This article investigates the essential cultural, gender, and contextual elements that shape the educational experiences and volunteerism attitudes of Arab and non-Arab students.

Supervised machine learning classifiers often encounter challenges related to performance, accuracy, and overfitting. This paper introduces the Artificial Liver Classifier (ALC), a novel supervised learning classifier inspired by the human liver's detoxification function. The ALC is characterized by its simplicity, speed, hyperparameters-free, ability to reduce overfitting, and effectiveness in addressing multi-classification problems through straightforward mathematical operations. To optimize the ALC's parameters, an improved FOX optimization algorithm (IFOX) is employed as the training method. The proposed ALC was evaluated on five benchmark machine learning datasets: Iris Flower, Breast Cancer Wisconsin, Wine, Voice Gender, and MNIST. The results demonstrated competitive performance, with the ALC achieving 100% accuracy on the Iris dataset, surpassing logistic regression, multilayer perceptron, and support vector machine. Similarly, on the Breast Cancer dataset, it achieved 99.12% accuracy, outperforming XGBoost and logistic regression. Across all datasets, the ALC consistently exhibited lower overfitting gaps and loss compared to conventional classifiers. These findings highlight the potential of leveraging biological process simulations to develop efficient machine learning models and open new avenues for innovation in the field.

Project management remains as one of the most popular post graduate management courses for international students at UK Universities. Students perceive or are advised that the skill set is highly transferable to a wide range of management opportunities and can be built upon almost any undergraduate discipline. However, many practitioners claim that the interconnectedness of the real life of projects can only ever be fully understood through on-the-job real life work experience. Hence the student dilemma, how to gain experience without experience? Project management simulation exercises attempt to bridge this gap. This research considers the effectiveness of a well proven simulation at a UK university comparing the applied knowledge of 17 different skill sets before and after the exercise through the lens of the student perspective. Furthermore, in depth semi-structured interviews with experienced tutors aim to decipher the mechanisms of learning so transferable principles can be prioritised for future exercise design. The study carried out during the 2020/21 academic year demonstrates that from a student perspective such educational approaches are highly effective with all the skill set analysed (a broad set of hard and soft skills) showing notable deltas before and after. In addition, students engage deeply with this method and find it fun and memorable which is important for future knowledge retrieval. The knowledge and experience of the tutors highlighted that game design was only 1 of 3 important elements crucial for learning to take place. Game management and the skill and motivation of the tutor have equal bearing on success. This research offers a conceptual framework to synthesis the findings and recommends that educationalists and employers collaborate and invest in future game development together rather than seek off the shelf solutions.

In research endeavors focused on the advancements of sixth-generation wireless communications, there is an urgent need to explore more efficient and flexible multiple-access schemes. Additionally, there is a growing demand for methods that can effectively conceal communication behavior, a necessity that is particularly crucial for security-critical applications. This study investigates the covertness and effective covert throughput (ECT) of uplink rate-splitting multiple access (RSMA) systems with overt and covert users in the finite block length regime. Through the derivation of the detection error probability for minimum error probability detection and the missed detection probability for Neyman-Pearson detection, it is demonstrated that the considered uplink RSMA scheme surpasses time division multiple access and frequency division multiple access counterparts with regard to covertness. Subsequently, an iterative optimization framework based on successive convex approximation is proposed to maximize the average amount of information reliably and covertly transmitted within the block length, namely, the ECT. Furthermore, the scenario where the covert user sends both covert and overt messages using RSMA is examined, and it is shown that the covert user can achieve higher effective throughput than the other schemes.

This work encompasses some relevant issues that arise when using a broadband method to characterize the even and odd mode propagation constants of symmetric microstrip coupled lines using four-port pure-mode differential and common mode S-Parameters. Correct excitations of differential and common modes play a significant role in experimental results. From these 4-port measurements, parameters corresponding to the even and odd modes can be extracted from the multi-mode generalized S-parameters obtained by the vector network analyzer. Differential and common mode sub-matrices are considered as two independent equivalent 2-port transmission lines. The method will be applied to extract the propagation constants of even and odd modes of coupled microstrip transmission lines. A discussion is included on ensuring the correct excitation of the modes in the structure and avoiding possible unwanted effects such as radiation, that negatively affects the final estimation, both in phase and attenuation. Two figures of merit are defined to evaluate how transitions affect the performance of the method. These figures of merit allow a reasonable estimation of how the final experiment is affected by selected excitation of modes. To assess the theory presented, two sets of seven lines of different lengths are manufactured. The experimental results show an excellent agreement, both with electromagnetic simulations and with the analytical model used, for the phase constant in the improved case of 30 • bends, and an improvement in the useful bandwidth of the attenuation constant. This fact verifies the correct functioning of the method. In addition, the measurements are presented in the range of 0.01-67 GHz, which represents a significant advance in bandwidth for this type of structure.

This paper explores the design and implementation of a cloud-based burst buffer system aimed at accelerating I/O performance for data-intensive applications in cloud environments. By leveraging the high throughput between compute nodes, the system buffers application data, effectively mitigating the I/O bottlenecks typical in shared cloud storage. A prototype implementation on Amazon EC2/S3 demonstrated significant performance enhancements, achieving up to 2.9 times improvement in execution time for real-world applications and reducing monetary costs by 50%. The proposed system not only addresses the critical issue of low I/O throughput but also offers scalable and cost-effective solutions for high-performance computing in the cloud.

The design of dual-band bandpass filters consisting of series short-circuited wire-bonded multiconductor transmission lines and shunt open stubs is thoroughly carried out in this paper. Two different configurations are studied and closed-form analytical design equations are derived to synthesize Chebyshev filtering functions. A novel, simplified and time-saving synthesis procedure to design dual-band filters with narrow or moderate broad bandwidths is finally presented, attending to the required final specifications and the physical and manufacturing limitations. The usefulness and validity of the proposed analytical equations are illustrated by designing, manufacturing, and measuring three different prototypes, showing an excellent agreement between analytical and measured results.

Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) by autonomous vehicles (AVs), deep analytics to fuse their outputs for a robust perception become imperative. However, existing fusion methods often make two assumptions rarely holding in practice: i) similar data distributions for all inputs and ii) constant availability for all sensors. Because, for example, lidars have various resolutions and failures of radars may occur, such variability often results in significant performance degradation in fusion. To this end, we present t-READi, an adaptive inference system that accommodates the variability of multimodal sensory data and thus enables robust and efficient perception. t-READi identifies variation-sensitive yet structure-specific model parameters; it then adapts only these parameters while keeping the rest intact. t-READi also leverages a cross-modality contrastive learning method to compensate for the loss from missing modalities. Both functions are implemented to maintain compatibility with existing multimodal deep fusion methods. The extensive experiments evidently demonstrate that compared with the status quo approaches, t-READi not only improves the average inference accuracy by more than 6% but also reduces the inference latency by almost 15× with the cost of only 5% extra memory overhead in the worst case under realistic data and modal variations.

Power-centric research conducted in the past few decades has identified many influencing factors that affect and contribute to the development of high voltage surface flashover. Among them, the surface condition—morphology, roughness, and texture—is one such physical characteristic of gas-solid interfaces that is believed to be exploitable, relatively inexpensively, as a method to improve surface flashover strength. High voltage pulsed power systems and equipment face similar challenges, but there presently exists limited literature focused on impulse-driven flashover and the effects of surface roughness. In this work, the impulsive flashover strengths of five polymers relevant to pulsed power system design (PVC, Delrin, Ultem, Torlon, Perspex) are reported under two different (∼20 nanosecond and ∼100 microsecond risetime) impulse waveforms in atmospheric air. Samples of different surface conditions— “as received” and “machined”—were subjected to flashover tests, complemented with surface profilometry measurements to evaluate the effects of various roughness characteristics on the impulse-driven breakdown strengths and times-to-breakdown. The results obtained indicate a general enhancement of the impulsive flashover strength with increased roughness with a corresponding prolongation of the time-to-breakdown. Rougher “machined” surfaces were therefore found to outperform the smoother “as received” surfaces. A correlation analysis between the measured surface roughness parameters and the breakdown data suggests that the short-wavelength components of the surface profile contributes more towards the enhancement of the flashover strength compared to longer-wavelength “waviness” components. The consistency of this result with the theory of increased streamer path length and streamer inhibition is discussed, as are the potential consequences to insulator surface modification for flashover mitigation.

The intricate challenge of maintaining semantic coherence and contextual alignment in text generation has driven the development of innovative embedding recalibration methodologies. Contextual Embedding Recalibration introduces a dynamic mechanism that iteratively adjusts token representations to reflect evolving semantic requirements within extended sequences. The recalibration framework leverages hierarchical token encoding and adaptive weight computation to balance localized fidelity and global coherence, addressing limitations of traditional static embedding strategies. Empirical evaluations across diverse tasks demonstrate substantial improvements in perplexity reduction, contextual consistency, and robustness under varied conditions, including noisy inputs and cross-domain generalization. A key feature of the recalibration layer is its seamless integration with existing transformer architectures, achieved through non-linear transformations and entropy-based regularization mechanisms. Quantitative analyses reveal that the recalibrated model excels in maintaining long-range dependencies, reducing semantic drift, and aligning tokens effectively in multi-turn dialogues and narrative completions. The ability to process out-of-vocabulary tokens with improved accuracy further highlights its adaptability to real-world linguistic variability. By incorporating computationally efficient techniques, the recalibration process achieves scalability while preserving performance gains across large datasets. The presented results demonstrate the efficacy of embedding recalibration in enhancing the representational depth and contextual reasoning capabilities of large-scale transformer models. Cross-domain evaluations confirm the robustness of the recalibrated model, indicating its versatility for applications requiring context-sensitive natural language generation. The findings not only address fundamental challenges in token representation but also offer a foundational framework for advancing machine learning methodologies in broader linguistic contexts. The proposed recalibration approach establishes a benchmark for future explorations into adaptive embedding mechanisms within artificial intelligence frameworks.

Additive manufacturing processes, such as laser powder bed fusion, offer great customization capabilities but often suffer from complexity and variability that can lead to defects and require costly post-process inspections. To enhance real-time, in-situ qualification and reduce the reliance on resource-intensive empirical testing, various machine learning models have been proposed. However, three critical challenges remain: (i) transferring knowledge across different fabrication settings without inducing negative transfer, (ii) achieving robust uncertainty quantification to account for the inherent stochasticity of AM processes, and (iii) ensuring computational efficiency and interpretability for real-time applications. In this work, we propose a novel transfer learning methodology based on the partially stochastic machine learning model enhanced with Gaussian Process priors over part-specific metadata. The proposed methodology facilitates flexible knowledge transfer, reduces the risk of negative transfer, and provides well-calibrated uncertainty estimates at reasonable computational costs. Numerical studies on the real-world additive manufacturing dataset are presented to evaluate the effectiveness and reliability of the proposed method and demonstrate the advantage of the proposed method over existing benchmark approaches. The source code will be publicly available upon paper publication.

Additive codes may have better parameters than linear codes. However, still very few cases are known and the explicit construction of such codes is a challenging problem. Here we show that a Griesmer type bound for the length of additive codes can always be attained with equality if the minimum distance is sufficiently large. This solves the problem for the optimal parameters of additive codes when the minimum distance is large and yields many infinite series of additive codes that outperform linear codes.

Respiratory diseases, including chronic obstructive pulmonary disease (COPD), are among the leading causes of deaths worldwide. Despite their prevalence, current medical practices lack a method to accurately quantify important parameters within specific lesion areas inside the lung. Our study introduces a catheter probe system designed to fill this critical gap. This novel system integrates a basket forceps with two pressure sensors to simultaneously measure exhalation airflow and static pressure within the pulmonary airway. Utilizing a Pitot tube mechanism, the pressure sensor modules are located outside the body, minimizing the risk of complications during measurement. Proof of concept experiments were performed using an artificial ventilator resembling the real respiration condition inside lung airway. Experimental results demonstrated the feasibility of the proposed concept using catheter probe of up to 550 mm in length, sufficiently allowing its access to human pulmonary airway through mouth. Consistent relation between the dynamic pressure and the mean airflow velocity was obtained independent of the airway size. Local tidal volume measurement was demonstrated at an airway mimicking the 4 th branches of bronchi with 5 mm inner diameter. This innovative approach promises to enhance our understanding and treatment of respiratory diseases by providing detailed insights into lung function previously unattainable through conventional methods.

Following a series of unplanned generation outages involving unexpected behaviors from inverter-based resources (IBRs), there has been a significant need for monitoring, control, and protection functions that can help system operators more rapidly understand and respond to these unexpected behaviors. This paper presents the IBR-rich transmission system datakit (IRTSD), which is focused on disturbances in transmission systems with high IBR penetration and consists of a dataset, power system model, and automation scripts. The data consists of 5500 events and 1.4M labelled signal recordings of power system disturbances produced using an electromagnetic transient model of a transmission system with roughly 40% IBR penetration. While the transmission system is small topologically, detailed models are used for IBR controls, conventional generators, and phasor measurement units (PMUs), resulting in a high-fidelity representation of a wide variety of power system phenomena from 0.5 Hz to 4 kHz. Events simulated include both the commonplace (e.g., stepping of voltage controls) and the emergent issues (e.g., forced oscillations or unexpected momentary cessation at an IBR plant). This paper covers the design and methodology behind the dataset, model, and automation scripts. It also provides an overview of the dataset, highlighting current research problems that the data and toolset are applicable to. The dataset, along with the models and automation scripts used to generate the data, are available to all users on IEEE DataPort and make the dataset easy for others to study, modify, and extend.

The rapid growth of AI-generated images in fields such as entertainment, e-commerce, and media has heightened the demand for robust evaluation methods to ensure high-quality, photorealistic outputs. However, current computational metrics often lack alignment with human perception, creating a gap in accurately assessing the quality of AI-generated visuals. This study introduces subjective human assessments named Visual Verity, alongside objective computational metrics, to evaluate photorealism, image quality, and text-image alignment in AI-generated images. We designed a comprehensive questionnaire and benchmarked these assessments against human judgments. The experiments are conducted using state-of-the-art models, including DALL•E 2, DALL•E 3, GLIDE, and Stable Diffusion by comparing their outputs with camera-generated images. Our findings show that while AI models excel in image quality, camera-generated images surpass them in photorealism and text-image alignment. Further analysis benchmarks traditional metrics, such as SSIM and PSNR, against human judgments and highlights the Interpolative Binning Scale as a more interpretable approach to metric scores. The framework provides a structured pathway for advancing the evaluation of AI-generated images and informing future developments in AI-driven visual media.

This paper presents a sensor-package co-simulation, design, and optimization approach for a multi-channel pressure scanning valve. The methodology combines finite element analysis to design both the sensor chip and its packaging, focusing on the interactions between these components to improve overall sensor performance. By systematically simulating and optimizing the structural characteristics of the chip-such as the thin-film diaphragm-and analyzing various packaging materials and configurations, we aim to mitigate the adverse effects of packaging-induced stress. Results demonstrate that careful selection of packaging materials and design parameters can effectively reduce packaging-related measurement errors, thereby improving the accuracy of the sensor.

To address the technical bottleneck caused by the domain mismatch problem in the sim-to-real rransfer process of humanoid robots, a targeted domain refinement (TDR) framework is proposed to solve the shortcomings of adaptive domain randomization (ADR). This research design uses quantitative simulation experiments to execute the code of TDR and ADR architecture through the Python 3.13 IDLE platform. Then the sample efficiency, adaptation speed, robustness, policy stability and domain gap of synthetic data are used to compare and analyze the two frameworks. The results show that TDR outperforms ADR on five indicators. It proves that it can make up for the shortcomings of ADR in solving domain mismatch problems, and provides reference experience for future research on improving sim-to-real rransfer.

Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the performance of HFCVs. To address this void, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. The suggested energy efficiency model employs real-time vehicle speed, acceleration, and roadway grade as input parameters for calculating instantaneous hydrogen energy consumption rates, battery energy consumption, and total energy consumption. The results suggest that the model's forecasts align well with real-world data, demonstrating average error rates of 0.0% and -0.1% for fuel cell energy and total energy consumption across all four cycles. However, it was observed that the error rate for a particular cycle, such as the UDDS, can be as high as 13.1%. Moreover, the study confirmed the reliability of the suggested model through validation with independent data. The findings indicated that the proposed model precisely predicted energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Additionally, instantaneous SOC predictions from the model closely match observed instantaneous SOC measurements, highlighting the model's effectiveness in estimating real-time changes in the battery SOC. The study investigated the energy impact of various intersection controls to assess the applicability of the proposed energy model. The simplicity of the proposed model enables easy estimation of energy consumption using traffic simulation models.