Abstract: Decentralised manufacturing is expanding as digitally controlled fabrication tools become accessible to SMEs, independent operators, and community workshops outside traditional factory settings, but the resulting heterogeneous, autonomously operated network introduces systemic uncertainty that no central authority governs. This paper proposes a systems-theoretic framework in which Free and Open Source Software (FOSS) governance acts as the structural interoperability layer of a distributed cyber-physical manufacturing system (CPS), and node-local digital twins --- each hosting a machine learning (ML) disturbance estimator --- provide local adaptive compensation without centralised data aggregation. A defining property of the architecture is automatic improvement propagation: learned corrections distribute via federated learning to structurally similar nodes without operator intervention, and the open, observable FOSS ecosystem enables advances in one fabrication modality to transfer to others through shared interface standards. The framework is applied analytically to three disturbance classes: regulatory restriction, technical process variability, and supply-chain disruption. Across cases, the analysis shows how open modular interfaces and local adaptation preserve functional continuity under perturbations that would more strongly affect centralised architectures. The contribution is a unified mathematical basis for robustness analysis in decentralised manufacturing CPS and a foundation for future simulation and empirical validation.

Abstract: The article describes the results of research on the power supply quality of selected fluorescent lamps and solid-state light sources powered by voltage with different waveforms and supply voltage values. The power factor, total harmonic distortion (THD) factor and values of individual harmonics were measured and their compliance with international standards was assessed. The measurement set-up used and the measurement results obtained with it are described. The results of the experimental research showed that the light sources under consideration did not meet the criteria specified in international standards for the THD factor and the values of individual harmonics, regardless of the shape of the supply voltage waveform. However, it was shown that supplying some light sources with a triangular voltage waveform can increase the illuminance value. On the other hand, the use of a rectangular voltage waveform leads to an increase in the power factor and a decrease in reactive power.

Abstract: Land use planning plays an important role in advancing sustainable development by integrating environmental, social, and economic dimensions to optimize land utilization and bolster climate resilience. The adoption of efficient practices contributes to the mitigation of land degradation, while strategically planned agricultural systems enhance food security and promote ecological balance. This study focused on the development of an environmental conservation framework for sustainable land use planning in Zambia. Employing a mixed-methods research design, data were collected from a sample of 150 respondents. Quantitative data were analysed using descriptive and inferential statistics, including regression analysis, while qualitative data were subjected to thematic analysis. The research identified key conflicts between agriculture and environmental conservation, including unsustainable farming practices (30.8%), resource competition (24.2%), and deforestation (23.3%). Approximately 40.3% of respondents reported occasional conflicts, while 33% experienced them often. Major barriers to sustainable land development included inadequate financial support (35%) and lack of knowledge (30%). Awareness of sustainable agricultural practices varied, with 38% of respondents indicating high awareness and 35.8% reporting low awareness. Conventional agriculture (35.8%), crop rotation (30%), and conservation agriculture (11.7%) were the most common practices, with crop rotation being the easiest to implement (42.2%), and climate-smart agriculture being the most challenging (37.8%). A chi-square analysis revealed no significant association between awareness levels and perceived barrier impacts (p=0.327). Regression analysis indicated that age negatively correlated with the type of conflict (β=-0.0283, p< 0.001), while location influenced conflict experiences, with certain areas, such as Section D (β=1.3799, p< 0.001) and Section G (β=1.6554, p< 0.001), reporting more frequent conflicts. Additionally, sex had a positive but marginally significant effect (β=0.2640, p=0.062). Qualitative findings highlighted the tension between agricultural production and environmental conservation, with economic pressures driving environmental degradation, such as deforestation and water pollution. Participants also pointed to limited knowledge, training, and financial barriers, including high costs and restricted access to credit, as key obstacles. The study proposed an environmental conservation framework to address these conflicts, integrating sustainable agricultural practices with effective land use planning. The framework advocates a multi-stakeholder approach involving policymakers, farmers, and environmental experts to promote balanced sustainable land use. The findings enhance the body of knowledge by providing empirical evidence on the conflicts between agriculture and environmental conservation in land use planning, highlighting key socio-economic and spatial factors influencing sustainability challenges. The proposed environmental conservation framework offers a practical guide for policymakers and stakeholders to integrate sustainable agricultural practices into land use planning.

Abstract: This study investigates the thermo-mechanical response of geocell-reinforced concrete pavements through scaled model tests and three-dimensional coupled finite element analyses. Static, cyclic, thermal, and coupled loading tests were conducted to clarify deformation evolution, strain distribution, and damage characteristics of the reinforced structure. The results show that, under static loading, pavement settlement evolves through three stages, namely initial compaction, plastic development, and stabilization, indicating progressive mobilization of geocell confinement. Under thermal loading, slab strain exhibits pronounced spatial and temporal non-uniformity, and the slab centre is identified as the thermally sensitive zone. Under coupled high-temperature and static loading, both strain and settlement show a non-monotonic increase–decrease trend at approximately 1.1–1.3 kN, suggesting a potential threshold for damage initiation. Under cyclic loading, permanent deformation accumulates with load repetitions and is highly sensitive to load amplitude. Numerical results further show that geocell reinforcement reduces the central settlement by 17.4% relative to plain concrete pavement and by 7.6% relative to a doweled pavement, while producing a smoother deflection basin and a more uniform stress distribution. Parametric analyses indicate that the optimum geocell height is approximately one-third of the surface course thickness; beyond this range, the marginal reinforcement benefit decreases. The results demonstrate that geocell reinforcement can significantly improve load transfer, deformation compatibility, and thermo-mechanical stability of concrete pavements.

Abstract: Train wheel wear is a critical factor affecting train operational safety, making the accurate and objective evaluation of wheel wear condition essential. However, current approaches are still constrained by inadequate measurement accuracy and incomplete evaluation methods, to address this issue, this study proposes an integrated method for the high-precision measurement and wear condition evaluation of train wheels. A mul-ti-sensor data fusion-based measurement method is developed to synchronously acquire key wear-related parameters, including wheel diameter, flange height, and flange thick-ness. Based on the measured data, an improved matter-element model combined with game-theoretic weighting is established to evaluate wheel wear condition. Experimental results show that the proposed online measurement method for in-service wheels achieves standard deviations below 0.15 mm, and the measurement errors satisfy the re-quirements of Chinese railway industry standards. The evaluation results derived from the high-precision measurement data indicate that wheel wear condition gradually dete-riorates with increasing service mileage, and that flange height wear is the dominant fac-tor affecting the wear grade. These findings are consistent with actual operating condi-tions. The proposed method integrates high-precision multi-parameter measurement with wear condition evaluation, providing a reliable technical basis for wheel condition moni-toring and predictive maintenance in rail transit.

Abstract: Laser-induced microjet assisted ablation is an emerging technology in the field of laser processing. However, the influence of solid boundaries on jet behavior and the associated material removal mechanism remain unclear. To address this, the present study systematically investigates the effect of the incidence angle on the processing efficiency and material removal mechanism in laser-induced microjet ablation. By controlling the laser power and liquid layer thickness, the dynamic behavior of the microjet, material removal performance, and surface morphology evolution under different inclination angles were explored. Based on video analysis and OpenCV processing, the regulation of jet morphology and impact mode by the incidence angle was revealed. Combined with white light interferometry and ultra-depth-of-field three-dimensional microscopy, the ablation depth and material removal rate were quantitatively characterized. The results show that under normal incidence, the maximum material removal rate of 0.091 mm³/s was achieved at 9 W, while further increases in power led to a decrease in removal rate due to bubble aggregation. When the sample was tilted to 15°, the material removal rate reached 0.163 mm³/s, representing a 106.3% improvement compared to that at 0°, and the ablation depth also peaked with an average maximum depth of 12.2 μm and a single-point maximum of 54.357 μm. Furthermore, scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) were employed to elucidate the microstructural features and elemental distribution under different process parameters. Through multi-parameter experiments, this study achieves process parameter optimization and clarifies the material removal mechanism influenced by different incidence angles, providing both a process reference and theoretical basis for efficient micro-machining of aerospace materials.

Abstract: Automatic speech recognition has advanced rapidly for high-resource languages, yet performance remains limited for low-resource languages such as Bangla, particularly in telehealth settings. Most systems rely on a standardized 16 kHz sampling rate, a design choice despite evidence that Bangla contains sibilant fricatives and other phonetic cues with substantial high-frequency energy that may be suppressed under bandwidth and latency constraints. This study evaluates audio sampling rate as a controllable signal-level parameter for Bangla telehealth ASR to identify an empirically grounded operating range balancing transcription accuracy, execution time, and network bandwidth. Twenty real-world Bangla doctor–patient consultations recorded at 32 kHz were deterministically resampled to 55 configurations between 8 kHz and 32 kHz and transcribed using a fixed cloud-based ASR system. Session-level Word Error Rate, execution latency, payload bandwidth, and high-frequency phonetic content were analyzed using a composite sibilant-likelihood score. WER decreased from 0.338 at 8 kHz to a local minimum of 0.232 at 18.75 kHz, with gains plateauing beyond this range despite substantial bandwidth increases. Elbow-point, Pareto frontier, weighted scoring, and Minimum Acceptable Trade-off analyses converged on an optimal region between 17.25 and 18.75 kHz, demonstrating that sampling-rate optimization improves ASR accuracy without proportional resource costs in telehealth settings.

Abstract: The integration of electric mobility and energy systems has emerged as a key research domain in the transition toward sustainable energy and decarbonized transport, yet a systematic quantitative overview of its scientific development remains limited. This study addresses this gap by conducting a bibliometric analysis of research activities across five domains central to electric vehicle–energy system integration: central energy management systems; renewable energy, hydrogen production, and large-scale storage; industrial applications; smart energy communities, virtual power plants, and vehicle-to-X; and urban high-power charging parks with local storage. Using publication data from Web of Science and Scopus, performance analysis and science mapping techniques were applied to examine publication dynamics, thematic structures, and intellectual linkages. Results indicate strong growth and consolidation around smart grids and decentralized flexibility solutions, particularly within energy management, renewable integration, and community-based energy systems, while industrial applications and high-power charging infrastructures remain comparatively underrepresented. The findings suggest a maturing interdisciplinary field characterized by expanding connections between mobility and energy research, alongside emerging opportunities related to industrial integration, charging infrastructure, and vehicle-to-grid deployment. The study provides a data-driven overview of research trends that can support future research prioritization and inform policy and strategic planning for integrated mobility-energy systems.

Abstract: Pavement texture is a critical element affecting road safety and ride quality. It is affected by traffic volume, climate conditions, aggregate properties, and asphalt volumetric properties. This research aims to study the effect of different parameters on pavement texture using statistical and machine learning models. Pavement profile data and multiple variables affecting texture were collected from 192 SPS sections from the Long-Term Pavement Performance (LTPP) database. After data collection, pavement texture data were obtained from the pavement profile using ProVAL software and Python. Thereafter, the pavement texture was clustered into four diverse groups using the Gaussian Mixture Model (GMM), and the research determined cluster-specific profiles by applying centroid-based optimization techniques. Finally, an ordered logistic regression model and different machine learning models using K-nearest neighbor, random forest, extra trees, extreme gradient boosting, cat boosting, neural network, and weighted ensemble algorithm were developed to explore the parameters affecting the texture at diverse levels. The important parameters obtained from the statistical model were International Roughness Index (IRI), Annual Average Daily Truck Traffic (AADTT), temperature, and untreated subgrade, and from machine learning models were precipitation, IRI, AADTT, and 18-kips ESAL. Overall, this study significantly contributed to advancing the understanding and application of diverse impactful factors for pavement surface characteristics, pavement safety, and ride quality.

Abstract: Deterministic lateral displacement (DLD) and related microfluidic sorting devices are typically evaluated based on the size distributions of particles collected at each outlet, even though the more relevant measure of performance is the probability that a particle of a given size ends up in a specific outlet. Here, we introduce a Bayesian framework that infers these size-dependent routing probabilities from experimentally accessible measurements of outlet size distributions, inlet size distributions, and outlet fractions. Using a DLD array designed to separate microspheres and microsphere clusters, we determine the probabilities that particles of different sizes are directed to each outlet and define a probabilistic critical size ($D_c$) at which particles are equally likely to follow zigzag or displacement trajectory. From these routing probabilities, we calculate key performance metrics, purity and yield. Our results demonstrate high-quality separations and show that routing probabilities provide a general and robust framework for benchmarking microfluidic sorting devices beyond traditional outlet-based analyses.

Abstract: The selection of an optimal biomaterial is a critical determinant of the long-term clinical success of dental implants, requiring a careful balance among competing mechanical, biological, and clinical performance criteria. This study develops a comprehensive evaluation framework employing the Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-PROMETHEE II) to conduct a systematic comparative analysis of 22 contemporary biomaterials across eight key criteria: elastic modulus, yield strength, ultimate tensile strength, density, osseointegration potential, corrosion resistance, biostability, and potential side effects. To address the inherent uncertainty in material property data, triangular fuzzy numbers (TFNs) were utilized to model both quantitative property intervals and qualitative linguistic variables—an approach justified by the fact that biomaterial properties are routinely reported as ranges rather than crisp scalar values. The Fuzzy-PROMETHEE method was selected over alternative MCDM approaches because of its capacity for pairwise outranking without the rank-reversal instability characteristic of TOPSIS, and its lower parametric burden compared to AHP when evaluating large alternative sets. The analysis identified the titanium alloy Ti-6Al-4V as the top-performing material, achieving the highest net outranking flow (Φnet = 0.3152), attributable to its uniquely balanced profile of fracture toughness, yield strength, and osseointegration potential. Zirconia ranked sixth (Φnet = 0.1659), reflecting a quantifiable mechanical trade-off relative to metallic alternatives despite its superior aesthetic properties. The robustness of the framework was corroborated by comparative analysis using TOPSIS (relative closeness = 0.839, identical top ranking) and confirmed stable by sensitivity analysis across Osseointegration criterion weight variations from 0% to 50%. This study presents a transparent, evidence-based decision-support tool to assist clinicians in navigating the complex trade-offs inherent in modern implantology.

Abstract: Educational robotics (ER) and robotics competitions offer an effective context for developing STEM (Science, Technology, Engineering, and Mathematics) competencies, technical skills and soft skills in engineering degrees. However, current platforms reveal a pedagogical and technical gap: closed commercial systems restrict access to hardware, while open solutions frequently lack a robust and structured architecture for educational settings. Moreover, in both cases, many platforms do not achieve the hardware requirements of the most demanding competitions. To address this issue, the present article presents the design, implementation, and validation of EASYbot, a modular open-hardware robotics platform based on Arduino. The system integrates a microcontroller, a dual USB–battery power supply, high-performance motor power stages, and a plug-and-play interface for input/output and communication peripherals, enabling its use in several competition categories such as mini-sumo or maze robots. The platform is complemented by a state-based programming model and supports libraries that facilitate a learning assessment. The platform provides a scalable ecosystem, enabling students to progress from initial prototyping to optimized hard-ware control. The validation process encompasses a range of assessments, including technical tests, usability and adoption evaluation through surveys.

Abstract: Pedestrian-involved road traffic accidents represent a serious challenge for traffic safety and require a comprehensive analysis of the interactions within the driver–vehicle–road–environment system. The objective of this study is to develop a methodology for risk assessment in road traffic accidents involving pedestrians based on the analysis of real court cases and dynamic modeling of vehicle motion. A database of 105 court cases was analyzed, enabling the identification of the main factors influencing the occurrence of pedestrian-related accidents. Based on this analysis, a system of 31 linguistic variables was developed to characterize driver behavior, vehicle technical characteristics, and road environment conditions. These variables were integrated into a mathematical model for quantitative risk assessment that enables the evaluation of the relative influence of different groups of factors on accident probability. In addition, a dynamic model of vehicle motion was developed to analyze the influence of driver reaction time, vehicle speed, and road surface conditions on the possibility of avoiding a collision. The results of the numerical analysis demonstrate that even minimal delays in hazard perception and driver reaction significantly increase the probability of pedestrian-related accidents. These findings highlight the importance of early hazard detection and automated emergency braking systems. The proposed methodology provides a framework for integrating intelligent driver assistance systems and automated braking control aimed at improving the safety of vulnerable road users.

Abstract: Composite Laminates are now finding prominence in many high-performance industries. They offer a high strength-to-weight ratio, design flexibility, and strong resistance to environmental damage, making them superior to many traditional materials. This review examines the key factors that determine the strength and long-term durability of composite laminates and the integration of Artificial Intelligence (AI) to standardize the selection of process parameters in conventional manufacturing and characterization techniques. The discussion is structured into three main areas: failure mechanisms, advances in modeling and simulation, and standardized methods for material qualification and testing. The study summarizes current knowledge, points out research gaps, and outlines likely future directions. It emphasizes a shift from viewing composites as uniform materials to an application-focused approach that combines multi-scale physics models with data-driven machine learning. It also evaluates the role of standardized testing in ensuring reliability and addresses challenges such as barely visible impact damage and long-term fatigue. The final section predicts the future of composite design, including intelligent manufacturing, self-healing materials, and predictive analytics.

Abstract: Engineer-to-Order (ETO) manufacturers face persistent cost and complexity challenges driven by product variety, including duplicate components, redundant variants, and inconsistent procurement setups. Although enterprise resource planning (ERP) and product lifecycle management (PLM) systems contain detailed Bills of Materials (BOMs) and procurement records, they typically lack portfolio-wide support for systematic cross-product commonality analysis without substantial manual effort. Prior approaches are either conceptual (e.g., indices and modularity frameworks) or ad hoc in practice, often relying on one-off spreadsheet analyses. This paper introduces the concept of Product Commonality Analysis Tools (PCATs) and develops and evaluates a lightweight PCAT in an action-research collaboration with a European ETO laser manufacturer. The PCAT operates on exported enterprise data to provide interactive portfolio-level views of component reuse and cross-product consistency. Its usefulness is evaluated through scenario-based think-aloud usability sessions and a functional comparison against Excel workarounds, standard ERP/PLM reporting, and vendor customizations. The results indicate that a lightweight PCAT can be integrated into existing ERP/PLM workflows with minimal disruption and can reduce the effort required to prepare reusable portfolio views for engineering and procurement reviews.

Abstract: Low-voltage distribution networks (LVDNs) serve as the final delivery layer of the electricity system, directly influencing reliability, public safety, customer service quality, and the integration of distributed energy resources. Despite their importance, LVDNs have historically received less monitoring than transmission and medium-voltage systems due to their scale, cost, and deployment complexity. Non-contact magnetic sensing has emerged as a promising alternative to invasive measurement methods for these networks. Among magnetic sensor types, giant magnetoresistive (GMR) devices are appealing because they offer high sensitivity, compactness, low power consumption, and compatibility with embedded electronics. This review assesses the current state of GMR-based monitoring for overhead and low-voltage applications, focusing on non-contact current measurement, fault detection, and fault classification. It first examines the operating characteristics of LVDNs and the unique challenge of detecting low- and high-impedance faults. Next, it outlines the physical principles behind GMR sensing, compares GMR with Hall, AMR, TMR, current transformer, and Rogowski-coil technologies, and discusses the use of multi-axis sensor heads to address cross-coupled magnetic fields in three-phase setups. Special focus is given to calibration, alignment, temperature effects, electromagnetic interference, packaging, wireless deployment, and data-driven classification. The review concludes that GMR sensors are well-suited for scalable, non-contact monitoring, but widespread adoption in the field will require better low-voltage fault datasets, standardized calibration procedures, long-term environmental testing, and closer integration with digital-twin and smart-meter infrastructures.

Abstract: In this work, the design and experimental validation of passive UHF RFID tag antennas are presented with the objective of evaluating the impact of chip placement and miniaturization approaches on tag performance. Four initial antenna layouts were developed by varying the position of the RFID integrated circuit within a coupling loop. Simulations and measurements confirmed that Antenna 1 achieved the best impedance matching, with a minimum reflection coefficient of −40 dB at 866 MHz and a power sensitivity of −16.3 dBm. Based on this reference design, a miniaturized version (Antenna 5) was obtained by integrating meander lines and capacitive end-loading, reducing the physical size while maintaining resonance at 866 MHz. Both structures were fabricated and evaluated using a Voyantic Tagformance measurement system, with read-range measurements performed under freespace conditions and in proximity to dielectric and metallic materials. The results demonstrated a maximum read range of 8.6 m for Antenna 1 in free space, while Antenna 5 preserved a read range of 6.3 m. In the presence of copper, Antenna 1 maintained a read range of 3 m, whereas Antenna 5 achieved approximately 0.5 m, confirming the robustness of the proposed designs in representative industrial environments.

Abstract: Environmental noise is increasingly recognized as a major environmental development challenge, with road traffic identified as the dominant source of acoustic pollution across Europe. Noise barriers are among the most widely implemented mitigation strategies. However, their spatial distribution and adequacy remain poorly documented, limiting their effectiveness for sustainable territorial planning. This study develops the first georeferenced database of highway noise barriers in Andalusia (Spain) and applies a reproducible, transdisciplinary geospatial workflow integrating field surveys, remote-sensing tools, and Geographic Information Systems (GIS). A total of 110 barriers were mapped, classified by material, geometry, and surrounding land use, and analyzed in relation to dwellings, schools, and hospitals. Results show that 1.6% of the Andalusian highway network is currently protected by barriers, with strong territorial disparities: over 50% of all structures are concentrated along coastal metropolitan corridors, while extensive inland areas remain unprotected. Misalignments were also detected between barrier placement and officially reported high-exposure segments, indicating limited correspondence between infrastructural deployment and acoustic priorities. Beyond generating a comprehensive regional dataset, the methodology provides a scalable basis for national and European initiatives seeking to harmonize the mapping and assessment of noise-mitigation infrastructures. By offering an open-access, transferable framework, this work supports policy professionals, environmental managers, and planners in evaluating mitigation gaps and informing more equitable and sustainable transportation and land-use strategies.

Abstract: Organizations are increasingly investing in Process Innovation with Analytics, i.e., the usage of analytics to innovate operational processes. Process innovation with analytics is a challenging and complex endeavor encompassing 1) redesign of processes, 2) development of digital infrastructure, and 3) analytics development. As a result, organizations need guidance on how to approach this complex challenge. While research on IT-enabled process innovation and analytics each offer valuable insights, process innovation with analytics necessitates contextualization of these knowledge bases due to its distinct characteristics. This paper aims to inspire further research into process innovation with analytics by 1) reconceptualizing analytics in the context of process innovation, and 2) proposing a research agenda, consisting of three research directions and five research challenges. The reconceptualization and research agenda are based on the authors’ experience from an Action Design Research study at a large global manufacturer and retailer focused on process innovation with analytics. Bridging analytics, process innovation, and infrastructure perspectives, the paper offers a foundation for future scholarly endeavors and calls for further research into 1) digital infrastructures for process innovation with analytics, 2) the relationship between process change and analytics development, and 3) governance of process innovation with analytics.

Abstract: Aim: Conventional free-fall kinematic models applied to plyometric push-up assessment treat the upper body as a vertically translating point mass, a simplification that ignores the curvilinear, arc-like trajectory imposed by the ankle pivot and systematically biases flight-time and height estimates. This study developed and analytically validated a novel rigid-body pendulum model to quantify plyometric push-up performance, deriving closed-form expressions for flight time, arc displacement, maximum height, and mean mechanical power at both the hand and whole-body center-of-mass reference levels. Methods: A planar rigid pendulum pivoting about the ankle axis was formulated using two independent derivation pathways, static moment equilibrium and a gravitational-torque center-of-mass coordinate approach, yielding the effective pendulum length L=(MW/M)×LOS. All performance indices were derived analytically from conservation of mechanical energy. Numerical simulations were conducted in R across seven pendulum arm lengths (LOW=0.50–2.00 m) and 500 uniformly spaced initial hand velocities per length, using adaptive Gauss-Kronrod quadrature with relative tolerance 10-10 and independent ODE cross-validation (maximum inter-method discrepancy &lt;2.5×10-7 s). Free-fall and pendulum model predictions were compared parametrically across the full physiologically admissible parameter space. Results: Both derivation pathways operationalize identical static rotational equilibrium conditions and yield the effective pendulum length (below); the geometric deviation between dOG and L remains below 4% for θ₀ ≤ 16°. Flight time equivalence between hands and center of mass (tH=tG) was formally established. The free-fall model systematically overestimated flight time by up to 18.82% (Δt=0.096 s at LOW= 0.50 m, VH,0=2.50 m/s) and maximum height by up to 28.43% (Δh=0.087 m at LOW= 0.50 m, t=0.50 s), with both errors increasing nonlinearly with initial velocity and flight time. Overestimation in height was proportionally greater at shorter pendulum arm lengths, reaching 18.18% at t=0.30 s for LOW=0.50 m versus 10.91% for LOW=1.00 m under identical conditions. Conclusion: The pendulum model provides a physically consistent, analytically tractable, and computationally validated framework for plyometric push-up performance assessment. It resolves the structural overestimation errors of the free-fall simplification, requires only four anthropometric measurements obtainable in field conditions, and supplies geometry-adjusted performance indices that improve measurement accuracy, particularly for athletes with shorter effective arm lengths or high take-off velocities.