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世界核心医学期刊文摘(神经病学分册) 2006年第2期 16-17页

作者： Scarmeas N. brandt j. Albert M. 郭俊 Columbia University Medical Center Presbyterian Hospital 622 W 168th St New York NY 10032 United States.Dr

Background: Delusions and hallucinations are common in Alzheimer disease (AD) and there are conflicting reports regarding their ability to predict cognitive decline, functional decline, and institutionalization. According to all previous literature, they are not associated with mortality. Obj.ctive: To examine whethe r the presence of delusions or hallucinations has predictive value for important outcomes in AD. Design, Setting, and Participants: A total of 456 patients with AD at early stages (mean Folstein Mini-Mental State Examination [MMSE] score of 21 of 30 at entry) were recruited and followed up semiannually for up to 14 years (mean, 4.5 years) in 5 university-based AD centers in the United States and Europe. Using the Columbia University Scale for Psychopathology in AD (administered every 6 months, for a total of 3266 visit-assessments, average of 7.2 per patient), the presence of delusions and hallucinations was extracted and examined as time-dependent predictors in Cox models. The models controlled for cohort effect, recruitment center, informant status, sex, age, education, a comorbidity index, baseline cognitive and baseline functional performance, behavioral sympt oms, and use of neuroleptics and cholinesterase inhibitors. Main Outcome Measure s: Cognitive (Columbia MMSE score of ≤20/57 [approximate Folstein MMSE score o f ≤10/30]), functional (Blessed Dementia Rating Scale [parts I and II] score of ≥10), institutionalization equivalent index, and death. Results: During the full course of follow-up, 38%of patients reached the cognitive, 41%the functiona l, 54%the institutionalization, and 49%the mortality end point. Delusions were noted for 34%of patients at baseline and 70%at any evaluation. Their presence was associated with increased risk for cognitive (risk ratio [RR], 1.50; 95%confidence interval [CI], 1.07-2.08) and functional decline (RR, 1.41; 95%CI, 1.02-1.94). Hallucinations were present in 7%of patients at initial visit and in 33%at any visit

关键词：阿尔茨海默病习惯化患者 AD 认知功能机能衰退幻觉感觉障碍

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阿尔茨海默病的运动症状预示不良转归

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世界核心医学期刊文摘（神经病学分册） 2005年第9期1卷 53-54页

作者： Scarmeas N. Albert M. brandt j. 陈海 Columbia University Medical Center 622 West 168th street New York NY 10032 United StatesDr.

Obj.ctive: To examine whether the presence of motor signs has predictive value for important outcomes in Alzheimer disease (AD). Methods: A total of 533 patients with AD at early stages (mean Folstein Mini-Mental State Examination [MMSE] 21/30 at entry) were recruited and followed semiannually for up to 13.1 years ( mean 3) in five University-based AD centers in the United States and European U nion. Four outcomes, assessed every 6 months, were used in Cox models: cognitive endpoint (Columbia Mini-Mental State Examination ≤20/57 [～MMSE ≤10/30]), functional endpoint (Blessed Dementia Rating Scale ≥10), institutionalization equ ivalent index, and death. Using a standardized portion of the Unified PD Rating Scale (administered every 6 months for a total of 3,149 visit-assessments, aver age 5.9 per patient), the presence of motor signs, as well as of individual moto r sign domains, was examined as time-dependent predictor. The models controlled for cohort, recruitment center, sex, age, education, a comorbidity index, and b aseline cognitive and functional performance. Results: A total of 39%of the pat ients reached the cognitive, 41%the functional, 54%the institutionalization, a nd 47%the mortality endpoint. Motor signs were noted for 14%of patients at bas eline and for 45%at any evaluation. Their presence was associated with increase d risk for cognitive decline (RR, 1.72; 95%CI, 1.24 to 2.38), functional declin e (1.80 [1.33 to 2.45]), institutionalization (1.68 [1.26 to 2.25]), and dea th (1.38 [1.05 to 1.82]). Tremor was associated with increased risk for reaching the cognitive and bradykinesia for reaching the functional endpoints. Posturalgait abnormalities carried increased risk for institutionalization and mortality. Fas ter rates of motor sign accumulation were associated with increased risk for all outcomes. Conclusions: Motor signs predict cognitive and functional decline, in stitutionalization, and mortality in Alzheimer disease. Different motor sign dom ain

关键词：运动症状阿尔茨海默病认知功能运动迟缓功能行为结构可靠性预后指标随访过程预测价值共济失调

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海马硬化性痴呆更类似于额颞性痴呆而非阿尔茨海默病

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世界核心医学期刊文摘（神经病学分册） 2005年第1期1卷 50-51页

作者： Blass D.M. Hatanpaa K.j. brandt j. 张玉龙 j.hns Hopkins Hospital Dept. of Psychiat. and Behav. Sci. 600 North Wolfe Street Baltimore MD 21287- 7279 United States Dr.

Obj.ctive: To characterize the clinical course of patholo gically diagnosed hippocampal sclerosis dementia (HSD). Background: Dementia associated with HSD is incompletely characterized. Previous studies suggest similarities to both Alzheimer disease (AD) and frontotemporal dementia (FTD). Methods: Case control analysis of the clinical course of patients with HSD, FTD, and AD from a neuropathology autopsy series conducted by a university hospital. Case histories were reviewed. Cumulative prevalence of behavioral, cognitive, psychiatric, and language symptoms were compared between groups, as was time of symptom onset. Clinical diagnostic criteria for FTD and AD were applied to case histories. Sensitivity and specificity of clinical FTD diagnostic criteria (Report of theWork Group on FTD and Pick’ s disease) were computed. Results: Cumulative prevalence of symptoms in HSD wasmost similar to that of FTD and differed from AD. Behavioral abnormalities such as decreased grooming and inappropriate behavior were more prevalent in HSD and FTD than AD. Hyperorality, inappropriate behavior, and decreased interest had earlier onset in HSD and FTD. Cognitive symptoms of disorientation, dyscalculia, apraxia, and agnosia were more prevalent in AD, as were psychiatric symptoms of hallucinations, delusions, and aggression. Most HSD patients met diagnostic criteria for FTD. Criteria sensitivity was 64.0% and specificity was 73.7% . Conclusions: FTD is a clinical syndrome associated with heterogeneous neuropathology. The clinical course of HSD is more similar to that of FTD than AD. These findings, together with the neuropathologic data presented in the accompanying article, support expanding the scope of FTD (Pick complex) to include HSD.

关键词：额颞性痴呆阿尔茨海默病硬化性认知障碍额颞痴呆累积发病率病理学确诊神经病理临床综合征病例对照分析

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Editorial for the Special Issue on Emerging Technology and Advanced Application of Nondestructive Detection for Power Equipment

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Chinese j.urnal of Electrical Engineering 2024年第1期10卷 1-2页

作者： Li j. Mei H. Li S. Tianj.n University China Tsinghua Shenzhen International Graduate School China Lanzhou j.aotong University China

The rapid development of clean and renewable energy technologies represented by wind and solar energy,as well as the continuous growth of load demand,have put forward higher requirements for the safe operation of the power *** performance of power equipment based on traditional dielectric materials is often limited by various defects generated during molding,transportation,assembly,and operation,which cannot meet the reliability requirements of power systems in the new *** detection is an essential technical method in industrial development,which can detect and evaluate the defect and damage status of in-service equipment,key components,and materials without damaging the obj.ct.

关键词： service Application operation

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Machine Fault Diagnosis Using Audio Sensors Data and Explainable AI Techniques-LIME and SHAP

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Computers, Materials and Continua 2024年第3期80卷 3463-3484页

作者： Zereen A.N. Das A. Uddin j. School of Data and Sciences Brac University Dhaka 1212 Bangladesh j. KIM College of Future Studies Endicott College Woosong University Daejeon 300-718 South Korea AI and Big Data Department Endicott College Woosong University Daejeon 300-718 South Korea

Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated maj.r potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning algorithms. Extreme Gradient Boosting, Support Vector Machine, Gaussian Naive Bayes, and Random Forest classifiers are used alongside Logistic Regression (LR) as a baseline model because their efficacy and simplicity are evaluated thoroughly with empirical analysis. The XAI is used as a targeted feature selection technique to select among 29 features of the time and frequency domain. The XAI approach is lightweight, trained with only targeted features, and achieved similar results as the traditional approach. The accuracy without XAI on baseline LR is 79.57%, whereas the approach with XAI on LR is 80.28%. Copyright © 2024 The Authors. Published by Tech Science Press.

关键词： Explainable AI feature selection machine fault diagnosis machine learning

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A New Method for Deriving High-Vertical-Resolution Wind Vector Data from the L-Band Radar Sounding System in China

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Advances in Atmospheric Sciences 2024年第11期41卷 2192-2202页

作者： Yuan F. Zhou Z. Liao j. National Meteorological Information Center China Meteorological Administration Beijing 100081 China

High-vertical-resolution radiosonde wind data are highly valuable for describing the dynamics of the meso- and microscale atmosphere. However, the current algorithm used in China’s L-band radar sounding system for calculating high-vertical-resolution wind vectors excessively smooths the data, resulting in significant underestimation of the calculated kinetic energy of gravity waves compared to similar products from other countries, which greatly limits the effective utilization of the data. To address this issue, this study proposes a novel method to calculate high-vertical-resolution wind vectors that utilizes the elevation angle, azimuth angle, and slant range from L-band radar. In order to obtain wind data with a stable quality, a two-step automatic quality control procedure, including the RMSE-F (root-mean-square error F) test and elemental consistency test are first applied to the slant range data, to eliminate continuous erroneous data caused by unstable signals or radar malfunctions. Then, a wind calculation scheme based on a sliding second-order polynomial fitting is utilized to derive the high-vertical-resolution radiosonde wind vectors. The evaluation results demonstrate that the wind data obtained through the proposed method show a high level of consistency with the high-resolution wind data observed using the Vaisala Global Positioning System and the data observed by the new Beidou Navigation Sounding System. The calculation of the kinetic energy of gravity waves in the recalculated wind data also reaches a level comparable to the Vaisala observations. © Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press 2024.

关键词： high-vertical-resolution radiosonde wind vectors L-band radar sounding system polynomial fitting quality control upper air

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Importance of timely and adequate source control in sepsis and septic shock

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j.urnal of Intensive Medicine 2024年第3期4卷 281-286页

作者： j.n j.De Waele Department of Intensive Care Medicine Ghent University HospitalGhentBelgium Department of Internal Medicine and Pediatrics Faculty of Medicine and Health SciencesGhent UniversityGhentBelgium

Source control is defined as the physical measures undertaken to eliminate the source of infection and control ongoing contamination, as well as restore anatomy and function at the site of infection. It is a key component of the management of patients with sepsis and septic shock and one of the main determinants of the outcome of infections that require source control. While not all infections may require source control, it should be considered in every patient presenting with sepsis;it is applicable and necessary in numerous infections, not only those occurring in the abdominal cavity. Although the biological rationale is clear, several aspects of source control remain under debate. The timing of source control may impact outcome;early source control is particularly relevant for patients with abdominal infections or necrotizing skin and soft tissue infections, as well as for those with more severe disease. Percutaneous procedures are increasingly used for source control;nevertheless, surgery—tailored to the patient and infection—remains a valid option for source control. For outcome optimization, adequate source control is more important than the strategy used. It should be acknowledged that source control interventions may often fail, posing a challenge in this setting. Thus, an individualized, multidisciplinary approach tailored to the infection and patient is preferable.

关键词： Sepsis Septic shock Source control Drainage Debridement

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Error field penetration in j.TEXT tokamak based on two-fluid drift-MHD model

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Chinese Physics B 2024年第4期33卷 545-551页

作者：王文徐涛张仪 the j.TEXT team State Key Laboratory of Advanced Electromagnetic Technology International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma PhysicsSchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China

An externally generated resonant magnetic perturbation can induce complex non-ideal MHD responses in their resonant *** have studied the plasma responses using Fitzpatrick's improved two-fluid model and program *** calculated the error field penetration threshold for *** addition,we find that the island width increases slightly as the error field amplitude increases when the error field amplitude is below the critical penetration ***,the island width suddenly j.mps to a large value because the shielding effect of the plasma against the error field disappears after the *** scanning the natural mode frequency,we find that the shielding effect of the plasma decreases as the natural mode frequency ***,we obtain the m/n=2/1 penetration threshold scaling on density and temperature.

关键词： plasma responses drift-MHD model error field penetration

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Value Function Mechanism in WSNs-Based Mango Plantation Monitoring System

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Computers, Materials and Continua 2024年第3期80卷 3733-3759页

作者： Sung W.-T. Isa I.G.T. Hsiao S.-j. Department of Electrical Engineering National Chin-Yi University of Technology Taichung 411030 Taiwan Department of Informatics Management Politeknik Negeri Sriwijaya Palembang 30139 Indonesia Department of Information Technology Takming University of Science and Technology Taipei City 11451 Taiwan

Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks (“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: “optimal,” “sub-optimal,” or “not-optimal” conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system. A value function-based will be employed to perform DRL model called deep Q-network (DQN) which contributes in optimizing the future reward and performing the precise decision recommendation to the agent and system behavior. The WSNs experiment result indicates the system’s accuracy by capturing the real-time environment parameters is 98.39%. Meanwhile, the results of comparative accuracy model experiments of the proposed DQN, individual Q-learning, uniform coverage (UC), and Naïve Bayes classifier (NBC) are 97.60%, 95.30%, 96.50%, and 92.30%, respectively. From the results of the comparative experiment, it can be seen that the proposed DQN used in the study has the most optimal accuracy. Testing with 22 test scenarios for “optimal,” “sub-optimal,” and “not-optimal” conditions was carried out to ensure the system runs well in the real-world data. The accuracy percentage which is generated from the real-world data reaches 95.45%. From the results of the cost analysis, the system can provide a low-cost system compared to the conventional system. Copyright © 2024 The Authors. Published by Tech Science Press.

关键词： deep Q-network (DQN) deep reinforcement learning (DRL) Intelligent monitoring system wireless sensor networks (WSNs)

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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

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Computers, Materials and Continua 2024年第3期80卷 4917-4945页

作者： Hu X. Tang Y. Pedrycz W. j.ang j. j.ang Y. The School of Computer Science and Engineering Southeast University Nanjing 211189 China The School of Computer and Information Hefei University of Technology Hefei 230601 China The Department of Electrical and Computer Engineering University of Alberta Edmonton T6R 2V4 AB Canada The Systems Research Institute Polish Academy of Sciences Warsaw 00-901 Poland The School of Information Engineering Nanjing University of Finance and Economics Nanjing 210023 China

Traditional Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) clustering algorithms are data-driven, and their obj.ctive function minimization process is based on the available numeric data. Recently, knowledge hints have been introduced to form knowledge-driven clustering algorithms, which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints. However, these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself; they require the assistance of evaluation indices. Moreover, knowledge hints are usually used as part of the data structure (directly replacing some clustering centers), which severely limits the flexibility of the algorithm and can lead to knowledge misguidance. To solve this problem, this study designs a new knowledge-driven clustering algorithm called the PCM clustering with High-density Points (HP-PCM), in which domain knowledge is represented in the form of so-called high-density points. First, a new data density calculation function is proposed. The Density Knowledge Points Extraction (DKPE) method is established to filter out high-density points from the dataset to form knowledge hints. Then, these hints are incorporated into the PCM obj.ctive function so that the clustering algorithm is guided by high-density points to discover the natural data structure. Finally, the initial number of clusters is set to be greater than the true one based on the number of knowledge hints. Then, the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination mechanism. Through experimental studies, including some comparative analyses, the results highlight the effectiveness of the proposed algorithm, such as the increased success rate in clustering, the ability to determine the optimal cluster number, and the faster convergence speed. Copyright © 2024 The Authors. Published by

关键词： Fuzzy C-Means (FCM) fuzzy logic knowledge-driven machine learning optimal number of clusters possibilistic clustering

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