bluuebunny/crossref_metadata_2025 huggingface dataset:
Silver Honeycomb Layered Tellurate Exhibiting Ag Subvalency and Metallophilic Bilayers
Authors: Kanyolo Godwill , Masese Titus , Miyazaki Yoshinobu , Saito Tomohiro
Published: December 2024

Silver (Ag+) conducting electrolytes are already being pursued for utilisation in solid-state batteries (e.g.

[1]
). Herein, we fortuitously synthesise a novel class of honeycomb layered tellurates, characterised by a global composition of Ag2
M
2TeO6 (where M = Co, Ni etc represents transition metals). These compounds feature argentophilic Ag-atom bilayers (Figure 1), as revealed through aberration-corrected transmission electron microscopy.
[2,3,4]
Due to metallophilicity and Ag subvalency, impalpable challenges reproducing the Ag bilayered structure exist within standard computational techniques.
[5]
Thus, the origin of the bilayers is theoretically understood via the emergence of non-commutative gauge interactions with three distinct valency states of Ag associated with well-known subvalent states, alongside the usual electromagnetic interaction.

[2,3,4,6]
Breaking these gauge symmetries generates a mass term, corresponding to the bifurcation of the Ag honeycomb lattice into argentophilic bilayers.

[3,4]
Moreover, since this novel class displays oxygen anion electrochemistry and relatively fast ionic conductivities, this work not only promises to inspire next-generation Ag-based honeycomb layered oxide cathodes but also provide a platform for further inquiry into the novel role of non-Euclidean and non-commutative geometries and gauge interactions respectively play within the broader class of honeycomb layered materials typically poised for secondary battery applications.
[3,6,7,8,9]

References:

[1] K. Kirshenbaum et al., Handbook of Solid State Batteries, 779-818 (2016)

[2]T. Masese, G. M. Kanyolo, Y. Miyazaki, T. Saito et al., Adv
. S
ci., 10, 2204672 (2024)

[3]G. M. Kanyolo, T. Masese, Y. Miyazaki, T. Saito et al., Prog
.
Mater.
S
ci., 141, 101205 (2024)

[4]G. M. Kanyolo, T. Masese et al., Mater
.
Today Chem., 33, 101657 (2024)

[5] K. Tada, T. Masese, and G. M. Kanyolo, Comput. Mater. Sci. 207 111322 (2022)

[6]G. M. Kanyolo and T. Masese, Mater
.
Today Phys., 39, 101271 (2024)

[7]G. M. Kanyolo, T. Masese et al., Chem. Soc. Rev
., 50, 3990 (2021)

[8] G. M. Kanyolo and T. Masese, Sci. Rep. 12 (1) 6465 (2022)

[9] G. M. Kanyolo and T. Masese, arXiv:2202.10323 [cond-mat.mtrl-sci]

Figure 1. HAADF-STEM image taken along [100] axis illustrating bilayer arrangement of silver atoms in honeycomb layered oxides with a global composition of Ag2Ni2TeO6.
[2,3]

Figure 1
Stochastic Economic Dispatch of a Power System With Solar Farm Considering Generation Flexibility and Reliability
Authors: Sabzevari Asghar , Moazzami Majid , Fani Bahador , Shahgholian Ghazanfar , Hashemi Mahnaz
Published: December 2024

In this paper, a novel approach is presented for optimizing economic dispatch (ED) in power systems featuring solar farms and flexible loads. The ED problem is formulated as a multiobjective optimization task with stochastic characteristics. To address this challenge, a hybrid multiobjective algorithm named hybrid differential evolution algorithm (hDE)‐multiobjective flower pollination algorithm (MOFPA) is proposed, which integrates principles from the differential evolution algorithm and the MOFPA, yielding enhanced performance. Two weighted objective functions are introduced to represent operation cost and the power system generation flexibility index (GFI) based on carefully selected scenarios. The occurrence probability of each scenario influences the objective function’s final value. Monte Carlo simulation (MCS) is utilized for scenario selection, enabling a comprehensive assessment of system performance. We first evaluate the proposed algorithm by optimizing standard benchmark functions and comparing results against those of other state‐of‐the‐art algorithms. The outcomes demonstrate the superior accuracy and efficiency of the hDE‐MOFPA algorithm. In the subsequent simulation phase, we optimally solve the ED problem, considering uncertainties and the involvement of flexible loads. A sensitivity analysis is conducted to examine the impact of uncertainties and flexible loads on both cost and emissions. The results reveal that the combined uncertainty of load and photovoltaic (PV) significantly influences the system. By adopting this novel approach, our proposed method offers valuable insights into optimizing the ED problem in power systems with solar farms and flexible loads, considering uncertainties and various scenarios. The sensitivity analysis indicates that considering uncertainties leads to a 4.9% increase in operational expenditures and a 4.1% decrease in GFI. Also, uncertainties in load and irradiation range from 5% to 20%, GFI experiences a decline from 4.08% to 20.18%, while costs undergo an increase from 4.92% to 18.93%.
Reproductive Health Literacy and Associated Factors Among High School Adolescents in Boke District, Eastern Ethiopia
Authors: Batu Getachew Bejiga , Oljira Lemessa , Sisay Mekonnen , Kebira Jemal Yusuf
Published: December 2024

Background: In recent years, the importance of health literacy in public health has attracted global interest as it affects people’s ability to make health decisions and is associated with health outcomes. Reproductive health literacy (RHL), in particular, affects adolescent’s ability to make sound sexual and reproductive health (SRH) decisions and is shown as a strong predictor of adolescent’s RH outcomes. This study aimed to determine the RHL status and associated factors among high school adolescents in Eastern Ethiopia.

Methods: Institutional‐based cross‐sectional study was conducted among 403 randomly selected secondary school adolescents in Boke District, Eastern Ethiopia, from January 1–5, 2021. Data were collected through pretested, structured, and self‐administered questionnaires, entered into Epidata version 3.1, and analyzed by Statistical Package for the Social Sciences (SPSS) version 23. A comprehensive questionnaires were designed to measure adolescents’ RHL in four functional dimensions (access, understand, appraise, and apply information). A participant’s RHL was categorized as limited or adequate, based on a transformed scale of measurements from 0 to 50. The predictors of the outcome variable were assessed using a logistic regression model and reported using adjusted odds ratio (AOR) with 95% confidence interval (CI). A statistical significance was declared at a p value of <0.05.

Results: The overall prevalence of limited RHL among adolescents was 81.6% with a 95% CI (77.5%−85.2%). In‐school male adolescents were 52% less likely to score in limited RHL status than their female counterparts (AOR = 0.48, 95% CI = 0.26, 0.88). Adolescents who reported attending reproductive health (RH)–related topics in class were 56% less likely to score in limited RHL status than their counterparts (AOR = 0.44, 95% CI = 0.23, 0.84). Furthermore, adolescents who reported using at least one RH service component were 60% times less likely to score in limited RHL status compared to their counterparts (AOR = 0.40, 95% CI = 0.23, 0.70).

Conclusion: A high proportion of school adolescents have limited RHL, suggesting school adolescents are at higher risk of RHL–related problems. Improving adolescent RHL with due attention to school, SRH education is warranted.
New Discovery of the Emergence Mechanism of High Clustering Coefficients
Authors: Ying Jun , Yan Chuankui , Wu Shouyan
Published: December 2024

In our statistical analysis, we have discovered that the distance distribution (referring to Euclidean distance) of many real networks follows certain patterns, especially the distances between connected nodes obey a scale‐free distribution. However, the classic BA model does not exhibit this characteristic. Furthermore, existing network models are mostly evolved based on degree‐preference mechanisms, without considering the potential influence of factors such as edge weights like spatial geographical factors on node‐edge connections in real networks. Taking distance‐weighted preferences as an example, this study proposes a network evolution model based on distance preference connections as the fundamental mechanism. By applying probability theory and mean‐field theory, the model’s degree distribution is calculated to be exponential, with a clustering coefficient greater than that of the BA model and consistent with data from some real networks. Our model reveals that this distance preference mechanism may be the fundamental mechanism underlying the emergence of high clustering in real networks. Additionally, by incorporating degree‐preference connection mechanisms, the model is further analyzed and improved to better match actual network evolution behaviors. The research results provide a possible explanation for resolving the controversy surrounding the scale‐free nature of networks.
Football as Foraging? Movements by Individual Players and Whole Teams Exhibit Lévy Walk Dynamics
Authors: Shpurov Ivan , Froese Tom , Ikegami Takashi
Published: December 2024

Many organisms, ranging from modern humans to extinct species, exhibit movement patterns that can be described by Lévy walk dynamics. It has been demonstrated that such behavior enables optimal foraging when resource distribution is sparse. Here, we analyze a dataset of football player trajectories, recorded during the matches of the Japanese football league, to elucidate the presence of statistical signatures of Lévy walks, such as the heavy‐tailed distribution of distances traveled between significant turns and the characteristic superdiffusive behavior. We conjecture that the competitive environment of a football game leads to bursty movement dynamics reminiscent of that observed in hunter‐gathering populations and more broadly in any biological organisms foraging for resources, whose exact distribution is unknown to them. Apart from analyzing individual players’ movements, we investigate the dynamics of the whole team by studying the movements of its center of mass (team’s centroid). Remarkably, the trajectory of the centroid also exhibits Lévy walk properties, marking the first instance of such type of motion observed at the group level. Our work concludes with a comparative analysis of different teams and some discussion on the relevance of our findings to sports science and science more generally.
Aseptic Meningitis and White Matter Disease in Childhood‐Onset Neuropsychiatric Lupus
Authors: Hsu Mei Lam , Chan Kwai Yu Winnie
Published: December 2024

We reported a 10‐year‐old girl who had an atypical demyelinating disease as the presentation of her neuropsychiatric lupus. The patient had a 4‐year history of systemic lupus erythematosus which had been on remission until she presented with fever and headache at the age of 10 years. Physical examination showed meningism. Extensive microbiological workup for infective meningitis was unrevealing. There was a radiographic finding of an extensive white matter hyperintensity on the magnetic resonance imaging (MRI) of the brain. At the initial stage of our case, as it was difficult to differentiate between infection of the central nervous system and neuropsychiatric manifestation of lupus, a course of intravenous immunoglobulin was given empirically instead of high‐dose corticosteroid while awaiting the microbiological workup results. The fever and headache subsided shortly after commencement of intravenous immunoglobulin without use of pulse corticosteroid. After the active neurological symptoms remitted, she was given a total of six monthly doses of intravenous immunoglobulin at 2 g/kg/cycle and six biweekly doses of intravenous cyclophosphamide at 500 mg/m2/month. Interval MRI showed resolution of the white matter hyperintensity. Despite the extensive demyelinating disease on initial presentation, she remitted successfully without residual neurological sequelae.
Effects of Family Environment on Depressive Symptoms in Postgraduate Students: Longitudinal Moderating Effect of Family Support and Mediating Effect of Psychological Resilience
Authors: Ren Minxuan , Song Jingjing , Zhou Chunyan , Hou Jinbo , Huang Hai , Li Lin
Published: December 2024

Background: Little is known about the mechanism of the relationship between family environment and depressive symptoms in Chinese master’s and PhD students. The objective of this study was to investigate the moderating effect of family support and the mediating effect of psychological resilience on depressive symptoms in a family environment. The differences between master’s and PhD students are also discussed in this study.

Methods: Data for 615 master’s and 78 PhD students were collected using the Family Environment Scale, Depression Self‐Rating Scale, Perceived Social Support Scale, and Psychological Resilience Scale in October every year for 3 years from 2021 to 2023. A latent growth curve mediation model was used to examine the potential mediating role of psychological resilience in the relationship between family environment and depressive symptoms over time.

Results: (1) There was a significant positive correlation among family environment, family support, and psychological resilience and a significant negative correlation between these factors and depressive symptoms. (2) Family support moderated the relationship between family environment and depressive symptoms among both master’s and PhD students. (3) Mediation analysis showed that psychological resilience mediated the relationship between family environment and depressive symptoms among master’s students.

Conclusion: Family environment is a significant risk factor for depressive symptoms. This association appears to be moderated by family support and mediated by psychological resilience. It is necessary to design depressive symptom prevention programs that consider the family environment of master’s and PhD students. Therefore, mental health services for this population should consider both family support and psychological resilience.
Safe Haven Ability of Energy and Agricultural Commodities Against G7 Stock Markets and Banking Indices During COVID‐19, Russia–Ukraine War, and SVB Collapse: Evidence From the Wavelet Coherence Approach
Authors: Snene Manzli Yasmine , Alnafisah Hind , Jeribi Ahmed
Published: December 2024

This article assesses the hedging and safe haven properties of energy and agricultural commodities (crude oil, natural gas, and wheat) against the G7 stock market indices and banking sector stock indices during the COVID‐19 pandemic, the Russia–Ukraine military conflict, and the Silicon Valley Bank (SVB) collapse. Using wavelet coherence analysis, our results showed dynamic correlations in which commodities shifted from diversifiers to strong safe havens during periods of turmoil. Particularly, WTI became a safe haven during the SVB collapse, natural gas acted primarily as a safe haven during the pandemic, and wheat evolved into a robust safe haven during crises. Moreover, our results with the G7 banking sector stock indices underscore the safe haven ability of commodities against these financial assets, furnishing valuable insights for investors during unstable financial situations.
Intelligent Optimization Analysis of the Cholera Epidemic Model
Authors: Awadalla Muath , Cheema Tahir Nawaz , Raza Ali , Rocha Eugenio , Ali Sharafat , Bilal Muhammad
Published: December 2024

Cholera is a global threat to public health and is an indicator of inequity and lack of social development. By the World Health Organization (WHO), there are 1.3–4.0 million cases of cholera and 21,000–143,000 deaths worldwide due to the infection each year. This innovative work discusses the spread of the cholera virus; the model of this disease was formulated mathematically and solved with the help of the artificial neural network technique. The developed model identified the nonlinear ordinary differential equations represented by susceptible (Sc), vaccinated (Vc), infectious (Ic), recovery (Rc), and concentration of cholera in water (Bc) and the cholera model reference dataset is formed using the explicit Runge–Kutta method. A dataset is arbitrarily used for each cyclic update in Levenberg–Marquardt backpropagation for the numerical study of cholera dynamics. The Levenberg–Marquardt backpropagation is implemented to refine the dataset of the cholera model for training, testing, and validation. The accuracy of the proposed technique is evaluated through mean squared error (MSE), error histograms, merit functions, reliable performance, and regression. These findings underscore the potential of intelligent optimization to enhance the precision of epidemic predictions and inform more effective, targeted cholera control strategies. Thus, intelligent optimization offers a valuable tool for public health response in vulnerable areas.
Impact of Green Technological Innovations, ICT, and Renewable Energy on CO2 Emissions in Emerging Economies
Authors: Iqbal Kashif , Hassan Syed Tauseef , Geng Yuqing , Cai Chang
Published: December 2024

To understand the impact of green technological innovation (GTI) and ICT technologies on carbon emissions (CO2), this study investigates both the direct and moderating impact of GTI, ICT technologies, renewable energy (RNE), gross domestic product (GDP), and structural change on CO2 emissions by employing a balanced panel dataset for selected emerging economies covering 1990–2020. In this study, we used continuously updated fully modified (CUP‐FM), continuously updated biased correction (CUP‐BC), and panel quantile regression approach to obtain robust model estimations to cross‐country dependencies and heterogeneity. The results show that GTI, RNE, and structural change significantly reduce (CO2) emissions in the chosen economies by advancing technological advancement. Technological advancement significantly reduces CO2 emissions in the selected economies by advancing green technologies leading to greater energy efficiency by encouraging carbon‐peaking and carbon‐neutrality objectives. In contrast, economic growth (GDP) favorably influences CO2 emissions, suggesting that increased economic activity may initially lead to higher emissions but can be mitigated by technological and policy advancements. As a result, better fiscal incentives, advancement in GTI, increased utilization of RNE, and the effective use of ICT technologies can help to reduce CO2 emissions in the selected emerging economies.
Influencing Factors for Depression and Depression Aggravation in Hospitalized Elderly Patients With Chronic Heart Failure
Authors: Wang Tao , Ma Lina , Wang Luling , Wang Zheng , Zhai Wenliang , Liu Fangyan , Zhang Zhongying , Li Yun
Published: December 2024

Studies on depression aggravation in hospitalized elderly patients with chronic heart failure (CHF) are scarce. This study analyzed the influencing factors for depression and depression aggravation in elderly CHF patients in China. A total of 196 elderly CHF patients admitted to Xuanwu Hospital from March 2019 to October 2022 were consecutively selected. Information such as demographics, comorbidities, primary disease and etiology, elderly hospitalization assessment, laboratory test outcomes, cardiac function, oral medications for CHF, and management during hospitalization were collected on admission. The patients were divided into the depressed and nondepressed groups according to their depression scores, and the independent predictors for depression were analyzed using multivariable logistic regression. Another depression assessment was performed at discharge, and independent factors for worsened depression during hospitalization were also analyzed. A high frailty index score was an independent predictor for depression in elderly CHF patients. The overall depression score of the patients at discharge was significantly higher than that on admission. Low educational attainment, a low left ventricular ejection fraction, long hospitalization, and admission to the intensive care unit (ICU) were independent predictors for depression exacerbation during hospitalization. Clinical assessment of depression is necessary for elderly hospitalized CHF patients, particularly for those who have low education, a high frailty index score, a low left ventricular ejection fraction, ICU admission, and long hospitalization. The findings of this study may deepen our understanding of depression in elderly CHF patients.
Fuelwood From Natural Forests’ Contribution to Households’ Energy Use and Its Effect on Carbon Dioxide Emission in Delanta District, Northeastern Ethiopia
Authors: Eshetu Sintayehu
Published: December 2024

Ethiopian rural households primarily rely on fuelwood as their energy source. This research was designed to quantify the amount of annual fuelwood collected from forests. The research uses a simple random sampling method for household selection that was used to invert the study, and the data were analyzed using Excel and Stata software. As a result, among the primary causes of deforestation and CO2 emissions are a strong reliance on fuelwood. The purpose of this study was to calculate the annual quantity of fuelwood harvested from the forests as well as the amount of CO2e and C produced throughout the process. An estimated 2725 kg of firewood and 26 kg of charcoal were used annually per households. In contrast, each household extracted an average of 3909.3 kg of firewood and 516.5 kg of charcoal annually from the forests to sale. The analysis of this study demonstrates that fuelwood from the forest accounted for 71% of the total energy sources that the sampled household had access to for both expenditure and sale. 66% of the overall energy sources came from firewood, and 5% came from charcoal. Consequently, 66% of fuelwood extracted from the forest is utilized for sale and serves as an alternate means of income generation, while 34% of fuelwood is used for household expenditure in the study area. From the total energy sources, fuelwood from the forest shares 51% of energy sources accessible for use in households and 96% of energy providers available for sale (income generation). However, in the examined areas, inefficient combustion of this fuel results in greenhouse gas emissions. Thus, each year, the burning of fuelwood taken from this forest contributes 974 tons of carbon dioxide equivalents (CO2e) to the atmosphere. This suggests that 265.6 tons of carbon is added to the household’s total carbon footprint as a result of using fuelwood from the forest as a domestic energy source. Annually, 7.74 tons and 1.96 tons of CO2e and carbon are emitted per household, respectively. Generally, fuelwood from the forest has a great role in either household energy use or income contribution. But without consideration of emissions, deforestation for fuelwood production has implications for emission. Conserving natural forests by adopting different forms of energy like electrical sources or establishing plantations on our own land is an essential approach to mitigating climate change in the future.
Olfactory Dysfunction as a Biomarker for Early Diagnosis of Cognitive Impairment in Patients With Type 2 Diabetes: A Systematic Review
Authors: Ramos-Cazorla Paula , Carazo-Barrios Lina , Reyes-Bueno Jose A. , Sagües-Sesé Elena , de Rojas-Leal Carmen , Barbancho Miguel A. , Garzón-Maldonado Francisco J. , de la Cruz-Cosme C. , García-Arnés Juan A. , García-Casares Natalia
Published: December 2024

Background: Olfactory dysfunction and cognitive impairment (CI) have been associated with Type 2 diabetes (T2DM), but the mechanisms underlying this association are broadly unknown. This systematic review tends to investigate the relationship between the onset of olfactory dysfunction and CI in patients with T2DM and to explore the potential role of olfactory dysfunction as an early diagnosis biomarker of CI.

Methods: We conducted a systematic review consulting PubMed and Scopus. The articles considered eligible included patients with T2DM and cognitive and olfactory test.

Results: The search identified a total of 145 articles, of which 13 were finally selected. The majority of these studies discovered a correlation between olfactory dysfunction and CI in individuals with T2DM. Additionally, other biomarkers such as functional magnetic resonance imaging demonstrated changes in brain regions associated with the sense of smell in T2DM patients.

Conclusions: Olfactory dysfunction could be a biomarker for early diagnosis of CI in T2DM. However, these alterations are highly heterogeneous and more studies that include neuroimaging need to be conducted.
Network Reconfiguration Using Hybrid Archimedes Optimization Algorithm for Multiobjective Functions
Authors: Adegoke Samson Ademola , Sun Yanxia , Wang Zenghui , Oladipo Stephen
Published: December 2024

Extension of the feeders and high power demand have resulted in more substantial power dissipation and reduced voltage decline. Therefore, minimizing power loss and enhancing the voltage profile are imperative for efficient energy distribution. This research introduces a novel approach called the Hybrid Archimedes Optimization Algorithm (HAOA), which combines the Levy flight and Archimedes Optimization Algorithm (AOA) to improve the performance of AOA. AOA replicated the upward impulse described by Archimedes principle in physics and experienced an imbalance between exploitation and exploration. Utilizing Levy flight results in an improved balance between exploration and exploitation, enhancing algorithm performance in finding optimal solutions. Therefore, hybridizing Levy flight with AOA to enhance its performance results in an improved trajectory balance between exploration and exploitation. This trajectory is utilized to update the object’s position in AOA and is regarded as a feasible method to enhance the slow performance of AOA. The HAOA algorithm was employed to optimize network reconfiguration to decrease power loss and voltage deviation (VD) and improve the voltage stability index (VSI) in radial distribution networks (RDN). The validation of HAOA was assessed using the IEEE 33 and 69 bus systems. The power loss for the IEEE 33 and 69 test systems dropped to 139.55 and 98.459 kW, respectively, compared to the base case values of 202.677 and 224.96 kW. The VD for the 33 and 69 bus systems is 0.00134 and 0.000306 p.u, respectively, compared to the base case values of 0.0035498 and 0.001443 p.u. The VSI has been enhanced to 0.785 p.u for the 33 bus system and 0.8126 p.u for the 69 bus system following the reconfiguration. The results indicate that HAOA outperforms other approaches in terms of lower power, VD, and voltage stability enhancement.
Enhancing the Phenolic Profile and Antioxidant Potency of Finger Millet Gruels Through Lactic Acid Fermentation and Incorporation
Authors: Lubaale John , Duodu Kwaku G.
Published: December 2024

This study evaluated the effects of souring (fermenting using Lactobacillus plantarum and back‐slopped inoculum and lactic acid acidification) and cooking of finger millet on phenolic content, radical scavenging properties, and inhibition of oxidative DNA damage in finger millet. Fermentation with Lactobacillus plantarum and back‐slopped inoculum, and lactic acid acidification significantly increased the total phenolic content and radical scavenging properties of finger millet extracts, whereas cooking resulted in the reduction of these bioactive properties. Liquid chromatography–mass spectrometry (LC–MS) analysis confirmed the presence of phenolic acids, flavonoids, and proanthocyanidins in finger millet extracts. Both total phenolic acids and total flavonoids were elevated by fermentation (back‐slopped inoculum and Lactobacillus plantarum inoculated) and lactic acid acidification but diminished by cooking. Notably, extracts from both unsoured and soured finger millet flour and gruel demonstrated protective properties against DNA damage. These findings suggest that souring enhances the phenolic content and antioxidant properties of finger millet, underscoring the potential of soured finger millet gruels for health promotion.
Optimization of the Reaction Conditions in Biodiesel Production: The Case of Baobab Seed Oil as Alternative Feedstock
Authors: Kodua Stephen Tweneboah , Boadi Nathaniel Owusu , Badu Mercy
Published: December 2024

Biodiesel production from underutilized seed oils offers a sustainable and environmentally friendly alternative to fossil fuels for energy generation. The present study focuses on employing response surface methodology (RSM) based on Box–Behnken experimental design (BBED) to optimize the reaction conditions in biodiesel production from baobab seed oil. The study investigates the impact of various experimental variables on the biodiesel yield. Specifically, the study explores the influence of reaction temperature (40°C–70°C), reaction time (60–120 min), catalyst concentration (0.5–1.5 wt. %), and oil‐to‐ethanol ratio (1:3–1:9 mol/mol) on the optimal yield. After screening the variables as mentioned above, the optimized reaction conditions were determined to be as follows: a reaction temperature of 55°C, a reaction time of 60 min, a catalyst concentration of 1.5 wt. %, and an oil‐to‐ethanol ratio of 1:6 mol/mol. These conditions gave a percentage yield of 98.6%. Notably, the biodiesel properties obtained met the recommended standards. Furthermore, the statistical analysis revealed an adjusted R2 value of 0.9465, closely aligned with the predicted R2 value of 0.8703, with a difference of less than 0.2. This indicates a congeniality of the experimental data and the model predictions. Additionally, the analysis of variance (ANOVA) derived from the quadratic model obtained from the RSM indicated that the model could accurately predict the biodiesel conversion with 97.32% accuracy, providing insights into the observed variations.
An Enhanced Deep Neural Network Model for the Detection of Anomalous Behavior of Drivers in Road Traffic
Authors: Khan Maleika Heenaye-Mamode , Nagwanshi Kapil Kumar , Muhammad Azhar Bhuheekhan , Girish Emerith , Baichoo Sunilduth , Sinha G. R. , Taylor Amelia
Published: December 2024

Over recent years, video‐surveillance systems have seen extensive adoption, largely driven by security imperatives, with radar‐based speed detection being a common feature in traffic monitoring. Despite its prevalence, broader anomaly detection in traffic patterns has not received equivalent focus. This research develops a sophisticated deep learning framework, drawing architectural inspiration from MobileNet, ResNet50, and VGG19, to not only detect and track vehicles but also analyze trajectory data to identify nonstandard behaviors. Specifically, our model detects four distinct anomalies: overspeeding, lingering in no‐stopping zones, insufficient spacing between vehicles, and violations of traffic light signals. To support this, we constructed a unique dataset comprising over 60,000 video frames. The YOLOv3 algorithm facilitated initial object recognition, which was complemented by data augmentation techniques to mitigate issues related to class imbalance and the limited availability of annotated datasets in this domain. Our enhanced model achieved an overall accuracy of 95%, with a detailed performance breakdown for each detected anomaly.
SO(3) Unscented Kalman Filter Design Based on Explicit Attitude Error Definition From Coordinate Frame Perspective
Authors: Zuo Yunlong , Zhang Xiaofeng , Yu Deying , Chang Lubin
Published: December 2024

When applying the unscented Kalman filter (UKF) in object orientation or attitude estimation, special treatment should be taken for the attitude constraint. A usual methodology is to formulate the attitude error as a state while making use of nonsingular parameterization for global attitude updates. In this respect, attitude error definition has an important effect on the filtering performance. However, many different attitude error forms in published works have confused the practitioners when designing UKF for attitude estimation (termed as SO(3)‐UKF). In this paper, the detailed designing procedures of SO(3)‐UKF are presented with the main focus on the attitude error definition from a coordinate frame perspective. Four attitude error forms with different expressed coordinate frames are presented. With the explicit coordinate frame denotation, the attitude sigma points construction and attitude update, which are crucial stages of the SO(3)‐UKF, can be carried out accordingly based on the attitude matrix multiplication chain rule. The performance of SO(3)‐UKF with different attitude error definitions is evaluated both on simulations and on real datasets.
Metal Defect Detection Models Fused EfficientNet and Involution
Authors: Wang Peng , Li Shixin , Wang Weixiao , Tang Kaiwen , Chen Fankai
Published: December 2024

This paper proposes an improved YOLOv5 model based on the EfficientNet‐B0 backbone, EIoU loss function, and dynamic convolution operator, which was evaluated using the NEU‐DET dataset. The results show that compared with the traditional YOLOv5 model, the proposed improvement achieves a higher detection accuracy with fewer model parameters, resulting in a more lightweight architecture. Using the EfficientNet‐B0 backbone not only enhances the detection performance but also reduces the parameter count of the model, thereby, effectively improving computational efficiency. The enhanced EIoU loss function provided a more accurate assessment of the quality of the detected bounding boxes than the traditional CIoU loss function. Additionally, the dynamic convolution operator utilized in the neck module enhances the representation capability of the feature map while reducing computational overhead. The simulation results demonstrate that the proposed improved model achieves higher mean average precision (mAP) values and requires fewer parameters for the NEU‐DET dataset, thereby, highlighting its promising practical applications.
Predicting BRAF Mutations in Cutaneous Melanoma Patients Using Neural Network Analysis
Authors: Dudin Oleksandr , Mintser Ozar , Gurianov Vitalii , Kobyliak Nazarii , Kaminskyi Dmytro , Matvieieva Alina , Shabalkov Roman , Mashukov Artem , Sulaieva Oksana
Published: December 2024

Point mutations at codon 600 of the BRAF oncogene are the most common alterations in cutaneous melanoma (CM). Assessment of BRAF status allows to personalize patient management, though the affordability of molecular testing is limited in some countries. This study aimed to develop a model for predicting alteration in BRAF based on routinely available clinical and histological data.

Methods: For identifying the key factors associated with point mutations in BRAF, 2041 patients with CM were recruited in the study. The presence of BRAF mutations was an endpoint. The variables included demographic data (gender and age), anatomic location, stage, histological subtype, number of mitosis, and also such features as ulceration, Clark level, Breslow thickness, infiltration by lymphocytes, invasiveness, regression, microsatellites, and association with nevi.

Results: A relatively high rate of BRAF mutation was revealed in the Ukrainian cohort of patients with CM. BRAF‐mutant melanoma was associated with younger age and location of nonsun‐exposed skin. Besides, sex‐specific differences were found between CM of various anatomic distributions and the frequency of distinct BRAF mutation subtypes.

A minimal set of variables linked to BRAF mutations, defined by the genetic input selection algorithm, included patient age, primary tumor location, histological type, lymphovascular invasion, ulceration, and association with nevi. To encounter nonlinear links, neural network modeling was applied resulting in a multilayer perceptron (MLP) with one hidden layer. Its architecture included four neurons with a logistic activation function. The AUROCMLP6 of the MLP model comprised 0.79 (95% CІ: 0.74–0.84). Under the optimal threshold, the model demonstrated the following parameters: sensitivity: 89.4% (95% CІ: 84.5%–93.1%), specificity: 50.7% (95% CІ: 42.2%–59.1%), positive predictive value: 73.1% (95% CІ: 69.6%–76.3%), and negative predictive value: 76.0% (95% CІ: 67.6%–82.8%). The developed MLP model enables the prediction of the mutation in BRAF oncogene in CM, alleviating decisions on personalized management of patients with CM.

In conclusion, the developed MLP model, which relies on the assessment of 6 variables, can predict the BRAF mutation status in patients with CM, supporting decisions on patient management.
Space–Time Analysis of Peste des Petits Ruminants in Mali and Identification of Risk Factors
Authors: Zannou Olivier Mahuton , Sow Ahmadou Nouh , Sissoko Boundiala , Fomba Cheick Oumar , Knight-Jones Theodore J. D. , Dione Michel
Published: December 2024

Livestock farming is an important part of Mali’s economy and a major source of income for the rural population especially women. One of the major constraints to this activity is high burden of animal disease, in particular peste des petits ruminants (PPR), which hinder the productivity of small ruminants and thus reduces the income of livestock farmers. This disease that has an effective vaccine is subjected to a worldwide eradication program. The aim of this study is therefore to develop risk maps and identify the disease’s risk factors to inform national vaccination strategy in Mali. This tool will help decisions‐makers rationalize the limited resources available for disease control. A compilation of retrospective cases of PPR from 2011 to 2023 was used to generate risk maps using multivariable regression models and geographically weighted regression. Results show that the southern regions of Mali are more at risk than the northern. PRR cases occur more during rainy and hot dry seasons. Parameters such as railroads length, rainfall, and watering points were identified as risk factors for the spread of the disease. These results point out high priority areas during a risk‐based vaccination campaign against PPR in Mali.
Research on Suppression of Vibration Caused by Combustion Pressure Pulsation in Aircraft Engines
Authors: Hong Liang , Zang Chao-Ping , Ji He-Jiong , Yu Qiu-Xia , Jiang Yun-Fan , Shen Lian
Published: December 2024

Abnormal high‐energy narrowband signals appeared in the aircraft engine test. By analyzing the characteristics of the vibration signals and the pressure pulsation signals in the measurement point data of the engine, it can be speculated that the abnormal vibration energy of the engine is not related to the operation of rotating parts, which upends a conventional wisdom on aeroengine’s vibration, i.e., mainly coming from rotor excitations. A special measurement test for the pulsation of the main combustion chamber components was designed. The pulsation characteristics, which exhibit a fixed ratio between the vibration/pulsation frequency value and the sound velocity during combustion chamber operation, of the main combustion chamber during component testing are similar to those under actual working conditions, which conform to the characteristics of plane wave theory. A three‐dimensional acoustic fluid finite element model was established to simulate the two stream flow in the main combustion chamber. The simulation results showed that the acoustic modal eigenvalues of the secondary flow path outside the main combustion chamber were consistent with the pulsation/vibration characteristics in the aircraft engine and component tests, proving that the main combustion chamber is the excitation source of the abnormal high‐energy narrow frequency vibration. By adjusting the air/fuel equivalence ratio at the head of the combustion chamber flame tube, the working conditions of the combustion chamber were optimized. Then, according to the results through the main combustion chamber component tests and overall machine tests, the narrow frequency high‐energy components in the airflow pulsation and overall machine vibration disappeared, which meant that the vibration of the aircraft engine was effectively suppressed. This investigation successfully achieved the positioning of the high‐energy vibration source in the aircraft engine and the vibration energy suppression of the excitation source under minimum engineering costs. Moreover, the suppression method for vibration caused by the combustion pulsation, which is proposed in this paper, can provide technical guidance for solving similar problems in practical applications.
Comparison of Shared Class I HLA-Bound Noncanonical Neoepitopes between Normal and Neoplastic Tissues of Pancreatic Adenocarcinoma
Authors: Zhang Tengyi , Celiker Betul , Shao Yingkuan , Gai Jessica , Hill Mark , Wang Chunyu , Zheng Lei
Published: December 2024

Purpose:

Developing T-cell or vaccine therapies for pancreatic ductal adenocarcinoma (PDAC) has been challenging because of a lack of knowledge regarding immunodominant, cancer-specific antigens as PDAC are characterized by a scarcity of genomic mutation-associated neoepitopes, and effective approaches to discover them are limited.

Experimental Design:

An advanced mass spectrometry approach was employed to compare the immunopeptidome of PDAC tissues and matched normal tissues from the same patients.

Results:

This study identified HLA class I–binding variant peptides derived from canonical proteins, which had single amino-acid substitutions not attributed to genetic mutations or RNA editing. These amino-acid substitutions appeared to result from translational errors. The variant peptides were predominantly found in tumor tissues, with certain peptides common among multiple patients. Importantly, several of these variant peptides were more immunogenic than their wild-type counterparts.

Conclusions:

The shared noncanonical neoepitopes identified in this study offer promising candidates for vaccine and T-cell therapy development, potentially providing new avenues for immunotherapy in PDAC.
The Use of Liver Support System Devices in Acute Liver Failure as a Consequence of Metastatic Melanoma in the Liver: A Case Report and Review of the Literature
Authors: Bukovica Petrc Anamarija , Salopek Tihana , Skočilić Iva , Zahirovic Dag , Orlic Lidija , Bubic Ivan , Matana-Kastelan Zrinka , Francetic Sara , Lisica Karla , Mikolasevic Ivana
Published: December 2024

Introduction: Melanoma often metastasizes to the liver, leading to significant morbidity and mortality. Liver injury can also occur due to hepatitis caused by immunotherapy used in the treatment of melanoma. Case Presentation: This case report presents a 38-year-old male diagnosed with advanced melanoma who experienced acute liver failure (ALF) initially thought to be a side effect of immunotherapy. Despite following aggressive supportive care as per the latest guidelines, the patient’s condition deteriorated rapidly. It was discovered that the patient had liver metastases. As the tumor had a positive BRAF mutation, we opted for invasive treatment with therapeutic plasma exchange to restore liver function and create the conditions for initiating treatment with BRAF/MEK inhibitors. After the use of a liver support device, the liver function was resolved, and a BRAF/MEK inhibitor was introduced. After 2 months of targeted therapy, a favorable effect and good melanoma control are observed. Conclusion: The report underscores the complexity of managing melanoma with liver metastasis and the urgent need for advancements in treatment modalities ALF in oncology patients. We suggest that invasive treatment methods, such as liver support system devices, should be considered in well-selected oncology patients, even in advanced stages of disease.
Incidence and associated factors of pre-hospital care-seeking delay in people with acute ischemic stroke: a systematic review and meta-analysis
Authors: zhao Yanjie , Xu Yuezhen , Fang Shuyan , Zhi Shengze , Ma Dongfei , Song DongPo , Gao Shizheng , Wu Yifan , Zhong Qiqing , Jin Changxu , Wang Rui , Sun Jiao
Published: December 2024

Background: Despite decades of educational efforts, patients with acute ischemic stroke (AIS) remain delayed in seeking medical care, which becomes the greatest obstacle to the successful management of the condition.
Objective: To systematically explore the incidence and influencing factors of pre-hospital care-seeking delay in acute ischemic stroke patients.
Methods: We systematically searched the PubMed, Embase, Cochrane Library, Web of Science, and Cumulative Index to Nursing and Allied Health Literature from database inception to September 30, 2023. Meta-analysis was conducted using the Stata 15.0 software package. The pooled incidence was calculated using a random-effects model. The quality of studies reporting incidence data was assessed using Joanna Briggs Institute's Critical Appraisal Checklist and Newcastle-Ottawa Scale. Subgroup analyses were performed according to study location, country income, recruitment date, and age.
Results: Finally, 30 related articles were included, involving a total of 287,102 people. The estimated incidence of pre-hospital care-seeking delay was 68%,and there were differences in this incidence in different countries (P= 0.035). Meta-analysis results showed that the delay rate was highest in low-income countries (85%) and lowest in high-income countries (62%). Patients who live farther from hospitals, have a lower level of education, diabetes, hyperlipidemia, or a history of stroke are more likely to experience delays (all P <0.05). Conversely, those who can recognize stroke symptom , perceive the severity of early symptom, understand thrombolysis treatment, atrial fibrillation, consciousness disturbance, visual disturbance, and symptom score at admission, Emergency Medical Services use, and immediate help-seeking have a lower risk of delay (all P < 0.05).
Conclusions: Pre-hospital care-seeking delays are common among patients with AIS, especially in low-income countries. To reduce delays, it is crucial to increase public awareness of stroke symptoms, improve education levels, and optimize healthcare accessibility.
Integrated Imaging-Pathologic Diagnosis and Treatment Overview of Mucinous Tubular and Spindle Cell Renal Cell Carcinoma: A Case Report and Systematic Review of the Literature
Authors: Zhang Yu , Long Yu , Wang Jie
Published: December 2024

Introduction: Mucinous tubular and spindle cell carcinoma (MTSCC) of the kidney is a rare, low-grade renal epithelial tumor with a unique histopathological profile. This case report aims to highlight the clinical, imaging, and immunohistochemical features of MTSCC, facilitating its differentiation from other renal tumors and providing insight into effective management strategies.
Case Presentation(s): A 52-year-old female presented with left-sided abdominal pain. Imaging studies, including CT and ultrasound, revealed a low-density mass in the left kidney. The patient subsequently underwent laparoscopic radical nephrectomy. Pathological examination identified spindle and cuboidal epithelial cells forming elongated tubules with mild atypia within a mucinous stroma. Immunohistochemical staining showed positivity for CK7, P504S, and PAX8, confirming the diagnosis of MTSCC. Postoperatively, the patient was managed conservatively without chemotherapy or radiotherapy and received only symptomatic treatment. During a 24-month follow-up, she demonstrated favorable recovery with no signs of recurrence or metastasis.
Conclusion: This case underscores the critical role of imaging, histopathology, and immunohistochemistry in accurately diagnosing MTSCC. Early detection and timely surgical intervention can lead to positive outcomes, as illustrated by this patient’s recovery. Further research is needed to explore the molecular mechanisms of MTSCC and establish robust guidelines for its management.