Cutoff scores for the “Interest game”, an application for the assessment of diminished interest in neurocognitive disorders

Diminished interest is a core feature of apathy that shows high prevalence in people with Mild and Major Neurocognitive disorders (NCD). In the clinical setting, apathy is mainly assessed using clinical scales and questionnaires, but new technologies are starting to be employed to complement classical instruments. Here, we explored the performance of the “Interest game,” a ludic application that assesses personal interests, in discriminating between persons with and without diminished interest based on the Apathy Diagnostic Criteria. Two hundred and twenty-seven elderly participants (56 healthy controls, 118 persons with mild-NCD, and 53 with major-NCD) completed the Interest game and were assessed by clinicians concerning the presence and the severity of apathy. Results showed that the application scores varied with the presence of apathy, the type of disorder, and the education level. Cutoff scores calculated for persons with Mild-NCD resulted in a sensitivity of 0.68 and a specificity of 0.65 for the main score index, suggesting the interest of employing this application in the clinical setting to complement the classical assessment

Detection of activities of daily living impairment in Alzheimer's disease and mild cognitive impairment using information and communication technology

International audienceBackground: One of the key clinical features of Alzheimer's disease (AD) is impairment in daily functioning. Patients with mild cognitive impairment (MCI) also commonly have mild problems performing complex tasks. Information and communication technology (ICT), particularly techniques involving imaging and video processing, is of interest in order to improve assessment. The overall aim of this study is to demonstrate that it is possible using a video monitoring system to obtain a quantifiable assessment of instrumental activities of daily living (IADLs) in AD and in MCI. Methods: The aim of the study is to propose a daily activity scenario (DAS) score that detects functional impairment using ICTs in AD and MCI compared with normal control group (NC). Sixty-four participants over 65 years old were included: 16 AD matched with 10 NC for protocol 1 (P1) and 19 MCI matched with 19 NC for protocol 2 (P2). Each participant was asked to undertake a set of daily tasks in the setting of a "smart home" equipped with two video cameras and everyday objects for use in activities of daily living (8 IADLs for P1 and 11 for P2, plus 4 temporal execution constraints). The DAS score was then computed from quantitative and qualitative parameters collected from video recordings. Results: In P1, the DAS score differentiated AD (DASAD,P1 = 0.47, 95% confidence interval [CI] 0.38-0.56) from NC (DASNC,P1 = 0.71, 95% CI 0.68-0.74). In P2, the DAS score differentiated MCI (DASMCI,P2 = 0.11, 95% CI 0.05-0.16) and NC (DASNC,P2 = 0.36, 95% CI 0.26-0.45). Conclusion: In conclusion, this study outlines the interest of a novel tool coming from the ICT world for the assessment of functional impairment in AD and MCI. The derived DAS scores provide a pragmatic, ecological, objective measurement which may improve the prediction of future dementia, be used as an outcome measurement in clinical trials and lead to earlier therapeutic intervention

A Batch Optimization Sofware for diffusion area scheduling in semiconductor manufacturing

International audienc

Impact of qualification management on scheduling in semiconductor manufacturing

International audienc

A Batch Optimization Solver for diffusion area scheduling in semiconductor manufacturing

16 pagesThis paper presents a method and a software for solving a batching and scheduling problem in the diffusion area of a semiconductor plant, the ATMEL fabrication unit in Rousset, France. The diffusion area is one of the most complex area in the fab. A significant number of lots has to be processed while satisfying complex equipment process and line management constraints. The purpose of this study is to investigate approaches to group lots in batches, to assign the batches on the equipment and to sequence these batches. Three indicators are used to evaluate the quality of a solution: the total number of moves, the batching coefficient and the X-factor. The problem is modeled through the disjunctive graph formulation. A constructive algorithm is proposed and improvement procedures based on iterative sampling and simulated annealing are developed. Computational experiments, carried out on actual industrial problem instances, show the ability of iterative sampling to improve significantly the initial solution. The proposed simulated annealing method brings in turn important enhancements to the results of iterative sampling. The software based on these methods, named Batch Optimization Solver (BOS), is currently used in the diffusion area of ATMEL. The disjunctive graph model allows in addition a high level of interactivity with the decision makers

Suèvres "Les Sables"(Loir-et-Cher) : un habitat gallo-romain et un four de tuilier du Ier s. aux marges d'une agglomération secondaire antique (10-120 ap. J.-C.)

A group of 1st century AD houses was discovered at the western limit of the small ancient town of Suèvres (Loir-et-Cher, France), along the road between Orléans, Tours and Blois which is thought to be Roman. It includes three parcels of land each of a minimum area of 900 to 1,500 m2 and containing domestic remains (cellars, wells, garbage dumps). Located on the outskirts of the town, this series of parcels follows the general directions defined by the road. Although this road was not precisely dated during the excavation, the building techniques used in its oldest phase are definitely of Antiquity. A tile kiln was also discovered along the route. It seems to have been mainly devoted to the production of tegulae, which were used locally both in the houses excavated on the site and inthe nearby town. The kiln’s production was short lived as was the settlement, which was gradually abandoned between 30/40 and 60/70 AD, and totally deserted before 120 AD.Un ensemble d’habitations du Ier s. a été mis au jour à la limite occidentale de l’agglomération secondaire antique de Suèvres (Loir-et-Cher), le long de la voie Orléans-Blois-Tours réputée romaine. Il regroupe trois parcelles, de 900 à 1 500 m2 de superficie minimale, qui comportent chacune des vestiges d’habitat (celliers, cave, puits, dépotoirs). Localisé à la périphérie de l’agglomération, cet ensemble parcellaire s’organise en fonction des principales orientations déterminées par la voie. Celle-ci n’a puêtre datée de façon absolue par la fouille, mais son état le plus ancien se réfère au mode de construction romain. Un four de tuilier a été découvert le long de la voie. Sa production semble surtout consacrée à des tegulae, probablement utilisées en premier lieu pour les constructions présentes sur le site et dans l’agglomération. Elle est de courte durée, à l’image de l’occupation du secteur, qui est majoritairement abandonné entre 30/40 et 60/70 ap. J.-C., et au plus tard vers 120 ap. J.-C

Batching, Scheduling, Disjunctive graph, Local search, Simulated Annealing, Wafer fabrication

International audienceThis paper proposes an efficient heuristic algorithm for solving a complex batching and scheduling problem in a diffusion area of a semiconductor plant. Diffusion is frequently bottleneck in the plant and also one of the most complex areas in terms of number of machines, constraints to satisfy and the large number of lots to manage. The purpose of this study is to investigate an approach to group lots in batches and to schedule these batches on machines. The problem is modeled and solved using a disjunctive graph representation. A constructive algorithm is proposed and improvement procedures based on iterative sampling and Simulated Annealing are developed. Computational experiments, carried out on actual industrial problem instances, show the ability of the iterative sampling algorithms to significantly improve the initial solution, and that Simulated Annealing enhances the results. Furthermore, our algorithm compares favorably to an algorithm of the literature on a simplified version of our problem. The constructive algorithm has been embedded in a software and is currently being used in a semiconductor plant

Measuring Stress in Health Professionals Over the Phone Using Automatic Speech Analysis During the COVID-19 Pandemic: Observational Pilot Study

International audienceBackground During the COVID-19 pandemic, health professionals have been directly confronted with the suffering of patients and their families. By making them main actors in the management of this health crisis, they have been exposed to various psychosocial risks (stress, trauma, fatigue, etc). Paradoxically, stress-related symptoms are often underreported in this vulnerable population but are potentially detectable through passive monitoring of changes in speech behavior. Objective This study aims to investigate the use of rapid and remote measures of stress levels in health professionals working during the COVID-19 outbreak. This was done through the analysis of participants’ speech behavior during a short phone call conversation and, in particular, via positive, negative, and neutral storytelling tasks. Methods Speech samples from 89 health care professionals were collected over the phone during positive, negative, and neutral storytelling tasks; various voice features were extracted and compared with classical stress measures via standard questionnaires. Additionally, a regression analysis was performed. Results Certain speech characteristics correlated with stress levels in both genders; mainly, spectral (ie, formant) features, such as the mel-frequency cepstral coefficient, and prosodic characteristics, such as the fundamental frequency, appeared to be sensitive to stress. Overall, for both male and female participants, using vocal features from the positive tasks for regression yielded the most accurate prediction results of stress scores (mean absolute error 5.31). Conclusions Automatic speech analysis could help with early detection of subtle signs of stress in vulnerable populations over the phone. By combining the use of this technology with timely intervention strategies, it could contribute to the prevention of burnout and the development of comorbidities, such as depression or anxiety

Validation of an Automatic Video Monitoring System for the Detection of Instrumental Activities of Daily Living in Dementia Patients

International audienceOver the last few years, the use of new technologies for the support of elderly people and in particular dementia patients received increasing interest. We investigated the use of a video monitoring system for automatic event recognition for the assessment of instrumental activities of daily living (IADL) in dementia patients. Participants (19 healthy subjects (HC) and 19 mild cognitive impairment (MCI) patients) had to carry out a standardized scenario consisting of several IADLs such as making a phone call while they were recorded by 2D video cameras. After the recording session, data was processed by a platform of video signal analysis in order to extract kinematic parameters detecting activities undertaken by the participant. We compared our automated activity quality prediction as well as cognitive health prediction with direct observation annotation and neuropsychological assessment scores. With a sensitivity of 85.31% and a precision of 75.90%, the overall activities were correctly automatically detected. Activity frequency differed significantly between MCI and HC participants (p < 0.05). In all activities, differences in the execution time could be identified in the manually and automatically extracted data. We obtained statistically significant correlations between manually as automatically extracted parameters and neuropsychological test scores (p < 0.05). However, no significant differences were found between the groups according to the IADL scale. The results suggest that it is possible to assess IADL functioning with the help of an automatic video monitoring system and that even based on the extracted data, significant group differences can be obtained

Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease

AbstractBackgroundTo evaluate the interest of using automatic speech analyses for the assessment of mild cognitive impairment (MCI) and early-stage Alzheimer's disease (AD).MethodsHealthy elderly control (HC) subjects and patients with MCI or AD were recorded while performing several short cognitive vocal tasks. The voice recordings were processed, and the first vocal markers were extracted using speech signal processing techniques. Second, the vocal markers were tested to assess their “power” to distinguish among HC, MCI, and AD. The second step included training automatic classifiers for detecting MCI and AD, using machine learning methods and testing the detection accuracy.ResultsThe classification accuracy of automatic audio analyses were as follows: between HCs and those with MCI, 79% ± 5%; between HCs and those with AD, 87% ± 3%; and between those with MCI and those with AD, 80% ± 5%, demonstrating its assessment utility.ConclusionAutomatic speech analyses could be an additional objective assessment tool for elderly with cognitive decline