Search Results (28)

To meet the growing demands for competency-based and personalized instruction in high school English reading, this study investigates a quantitative approach to modeling learning pathways and progressions. Traditional assessments often fail to capture students’ fine-grained cognitive differences and provide limited guidance for individualized teaching. Based on cognitive diagnostic theory, this study analyzes large-scale empirical data to construct a progression framework reflecting both the sequencing of cognitive skill development and the hierarchical structure of reading abilities. A Q-matrix was calibrated through expert consensus. A hybrid cognitive diagnostic model was used to infer students’ knowledge states, followed by cluster analysis and item response theory to define progression levels, which were mapped to national curriculum standards. The findings reveal that students’ mastery of cognitive attributes follows a stepwise developmental pattern, with dominant learning trajectories. The constructed learning progression aligns well with curriculum-based academic quality levels, while uncovering potential misalignments in the positioning of some skill levels. Students with identical scores also showed significant variation in cognitive structures. The proposed model provides a data-informed foundation for adaptive instruction and offers new tools for personalized learning in English reading comprehension. Full article

This paper presents a comprehensive reliability analysis framework for the Chris–Jerry (CJ) lifetime distribution under an improved adaptive progressive Type-II censoring plan. The CJ model, recently introduced to capture skewed lifetime behaviors, is studied under a modified censoring structure designed to provide greater flexibility in terminating life-testing experiments. We derive maximum likelihood estimators for the CJ parameters and key reliability measures, including the reliability and hazard rate functions, and construct approximate confidence intervals using the observed Fisher information matrix and the delta method. To address the intractability of the likelihood function, Bayesian estimators are obtained under independent gamma priors and a squared-error loss function. Because the posterior distributions are not available in closed form, we apply the Metropolis–Hastings algorithm to generate Bayesian estimates and two types of credible intervals. A comprehensive simulation study evaluates the performance of the proposed estimation techniques under various censoring scenarios. The framework is further validated through two real-world datasets: one involving rainfall measurements and another concerning mechanical failure times. In both cases, the CJ model combined with the proposed censoring strategy demonstrates superior fit and reliability inference compared to competing models. These findings highlight the value of the CJ distribution, together with advanced censoring methods, for modeling lifetime data in physics and engineering applications. Full article

Background: The COVID-19 pandemic has claimed thousands of lives worldwide. While infection rates have declined in recent years, emerging variants remain a deadly threat. Accurate diagnosis is critical to curbing transmission and improving treatment outcomes. However, the similarity of COVID-19 symptoms to those of the common cold and flu has spurred the development of automated diagnostic methods, particularly through lung computed-tomography (CT) scan analysis. Methodology: This paper proposes a novel deep learning-based approach for detecting diverse COVID-19 variants using advanced textural feature extraction. The framework employs a dual-channel convolutional neural network (CNN), where one channel processes texture-based features and the other analyzes spatial information. Unlike existing methods, our model dynamically learns textural patterns during training, eliminating reliance on predefined features. A modified local binary pattern (LBP) technique extracts texture data in matrix form, while the CNN’s adaptable internal architecture optimizes the balance between accuracy and computational efficiency. To enhance performance, hyperparameters are fine-tuned using the Adam optimizer and focal loss function. Results: The proposed method is evaluated on two benchmark datasets, COVID-349 and Italian COVID-Set, which include diverse COVID-19 variants. Conclusions: The results demonstrate its superior accuracy (94.63% and 95.47%, respectively), outperforming competing approaches in precision, recall, and overall diagnostic reliability. Full article

Hyperspectral image (HSI) denoising is an important preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for denoising tasks. In recent years, denoising methods based on low-rank subspaces have garnered widespread attention. In the low-rank matrix factorization framework, the restoration of HSI can be formulated as a task of recovering two subspace factors. However, hyperspectral images are inherently three-dimensional tensors, and transforming the tensor into a matrix for operations inevitably disrupts the spatial structure of the data. To address this issue and better capture the spatial-spectral priors of HSI, this paper proposes a modeling approach named low-rank Tucker decomposition with subspace implicit neural representation (LRTSINR). This data-driven and model-driven joint modeling mechanism has the following two advantages: (1) Tucker decomposition allows for the characterization of the low-rank properties across multiple dimensions of the HSI, leading to a more accurate representation of spectral priors; (2) Implicit neural representation enables the adaptive and precise characterization of the subspace factor continuity under Tucker decomposition. Extensive experiments demonstrate that our method outperforms a series of competing methods. Full article

A novel unified progressive hybrid censoring is introduced to combine both progressive and hybrid censoring plans to allow flexible test termination either after a prespecified number of failures or at a fixed time. This work develops both frequentist and Bayesian inferential procedures for estimating the parameters, reliability, and hazard rates of the inverted Nadarajah–Haghighi lifespan model when a sample is produced from such a censoring plan. Maximum likelihood estimators are obtained through the Newton–Raphson iterative technique. The delta method, based on the Fisher information matrix, is utilized to build the asymptotic confidence intervals for each unknown quantity. In the Bayesian methodology, Markov chain Monte Carlo techniques with independent gamma priors are implemented to generate posterior summaries and credible intervals, addressing computational intractability through the Metropolis—Hastings algorithm. Extensive Monte Carlo simulations compare the efficiency and utility of frequentist and Bayesian estimates across multiple censoring designs, highlighting the superiority of Bayesian inference using informative prior information. Two real-world applications utilizing rare minerals from gold and diamond durability studies are examined to demonstrate the adaptability of the proposed estimators to the analysis of rare events in precious materials science. By applying four different optimality criteria to multiple competing plans, an analysis of various progressive censoring strategies that yield the best performance is conducted. The proposed censoring framework is effectively applied to real-world datasets involving diamonds and gold, demonstrating its practical utility in modeling the reliability and failure behavior of rare and high-value minerals. Full article

This paper presents an integrated digital training twin framework for adaptive aircraft maintenance education, combining real-time competence modeling, algorithmic orchestration, and cloud–edge deployment architectures. The proposed system dynamically evaluates learner skill gaps and assigns individualized training resources through a multi-objective optimization function that balances skill alignment, Bloom’s cognitive level, fidelity tier, and time efficiency. A modular orchestration engine incorporates reinforcement learning agents for policy refinement, federated learning for privacy-preserving skill analytics, and knowledge graph-based curriculum models for dependency management. Simulation results were conducted on the Pneumatic Systems training module. The system’s validation matrix provides full-cycle traceability of instructional decisions, supporting regulatory audit-readiness and institutional reporting. The digital training twin ecosystem offers a scalable, regulation-compliant, and data-driven solution for next-generation aviation maintenance training, with demonstrated operational efficiency, instructional precision, and extensibility for future expansion. Full article

This study addresses the critical gap between academic training and the competency demands of the maritime logistics and port management sector. Using a mixed-methods approach, it integrates benchmarking of postgraduate programs from leading universities, interviews with 15 stakeholders representing diverse industry profiles, and an in-depth curriculum analysis. The research identifies and categorizes essential technical, management, and interpersonal competencies, culminating in the development of a Competency Matrix to guide the alignment of academic curricula with industry requirements. Key competencies identified include strategic decision-making, operations management, data analysis, adaptability, teamwork, and customer engagement, all of which are critical to ensuring efficiency and competitiveness in the sector. This study introduces an innovative framework by combining benchmarking with qualitative insights, addressing a crucial gap in the literature while offering actionable strategies for academia to enhance training programs. The findings highlight the urgent need for universities to develop courses tailored to global challenges, such as digitalization, sustainability, and supply chain resilience. Although this study is exploratory and based on a limited sample size, it provides meaningful insights into the Portuguese maritime and port logistics sector, laying a solid foundation for future research. Further studies should investigate how innovation and emerging technologies, such as artificial intelligence and blockchain, are reshaping competency requirements in this dynamic and globalized industry. Full article

“The Emergence of Neuroleadership in the Knowledge Economy” explores the field of neuroleadership in today’s constantly changing economy, highlighting the transition from traditional leadership to neuroleadership. Neuroleadership renders itself as a novel approach to the leadership theory, which brings together insights from neuroscience, psychology, and leadership studies. It emphasizes understanding the workings of the brain and human behavior in order to drive leadership effectiveness, at individual, team, and organizational levels. Additionally, the knowledge economy is characterized by the significant role of knowledge and intellectual capital when it comes to driving economic growth and organizational development. It highlights the creation, dissemination, and sharing of knowledge as important pillars for productivity and competitive advantage, shaping industries and transforming leadership traditional models. Through an extensive literature review and by employing the Dulewicz and Higgs leadership model, the authors showcase what are the intellectual, managerial, and emotional competencies that make neuroleadership the next natural step in leading teams and organizations. This article proposes a comparative matrix between traditional leaders and neuroleaders, and highlights a novel framework for better understanding neuroleadership. Full article

Protein farnesylation is a post-translational modification where a 15-carbon farnesyl isoprenoid is appended to the C-terminal end of a protein by farnesyltransferase (FTase). This process often causes proteins to associate with the membrane and participate in signal transduction pathways. The most common substrates of FTase are proteins that have C-terminal tetrapeptide CaaX box sequences where the cysteine is the site of modification. However, recent work has shown that five amino acid sequences can also be recognized, including the pentapeptides CMIIM and CSLMQ. In this work, peptide libraries were initially used to systematically vary the residues in those two parental sequences using an assay based on Matrix Assisted Laser Desorption Ionization–Mass Spectrometry (MALDI-MS). In addition, 192 pentapeptide sequences from the human proteome were screened using that assay to discover additional extended CaaaX-box motifs. Selected hits from that screening effort were rescreened using an in vivo yeast reporter protein assay. The X-ray crystal structure of CMIIM bound to FTase was also solved, showing that the C-terminal tripeptide of that sequence interacted with the enzyme in a similar manner as the C-terminal tripeptide of CVVM, suggesting that the tripeptide comprises a common structural element for substrate recognition in both tetrapeptide and pentapeptide sequences. Molecular dynamics simulation of CMIIM bound to FTase further shed light on the molecular interactions involved, showing that a putative catalytically competent Zn(II)-thiolate species was able to form. Bioinformatic predictions of tetrapeptide (CaaX-box) reactivity correlated well with the reactivity of pentapeptides obtained from in vivo analysis, reinforcing the importance of the C-terminal tripeptide motif. This analysis provides a structural framework for understanding the reactivity of extended CaaaX-box motifs and a method that may be useful for predicting the reactivity of additional FTase substrates bearing CaaaX-box sequences. Full article

The development of competency frameworks serves as the foundation for the development of competency-based education. It is vital to develop a country-specific framework to address the specific needs of the local population for pharmacy services. This study aimed to describe the development process of a competency framework for undergraduate pharmacy education in Kuwait with a unique matrix structure. The process started with the development of guiding principles for curriculum revision and implementation, as well as the identification of global educational outcomes. This process was followed by: (A) a needs assessment with key stakeholders; (B) development of the initial competency framework; and (C) refinement of the framework. Qualitative data were thematically analyzed to identify the main competency domains that students need to perform the identified entrustable professional activities (EPAs). Five population needs were identified by the needs assessment, with 17 EPAs suggested to fulfill those needs. In addition, 11 competency domains were identified. The initial competency framework was created as a 3 × 8 matrix, with 3 professional and 8 transversal competency domains. Refinement of the framework resulted in the removal of redundancies and the development of a global behavior competency profile. The development of a matrix competency framework and associated EPAs for Kuwait serves as a foundation for preparing pharmacists to fulfill local population needs and expanding the scope of practice in the country. Full article

This paper reports the results of a study on the implementation of a sustainable teaching model based on the OBE (Outcome-Based Education) concept and the TSEM (Teach, Study, Evaluate, and Manage) framework in computer science and technology at NingboTech University, China. In the context of digital education, the OBE concept and the TSEM framework are integrated to explore sustainable teaching and learning models based on “artificial intelligence and education”. Based on the core concept of engineering professional education accreditation, the course is designed by using the PCCM (Professional Competency Correlation Matrix) method to build a model based on big data analysis, deepen the classroom teaching reform of “artificial intelligence and education”, and explore the integrated digital sustainable teaching mode of “teaching, learning, evaluation, and management”. The aim of this study is to explore the effectiveness of the teaching model based on OBE and the TSEM framework on students’ sustainable development. The results show that students deepen their learning in computer science while enhancing their own learning initiative, teamwork skills, innovation skills, and awareness of sustainable development. Research shows that our teaching model plays an important role in the development of student sustainable education, enhancing student engineering practice and innovation capabilities and cultivating applied innovative talents. The efficacy of the teaching model based on the OBE concept and the TSEM framework for improving students’ competence in sustainable education warrants further investigation. Full article

Micron-sized metal–organic framework (MOF) sheets were prepared using organic molecules with aggregation-induced emission (AIE) properties as ligands. The intermolecular ligand-to-metal charge transfer (LMCT) process occurs in MOF structures, resulting in the disappearance of the matrix coordination-induced emission (MCIE) effect and emergence of the aggregation-caused quenching (ACQ) effect. Here, we demonstrate that molecules with electron donors can compete with the LMCT process in MOF structures, thereby changing the transfer path of the excitation energy and returning it to the ground state, mainly in the form of fluorescence. Organic molecules with amino or sulfhydryl groups can act as effective electron donors, reducing the LMCT process and causing the MCIE effect of the MOF sheet. The coexistence of amino and sulfhydryl groups will strongly inhibit the LMCT process of the MOF sheet, thereby greatly enhancing the MCIE effect. Therefore, these types of molecules can be used to regulate the photoluminescence intensity of AIE-based MOF materials. In addition, there are some organic molecules with multiple carboxyl or hydroxyl groups which can produce similar effects. Finally, it was confirmed that the intermolecular LMCT process is highly sensitive, and the MOF sheet showed distinguishable fluorescence results even with the addition of small molecules in the amount of 10⁻⁹ M. Thus, it is a feasible idea to use the fluorescence changes induced by the LMCT process as a sensitive sensing method for small molecules. Full article

During imaging and transmission, images are easily affected by several factors, including sensors, camera motion, and transmission channels. In practice, images are commonly corrupted by a mixture of Gaussian and impulse noises, further complicating the denoising problem. Therefore, in this work, we propose a novel mixed noise removal model that combines a deterministic low-rankness prior and an implicit regularization scheme. In the optimization model, we apply the matrix logarithm norm approximation model to characterize the global low-rankness of the original image. We further adopt the plug-and-play (PnP) scheme to formulate an implicit regularizer by plugging an image denoiser, which is used to preserve image details. The above two building blocks are complementary to each other. The mixed noise removal algorithm is thus established. Within the framework of the PnP scheme, we address the proposed optimization model via the alternating directional method of multipliers (ADMM). Finally, we perform extensive experiments to demonstrate the effectiveness of the proposed algorithm. Correspondingly, the simulation results show that our algorithm can recover the global structure and detailed information of images well and achieves superior performance over competing methods in terms of quantitative evaluation and visual inspection. Full article

With the development of intelligent transportation, radar and communication on moving platforms are competing for the spectrum. In this paper, we propose and demonstrate a new algorithmic framework for radar-communication spectral coexistence system on moving platform with mutual interference suppression, in which communication rate and the radar signal-to-interference-plus-noise ratio (SINR) are simultaneously optimized, under the energy constraints for the two systems and the radar constant modulus constraint. The radar spatial-temporal filter at the receiver and transmitting waveform are optimized, while the codebook matrix is optimized for the communication system. To cope with the established non-convex problem with triplet variables, we decouple the original problem into multiple subproblems, for which an alternating algorithm based on iterative procedures is derived with lower computational complexity. Specifically, the subproblems of communication codebook and radar filter design are convex and the closed-form solutions can be easily obtained, while the radar waveform optimization is non-convex. Then we propose a novel scheme by exploiting the alternating direction method of multipliers (ADMM) based on minorization-maximization (MM) framework. Finally, to reveal the effectiveness of the proposed algorithm in different scenarios, numerical results are provided. Full article

Statistical models play a significant role in designing competent breeding programs related to complex traits. Recently; the holo-omics framework has been productively utilized in trait prediction; but it contains many complexities. Therefore; it is desirable to establish prediction accuracy while combining the host’s genome and microbiome data. Several methods can be used to combine the two data in the model and study their effectiveness by estimating the prediction accuracy. We validate our holo-omics interaction models with analysis from two publicly available datasets and compare them with genomic and microbiome prediction models. We illustrate that the holo-omics interactive models achieved the highest prediction accuracy in ten out of eleven traits. In particular; the holo-omics interaction matrix estimated using the Hadamard product displayed the highest accuracy in nine out of eleven traits, with the direct holo-omics model and microbiome model showing the highest prediction accuracy in the remaining two traits. We conclude that comparing prediction accuracy in different traits using real data showed important intuitions into the holo-omics architecture of complex traits. Full article