ABSTRACT This paper presents a detailed mathematical framework for computing a Model Validation Level (MVL), a metric for quantifying the amount of trust that can be placed in the results of a model. The framework is founded in three key pillars of validation: fidelity, referent authority, and scope. Each of these three pillars can be quantified to enable an objective and automatable comparison between model results and referent data. Combined, these pillars result in an MVL between zero and nine for the model that quickly communicates the model’s validity for a given intended use case. Additionally, the framework provides opportunities for identifying areas of model improvement to increase trust. The paper presents analysis on several metrics, demonstrating results for a variety of possible validation scenarios. Ultimately, the MVL framework provides an objective methodology for automating validation, enabling models to be quickly evaluated for trust in the ongoing digital transformation.

The detection and classification of eye diseases, including Diabetic Retinopathy, Cataract, and Normal conditions, are critical in medical imaging for early diagnosis and treatment. This study proposes a hybrid CNN-Haralick model, leveraging the lightweight MobileNetV2 CNN architecture for spatial feature extraction and Haralick texture features extraction for texture analysis to enhance the accuracy of eye disease classification. A dual-branch architecture is employed, which fuses features from both the Convolutional Neural Network and the Haralick-based texture analysis at an early stage. The model is evaluated on a dataset consisting of images from multiple sources. Experimental results show that the hybrid CNN-Haralick model achieves an overall accuracy of 98% on the validation set, outperforming traditional CNN models. The model demonstrates exceptional performance, with a macro average F1-score of 98% for the three classes, and AUC-ROC scores of 100% for each category. The confusion matrix and classification report further validate the model's capability to accurately classify eye diseases, providing reliable decision support for clinicians. Additionally, the model's effectiveness is discussed in comparison with existing works, highlighting its superior performance in terms of both accuracy and computational efficiency.

This study addresses the limited research on IT-enabled support for internal staffing by employing a design science research (DSR) methodology to design, develop, and evaluate a mobile-based staffing and task assignment management system (M-STAMS). Tailored to a public university's human resource operations, the artifact leverages cloud and mobile technologies to enhance staff-task matching, operational efficiency, and coordination. A survey of 180 users was conducted using an extended Technology Acceptance Model (TAM) that incorporates user habits as an external factor to assess both technical effectiveness and user acceptance. The results demonstrate that perceived ease of use, usefulness, and habitual technology behavior significantly influence adoption intentions. The findings contribute to digital HRM research by validating the role of mobile technologies in staffing effectiveness and emphasizing the value of DSR in bridging the gap between information systems and human resource specialists to develop socio-technical HR solutions tailored to organizational needs and constraints.

This study investigates the factors affecting information sharing for the identification of road transport accident victims by the National Union of Road Transport Workers (NURTW) and the Federal Road Safety Corps (FRSC) in Kwara State, Nigeria. The study specifically aims to assess the effectiveness of inter-agency collaboration, evaluate the accuracy and timeliness of shared information, and identify the challenges hindering effective data exchange between the two organisations. A quantitative research methodology was employed using a cross-sectional survey design to objectively analyze measurable variables across the three senatorial zones of the state. A multistage random sampling technique was applied to select 485 respondents from a total population of 2,954 staff members of both agencies. The findings revealed that although collaboration between NURTW and FRSC is generally perceived as beneficial, it is hindered by poorly defined roles, weak communication systems, and ineffective use of shared information. Incomplete records and the destruction of vital data during accidents were also identified as major challenges. These issues reflect broader structural and procedural shortcomings that compromise timely and accurate victim identification. Based on the findings, the study recommends the development of formal collaboration protocols, adoption of digital data systems, and establishment of joint operational frameworks to enhance inter-agency coordination and road accident response in Kwara State.

Event-based vision sensors (EVSs), often referred to as neuromorphic cameras, operate by responding to changes in brightness on a pixel-by-pixel basis. In contrast, traditional framing cameras employ some fixed sampling interval where integrated intensity is read off the entire focal plane at once. Similar to traditional cameras, EVSs can suffer loss of sensitivity through scenes with high intensity and dynamic clutter, reducing the ability to see points of interest through traditional event processing means. This paper describes a method to reduce the negative impacts of these types of EVS clutter and enable more robust target detection through the use of individual pixel frequency analysis, background suppression, and statistical filtering. Additionally, issues found in normal frequency analysis such as phase differences between sources, aliasing, and spectral leakage are less relevant in this method. The statistical filtering simply determines what pixels have significant frequency content after the background suppression instead of focusing on the actual frequencies in the scene. Initial testing on simulated data demonstrates a proof of concept for this method, which reduces artificial scene noise and enables improved target detection.

The proposed mechanisms of spinal cord stimulation (SCS) follow the polarization of dorsal column axons; however, the development of subparesthesia SCS has encouraged the consideration of different targets. Given their relative proximity to the stimulation electrodes and their role in pain processing (eg, synaptic processing and gate control theory), spinal cord dorsal horn interneurons may be attractive stimulation targets. We developed a computational modeling pipeline termed "quasiuniform-mirror assumption" and applied it to predict polarization of dorsal horn interneuron cell types (islet type, central type, stellate/radial, vertical-like) to SCS. The quasiuniform-mirror assumption allows the prediction of the peak and directional axes of dendrite polarization for each cell type and location in the dorsal horn, in addition to the impact of the stimulation pulse width and electrode configuration. For long pulses, the peak polarization per milliampere of SCS with a spaced bipolar configuration was islet type 3.5mV, central type 1.3mV, stellate/radial 1.4mV, and vertical-like 1.6mV. For stellate/radial, the peak dendrite polarization was dorsal-ventral, and for islet-type, the peak dendrite polarization was in the rostral-caudal axis. For islet type and central type cells, peak dendrite polarization was between stimulation electrodes, whereas for stellate/radial and vertical-like cells, peak dendrite polarization was under the stimulation electrodes. The impact of the pulse width depends on the membrane time constants. Assuming a 1-millisecond time constant, for a 1-millisecond or 100-μs pulse width, the peak dendrite polarization decreases (from direct current values) by approximately 33% and approximately 88%, respectively. Increasing the interelectrode distance beyond approximately 3 cm did not significantly increase the peak polarization but expanded the region of interneuron polarization. Predicted maximum polarization of islet-cells in the superficial dorsal horn at locations between electrodes is 4.6mV for 2 mA, 1-millisecond pulse SCS. A polarization of a few millivolts is sufficient to modulate synaptic processing through subthreshold mechanisms. Our simulations provide support for SCS approaches optimized to modulate the dendrites of dorsal horn neurons.

The recently discovered wurtzite ferroelectrics (FEs) have been at the center of electronic materials research because of their process compatibility and remarkably high Curie temperatures (above 1100 °C), qualities required for high-temperature nonvolatile memory. Among the wurtzite FEs, aluminum scandium nitride (AlScN) is one of the most studied, and although significant progress has been made toward its implementation, questions remain regarding its high and polarization-dependent leakage current. In this manuscript, we discuss the origin of this polarization-dependent leakage in sputter-deposited AlScN FE films by analyzing temperature-dependent current–voltage (I–V) characteristics of metal-FE-metal devices. The results suggest that the difference in current density with polarization is due to bulk properties more than the electrode–FE interface. Further statistical analysis showed that if the Poole–Frenkel conduction model is used, this current density difference can be attributed to changes in the electron mobility and/or carrier (or trap) density with polarization and not due to changes in the trap depth.

Abstract Ranque–Hilsch vortex tubes split an incoming fluid stream into two outgoing streams—one cooler and one warmer than the inlet stream, all without any supplied external power or moving parts. These impressive devices have been the subject of a great deal of research over the years, but little progress has been made in characterizing their behavior at elevated inlet temperatures. With promising potential as a thermal management device in high-temperature applications, characterization of vortex tube behavior at elevated temperatures is necessary. In the present work, a commercial Ranque–Hilsch vortex tube was modified, replacing a polymer vortex generator with a brass version to allow for higher temperature operation. Inlet total temperatures were varied between 350 K and 500 K, the highest applied to vortex tubes of which the authors are aware in the open literature, and the resulting temperature separation characteristics were examined. The high-temperature vortex tube experiments necessitated a wider range of higher Mach number inlet conditions than studied previously, and the results suggest a strong dependence of the temperature separation on the inlet Mach number. A dependence on the inlet Reynolds number was also observed, with greater sensitivity at lower Reynolds numbers.

This study employs molecular dynamics simulations to investigate the fracture behavior of four binary refractory alloys WxM1−x (M = V, Mo, Ta, Re) and their dependence on crystallographic orientation, composition, and grain boundary (GB) structure, focusing on six distinct low-sigma grain boundaries. The simulations reveal that the effect of composition is complex with the most pronounced effect, accompanied by the maximum or minimum stress intensity factor, generally occurring at intermediate compositions. All compositions showed a higher fracture resistance in the [110] orientation compared to the [100] orientation. There was a strong thermodynamic tendency for Mo and V, and Ta to a lesser extent, to segregate to GBs specifically at the low temperatures. The segregation behavior was more striking in tilt compared to twist GBs and was generally associated with GB embrittlement. A strengthening effect was, however, also observed for specific grain boundaries and segregating elements, demonstrating the significance of the effect of GB structure on overall behavior. Finally, twist GBs typically had higher strength and showed a stronger dependence on strain rate in most cases when compared to tilt GBs. These results may help inform the design of next generation structural materials for extreme environments.

Abstract A Stirling engine thermodynamic cycle was computationally simulated and probabilistically evaluated in view of the several uncertainties in the performance parameters. Cumulative distribution functions and sensitivity factors were computed for the overall thermal efficiency and net specific power output due to the thermodynamic random variables. These results can be used to quickly identify the most critical design variables in order to optimize the design, enhance performance, increase system availability and make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in the Stirling engine health determination and to the identification of both the most critical measurements and parameters. Probabilistic analysis aims at unifying and improving the control and health monitoring of Stirling engine by increasing the quality and quantity of information available about the engine’s health and performance.