MR-tomographischer Befund bei Patienten mit Kniegelenkbeschwerden in Abhängigkeit von der beruflichen und außerberuflichen Gelenkbelastung

Die Gonarthrose als multikausale chronische Erkrankung des Kniegelenks ist die weltweit häufigste Gelenkerkrankung. Ursächlich liegt ein Missverhältnis von Belastbarkeit und mechanischer Beanspruchung des Gelenkknorpels zugrunde, das zur progredienten Schädigung der kartilaginären und ossären Gelenkstrukturen sowie des umgebenden Weichteilmantels führt. Die vorliegende Arbeit untersucht detailliert den MR-tomographischen Befund am Kniegelenk in Abhängigkeit von der Kniebelastung in Beruf und Freizeit. Die Gelenkbelastung wurde individuell im strukturierten Interview mittels eines modifizierten Tegner-Scores erfasst. Es ergeben sich erstmalig Hinweise auf ein belastungskonformes Schadensbild einer durch berufliches Knien und Hocken im Sinne der neuen Berufskrankheit Gonarthrose BK 2112

Edge computing service deployment and task offloading based on multi-task high-dimensional multi-objective optimization

The Mobile Edge Computing (MEC) system located close to the client allows mobile smart devices to offload their computations onto edge servers, enabling them to benefit from low-latency computing services. Both cloud service providers and users seek more comprehensive solutions, necessitating judicious decisions in service deployment and task offloading while balancing multiple objectives. This study investigates service deployment and task offloading challenges in a multi-user environment, framing them as a multi-task high-dimensional multi-objective optimization (MT-HD-MOO) problem within an edge environment. To ensure stable service provisioning, beyond considering latency, energy consumption, and cost as deployment objectives, network reliability is also incorporated. Furthermore, to promote equitable usage of edge servers, load balancing is introduced as a fourth task offloading objective, in addition to latency, energy consumption, and cost. Additionally, this paper designs a MT-HD-MOO algorithm based on a multi-selection strategy to address this model and its solution. By employing diverse selection strategies, an environment selection strategy pool is established to enhance population diversity within the high-dimensional objective space. Ultimately, the algorithm's effectiveness is verified through simulation experiments

Optimizing spatial accessibility and equity of hierarchical older adult care facilities using a multi-modal two-step floating catchment area method: a case study of Lin'an District, Hangzhou

The global aging trend is becoming increasingly pronounced, and the accessibility and equity of older adult care facilities directly influence the health and quality of life of the older adult population, thus representing a critical issue in public health research and policy-making. Using Lin'an District, Hangzhou as an illustrative case, this research examines urban-rural integrated areas specifically, addressing the persistent challenge of supply-demand mismatches in older adult care facility allocation and seeking to optimize their spatial configuration. A comprehensive analytical framework based on the multi-modal two-step floating catchment area (2SFCA) method was established, integrating the Gini coefficient, Lorenz curve, and local spatial autocorrelation analysis to systematically evaluate the spatial accessibility and equity of older adult care facilities. The results demonstrate significant spatial heterogeneity in facility accessibility, revealing a clear distribution pattern characterized by higher accessibility in the eastern urban core and markedly lower accessibility in western rural regions, thereby highlighting notable supply-demand imbalances between urban and rural contexts. Furthermore, the application of local spatial autocorrelation effectively identified key regions characterized by pronounced inequities, notably rural areas in the west suffering severe resource deficiencies and transitional urban-rural zones where supply-demand conflicts prominently occur. The study further investigates critical factors underlying accessibility and equity disparities, including differences in transportation infrastructure, uneven older adult population distributions, and hierarchical classifications of service facilities. Ultimately, the findings provide valuable empirical insights and policy recommendations applicable to urban-rural integration contexts globally, contributing meaningfully to the advancement of age-friendly societies

Development of number line estsimation in Chinese preschoolers: a comparison between numerical and non-numerical symbols

To examine the level of number line estimation (NLE) in Chinese children with respect to representations of both numerical (Arabic numerals) and non-numerical symbols (dots), a total of 192 Chinese preschoolers aged between 4 and 5 years participated in four different NLE tasks. These tasks were paired to evaluate the accuracy and patterns of children’s estimations in both numerical and non-numerical symbol contexts. Our findings indicate that, for Chinese preschoolers, relatively precise numerical symbol representations begin to emerge as early as 4 years of age. The accuracy of number line estimates for both 4- and 5-year-old children gradually increases in tasks involving both numerical and non-numerical symbols. Additionally, the development and patterns observed in the number line estimates of 4- and 5-year-old Chinese preschoolers are similar in both numerical symbol and non-numerical symbol tasks. These results indicate that the initiation of relatively precise numerical symbol representation and the turning point in the developmental trajectory, where the relatively precise representation for numerical symbols surpasses that of non-numerical ones, occur earlier in Chinese children than in their Western counterparts

The Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation

Aiming at the two characteristics of premature convergence of particle swarm optimization that the particle velocity approaches 0 and particle swarm congregate, this paper learns from the annealing function of the simulated annealing algorithm and adaptively and dynamically adjusts inertia weights according to the velocity information of particles to avoid approaching 0 untimely. This paper uses the good uniformity of Anderson chaotic mapping and performs chaos perturbation to part of particles based on the information of variance of the population’s fitness to avoid the untimely aggregation of particle swarm. The numerical simulations of five test functions are performed and the results are compared with several swarm intelligence heuristic algorithms. The results shows that the modified algorithm can keep the population diversity well in the middle stage of the iterative process and it can improve the mean best of the algorithm and the success rate of search

Angiopoietin-like protein 4 dysregulation in kidney diseases: a promising biomarker and therapeutic target

The global burden of renal diseases is increasingly severe, underscoring the need for in-depth exploration of the molecular mechanisms underlying renal disease progression and the development of potential novel biomarkers or therapeutic targets. Angiopoietin-like protein 4 (ANGPTL4) is a multifunctional cytokine involved in the regulation of key biological processes, such as glucose and lipid metabolism, inflammation, vascular permeability, and angiogenesis, all of which play crucial roles in the pathogenesis of kidney diseases. Over the past 2 decades, ANGPTL4 has been regarded as playing a pivotal role in the progression of various kidney diseases, prompting significant interest from the scientific community regarding its potential clinical utility in renal disorders. This review synthesizes the available literature, provides a concise overview of the molecular biological effects of ANGPTL4, and highlights its relationship with multiple renal diseases and recent research advancements. These findings underscore the important gaps that warrant further investigation to develop novel targets for the prediction or treatment of various renal diseases

Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices

Primary hepatocellular carcinoma (PHC) is associated with high rates of morbidity and malignancy in China and throughout the world. In clinical practice, a combination of ultrasound and alpha-fetoprotein (AFP) measurement is frequently employed for initial screening. However, the accuracy of this approach often falls short of the desired standard. Consequently, this study aimed to investigate the enhancement of precision of preliminary detection of PHC by ensemble learning techniques. To achieve this, 712 patients with PHC and 1887 healthy controls were enrolled for the assessment of four ensemble learning methods, namely, Random Forest (RF), LightGBM, Xgboost, and Catboost. A total of eleven characteristics, comprising nine serological indices and two demographic indices, were selected from the participants for use in detecting PHC. The findings identified an optimal feature subset consisting of eight features, namely AFP, albumin (ALB), alanine aminotransferase (ALT), platelets (PLT), age, alkaline phosphatase (ALP), hemoglobin (Hb), and body mass index (BMI), that achieved the highest classification accuracy of 96.62%. This emphasizes the importance of the collective use of these features in PHC diagnosis. In conclusion, the results provide evidence that the integration of serological and demographic indices together with ensemble learning models, can contribute to the precision of preliminary diagnosis of PHC

Colloidal quantum dots and metal halide perovskite hybridization for solar cells stability and performance enhancement

Metal halide perovskites and colloidal quantum dots (QDs) are two emerging class of photoactive materials that has been attracted considerable attention for next-generation high-performance solution-processed solar cells. In particular, the hybridization of these two materials has been recently demonstrated remarkable performance enhancement due to the complementary nature of the two constituents. In this review, we will highlight the recent progress of QDs and perovskite hybridization in solar cell applications. More specifically, the unique properties of monophase perovskite QDs will be summarised which are demonstrated by homogeneously hybridizing perovskite QDs into perovskite lattice. We also discuss the recent progress in heterogeneously hybridizing discrete colloidal QDs into perovskite layers which exhibit significant perovskite film stability enhancement as well as corresponding solar cell performance improvement. PbS QDs, other chalcogenides QDs, as well as emerging two-dimensional QDs, are further accounted through multiple methods, such as bilayer architectures, core-shell structures or blending multiple QDs into perovskite layers. In the end, an outlook perspective of this field has been proposed to point out several challenges and possible solutions

Bidentate ligand modification strategy on supported Ni nanoparticles for photocatalytic selective hydrogenation of alkynes

The design of selective and stable non-precious metal catalysts for hydrogenation of alkyne is highly desirable. In this study, L-lysine modification strategy is applied to support Ni nanoparticles, which greatly improves the stability and photocatalytic performance in the hydrogenation of phenylacetylene to styrene. The robust stability is attributed to that both amino and carboxyl groups of L-lysine can function simultaneously as the anchor, much stronger than a single group, to strongly interact with metallic Ni via N and O coordination. The high selectivity to styrene is due to that L-lysine modification results in a larger adsorption energy difference between styrene and phenylacetylene on the surface of Ni, therefore phenylacetylene is preferentially adsorbed on Ni surface. This protocol shows that the modulation of interaction between ligands and Ni is favorable to design stable, active and selective catalysts for hydrogenation of alkynes

Growth mindset and well-being in social interactions: countering individual loneliness

IntroductionLoneliness is a prevalent negative emotion experienced by college students. This study explores the relationship between a growth mindset and loneliness among college students.MethodsA total of 560 college students completed the Growth Mindset Scale (GMS), UCLA Loneliness Scale (UCLA), Interpersonal Relationships Assessment Scale (IRS), and two measures assessing distinct facets of well-being the Satisfaction with Life Scale (SWLS) and the revised Positive Affect and Negative Affect Scale (PANAS).Results and discussionThe results found a significant negative correlation between a growth mindset and loneliness. A growth mindset negatively predicted loneliness through the chain-mediated effects of interpersonal distress and well-being. These findings underscore the important role of a growth mindset in influencing loneliness, providing teachers and practitioners a new perspective to understand and intervene college students’ psychological challenges