bluuebunny/crossref_metadata_2025 huggingface dataset:
Perceived drivers of the Ebola virus disease outbreak in Mubende and Kassanda districts, Uganda: a qualitative study
Authors: Ninsiima Lesley Rose , Mor Siobhan M , Romano Javier Sánchez , Namakula Lydia Nabawanuka , Kankya Clovice , Kungu Joseph , Mugisha Lawrence , Klein Jörn , Nyakarahuka Luke
Published: December 2024

Introduction

During the most recent Ebola virus disease (EVD) outbreak in Uganda, a cluster of community deaths with epidemiological linkages to the first reported case were identified to have occurred in Mubende, Kassanda and Kampala districts in September 2022. This study aimed to explore perceived drivers of EVD outbreak among affected communities in Mubende and Kassanda districts, Uganda.

Methods

We conducted a descriptive qualitative and participatory epidemiology study using focus group discussions (n=4), in-depth interviews (n=12), key informant interviews (n=12) and participatory landscape mapping. The subcounties of Madudu (Mubende district) and Kikandwa (Kassanda district) were purposively selected within each district because Ebola cases were known to have occurred within these areas. The community expressed their own understanding and perceptions of the drivers of Ebola virus outbreak within these subcounties. Qualitative data were analysed using thematic content analysis in Nvivo V.12 software. Data were analysed using both inductive and deductive approaches, where codes, subthemes and themes in the data were merged with global themes. The results were interpreted in the context of the broader literature on the topic using the social-ecological model and the epidemiological triad using the specific experiences and insights of the study participants. Participant responses were categorised in terms of their themes.

Results

A total of five themes were identified which described the perceived drivers of Ebola virus outbreaks. These included (1) individual: knowledge about EVD (source of the disease and fear due to death of some suspected cases); (2) interpersonal: perceived sources of Ebola virus spillover (ecological, anthropogenic, environmental and cultural); (3) community: impact of EVD to the community (economic loss and survivors lack of support from the government); (4) organisational: health system challenges in outbreaks (delayed laboratory results, poor recording and reporting systems in the facilities and poor surveillance); and (5) policy: recommendations (use of One Health approach and continuous sensitisation).

Conclusions

This study underscores the complex interplay of factors shaping the dynamics of EVD. Understanding Ebola requires not only scientific knowledge but also an appreciation of sociocultural contexts and systemic vulnerabilities within health systems. We therefore recommend comprehensive approaches which integrate scientific expertise with community participation, strengthen health systems and foster collaboration across sectors to mitigate the impact of future outbreaks to address these challenges effectively. Additionally, raising awareness, sensitising the public and safeguarding natural habitats are crucial steps to mitigate the risk of future disease outbreaks.
Tetrodotoxin toxicity: an increasing threat
Authors: Burton Emily , Ward Alastair , Tamlin Lachlan , El-Masri Shaddy , Goh Rudy , Ghaoui Roula , Bacchi Stephen
Published: December 2024

A man aged in his sixties presented to the emergency department with vomiting, dizziness and generalised weakness preceded by perioral and peripheral paraesthesias for several hours. He did not speak English and was visiting from overseas. Examination revealed multidirectional nystagmus, subtle bilateral ptosis, marked bilateral upper limb dysmetria and heel-shin ataxia, with mild proximal limb weakness. Brain imaging was unremarkable. Further history revealed the man had gone fishing and eaten a fish he had caught earlier that day. He was subsequently diagnosed with tetrodotoxin toxicity from pufferfish ingestion. He was managed supportively and made a complete recovery. Tetrodotoxicity has been hitherto considered rare outside of Southeast Asia, but rising water temperatures and human interventions have facilitated the migration of marine species containing tetrodotoxin to a wider geographical range.
Handheld fans for dyspnoea in respiratory failure: randomised controlled trial
Authors: Yue Dan , Xie Xuqin , Chen Gaoyu , Luo Zhimei , Fan Linjie , Sun Desheng
Published: December 2024

Objective

Fan therapy is widely acknowledged as an essential component in the management of dyspnoea with numerous studies supporting its efficacy in alleviating dyspnoea among patients with chronic illnesses. However, there is limited evidence regarding the effectiveness of fan therapy in reducing dyspnoea in patients with respiratory failure undergoing continuous oxygen therapy. This study aimed to assess the efficacy of fan therapy in mitigating dyspnoea in this specific patient population through a randomised controlled trial.

Methods

Participants meeting the inclusion criteria were randomly assigned to either an experimental group or a control group. In the experimental group, a handheld fan (HHF) was directed at the face, while in the control group the fan was aimed at the legs. Both interventions were conducted at a distance of 15–30 cm for 10 min. Key physiological and subjective measures, including heart rate, respiratory rate, blood pressure, blood oxygen saturation, facial skin temperature and Visual Analogue Scale (VAS) scores, were recorded immediately after fan therapy.

Results

The experimental group demonstrated a statistically significant reduction in VAS scores (p<0.05) compared with the control group indicating that fan therapy effectively alleviates dyspnoea in patients with respiratory failure receiving continuous oxygen therapy.

Conclusion

HHFs are affordable, widely accessible and highly effective in relieving dyspnoea with minimal risk. Therefore, fan therapy should be considered as a complementary treatment for patients with respiratory failure and incorporated alongside standard therapeutic interventions for the condition.
Finite element study on seismic performance of reinforced concrete bridge pier with kinked rebars
Authors: Wang Chengquan , Xie Changgen , Wang Xinquan , Zhuang Yizhou , Qian Haimin , Wu Xi , Sun Miaomiao
Published: December 2024

This paper presents a new design for cost-effective and easily constructed kinked rebar reinforced concrete bridge piers to improve their deformation capacity and energy dissipation during earthquakes, addressing issues of uneven concrete damage and challenging post-disaster repairs. Based on uniaxial tensile tests, mechanical properties of kinked rebars are determined, focusing on steel bar diameter ( d) and kinked ratio αkb. In ABAQUS simulations, three pier models are created: a regular reinforced concrete bridge pier and two reinforced concrete bridge piers with kinked rebars, varying the kink ratio. Under 0.1 axial compression ratio, these piers undergo low-cycle reciprocating loading; their seismic performance is assessed through hysteresis curves, skeleton curves, residual displacements, and energy dissipation capacities. Results demonstrate that kinked rebars effectively concentrate concrete damage and enhance ductility and energy absorption. Piers exhibit increased post-yield stiffness and gradual capacity growth in later loading stages, providing robustness throughout the loading process. This validates the use of kinked steel bars in bridge piers.
FOSTERING ENERGY TRANSITION IN SMES OF SOUTHERN EUROPE THROUGH GREEN FINANCE
Authors: FENG YE , JIN CHENG , LI ZENGRONG
Published: December 2024

This study examines the relationship between the market size of sustainable finance and the energy expenditure of Small and Medium-sized Enterprises (SMEs) in Southern Europe from 2000 to 2019, using the CUP-FM method and panel cointegration estimation. It finds a significant negative correlation: a 1% increase in sustainable finance is associated with a 0.69% decrease in SMEs’ energy spending. Additionally, the study highlights a positive correlation between inflation rates and higher energy consumption by SMEs, suggesting that rising operational costs may lead to increased energy use. To support the energy transition of SMEs, practical policies are needed in Southern European economies, focusing on developing green finance markets, promoting sustainable digitalization, creating green jobs and encouraging environmentally friendly economic growth.
DIGITALIZATION FOR ENTREPRENEURIAL EMPOWERMENT AND SMALL BUSINESS SUCCESS
Authors: LIU XINHUI , ZHANG CHUO
Published: December 2024

This paper investigates the impact of digitalization on entrepreneurship capacity in developing economies using a sample of 20 countries from 2009 to 2021. Findings reveal a significant positive correlation between digitalization indicators, such as internet and mobile phone users, and entrepreneurship capacity, emphasizing digitalization’s role in enhancing communication, information dissemination and market access for entrepreneurs. However, factors like patent applications and trade liberalization show insignificant correlations with entrepreneurship empowerment. Additionally, higher inflation rates are associated with decreased entrepreneurship capacity, while urbanization emerges as a significant motivator. To foster entrepreneurship capacity for SMEs, governments should prioritize digitalization and ICT diffusion policies, promote digital literacy, support patent registration and develop smart cities to create conducive environments for entrepreneurial activities.
LEVERAGING ON INTELLIGENT COMPUTING NEURO-STRUCTURES FOR POPULATION DYNAMICS OF FINANCIAL BUBBLE MODEL
Authors: ALI SYED FARWAH , FANG KWO-TING , KAUSAR KIANI ADIQA , SHOAIB MUHAMMAD , ASIF ZAHOOR RAJA MUHAMMAD
Published: December 2024

This study aims to exploit the Artificial intelligence (AI)-based computing paradigm to analyze the economic system to define the price movements of unsustainable expansion, rapid collapse, and eventual equilibrium that characterize financial bubbles represented with differential equations to portray the role of societal contagion and group mentality from a behavioral viewpoint with the market population classified as bull, i.e., optimistic, neutrals, bear, i.e., pessimistic, and quitter categories. The concept of the financial bubble is characterized as an unexpected rise in prices that is rapidly followed by a shrill decline and retrospectively appears as a consequence of such uncertainty in price value. AI-based applications facilitate financial analysts with innovative computational paradigms for gaining deep insights, improving predictive accuracy, and creating sustained vigorous risk management stratagems for the financial bubble framework and solving with supervised nonlinear autoregressive exogenous networks with optimized Bayesian regularization algorithm to accomplish the reasonable predictive accuracy and malleability for the solution of financial bubble behavioral dynamics. The Adams numerical solver accomplishes the acquisition of synthetic data for the execution of a multi-layer structure of exogenous networks to solve for financial bubble parameters termed as contagion rate of optimistic behavior, bearish behavior, pessimist’s average time of staying in the bearish group, the pessimist’s population effect on the autonomous supply and the rate of optimists conversion to pessimists group while assuming other parameters values to be fixed for demand and supply functions. A consistent overlap between proposed results and synthetic numerical values of a financial bubble model is indicated by negligible error value that verifies the exogenous network’s effectiveness and is verified by the enclosure of several evaluation measures of the precision and efficiency, through mean square error objective functions, adaptive amendable parameters, error dispersal, and input-error correlation analyses.
MHD slip flow through nanofluids for thermal energy storage in solar collectors using radiation and conductivity effects: A novel design sequential quadratic programming-based neuro-evolutionary approach
Authors: Butt Zeeshan Ikram , Ahmad Iftikhar , Raja Muhammad Asif Zahoor , Hussain Syed Ibrar , Ilyas Hira , Shoaib Muhammad
Published: December 2024

In this research, a novel design stochastic numerical technique is presented to investigate the unsteady form magnetohydrodynamic (MHD) slip flow along the boundary layer to analyze the transportation and heat transfer in a solar collector through nano liquids which is a revolution in the field of neurocomputing. Thermal conductivity in variable form is dependent on temperature and wall slips are assumed over the boundary. For mathematical modeling, the solar collector is assumed in the form of a nonlinear stretching sheet and a quite new artificial neural networks (ANNs) based approach is used to solve the current problem in which inverse multiquadric radial basis (IMRB) kernel is sandwiched between a global search solver named genetic algorithms (GAs) and a highly effective local solver named sequential quadratic programming (SQP) i.e. IMRB-GASQP solver. The governing boundary value problem is altered in the form of a system of nonlinear ordinary differential equations (ODEs) through the utilization of similarity transformation and then the obtained system of ODEs is solved using IMRB-GASQP solver by altering the values of distinguished parameters involved in it to observe the fluctuation in the velocity and temperature profiles of nanofluid. The obtained results are effectively compared with the reference solutions using the Adams numerical technique in graphical and tabulated form. An exhaustive error analysis using performance operators is presented while the efficacy of the designed solver using various statistical operators is also part of this research.
The A-polynomial and knot volume
Authors: Schilder Marc B.
Published: December 2024

In this paper, we conjecture a connection between the [Formula: see text]-polynomial of a knot in [Formula: see text] and the hyperbolic volume of its exterior [Formula: see text]: the knots with zero hyperbolic volume are exactly the knots with an [Formula: see text]-polynomial where every irreducible factor is the sum of two monomials in [Formula: see text] and [Formula: see text]. Herein, we show the forward implication and examine cases that suggest the converse may also be true. Since the [Formula: see text]-polynomial of hyperbolic knots are known to have at least one irreducible factor which is not the sum of two monomials in [Formula: see text] and [Formula: see text], this paper considers satellite knots which are graph knots and some with positive hyperbolic volume.
The braid indices of pretzel links: A comprehensive study, Part I
Authors: Diao Yuanan , Ernst Claus , Hetyei Gabor
Published: December 2024

In this paper and its sequel, we study the braid indices for all non-alternating pretzel links by a systematic approach. Pretzel links can be divided into three types according to the Seifert circle decompositions of their standard link diagrams. In this paper, we present our results on Type 1 and Type 2 pretzel links, namely the complete formulation of the braid indices for all non-alternating Type 1 and Type 2 pretzel links. Since the braid indices are already known for all alternating pretzel links from our previous work, it means that we have now completely determined the braid indices for all Type 1 and Type 2 pretzel links.
Charged-particle multiplicity distribution of heavy flavor decay leptons in proton–proton collisions using Pythia8 at LHC energies
Authors: Islam Md. Samsul , Sinha Tinku
Published: December 2024

In this report, the production of heavy-flavor (HF) hadron decay leptons as a function of self-normalized charged-particle multiplicity ([Formula: see text]) for proton–proton (p-p) collisions at [Formula: see text] = 13[Formula: see text]TeV has been discussed. The production of heavy-flavor (charm and bottom quarks) decay electrons (HFE) and muons (HFM) are measured at mid- ([Formula: see text]) and forward- (2.5 [Formula: see text] 4) rapidity regions, respectively. The charged-particle multiplicity ([Formula: see text]) is estimated at [Formula: see text]. The recent studies of ALICE experiment on charm production as a function of multiplicity in p-p collisions at [Formula: see text] = 7[Formula: see text]TeV and also inclusive and multiplicity-dependent production from heavy-flavor hadron decays in p-p and p-Pb collisions at [Formula: see text] = 13[Formula: see text]TeV and [Formula: see text] = 8.16[Formula: see text]TeV, respectively, are done. These studies motivate to calculate multiplicity dependence of HF meson decay leptons i.e., for electron and muon channels both. The multiplicity dependence HFE and HFM production are calculated using PYTHIA8 event generator. The multiple-parton interactions (MPI)-based color reconnection (CR) mechanism is added to the standalone PYTHIA8, to observe the signature of collectivity. To observe the impact of MPI effect, the simulation results (w/ and w/o CR mechanism) for pT-differential production cross-section of HFE in p-p collisions at [Formula: see text] = 13 TeV at mid-rapidity in a pT range (0.5 [Formula: see text] 16[Formula: see text]GeV/c) compared with ALICE data. At LHC, a linear increase of relative J[Formula: see text] as a function of charged-particle multiplicity is observed in p-p collisions. We have added the results of average pT ([Formula: see text] and [Formula: see text]) contributions as a function of multiplicity for both HFE and HFM productions. The same increasing trend is observed for relative HFE and HFM versus multiplicity. However, the results of mean pT ([Formula: see text] and [Formula: see text]) for HFE show much sharper increase with respect to HFM production. The results of nuclear modification factors like observables ([Formula: see text] and [Formula: see text]) versus pT could make us understand about the formation of quantum chromodynamics (QCD) medium in high multiplicity p-p collisions.
THE FOURTH-ORDER EXPLICIT GROUPING METHOD BASED ON CRANK–NICOLSON APPROACH FOR SOLVING THE TWO-DIMENSIONAL FRACTIONAL MOBILE/IMMOBILE EQUATION
Authors: KHAN MUHAMMAD ASIM , ALIAS NORMA , AYUB AYESHA , ALI UMAIR , CHING DENNIS LING CHUAN , ALJOHANI A. F. , KHAN ILYAS , MEMON ABID ALI
Published: December 2024

This paper introduces a novel fourth-order explicit group iterative technique (FEGS) for solving the two-dimensional (2D) fractional mobile/immobile equation (FME). The key objective of this study is to create a reliable and accurate numerical technique for solving the FME, which represents complex physical phenomena that involve anomalous diffusion processes. The suggested approach is based on the fourth-order Crank–Nicolson (C–N) finite difference method. It uses the Caputo fractional derivative for discretizing time and a fourth-order numerical technique for discretizing space. Furthermore, a theoretical investigation validates the stability and convergence of the suggested method. This paper presents various numerical examples to assess the accuracy and efficiency of the FEGS, showcasing its accuracy in comparison to other existing approaches. The results demonstrate the scheme’s stability and capacity for practical applications in modelling and simulating activities that encompass fractional diffusion equations.
Ramsey, expanders, and Borel chromatic numbers
Authors: Grebík Jan , Vidnyánszky Zoltán
Published: December 2024

We construct bounded degree acyclic Borel graphs with large Borel chromatic number using a graph arising from Ramsey theory and limits of expander sequences.
Probabilistic Machine Learning-Based Frequency Normalization Method for Bridge Damage Detection Considering Environmental Variations
Authors: Pei Xue-Yang , Zhang He-Tang , Huang Hai-Bin , Liang Dong
Published: December 2024

The bridge natural frequency changes caused by structural damages are often masked by the variations of environmental factors (especially the temperature), thus the ability to detect early damage is usually weakened. In order to tolerate the environmental effects, this paper proposes to normalize the natural frequency data based on probabilistic machine learning for the early damage detection of bridge structures. First, the probabilistic nonlinear relationships between multi-order natural frequencies in the intact state are modeled by the Gaussian process regression or the relevance vector machine. Second, the normalized frequency residuals for each order of frequency are individually calculated through their conditional means and variances estimated from the built probabilistic nonlinear relationship models. Third, the lifting residual vectors corresponding to the normalized frequency residuals are constructed according to the statistical localization approach. And finally, the Mahalanobis squared distance of the residual vector is defined as the damage indicator, afterwards the corresponding damage threshold is determined by the kernel density estimation technique. The long-term natural frequency data from a numerical simulation and an actual bridge are used to verify the effectiveness of the proposed method. The results demonstrate that the normalized frequency residuals are scarcely affected by the environmental variations, while remaining high sensitivity to the structural damages; compared to the normalized frequency residual, the damage indicator defined from the lifting residual vector is more suitable for detecting the early damage of bridge structures.
Response Mechanism of Nonlinear Energy Sink with Geometrically Nonlinear Damping Attached to Damped Primary System
Authors: Zhao Hongzhen , Li Jing , Zhu Shaotao , Zhang Yufeng
Published: December 2024

With the increasing performance requirements of vibration suppression devices for precision instruments and structural safety, there is an urgent need to explore efficient and stable vibration absorbers. This paper aims to investigate the response mechanism of a geometrically nonlinear coupled system under harmonic excitation, taking into account the damping of the primary system, which is often overlooked in research. The slow variation equation of the system is derived by the complexification-averaging method, from which the bifurcation analysis is conducted, and the influences of the two types of damping on the bifurcation are thoroughly discussed. Moreover, two motion triggering conditions are analyzed, namely, the threshold for the transition from intra-well periodic to inter-well chaos, and the threshold for the occurrence of strongly modulated response, some typical parameters are selected for numerical verification. Furthermore, the damping dissipation ratio is defined to evaluate the damping efficiency of the system, and the numerical results indicate that the optimal working efficiency can be achieved when the system first performs steady-state motion across two potential energy wells. Finally, the energy spectrum is calculated by the Romberg algorithm to verify the vibration reduction performance of the proposed model.
Two-Stage Integrated Dynamic Assessment Method for Urban Resilience Based on Multisource Data
Authors: Shen Lulu , Li Jianping , Sun Xiaolei , Suo Weilan
Published: December 2024

Urban resilience assessment (URA) is challenging because of urban system complexity and dynamic resilience variability. This paper develops a URA method with comprehensive feature consideration and integrated data use and then constructs a hierarchical URA criteria system. Subsequently, a two-stage integrated dynamic assessment method based on multisource data is presented, wherein the subjective–objective combination weights are determined, and the dimensional and overall urban resilience (UR) indexes are constructed. The applicability and superiority of the proposed method to existing methods are verified using a case study in Beijing. The results showed that UR in Beijing has improved substantially in 2016–2020; social and ecological dimensions are important for UR improvement; and synergies exist between different UR dimensions, which are crucial for resilient urban development. This study provides a systematic solution for URA that features a dynamic perspective, multisource data utilization, and subjective–objective combination weights, enhancing URA comprehensiveness and accuracy.
Nanodiamonds as Emerging Biomaterials for Biomedical Applications
Authors: Kumar Ankesh , Yadav Pankaj , Bhatia Dhiraj
Published: December 2024

Nanodiamonds (NDs) have unique optical and mechanical characteristics, surface chemistry, extensive surface area and biocompatibility, and they are nontoxic, rendering them suitable for a diverse range of applications. Recently, NDs have received significant attention in nano-biomedical engineering. This review discusses the recent advancement of NDs’ biomedical engineering, historical background, basic introduction to nanoparticles and development. We summarize NDs’ synthesis technique, properties and applications. Two methodologies are used in ND synthesis: bottom-up and top-down. We cover synthesis methods, including detonation, ball milling, laser ablation, chemical vapor deposition (CVD) and high pressure and high temperature (HPHT); discuss the properties of NDs, such as fluorescence and biocompatibility. Due to these properties, NDs have potential applications in biomedical engineering, including bioimaging, biosensing, drug delivery, tissue engineering and protein mimics. Further, it provides an outlook for future progress, development and application of NDs in biological and biomedical areas.
Plasma-Assisted Synthesis of N-Doped ZnO for Photocatalytic H2O2 Production
Authors: Di T. , Liu H. , Luan Y. , Li T. , Ke R. , Tao H. , Chen Z. , Zhao H.
Published: December 2024

Nonthermal plasma is a convenient, efficient and environmentally friendly technology for the synthesis and modification of materials. In this study, microwave plasma, a type of nonthermal plasma, was utilized to facilitate the rapid synthesis of hierarchical porous ZnO materials with N2 as feeding gas. It was observed that ZnO precursor could be transformed into hexagonal ZnO crystals after a short duration of plasma treatment while successfully incorporating N atoms into its lattice structure. The plasma-treated samples exhibit an increased number of smaller mesopores without compromising the hierarchical structure of the ZnO precursor. Furthermore, due to their enlarged specific surface area and extended visible light response range, the N-doped ZnO samples demonstrate excellent performance in photocatalytic H2O2 production performance. This work provides novel insight on exploring a new highly effective and efficient tools for material synthesis.
Fabrication of Perfect Branched PVDF/TiO2 Nanofibers for Keeping Food Fresh via One-Step Electrospinning
Authors: Zaarour Bilal , Mansour Ghaytha
Published: December 2024

Nowadays, the attention of researchers about titanium dioxide (TiO[Formula: see text] nanomaterials has increased due to their bactericidal, nontoxic, safe, cost-effective, stable, noncarcinogenic and industrial applications. This study aims to fabricate the electrospun branched PVDF/TiO2 nanofiber webs with tiny diameters and evaluate their performance in food fresh-keeping. The surface morphology, infrared spectroscopy, antibacterial properties and air permeability of the electrospun nanofiber web have been tested. Our results showed that the branched PVDF/TiO2 nanofibers with a diameter of less than 4[Formula: see text]nm can be electrospun smoothly. Furthermore, these webs have exhibited great potential in preserving the core of bananas fresh owing to their antibacterial properties, nonpoisonous, edible, edible and breathability.
An artificial electric field algorithm with a variable spiral search and an optimal solution mutation strategy
Authors: Cheng Jiatang , Feng Zhichao
Published: December 2024

Artificial electric field algorithm (AEFA) inspired by Coulomb’s law of electrostatic force is a swarm intelligent optimization algorithm. It utilizes the electrostatic force to enable information transmission and evolution of individuals. The traditional AEFA has achieved some successful applications, but it still has problems with premature convergence and low search ability. To solve these problems, an AEFA with a variable spiral search strategy and an optimal solution mutation strategy (VOAEFA) is proposed. The variable spiral search strategy which includes spiral search and skip search can improve the global exploration capability and reduce the probability of the algorithm falling into local optima. The optimal solution mutation strategy can increase the diversity of population. To evaluate the performance of VOAEFA, it is compared with AEFA algorithms and other algorithms on 57 benchmark functions and three engineering design problems. The comparison results on benchmark functions indicate that VOAEFA is superior to its competitors. In addition, VOAEFA has the advantage of high accuracy in solving challenging problems.
Navigating the Future of Infectious Diseases Management Through Carbon Nanotubes: A Review
Authors: Malhan Arprita , Guleria Mridul , Das Ushasi , Majee Sutapa Biswas , Singh Sudarshan , Nagime Pooja V. , Syukri Dwi Marlina , Chidrawar Vijay R. , Arania Resti , Shafi Sheeba
Published: December 2024

Infectious diseases are illnesses caused by pathogenic microorganisms, including viruses, bacteria, fungi, and parasites. These diseases can be transmitted from one individual to another, as well as through contaminated food, water, and insect bites, infectious agents invade the body, multiply, and disrupt normal bodily functions, leading to various health issues. However, due to antimicrobial resistance, the need to develop nanoenabled medicine gained significant attention recently. The management of infectious diseases using carbon nanotubes (CNTs) is an emerging field that leverages the unique properties of these nanostructures for enhanced drug delivery and therapeutic applications. Therefore, this review explores the transformative potential of CNTs in the diagnosis, prevention, and treatment of infectious diseases. As global health challenges escalate due to emerging pathogens and increasing drug resistance, the need for innovative solutions becomes critical. Moreover, the review systematically examines the unique properties of CNTs, including their mechanical, thermal, and electrical characteristics, that make them suitable for various biomedical applications. Further, the review highlights recent advancements in CNTs-based technologies, focusing on their roles in biosensing, drug deliver, and antiviral agents. Furthermore, the review also discusses how CNTs enhance the sensitivity and specificity of diagnostic tools, enabling rapid detection of infectious agents. Additionally, the multifunctional capabilities of CNTs in therapeutic applications, such as targeted drug delivery and pathogen inactivation, are also discussed. Challenges related to the clinical translation of CNTs technologies, including safety, biocompatibility, and regulatory concerns, are critically analyzed. In addition, the review concludes with clinical data by emphasizing the need for interdisciplinary collaboration to harness the full potential of CNTs in the management of infectious diseases, paving the way for future research and development in this promising field.
FRIEND OR FOE? HIGHER TEMPERATURE AND PERFORMANCE OF LEISURE FARMS
Authors: QIANG HONGJIE , CHIU CHIA-NING
Published: December 2024

This study examines the effect of weather on outdoor recreation and specifically leisure farms in Taiwan. We find that temperature has a complex effect on visits, revenues, and occupancy rates. Cool temperatures are the least desirable, very cold temperatures and warm temperatures are the most beneficial, and hot temperatures are less beneficial. Sites designed for winter activities benefit the most from cold temperatures whereas sites designed for walking and boating do best in warm temperatures. Global warming is likely to harm the sites suited for winter activities but should prolong the outdoor season for most leisure farms in Taiwan. But leisure farms may want to find ways to cope with hot temperatures.
A Feature Selection Method Combining Filter and Wrapper Approaches for Medical Dataset Classification
Authors: Yavuz Gürcan , Moghanjoughi Mohammad Karrari , Dumlu Hatem , Çakir Halil İbrahim
Published: December 2024

The curse of dimensionality is a problem that arises in datasets containing high-dimensional features. One of the primary methods used to address this issue is feature selection (FS), an effective technique for reducing insignificant features in a dataset. In this study, a two-stage approach called JASAL, which combines Filter and Wrapper FS methods, is proposed for high-dimensional medical datasets. In the first stage, preprocessing is applied to the high-dimensional datasets, and the ReliefF filter method is selected for this purpose. In the subsequent stage, continuous optimization algorithms, such as the Sine-Cosine Algorithm (SCA) and JAYA algorithms, are hybridized for binary optimization. A wrapper method that stochastically selects one of these two algorithms is used. Additionally, to overcome the local optimum problem of the JAYA algorithm, the Lévy flight strategy is incorporated into JAYA’s existing solution update strategy. To evaluate the performance of the JASAL algorithm, experiments are conducted on eight medical datasets, seven of which are high-dimensional. The results of the proposed algorithm are compared with 11 other metaheuristic algorithms. Various metrics, such as mean accuracy, mean number of selected features, and standard deviation, are considered in the experiments. The experimental results reveal that the JASAL algorithm achieves higher average accuracy than the compared algorithms. Furthermore, it is observed that these accuracy values are obtained by selecting relatively fewer features.
Distributed Reduced Order Observer for LTI Systems Under Impulsive Communication Disturbances
Authors: Wu Yali , Sun Yaping , Liu Jason J. R. , Shkodyrev Viacheslav P. , Su Housheng
Published: December 2024

In the process of signal transmission, an inevitable phenomenon is that the signal may change abruptly at certain discrete moments, negatively contributing to the performance of the system. In this paper, we focus on the distributed state estimation problem for continuous-time linear time-invariant (LTI) systems with intermittent abrupt jumps in communication information. First, the phenomenon of intermittent sudden jumps during information transmission is characterized as impulsive communication disturbances. Under impulsive communication disturbances, a distributed reduced order observer (DROO) is designed with each local observer grouped with two modules: a reduced-order observer and a consensus-based observer. Then, sufficient conditions for the existence of the DROO are presented, by using the graph theory and impulsive analysis. The relationship between the feedback gain and the unstable system matrix, the network topology, the impulse intensity, and the average impulsive interval is obtained. Finally, the validity of the proposed DROO under impulsive communication disturbances is substantiated by numerical simulations.
Self-Triggering Secure Consensus Against Adversarial Attacks
Authors: Liao Zirui , Shi Jian , Wang Shaoping , Zhang Yuwei , Mu Rui , Sun Zhiyong
Published: December 2024

The problem of secure consensus for multi-agent systems (MASs) is tackled in this study. The self-triggering strategy is designed to enable each healthy agent to estimate its next triggering step at the current triggering step. Thus, each healthy agent only needs to sense and broadcast at its triggering steps, and to monitor the latest broadcast states of their neighbors at their triggering steps. The frequent monitoring is thereby mitigated. Subsequently, a self-triggering secure consensus algorithm is developed to guarantee that the state variables of healthy agents reach consensus despite the influence of faulty agents in the network. The convergence analysis of the proposed method is conducted with graph tools and Lyapunov theory. Numerical examples are given to illustrate the superior performance of the proposed self-triggering secure consensus algorithm compared with the existing methods based on the static and dynamic event-triggering mechanisms.
Automatic Programming: Large Language Models and Beyond
Authors: Lyu Michael R. , Ray Baishakhi , Roychoudhury Abhik , Tan Shin Hwei , Thongtanunam Patanamon
Published: December 2024

Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance.
MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning
Authors: Sartaj Hassan , Ali Shaukat , Gjøby Julie Marie
Published: December 2024

Testing healthcare Internet of Things (IoT) applications at system and integration levels necessitates integrating numerous medical devices. Challenges of incorporating medical devices are: (i) their continuous evolution, making it infeasible to include all device variants, and (ii) rigorous testing at scale requires multiple devices and their variants, which is time-intensive, costly, and impractical. Our collaborator, Oslo City’s health department, faced these challenges in developing automated test infrastructure, which our research aims to address. In this context, we propose a meta-learning-based approach (MeDeT) to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices. We evaluate MeDeT in Oslo City’s context using five widely-used medical devices integrated with a real-world healthcare IoT application. Our evaluation assesses MeDeT’s ability to generate and adapt DTs across various devices and versions using different few-shot methods, the fidelity of these DTs, the scalability of operating 1000 DTs concurrently, and the associated time costs. Results show that MeDeT can generate DTs with over 96% fidelity, adapt DTs to different devices and newer versions with reduced time cost (around one minute), and operate 1000 DTs in a scalable manner while maintaining the fidelity level, thus serving in place of physical devices for testing.
Efficient Model Verification at Runtime through Adaptive Dynamic Approximation
Authors: Alidoost Nia Mehran , Calinescu Radu , Kargahi Mehdi , Abate Alessandro
Published: December 2024

In dynamic environments, safety-critical autonomous systems must adapt to environmental changes without violating safety requirements. Model verification at runtime supports adaptation through the periodic analysis of continually updated models. A major limitation of the technique is the high overhead associated with the regular analyses of large state-space models. Our paper introduces an
adaptive approximation strategy
that tackles this limitation by delaying unnecessary model updates, significantly reducing the overheads of these analyses. The strategy is applicable to Markov decision processes (MDPs) and is partitioned into components that can be analyzed independently and approximately. Each component is assigned a priority that depends on its impact on the accuracy of verification, and only the highest-priority components affected by changes are scheduled for updating/approximating. A complete update and verification of the entire model is triggered infrequently when the accuracy drops below a predefined threshold. We provide theoretical guarantees and proofs which ensure that our strategy can be applied without impacting the overall safety of the verified autonomous system. The experimental results from a case study in which we applied the strategy to a rescue robot team show that it is fully robust against safety-critical errors and can achieve a decision accuracy of over 97%.
METTL14 promotes ferroptosis in smooth muscle cells during thoracic aortic aneurysm by stabilizing the m6A modification of ACSL4
Authors: Wang Wenjun , Chen Jiayi , Lai Songqing , Zeng Ruiyuan , Fang Ming , Wan Li , Li Yiying
Published: December 2024

Our study confirmed that METTL14 can induce ferroptosis in vascular smooth muscle cells during the progression of thoracic aortic aneurysm by mediating the m6A modification of ACSL4 mRNA.
Spondias mombin Ameliorates Copper‐Induced Memory Impairment in Mice by Mitigating Oxidative Stress and Modulating Cholinergic Activity
Authors: Agboola James Busayo , Joshua Amos Rotimi , Ogunsanya Sanmi Tunde , Ehigie Adeola Folashade , Oke Janet Olufumilayo , Ogunruku Omodesola Oluwafisayo
Published: December 2024

The study unveils the importance of Spondias mombin leaf extract (S. mombin: 100 mg/kg b.wt.) in mitigating the toxic insult mediated by copper (16 mg/kg) on the cholinergic system via oral gavage. Male mice were weighed and distributed into five groups, six mice per group. Group A represents the basal control group given only distilled water; Group B and C were given 16 mg/kg of copper sulphate and 100 mg/kg of S. mombin methanol leaf extract, respectively, for 31 days; Group D was given 16 mg/kg of copper sulphate for 31 days, followed by treatment with 100 mg/kg of S. mombin methanol leaf extract for 15 days; and Group E was assigned only S. mombin. The copper concentration was evaluated using atomic absorption spectroscopy, while the cognitive abilities were assessed using Morris water maze and elevated plus‐maze. The result showed that S. mombin reversed the disparities in cholinesterase and choline acetyltransferase activities in vivo due to copper neurotoxicity, with a relative (p < 0.05) decrease in brain and blood copper concentration in co‐ and posttreatment groups. Furthermore, S. mombin reported decreased the disparity between pro‐ and antioxidant occurrence caused by copper toxicity by lowering brain lipid peroxidation, raising thiol levels, and increasing the activities of antioxidant enzymes such as glutathione transferase, catalase, and superoxide dismutase. The result showed that the extract had a protective influence on memory deficits (p < 0.05), significantly reduced anxiety levels, and increased the overall number of neurons in the hippocampus subfields. In conclusion, S. mombin ameliorates Cu‐induced neurotoxicity and neurocognitive dysfunctions by virtue of enhancement of cholinergic signalling and antioxidant activities in the brain.
A Pilot Study on Developed Shoes That Enhance Gait Parameters Without Increasing Muscle Activity
Authors: Abiko Teppei , Murata Shin , Kai Yoshihiro , Nakano Hideki , Sakamoto Masashi , Suzuki Keita , Matsuo Dai , Kawaguchi Michio
Published: December 2024

This pilot study investigated the potential of a newly developed shoe design to improve gait parameters without altering muscle activity in healthy women. The shoe design features a V‐shaped heel and a high‐elasticity midsole, which are intended to enhance stability during heel contact and promote efficient load transfer throughout the gait cycle. Ten study participants underwent a randomized crossover design, wearing developed and general shoes during the trials. Spatiotemporal gait data and muscle activity were measured to assess the impact of the shoe design developed on gait efficiency. Significant improvements in gait speed, step and stride length, and swing time were observed with the developed shoes, indicating improved gait efficiency. Importantly, these improvements were achieved without significant changes in muscle activity, suggesting that the developed shoe design improves gait efficiency without increasing muscle workload. Considering the limitations of the small sample size and the exploratory nature of this pilot study, further research with a larger cohort is necessary to validate these preliminary findings.

Trial Registration: Clinical Trial Registry identifier: UMIN000054260
Three‐Dimensional Dynamic Response of Multilayered Orthotropic Anisotropic Foundation Under Moving Loads
Authors: Zhang Chunli , Hong Long , Qin Mengdi , Qi Yunfei , Zhu Yanzhi , Hui Cun
Published: December 2024

Based on the basic control equations of orthotropic anisotropic half‐space under moving harmonic loads, the transfer matrix solution of a single‐layer foundation is derived by introducing the moving coordinate system, Fourier integral transform, and Cayley–Hamilton theorem. The three‐dimensional (3D) dynamic mechanical model of layered orthotropic anisotropic foundation under the rectangular coordinate system is established. The dynamic response of the layered foundation along the depth direction in the frequency domain is derived by using the transfer matrix method, combining boundary conditions, interlayer contact conditions, and continuity conditions. The analytical expressions of the displacements and stresses in the layered foundation are obtained by using Fourier inverse transformation. Based on the derived theoretical method, degradation verification, and corresponding calculation program are prepared for numerical calculation, and numerical example parameter analysis was carried out in combination with the finite element analysis software ABAQUS to study the influence law of the stratification characteristics, load moving speed, load vibration frequency and orthotropic anisotropic properties on the dynamic response of layered foundation. The 3D dynamic characteristics of layered orthotropic anisotropic foundations under the moving load are revealed. The results show that the stratification characteristics of the foundation have a significant effect on the vertical displacement of the surface, and the changes in elastic modulus and shear modulus parameters of the first layer of soil have a greater effect on the displacement dynamic response than that of the subsoil. Within a certain range, the vertical displacement of the foundation surface increases with the increase of load moving speed and decreases with the increase of load vibration frequency. Compared with isotropy, the orthogonal anisotropy of the surface foundation has an obvious influence on the vertical displacement of the foundation surface. In practical engineering, the orthogonal anisotropy of the foundation should be considered in order to obtain more accurate results.