Strict Standards Research Articles

Efficiency of Police Cooperation in Criminal Investigations

Police cooperation is essential not only to investigate crime, but also to prevent it. Sharing information and best practices between law enforcement agencies allows early identification of threats, which can lead to prompt and effective interventions. At the same time, working together can reduce legislative or procedural disparities between states, which helps to strengthen joint efforts to fight crime. In addition to legal and operational challenges, police cooperation also raises ethical, privacy and data protection issues. In the digital age, the exchange of information between police agencies must comply with strict data protection standards in line with national and international legislation, such as the General Data Protection Regulation (GDPR) in the European Union. Thus, police cooperation is an indispensable tool in criminal investigations, offering the possibility to go beyond the geographical and legal limits imposed by national borders. However, its effectiveness depends on a clear legislative framework, mutual trust between partners and the adoption of modern technologies to facilitate real-time communication and information exchange. Police cooperation is an essential element in the fight against contemporary crime, but its effectiveness depends on resolving the legislative and operational problems that sometimes prevent effective inter-agency cooperation. Technological development and adaptation to new forms of crime also remain major challenges in this area.

Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system

In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizing a dataset encompassing various physical characteristics. The comparative analysis results reveal the strengths and weaknesses of each approach in addressing this classification task. In this research, two classification systems for different varieties of rice-medium and premium—are compared. CNN and ANFIS are the techniques applied. The CNN accuracy on the rice picture is 62.5%. Thus, a contrast enhancement procedure was applied and had better accuracy at 75%. However, when contrasted with the classification made using the ANFIS approach, the ANFIS method continued to yield the best accuracy, 82.25%.

Enhanced nitrogen removal for low C/N wastewater via preventing futile carbon oxidation and augmenting anammox

Efficient carbon use is crucial for biological nitrogen removal. Traditional aerobic processes can waste carbon sources, exacerbating carbon deficiency. This study explores an anaerobic/oxic/anoxic system with sludge double recirculation to improve nitrogen removal in low C/N wastewater. This system integrated aerobic nitrification after the carbon intracellular storage, separating carbon and nitrogen by denitrifying glycogen-accumulating organisms (DGAOs) with endogenous partial denitrification and Anammox within the anoxic units. A significant efficiency of 91.02±7.01% chemical oxygen demand (COD) was converted into intracellular carbon in anaerobic units, significantly reducing carbon futile oxidation in the aerobic units by effectively separating COD from ammonia. Intracellular storage of carbon sources and microbial adaptation to carbon scarcity prevent futile oxidation of COD in the aerobic units even with short-term high dissolved oxygen (DO), thereby enhancing nitrogen removal under anoxic conditions with sufficient intracellular carbon source. The microbial analysis identified Candidatus Brocadia as the dominant anammox bacteria, in combination with the activity of DGAOs and other related microbial communities, accounting for 37.0% of the TN removal. Consequently, the system demonstrated remarkable nitrogen removal efficiencies, achieving 81.3±3.3% for total nitrogen (TN) and 98.5±0.9% for ammonia nitrogen while maintaining an effluent COD concentration of 17.2±9.1 mg/L, treating the low C/N of 4.18 in the influent wastewater. The findings in this study provide a sustainable and energy-saving technique for conventional WWTPs to meet strict discharge standards by avoiding futile oxidation of COD and encouraging anammox contributions.

Ethical Application of Generative Artificial Intelligence in Medicine.

Generative artificial intelligence(AI) may revolutionize healthcare, providing solutions that range from enhancing diagnostic accuracy to personalizing treatment plans. However, its rapid and largely unregulated integration into medicine raises ethical concerns related to data integrity, patient safety, and appropriate oversight. One of the primary ethical challenges lies in generative AI's potential to produce misleading or fabricated information, posing risks of misdiagnosis or inappropriate treatment recommendations, which underscore the necessity for robust physician oversight. Transparency also remains a critical concern as the closed-source nature of many large-language models(LLMs) prevents both patients and healthcare providers from understanding the reasoning behind AI-generated outputs, potentially eroding trust. The lack of regulatory approval for AI as a medical device, combined with concerns around the security of patient-derived data and AI-generated synthetic data, further complicates its safe integration into clinical workflows. Furthermore, synthetic datasets generated by AI, while valuable for augmenting research in areas with scarce data, complicate questions of data ownership, patient consent, and scientific validity. Additionally, generative AI's ability to streamline administrative tasks risks depersonalizing care, further distancing providers from patients. These challenges compound the deeper issues plaguing the healthcare system, including the emphasis of volume and speed - over value and expertise. The utilization of generative AI in medicine brings about mass scaling of synthetic information, thereby necessitating careful adoption to protect patient care and medical advancement. Given these considerations, generative AI applications warrant regulatory and critical scrutiny . Key starting points include establishing strict standards for data security and transparency, implementing oversight akin to Institutional Review Boards(IRBs) to govern data usage, and developing interdisciplinary guidelines that involve developers, clinicians, and ethicists. By addressing these concerns, we can better align generative AI adoption with the core foundations of humanistic healthcare - preserving patient safety, autonomy, and trust while harnessing AI's transformative potential. LEVEL OF EVIDENCE: Level V: Expert Opinion.

Addressing Bias and Fairness Using Fair Federated Learning: A Synthetic Review

The rapid increase in data volume and variety within the field of machine learning necessitates ethical data utilization and adherence to strict privacy protection standards. Fair federated learning (FFL) has emerged as a pivotal solution for ensuring fairness and privacy protection within distributed learning environments. FFL not only enhances privacy safeguards but also addresses inherent limitations of existing federated learning (FL) systems by fostering equitable model training across diverse participant groups, mitigating the exclusion of individual users or minorities, and improving overall model fairness. This study examines the causes of bias and fairness within existing FL systems and categorizes solutions according to data partitioning strategies, privacy mechanisms, applicable machine learning models, communication architectures, and technologies designed to manage heterogeneity. To mitigate bias, enhance fairness, and strengthen privacy protections in FL, this study also explores fairness evaluation metrics, relevant applications, and associated challenges of FFL. Addressing bias, fairness, and privacy concerns across all mechanisms serves as a valuable resource for practitioners aiming to develop efficient FL solutions.

Advancing Healthcare: Intelligent Speech Technology for Transcription, Disease Diagnosis, and Interactive Control of Medical Equipment in Smart Hospitals

Intelligent Speech Technology (IST) is revolutionizing healthcare by enhancing transcription accuracy, disease diagnosis, and medical equipment control in smart hospital environments. This study introduces an innovative approach employing federated learning with Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) neural networks to improve IST performance. Leveraging the “Medical Speech, Transcription, and Intent” dataset from Kaggle, comprising a variety of speech recordings and corresponding medical symptom labels, noise reduction was applied using a Wiener filter to improve audio quality. Feature extraction through MLP and sequence classification with GRU highlighted the model’s robustness and capacity for detailed medical understanding. The federated learning framework enabled collaborative model training across multiple hospital sites, preserving patient privacy by avoiding raw data exchange. This distributed approach allowed the model to learn from diverse, real-world data while ensuring compliance with strict data protection standards. Through rigorous five-fold cross-validation, the proposed Fed MLP-GRU model demonstrated an accuracy of 98.6%, with consistently high sensitivity and specificity, highlighting its reliable generalization across multiple test conditions. In real-time applications, the model effectively performed medical transcription, provided symptom-based diagnostic insights, and facilitated hands-free control of healthcare equipment, reducing contamination risks and enhancing workflow efficiency. These findings indicate that IST, powered by federated neural networks, can significantly improve healthcare delivery, accuracy in patient diagnosis, and operational efficiency in clinical settings. This research underscores the transformative potential of federated learning and advanced neural networks for addressing pressing challenges in modern healthcare and setting the stage for future innovations in intelligent medical technology.

Deep learning for the detection of good hygienic practices control measures for food handlers

Hygiene habits and behaviors of food handlers are critical to ensuring food safety during production. Good Hygiene Practices (GHP) require food handlers to follow strict hygiene standards, including proper handwashing procedures and the use of personal protective equipment (PPE) such as hairnets, masks, gloves, protective clothing, pants, and work shoes to prevent cross-contamination in food factories. However, manual inspections in large-scale operations are often inefficient and impractical. To address this, this study proposes a deep learning-based system that leverages computer vision and Convolutional Neural Networks (CNNs) to automatically detect hand hygiene and PPE compliance before workers enter the production area. This ensures adherence to GHP standards, enhances food safety, improves inspection efficiency, and reduces costs. A dataset of 20,222 entries, comprising 12 handwashing actions from 8 angles and color-coded PPE conditions, was used to train and evaluate 10 models: YOLOv8, YOLOv7, YOLOv6, YOLOv5, ResNet, Dense-Net, MobileNetv2, EfficientNetv2, VGG, and Vision Transformer (ViT). YOLOv6 and YOLOv8 achieved the highest accuracy (0.999) for handwashing recognition, while Dense-Net achieved the highest accuracy (0.956) for PPE detection. This system offers an efficient and automated solution for monitoring hygiene habits, helping to prevent cross-contamination and ensure food safety within factory environments.

The Epistemic Policies of Anti-Ageing Medicines in the European Union.

Anti-ageing medicines are products intended to extend lifespan and healthspan in humans that have a good potential use in public health policies. In the European Union, their development, production and consumption are dependent on regulatory science performed by the European Medicines Agency and its associated epistemic policies. They impose, among other things, an unfavourable burden of proof, a strict standard of proof and meta-methodological constrictions related to some theoretical issues. This results in a distribution of errors that tends to reduce false positives while increasing false negatives, leading to a set of social consequences that are generally accepted when the focus is placed on conventional medicines. However, when the same epistemic policies are applied to anti-ageing medicines, the distribution of errors is imbalanced, and undesirable outcomes like research discouragement and waiting time extensions appear. Three possible strategies that policymakers could implement to unblock the situation are presented for future reflection: the consideration of ageing as a disease, the application of methodological asymmetry and the use of biomarkers during clinical research.

A mobile electrical stimulator for therapeutic modulation of the vestibular system - design, safety, and functionality.

Low-intensity noisy galvanic vestibular stimulation (nGVS) is a promising non-invasive treatment for enhancing vestibular perceptual performance and postural control in patients with chronic vestibular hypofunction. However, this approach has so far been studied mainly under laboratory conditions. Evidence indicates that continuous application of nGVS in daily life is necessary for it to be effective. To address this need, we have developed a mobile nGVS stimulator and conducted a series of pilot studies to evaluate its safety, tolerability, functionality, and therapeutic effects. The device is a lightweight, compact, and portable AC stimulator featuring a user-friendly interface for the individualized adjustment of nGVS parameters. It includes an integrated motion sensor that automatically activates stimulation during body movement and deactivates it during inactivity, optimizing its practical use in real-world settings. The stimulator adheres to strict safety standards and, in initial long-term use, has exhibited only mild side effects (e.g., skin irritation and headaches), likely attributable to the current electrode placement, which requires further optimization. As expected, the device consistently elicits known vestibular sensorimotor reflex responses in healthy individuals. Importantly, further pilot studies in healthy participants demonstrate that the device can reliably replicate known facilitating effects on vestibular perception and postural control. Together, these findings suggest that this mobile stimulation device can facilitate the translation of nGVS into therapeutic everyday use.

Next generation tissue biobanking: quality assured, financed, and integrated?

Biobanks are essential for biomedical research, particularly in the era of personalized medicine. In Germany, 36biobanks have been established over the past decade that are connected under the German Biobank Alliance (GBA). These biobanks store high-quality biological samples along with clinical data to support research projects. Biobanks can be integrated, handling both tissue and liquid samples, or set up as separate entities depending on specific requirements.Tissue biobanking is especially complex due to the invasive nature of tissue collection and the non-replicability of the samples. Close collaboration between clinics, pathologists, IT specialists, and biobank managers is crucial to ensure the quality of samples and promote interdisciplinary research.The integration of pathology and biobanking is key, both organizationally and technically. Shared IT systems, standardized protocols, and collaborative governance structures are vital for efficient data management. Quality assurance, ethical guidelines, and data protection are critical to maintaining public trust and legal compliance.Long-term financial models are needed to ensure the sustainability of biobanks. The GBA supports emerging biobanks through its "Starterkit" initiative, offering guidance and best practices to help new biobanks develop.Tissue biobanks are indispensable for advancing the understanding of diseases and developing new therapies. However, they must adhere to strict ethical and legal standards to maximize their scientific and societal value.

Industry 5.0 for Society 5.0 a new Transformation

Industry 5.0 represents a transformative approach within Society 5.0, leveraging advanced technologies like AI, IoT, Robotics, and Blockchain to enhance productivity, automation, and decision-making across various sectors. This abstract examines how privacy, security, and ethical issues are addressed by modern technologies to create a societal structure that is morally and humanely sound. IoT improves smart city infrastructure and environmental monitoring, while AI enables tailored education and predictive healthcare. Blockchain guarantees safe and transparent data transfers, while robotics helps with accurate healthcare operations. Collectively, these technologies support Society 5.0's sustainability, inclusivity, and resilience. They also hold out hope for a time when technological breakthroughs will preserve strict ethical standards and cybersecurity safeguards while prioritizing the needs of people and society.

Recycled graphite enabled superior performance for lithium ion batteries

Recycling graphite attracts growing attention since cumulative amount of spent Li-ion batteries and the shortage of graphite supply chain. Although various recycling methods have been reported, the recycled graphite cannot reach the strict commercial standards of purity, scalability, efficiency, and capacity, preventing it from battery manufacturing. Herein, the important roles of defects and functional groups on the graphite surface are deeply studied, and a closed-loop graphite recycling process with the surface recovery and modification for the graphite from the end-of-life batteries is demonstrated. The recovered graphite delivers a purity of over 99.9 % and an average initial coulombic efficiency of 91.5 %. Compared with commercial graphite in industrial standard battery testing parameters, full cells with recovered graphite possess enhanced rate reversibility, doubled cycle life, over 10 % higher capacity along with half anode material cost. These impressive results not only underscore the transformative potential of surface reconstruction and modification in graphite recycling, but also present economic feasibility and sustainable pathway for significantly improving battery performance and addressing global resource challenges via integration with the hydrometallurgical recycling process.

Fabrication of SiC-Al2O3 foam ceramic and its application in fluoride-containing water

Silicon carbide (SiC) foam ceramics demonstrate excellent filtration efficiency as filter supports due to their complex pore network and chemical stability. However, their fabrication demands high sintering temperatures (above 2100 °C), resulting in substantial energy consumption and production costs, which restricts their wider use in separation and purification application processes. To address this, we utilized fly ash, a by-product of coal combustion rich in inorganic Al₂O₃, as a sintering aid to develop a porous silicon carbide–alumina foam ceramic (SAFC) and investigated the effects of Al₂O₃ on its performance. Additionally, we introduced SAFC as an adsorbent material, synthesizing the fluoride adsorbent (La@SAFC) and evaluating its fluoride removal efficacy through batch experiments. Results indicate that Al₂O₃, as a sintering aid, promotes the sintering process by forming a liquid phase that enhances Si and C diffusion at lower sintering temperatures. SAFC containing 16 wt.% Al₂O₃ achieved optimal porosity, low bulk density, and compressive strength at a sintering temperature of 1150 °C. The addition of Al₂O₃ also facilitated mullite formation, significantly enhancing the ceramic's mechanical properties. However, excess Al₂O₃ can produce an overabundant liquid phase and gas release, potentially compromising the mechanical integrity of SAFC. Batch experiments revealed that La@SAFC exhibited strong fluoride removal from water containing competing ions across a pH range of 3–7, achieving effluent within strict discharge standards. Mechanism analysis suggested fluoride removal via ion exchange and electrostatic interaction. La@SAFC maintained high fluoride removal efficiency across five regeneration cycles, underscoring its promise for practical applications.

Simplified process for preparing native and depolymerized capsular polysaccharides of Streptococcus pneumoniae

Streptococcus pneumoniae is a major pathogen of bacterial pneumonia, meningitis, sepsis, and otitis media. The pathogenicity of this bacterium is largely attributed to its polysaccharide capsule, a protective layer around bacterial cell that enables bacteria to resist against host defense. Capsular polysaccharides (CPSs) of S. pneumoniae have been used as antigens to develop a variety of pneumococcal vaccines against invasive pneumococcal disease (IPD). These vaccines have been proven to be effective in reducing the incidence of IPD cases that are caused by vaccine-covered serotypes at the global scale. A crucial step in the manufacture of pneumococcal polysaccharide and conjugate vaccines is to purify native and depolymerized CPSs to meet strict quality standards in purity and structural integrity. The major impurities comprise proteins, nucleic acids and cell wall polysaccharides (CWPS). Traditionally, the removal of impurities to obtain purified native CPSs involves a complex process of purification, after which purified CPSs need to be further size-reduced to obtain depolymerized CPSs by multi-step approaches. In this study, we streamlined the process of CPS purification, which involves firstly ultrafiltration, followed by one-step acid precipitation, and finally diafiltration to obtain pure native CPSs. Furthermore, hydrolysis using trifluoroacetic acid (TFA) was integrated into the process to obtain purified depolymerized CPSs. The native and depolymerized CPSs produced by this optimized process were comparable to the materials obtained by the traditional approaches in purity and structural integrity, which would meet the quality standards of CPSs for vaccine production in the current edition of the European Pharmacopeia.

INTERNATIONAL EXPERIENCE OF STATE REGULATION OF THE FOOD INDUSTRY IN THE CONTEXT OF DIGITAL TRANSFORMATION

The article is devoted to researching the international experience of state regulation of the food industry in the conditions of digital transformation and determining ways of its adaptation to Ukrainian realities. The experience of the EU, the USA, China, Canada, Australia and India in the application of digital transformation technologies and innovations in the state regulation of food business was studied. The key factors for the successful implementation of digital technologies in the regulatory mechanisms of the food industry have been identified, including: the creation of a legal framework, investment in the development of digital infrastructure, increasing the digital literacy of employees and consumers, as well as cooperation between state bodies. The results of the conducted research emphasize the need for an integrated approach combining technological innovation, cooperation between governments, industry and scientific institutions, as well as strict quality standards. The integration of advanced technologies and partnerships between the government and the private sector are key elements for the sustainable development of the food industry.

Underpowered studies in muscle metabolism research: Determinants and considerations

Biomedical research frequently employs null hypothesis testing to determine whether an observed difference in a sample is likely to exist in the broader population. Null hypothesis testing generally assumes that differences between groups or interventions are non-existent, unless proven otherwise. Because biomedical studies with human subjects are often limited by financial and logistical resources, they tend to have low statistical power, i.e. a low probability of statistically confirming a true difference. As a result, small but potentially clinically important differences may be overseen or ignored simply due to the absence of a statistically significant difference. This absence is often misinterpreted as ‘equivalence’ of treatments. In this educational paper, we will use practical examples related to the effects of exercise and nutrition on muscle protein metabolism to illustrate the most important determinants of statistical power, as well as their implications for both investigators and readers of scientific articles.Changes in muscle mass occur at a relatively slow rate, making it practically challenging to detect differences between treatment groups in a long-term setting. One way to make it ‘easier’ to differentiate between groups and hence increase statistical power is to have a sufficiently long study duration to allow treatment effects to become apparent. This is especially relevant when comparing treatments with relatively small expected differences such as the effect of modest changes in daily protein intake. Secondly, one could try to minimize the variance and response heterogeneity within groups, for example by using strict inclusion criteria and standardization protocols (e.g., meal provision), by using cross-over designs, or even within-subject designs where two interventions are compared simultaneously (e.g., studying an exercised limb vs a contralateral control limb) although this might limit the generalizability of the findings (e.g. such single-limb exercise training is not common in practice). In terms of data interpretation, investigators should obviously refrain from drawing strong conclusions from underpowered studies. Yet, such studies still provide valuable data for meta-analyses. Finally, because muscle protein synthesis rates are highly responsive to anabolic stimuli, acute metabolic studies are more sensitive to detect potentially clinically relevant differences in the anabolic response between treatments. Apart from further elaborating on these topics, this educational article encourages readers to more critically question null findings and scientists to more clearly discuss limitations that may have compromised statistical power.

REGULAÇÃO LEGAL DAS REDES SOCIAIS: PROTEÇÃO DE DADOS E LIBERDADE DE EXPRESSÃO

The legal regulation of social networks is a field in constant evolution, which seeks to balance two fundamental pillars in the digital era: data protection and freedom of expression. Social networks manage a huge amount of personal data, which makes the existence of legal frameworks that protect users’ privacy imperative. Regulations such as the General Data Protection Regulation (GDPR) in the European Union and the General Data Protection Law (LGPD) in Brazil establish strict standards on how platforms must manage personal information. These laws not only allow users to access, correct or delete their data, but also impose sanctions on companies that do not comply with these requirements, ensuring a high level of security and transparency. At the same time, freedom of expression is a fundamental right that must be protected in the digital environment. However, exercising this right on social media often conflicts with the need to moderate content that may be considered offensive, dangerous or even illegal. Platforms implement moderation policies to mitigate the spread of hate speech, misinformation, and other harmful content. This, however, creates significant challenges in relation to censorship and the delimitation of permitted speech, which can affect the free expression of ideas and opinions. The challenge lies in finding an appropriate balance between protecting users’ privacy and data and enabling robust freedom of expression. An excessive focus on data protection can limit users’ ability to express themselves freely, while unrestricted freedom of expression can put individuals’ security and privacy at risk. As social media becomes more deeply integrated into everyday life and becomes a key platform for communication and the exchange of ideas, it is essential that regulations continue to adapt to address these dilemmas in a balanced and fair way. The future of regulation in this field will depend on the ability of legislators and platforms to adapt to rapid technological and social changes. ensuring that both rights are protected effectively and equitably.

Lost vegetables, lost value: Assessment of carrot downgrading and losses at a large producer organisation

Retailers usually impose strict private quality standards on fresh fruit and vegetable products, mainly focused on cosmetic features. At the supplier level, this generates a stream of suboptimal products that cannot be sold on the fresh market. This article studies the impact of these standards on suppliers by means of a direct quantification conducted in 2023 in the carrot supply chain of an Italian Producer Organisation. 34.64% of the carrots processed in terms of mass is downgraded because of non-compliance with the standards imposed by retailers, while 12.57% is lost at the primary stage due to food safety reasons. Downgrading occurs as a result of cosmetic imperfection (33.28% of mass) or because carrots are out-of-size (66.72%). The downgraded carrots are re-processed for industry, generating an additional loss of 15.27% of mass along the way. The economic loss linked to downgrading would account for 22.05% of potential turnover if no cosmetic quality standards were applied. By modelling a series of hypothetical scenarios, this article discusses the impact on the producer if the private quality standards imposed by retailers were more flexible.

Research on smoke cleaning based on Mg(OH)2/NaClO2 seawater solution

Abstract Ship exhaust emissions contain a large amount of sulfur oxides and nitrogen oxides, which have attracted widespread attention from the international community for their pollution of the atmospheric environment. In order to reduce the emissions of these pollutants, the International Maritime Organization (IMO) and other national institutions have established strict emission standards and promoted the research and application of ship exhaust desulfurization and denitrification technology. Experimental and simulation analyses were conducted on the denitrification effect of Mg(OH)2/NaClO2 solution using a cyclic spray method. Simulation analysis was carried out using cyclic spraying. The experiment compared the reaction degree and reaction time of NO in NaClO2 seawater, NaClO2 water, and Mg(OH)2/NaClO2 marine solution. The experimental result is that in the process of cyclic spray, the removal rate of NO by these three solutions can reach 100%. The reaction time of NO is 71, 86, and 90 minutes, respectively. Under the same conditions, the simulation results showed that the reaction rate of NaClO2 water solution with NO was faster, followed by NaClO2 seawater solution. The reaction rate of Mg(OH)2/NaClO2 seawater solution with NO was the slowest of the three. When the pH value exceeds 7, it effectively inhibits the production of ClO2 gas by NaClO2. The removal rate of Mg(OH)2/NaClO2 seawater solution is directly proportional to the initial concentration of NaClO2. In addition, it could be seen from the simulation cloud diagram that the removal speed of NO in the spray reactor slows down as the concentration of NO increases.

Finite element modeling and analysis of tire creep test

Abstract The tire is one of the most complex components of the vehicle, which justifies strict standards and regulations set by governments worldwide to ensure the performance, durability, and reliability of tires approved for use in the transport sector. Among these requirements, tire durability testing involves the assessment of short-and long-term tire response to determine optimum conditions of use and service life. Therefore, this paper focuses on the creep performance of tires using the finite element method in ABAQUS. As a case study, a selected tire with visco-hyperelastic rubber compounds and linear elastic reinforcements is subjected to a constant prescribed force for a period of time, and the resulting strain evolution is recorded till the release of the applied load. Several parameters are accounted for, such as the inflation pressure, material characteristic times, and load amplitude. The post-processing of results enabled the prediction of the overall creep response of the tire over time with respect to the prescribed load. The knowledge gained in this study can be exploited to predict tire failure caused by excessive strain and pinpoint the areas of the tire that are the most exposed to creep failure.