Transmission Rate Research Articles

Identifying Interest Rate Transmission Mechanism under a Bayesian Network

This study examines causal relationships among various short- and long-term interest rates in the Korean financial market to identify transmission channels. Monthly time series data from January 2015 to February 2024 were used, covering nine interest rates, including call rates, commercial paper (CP) rates, bank lending rates, and Treasury bond yields of different maturities. The study employs a Bayesian network to identify an acyclic causal structure between interest rates alongside a vector error correction model (VECM) to capture long-term equilibrium relationships and short-term dynamics. The findings reconfirm the traditional call rate transmission mechanism, aligning with conventional monetary policy views, demonstrating the call rate’s significant influence on bank lending rates, which affects corporate bond spreads and CP rates. Additionally, it reveals that 10-year Treasury bond yields form an independent interest rate transmission pathway, a finding not previously identified in the literature. These results underscore the need for coordinated monetary and fiscal policies due to the distinct transmission pathways of Treasury yields for sustainable macroeconomic management and growth.

The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model.

In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the existing SSVEP brain control methods cannot adapt to dynamic or unstructured environments. Moreover, the recognition accuracy from the conventional decoding algorithm still needs to improve. To address the above problems, this study proposed a steady-state hybrid visual evoked potentials (SSHVEP) paradigm using the grasping targets in their environment to improve the connection between the subjects' and their dynamic environments. Moreover, a novel EEG decoding method, using the multivariate variational mode decomposition (MVMD) algorithm for adaptive sub-band division and convolutional neural network (CNN) for target recognition, was applied to improve the decoding accuracy of the SSHVEPs. 18 subjects participated in the offline and online experiments. The offline accuracy across 18 subjects by the 9-target SSHVEP paradigm was up to 95.41 ± 2.70%, which is a 5.80% improvement compared to the conventional algorithm. To further validate the performance of the proposed method, the brain-controlled grasping robot system using the SSHVEP paradigm was built. The average accuracy reached 93.21 ± 10.18% for the online experiment. All the experimental results demonstrated the effectiveness of the brain computer interaction method based on the SSHVEP paradigm and the MVMD combined CNN algorithm studied in this paper.

Quantifying the Health–Economy Trade-Offs: Mathematical Model of COVID-19 Pandemic Dynamics

The COVID-19 pandemic has presented a complex situation that requires a balance between control measures like lockdowns and easing restrictions. Control measures can limit the spread of the virus but can also cause economic and social issues. Easing restrictions can support economic recovery but may increase the risk of virus transmission. Mathematical approaches can help address these trade-offs by modeling the interactions between factors such as virus transmission rates, public health interventions, and economic and social impacts. A study using a susceptible-infected-susceptible (SIS) model with modified discrete time was conducted to determine the cost of handling COVID-19. The results showed that, without government intervention, the number of patients rejected by health facilities and the cost of handling a pandemic increased significantly. Lockdown intervention provided the least number of rejected patients compared to social distancing, but the costs of handling the pandemic in the lockdown scenario remained higher than those of social distancing. This research demonstrates that mathematical approaches can help identify critical junctures in a pandemic, such as limited health system capacity or high transmission rates, that require rapid response and appropriate action. By using mathematical analysis, decision-makers can develop more effective and responsive strategies, considering the various factors involved in the virus’s spread and its impact on society and the economy.

Coherently and Incoherently Pumped Telecom C-Band Single-Photon Source with High Brightness and Indistinguishability.

Long-range, terrestrial quantum networks require high-brightness single-photon sources emitting in the telecom C-band for maximum transmission rates. For solid-state quantum emitters, the underlying pumping process, i.e., coherent or incoherent excitation schemes, impacts several photon properties such as photon indistinguishability, single-photon purity, and photon number coherence. These properties play a major role in quantum communication applications, the latter in particular for quantum cryptography. Here, we present a versatile telecom C-band single-photon source that is operated coherently and incoherently using two complementary pumping schemes. The source is based on a quantum dot coupled to a circular Bragg grating cavity, whereas coherent (incoherent) operation is performed via the novel SUPER scheme (phonon-assisted excitation). In this way, high end-to-end-efficiencies (ηend) of 5.36% (6.09%) are achieved simultaneously with a small multiphoton contribution g(2)(0) of 0.076 ± 0.001 [g(2)(0) of 0.069 ± 0.001] for coherent (incoherent) operation.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm–grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Fabrication and characterization of multi-layered coaxial agar-based electrospun biocomposite mat, novel replacement for transdermal patches

In the performed study, a novel fabrication of agar-based nanofibers was electrospun in an asymmetric bilayer dressing for biomedical transdermal patches. The optimal parameters for the fabrication of agar-based nanofibers after optimization were a feed rate of 10 μL/min, a 7 cm collector-to-nozzle distance, a 15 kV applied voltage, and a 700-rpm rotating collector speed. Coaxial nanofibers, as a second asymmetric layer, were produced using polyvinyl alcohol (PVA) with cephalexin hydrate, an antibacterial drug, as the core and agar-PCL as the sheath. The morphology of the developed uniaxial and coaxial nanofibrous layers was analysed using a scanning electron microscope and transmission electron microscopy, respectively. For the formation of bilayer asymmetric structures, the agar-PCL uniaxial layer was fabricated over the layer of coaxial PVA and agar-PCL layers for sustained drug release. The agar-based nanofibrous mats exhibited tensile strength of 7 MPa with 40 % elongation failure, 8-fold increased swelling, enhanced wettability (60° contact angle), and a moisture transmission rate of 2174 g/m2/day. The developed coaxial bilayer mats exhibited antimicrobial activity, hemocompatibility, and cytocompatibility. Overall, this novel agar nanofibrous dressing offers promising potential for advanced biomedical applications, particularly as transdermal patches for efficient drug delivery systems.

Spatiotemporally Explicit Epidemic Model for West Nile Virus Outbreak in Germany: An Inversely Calibrated Approach.

Since the first autochthonous transmission of West Nile Virus was detected in Germany (WNV) in 2018, it has become endemic in several parts of the country and is continuing to spread due to the attainment of a suitable environment for vector occurrence and pathogen transmission. Increasing temperature associated with a changing climate has been identified as a potential driver of mosquito-borne disease in temperate regions. This scenario justifies the need for the development of a spatially and temporarily explicit model that describes the dynamics of WNV transmission in Germany. In this study, we developed a process-based mechanistic epidemic model driven by environmental and epidemiological data. Functional traits of mosquitoes and birds of interest were used to parameterize our compartmental model appropriately. Air temperature, precipitation, and relative humidity were the key climatic forcings used to replicate the fundamental niche responsible for supporting mosquito population and infection transmission risks in the study area. An inverse calibration method was used to optimize our parameter selection. Our model was able to generate spatially and temporally explicit basic reproductive number (R0) maps showing dynamics of the WNV occurrences across Germany, which was strongly associated with the deviation from daily means of climatic forcings, signaling the impact of a changing climate in vector-borne disease dynamics. Epidemiological data for human infections sourced from Robert Koch Institute and animal cases collected from the Animal Diseases Information System (TSIS) of the Friedrich-Loeffler-Institute were used to validate model-simulated transmission rates. From our results, it was evident that West Nile Virus is likely to spread towards the western parts of Germany with the rapid attainment of environmental suitability for vector mosquitoes and amplifying host birds, especially short-distance migratory birds. Locations with high risk of WNV outbreak(Baden-Württemberg, Bavaria, Berlin, Brandenburg, Hamburg, North Rhine-Westphalia, Rhineland-Palatinate, Saarland, Saxony-Anhalt and Saxony) were shown on R0 maps. This study presents a path for developing an early warning system for vector-borne diseases driven by climate change.

Seasonal Variability and Stochastic Branching Process in Malaria Outbreak Probability

Background: Malaria is the world’s most fatal and challenging parasitic disease, caused by the Plasmodium parasite, which is transmitted to humans by the bites of infected female mosquitoes. Bangladesh is the most vulnerable region to spread malaria because of its geographic position. In this paper, we have considered the dynamics of vector-host models and observed the stochastic behavior. This study elaborates on the seasonal variability and calculates the probability of disease outbreaks. Methods: We present a model for malaria disease transmission and develop its corresponding continuous-time Markov chain (CTMC) representation. The proposed vector-host models illustrate the malaria transmission model along with sensitivity analysis. The deterministic model with CTMC curves is depicted to show the randomness in real scenarios. Sequentially, we expand these studies to a time-varying stochastic vector-host model that incorporates seasonal variability. Phase plane analysis is conducted to explore the characteristics of the disease, examine interactions among various compartments, and evaluate the impact of key parameters. The branching process approximation is developed for the corresponding vector-host model to calculate the probability outbreak. Numerous numerical results are accomplished to observe the analytical investigation. Results: Seasonality and contact patterns affect the dynamics of disease outbreaks. The numerical illustration provides that the probability of a disease outbreak depends on the infected host or vector. Additionally, periodic transmission rates have a great influence on the probability outbreak. The basic reproduction number (R0) is derived, which is the main justification for studying the dynamical behavior of epidemic models. Conclusions: Seasonal variability significantly impacts malaria transmission, and the probability of disease outbreaks is influenced by time and the initial number of infected individuals. Moreover, the branching process approximation is applicable when the population size is large enough and the basic reproduction number is less than 1. In the future, such analysis can help decision-makers understand the impact of various parameters and their stochastic behavior in the vector-host model to prevent such types of disease outbreaks.

COVID-19 in Egyptian Children on Regular Hemodialysis: Experience of a Single Center

Abstract Background COVID-19 is clinically characterized by high rates of transmission, and pathological abnormalities. Pediatric patients with CKD are at high risk of COVID-19 infection & morbidity. Aim of the Work We aimed at studying the rate of infection by COVID-19 in pediatric patients following up at our Pediatric dialysis & Nephrology unit, Ain Shams University. Patients and Methods This retrospective study was conducted over 12 months from 1st of March 2020 to end of February 2021 on 70 pediatric patients with chronic kidney disease stage 5 on regular hemodialysis (CDK5d). Results The mean age & dialysis duration of our studied patients was 11.89 (±3.01) years & 2.17(±1.2)years respectively, where the incidence of COVID-19 infection confirmed by PCR on nasal swab sampleswas 7%. The younger patients had a higher rate of COVID-19 infection than older ones (p = 0.05). Using public transportation during past 14 days before getting COVID-19 infection was significant in COVID-19 positive patients (p = 0.01). The common associated symptoms in our patients were fever, joint ache, muscle ache, loss of appetite, headache, shortness of breath, fatigue, cough, sore throat, nausea, loss of smell or taste, diarrhea, vomiting. Conclusion COVID-19 infection among our CDK5d patients was not uncommon with incidence of 7%, where younger patients were infected more frequently than older patients The common associated symptoms in our patients were fever, joint ache, muscle ache, loss of appetite, headache, shortness of breath, fatigue, cough, sore throat, nausea, loss of smell or taste, diarrhea, vomiting.

Progress in research and development of preventive vaccines for children in China

The infant and child stage is an important stage for the continuation and development of human society. The initial years of life have a lasting impact on a child's future. Children under the age of 5 have an immature immune system, especially infants and young children under 6 months of age. At this stage, the population has a low immunity to pathogen infections, making them vulnerable to bacteria and viruses. Vaccination can enhance the immunity of infants and children to specific diseases, reduce the transmission rate of infectious diseases, and promote the development of global public health. This article summarizes the current application status of Rotavirus (RV) vaccine, Hand-foot -mouth disease (HFMD) vaccine, and Pneumococcal Conjugate Vaccine (PCV) in China, as well as the research progress of clinical trial vaccine, laying a foundation for subsequent vaccine development.

A mathematical model of COVID-19 with multiple variants of the virus under optimal control in Ghana.

In this paper, we suggest a mathematical model of COVID-19 with multiple variants of the virus under optimal control. Mathematical modeling has been used to gain deeper insights into the transmission of COVID-19, and various prevention and control strategies have been implemented to mitigate its spread. Our model is a SEIR-based model for multi-strains of COVID-19 with 7 compartments. We also consider the circulatory structure to account for the termination of immunity for COVID-19. The model is established in terms of the positivity and boundedness of the solution and the existence of equilibrium points, and the local stability of the solution. As a result of fitting data of COVID-19 in Ghana to the model, the basic reproduction number of the original virus and Delta variant was estimated to be 1.9396, and the basic reproduction number of the Omicron variant was estimated to be 3.4905, which is 1.8 times larger than that. We observe that even small differences in the incubation and recovery periods of two strains with the same initial transmission rate resulted in large differences in the number of infected individuals. In the case of COVID-19, infections caused by the Omicron variant occur 1.5 to 10 times more than those caused by the original virus. In terms of the optimal control strategy, we formulate three control strategies focusing on social distancing, vaccination, and testing-treatment. We have developed an optimal control model for the three strategies outlined above for the multi-strain model using the Pontryagin's Maximum Principle. Through numerical simulations, we analyze three optimal control strategies for each strain and also consider combinations of the two control strategies. As a result of the simulation, all control strategies are effective in reducing disease spread, in particular, vaccination strategies are more effective than the other two control strategies. In addition the combination of the two strategies also reduces the number of infected individuals by 1/10 compared to implementing one strategy, even when mild levels are implemented. Finally, we show that if the testing-treatment strategy is not properly implemented, the number of asymptomatic and unidentified infections may surge. These results could help guide the level of government intervention and prevention strategy formulation.

Epidemiology and Management of Bean Common Mosaic Virus (BCMV) in Traditional Phaseolus vulgaris L. Landraces within Protected Geographical Indications

Protected geographical indications (PGIs) share health problems related to plant propagation material. The PGI “Faba de Lourenzá” encompasses a 1660 km2 area in northern Galicia, Spain, renowned for its “Faba Galaica” (FG) and Faba do marisco” (FM) bean cultivars. The lack of certified virus-free seeds poses a challenge. From 2019 to 2023, seeds from 60 lots were tested for BCMV. Plants from several plots were tested periodically to develop disease progress curves (DPCs). Control methods (plots out PGI zone, virus-free seedlings, rogueing, corn borders, and intercropping) were tested. Yields in five plots were used to assess BCMV’s economic impact. Seed lots were 22.3% FG-infected and <5% FM-infected. The transmission rate of BCMV from infected FG plants to their seeds was 25.5 ± 5%, while for FM it was 12 ± 3%. FG yield losses were on average 31.6 ± 4.5%. Combining virus-free seedlings and infected plant removal in plots outside the PGI area proved effective at reducing infection rates; combining with intercropping resulted in the lowest incidence in an FG plot. Farmer training and off-site plot selection to produce healthy sowing beans are key to improving results.

Instabilities and self–organization in spatiotemporal epidemic dynamics driven by nonlinearity and noise

Theoretical analysis of epidemic dynamics has attracted significant attention in the aftermath of the COVID–19 pandemic. In this article, we study dynamic instabilities in a spatiotemporal compartmental epidemic model represented by a stochastic system of coupled partial differential equations (SPDE). Saturation effects in infection spread–anchored in physical considerations–lead to strong nonlinearities in the SPDE. Our goal is to study the onset of dynamic, Turing–type instabilities, and the concomitant emergence of steady–state patterns under the interplay between three critical model parameters–the saturation parameter, the noise intensity, and the transmission rate. Employing a second–order perturbation analysis to investigate stability, we uncover both diffusion–driven and noise–induced instabilities and corresponding self–organized distinct patterns of infection spread in the steady state. We also analyze the effects of the saturation parameter and the transmission rate on the instabilities and the pattern formation. In summary, our results indicate that the nuanced interplay between the three parameters considered has a profound effect on the emergence of dynamical instabilities and therefore on pattern formation in the steady state. Moreover, due to the central role played by the Turing phenomenon in pattern formation in a variety of biological dynamic systems, the results are expected to have broader significance beyond epidemic dynamics.

Essential oils loaded biodegradable PBAT/PBS films as young coconut packaging after harvest

The polybutyrate adipate terephthalate/polybutylene succinate (PBAT/PBS) films were produced by the single screw blown-film extrusion method for the young coconut packaging application. The antifungal performance of the film was enhanced by the addition of essential oils, including cinnamon essential (CIN) and citrus essential (CT) oils. The mechanical properties, physical properties, thermal properties, oxygen transmission rate (OTR) and water vapor transmission rate (WVTR) of the film with essential oils were evaluated. The results found that 10 wt% of PBAT content in the PBS matrix was suitable for biodegradable film creation. In the film packaging, essential oils at 5 phr might be a plasticizer by increasing the tensile strength, elongation at break and toughness, while the tensile modulus decreased. The oxygen and water vapor transmission rates (OTR, WVTR) trended to increase, especially of the 10CT_10PBAT/PBS film. The essential oils could show antifungal performance by inhibiting the A. Niger growth. In the case of film packaging application, the film with essential oils could enhance the storage time of young coconut to 21 days without yeast growth on the surface, while the quality of coconut water did not change.

Permeability and barrier layer properties of a woven carbon fibre polymer composite as battery packaging

Carbon fibre reinforced polymer (CFRP) laminates are used in various studies as Li-ion polymer (LiPo) batteries packaging. However, their suitability as a packaging material is not well understood. The present study investigates the liquid absorption (water and lithium bis(trifluoromethanesulfonic)imide (LiTFSI) electrolyte immersion tests) and barrier layer properties (water vapour transmission rate (WVTR) and oxygen transmission rate (OTR) tests) of a 200 gsm woven CFRP laminate to assess its suitability as a battery packaging material. This is the first study to show that prolonged immersion in battery electrolyte does not change the mechanical of a woven CFRP laminate. Hence, the CFRP laminate may be suitable for structural battery components, such as current collectors and electrodes. However, CFRP should not be used as battery packaging, as OTR and WVTR values of the CFRP laminate were found to be five and one order of magnitudes higher than typical battery pouch materials (i.e. aluminium foil), respectively.

Non-optimal and optimal fractional control analysis of measles using real data

This study employs fractional, non-optimal, and optimal control techniques to analyze measles transmission dynamics using real-world data. Thus, we develop a fractional-order compartmental model capturing measles transmission dynamics. We then formulate an optimal control problem to minimize the disease burden while considering constraints such as vaccination resources and intervention costs. The proposed model’s positivity, boundedness, measles reproduction number, and stability are obtained. The sensitivity analysis using the partial rank correlation coefficient is shown for the fractional orders of 0.99 and 0.90. It is noticed that the rate of recruitment into the susceptible population (π), the rate at which individuals in the latent class become asymptomatic (α1), and the transmission rate (β) contribute positively to the spread of the disease, while the rate at which individuals in the asymptomatic class become symptomatic (α2), the vaccination rate for the first measles dose (γ1), and the rate at which individuals in the latent class recover from measles (δ1) contribute significantly to the reduction of measles in the community. Utilizing numerical simulations and sensitivity analyses, we identify optimal control strategies that balance the trade-offs between intervention efficacy, resource allocation, and societal costs. Our findings provide insights into the effectiveness of fractional optimal control strategies in mitigating measles outbreaks and contribute to developing more robust and adaptive disease control policies in real-world scenarios.

Kū Like Kākou: Utilizing a Community-led Model of Collaboration to Respond to the COVID-19 Pandemic.

The outbreak of COVID-19 in 2020 brought significant challenges for Native Hawaiians (NH), Pacific Islanders (PI), and other communities of color worldwide. Rapidly increasing rates of infection and transmission of the virus in Native Hawaiian and Pacific Islander (NHPI) communities and incomplete or unavailable data signaled to Hawai'i's leaders that advocacy and action needed to take place to minimize the impact of COVID-19. The Native Hawaiian and Pacific Islander Response, Recovery, and Resilience team (NHPI 3R Team) emerged from an effort to lead and fill gaps in response to COVID-19. Through the swift, intentional, and collaborative work of the team and its partners, NHPI communities and the entities that serve them were better equipped to navigate the pandemic, improve health outcomes, and contribute to a reduction in the number of infections, a rise in vaccination uptake, and an increase in NH and PI representation on various levels of government agencies. As the world shifts its focus from COVID-19 to broader health topics, the NHPI 3R Team will continue to serve as a hub for the exchange of resources and a model of community-led work that can be used to tackle issues like COVID-19 and beyond.

Solving satellite image data downlink scheduling problem with family attribute via a bi-stage differential evolutionary algorithm

Due to the asynchronous development of observation and transition capabilities, the original image data (OID) obtained by a one-time observation cannot be completely transmitted in one transmission cycle between the EOS and GS, which is named a visible time window (VTW). First, it is needed to segment the OID into several segmented image datasets (SIDs), which are then transmitted in several VTWs. Furthermore, this can cause the satellite image data downlink scheduling problem (SIDSP). This study defines an innovative SIDSP as a satellite image data downlink scheduling problem with the family attribute (SIDSPFA), where massive OID are segmented using a fast segmentation operator, and all SIDs and other no-segmented OID are transmitted in the second step. In addition, two optimization objectives, namely the failure rate (FR) and Segmentation Time (ST) of the image data transmission, are defined to formalize the SIDSPFA as a bi-objective discrete optimization model. Further, a bi-stage differential evolutionary algorithm (DE-CD) is developed. The results of the extensive simulation tests verify the efficiency of the models, strategies, algorithms, and operators used in this study.

Evaluation and validation of a PrintrLab-based LAMP assay to identify Trypanosoma cruzi in newborns in Bolivia: a proof-of-concept study

Vertical transmission of Trypanosoma cruzi represents approximately 20% of new Chagas disease cases. Early detection and treatment for women of childbearing age and newborns is a public health priority, but the lack of a simple and reliable diagnostic test remains a major barrier. We aimed to evaluate the performance of a point-of-care loop-mediated isothermal amplification (LAMP) assay for the detection of Tcruzi. In this proof-of-concept study, we coupled a low-cost 3D printer repurposed for sample preparation and amplification (PrintrLab) to the Eiken Tcruzi-LAMP prototype to detect vertically transmitted Tcruzi, which we compared with standardised PCR and with the gold-standard algorithm (microscopy at birth and 2months and serological study several months later). We screened pregnant women from two hospitals in the Bolivian Gran Chaco province, and those who were seropositive for Tcruzi were offered the opportunity for their newborns to be enrolled in the study. Newborns were tested by microscopy, LAMP, and PCR at birth and 2months, and by serology at 8months. Between April 23and Nov 17, 2018, 986mothers were screened, among whom 276were seropositive for Tcruzi (28·0% prevalence, 95% CI 25·6-31·2). In total, 224infants born to 221seropositive mothers completed 8months of follow-up. Congenital transmission was detected in nine of the 224newborns (4·0% prevalence, 1·9-7·5) by direct microscopy observation, and 14more cases were diagnosed serologically (6·3%, 3·6-10·3), accounting for an overall vertical transmission rate of 10·3% (6·6-15·0; 23of 224). All microscopy-positive newborns were positive by PrintrLab-LAMP and by PCR, while these techniques respectively detected four and five extra positive cases among the remaining 215 microscopy-negative newborns. The PrintrLab-LAMP yielded a higher sensitivity than microscopy-based analysis. Considering the simpler use and expected lower cost of LAMP compared with PCR, our findings encourage its evaluation in a larger study over a wider geographical area. Inter-American Development Bank.

Films based on TEMPO-oxidized chitosan nanoparticles: Obtaining and potential application as wound dressings

A series of novel films based on TEMPO-oxidized chitosan nanoparticles were prepared by casting method. Fourier transform infrared spectroscopy (FTIR) was employed to ascertain the chemical structure of TEMPO-oxidized chitosan. The surface morphology of the TEMPO-oxidized chitosan nanoparticles was analyzed by atomic force microscopy (AFM). The physicochemical (area density, thickness, iodine sorption, roughness), functional (moisture sorption, liquid absorption capacity, weight loss upon contact with the liquid, and water vapor transmission rate), antibacterial, and antioxidant properties of films based on TEMPO-oxidized chitosan nanoparticles were also investigated. The physicochemical properties of the films varied widely: area density ranged from 77.83 ± 0.06 to184.46 ± 0.05 mg/cm2, thickness varied between 80.5 ± 1.6 and 200.5 ± 1.6 μm, iodine sorption spanned from 333.7 ± 2.1 to166.4 ± 2.2 mg I2/g, and roughness ranged from 4.1 ± 0.2 to 5.6 ± 0.3 nm. Similarly, the functional properties also varied significantly: moisture sorption ranged from 4.76 ± 0.03 to 9.62 ± 0.11 %, liquid absorption capacity was between 129.04 ± 0.24 and 159.33 ± 0.73 % after 24 h, weight loss upon contact with the liquid varied between 31.06 ± 0.35 and 45.88 ± 0.58 % after 24 h and water vapor transmission rate ranged from 1220.10 ± 2.91to1407.77 ± 5.22 g/m2 day. Despite the wide variations in physicochemical and functional properties, all films showed maximum bacterial reduction of Staphylococcus aureus and Escherichia coli, although they exhibited low antioxidant activity. The results suggest that the films could be effectively utilized as antibacterial wound dressings.