Assessing gray matter volume in patients with idiopathic rapid eye movement sleep behavior disorder

Idiopathic rapid eye movement sleep behavior disorder(iRBD) is often a precursor to neurodegenerative disease. However, voxel-based morphological studies evaluating structural abnormalities in the brains of iRBD patients are relatively rare. This study aimed to explore cerebral structural alterations using magnetic resonance imaging and to determine their association with clinical parameters in iRBD patients. Brain structural T1-weighted MRI scans were acquired from 19 polysomnogram-confirmed iRBD patients(male:female 16:3; mean age 66.6 ± 7.0 years) and 20 age-matched healthy controls(male:female 5:15; mean age 63.7 ± 5.9 years). Gray matter volume(GMV) data were analyzed based on Statistical Parametric Mapping 8, using a voxel-based morphometry method and two-sample t-test and multiple regression analysis. Compared with controls, iRBD patients had increased GMV in the middle temporal gyrus and cerebellar posterior lobe, but decreased GMV in the Rolandic operculum, postcentral gyrus, insular lobe, cingulate gyrus, precuneus, rectus gyrus, and superior frontal gyrus. iRBD duration was positively correlated with GMV in the precuneus, cuneus, superior parietal gyrus, postcentral gyrus, posterior cingulate gyrus, hippocampus, lingual gyrus, middle occipital gyrus, middle temporal gyrus, and cerebellum posterior lobe. Furthermore, phasic chin electromyographic activity was positively correlated with GMV in the hippocampus, precuneus, fusiform gyrus, precentral gyrus, superior frontal gyrus, cuneus, inferior parietal lobule, angular gyrus, superior parietal gyrus, paracentral lobule, and cerebellar posterior lobe. There were no significant negative correlations of brain GMV with disease duration or electromyographic activity in iRBD patients. These findings expand the spectrum of known gray matter modifications in iRBD patients and provide evidence of a correlation between brain dysfunction and clinical manifestations in such patients. The protocol was approved by the Ethics Committee of Huashan Hospital(approval No. KY2013-336) on January 6, 2014. This trial was registered in the ISRCTN registry(ISRCTN18238599).

In situ atomic-scale observation of size-dependent (de) potassiation and reversible phase transformation in tetragonal FeSe anodes

Potassium-ion batteries(PIBs)are considered promising alternatives to lithium-ion batteries owing to cost-effective potassium resources and a suitable redox potential of-2.93 V(vs.-3.04 V for Li+/Li).However,the exploration of appro-priate electrode materials with the correct size for reversibly accommodating large K+ions presents a significant challenge.In addition,the reaction mecha-nisms and origins of enhanced performance remain elusive.Here,tetragonal FeSe nanoflakes of different sizes are designed to serve as an anode for PIBs,and their live and atomic-scale potassiation/depotassiation mechanisms are revealed for the first time through in situ high-resolution transmission electron micros-copy.We found that FeSe undergoes two distinct structural evolutions,sequen-tially characterized by intercalation and conversion reactions,and the initial intercalation behavior is size-dependent.Apparent expansion induced by the intercalation of K+ions is observed in small-sized FeSe nanoflakes,whereas unexpected cracks are formed along the direction of ionic diffusion in large-sized nanoflakes.The significant stress generation and crack extension originating from the combined effect of mechanical and electrochemical interactions are elucidated by geometric phase analysis and finite-element analysis.Despite the different intercalation behaviors,the formed products of Fe and K_(2)Se after full potassiation can be converted back into the original FeSe phase upon depotassiation.In particular,small-sized nanoflakes exhibit better cycling perfor-mance with well-maintained structural integrity.This article presents the first successful demonstration of atomic-scale visualization that can reveal size-dependent potassiation dynamics.Moreover,it provides valuable guidelines for optimizing the dimensions of electrode materials for advanced PIBs.

Neurophysiological, histological, and behavioral characterization of animal models of distraction spinal cord injury: a systematic review

Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.

Increased excitatory amino acid transporter 2 levels in basolateral amygdala astrocytes mediate chronic stress–induced anxiety-like behavior

The conventional perception of astrocytes as mere supportive cells within the brain has recently been called into question by empirical evidence, which has revealed their active involvement in regulating brain function and encoding behaviors associated with emotions.Specifically, astrocytes in the basolateral amygdala have been found to play a role in the modulation of anxiety-like behaviors triggered by chronic stress. Nevertheless, the precise molecular mechanisms by which basolateral amygdala astrocytes regulate chronic stress–induced anxiety-like behaviors remain to be fully elucidated. In this study, we found that in a mouse model of anxiety triggered by unpredictable chronic mild stress, the expression of excitatory amino acid transporter 2 was upregulated in the basolateral amygdala. Interestingly, our findings indicate that the targeted knockdown of excitatory amino acid transporter 2 specifically within the basolateral amygdala astrocytes was able to rescue the anxiety-like behavior in mice subjected to stress. Furthermore, we found that the overexpression of excitatory amino acid transporter 2 in the basolateral amygdala, whether achieved through intracranial administration of excitatory amino acid transporter 2agonists or through injection of excitatory amino acid transporter 2-overexpressing viruses with GfaABC1D promoters, evoked anxiety-like behavior in mice. Our single-nucleus RNA sequencing analysis further confirmed that chronic stress induced an upregulation of excitatory amino acid transporter 2 specifically in astrocytes in the basolateral amygdala. Moreover, through in vivo calcium signal recordings, we found that the frequency of calcium activity in the basolateral amygdala of mice subjected to chronic stress was higher compared with normal mice.After knocking down the expression of excitatory amino acid transporter 2 in the basolateral amygdala, the frequency of calcium activity was not significantly increased, and anxiety-like behavior was obviously mitigated. Additionally, administration of an excitatory amino acid transporter 2 inhibitor in the basolateral amygdala yielded a notable reduction in anxiety level among mice subjected to stress. These results suggest that basolateral amygdala astrocytic excitatory amino acid transporter 2 plays a role in in the regulation of unpredictable chronic mild stress-induced anxiety-like behavior by impacting the activity of local glutamatergic neurons, and targeting excitatory amino acid transporter 2 in the basolateral amygdala holds therapeutic promise for addressing anxiety disorders.

Trust beyond Technology Algorithms: A Theoretical Exploration of Consumer Trust and Behavior in Technological Consumption and AI Projects

In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.

THE DYNAMICAL BEHAVIOR OF FULLY DISCRETE SPECTRAL METHOD FOR NONLINEAR SCHRODINGER EQUATION WITH WEAKLY DAMPED

Nonlinear Schrodinger equation (NSE) arises in many physical problems. It is a very important equation. A lot of works studied the wellposed, the existence of solution of NSE etc. And there are many works studied the numerical methods for it. Recently, since the development of infinite dimensional dynamic system the dynamical behavior of NSE has been investigated. The paper [1] studied the long time wellposedness, the existence of universal attractor and the estimate of Lyapunov exponent for NSE with weakly damped. At the same time it was need to study the large time new computational methods and to discuss its convergence error estimate, the existence of approximate attractors etc. In this pape we study the NSE with weakly damped (1.1). We assume,where 0【λ【2 is a constant. If we wish to construct the higher accuracy computational scheme, it will be difficult that staigh from the equation (1.1). Therefore we start with (1. 4) and use fully discrete Fourier spectral method with time difference to

Dynamic Behavior and Its Consideration of EHD Liquid Extraction Phenomenon Causing under DC or Low-Frequency AC Voltage

This paper describes an experimental and theoretical study on an extraction phenomenon of liquids occurring at an air gap between the liquid surface and the electrode by applying a direct current (DC) or low-frequency alternating current (AC) voltage. Three liquids with a different physical property;2,3-dihydrodecafluoropenten, palm fatty acid ester oil and crude rapeseed oil are used as working liquids. The electrode configuration is the sphere or plane (high voltage electrode) to grounded plane electrode. The grounded plane electrode is fixed to the bottom of the test vessel with working liquid and the high voltage electrode is installed in an air above the liquid surface against the grounded plane electrode. The liquid surface swells towards the high voltage electrode by the increase of voltage and the liquid is extracted in a short time, thereafter the air gap between the liquid surface and the high voltage electrode is bridged at a thick liquid column. Such the liquid behavior displays unique features with voltage polarity effect for each working liquid. The relationship between the applied voltage, current variation, height of swollen liquid, force pulling liquid and dynamic feature of liquid is examined experimentally. The liquid behavior is considered theoretically based on experimental observations.

Dynamic changes of behaviors, dentate gyrus neurogenesis and hippocampal miR-124 expression in rats with depression induced by chronic unpredictable mild stress

The depression-like behavior phenotype,neurogenesis in the dentate gyrus and miR-124 expression in the hippocampus are the focus of current research on the pathogenesis of depression and antidepressant therapy.The present study aimed to clarify the dynamic changes of depression-like behavior,dentate gyrus neurogenesis and hippocampal miR-124 expression during depression induced by chronic stress to reveal pathological features at different stages of depression and to further provide insight into depression treatment.Chronic unpredictable mild stress depression models were established by exposing Sprague-Dawley rats to various mild stressors,including white noise,thermal swimming,stroboscopic illumination,soiled cages,pairing with three other stressed animals,cold swimming,tail pinch,restraint and water and food deprivation.Chronic unpredictable mild stress model rats underwent dynamic observation from 1 to 8 weeks and were compared with a control group(normal feeding without any stressors).To observe changes in the depression-like behavior phenotype during chronic unpredictable mild stress-induced depression,a sucrose preference test was used to evaluate the degree of anhedonia.An open-field test was used to evaluate locomotor activity and anxiety status.Compared with the control group,chronic unpredictable mild stress rats lost weight but did not have a depression-like behavioral phenotype at 1-4 weeks.Chronic unpredictable mild stress rats presented decreased sucrose preference and locomotor activity at 5-8 weeks.In addition,chronic unpredictable mild stress rats did not have significant anxiety-like behavior during 1-8 weeks of modeling.To observe neurogenesis dysfunctions and changes in neuronal number in the dentate gyrus during chronic unpredictable mild stress-induced depression,markers(DCX and DCX/BrdU)of neural proliferation and differentiation and the neuronal marker NeuN were assessed by immunofluorescence.Compared with the control group,neurogenesis and the neuronal number in the dentate gyrus did not change from 2 to 6 weeks;however,neural proliferation and differentiation in the dentate gyrus decreased,and the number of neurons decreased until the eighth week in the chronic unpredictable mild stress group.Real-time quantitative reverse transcription polymerase chain reaction assays and fluorescence in situ hybridization were used to measure the expression of hippocampal miR-124 during chronic unpredictable mild stress-induced depression.The results showed that the expression of hippocampal miR-124 was unchanged during the first 4 weeks but increased from 5 to 6 weeks and decreased from 7 to 8 weeks compared with the control group.These findings indicate that during chronic unpredictable mild stress-induced depression,the behavioral phenotype,miR-124 expression in the hippocampus,neurogenesis in the dentate gyrus and neuronal numbers showed dynamic changes,which suggested that various pathological changes occur at different stages of depression.All experimental procedures and protocols were approved by the Experimental Animal Ethics Committee of Guangzhou University of Chinese Medicine of China in March 2015.

Comparative Studies on Calculation Methods of Shear Behavior of Reinforced Concrete Beams of Chinese Modern Reinforced Concrete Buildings

In order to study the calculation methods of shear behavior of reinforced concrete beams of Chinese modern reinforced concrete buildings,this paper carried out tests on the concrete compressive strength of 12 Chinese modern concrete buildings,the mechanical properties of 66 rebars from different Chinese modern concrete buildings,and the concrete cover thickness of 9 Chinese modern concrete buildings,and the actual material properties and structural configurations have been obtained. Then,the comparison on calculation methods include the Chinese original calculation method,the Chinese present calculation method,the American present calculation method and the European present calculation method is studied with case analysis method.The results show that the Chinese original calculation method of shear behavior of reinforced concrete beams is based on the allowable stress calculation method,and the design safety factors are 3. 55- 4. 00. The standard value of the compressive strength of concrete cubes is 8. 48 MPa,the standard value of the concrete tensile strength is 1.20 MPa,the standard value of the yield strength of rectangular rebars is 229. 56 MPa,and the standard value of the yield strength of round rebars is 276. 82 MPa. The average value of the concrete cover thickness of beams and columns is 35.96 mm. In term of calculation area of hoop rebars of reinforced concrete beams,without considering earthquake loads,the Chinese original structural calculation method is safer than the Chinese present structural calculation method,but is more unsafe than the American present structural calculation method and the European present structural calculation method. The results can provide the support for structural safety assessments and repair designs of Chinese modern reinforced concrete buildings.

Alteration of chemical behavior of L-ascorbic acid in combination with nickel sulfate at different pH solutions in vitro

Objective:To evaluate the alteration of chemical behavior of L-ascorbic acid(vitamin C) with metal ion(nickel) at different pH solutions in vitro.Methods:Spectra of pure aqueous solution of L-ascorbic acid(E mark) compound and NiSO_4(H_2O)(sigma USA) were evaluated by UV visible spectrophotometer.Spectral analysis of L-ascorbic acid and nickel at various pH(2.0, 7.0,7.4 and 8.6) at room temperature of 29℃ was recorded.In this special analysis,combined solution of L-ascorbic acid and nickel sulfate at different pH was also recorded.Results:The result revealed that λ_(max)(peak wavelength of spectra) of L-ascorbic acid at pH 2.0 was 289.0 run whereas at neutral pH 7.0,λ_(max) was 29S.4 run.In alkaline pH 8.6,λ_(max) was 295.4 nm and at pH 7.4 the λ_(max) of L-ascorbic acid remained the same as 295.4 nm.Nickel solution at acidic pH 2.0 was 394.5 nm,whereas at neutral pH 7.0 and pH 7.4 were the same as 394.5 nm.But at alkaline pH 8.6,λ_(max) value of nickel sulfate became 392.0 nm.The combined solution of L-ascorbic acid and nickel sulfate(6 mg/mL each) at pH 2.0 showed 292.5 nm and 392.5 nm,respectively whereas at pH 7.0,L-ascorbic acid showed 296.5 nm and nickel sulfate showed 391.5 nm.At pH 7.4,L-ascorbic acid showed 297.0 nm and nickel sulfate showed 394.0 nm in the combined solution whereas at pH 8.6(alkaline) L-ascorbic acid and nickel sulfate were showing 297.0 and 393.5 nm,respectively. Conclusions:Results clearly indicate an altered chemical behavior of L-ascorbic acid either alone or in combination with nickel sulfate in vitro at different pH.Perhaps oxidation of L-ascorbic acid to L-dehydro ascorbic acid via the free radical(HSc*) generation from the reaction of H,ASc + Ni(Ⅱ) is the cause of such alteration of λ_(max),value of L-ascorbic acid in the presence of metal nickel.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

Experimental seismic behavior of squat shear walls with precast concrete hollow moulds

This study proposes an innovative precast shear wall system, called an EVE precast hollow shear wall structure (EVE-PHSW). Precast panels in EVE-PHSW are simultaneously precast with vertical and horizontal holes. Noncontact lap splices of rebars are used in vertical joints connecting adjacent precast panels for automated prefabrication and easy in situ erection. The seismic behavior of EVE walls was examined through a series of tests on six wall specimens with aspect ratios of 1.0~1.3. Test results showed that EVE wall specimens with inside cast-in situ concrete achieved the desired “strong bending and weak shear” and failed in shear mode. Common main diagonal cracks and brittle shear failure in squat cast-in situ walls were prevented. Inside cast-in situ concrete could signifi cantly improve the shear strength and stiff ness of EVE walls. The details of boundary elements (cast-in situ or prefabricated) and vertical joints (contiguous or spaced) had little eff ect on the global behavior of EVE walls. Noncontact lap splices in vertical joints could enable EVE walls to exhibit stable load-carrying capacity through extensive deformations. Evaluation on design codes revealed that both JGJ 3-2010 and ACI 318-14 provide conservative estimation of shear strength of EVE walls, and EVE walls achieved shear strength reserves comparative to cast-in situ walls. The recommended eff ective stiff ness for cast-in situ walls in ASCE 41-17 appeared to be appropriate for EVE walls.

The Mental Protection System for Protective Behaviors: The Social Brain and the Mental Immune System

The physical protection system of the body consists of the protective organs for vulnerable body parts-functions and the protective countermeasures against invaders (pathogens), but to survive, the body also requires the protective social groups for vulnerable social members-functions and the protective instinctive mental countermeasures against adversities such as hardship, danger, and unfamiliarity-uncertainty. As a result, this paper proposes that the mental protection system of the body consists of the social brain to set up the protective social groups for vulnerable social members-functions and the mental immune system to produce the protective mental countermeasures against adversities. This paper proposes that from the social brain, the protective social groups include alliance group for vulnerable individuals, kinship-friendship group for vulnerable children, interdependent specialists group for vulnerable pregnant females, territorial group for social boundary, connective group for social connection, and competitive group for social competition. From the mental immune system, the mental protective countermeasures include comforter against hardship, hyperactivity against danger, phobia against unfamiliarity-uncertainty, and rationality against unfamiliarity-uncertainty. The overactive mental immune system causes mental allergies and auto immune diseases as personality-mental disorders against ubiquitous harmful and harmless perceived adversities, correlating to physical allergies and auto immune diseases against ubiquitous harmful and harmless detected invaders. The mental protection system also produces personality traits, social moralities, social organizations, social systems, religions, and cultures as described in this paper. The mental protective system is the source of protective behaviors.

Very High Cycle Fatigue Behaviors and Surface Crack Growth Mechanism of Hydrogen-Embrittled AISI 304 Stainless Steels

The influence of hydrogen embrittlement on the fatigue behaviors of AISI 304 stainless steel is investigated. The fatigue endurance limits of the untreated and hydrogen-embrittled materials were almost the same at 400 MPa, and hydrogen embrittlement had little influence even though the sample contained about 8.1 times more hydrogen. Thus, the sensitivity of hydrogen gas in this material is very low. A surface crack initiation, growth, coalescence, and micro ridge model is proposed in this study. Slip line formation?⇒microcrack formation?⇒increases in the crack width, and blunting of the crack tip as it grows?⇒formation of many slip lines because of deformation in the shear direction?⇒growth of the crack in the shear direction, forming micro ridges, coalescence with adjacent cracks ⇒?continuous initiation, growth, coalescence, and ridge formation of surface cracks and specimen breakage.

Efficiency of employing fiber-based finite-length plastic hinges in simulating the cyclic and seismic behavior of steel hollow columns compared with other common modelling approaches

The accuracy and effi ciency of the modelling techniques utilized to model the nonlinear behavior of structural components is a signifi cant issue in earthquake engineering. In this study, the suffi ciency of three diff erent modelling techniques that can be employed to simulate the structural behavior of columns is investigated. A fi ber-based fi nite length plastic hinge (FB-FLPH) model is calibrated in this study. In order to calibrate the FB-FLPH model, a novel database of the cyclic behavior of hollow steel columns under simultaneous axial and lateral loading cycles with varying amplitudes is used. By employing the FB-FLPH model calibrated in this study, the interaction of the axial force and the bending moment in columns is directly taken into account, and the deterioration in the cyclic behavior of these members is implicitly considered. The superiority of the calibrated FB-FLPH modelling approach is examined compared with the cases in which conventional fi ber-based distributed plasticity and concentrated plasticity models are utilized. The effi ciency of the enumerated modelling techniques is probed when they are implemented to model the columns of a typical special moment frame in order to prove the advantage of the FB-FLPH modelling approach.

Awareness, self-management behaviors, health literacy and kidney function relationships in specialty practice

AIM To determine the relationship between chronic kidney disease(CKD) awareness(CKD-A), self-management behaviors(CKD-SMB) knowledge, performance of CKDSMBs, health literacy(HL) and kidney function. METHODS Participants were eligible patients attending an outpatient nephrology clinic. Participants were administered: Newest Vital Sign to measure HL, CKD self-managementknowledge tool(CKD-SMKT) to assess knowledge, past performance of CKD-SMB, CKD-A. Estimated GFR(e GFR) was determined using the MDRD-4 equation. Duration of clinic participation and CKD cause were extracted from medical charts. RESULTS One-hundred-fifty patients participated in the study. e GFRs ranged from 17-152 m L/min per 1.73 m2. Majority(83%) of respondents had stage 3 or 4 CKD, low HL(63%), and were CKD aware(88%). Approximately 40%(10/25) of patients in stages 1 and 2 and 6.4%(8/125) in stages 3 and 4 were unaware of their CKD. CKD-A differed with stage(P < 0.001) but not by HL level, duration of clinic participation, or CKD cause. Majority of respondents(≥ 90%) correctly answered one or more CKD-SMKT items. Knowledge of one behavior, "controlling blood pressure" differed significantly by CKD-A. CKD-A was associated with past performance of two CKD-SMBs, "controlling blood pressure"(P = 0.02), and "keeping healthy body weight"(P = 0.01). Adjusted multivariate analyses between CKD-A and:(1) HL; and(2) CKD-SMB knowledge were nonsignificant. However, there was a significant relationship between CKD-A and kidney function after controlling for demographics, HL, and CKD-SMB(P < 0.05). CONCLUSION CKD-A is not associated with HL, or better CKD-SMBs. CKD-A is significantly associated with kidney function and substantially lower e GFR, suggesting the need for focused patient education in CKD stages 1.

Experimental Study on Seismic Behavior ofSeismic-damaged Lateral Joints in CompositeFrame Consisting of CFSST Columns and SteelBeams Strengthened with Enclosed ReinforcedConcrete

A new composite strengthening method of seismic-damaged lateral joints in composite frame consisting of Concrete-Filled SquareSteel Tubes （CFSST） columns and steel beams strengthened with enclosed Reinforced Concrete （RC） at the ends of columns andwelding steel plates at the ends of beams was presented. Based on the current design specifications, one half scaled models of 4lateral joints in composite frame consisting of CFSST columns and steel beams were designed and manufactured. One model wasoriginal control specimen, one was strengthened by enclosed RC, and the others were strengthened after pre-damage. The destructiontests under lateral cyclic load on the models were carried. The effectiveness of seismic-damaged joints strengthened with enclosedRC and the reinforcement effect on different levels of seismic damage were studied. The test results show that seismic- damagedjoints in composite frame consisting of CFSST columns and steel beams strengthened with enclosed RC meets the strongcolumn-weak beam joints requirement of seismic design, and the failure modes are of all joints are the bending failure of steel beam.The reinforcement with enclosed RC has a significant on increasing the ultimate capacity and the seismic behaviors of joints. Thestudy indicated the rehabilitated joints recover the level of their original seismic performances before seismic damage in a certainextent damage level. Based on the test data, namely the ultimate capacity, limit displacement, ductility, the energy consumptioncoefficient, limit displacementthe strengthening method of seismic-damaged joints by strengthened with enclosed RC is an effectivemethod for seismic strengthening.

Coke behavior with H_(2)O in a hydrogen-enriched blast furnace:A review

Hydrogen-enriched blast furnace ironmaking has become an essential route to reduce CO_(2)emissions in the ironmaking process.However,hydrogen-enriched reduction produces large amounts of H_(2)O,which places new demands on coke quality in a blast furnace.In a hydrogen-rich blast furnace,the presence of H_(2)O promotes the solution loss reaction.This result improves the reactivity of coke,which is 20%-30%higher in a pure H_(2)O atmosphere than in a pure CO_(2)atmosphere.The activation energy range is 110-300 kJ/mol between coke and CO_(2)and 80-170 kJ/mol between coke and H_(2)O.CO_(2)and H_(2)O are shown to have different effects on coke degradation mechanisms.This review provides a comprehensive overview of the effect of H_(2)O on the structure and properties of coke.By exploring the interactions between H_(2)O and coke,several unresolved issues in the field requiring further research were identified.This review aims to provide valuable insights into coke behavior in hydrogen-rich environments and promote the further development of hydrogen-rich blast furnace ironmaking processes.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.