This systematic review discusses the methodological approaches and statistical confirmations of cross-cultural adaptations of cognitive evaluation tools used with different populations. The review considers six seminal studies on the methodology of cultural adaptation in Europe, Asia, Africa, and South America. The results indicate that proper adaptations need holistic models with demographic changes, and education explained as much as 26.76% of the variance in MoCA-H scores. Cultural-linguistic factors explained 6.89% of the variance in European adaptations of MoCA-H; however, another study on adapted MMSE and BCSB among Brazilian Indigenous populations reported excellent diagnostic performance, with a sensitivity of 94.4% and specificity of 99.2%. There was 78.5% inter-rater agreement on the evaluation of cultural adaptation using the Manchester Translation Evaluation Checklist. A paramount message of the paper is that community feedback is necessary for culturally appropriate preparation, standardized translation protocols also must be included, along with robust statistical validation methodologies for developing cognitive assessment instruments. This review supplies evidence-based frameworks for the further adaptation of cognitive assessments in increasingly diverse global health settings.

This work introduces a multi-output classification (MOC) fra mework designed for domain adaptation in fault diagnosis, particularly under partially la beled (PL) target domain scenarios and comp ound fault conditions in rotating machinery. Unlike traditional multi-class classification (MCC) methods that treat each fault combination as a distinct class, the proposed approach independently estimates the severity of each fau lt type, improving both interpretability and diagnostic accuracy. The model incorporates multi-ke rnel maximum mean discrepancy (MK-MMD) and entropy minimization (EM) losses to facilitate feature tran sfer from the source to the target domain. In addition, frequency layer normalization (FLN) is applied to preserve structural properties in the frequen cy domain, which are strongly influenced by system dynamics and are often stationary with respect to changes in rpm. Evaluations across six domain ad aptation cases with PL data demonstrate that MOC outperforms baselin e models in macro F1 score. Moreover, MOC consistently achieves better classification performance for individual fault types, and FLN shows superior adaptability compared to other normalization techniques.

Although numerous machine learning models exist to detect issues like rolling bearing strain and deformation, typically caused by improper mounting, overloading, or poor lubrication, these models often struggle to isolate faults from the noise of real-world operational and environmental variability. Conditions such as variable loads, high temperatures, stress, and rotational speeds can mask early signs of failure, making reliable detection challenging. To address these limitations, this work proposes a continual deep learning approach capable of learning across domains that share underlying structure over time. This approach goes beyond traditional accuracy metrics by addressing four second-order challenges: catastrophic forgetting (where new learning overwrites past knowledge), lack of plasticity (where models fail to adapt to new data), forward transfer (using past knowledge to improve future learning), and backward transfer (refining past knowledge with insights from new domains). The method comprises a feature generator and domain-specific classifiers, allowing capacity to grow as new domains emerge with minimal interference, while an experience replay mechanism selectively revisits prior domains to mitigate forgetting. Moreover, nonlinear dependencies across domains are exploited by prioritizing replay from those with the highest prior errors, refining models based on most informative past experiences. Experiments show high average domain accuracy (up to 88.96%), with forgetting measures as low as .0027 across non-stationary class-incremental environments.

Autism diagnoses have increased sharply since 2000, according to government figures, and by 2020 the rate among 8-year-olds reached 2.77%, according to the US Centers for Disease Control and Prevention (CDC). Scientists attribute at least part of the rise to increased awareness of autism and an expanding definition of the disorder. Researchers have also been investigating environmental factors. The US National Institutes of Health (NIH), a government agency, spends more than 300m ( 230m) per year researching autism. Kennedy did not give details on the research project or how much funding will be devoted to autism research.

Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients.

Real-world networks grow over time; statistical models based on node exchangeability are not appropriate. Instead of constraining the structure of the \textit{distribution} of edges, we propose that the relevant symmetries refer to the \textit{causal structure} between them. We first enumerate the 96 causal directed acyclic graph (DAG) models over pairs of nodes (dyad variables) in a growing network with finite ancestral sets that are invariant to node deletion. We then partition them into 21 classes with ancestral sets that are closed under node marginalization. Several of these classes are remarkably amenable to distributed and asynchronous evaluation. As an example, we highlight a simple model that exhibits flexible power-law degree distributions and emergent phase transitions in sparsity, which we characterize analytically. With few parameters and much conditional independence, our proposed framework provides natural baseline models for causal inference in relational data.

To satisfy the requirements of the end-to-end fault diagnosis of gears, an integrated intelligent method of fault diagnosis for gears using acceleration signals was proposed, which was based on Gabor-based Adaptive Short-Time Fourier Transform (Gabor-ASTFT) and Dual-Tree Complex Wavelet Transform(DTCWT) algorithms, Dilated Residual structure and feature fusion layer, is proposed in this paper. Initially, the raw one-dimensional acceleration signals collected from the gearbox base using vibration sensors undergo pre-segmentation processing. The Gabor-ASTFT and DTCWT are then applied to convert the original one-dimensional time-domain signals into two-dimensional time-frequency representations, facilitating the preliminary extraction of fault features and obtaining weak feature maps.Subsequently, a dual-channel structure is established using deconvolution and dilated convolution to perform upsampling and downsampling on the feature maps, adjusting their sizes accordingly. A feature fusion layer is then constructed to integrate the dual-channel features, enabling multi-scale analysis of the extracted fault features.Finally, a convolutional neural network (CNN) model incorporating a residual structure is developed to conduct deep feature extraction from the fused feature maps. The extracted features are subsequently fed into a Global Average Pooling(GAP) and a classification function for fault classification. Conducting comparative experiments on different datasets, the proposed method is demonstrated to effectively meet the requirements of end-to-end fault diagnosis for gears.

With the widespread adoption of Ethereum, financial frauds such as Ponzi schemes have become increasingly rampant in the blockchain ecosystem, posing significant threats to the security of account assets. Existing Ethereum fraud detection methods typically model account transactions as graphs, but this approach primarily focuses on binary transactional relationships between accounts, failing to adequately capture the complex multi-party interaction patterns inherent in Ethereum. To address this, we propose a hypergraph modeling method for the Ponzi scheme detection method in Ethereum, called HyperDet. Specifically, we treat transaction hashes as hyperedges that connect all the relevant accounts involved in a transaction. Additionally, we design a two-step hypergraph sampling strategy to significantly reduce computational complexity. Furthermore, we introduce a dual-channel detection module, including the hypergraph detection channel and the hyper-homo graph detection channel, to be compatible with existing detection methods. Experimental results show that, compared to traditional homogeneous graph-based methods, the hyper-homo graph detection channel achieves significant performance improvements, demonstrating the superiority of hypergraph in Ponzi scheme detection. This research offers innovations for modeling complex relationships in blockchain data.

Interference bias is a major impediment to identifying causal effects in real-world settings. For example, vaccination reduces the transmission of a virus in a population such that everyone benefits -- even those who are not treated. This is a source of bias that must be accounted for if one wants to learn the true effect of a vaccine on an individual's immune system. Previous approaches addressing interference bias require strong domain knowledge in the form of a graphical interaction network fully describing interference between units. Moreover, they place additional constraints on the form the interference can take, such as restricting to linear outcome models, and assuming that interference experienced by a unit does not depend on the unit's covariates. Our work addresses these shortcomings. We first provide and justify a novel definition of causal models with local interference. We prove that the True Average Causal Effect, a measure of causality where interference has been removed, can be identified in certain semi-parametric models satisfying this definition. These models allow for non-linearity, and also for interference to depend on a unit's covariates. An analytic estimand for the True Average Causal Effect is given in such settings. We further prove that the True Average Causal Effect cannot be identified in arbitrary models with local interference, showing that identification requires semi-parametric assumptions. Finally, we provide an empirical validation of our method on both simulated and real-world datasets.

There are many approaches for training decision trees. This work introduces a novel gradient-based method for constructing decision trees that optimize arbitrary differentiable loss functions, overcoming the limitations of heuristic splitting rules. Unlike traditional approaches that rely on heuristic splitting rules, the proposed method refines predictions using the first and second derivatives of the loss function, enabling the optimization of complex tasks such as classification, regression, and survival analysis. We demonstrate the method's applicability to classification, regression, and survival analysis tasks, including those with censored data. Numerical experiments on both real and synthetic datasets compare the proposed method with traditional decision tree algorithms, such as CART, Extremely Randomized Trees, and SurvTree. The implementation of the method is publicly available, providing a practical tool for researchers and practitioners. This work advances the field of decision tree-based modeling, offering a more flexible and accurate approach for handling structured data and complex tasks. By leveraging gradient-based optimization, the proposed method bridges the gap between traditional decision trees and modern machine learning techniques, paving the way for further innovations in interpretable and high-performing models.