Advanced delta domain model order reduction and control of high order interleaved DC-DC converters using metaheuristic optimization: a unified fractional-order system approach

Rates of recreational cannabis (ie, marijuana) use in the United States have increased in recent years as have rates of cannabis use disorder. The aim of this study was to assess theoretical correlates of intentions to initiate and sustain marijuana cessation among adult user-using domains of the Multi-Theory Model (MTM) of health behavior change. A cross-sectional survey was administered to a sample of adult cannabis users (n = 227; mean age = 40.9 years; 48.5% female; 85.5% non-Hispanic white). We assessed intention to quit cannabis using MTM constructs of initiation (participatory dialogue, behavioral confidence, changes in physical environment) and sustenance (emotional transformation, practice for change, changes in social environment). The structure of the MTM scale was assessed using confirmatory factor analysis. Hierarchical linear regression models were used to assess constructs of initiation and sustenance of cannabis use controlling for demographic characteristics and Cannabis Use Disorder Identification Test score (<12 and >12). The MTM scale evidenced good reliability (Cronbach's alphas > .7) and good model fit. Results indicated that only the change in physical environment construct was significantly associated with the intention of initiating cannabis cessation (B = 0.27, P < .001) while practice for change (B = 0.27, P < .001) and changes in social environment (B = 0.19, P = .001) was positively associated with the intentions of sustaining cannabis cessation. Results indicate that changes to physical and social environment are key features of initiating and sustaining cannabis use cessation, which aligns with research on tobacco cessation. Future research should explore how these factors interact and investigate additional influences on long-term cessation success.

The Takini/Survivor project examined factors promoting healing and resilience among women survivors of domestic violence in primarily South Dakota, with particular attention to American Indian/Native American (Native hereinafter) and rural experiences through the resilience portfolio model. Using a phenomenological design, this study explored the narratives of 31 Native women using semistructured qualitative interviews. When appropriate, the study also delineated between narratives of Native rural (10) and nonrural women (21). Participants described "poly-strengths" sequences in which environmental strengths (such as housing and transportation) enabled them to draw on their other strengths across resilience portfolio model domains. Rural participants emphasized how geographic isolation, limited mobility, and safety concerns in small communities constrained access to additional resources such as interpersonal supportive relationships. Survivors contextualized abuse within intergenerational trauma, drew on cultural identity and spirituality as distinct meaning-making pathways, and cited children/grandchildren and helping others as central purposes. Healing occurs through reinforcing poly-strengths rather than isolated protective factors. Our findings contribute to resilience portfolio model by building on the importance of environmental strengths and how cultural identities create distinct resilience pathways. Implications include culturally responsive and supportive services, innovative service delivery in rural areas, and reforms to transportation policies. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Abstract. Cold-water corals form complex three-dimensional structures on the seafloor, providing habitat for numerous species, and act as a carbon cycling hotspot in the deep-sea. The distribution of these important ecosystems is often predicted by statistical habitat suitability models, using variables such as terrain characteristics, temperature, salinity, and surface productivity. While useful, these models do not provide a mechanistic understanding of the processes that facilitate cold-water coral occurrence, and how this may change in the future. Here, we present the results of a mechanistic process-based model in which coral biomass and respiration are predicted based on hydrodynamics, organic matter transport and coral physiology. The model domain comprises the cold-water coral mounds of south-east Rockall Bank in the north-east Atlantic Ocean. Hydrodynamic forcing is provided by a high-resolution Regional Ocean Modelling System (ROMS) model, which drives the transport of reactive suspended particulate organic matter in the region. The physiological cold-water coral model, with coral food uptake, assimilation, and respiration as key variables and with model parameters estimated from available experimental reports, is coupled to the reactive transport model of suspended particulate organic matter. Cold-water coral biomass was mainly predicted on coral mounds and ridges in the area. Model predictions agree with coral reef biomass and respiration observations in the study area and coral occurrences agree with predictions from previously published habitat suitability models. Filter feeding activity by cold-water corals proved to strongly deplete food particles in the bottom waters. Replenishment of food particles by tidal currents was therefore vital for cold-water coral growth. This mechanistic modelling approach has the advantage over statistical and machine learning-based predictions that it can be used to obtain an understanding of the effect of changing environmental conditions such as ocean temperature, surface production export, or ocean currents on cold-water coral biomass distribution and can be applied to other study areas and/or species.

The asthma diary has been established as a self-management tool to help patients achieve good disease control. However, asthma diary compliance is low, indicating a need to assess the diary's usability. Given the scarcity of a validated tool for assessing asthma diary usability, this study aims to adapt the Malay version of the mHealth App Usability Questionnaire (M-MAUQ) into the Asthma Diary Usability Questionnaire (ADU-Q) (Malay) and to assess its psychometric properties. In the first phase, the M-MAUQ items were adapted and rearranged into the four domains of the Nielsen usability model. In the second phase, content validity was assessed in two stages: domain validation by 3 experts, followed by item content validation by 7 experts. Face validation was then conducted by 10 patients with asthma. In the third phase, a cross-sectional study of 115 asthma patients attending the primary care clinic and the respiratory clinic follow-up was conducted. All patients completed the ADU-Q (Malay), and 62 patients were contacted 2 weeks later to complete the ADU-Q again. Exploratory factor analysis was performed with promax rotation, while reliability was assessed using Cronbach's alpha and the intraclass correlation coefficient. The item content validity index was 1.0, and item face validity indices ranged from 0.9 to 1.0. Exploratory factor analysis identified a three-factor structure with factor loadings >0.5 for all items; corrected item-total correlations exceeded 0.5, Cronbach's alpha was 0.982, and the intraclass correlation coefficient was 0.988, demonstrating excellent internal consistency and test-retest reliability. Based on these findings, ADU-Q is a valid, reliable and stable tool for assessing the usability of an asthma diary among adults with asthma.

Abstract The hydroxyl radical (OH) is chemically coupled to other atmospheric constituents including water vapor, NO x , ozone, CO, and methane that provide the sources and sinks of OH. These species have longer lifetimes than OH itself and consequently undergo atmospheric transport, allowing dynamics to indirectly affect OH. We investigated whether a combination of meteorological variables and idealized tracers can predict the OH distributions for 40°S–40°N simulated by multiple models. We find that they can explain 70% or more of the variance in July spatial anomalies in OH with the zonal mean removed at 400 hPa, and 59% or more for tropospheric column OH (tcolOH). We find two constituents observed from space, water vapor and NO 2 , can together serve as proxies for much of the 40°S–40°N spatial variability in OH at 400 hPa, especially over the ocean. Multiple linear regression (MLR) on water vapor and NO 2 columns versus tcolOH results in r 2 &gt; 0.5 for the interannual variability in January tcolOH over more than half of the 40°S–40°N domain in most models. These results highlight the value of satellite observations of water vapor and NO 2 for constraining simulated OH variability. However, the relative sensitivity of OH to each of these two variables differs between models. Consequently, understanding individual models' relative sensitivities can help maximize the value of these observational constraints. The results of our proof‐of‐concept study are encouraging and justify additional research to fully explore the potential of other satellite‐observable variables for the development of process‐based diagnostics and constraining the spatiotemporal variations of tropospheric OH.

Identifying the time-domain waveform type under broadband conditions is a basic but very challenging task. Traditional methods based on frequency domain or training models generally have the problems of high resource consumption, large delay, and unsuitability for hardware. This paper proposes a time-domain waveform recognition architecture based on an FPGA, which is integrated with multi-feature voting. Several lightweight time domain characteristics, such as high amplitude ratio, symmetry, slope uniformity, slope change rate, and flat-top characteristics, are extracted and directly used for waveform classification. Then classify sine waves, square waves, triangular waves, and noise in the time domain according to the decision-making mechanism of voting. In order to improve reliability under non-ideal conditions, adaptive thresholds and noise perception decision-making logic are used to suppress misclassifications caused by random fluctuations and jitter. The whole engineering design focuses on resource consumption and hardware efficiency, using a fully pipeline FPGA architecture. The experimental results prove that the system has the ability of high-precision identification, low power consumption, and real-time processing in the wide frequency band, providing an efficient and practical solution for embedded waveform recognition applications.

BackgroundCancer risk prediction models are vital for precision prevention, enabling individualized assessment of cancer susceptibility based on genetic, clinical, environmental, and lifestyle factors. However, the practical use of these models is hindered by fragmented resources, heterogeneous reporting, and the absence of transparent, structured systems for systematic discovery and comparison.ObjectiveThis study aimed to develop a retrieval-augmented, knowledge-guided system that provides accurate recommendations for cancer risk prediction models.MethodsWe developed CanRisk-RAG, a recommendation platform underpinned by a precisely constructed knowledge base comprising more than 800 peer-reviewed cancer risk prediction models spanning diverse cancer types, modeling approaches, and predictive variables. The system integrates (1) large language model (LLM)–based semantic tag extraction, (2) embedding vectorization of structured metadata and abstracts, (3) a multifactor ranking algorithm combining semantic similarity with multiple quality indicators, and (4) LLM-generated literature summarization to support rapid user interpretation. Performance was evaluated across 4 types of representative queries. Eight domain experts independently assessed retrieval quality. CanRisk-RAG was benchmarked against PubMed, ChatGPT-4o, ScholarAI, and Gemini 1.5 Flash.ResultsOn the independent validation set, CanRisk-RAG consistently outperformed all 4 baseline applications, achieving the highest overall relevance (8.30 [SD 0.59]) and reliability (7.62 [SD 0.76]) scores on a 10-point scale (P<.05). It also demonstrated high authenticity, data completeness, and consistency. Baseline applications frequently returned incomplete, inconsistent, or fabricated results, especially for complex, multifactorial queries, whereas CanRisk-RAG delivered accurate and structured recommendations grounded in validated evidence.ConclusionsCanRisk-RAG presents a transparent, domain-specific, and semantically enriched framework for discovering cancer risk prediction models, addressing several limitations of existing keyword-based search tools and general-purpose LLMs. By integrating structured knowledge, multifactor ranking, and LLM-based reasoning, the system aims to improve the precision, reproducibility, and usability of model selection in cancer risk prediction. While our evaluation demonstrates encouraging performance compared with baseline systems, further validation in broader clinical contexts and real-world applications is warranted. The framework’s general design may also be adaptable to other clinical model domains, providing a potential foundation for advancing evidence-based model discovery in precision medicine.

Goat (Capra hircus) welfare is an important issue in any farming system. The aim of the study was a comprehensive analysis of the impact of environmental factors and farming practices on the welfare of goats, with particular attention to physical, behavioural, and emotional aspects. It includes a review of the up-to-date literature on the effects of environmental conditions including air temperature, air humidity, space, feeding systems, social relationships (mother-offspring, human-animal, animal-animal), zootechnical procedures (dehorning, castration, hoof trimming) and welfare assessment methods. It compares the AWIN, Anzuino, Muri and Leite protocols for assessing goat welfare and their application in the Five Domain Model. Goat welfare is strongly influenced by their environment, nutrition and socialisation: heat stress and confined space cause physiological disorders, decreased immunity and increased aggressive behaviour and a monotonous diet leads to frustration and reduced cognitive activity, whereas positive early contact with humans reduces anxiety and maintaining the mother-kid bond supports the social development of young goats. Furthermore, significant improvements in welfare and stress reduction can be achieved by providing anaesthesia and painkillers where necessary to minimise pain and enriching the environment with items that support natural behaviour, such as platforms, brushes and items for cognitive tasks. In general, the keeper should take a holistic approach, combining environmental optimisation, humane husbandry practices and regular monitoring using validated assessment protocols to improve welfare. These measures are both an ethical obligation and a prerequisite for animal health and production efficiency. Nevertheless, there is a need for further research focussing on the development of non-invasive assessment methods and innovative forms of environmental enrichment.

Driven by the global energy transition, mechanized construction—characterized by enhanced safety, efficiency, and quality—is becoming the mainstream approach in power grid development. Mechanization assessment serves as a critical tool for guiding and optimizing this process, yet current practices remain largely manual, resulting in inefficiency, time-consuming operations, and a lack of real-time insights, which severely limit its practical utility for dynamic project guidance. To address these challenges, this study proposes a novel framework that integrates semantic technology (i.e., ontology) and large language models (LLMs). The framework first constructs a semantic model of the power grid construction domain using ontology. An LLM is then employed to convert multi-source project data into structured ontological instances. Building on this, mechanization assessment criteria are formalized into machine-executable Semantic Web Rule Language (SWRL) rules, which enable automated reasoning and scoring through an ontological reasoner. Furthermore, the LLM is utilized to generate comprehensive and intelligible assessment reports based on the reasoning outputs. To validate the proposed method, 126 real-world project cases were applied to the system. The results demonstrate a 96% accuracy rate in mechanization assessment outcomes compared to expert evaluations. The approach facilitates an objective, standardized, and dynamic evaluation of construction mechanization levels, providing a foundation for intelligent and scalable management models in power grid construction.

Face recognition models are vulnerable to spoofing of adversarial patches in the physical world. Attackers can enable face recognition models to make false identity judgments by simply pasting a sticker with a special pattern on the face. However, existing attacks lack the ability to transfer black-box models, and the improvement of the transferability is mainly focused on adversarial perturbations based the p-norm. To further improve the attack performance and transferability, a high transferable face recognition adversarial patches generation method named as AdvDiffusion is proposed. It first determines the region for adversarial patches generation based on facial gradient maps, and then an image is reconstructed to generate an adversarial patch by adding noise and denoising it with a pre-trained diffusion model. In the denoising, an adversarial loss is used to fine-tune the model and control the image to generate an adversarial patch with spoofing capability. Experiments and analysis show that the adversarial patches generated by have good adversarial attack capability on black-box face recognition models in both digital and physical domains, and also have better robustness under the changes of a complex physical environment compared with some state-of-the-art methods. It has great potential application for black-box attacks in the physical domain.

The success of machine learning in chemistry is fundamentally underpinned by the information fidelity of molecular representations. Despite their widespread adoption for efficiency and interpretability, Morgan fingerprints harbor a long-overlooked and fundamental flaw: bit collisions. This phenomenon erroneously maps distinct chemical substructures to identical positions, systematically corrupting structure-property relationships and severely compromising model interpretability. To address this challenge, we introduce Collision-Free Morgan Fingerprints (CF-MF), a principled framework that guarantees the integrity of substructure information through an adaptive, data-driven sizing mechanism. Through a comprehensive evaluation across 25 diverse datasets (> 50,000 molecules) and multiple machine learning paradigms, we demonstrate that CF-MF delivers consistent and significant performance gains up to 16.81% RMSE reduction in regression and 11.1% accuracy increase in classification. More critically, by eliminating attribution errors caused by collisions, CF-MF fundamentally restores chemical interpretability and expands the reliable prediction domains of models by 60-100%. Our information-theoretic analysis reveals a strong correlation between collision-induced entropy loss and performance degradation (R2 = 0.854, p < 0.001), establishing information fidelity as a fundamental design principle for next-generation molecular representations. It also achieves performance competitive with state-of-the-art deep learning models while retaining the simplicity and intuitiveness of traditional fingerprints. This work provides a more reliable and trustworthy foundation for AI-driven drug discovery, materials science, and environmental assessment.Scientific contributionWhile bit collisions in Morgan fingerprints have been acknowledged for decades, this study is the first to systematically quantify their impact on machine learning performance and provide a principled, reproducible solution applicable to any molecular dataset. We establish a novel information-theoretic framework that directly links collision-induced entropy loss to predictive degradation, offering the field a quantitative criterion for evaluating molecular representation fidelity. Beyond performance gains, our work uniquely demonstrates that eliminating collisions restores chemically valid SHAP attributions-addressing a critical but previously unrecognized barrier to trustworthy AI interpretation in chemistry.

In this paper, we propose a three-dimensional (3D) geometry-based stochastic model (GBSM) for double-reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) air-to-ground (A2G) communication systems. We develop the GBSM for dual-RIS channels, where RIS arrays are strategically mounted on unmanned aerial vehicles (UAVs) to reflect signals from the UAV transmitter towards the ground receiver via cascaded RIS links. The flexible trajectories and on-demand deployment of UAVs effectively mitigate the degradation caused by obstructive elements like buildings and trees. Furthermore, we incorporate a beam-domain channel model (BDCM) into the geometric framework to systematically analyze its propagation framework. This approach reduces computational complexity and enables systematic analysis of the system’s propagation mechanisms. The model captures dynamic behaviors in realistic scenarios by integrating real-time kinematic parameters, including velocities and accelerations of the UAV transmitter, ground receiver, and RIS-mounted UAVs. Key propagation characteristics, such as cross-correlation functions (CCFs), autocorrelation functions (ACFs), frequency correlation functions (FCFs), and channel capacity, are analyzed by comparing the proposed beam-domain approach with conventional geometric methods. Simulation results demonstrate that the statistical properties obtained from the beam-domain channel model closely match those derived from the geometry-based stochastic model, validating the accuracy of the proposed approach. Moreover, the beam-domain method significantly reduces computational complexity over traditional geometric techniques, offering valuable insights for designing efficient distributed RIS-assisted A2G communication systems.

This paper introduces the Best Sharia-based Capital Asset Pricing Model (BSCAPM), a mathematical modification of the BCAPM model integrating Islamic finance principles. The study focuses on optimizing the beta in the model, incorporating factors aligned with Islamic principles, such as zakat and purification, while excluding short selling. Using data from the Jakarta Islamic Index (JII) from June 2020 to May 2024, the BSCAPM portfolio outperforms the BCAPM portfolio in terms of the Sharpe ratio. The results suggest that BSCAPM could serve as an effective alternative for modeling in Islamic investments, providing Muslim investors with a Shariah-compliant, optimal portfolio formation model. The research contributes to the underexplored domain of portfolio selection modeling in the Islamic sector, enriching references on asset pricing of Shariah portfolios, particularly in the Indonesian Shariah stock market.

Home parenteral nutrition is a life-sustaining therapy for patients with chronic intestinal failure, increasingly delivered in the home setting worldwide. While home parenteral nutrition supports nutritional status, survival, and functional independence, it also transfers complex clinical responsibilities to patients and family caregivers, raising concerns about care quality and safety. This study aimed to explore patients' and primary caregivers' experiences of home parenteral nutrition and to identify structural, process-related, and outcome-related factors influencing care quality and safety, guided by the Donabedian model. An interpretive descriptive qualitative design was used. Semi-structured interviews and structured home-based observational assessments were conducted with 22 participants (11 adult patients and 11 primary caregivers) recruited from a tertiary university hospital in Turkiye. Interviews explored lived experiences of home parenteral nutrition, while observations focused on caregivers' adherence to aseptic technique during parenteral nutrition preparation, catheter care, and infusion procedures. Data were analysed using directed content analysis guided by Donabedian's structure-process-outcome framework. Nine themes were identified across the three domains of the Donabedian model. Structural factors included the critical role of comprehensive discharge education, challenges in establishing safe home environments, and the absence of professional home care services. Process-related findings revealed frequent deviations from standard protocols, influenced by procedural complexity, emotional distress, and limited access to reimbursed supplies. Outcome-related experiences encompassed catheter-related complications, reduced quality of life, substantial caregiver burden, and mixed satisfaction with care. This study demonstrates that safe and high-quality home parenteral nutrition depends on more than technical training alone. Integrated post-discharge support, continuous caregiver education, and accessible home care services are essential to reduce complications and caregiver burden. Strengthening health system structures is crucial to ensure quality and safety in home parenteral nutrition care.

Price volatility in steel reinforcement bars (rebar) plays a pivotal role in managing construction project costs, with precise forecasting being essential for maintaining corporate profitability and ensuring market stability. This research conducts a comprehensive evaluation of five prominent forecasting models—Autoregressive Integrated Moving Average (ARIMA), eXtreme Gradient Boosting (XGBoost), Prophet, Long Short-Term Memory (LSTM), and Transformer—specifically applied to steel rebar price prediction. The study emphasizes the influence of feature selection, defined as the number of historical price data points utilized for prediction, on the accuracy of these models. Furthermore, it develops a hybrid forecasting framework grounded in a residual complementarity mechanism aimed at improving long-term predictive performance. The results reveal that the ARIMA model delivers consistent and reliable short-term forecasts, particularly within a two-month horizon, whereas the Prophet model effectively captures long-term price trends but suffers from notable short-term bias. A two-stage hybrid model (referred to as Combination Model II), which integrates ARIMA and Prophet through residual inversion, demonstrates superior forecasting accuracy over a six-month period. This hybrid approach surpasses the standalone ARIMA model by more than 70% across key evaluation metrics—including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), and Mean Absolute Scaled Error (MASE)—and exceeds the performance of the standalone Prophet model by over 90%. This integration effectively combines the high short-term precision of ARIMA with the long-term trend stability of Prophet. Within the domain of machine learning and deep learning models, XGBoost achieves optimal predictive accuracy when utilizing between one and four features. The predictive performance of LSTM does not exhibit a straightforward linear relationship with the number of features; however, certain feature combinations enable it to outperform other models. Transformer models maintain stable accuracy when employing feature sets ranging from one to five and twelve to seventeen, but display considerable variability in performance when the feature count lies between five and twelve. This investigation delineates the optimal parameter ranges and contextual applicability for each model. The proposed hybrid forecasting methodology, alongside a model transfer strategy encompassing data preprocessing adjustments, parameter optimization, and weight adaptation, offers practical applicability to other commodity markets such as cement and concrete. Consequently, this research provides a scientifically grounded framework to support procurement decision-making processes within construction enterprises.

BackgroundMild behavioral impairment (MBI) captures later-life onset neuropsychiatric symptoms (NPS) that may herald neurodegeneration. The emotional impact of these early behavioral changes on caregivers is under-measured in pre-dementia care.ObjectiveTo develop a brief, domain-aligned caregiver distress scale for MBI (MBI-D) and examine clinical correlates of MBI-related caregiver distress in mild cognitive impairment due to AD (MCI-AD).MethodsOne hundred and four participant-informant dyads with MCI-AD at a Greek memory clinic were included. Caregivers completed the Greek MBI-C and the new five-item MBI-D (one item per ISTAART-AA MBI domain). Internal consistency (Cronbach’s α), non-parametric tests, and Spearman correlations assessed bivariate associations. Multiple linear regression identified independent correlates of MBI-D total. Prespecified covariates were age, education, sex, global cognition (MMSE or ACE-R), disease duration, and MBI-C (total or domains).ResultsInternal consistency of the MBI-D was moderate (α = 0.617; standardized α = 0.627; mean inter-item r = 0.25). MBI-D total correlated strongly with MBI-C total (ρ = 0.789, p < 0.001), and each MBI-D domain correlated with its corresponding MBI-C domain (ρ = 0.478–0.850, all p < 0.001). Disease duration was associated with MBI-D total and with apathy-related distress (ρ = 0.302, p = 0.002 and ρ = 0.392, p < 0.001, respectively). In multivariable regression, MBI-C total and education were independent predictors of MBI caregiver distress (β = 0.804, p < 0.001, and β = 0.135, p = 0.017, respectively). In the MBI-C domains model, impulse dyscontrol, apathy and emotional dysregulation independently related to higher distress (B = 0.513, β = 0.482, p < 0.001, B = 0.315, β = 0.278, p < 0.001, and B = 0.289, β = 0.227, p = 0.001 respectively), while cognitive performance (MMSE and ACE-R) did not have a significant impact.ConclusionThe MBI-D, strongly coupled with MBI-C, is a concise, clinically practical and scalable measure of MBI-related caregiver distress in MCI-AD, capturing both symptom burden and domain-specific distress in a single administration. Impulsivity, apathy, and affective dysregulation are highlighted as priority targets for early, caregiver-focused interventions advancing innovative, prevention-oriented dementia care delivery.

Asset-intensive industries are experiencing a profound shift in maintenance methodologies through artificial intelligence and sophisticated data analytics. Intelligent platforms, which offer prediction, prescription, and increasingly autonomous task execution, are replacing conventional maintenance frameworks characterized by failure-response protocols and calendar-based servicing schedules. This investigation examines how AI algorithms, trained using IoT sensor information, operational logs, and equipment histories, detect early deterioration indicators across vibration, thermal, and sound-based monitoring domains. Generative AI assistance tools are reshaping technical support by producing contextual action plans derived from documented failure patterns and service records. Combined architecture that bring together large-scale foundation algorithms and specialized domain models make it possible to analyze data and solve problems in real time in industrial settings. The use of digital replicas, voice-activated technician aids, and self-governing scheduling platforms points to a shift toward maintenance environments where machines can monitor themselves, find problems, plan work, and take corrective action with little help from people. Edge processing developments enable rapid anomaly identification, essential for geographically isolated or mission-critical equipment. The evolution of equipment servicing is moving away from manual assessment and fixed scheduling toward intelligent, self-directed, and adaptive learning platforms, converting maintenance operations from expense categories into strategic organizational strengths for enterprises operating sophisticated equipment portfolios.

Cognitive domains (e.g. memory, attention, or executive functions) play a crucial role in cognitive diagnostics. However, cognitive domain definitions and the assignment of cognitive tests to these domains lack both consensus and empirical justification in current guidelines for cognitive diagnostics. Focusing on this issue in Parkinson's disease (PD), this scoping review aims to provide an overview of empirical findings from dimensionality reduction analyses applied to cognitive test scores of individuals with PD. The research addressed three questions: (i) Which methods were used to empirically evaluate cognitive domain differentiations in PD? (ii) Could previously assumed cognitive domains in PD be reproduced by empirical analyses? (iii) Which cognitive domain models did the studies use as its theoretical framework? An a priori defined systematic literature search identified 22 eligible studies published between 1963 and 2022, mainly using exploratory methods such as Principal Component Analysis or Exploratory Factor Analysis to identify a cognitive domain structure in people with PD. These studies were highly heterogeneous in specific methodological configurations of statistical methods, such as rotation and extraction methods. Only few studies referred to the Movement Disorder Society (MDS) guidelines for cognitive diagnostics in PD, and none successfully reproduced previously assumed cognitive domain structures by (exploratory) empirical analysis. The inconsistent results regarding the structure of cognitive functioning might result from heterogeneous samples and methods. Overall, the results challenge the validity and reliability of cognitive diagnoses in PD, highlighting a need for more robust empirical support to improve diagnostic frameworks.

The task of pose-guided person image generation has long been confronted with two major challenges: high-frequency texture details tend to blur and be lost during appearance transfer, while the semantic identity of the person is difficult to maintain consistently during pose changes. To address these issues, this paper proposes a diffusion-based generative framework that integrates frequency awareness and global semantic alignment. The framework consists of two core modules: a multi-level fractional-order Gabor frequency-aware network, which accurately extracts and reconstructs high-frequency texture features such as hair strands and fabric wrinkles, enhances image detail fidelity through fractional-order filtering and complex domain modeling; and a global semantic-pose alignment module that utilizes a cross-modal attention mechanism to establish a global mapping between pose features and appearance semantics, ensuring pose-driven semantic alignment and appearance consistency. The collaborative function of these two modules ensures that the generated results maintain structural integrity and natural textures even under complex pose variations and large-angle rotations. The experimental results on the DeepFashion and Market1501 datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches in terms of SSIM, FID, and perceptual quality, validating the effectiveness of the model in enhancing texture fidelity and semantic consistency.