Value Of Function Research Articles

Comment on “Tunneling-tip-induced collapse of the charge gap in the excitonic insulator Ta2NiSe5 ”

In this study, we investigate the discrepancy between the estimate of Q. He [], who observed a remarkable collapse of the exciton gap in Ta2NiSe5 due to the electrostatic field between the scanning tunneling microscope (STM) tip and the sample, and that of a recent angle-resolved photoemission spectroscopy investigation [C. Chen , ]. It is proposed that a critical factor contributing to this discrepancy is due to He 's assumption of a constant work function of the STM tip. This assumption led to an underestimation of the tip-induced electric field. Using a literature value for the sample work function, a more substantial electric field strength is obtained, which resolves the apparent conflict between the doping estimates of these two techniques. Furthermore, our findings highlight the importance of the STM tip condition, which can significantly impact the tip work function and, consequently, influence the doping estimation in experiments involving tip-induced electric fields. Published by the American Physical Society 2024

A Shark Inspired Ensemble Deep Learning Stacks for Ensuring the Security in Internet of Things (IoT)-Based Smart City Infrastructure

Recent advancements in the Internet of Things (IoT) have paved the way for intelligent and sustainable solutions in smart city environments. However, despite these advantages, IoT-connected devices present significant privacy and security risks, as network attacks increasingly exploit user-centric information. To protect the network against the rapidly growing number of cyber-attacks, it is essential to employ a cognitive intrusion detection system (CIDS) capable of handling complex and voluminous network data. This research presents a novel ensemble deep learning framework designed to enhance cybersecurity in IoT-based smart city ecosystems. The proposed architecture integrates Self-Attention Convolutional Neural Networks, Bidirectional Gated Recurrent Units, and Shark Smell Optimized Feed Forward Networks to create a robust, adaptive system for detecting and mitigating cyber threats. By leveraging fog computing, the model significantly reduces latency and computational overhead, making it highly suitable for large-scale IoT deployments. Extensive experimentation using the ToN-IoT dataset demonstrates the framework's exceptional performance, achieving a 99.78% detection rate across various attack types and an AUC of 0.989. The proposed model outperforms existing state-of-the-art approaches, achieving a mean fitness function value of 0.85640 and a standard deviation of 0.037630 in binary classification outcomes. In multi-class classification, the model maintains a mean fitness function value of 0.8230 and a variance of 2.28930 × 104, significantly outperforming other meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The model exhibits superior accuracy and efficiency compared to existing state-of-the-art approaches, particularly in identifying complex and emerging threats. This research makes significant contributions by introducing innovative feature extraction techniques, optimizing model performance for resource-constrained environments, and providing a scalable solution for securing smart city infrastructure. The findings highlight the potential of ensemble deep learning approaches to fortify IoT networks against cyberattacks, paving the way for more resilient and secure smart cities.

RP-CAD for Lipid Quantification: Systematic Method Development and Intensified LNP Process Characterization.

Lipid nanoparticles (LNPs) and their versatile nucleic acid payloads bear great potential as delivery systems. Despite their complex lipid composition, their quality is primarily judged by particle characteristics and nucleic acid encapsulation. In this study, we present a holistic reversed-phase (RP)-charged aerosol detection (CAD)-based method developed for commonly used LNP formulations, allowing for intensified LNP and process characterization. We used an experimental approach for power function value (PFV) optimization termed exploratory calibration, providing a single PFV (1.3) in an appropriate linearity range for all six lipids. Followed by the procedure of method calibration and validation, linearity (10-400 ng, R2 > 0.996), precision, accuracy, and robustness were effectively proven. To complement the commonly determined LNP attributes and to evaluate the process performance across LNP processing, the developed RP-CAD method was applied in a process parameter study varying the total flow rate (TFR) during microfluidic mixing. The RP-CAD method revealed a constant lipid molar ratio across processing but identified deviations in the theoretical lipid content and general lipid loss, which were both, however, entirely TFR-independent. The deviations in lipid content could be successfully traced back to the lipid stock solution preparation. In contrast, the observed lipid loss was attributable to the small-scale dialysis following microfluidic mixing. Overall, this study establishes a foundation for employing RP-CAD for lipid quantification throughout LNP processing, and it highlights the potential to extend its applicability to other LNPs, process parameter studies, or processes such as cross-flow filtration.

Research on the Planning and Design of Med–Small-Sized Hospitals in China Based on Patient Perceived Value in the Context of Healthcare Model Transformation

China’s aging society has led to a major transformation of the healthcare model from traditional therapeutic medical care to life support healthcare. Med–small-sized hospitals (with 20–299 beds) are expected to play a major role in this transition compared to large hospitals. However, hospitals often take 3–5 years or more from design to construction. In order to guide architects in planning and designing for future needs, this study aims to identify the key points of planning and design that can enhance the value of med–small-sized hospitals in terms of functional configuration, spatial environment, etc. In this study, a questionnaire survey was conducted on the perceived value of 600 patients in Nanjing, China. The data were used to construct a structural equation model (SEM) of perceived value for med–small-sized hospitals in China. There are 24 observed variables in six dimensions, including cost value, functional value, social value, cognitive value, patient satisfaction, and patient loyalty. The final SEM indicates that optimizing functional configurations and spatial environment design can significantly increase the value of hospitals, while patients’ willingness to go to the inpatient department is quite low. Finally, based on the survey findings from Nanjing, we proposed four optimization strategies: (1) promoting diversified development and collaboration based on regional characteristics, (2) optimizing functional configurations, (3) maintaining an efficient and concise patient experience, and (4) optimizing the inpatient department.

Improved Bayesian model updating of geomaterial parameters for slope reliability assessment considering spatial variability

In engineering practice, Bayesian model updating using field data is often conducted to reduce the substantial inherent epistemic uncertainties in geomaterial properties resulting from complex geological processes. The Bayesian Updating with Subset simulation (BUS) method is commonly employed for this purpose. However, the wealth of field data available for engineers to interpret can lead to challenges associated with the “curse of dimensionality”. Specifically, the value of the likelihood function in the BUS method can become extremely small as the volume of field data increases, potentially falling below the accuracy threshold of computer floating-point operations. This undermines both the computational efficiency and accuracy of Bayesian model updating. To effectively address this technical challenge, this paper proposes an improved BUS method developed based on parallel system reliability analysis. Leveraging the Cholesky decomposition-based midpoint method, the total failure domain in the original BUS method, which involves a low acceptance rate, is subdivided into several sub-failure domains with a high acceptance rate. Facilitated with an improved Metropolis-Hastings algorithm, the improved BUS method enables the consideration of a large volume of field data and spatial variability of geomaterial properties in the probabilistic back analysis. The results of an illustrative soil slope, involving spatially variable undrained shear strength, demonstrate that the improved BUS method is effective in simultaneously incorporating a substantial volume of field measurements and observations in the model updating process. Through a comparison with the original BUS method, the improved BUS method is shown to be useful for Bayesian model updating of high-dimensional spatially variable geomaterial properties and slope reliability assessment.

Enhancing Unmanned Marine Vehicle Security: A Periodic Watermark-Based Detection of Replay Attacks

This paper explores a periodic watermark-based replay attack detection method for Unmanned Marine Vehicles modeled in the framework of the Takagi–Sugeno fuzzy system. The precise detection of replay attacks is crucial for ensuring the security of Unmanned Marine Vehicles; however, traditional timestamp-based or encoded measurement-dependent detection approaches often sacrifice system performance to achieve higher detection rates. To reduce the potential performance degradation, a periodic watermark-based detection scheme is developed, in which a compensation signal together with a periodic Gaussian watermark signal is integrated into the actuator. By compensation calculations conducted with all compensatory signals in each period, the position corresponding to a minimum value of the detection function can be derived. Then, the time that the attacks occurred can be ensured with the aid of the comparison between this position with the watermark signal in the same period. An application on a UMV is shown to demonstrate the effectiveness of the presented scheme in detecting replay attacks while minimizing control costs.

Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

AbstractWireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing methods, highlighting its potential for enhancing WMN performance. Specifically, CFL-PSO achieves up to a 66.5% improvement in network connectivity, a 16.56% improvement in coverage, and a 21.4% improvement in the objective function value when compared to the standard FLA.

Load frequency control performance enhancement of an electric vehicle integrated time-delay multi-microgrid system

ABSTRACT Frequency control in a microgrid (μG) is problematic due to low inertia of distributed generators (DGs) and uncertainty of the renewable energy source-dependent DGs. Consequently, deploying distributed storage units (DSUs) in a μG is crucial for quickly arresting frequency deviations. The electric vehicle (EV) technology, as DSU, is being preferred over conventional energy storage due to its lower cost and slower degradation rate, facilitating demand side response in a μG. The adoption of the EV technology necessitates an open communication infrastructure in the μG, with a pre-existing communication time delay (CTD). This article explores the impact of EV integration on load frequency control (LFC) performance of a multi-microgrid (MμG) system and determines an optimal CTD value simultaneously. A cyclical parthenogenesis algorithm-optimized 2DOF-FOPID controller is implemented to obtain dynamic responses of the MμG system. Simulation results reveal significant improvements in dynamic responses when integrating EVs in the MμG, with a 97.33% increase in objective function value. The recommended controller also outperforms standard controllers in terms of how quickly frequency and tie-line power variations settle down to zero. Thus, the proposed controller meets the LFC requirements. Robustness of the proposed LFC scheme is tested against parametric variations in the MμG system.

Surrogate-assisted push and pull search for expensive constrained multi-objective optimization problems

In many real-world engineering optimizations, a large number of objective and constraint function values often need to be obtained through simulation software or physical experiments, which incurs significant computational costs and/or time expenses. These problems are known as expensive constraint multi-objective optimization problems (ECMOPs). This paper combines the push and pull search (PPS) framework and proposes a surrogate-assisted evolutionary algorithm to solve ECMOPs through Bayesian active learning, naming it the surrogate-assisted PPS (SA-PPS). Specifically, during the push search stage, candidate solutions are selected based on two indicators: hypervolume improvement and objective uncertainty. These aim to quickly guide the population towards the unconstrained Pareto front while ensuring diversity. During the pull search stage, the population is partitioned into many subregions through reference vectors, and different selection strategies are assigned to each subregion based on its state, aiming to guide the population towards the constrained Pareto front while ensuring diversity. Furthermore, we introduce a batch data selection strategy that utilizes Bayesian active learning to enable the surrogate model to focus on regions of interest in the pull search stage. Extensive experimental results have shown that the proposed SA-PPS algorithm exhibits superior convergence and diversity compared to 9 state-of-the-art algorithms across a variety of benchmark problems and a real-world optimization problem.

The Prognostic Value of Left Atrial Function in Patients with Acute Myocardial Infarction.

The prognostic value of left atrial (LA) volume is well-established in acute myocardial infarction (AMI) patients. LA strain provides further patophysological insights. In the present study, we evaluated LA volume and LA strain in AMI patients including those with atrial fibrillation (AF). The aim of the study was to determine if LA strain provide additional prognostic value. Patients with AMI underwent two-dimensional echocardiography within 72 h of admission. The primary outcome was a composite of all-cause mortality and major adverse cardiovascular events. Cox regression analyses were performed. We included 501 patients and during follow-up, 132 patients (26.4%) met the primary outcome. Left ventricular (LV) global longitudinal strain (GLS) (HR 0.94 [95% CI 0.88-0.99], p = 0.029), indexed LA volume (LAVi) (HR 1.02 [95% CI 1.00-1.04], p = 0.015), and LA reservoir strain (HR 0.96 [95% CI 0.93-0.99], p = 0.017) were all independently associated with the primary outcome. A univariate Cox model conducted on the AF patients (n = 32) revealed that LA reservoir strain remained significantly associated with the primary outcome, while LV GLS and LAVi were not significant. The prognostic value of LA reservoir strain was comparable to LA volume and LV GLS, and might even be better in AF patients.

Seismic performance of friction damped posttensioned steel frame equipped with SMA strands

The post-tensioned friction dissipating (PTFD) connection for steel frames has drawn significant attention from researchers due to its excellent seismic performance. A simplified numerical algorithm suitable for modeling the SMA strands is proposed. The simplified numerical model of the SMA-PTFD is established. Parametric analysis is performed based on hysteresis analysis and transient dynamic analysis to reveal the influence of SMA strands on the seismic performance of SMA-PTFD frames. The changing rule of seismic response of SMA-PTFD frames, along with the fmax for SMA strands and the Fmax for the friction device, is systematically investigated. The value of the objective function can be reduced by about 50 % when the fmax is increased from 1 kN to 13 kN. The fundamental frequency is 1.25 Hz for three analyzed conditions. The value of the second frequency is significantly affected by Fmax. The values of the second frequency are 1.5 Hz, 1.58 Hz, and 1.42 Hz. The characteristics of seismic response, including displacement and cable force, are deeply studied. The seismic response is also investigated in the frequency domain. The results presented in this work reveal the collaborative energy dissipation mechanism of the friction device and SMA strands.

Global effects of livestock grazing on ecosystem functions vary with grazing management and environment

Grasslands support multiple ecosystem functions and services, and diverse biota, and are critical for human well-being. Grazing is the most pervasive land use in grasslands, but can have damaging effects when poorly managed. How grazing management and the environment interact to affect ecosystem functions globally is less well understood. Addressing this knowledge gap is important if we are to evaluate where (climate region, soil texture, and grassland type), what (livestock type), and how (grazing intensity, grazing regime, and duration) grazing might minimize grassland degradation and sustain healthy grassland functions. We used a systematic meta-analysis to explore the effects of grazing on ecosystem functions (primary production, carbon sequestration, water conservation, nutrient cycle, and decomposition) based on 3917 paired data from 148 studies across the globe. We found that grazing substantially reduced plant productivity (-26%), followed by water conservation (-18%) and carbon sequestration (-19%). The value of most ecosystem functions declined with increasing grazing intensity, and more pronounced negative effects of grazing with mixed-herbivore than single species grazing. Grazing impacts also varied with environmental conditions, with light grazing increasing carbon sequestration in arid regions, but reducing it in semi-arid regions. Further, increasing aridity indirectly weakened the positive impacts of light grazing on ecosystem functions by suppressing grazing effects. Our study suggests that the interactions between grazing management and environmental conditions are critical when assessing the effects of grazing on grassland functions, and this will likely be more important as climates become hotter and drier.

Study on long short-term memory based on vector direction of flood process for flood forecasting

Accurate flood forecasting is crucial for flood prevention and mitigation, safeguarding the lives and properties of residents, as well as the rational use of water resources. The study proposes a model of long and short-term memory (LSTM) combined with the vector direction (VD) of the flood process. The Jingle and Lushi basins were selected as the research objects, and the model was trained and validated using 50 and 49 measured flood rainfall-runoff data in a 7:3 division ratio, respectively. The results indicate that the VD-LSTM model has more advantages than the LSTM model, with increased NSE, and reduced RMSE and bias to varying degrees. The flow simulation results of VD-LSTM better match the observed flow hydrographs, improving the underestimation of peak flows and the lag issue of the model. Under the same task and dataset, with the same hyperparameter settings, VD-LSTM can more quickly reduce the loss function value and achieve a better fit compared to LSTM. The proposed VD-LSTM model couples the vectorization process of flood runoff with the LSTM neural network, which contributes to the model better exploring the change characteristics of rising and receding water in flood runoff processes, reducing the training gradient error of input–output data for the LSTM model, and more effectively simulating flood process.

Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for Nodes.

With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node's Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely "the alternating backpack problem", and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes' energy requirements and MC's capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.

Design of short-focus near-eye optical system for virtual reality using polarization-insensitive metasurface

Near-eye optical systems, as an important component of virtual reality displays, have attracted great research interest recently. However, current systems have complex structures and face the design challenge of combining compact, short-focus design with wide field of view and high angular resolution. In this paper, we propose a short-focus near-eye optical system with wide field of view and high angular resolution, referred to as a meta-eyepiece, by patterning a single-layer polarization-insensitive metasurface on a substrate. The metasurface, featuring a quasi-periodic nanopillar arrangement, enables precise phase modulation and enhances design flexibility. The desired metaform phase can be obtained by modeling the light propagation of the meta-eyepiece to determine key design parameters, utilizing metaform phase polynomials, customizing the objective merit function and employing advanced optimization algorithms. Our system achieves a short focal length of approximately 22 mm with an 80° field of view, offering compactness superior to conventional virtual reality optics and a minimum resolvable angle less than 1.25 arcminutes, ensuring high angular resolution. It also exhibits excellent imaging performance with full-field modulation transfer function values exceeding 0.5 at 62.5 lp/mm. Although the initial system utilizes ray optics, the scaled version is validated for its feasibility and scalability through full-wave simulations. Our meta-eyepiece structure and design method show the potential of metasurfaces for applications in virtual reality, offering valuable support for technological development in this field.

Purchase intention toward E-pharmacy: the consumption value perspective

Purpose This study aims to examine consumer purchase behavior towards E-pharmacy from a consumption value perspective. For this purpose, the influence of consumption values on consumer purchase intention is evaluated. Further, the role of consumer involvement in online purchase setup in the context of E-pharmacy is observed using moderation analysis. Design/methodology/approach The study used a cross-sectional survey design, wherein the respondents in the Delhi-National Capital Region of India were approached using a purposive sampling method. Responses received through the structured questionnaire were subjected to analysis using Smart PLS Version 4.0.9.6. Findings Results indicate a significant influence of functional (partial), emotional, social and conditional values on consumers’ purchase intention. Moreover, the moderation effect of consumer online involvement is seen in functional value (partial) and emotional value–purchase intention relationships. Research limitations/implications This study reinforces the use of the consumption value perspective to explain consumer purchase behavior toward information system (IS)-based platforms such as E-pharmacy. Some of the results, in this context, that did not establish significant relationships between a given form of consumption values and purchase intention, open up the possibilities of retesting the said relationships in the future. The researchers would realize the benefit of such examinations, as the E-pharmacy sector further evolves and matures in the future. Further, the successful establishment of a significant moderating role of consumer online involvement in certain consumption value-purchase intention relationships such as safety, perceived convenience and comfort value paves the way for future researchers to explore consumer online involvement for a similar role in IS research. Practical implications This study provides cues for E-pharmacy marketers to focus on enhancing product safety, usage comfort, users’ social image and awareness toward health consciousness that will help build purchase intention and assist in overcoming the challenges in the long run. It further suggests that marketers should have a strong re-look at forming price value perceptions and they must enhance the interactive features of the company’s mobile apps/websites with the help of the emerging artificial intelligence tools to re-orient the epistemic value and perceived convenience value toward E-pharmacy services. Furthermore, this study recommends following digital marketing practices to increase consumer online involvement, which would help strengthen perceived convenience formation that would otherwise be difficult to attain for E-pharmacy consumers. Originality/value This study provides novel insight into consumer purchase intention evaluation through the consumption value perspective in an emerging E-pharmacy market. The consumption value frame of reference helps to understand consumers’ buying rationale that affects their buying behavior beyond the initial adoption. Further, the moderation analysis of consumer online involvement provides an additional dimension to understanding consumer buying behavior concerning E-pharmacy.

The impact of brain functional connectivity on skill and physical performance in soccer players: A resting state fMRI study

ObjectiveIdentifying connections between various aspects of physical performance, motor skills, and cognitive abilities with the brain connectivity networks is essential for determining important brain regions associated with soccer performance. This study aimed to carry out the relationship between soccer-specific parameters and resting-state functional connectivity in soccer players. Materials and methodsTwenty-five soccer players (Vo2max; 50.68 ± 3.76 ml/min/kg) were participated voluntarily. The study encompassed a comprehensive assessment, including measures of physical performance, skill performance, and executive function (design fluency). Each participants underwent resting-state functional magnetic resonance imaging (fMRI) also allowing for the acquisition of data. The Linear regression were conducted between sport-specific parameters and functional connectivity values. ResultsOur findings revealed a significant positive correlation between agility and linear sprinting with the dorsal attention network (DAN). Conversely, the aerobic fitness parameter demonstrated exhibited a significant negative correlation with the DMN. Skill performance (speed dribbling, passing, shooting) displayed a significant positive correlation with both sensorimotor network (SMN), and DAN. Moreover, design fluency test displayed a significant positive correlation with the DMN. Notably, IPSL and IPSR nodes within the DAN consistently demonstrated the highest degree of centrality across various sports parameters. ConclusionThis study provides valuable insights into the intricate relationship among physical performance, skill proficiency, and executive function, as they relate to the functional connectivity of specific neural networks in soccer players. The outcomes establish a foundational understanding for future exploration and potential applications in sports science, and cognitive neuroscience in order to improve soccer performance.

Impact of the Diverting Stoma on Renal Function.

Although loop ileostomy as a diverting stoma has been considered to affect renal dysfunction, few reports have compared loop colostomy with loop ileostomy regarding renal function. This is an important issue in the current setting of increased opportunities to perform surgery on patients with poor renal function. This study aims to reveal the effect of ileostomy on renal dysfunction compared to colostomy following sphincter-preserving rectal surgery. This study was a retrospective analysis. We compared preoperative and postoperative blood urea nitrogen, serum creatinine and estimated glomerular filtration rate values. The study was conducted at a single academic institution in Osaka, Japan. From October 2013 to November 2021, 135 consecutive patients underwent rectal surgery with diverting stoma are included. Differences in pre- and postoperative renal function values by stoma creation site in patients with preoperative chronic kidney disease. Risk factors for patients with newly kidney disease after stoma creation. In the preoperative chronic kidney disease (+) patients, the differences between the pre- and post-values in the blood urea nitrogen (p = 0.047) and the serum creatinine (P = 0.028) values were higher than in the preoperative chronic kidney disease (-) patients. In the preoperative chronic kidney disease (+) patients, ileostomy was significantly associated with an elevation of the serum creatinine value (p = 0.025) and a decrease in estimated glomerular filtration rate value (p = 0.041) from the pre-operative one compared with that of colostomy. In multivariate analysis, ileostomy (odds ratio; 7.443, p = 0.011) and hypertension (4.226, p = 0.008) were independent risk factors of newly kidney disease postoperatively. Limitations to our study includes its retrospective nature and bias due to the stoma site being determined by each surgeon. We should take care to choose diverting stoma especially in patients with a risk of kidney disfunction. See Video Abstract.

Factors associated with cognitive reserve according to education level.

We investigated distinctive factors associated with cognitive reserve (CR) based on education level. Among 1247 participants who underwent neuropsychological assessment, amyloid positron emission tomography, and brain magnetic resonance imaging, 336 participants with low education (≤6 years) and 697 with high education (≥12 years) were selected. CR was measured as the difference between the predicted and observed value of cognitive function based on cortical thickness. Multiple linear regression was conducted in each group after controlling for age and sex. In the low-education group, low literacy, long sleep duration(>8h/day), and diabetes were negatively associated with CR, whereas cognitive and physical activity were positively associated with CR. In the high-education group, cognitive activity was positively related to CR, whereas low literacy, long sleep duration (>8h/day), and depression were negatively related to CR. This study provides insights into different strategies for enhancing CR based on educational background. Factors associated with cognitive reserve (CR) varied according to the education level. Diabetes and physical activity were associated with CR in the low-education group. Depression was related to CR in the high-education group. Low literacy, sleep duration, and cognitive activity were associated with CR in both groups. Dementia-prevention strategies should be tailored according to educational level.

Slow Gait Speed Predicts Incident Dementia, Mortality, and Long-Term Functional Outcome in Cerebral Small-Vessel Disease.

Gait impairment is observed in patients with small vessel disease (SVD); however, the association between gait function and long-term outcome remains unclear. This study aimed to clarify the predictive value of gait function on incident dementia, survival and functional outcome. Data were derived from a Japanese cohort of patients with SVD. This study included 522 participants who underwent 3-m timed up and go test (TUG), and gait speed, TUG time, was divided into tertiles. Magnetic resonance imaging was used to evaluate severity of white matter hyperintensities, lacunes, and medial temporal atrophy. Primary outcome was dementia. All-cause death and functional outcome by modified Rankin scale at the last visit was also evaluated. The median age was 71 years, and median TUG time was 9.91 s. During follow-up period of 4.8 years, 32 cases of dementia occurred. Cox proportional hazard analysis revealed that slow gait speed (TUG time > 10.88 s) was associated with a significantly higher risk of incident dementia than fast (TUG time < 9.03) and middle (TUG time, 9.04-10.87 s) speeds after adjusting risk factors, Mini-Mental State Examination, SVD severity and brain atrophy (adjusted hazard ratio, 2.73; 95% confidence interval, 1.16-6.42, p = 0.022). Slow speed was also associated with mortality and poor functional outcome compared with other speeds (adjusted odds ratio, 4.19; 95% confidence interval 1.92-9.18, p < 0.001). Gait function was associated with incident dementia, mortality and poor functional outcome independently of cognitive function, brain atrophy, and SVD severity.