Global Domain Research Articles

Sex Differences in the Trajectories of Cognitive Decline and Affected Cognitive Domains Among Older Adults With Controlled and Uncontrolled Glycemia.

We aimed to analyze the trajectories of cognitive decline as a function of the presence of type 2 diabetes and glycemic control in analyzes stratified by sex in an 8-year follow-up period. A total of 1752 men and 2232 women aged ≥50 years who participated in the English Longitudinal Study of Ageing (ELSA), conducted from 2004 to 2012, were analyzed. The outcomes of interest were performance on the cognitive domains of memory, executive function, and temporal orientation as well as the global cognition score. Cognitive performance was standardized in z-scores in strata based on schooling and age. The participants were classified as without diabetes, with controlled glycemia, and with uncontrolled glycemia, according to medical diagnosis, glucose-lowering medications use and HbA1c levels. Generalized linear mixed models controlled by sociodemographic, behavioral, and health-related characteristics were used for the trajectory analyses. No differences in z-scores were found for global cognition or cognitive domains based on diabetes classification in men and women at baseline. More than 8 years of follow up, women with uncontrolled glycemia had a greater decline in z-scores for global cognition (-0.037 SD/year [95% CI: -0.073; -0.001]) and executive function (-0.049 SD/year [95% CI: -0.092; -0.007]) compared with those without diabetes. No significant difference in trajectories of global cognition or any cognitive domain was found in men as a function of diabetes classification. Women with uncontrolled glycemia are at greater risk of a decline in global cognition and executive function than those without diabetes.

Sleep disturbances, cognitive decline, and AD biomarkers alterations in early Parkinson's disease.

We aimed to investigate whether each type of sleep disturbances (i.e., pRBD, EDS, and insomnia) is specifically associated with faster decline in global cognition and different cognitive domains among de novo PD patients. We also assessed the influence of sleep disturbances on core AD CSF biomarkers alterations and conversion to dementia. Prospectively longitudinal data were obtained from the PPMI cohort. Sleep disturbances and cognition ability were assessed by questionnaires at baseline and follow-up visits. Generalized linear mixed models were utilized to assess the effect of sleep disturbances on cognitive decline and core AD CSF biomarkers change. The associations between sleep disturbances and conversion to dementia were analyzed using Cox regression analysis. Baseline pRBD was associated with faster decline in global cognition and all cognitive domains, including verbal episodic memory, visuospatial ability, executive function, language, and processing speed. EDS was associated with faster decline in three cognitive domains, including verbal episodic memory, executive function/working memory, and processing speed. Insomnia was associated with faster decline in global cognition and verbal episodic memory. Meanwhile, pRBD and EDS were associated with longitudinal decrease of CSF Aβ42. Baseline pRBD increased the risk of conversion to dementia. The risk of dementia in PD patients with multiple sleep disturbances also increased compared with those without sleep disturbance. Sleep disturbances (i.e., pRBD, EDS, and insomnia) were associated with cognitive decline in early PD. EDS and pRBD were associated with decrease of CSF Aβ42. Moreover, pRBD was associated with conversion to dementia.

Incorporation of a global perspective into data-driven analysis of maritime collision accident risk

Ship collision accidents are one of the most frequent accident types in global maritime transportation. Nevertheless, conducting an in-depth analysis of collision prevention poses a formidable challenge due to the constraints of limited Risk Influential Factors (RIFs) and available datasets. This paper aims to incorporate a global perspective into a new data-driven risk model, scrutinize the root causes of collision accidents, and advance measures for their mitigation. Additionally, it seeks to analyze the spatial distribution and conduct a comprehensive comparative study on collision characteristics for both pre- and post-COVID-19, utilizing the real accident dataset collected from two reputable organizations: Global Integrated Shipping Information System (GISIS) and Lloyd's Register Fairplay (LRF). The research findings and implications encompass several crucial aspects: 1) the constructed model demonstrates its reliability and accuracy in predicting collision accidents, as evident from its prediction performance and various scenario analysis; 2) the most hazardous voyage segment for collision accidents is identified to provide valuable guidance to different stakeholders; and 3) the hierarchical significance of various ship types in the context of collision accident is highlighted regarding the most probable scenario for collision occurrences; 4) During the pandemic, the rise in collision probabilities, particularly involving older vessels and bulk carriers, implies heightened operational challenges or maintenance issues for these ship types; (5) The prominence of favorable and adverse sea conditions in collision reports underscores the significant influence of weather on accidents during the pandemic. These findings and implications help enhance safety protocols, ultimately reducing the frequency of collision accidents in the global maritime domain.

Satisfaction With and Adherence to Off-Label Corticosteroids in Adolescents and Adults With Eosinophilic Esophagitis: Results of a Web-Based Survey in the United States.

We assessed satisfaction with and adherence to off-label corticosteroids in patients with eosinophilic esophagitis (EoE) in the United States. EoE is a chronic inflammatory disease for which there are currently no US Food and Drug Administration-approved swallowed topical corticosteroids. This noninterventional, cross-sectional, web-based survey included caregivers of adolescents (aged 11 to 17 y) and adults (aged 18 years or older) with a self-reported [or caregiver-reported (adolescents)] physician diagnosis of EoE who were receiving corticosteroids. Participants were recruited through 2 nonprofit, patient advocacy groups. The 9-item Treatment Satisfaction Questionnaire for Medication (TSQM-9) was used to assess satisfaction across effectiveness, convenience, and global satisfaction domains (scale: 1 to 100 per domain); higher scores indicated greater satisfaction. The 4-item Morisky Green Levine Medication Adherence Scale (MGL-4) was used to assess adherence; an MGL-4 score of <3 indicated adherence. Participants also reported reasons for nonadherence. Overall, 201 participants (caregivers of adolescents, n=98; adults, n=103) were included in this study. Mean TSQM-9 scores indicated low satisfaction with off-label corticosteroids across all 3 satisfaction domains in adolescents (≤61.1) and adults (≤55.7). Slightly fewer adolescents (37.1%) than adults (40.8%) were considered adherent. Forgetfulness was the most frequently reported reason for nonadherence; some patients chose not to take their medications, owing to poor palatability (adolescents), difficulty taking medications at specific times (adults), or feeling depressed/overwhelmed (adolescents and adults). Satisfaction with and adherence to off-label corticosteroids were low in this web-based survey of adolescents and adults with EoE in the United States.

Identifying interdisciplinary emergence in the science of science: combination of network analysis and BERTopic

Global scientific output is expanding exponentially, which in turn calls for a better understanding of the science of science and especially how the boundaries of scientific fields expand through processes of emergence. The present study proposes the application of embedded topic modeling techniques to identify new emerging science via knowledge recombination activities as evidenced through the analysis of research publication metadata. First, a dataset is constructed from metadata derived from the Web of Science Core Collection database. The dataset is then used to generate a global map representing a categorical scientific co-occurrence network. A research field is defined as interdisciplinary when multiple science categories are listed in its description. Second, the co-occurrence networks are subsequently compared between periods to determine changing patterns of influence in light of interdisciplinarity. Third, embedded topic modeling enables unsupervised association of interdisciplinary classification. We present the results of the analysis to demonstrate the emergence of global interdisciplinary sciences and further we perform qualitative validation on the results to identify what the sources of the emergent areas are. Based on these results, we discuss potential applications for identifying emergence through the merging of global interdisciplinary domains.

Distributed Physics-Informed machine learning strategies for two-phase flows

This study delves into applying Distributed Learning Machines (DLM), a subset of Physics-Informed Neural Networks (PINN), in tackling benchmark challenges within two-phase flows. Specifically, two variants of DLM, namely Distributed Physics-Informed Neural Networks (DPINN) and Transfer Physics-Informed Neural Networks (TPINN), are studied. The DLM architecture strategically divides the global domain into distinct non-overlapping sub-domains, with interconnected solutions facilitated by interface conditions embedded in the loss function. Forward and inverse benchmark problems in two-phase flows are explored: (a) bubble in a reversing vortex and (b) Bubble Rising under Buoyancy. The Volume of Fluid (VOF) method handles interface transport in both scenarios, with the inverse problem incorporating interface-position data during the training phase. The forward problem highlights the effectiveness of DPINN in capturing the interface using a simple transport equation. The distinctive contribution of this work lies in its exploration of the inverse problem, offering insights into the scalability of distributed architectures when dealing with a system of governing equations. Following the validation of an initial PINN model against Computational Fluid Dynamics (CFD) data, the study extends to DPINN and TPINN. A parametric study optimizes network hyperparameters, emphasizing the regularization of loss terms within the DPINN loss function. A self-adaptive weighting strategy based on a Gaussian probabilistic model dynamically adjusts loss weights during training to overcome challenges associated with manual parameter tuning. The evaluation of accuracy against CFD data and published results underscore the efficacy of DLMs in addressing two-phase flow problems. Additionally, the computational efficiency of distributed networks is explored compared to traditional PINNs.

Association of categorical diagnoses and psychopathologies with quality of life in patients with depression, anxiety, and somatic symptoms: A cross-sectional study

ObjectiveMajor depressive disorder (MDD), anxiety disorders, and somatic symptom disorder (SSD) are associated with quality of life (QoL) reduction. This cross-sectional study investigated the relationship between these conditions as categorical diagnoses and related psychopathologies with QoL, recognizing their frequent overlap. MethodsWe recruited a total of 403 clinical patients and healthy individuals, administering diagnostic interviews based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. QoL and psychopathologies were assessed using the WHO Quality of Life-BREF (WHOQOL-BREF) and several self-administered questionnaires, respectively. Multiple linear regression analyses examined the associations between psychiatric diagnoses, psychopathologies, and QoL. ResultsSSD and MDD were independently associated with impaired global (β = −0.318 and − 0.287) and all QoL domains (β = −0.307, −0.150, −0.125, and − 0.133, in physical, psychological, social, and environmental domains respectively for SSD; β = −0.278, −0.344, −0.275, and − 0.268 for MDD). The Beck Depression Inventory-II score showed pervasive associations with QoL (β = −0.390, −0.408, −0.685, −0.463, and − 0.420, in global, physical, psychological, social, and environmental domains). The Patient Health Questionnaire-15 and Health Anxiety Questionnaire scores were associated with global (β = −0.168 and − 0.181) and physical (β = −0.293 and − 0.121) QoL domain, while the Cognitions About Body and Health Questionnaire score was only associated with environmental QoL domain (β = −0.157). ConclusionSSD and MDD were independently associated with QoL impairment. Depressive symptoms were associated with all QoL domains, whereas somatic symptom burden and health anxiety primarily affected the physical QoL domain. Clinicians should consider concomitant psychopathologies when managing patients with depression, anxiety, or somatic symptoms.

The relation of a cerebrospinal fluid profile associated with Alzheimer’s disease with cognitive function and neuropsychiatric symptoms in sporadic cerebral amyloid angiopathy

BackgroundPatients with sporadic cerebral amyloid angiopathy (sCAA) frequently report cognitive or neuropsychiatric symptoms. The aim of this study is to investigate whether in patients with sCAA, cognitive impairment and neuropsychiatric symptoms are associated with a cerebrospinal fluid (CSF) biomarker profile associated with Alzheimer’s disease (AD).MethodsIn this cross-sectional study, we included participants with sCAA and dementia- and stroke-free, age- and sex-matched controls, who underwent a lumbar puncture, brain MRI, cognitive assessments, and self-administered and informant-based-questionnaires on neuropsychiatric symptoms. CSF phosphorylated tau, total tau and Aβ42 levels were used to divide sCAA patients in two groups: CAA with (CAA-AD+) or without a CSF biomarker profile associated with AD (CAA-AD-). Performance on global cognition, specific cognitive domains (episodic memory, working memory, processing speed, verbal fluency, visuoconstruction, and executive functioning), presence and severity of neuropsychiatric symptoms, were compared between groups.ResultssCAA-AD+ (n=31; mean age: 72 ± 6; 42%, 61% female) and sCAA-AD- (n=23; 70 ± 5; 42% female) participants did not differ with respect to global cognition or type of affected cognitive domain(s). The number or severity of neuropsychiatric symptoms also did not differ between sCAA-AD+ and sCAA-AD- participants. These results did not change after exclusion of patients without prior ICH.ConclusionsIn participants with sCAA, a CSF biomarker profile associated with AD does not impact global cognition or specific cognitive domains, or the presence of neuropsychiatric symptoms.

Thermal-structural Modeling of power electronic package: effects of deposition geometry and dry spot on the stress distributions

A Power Electronics package is a heterogeneous system made of semiconductor devices (dies), metallic-ceramic substrate, baseplate and encapsulating material.The electronic devices can be MOSFETs (Metal Oxide Semiconductors Field Effect Transistors), diodes, IGBTs (Insulated Gate Bipolar Transistors) and passive components such as capacitors, resistors and sensors integrated on support circuit; the complete set of semiconductor devices installed on a package form the so-called “power modulus”.These devices play an important role in the transmission and conversion of energy in electric and hybrid vehicles. Due to the elevated currents and, consequently, the elevated temperatures at which these systems work, and due to the mismatch of the thermal-mechanical properties of the materials from which they are made, stresses and strains develop within the devices.Such stresses can be locally concentrated and amplified, depending on the deposition geometry of the various layers constituting the semiconductor device. Edge termination structures are essential to decrease the electric field at die’s edges, and, including brittle compounds in their composition, like Si3N4 or SiO, they are quite sensitive to mechanical stress.Another reason that may cause the stresses concentrations is the presence of dry spots. Dry spots are areas of various size, located at the interface between die or leadframe and the encapsulating resin, where there is a total or partial lack of adhesion.The aims of this work are, mainly, two. At first, four linear finite element analyses have been performed in order to evaluate the stresses concentrations at the corners of edge termination structure.The first case concerns sharp corners and the other cases concern differently filleted corners. In the second part of this work, it has been analysed the dry spot effect on stress distributions. Due to the small size of the defect with respect to the whole package, a Global-Local FEM approach has been used, creating a local subdomain where the mesh is denser than the global domain and a running a further separate numerical analysis which takes its boundary conditions from the global analysis, so delivering reliable small-scale stress and strain gradients. This approach permits to achieve accurate analyses focused on zones of interest of the global domain, limiting the increase of the computational cost of the modelling.

Research on multi‐center assisted diagnosis of ASD based on multimodal feature fusion

AbstractAutism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, and structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) provide information about brain structure and function, aiding in objective ASD diagnosis. However, existing ASD classification methods face challenges such as sample scarcity, inter‐imaging center variations, insufficient single‐modality information, and inconsistent feature dimensions. This study introduced a method based on the Local Global Multimodal Domain Adaptation (LGMDA)‐Sparse Adaptive Prior Coupled Dictionary Learning (SACDL) framework. Initially, the LGMDA method was introduced to achieve multi‐source DA. By minimizing differences between different data domains while maximizing inter‐class differences within the same domain, it expands the sample size of multi‐modal data, addressing the issues of sample scarcity and heterogeneity in ASD data. Subsequently, the SACDL method was employed for multimodal fusion. It initialized dictionaries using the ATGP algorithm, combined sMRI and fMRI data for dictionary learning, adaptively adjusted sparsity parameters, and integrated ASD phenotype data for constrained optimization. It enables joint learning of shared and modality‐specific features, balancing differences in feature dimensions. Experimental results show that this model effectively utilizes multi‐center, multi‐modal information to achieve better auxiliary diagnosis than single‐modal small samples. This method has the potential to provide effective solutions for ASD multi‐source and multi‐modal classification problems, which are significant for ASD research and clinical diagnosis.

Testing analytical methods to derive the cosmic-ray ionisation rate in cold regions via synthetic observations

Context. Cosmic rays (CRs) heavily impact the chemistry and physics of cold and dense star-forming regions. However, the characterisation of their ionisation rate continues to pose a challenge from the observational point of view. Aims. In the past, a few analytical formulas have been proposed to infer the cosmic-ray ionisation rate, ζ2, from molecular line observations. These have been derived from the chemical kinetics of the involved species, but they have not yet been validated using synthetic data processed with a standard observative pipeline. In this work, we aim to bridge this gap. Methods. We performed a radiative transfer on a set of three-dimensional magneto-hydrodynamical simulations of prestellar cores, exploring different initial ζ2, evolutionary stages, types of radiative transfer (for instance assuming local-thermodynamic-equilibrium conditions), and telescope responses. We then computed the column densities of the involved tracers to determine ζ2, employing a recently proposed method based on the detection of H2D+. We compared this approach with a previous method, based on more common tracers. Both approaches are commonly used. Results. Our results confirm that the equation based on the detection of H2D+ accurately retrieves the actual ζ2 within a factor of two to three in the physical conditions explored in our tests. Since we have also explored a non-local thermodynamic equilibrium (non-LTE) radiative transfer, this work indirectly offers insights into the excitation temperatures of common transitions at moderate volume densities (n ≈ 105 cm−3). We also performed a few tests using a previous methodology that is independent of H2D+, which overestimates the actual ζ2 by at least two orders of magnitude. We considered a new derivation of this method, however, we found that it still leads to high over-estimations. Conclusions. The method based on H2D+ is further validated in this work and demonstrates a reliable method for estimating ζ2 in cold and dense gas. On the contrary, the former analytical equation, as already pointed out by its authors, has no global domain of application. Thus, we find that it ought to be employed with caution.

Combining the Emission Preprocessor HERMES with the Chemical Transport Model TM5-MP

Emission inventories (EIs) are vital for air quality modeling. Specific research goals often require modifying EIs from diverse data sources, demanding significant code development. In this study, we utilized and further developed the High Elective Resolution Modeling Emission System version three for Global and Regional domains (HERMESv3_gr). This user-friendly processing system was adapted for generating EIs compatible with the Chemistry Transport Model Tracel Model 5 Massive Parallel (TM5-MP). The results indicate that HERMESv3_gr is capable of generating EIs with negligible biases (10−7 relative differences) for TM5-MP, showcasing its effectiveness. We applied HERMESv3_gr to integrate the EI Regional Emission inventory in Asia (REAS) into the global EI Community Emission Data System (CEDS). Comparison of model results using CEDS alone and the integrated EI against measurement data for various pollutants globally revealed small improvements for carbon monoxide (1%) ethane (1–2%), and nitrogen oxide (2%) and larger for propane (5–7%). Ozone in the northern hemisphere improved by about 2% while in the southern hemisphere improvements of 5% could be observed. Our findings highlight the importance of carefully considering the effects of EI integration for accurate air quality modeling.

A multimesh finite element method for integral nonlocal elasticity using mesh-decoupling technique

This study presents a generalized multimesh nonlocal finite element method (M2-FEM) that addresses several long-standing challenges in the numerical simulation of Eringen’s integral elasticity theories, often used to model nonlocal effects. The major challenges in the numerical simulation of integral boundary value problems are primarily rooted in the coupling of the spatial discretization of the global (parent) and integral (child) domains, which severely restricts both the applicability and the computational efficiency of existing algorithms by imposing an implicit trade-off between the accuracy of the child domain and the resources required by the overall parent domain. One of the defining contributions of this study consists in the development of a mesh-decoupling technique that generates isolated sets of meshes such that the parent and child domains can be discretized and approximated independently. This mesh-decoupling has multi-fold advantages on the simulation of integral theories such that, when compared to existing state-of-the-art techniques, the proposed algorithm achieves a greater ability to adopt generalized integral kernel functions, the ability to handle non-regular (non-rectangular) domains via unstructured meshing, and simultaneously better numerical accuracy and efficiency (hence allowing greater flexibility in both mesh size and computational cost trade-off decisions). Both benchmark studies and several case studies are conducted to assess the effectiveness of M2-FEM. Simulation results confirm that M2-FEM can accurately simulate integral nonlocal problems and demonstrate its multi-fold computational advantages over existing mesh-coupling-based nonlocal finite element methods. Overall, the proposed M2-FEM algorithm is very general and it can be applied to a variety of integral theories, even beyond nonlocal elasticity.

Relationship between prognostic nutritional index and post-stroke cognitive impairment

ABSTRACT Background The Prognostic Nutritional Index (PNI) has been described as a useful screening tool for patient prognosis in several diseases. As a potential diagnostic index, it has attracted the interest of many physicians. However, the correlation between the PNI and post-stroke cognitive impairment (PSCI) remains unclear. Methods A total of 285 patients with acute ischemic stroke were included. PNI was assessed as serum albumin (g/L) + 5× lymphocyte count (109/L) and was dichotomized according to the prespecified cut-off points 48.43 for the high and low groups. PSCI was defined as Mini-Mental State Examination (MMSE) < 27 at the 6–10 months follow-up. Multiple logistic regression and linear regression analyses were performed to examine the association between PNI and cognitive outcomes. Results A low PNI was independently associated with PSCI after adjusting for age, sex, education, National Institutes of Health Stroke Scale (NIHSS), deep white matter hyperintensity (DWMH), and stroke history (odds ratio [OR]: 2.158; 95% confidence interval [CI]: 1.205–3.863). The PNI scores were significantly associated with MMSE and attention domain (β = 0.113, p = 0.006; β = 0.109, p = 0.041, respectively). The PNI improved the model’s discrimination when added to the model with other clinical risk factors. Conclusions A low PNI was independently associated with the occurrence of PSCI and the PNI scores were specifically associated with the scores of global cognition and attention domain. It can be a promising and straightforward screening indicator to identify the person with impaired immune-nutritional status at higher risk of PSCI.

Pre- and Post-Treatment Quality of Life Among Patients with Advanced Stage Cervical Cancer at Tikur Anbessa Specialized Hospital, Ethiopia.

The development of health concepts beyond traditional markers of illness and death has made the evaluation of quality of life (QoL) crucial to patient care. Yet, there is little research evaluating the pre- and post-treatment QoL of cervical cancer survivors in Ethiopia. This study aimed to assess the pre- and post-treatment QoL of women diagnosed with advanced-stage cervical cancer. A cohort design was conducted at the Tikur Anbessa Specialized Hospital Oncology Center. A total of 166 cervical cancer patients were recruited consecutively. Data was collected through interviews with standardized questionnaires before and after treatment. The Wilcoxon rank test was used to assess the significant differences in pre-treatment and post-treatment quality of life. Additionally, the Mann-Whitney U-test was also employed. Statistical significance was determined with p-values <0.05 and a 95% confidence interval. Women who were in stages IVA and IVB were 24.7% and 10.2%, respectively. Both the global health scale (66.67 [47.92-75] to 83.33 [66.67-83.33]) and the functional domain QoL (66.67 [40-80] to 70 [46.67-86.66]) showed statistically significant improvements from pre-treatment to post-treatment QoL. Women under the age of 45 were found to have higher global health QOL (P < 0.001) and functional domain QOL (P = 0.029). Women presented in stages II and III had comparatively higher global health QoL (P = 0.008) and functional domain QoL (P = 0.021). Global health QOL and the majority of functional quality of life significantly improved following six months of cancer treatment. But there was no discernible change in terms of sexual enjoyment, sexual function, or activity. Age, marital status, the duration since diagnosis, the stage of the cancer, and the presence of comorbidities were the factors that affected the improvement of post-treatment quality of life.

Visual arts talent among adolescents with intellectual disability: The global domain approach

Talent in the population with intellectual disability (ID) has to date been investigated in the context of the phenomenon of the idiot savant (Miller, 2005). This pioneering study examined talent in a population with ID through the prism of giftedness and talent theories in the typical population. The goals were to examine: (a) whether the talent of adolescents with ID is expressed only in their drawing ability (specific domain) or whether they would demonstrate high cognitive abilities compared to adolescents with ID without this talent (global domain); (b) the contribution of the participants’ endogenous background characteristics (fluid intelligence, cognitive flexibility and creative thinking) as well as the contribution of an exogenous factor (participation in an art class) to their art measures. An intelligence and cognitive tests battery was administered among adolescents with ID with drawing talent (N = 29) and without such talent (N = 26). Morphological Structural Conceptual Analysis (Elkosho, 2007) was used to assess the drawing ability of students with/without drawing talent as well as their level of commitment to visual arts. The ANOVAs indicated that adolescents with ID with artistic talent exhibited relatively higher drawing skills as well as fluid intelligence, cognitive flexibility and creative thinking. Regression analyses indicated that participation in an art class contributed to the explained variance of the art measures. The findings support the global domain approach and the nurture thesis in visual arts (drawing) in the population with ID. Renzulli's (2020) three-ring model of giftedness might be applicable for the population with ID.

Modified Nested Saturated Control for Uncertain Multiple Integrators With High-Order Nonlinear Perturbation.

This article studies the global asymptotic stability problem of multiple integrators subject to uncertain parameters and high-order nonlinear perturbation. By introducing a modified saturation function with positive tunable parameters, three types of modified nested saturated controllers with different parameter assignments are proposed to handle the uncertain integrators with high-order nonlinear perturbation. The specific mechanism of the modified nested saturated controllers with different parameter assignments, in which control parameters appear before states, saturation functions, or saturation levels, is comprehensively analyzed. With the help of a modified saturation function, the global convergence domain of each state can be kept invariable in the saturation reduction analysis. Meanwhile, a large saturation level is obtained by adjusting positive tunable parameters. Combining with derivatives calculation and M -matrix-based comparison theory, proposed saturated control schemes are utilized to handle the considered system. Finally, an active magnetic bearing (AMB) system is revisited as an uncertain mechanical system and used to verify the feasibility of the proposed control designs.

A federated cross-machine diagnostic framework for machine-level motors with extreme label shortage

A start-up company is usually only able to collect normal samples, resulting in extreme label shortage and inability to establish effective intelligent diagnostic models. Especially for machine-level motors with more expensive experimental labeling, cooperation from multiple partners is often required. The phenomenon of signal global domain shift caused by inherent assembly differences and dynamic testing environments becomes more pronounced in scenarios from multiple sources. Furthermore, due to data security and privacy concerns, it is not advisable to directly share local data from multiple companies. This article proposes a federated cross-machine diagnostic framework (FedCMD) to address these challenges, which not only considers the security of various companies but also evaluates the generalization ability of machine-level motors on new individuals. A multiscale pruning transformer (MSPT) is developed with three strategies to provide an effective solution. Comprehensive experiments and visual analyses are performed on real production line signals, which demonstrates the superiority of the proposed model with highest generalization performance of 97.42% mean cross machine accuracy (CMA).

Spiritual Nexus: The Interwoven Sacred Sites of the Kailash Region

This article investigates the complex network of sacred sites within the Kailash region, an axis of profound spiritual significance for a multitude of faiths. The central figure of this sacred web is Mount Kailash, surrounded by the mystical Lake Manasarovar and Rakshas Tal, the restorative springs of Tirthapuri, and the reflective sanctuary of the Bon Monastery. This study delves into the spiritual importance, historical connections, and intricate cultural practices maintained across these sites. Darchen, the starting point for the spiritually charged Kailash Kora, marks the pilgrims' journey towards potential enlightenment. Moreover, the paper investigates the ecocultural dynamics, emphasizing the monasteries around Manasarovar, including the Bon Monastery, as enduring custodians of spiritual wisdom despite historical tribulations. Harnessing a variety of interdisciplinary perspectives, the article portrays how these sites collectively constitute a spiritual heritage that continually shapes the socio-religious fabric of the region. It also addresses modern challenges due to increased pilgrimage, advocating for sustainable conservation efforts. By intertwining the cosmic symbolism and earthly presence of these sites, this article contributes a holistic view of the Kailash region's perpetual sacredness and its essential place within the global spiritual domain.

Do Complications After Pancreatoduodenectomy Have an Impact on Long-Term Quality of Life and Functional Outcomes?

Our aim was to assess whether complications after pancreatoduodenectomy (PD) impact long-term quality of life (QoL) and functional outcomes. There is an increasing number of long-term post-PD survivors, but few studies have evaluated long-term QoL outcomes. The EORTC QLQ-C30 and QLQ-PAN26 questionnaires were administered to patients who survived >5 years post-PD. Clinical relevance (CR) was scored as small (5-10), moderate (10-20), or large (>20). Patients were stratified based on whether they experienced a complication during the index hospitalization. Of 305 patients >5 years post-PD survivors, with valid contact information, 248 completed the questionnaires, and 231 had complication data available. Twenty-nine percent of patients experienced a complication, of which 17 (7.4%) were grade 1, 27 (11.7%) were grade 2, and 25 (10.8%) were grade 3. Global health status and functional domain scores were similar between both groups. Patients experiencing complications reported lower fatigue (21.4 vs 28.1, P < 0.05, CR small) and diarrhea (15.9 vs 23.1, P < 0.05, CR small) symptom scores when compared to patients without complications. Patients experiencing complications also reported lower pancreatic pain (38.2 vs 43.4, P < 0.05, CR small) and altered bowel habits (30.1 vs 40.7, P < 0.01, CR moderate) symptom scores. There was a lower prevalence of worrying (36.2% vs 60.5%, P < 0.05) and bloating (42.0% vs 56.2%, P < 0.05) among PD survivors with complications. Post-PD complication rates were not associated with long-term global QoL or functionality, and may be associated with less severe pancreas-specific symptoms.