Time Investment Research Articles

Assessing the impact of risk-based data monitoring on outcomes for a paediatric multicentre randomised controlled trial.

Regulatory guidelines recommend that sponsors develop a risk-based approach to monitoring clinical trials. However, there is a lack of evidence to guide the effective implementation of monitoring activities encompassed in this approach. The aim of this study was to assess the efficiency and impact of the risk-based monitoring approach used for a multicentre randomised controlled trial comparing treatments in paediatric patients undergoing cardiac bypass surgery. This is a secondary analysis of data from a randomised controlled trial that implemented targeted source data verification as part of the risk-based monitoring approach. Monitoring duration and source to database error rates were calculated across the monitored trial dataset. The monitored and unmonitored trial dataset, and simulated trial datasets with differing degrees of source data verification and cohort sizes were compared for their effect on trial outcomes. In total, 106,749 critical data points across 1,282 participants were verified from source data either remotely or on-site during the trial. The total time spent monitoring was 365 hours, with a median (interquartile range) of 10 (7, 16) minutes per participant. An overall source to database error rate of 3.1% was found, and this did not differ between treatment groups. A low rate of error was found for all outcomes undergoing 100% source data verification, with the exception of two secondary outcomes with error rates >10%. Minimal variation in trial outcomes were found between the unmonitored and monitored datasets. Reduced degrees of source data verification and reduced cohort sizes assessed using simulated trial datasets had minimal impact on trial outcomes. Targeted source data verification of data critical to trial outcomes, which carried with it a substantial time investment, did not have an impact on study outcomes in this trial. This evaluation of the cost-effectiveness of targeted source data verification contributes to the evidence-base regarding the context where reduced emphasis should be placed on source data verification as the foremost monitoring activity.

A qualitative study on the facilitators and barriers to adopting the N-of-1 trial methodology as part of clinical practice: potential versus implementation challenges

ABSTRACT Purpose To investigate opinions among healthcare stakeholders whether implementation of the N-of-1 trial approach in clinical practice is a feasible way to optimize evidence-based treatment results for unique patients. Methods We interviewed clinicians, researchers, and a patient advocate (n = 13) with an interest in or experience with N-of-1 trials on the following topics: experience with N-of-1, measurement, validity and reliability, informally gathered data usability, and influence on physician-patient relationship. Interviews were analysed using qualitative, thematic analysis. Results The N-of-1 approach has the potential to shift the current healthcare system towards embracing personalized medicine. However, its application in clinical practice carries significant challenges in terms of logistics, time investment and acceptability. New skills will be required from patients and healthcare providers, which may alter the patient-physician relationship. The rise of consumer technology enabling self-measurement may leverage the uptake of N-of-1 approaches in clinical practice. Conclusions There is a strong belief that the N-of-1 approach has the potential to play a prominent role in transitioning the current healthcare system towards embracing personalized medicine. However, there are many barriers deeply ingrained in our healthcare system that hamper the uptake of the N-of-1 approach, making it momentarily only interesting for research purposes.

A versatile design method applied to reconfigurable metasurfaces

This paper introduces a versatile design method for reconfigurable metasurfaces based on the Pancharatnam–Berry phase theory. Unlike traditional reconfigurable metasurfaces that require designing independent surfaces for specific applications, leading to a significant time investment for designers to learn and create, this study proposes a foundational, invariant metasurface. By selectively metallizing holes or inserting metal cylinders, it achieves nine available functionalities. Regardless of the chosen operating function, the metasurface demonstrates high efficiency at 8.2 GHz, with amplitude loss less than 1 dB. Additionally, when operating in phase modulation mode, the design provides a 360° phase adjustment range and a 30° phase step. A prototype containing 31×31 units (425.6×425.6mm2) has been fabricated and tested under function 7 (TM and RHCP transmission phase modulation). Measurement results confirm the metasurface’s capability for polarization conversion and phase modulation at the frequency of 8.2 GHz.

A new approach for the flashover voltage prediction using an arc propagation reproduction on a high-voltage insulator

PurposeThis paper aims to introduce a new numerical model that predicts the flashover voltage (FOV) value in the presence of polluted air surrounding a high-voltage insulator. The model focuses on simulating the propagation of arcs and aims to improve the accuracy and reliability of FOV predictions under these specific conditions.Design/methodology/approachThis arc propagation method connecting the high voltage fitting and the grounded insulator cap involves a two-step process. First, the electric field distribution in the vicinity of the insulator is obtained using finite element method analysis software. Subsequently, critical areas with intense electric field strength are identified. Random points within these critical areas are then selected as initial points for simulating the growth of electric arcs.Findingsby increasing the electric voltage applied to the insulator fittings, the arc path is, step by step, generated until a breakdown occurs on the polluted air surrounding the insulator surface, and thus a prediction of the FOV value.Practical implicationsThe proposed model for the FOV prediction can be a very interesting alternative to dangerous and costly experimental tests requiring an investment in time and materials.Originality/valueSome works were done trying to reproduce discharge propagation but it was always with simplified models such as propagation in one direction from a point to a plane. The difficulty and the originality of the present work is the geometry complexity of the insulator with arc propagation in three distinct directions that will require several proliferation conditions.

Machine learning-based optimization of a multi-step ion exchange chromatography for ternary protein separation

Ion-exchange chromatography is an essential but complicated step in the biopharmaceutical downstream process, with multiple factors affecting the separation efficiency. Model-based optimization can help expedite process developments with limited time and resource investments. To address the mechanistic model's high computational complexity, a machine learning (ML)-based optimization framework was introduced. Specifically, Gaussian Process Regression models were utilized as substitutes for the mechanistic model in calculating the constraints and objective function. To further reduce the required sampling, feasibility and optimization stages were incorporated into the framework, with a penalty introduced at each stage to guide the search. This ML-based framework was applied to a case study to separate a protein mixture. Compared to the results obtained through genetic algorithm, this approach enhanced productivity by 50.1 % and reduced computation time by 70.8 % simultaneously. The effects of peak cutting criteria on the optimal results were examined, followed by a detailed analysis of design space.

Stability Study of New Power System Based On Multi-Intelligent Body Collaboration

Developing, implementing, and maintaining a multi-intelligent body collaboration system necessitates significant investments in finances, time, and expertise. While multi-intelligent body collaboration has the potential to enhance power system stability significantly, it also comes with challenges related to interoperability, security, system complexity, and resource allocation. Resource allocation and training costs can be substantial. Addressing these challenges is crucial to harnessing the full benefits of this approach and ensuring the reliable and efficient operation of power systems. Effective communication and coordination strategies among intelligent agents are integral to maintaining power system stability. Timely information exchange, load balancing, disturbance management, and the integration of AI contribute to a more resilient and adaptive energy grid. As technology advances, refining these strategies will be essential to meet the growing demands of an ever-evolving power landscape. As technology marches forward, it becomes increasingly evident that the refinement of these strategies is paramount. The dynamism of the power landscape, driven by technological advancements and evolving needs, necessitates an agile and adaptable power system. The fusion of multi-intelligent bodies and modern technology stands as a testament to our collective pursuit of a more reliable, efficient, and sustainable energy future. In this ever-evolving landscape, the innovation and enhancement of these strategies are our compass, guiding us toward a brighter and more efficient future.

BloomDT - An improved privacy-preserving decision tree inference scheme

Outsourcing decision tree models to cloud servers can allow model providers to distribute their models at scale without purchasing dedicated hardware for model hosting. However, model providers may be forced to disclose private model details when hosting their models in the cloud. Due to the time and monetary investments associated with model training, model providers may be reluctant to host their models in the cloud due to these privacy concerns. Furthermore, clients may be reluctant to use these outsourced models because their private queries or their results may be disclosed to the cloud servers. In this paper, we propose BloomDT, a privacy-preserving scheme for decision tree inference, which uses Bloom filters to hide the original decision tree's structure, the threshold values of each node, and the order in which features are tested while maintaining reliable classification results that are secure even if the cloud servers collude. Our scheme's security and performance are verified through rigorous testing and analysis.

An Exploration of Dutch Dermatologists' Experience and Satisfaction With Teledermatology: Sociotechnical and Complex Adaptive System Perspective.

Despite the global upscale of teledermatology during the COVID-19 pandemic, persistent barriers, such as the poor anamnesis and photo quality, hinder its effective use in practice. Understanding Dutch dermatologists' experiences and satisfaction with using the teledermatology system in the Dutch health care system is needed. A holistic evaluation may provide valuable insight to understand how barriers interrelate which is deemed necessary for the innovation of teledermatology in practice. Guided by a complex adaptive system perspective, this study aims to understand Dutch dermatologists' experience and satisfaction with their training, support communication, interaction, and usage of a teledermatology platform of a Dutch digital hospital during the COVID-19 pandemic, uncovering insights to improve teledermatology services for the future. A web-based questionnaire was sent in December 2021 to Dutch dermatologists who (1) had an active teledermatology platform account, and (2) responded to a teledermatology consultation between October 1, 2019, and September 30, 2021. The questionnaire consisted of the validated Store-and-Forward Telemedicine Service User-satisfaction Questionnaire (SAF-TSUQ) questionnaire, and new questions regarding; demographics of teledermatologists, the use of teledermatology during the COVID-19 pandemic, the performance of teledermatology by general practitioners (GP), and the role of dermatologists in the teledermatology process. The open-ended questions were analyzed by a grounded theory approach guided by a sociotechnical model and complemented by a complex adaptive system perspective. A panel discussion with 3 dermatologists was performed to provide additional insight into the responses to the questionnaire. We obtained responses from 25 out of the 249 (10%) invited dermatologists. Overall, dermatologists had a positive experience with teledermatology. Interestingly, teledermatology use frequency remained unaffected by the COVID-19 pandemic. However, the insufficient quality and incompleteness of the clinical content (photos and anamneses information) of the teledermatology consultation impacted the efficiency of the teledermatology workflow. Dermatologists expressed the need for improvement to avoid time-consuming processes or physical referrals. The panel discussion enriched and confirmed the responses, suggesting solutions like mandatory fields for the GPs for a complete anamnesis. Dutch Dermatologists view teledermatology as a valuable tool to provide access to dermatology care. However, improvements regarding the quality and completeness of the provided clinical content are necessary for the effectiveness and efficiency of the complex teledermatology system in Dutch health care. This could increase both the dermatologists' satisfaction and the quality of teledermatology services. Managing trade-offs, such as time investments versus image quality, is crucial for teledermatology implementation and should be assessed from a complexity perspective to understand trade-offs and prevent unintended consequences.

Does less workload lead to more family educational care? Evidence from rural China

ABSTRACT Based on the theory of work – family balance, this paper employs a two-way fixed-effects model to explore the relationship between agricultural mechanization and investment in children’s education. The findings indicate that a county-level increase in the level of mechanization can stimulate financial and time investments in children’s education. Further heterogeneity analysis suggests that this impact is more pronounced on girls and primary school-aged children. The findings imply that reducing the labour burden on farmers will enable them to focus more on caring for their children.

Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model

Abstract. Global storm-resolving models (GSRMs) use strongly refined horizontal grids compared with the climate models typically used in the Coupled Model Intercomparison Project (CMIP) but employ comparable vertical grid spacings. Here, we study how changes in the vertical grid spacing and adjustments to the integration time step affect the basic climate quantities simulated by the ICON-Sapphire atmospheric GSRM. Simulations are performed over a 45 d period for five different vertical grids with between 55 and 540 vertical layers and maximum tropospheric vertical grid spacings of between 800 and 50 m, respectively. The effects of changes in the vertical grid spacing are compared with the effects of reducing the horizontal grid spacing from 5 to 2.5 km. For most of the quantities considered, halving the vertical grid spacing has a smaller effect than halving the horizontal grid spacing, but it is not negligible. Each halving of the vertical grid spacing, along with the necessary reductions in time step length, increases cloud liquid water by about 7 %, compared with an approximate 16 % decrease for halving the horizontal grid spacing. The effect is due to both the vertical grid refinement and the time step reduction. There is no tendency toward convergence in the range of grid spacings tested here. The cloud ice amount also increases with a refinement in the vertical grid, but it is hardly affected by the time step length and does show a tendency to converge. While the effect on shortwave radiation is globally dominated by the altered reflection due to the change in the cloud liquid water content, the effect on longwave radiation is more difficult to interpret because changes in the cloud ice concentration and cloud fraction are anticorrelated in some regions. The simulations show that using a maximum tropospheric vertical grid spacing larger than 400 m would increase the truncation error strongly. Computing time investments in a further vertical grid refinement can affect the truncation errors of GSRMs similarly to comparable investments in horizontal refinement, because halving the vertical grid spacing is generally cheaper than halving the horizontal grid spacing. However, convergence of boundary layer cloud properties cannot be expected, even for the smallest maximum tropospheric grid spacing of 50 m used in this study.

Leisure Time and Social development: Contemporary Issues and Challenges

Today's era is the era of consumerism driven by technology. It has made human beings very materialistic. To fulfill these materialistic needs, we work day and night like a machine. This means less leisure time with family, friends and society. Lack of investment of time and energy in nurturing personal, family and social life can lead, and in many cases, have led to negative outcomes. At personal level, mental and physical well-being is affected. Lack of leisure time can lead to stress and unhealthy lifestyle which in long term can give rise to serious conditions like depression, hyper tension, and heart stroke. The family is affected as well. Lack of engagement between spouses can lead to disintegration and break-down of family structure. This has serious consequences for children within the family.This leads us to think about nature of development. Economic as well as social development is important for development and happiness of individual, family, and the society.In this research paper, we assess the relationship between leisure time and social development. This research paper is based on secondary sources.

A Hybrid QOGWO-GPR Algorithm for Antenna Optimization

Optimization of antenna performance is a non-linear, multi-dimensional and complex issue, which entails a significant investment of time and labor. In this paper, a hybrid algorithm of quasi-opposition grey wolf optimization (QOGWO) and Gaussian process regression (GPR) model is proposed for antenna optimization. The QOGWO is prone to global optimality, high precision for complex problems, and fast convergence rate at the later stage. The GPR model can reduce time cost of antenna samples generation. After being optimized by the proposed approach, a stepped ultrawideband monopole antenna and a dual-band MIMO antenna for WLAN can achieve wider bandwidth and higher gain or isolation at low time cost, compared to other intelligent algorithms and published literatures.

Non-compensable regulation and regulatory expropriation: where is the line?

Abstract Having examined the police powers doctrine and clarified expropriation clauses, this study concludes that neither of the two instruments can draw the line between non-compensable regulation and regulatory expropriation in a predictable and consistent manner. The outcome of expropriation cases depends significantly on tribunals’ discretion and their ex post interpretation of State actions. In this paradigm, investors and States cannot assess their risks at the time of investment or regulation. Every decision to invest or to regulate becomes a gamble. There is a need for more objective and clear rules to distinguish expropriation and regulation in international investment law.

Improving the Methodology for Determining the Biomass/Coal Co-Combustion Ratio: Predictive Modeling of the 14C Activity of Pure Biomass

Sampling and 14C detection of biomass are now essential steps to ensure the accuracy of the 14C method, but they require additional time and economic investment. When there are multiple types of biomass fuels, it is not possible to guarantee the uniformity of sampling. The 14C activity of biomass fuels exhibits variability, and this value significantly impacts the precision of the 14C method. Therefore, this study aims to investigate the influencing factors of 14C activity in biomass fuels. It also provides predicted values of 14C activity for different types of biomass fuels for each year from 2020 to 2030. Additionally, this study discusses the potential blending ratio measurement errors that may arise due to the uncertainties of the predicted values. The reduction in the 14C activity of biomass fuels can occur due to the utilization of fossil fuels, human activities, and the photosynthesis mode of C3 plants. This study presents a prediction method for determining the reduction factor. The other component of the prediction methodology involves determining the original 14C activity of biomass fuels. The 14C activity of the annual biomass is equal to the 14CO2 activity (the 14C activity of CO2) of the surrounding environment, and it experiences a decline of 0.355 pMC/year. The 14C activity has ranges of five types of perennial biomass fuels, including wood chips and branches, bark, leaves, wasted furniture, and abandoned building wood, for the time period between 2020 and 2030, are 97.34~102.84, 96.35~106.27, 96.35~102.64, 111.00~118.60, and 111.32~129.47 pMC, respectively. Based on these, this study introduces a new formula for calculating blending ratios, which enhances the current methodology. The calculation errors of blending ratios caused by the uncertainties of the predicted values are generally negligible, with the exception of wasted furniture and construction wood. The annual decrease in the blending ratio calculation error, caused by the uncertainty associated with the predicted value, can be observed. This study aims to reduce the implementation time and economic cost of the 14C method while ensuring the accuracy of biomass blending ratio detection.

Do NHS talking therapies services and couple therapy for depression have a relationship problem?

PHOTO CREDIT: Antoni ShkrabaCouple Therapy for Depression (CTfD) is unique being the only NHS Talking Therapies Services (NHSTTS) therapy involving partners. As a couple therapy it is systemic as well as behavioural. CTfD is an effective intervention for both depression and relationship distress and achieves good recovery-rates for depression and anxiety compared with other ‘high-intenstty’ therapies.Delivery of CTfD in NHSTTS is inadequate. Over a third of NHSTTS provide no CTfD. When they do, delivery is so minimal that depressed clients cannot generally access CTfD. Since 2010, 1172 CTfD therapists have been trained yet, in 2021/2, only 1574 couples received CTfD (approximately one couple seen per year per therapist). This is wasteful given the investment of time and money.Constraints affecting delivery e.g. individually-focused organisational culture of NHSTTS, misunderstanding of the role of CTfD therapists, inappropriate referrals, inadequate supervision and delivery on a minimal scale, are discussed and recommendations outlined.Clients deserve genuine choice, staff need support to deliver CTfD effectively, and referral quantity and quality require urgent attention.

The price behavior characteristics of China and Europe carbon emission trading market based on the perspective of time scaling and expected returns.

China has the world's largest carbon market in terms of greenhouse gas emissions, but its system needs to be improved and enhanced. In comparison, the European carbon market stands as the most mature and well-developed carbon market globally. Carbon trading prices, serving as a barometer for the carbon market, are significantly influenced by investor behavior. Therefore, it is necessary to analyze the characteristics of carbon trading prices in both China and Europe, considering the impact of investor trading intervals and psychological expected returns. This study utilizes the Zipf method to characterize the dynamic behavior of carbon trading prices between China and Europe, conducting a comparative analysis. The results show distinctive asymmetry in the behavior of carbon trading prices in both markets. In the Chinese market, when τ < 277, the absolute deviation da(τ, ε) value gradually changes but consistently indicates a bullish trend. However, when τ ≥ 277, the da(τ, ε) value surges rapidly, reflecting a pronounced bullish sentiment among investors toward carbon trading prices in China. In the European market, within the sample period, regardless of variations in τ and ε, the da(τ, ε) value shows a linear upward trend, indicating a significant overall bullishness in prices. This suggests a higher probability of long-term bullishness in carbon trading prices. Investors' investment time scale (τ) and expected returns (ε) both influence the behavior of carbon trading prices in both China and Europe. Generally, a longer τ implies a higher probability of bullishness. As for ε, higher values lead to more extreme judgments on price movements, resulting in greater distortion in carbon trading prices. Short-term investors (τ<1 month) anticipate extreme fluctuations, exhibiting random behavior when ε < 0.15 and converging rapidly to extreme values of 1 or 0 when ε ≥ 0.15. Long-term investors (τ>quarter) are less biased, expressing a bullish outlook on both Chinese and European carbon prices. With increasing ε, the probability of bullishness either increases or decreases rapidly until reaching the saturation point. Once saturated, there is no further distortion in carbon price behavior. Furthermore, the Chinese carbon market displays a positive trend in carbon trading prices and a higher probability of long-term bullishness. For the European market, lower expected returns contribute to considerable carbon trading price fluctuations, exacerbating risk and uncertainty. The results of this study contribute to understanding the diverse trading behaviors in Chinese and European carbon markets and provide guidance for avoiding extreme volatility in carbon trading prices.

Automated eccentricity measurement from raw eclipsing binary light curves with intrinsic variability

Eclipsing binary systems provide the opportunity to measure the fundamental parameters of their component stars in a stellar-model-independent way. This makes them ideal candidates for testing and calibrating theories of stellar structure and (tidal) evolution. Large photometric (space) surveys provide a wealth of data for both the discovery and the analysis of these systems. Even without spectroscopic follow-up there is often enough information in their photometric time series to warrant analysis, especially if there is an added value present in the form of intrinsic variability, such as pulsations. Our goal is to implement and validate a framework for the homogeneous analysis of large numbers of eclipsing binary light curves, such as the numerous high-duty-cycle observations from space missions like TESS. The aim of this framework is to be quick and simple to run and to limit the user's time investment when obtaining, amongst other parameters, orbital eccentricities. We developed a new and fully automated methodology for the analysis of eclipsing binary light curves with or without additional intrinsic variability. Our method includes a fast iterative pre-whitening procedure that results in a list of extracted sinusoids that is broadly applicable for purposes other than eclipses. After eclipses are identified and measured, orbital and stellar parameters are measured under the assumption of spherical stars of uniform brightness. We tested our methodology in two settings: a set of synthetic light curves with known input and the catalogue of Kepler eclipsing binaries. The synthetic tests show that we can reliably recover the frequencies and amplitudes of the sinusoids included in the signal as well as the input binary parameters, albeit to varying degrees of accuracy. Recovery of the tangential component of eccentricity is the most accurate and precise. Kepler results confirm a robust determination of orbital periods, with 80.5&lt;!PCT!&gt; of periods matching the catalogued ones. We present the eccentricities for this analysis and show that they broadly follow the theoretically expected pattern as a function of the orbital period. Our analysis methodology is shown to be capable of analysing large numbers of eclipsing binary light curves with no user intervention, and in doing so provide a basis for a further in-depth analysis of systems of particular interest as well as for statistical analysis at the sample level. Furthermore, the computational performance of the frequency analysis, extracting hundreds of sinusoids from Kepler light curves in a few hours, demonstrates its value as a tool for a field like asteroseismology.

Using a model of domain learning to understand the development of creativity

Creative thinking is a process through which individuals generate ideas that are simultaneously novel and meaningful within a given social context. Historically, psychologists have closely studied the general creative capacity of young learners, as well as the domain-specific creativity of experts. However, the developmental trajectory from children’s general creativity to experts’ domain-specific creativity remains largely unmapped. In this article, we work to address this issue theoretically by drawing on one established conceptual framework of academic development, the Model of Domain Learning (MDL). The MDL contains specific hypotheses about how learners’ declarative and procedural knowledge, motivation, and performance within a domain change as they learn, and we here delineate our hypothesized ways in which creative thinking could be expected to concomitantly progress throughout that development. We suggest that domain creative thinking develops from a largely self-referenced process when domain knowledge is low, to a more highly socially-referenced process as domain knowledge grows. In addition, we argue that creativity can both support, and be supported by, domain learning and offer specific suggestions for incorporating creative thinking into instruction at each stage of domain learning. We also show that, as learners develop academically within a domain, creative thinking requires a progressively greater investment of time and effort, which contributes to the riskiness of creative innovation and the rareness of creative experts.

Reselling or dual selling? The role of consumer‐to‐manufacturer e‐commerce platforms' voluntary investment

AbstractRecent years have witnessed e‐commerce platforms that voluntarily invest in new digital technologies to help their suppliers reduce production costs. To examine its impact on channel structure, we develop a supply chain model consisting of a supplier and an e‐commerce platform who purchases products from the supplier at a wholesale price and sells them to end markets. In addition, the supplier has the option to accept the marketplace by paying a commission fee charged by the platform. We show that the presence of voluntary investment on production improvement can overturn some classical insights from prior studies. For example, a higher marketplace commission fee can lead to a lower selling quantity in the marketplace, but a higher order quantity in the reselling channel under certain conditions. This stands in strict contrast to the opposite finding when the platform invests instead on demand enhancement. In addition, previous studies suggest that offering the supplier the marketplace option is beneficial to the supplier but is detrimental to the platform. However, with voluntary investment, the platform can benefit from allowing the supplier to encroach via the marketplace, and in some situations the supplier suffers from having such an option. We find that the commission fee and the production cost are two interacting forces steering equilibrium decisions for supply chain members, and we characterize the concise operating regimes for the channel structure choices on behalves of the supplier and the platform. A number of extensions are discussed, including product substitution level, investment cost structure, whether the quantity decisions are made simultaneously or sequentially, voluntary investment timing, and endogenous commission fee.

An Overview of Polymeric Nanoplatforms to Deliver Veterinary Antimicrobials.

There is an urgent need to find new solutions for the global dilemma of increasing antibiotic resistance in humans and animals. Modifying the performance of existing antibiotics using the nanocarrier drug delivery system (DDS) is a good option considering economic costs, labor costs, and time investment compared to the development of new antibiotics. Numerous studies on nanomedicine carriers that can be used for humans are available in the literature, but relatively few studies have been reported specifically for veterinary pharmaceutical products. Polymer-based nano-DDS are becoming a research hotspot in the pharmaceutical industry owing to their advantages, such as stability and modifiability. This review presents current research progress on polymer-based nanodelivery systems for veterinary antimicrobial drugs, focusing on the role of polymeric materials in enhancing drug performance. The use of polymer-based nanoformulations improves treatment compliance in livestock and companion animals, thereby reducing the workload of managers. Although promising advances have been made, many obstacles remain to be addressed before nanoformulations can be used in a clinical setting. Some crucial issues currently facing this field, including toxicity, quality control, and mass production, are discussed in this review. With the continuous optimization of nanotechnology, polymer-based DDS has shown its potential in reducing antibiotic resistance to veterinary medicines.