Practical Requirements Research Articles

Triple network hydrogel-based structure triboelectric nanogenerator for human motion detection and structural health monitoring

In this study, we developed a high-performance triple network (TN) PVA/PAAM/PEDOT/Zn2+ (PMPZ) hydrogel with exceptional mechanical properties and stable output performance. The robust and highly tough TN structure, fabricated via a Zn2+ pinned hydrogel and multi-network interpenetrating polymerization process, demonstrates high mechanical strength alongside exceptional mechanical properties. The PMPZ hydrogels exhibit notable mechanical sensing capabilities (gauge factor: 48.6) while maintaining excellent tensile (83.79 KPa) and compressive (96 MPa) strengths. As a triboelectric nanogenerator (TENG), the PMPZ-TENG shows outstanding flexibility and integrability, achieving a maximum open-circuit voltage (Voc) of 326.89 V and a peak power density of 368.3 μW/cm² with a load resistance of 10E8 Ω. Furthermore, the PMPZ-TENG demonstrates enduring practicality and responsiveness as a self-powered sensor. These properties make the PMPZ-TENG highly suitable for applications in human health monitoring. Additionally, we conducted computational simulations on the hydrogel and seismic-resistant civil models. 48 prototype structures simulated different seismic hazard levels to effectively capture plastic hinge deformation within high-strength steel composite K-shaped eccentric braced steel frames. The plastic hinge deformations informed the establishment of physical seismic models for structural health monitoring (SHM). This study not only introduces a high-performance PMPZ-TENG meeting practical requirements but also establishes a novel pathway for integrating TENGs in SHM.

Spectrophotometric-Based Sensor for the Detection of Multiple Fertilizer Solutions

The online detection of fertilizer solution information is a crucial link in the implementation of intelligent and precise variable fertilization techniques. However, achieving simultaneous rapid online detection of multiple fertilizer components is still challenging. Therefore, a rapid detection method based on spectrophotometry for qualitative and quantitative identification of four fertilizers (typical N, P, and K fertilizers: KNO3, (NH4)2SO4, KH2PO4, and K2SO4) was proposed in this work. Full-scan absorption spectra of fertilizer solutions at varying concentrations were obtained using a UV–visible/near-infrared spectrophotometer. By assessing the linear fit between fertilizer concentration and absorbance at each wavelength within the characteristic band, the characteristic wavelengths for KNO3, (NH4)2SO4, KH2PO4, and K2SO4 were identified as 214 nm, 410 nm, 712 nm, and 1708 nm, respectively. The identification method of fertilizer type and the prediction model of concentration were constructed based on characteristic wavelength and the Lambert–Beer law. Based on the above analysis, a four-channel photoelectric sensor was designed with four LEDs emitting wavelengths closely matched to characteristic wavelengths for fertilizer detection. A detection strategy of “qualitative analysis followed by quantitative detection” was proposed to realize the online detection of four fertilizer types and their concentrations. Evaluation of the sensor’s performance showed its high stability, with an accuracy of 81.5% in recognizing fertilizer types. Furthermore, the relative error of the sensor detection was substantially less than ±15% for the fertilizer concentrations not exceeding 80 mg/L. These results confirm the capability of the sensor to meet the practical requirements for online detection of four fertilizer types and concentrations in the field of agricultural engineering.

OPTIMAL CONTROL OF A HYBRID PV-WIND POWER SYSTEM FOR ISLANDED HYBRID MICROGRID USING MOGA-FUZZY LOGIC CONTROLLER

The paper presents a comprehensive approach to managing the complexities associated with the integration of photovoltaic (PV) and wind energy systems in islanded microgrid environments. These microgrids, often isolated from the main power grid, face unique challenges due to the variability and intermittency of renewable energy sources, which can lead to instability and inefficiencies in power supply. To address these challenges, the study introduces a novel control strategy that combines a Multi-Objective Genetic Algorithm (MOGA) with a Fuzzy Logic Controller. The MOGA is utilized to explore a broad solution space and identify optimal control parameters that balance multiple objectives, including minimizing power fluctuations, maximizing the utilization of renewable energy, and ensuring a stable and reliable power supply. This optimization is crucial in islanded microgrids, where the lack of a larger grid connection necessitates highly efficient and responsive energy management systems to maintain stability. The Fuzzy Logic Controller, on the other hand, provides a flexible and adaptive control mechanism that responds to the dynamic and often unpredictable nature of renewable energy generation. By interpreting input variables in a way that mimics human decision-making, the Fuzzy Logic Controller can effectively handle the inherent uncertainties and non-linearities in the power system, adjusting the operation of the PV and wind power sources, as well as any supplementary energy storage systems, to optimize performance. This adaptive capability is particularly beneficial in scenarios where rapid changes in weather conditions can significantly impact energy generation and consumption patterns. The integration of MOGA with Fuzzy Logic not only enhances the decisionmaking process but also allows for the simultaneous consideration of multiple objectives, which is a critical advancement over traditional single-objective optimization techniques. This dual approach ensures that the hybrid PV-wind power system operates at its highest efficiency, balancing the need for sustainability with the practical requirements of reliability and economic viability. The results from extensive simulations, which model various operational scenarios and disturbances, demonstrate that the proposed MOGA-Fuzzy Logic Controller significantly improves the stability and efficiency of the islanded microgrid. The system is shown to effectively manage power flows, reduce dependency on fossil fuel-based backup generators, and increase the overall penetration of renewable energy. The proposed MOGA-Fuzzy Logic Controller stands out as a promising solution for the optimal control of hybrid PV-wind power systems, offering a viable pathway for achieving sustainable energy goals in islanded and other decentralized grid settings. This work not only advances academic knowledge but also has practical implications for the design and operation of future energy systems, making it a significant contribution to the field of renewable energy and power system engineering.

Design of Inductive Power Transfer Charging System with Weak Coupling Coefficient

Inductive power transfer (IPT) technology is used in various applications owing to its safety features, robust environmental adaptability, and convenience. In some special applications, the charging pads are required to be as compact as possible to accommodate practical spatial requirements, and even size requirements dictate that the diameter of the charging pad matches the air gap. However, such requirements bring about a decrease in the transmission efficiency, power, and tolerance to misalignment of the system. In this paper, by comparing a double-sided inductor–capacitor–capacitor (LCC), double-sided inductor–capacitor–inductor (LCL), series–series (SS), and inductor–capacitor–capacitor–series (LCC-S) compensation topologies in IPT systems, we identified a double-sided LCC compensation topology that is suitable for weak coupling coefficients. Furthermore, this study modeled and simulated the typical parameters of coreless coils in circular power pads, such as the number of coil layers, turns, wire diameter, and wire spacing, to enhance the mutual inductance of the magnetic coupler during misalignment and long-distance transmission. A wireless charging system with 640 W output power was built, and the experimental results show that a maximum dc-dc efficiency of over 86% is achieved across a 200 mm air gap when the circular power pad with a diameter of 200 mm is well aligned. The experimental results show that using a suitable compensation topology and optimizing the charging pad parameters enables efficient IPT system operation when the coupling coefficient is 0.02.

Preface

The 2024 4th International Conference on Advanced Materials and Mechatronics (ICAMM 2024) was held via virtual form in Xi’an, China from May 10th to 12th, 2024. Previous conferences in the past three years of this series have been successfully held and it was agreed to hold the conference once a year, with the aim to establish and develop a constant international collaboration. In recent years, interest in advanced materials and mechatronics has been flourishing all over the globe due to both theoretical and practical requirements. Considering the trend, the fourth conference of ICAMM series was organized in order to provide an international academic forum for developing research cooperation and to promote research and other academic activities in related fields. We entertained about 80 participants at ICAMM 2024 and arranged various speeches and presentations which cover a wide range of topics related to the Conference theme. Many scholars and researchers all over the world have contributed to the emerging technology and application of advanced materials and mechatronics. The wonderful keynote speeches included Semiconductor Lighting and Display: Design and Manufacturing Technology for High Efficiency GaN-based LED Chips (by Prof. Shengjun Zhou, Wuhan University, China), Materials, Processing and Reliability of Low Temperature Bonding in 3D Chip Stacking (by Prof. Liang Zhang, Xiamen University of Technology, China), Monitoring Applications of Electronic Devices in the Shipbuilding Industry (by Assoc. Prof. Fengming Du, Dalian Maritime University, China), and Addictive Manufacturing and Intelligent Processing Control of Advanced Metal Materials (by Prof. Kun Li, Chongqing University, China). ICAMM 2024 has been endorsed and supported by many national and international engineering-related universities and organizations and was a complete success. The topics of papers collected and undergone rigorous peer review in the Proceedings of ICAMM 2024 can be classified as follows: Semiconductor Materials, Mechanics of Materials, Material Forming Process and Equipment, Advanced Electromagnetism, Laser Physics and Technology, Irradiation electronics, etc. Last but not the least is our gratitude. We would like to express our sincere thanks to all the keynote speakers, authors, committee members for the success of the Conference, which has given rise to this present volume of selected papers. We would also like to acknowledge the staff of Journal of Physics: Conference Series for their effective work to make this Proceedings published. The Committee of ICAMM 2024 List of Committee Member is available in this Pdf.

Development and prospect of acoustic reflection imaging logging processing and interpretation method

Acoustic reflection imaging logging technology can detect and evaluate the development of reflection anomalies, such as fractures, caves and faults, within a range of tens of meters from the wellbore, greatly expanding the application scope of well logging technology. This article reviews the development history of the technology and focuses on introducing key methods, software, and on-site applications of acoustic reflection imaging logging technology. Based on the analyses of major challenges faced by existing technologies, and in conjunction with the practical production requirements of oilfields, the further development directions of acoustic reflection imaging logging are proposed. Following the current approach that utilizes the reflection coefficients, derived from the computation of acoustic slowness and density, to perform seismic inversion constrained by well logging, the next frontier is to directly establish the forward and inverse relationships between the downhole measured reflection waves and the surface seismic reflection waves. It is essential to advance research in imaging of fractures within shale reservoirs, the assessment of hydraulic fracturing effectiveness, the study of geosteering while drilling, and the innovation in instruments of acoustic reflection imaging logging technology.

Design and Implementation of an Active Prosthetic Ankle and Adaptive Equipment for Bike Riding In Lower Limb Amputees

Lower limb amputees often face challenges in regaining mobility andparticipating in physical activities such as bike riding. Existing prosthetic systemsstrive to replicatehuman locomotion; however, conventional prostheticfeetutilized in India typically feature stationary ankles. Despite advancements inprosthetic technology, including hydraulic or externally powered systems, thesesolutions fail to meet the practical requirements of motorcycle riding for thispatient population. To address this issue, this study proposes a cost-effectivesolutioninvolvingthemodificationofmotorcycleswithleft-handthumb-controlledgearshiftingandfore footup-and-downmotion.Thisinnovativeapproach aims to improve mobility and alleviate discomfort for lower limbamputees,facilitatingtheirengagementinmotorcycletransportationwithgreatereaseandsafety.

Wearable gait recognition system based on time domain features of Bluetooth inertial sensor network

Abstract As an important part of medical monitoring, gait recognition has been used in lower limb rehabilitation diagnosis and gait assessment. To meet practical requirements, we designed a wearable gait recognition system based on time domain features of Bluetooth inertial sensor network, which can be used for real-time acquisition and evaluation of gait signals. In this research, we used 6 sensor units to integrate 10 kinds of gait information, and designed a gait monitoring system covering the main joints of lower limbs based on Bluetooth network. The gait monitoring system can collect the inertial sensing data of lower limbs at a maximum rate of 90 Hz through the 6 sensor units, and has good electrical performance. In daily use, each unit can work for more than 3 hours and maintain a received signal strength indicator (RSSI) of more than -90 dBm at a spatial distance of 10 meters. On the gait dataset collected in the experiment, we used sliding window to segment the time domain signals and extract 10 kinds of time domain features for classification algorithm. We achieved 97.1% average identification accuracy of 7 different gait patterns by support vector machine (SVM).

The economic essence of the concept "Manufacturing process in beekeeping" according to the requirements of international accounting standards

The paper examines the economic essence of the concept of "production process" from the standpoint of the category as a value characteristic. The elements of the basic model of the production process - production resources, technological environment, product - were adapted to the conceptual apparatus of international standards, taking into account the specific features of the beekeeping industry. The definition of the concept of "production process in beekeeping" is proposed in accordance with the requirements of IAS 41 "Agriculture" and IAS 2 "Inventories". It has been established that bee colonies meet the mandatory conditions stipulated by IAS for recognizing assets as biological, therefore, in order to unify the concept of the production process of beekeeping with the requirements of international accounting practice, bee colonies should be considered as a biological asset, which will allow the evaluation of bee colonies and form information adapted to international standards in current accounting and accounting. However, according to IAS 41 "Agriculture", which relate to the agricultural stage of the beekeeping production process, "the output obtained from the biological assets of the enterprise should be valued at fair value less costs to sell, which are determined at the time of receipt products", and not at its actual cost price, which is not used in international accounting practice. It is recommended to supplement the accounting of beekeeping products in farms that prepare financial statements.

Optimizing binders with maximum using of industrial waste to develop low carbon ultra-high performance concrete

Optimizing binders by using industrial waste and then adjusting microstructure has potential to develop low production cost and low carbon emission ultra-high performance concrete (UHPC) to meet different practical engineering requirements. This study first investigates the influence of silica fume, cenosphere and fly ash on paste mixing time, flowability, flexural and compressive strength of UHPC by using three-factor and four-level orthogonal test. Moreover, the effect of fly ash and slag content were further discussed, and the modification mechanisms of binders were revealed by analyzing pore structure, interface and characteristics of hydration products. The results show that silica fume and cenosphere are the most sensitive factors affecting paste mixing time and flowability of UHPC, respectively. The sensitivity of binders on compressive and flexural strength of UHPC can be ranked according to silica fume > fly ash > cenosphere and cenosphere > fly ash > silica fume, respectively. The binders’ composition for low-carbon UHPC with compressive strength grade of 120 MPa is that cement: cenosphere: silica fume: fly ash equals to 1:0.33:0.33:0.56, and the total amount of fly ash reaches 40%. Meanwhile, the 28d compressive/flexural strength decreases and the flowability increases with the increase of fly ash content, resulting from pore volume with size of 5–50 nm and Ca/Si ratio on the interface between steel fiber and matrix increase. The flowability of UHPC with slag is reduced and the cumulative pore volume with size of 50 nm–5 um pores is increased due to the influence of slag morphology, but the porosity and interfacial Ca/Si ratio is reduced because of the high pozzolanic activity of slag, leading to similar compressive and flexural strength with that of UHPC with fly ash.

PHÁT TRIỂN NĂNG LỰC TỔ CHỨC HOẠT ĐỘNG TRẢI NGHIỆM CHO SINH VIÊN SƯ PHẠM

The 2018 General Education Curriculum identifies experience and career-oriented as compulsory educational activities carried out from grade one to grade twelve. These activities replace extracurricular activities, but with changes in name, objectives, content, methods, and regulations for testing and evaluating outcomes. This raises the question of what measures teacher training institutions need to take to develop the competency of organizing experiential activities for pre-service teachers, ensuring they meet practical requirements immediately after graduation. By examining curriculum documents and drawing on practical research results, this article proposes measures to develop the competency of organizing experiential activities for pre-service teachers during their training. The proposed measures emphasize integrating and embedding the development of this competency through the teaching of specialized subjects, pedagogical subjects, and Youth Union activities. Keywords: Experiential activities; competency in organizing experiential activities; competency development; pre-service teachers.

<span>Dạy học chủ đề ba đường conic Toán 10 theo định hướng kết nối tốn học với thực tiễn</span>

Teaching connecting mathematics with practice has a very important meaning. Doing this well will create motivation for learners to enjoy learning, contributing to the development of qualities and abilities, especially the ability to learn and apply mathematics into practice. The topic of three conics with highly practical requirements to meet makes up the majority of the topic of three conics. The article proposes some measures to strengthen the connection between mathematics and practice in teaching the topic Three conic sections - Math 10 for students in high school.

DeepPGD: A Deep Learning Model for DNA Methylation Prediction Using Temporal Convolution, BiLSTM, and Attention Mechanism.

As part of the field of DNA methylation identification, this study tackles the challenge of enhancing recognition performance by introducing a specialized deep learning framework called DeepPGD. DNA methylation, a crucial biological modification, plays a vital role in gene expression analyses, cellular differentiation, and the study of disease progression. However, accurately and efficiently identifying DNA methylation sites remains a pivotal concern in the field of bioinformatics. The issue addressed in this paper is the presence of methylation in DNA, which is a binary classification problem. To address this, our research aimed to develop a deep learning algorithm capable of more precisely identifying these sites. The DeepPGD framework combined a dual residual structure involving Temporal convolutional networks (TCNs) and bidirectional long short-term memory (BiLSTM) networks to effectively extract intricate DNA structural and sequence features. Additionally, to meet the practical requirements of DNA methylation identification, extensive experiments were conducted across a variety of biological species. The experimental results highlighted DeepPGD's exceptional performance across multiple evaluation metrics, including accuracy, Matthews' correlation coefficient (MCC), and the area under the curve (AUC). In comparison to other algorithms in the same domain, DeepPGD demonstrated superior classification and predictive capabilities across various biological species datasets. This significant advancement in algorithmic prowess not only offers substantial technical support, but also holds potential for research and practical implementation within the DNA methylation identification domain. Moreover, the DeepPGD framework shows potential for application in genomics research, biomedicine, and disease diagnostics, among other fields.

Preparation and anti-counterfeiting application of Sr3Y2Ge3O12:Bi2+ red long afterglow luminescent

Red, as one of the three primary colors, is crucial for blending the luminescent color of long-lasting materials. This project selected Sr3Y2Ge3O12 as the substrate and Bi2+ as the activator. A solid-state method at high temperature was used to prepare a new type of visible red long-afterglow luminescent material, by adjusting the doping concentration of Bi2+ and changing the calcination temperature. When the doping concentration of Bi2+ was 0.50mol% and the calcination temperature was 1150 °C, the prepared Sr3Y2Ge3O12:Bi2+ had the best luminescent properties. After being excited by 254 nm ultraviolet light, the material shows the red afterglow luminous intensity, duration and stability that meet the practical application requirements. Through thermoluminescence, photoluminescence and fluorescence spectra, the trap characteristics and internal mechanism of long afterglow were studied comprehensively. Finally, the film and printing ink were prepared by using the Sr3Y2Ge3O12:Bi2+ long afterglow luminescent powder with the best luminescent performance, and the pattern anti-counterfeiting was successfully realized.

FedGMMAT: Federated generalized linear mixed model association tests.

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.

Fracture modeling of CNT/epoxy nanocomposites based on phase-field method using multiscale strategy

The computational modeling of fracture, particularly in structures with complex crack topologies, remains challenging due to significant computational costs, especially in simulating two- and three-dimensional brittle fracture. This study presents an efficient phase-field model to address these challenges. By leveraging the user (UMAT) subroutine in ABAQUS and establishing an analogy between the phase-field evolution law and the heat transfer equation, the method efficiently tackles complex fracture problems. The model is verified through analysis of typical 2D and 3D fracture benchmarks with different failure modes, demonstrating accuracy and efficiency compared to experimental and numerical data. Additionally, the model is applied to explore brittle fracture in carbon nanotubes (CNTs)/epoxy nanocomposites, revealing insights into the impact of CNT weight fraction on fracture phenomena prediction. The incorporated CNTs in the matrix are considered uniformly dispersed and randomly oriented. Overall, the developed model and computational implementation show promise for meeting the requirements of structural-level engineering practices.

Adaptive Real-Time Tracking of Molten Metal Using Multi-Scale Features and Weighted Histograms

In this study, we study the tracking of the molten metal region in the dross removal process during metal ingot casting, and propose a real-time tracking method based on adaptive feature selection and weighted histogram. This research is highly significant in metal smelting, as efficient molten metal tracking is crucial for effective dross removal and ensuring the quality of metal ingots. Due to the influence of illumination and temperature in the tracking environment, it is difficult to extract suitable features for tracking molten metal during the metal pouring process using industrial cameras. We transform the images captured by the camera into a multi-scale feature space and select the features with the maximum distinction between the molten metal region and its surrounding background for tracking. Furthermore, we introduce a weighted histogram based on the pixel values of the target region into the mean-shift tracking algorithm to improve tracking accuracy. During the tracking process, the target model updates based on changes in the molten metal region across frames. Experimental tests confirm that this tracking method meets practical requirements, effectively addressing key challenges in molten metal tracking and providing reliable support for the dross removal process.

Random flutter analysis of a novel binary airfoil with fractional order viscoelastic constitutive relationship

The fractional order model emerges naturally when the integer order model fails to meet practical requirements and finds extensive applications in engineering practice. However, there is still limited research on the random flutter of fractional order viscoelastic binary airfoils. This study aims to explore the random flutter of such airfoils under the influence of Gaussian white noise excitation by utilizing the pth moment Lyapunov exponent. Firstly, a two-degree-of-freedom fractional order stochastic dynamic equation is established for the viscoelastic binary airfoil through the introduction of fractional order damping. The singular perturbation method is then employed to derive the pth moment Lyapunov exponent of the system. An approximate analytical solution of the pth moment Lyapunov exponent is obtained through Fourier series expansion, which exhibits good agreement with numerical results obtained from Monte Carlo simulations. Subsequently, a comprehensive analysis is conducted to examine the impacts of fractional order, airflow velocity, noise intensity, and key structural parameters on the stochastic flutter of the fractional order viscoelastic binary airfoil. The findings indicate that compared with the typical binary airfoil model, the flutter speed of the fractional-order viscoelastic binary airfoil is significantly increased, the stability of the system is highly sensitive to the natural frequency of the system, and the fractional-order factor has a positive effect on the stochastic stability of the binary airfoil before the critical value. The above research results can guide the design optimization of the fractional-order viscoelastic airfoil.

Optimization of the cold‐side heat exchanger design to improve the performance of the motorcycle exhaust thermoelectric generator

AbstractThis study optimizes the main parameters of the cold‐side heat exchanger (CHE) longitudinal fin, including fin quantity (Nf), fin thickness (Tf), and fin height (Hf), to enhance the performance of motorcycle exhaust thermoelectric generator units (TGUs) utilizing the computational fluid dynamics approach. The investigation shows that these parameters significantly affect the dissipation area of the CHE heat and the outside air velocity distribution in fin gaps, resulting in the fluctuation of TGU output power. The output power increases with respect to Hf; nevertheless, it first increases as Nf and Tf increase but decreases dramatically when Nf or Tf becomes too large. Besides, Hf significantly affects output power, and its impact is almost independent of Tf and Nf, and vice versa; meanwhile, Tf and Nf have a strong relation. This study proposes two Hf of 40 and 60 mm along with the optimal Tf of 1 mm and Nf of 29, providing significantly high output power, low weight, and compact size for TGU. This work contributes insight into the effect of CHE parameters on the TGU performance, and it is a crucial case study for selecting suitable heat sink parameters for TGU, considering practical requirements and conditions.

Safety of Gonadal Tissue-Derived Mesenchymal Stem Cell Therapy in Geriatric Dogs with Chronic Disease.

Ensuring the safety of mesenchymal stem cell (MSC) therapy is a fundamental requirement in clinical practice. This study aimed to assess the safety of using gonadal tissue-derived MSCs (n = 10) compared to the commonly utilized adipose tissue-derived MSCs (n = 9) in geriatric dogs with chronic diseases. All participants received allogeneic MSC therapy, and no allergic reactions due to allogeneic cell immunogenicity were noted. Both groups showed no adverse changes in physical exams or hematological parameters before and after therapy. Importantly, there were no instances of tumor formation or growth post-treatment in either group. The findings demonstrated that dogs treated with gonadal tissue-derived MSCs experienced no clinical adverse effects. However, clinical adverse effects were reported in one case of adipose tissue-derived MSC therapy. Despite limitations in monitoring beyond one year and constraints due to a small and diverse patient group, this pioneering study validates the safe use of gonadal tissue-derived MSCs in aged companion animals. It underscores the potential of utilizing tissues from neutering procedures to advance regenerative medicine and expand cell banks and therapy options for companion animals.