Final environmental impact statement. Proposed rulemaking concerning passenger automotive average fuel economy. NHTSA technical report.

Mode of access: Internet.

Treatment patterns and health care resource utilization in a 1-year observational cohort study of outpatients with schizophrenia at risk of nonadherence treated with long-acting injectable antipsychotics

Purpose: To describe (1) the clinical profiles and the patterns of use of long-acting injectable (LAI) antipsychotics in patients with schizophrenia at risk of nonadherence with oral antipsychotics, and in those who started treatment with LAI antipsychotics, (2) health care resource utilization and associated costs. Patients and methods: A total of 597 outpatients with schizophrenia at risk of nonadherence, according to the psychiatrist's clinical judgment, were recruited at 59 centers in a noninterventional prospective observational study of 1-year follow-up when their treatment was modified. In a post hoc analysis, the profiles of patients starting LAI or continuing with oral antipsychotics were described, and descriptive analyses of treatments, health resource utilization, and direct costs were performed in those who started an LAI antipsychotic. Results: Therapy modifications involved the antipsychotic medications in 84.8% of patients, mostly because of insufficient efficacy of prior regimen. Ninety-two (15.4%) patients started an LAI antipsychotic at recruitment. Of these, only 13 (14.1%) were prescribed with first-generation antipsychotics. During 1 year, 16.3% of patients who started and 14.9% of patients who did not start an LAI antipsychotic at recruitment relapsed, contrasting with the 20.9% who had been hospitalized only within the prior 6 months. After 1 year, 74.3% of patients who started an LAI antipsychotic continued concomitant treatment with oral antipsychotics. The mean (median) total direct health care cost per patient per month during the study year among the patients starting any LAI antipsychotic at baseline was 1,407 ( 897.7). Medication costs (including oral and LAI antipsychotics and concomitant medication) represented almost 44%, whereas nonmedication costs accounted for more than 55% of the mean total direct health care costs. Conclusion: LAI antipsychotics were infrequently prescribed in spite of a psychiatrist-perceived risk of nonadherence to oral antipsychotics. Mean medication costs were lower than nonmedication costs.

Treatment patterns and health care resource utilization in a 1-year observational cohort study of outpatients with schizophrenia at risk of nonadherence treated with long-acting injectable antipsychotics

Purpose: To describe (1) the clinical profiles and the patterns of use of long-acting injectable (LAI) antipsychotics in patients with schizophrenia at risk of nonadherence with oral antipsychotics, and in those who started treatment with LAI antipsychotics, (2) health care resource utilization and associated costs. Patients and methods: A total of 597 outpatients with schizophrenia at risk of nonadherence, according to the psychiatrist's clinical judgment, were recruited at 59 centers in a noninterventional prospective observational study of 1-year follow-up when their treatment was modified. In a post hoc analysis, the profiles of patients starting LAI or continuing with oral antipsychotics were described, and descriptive analyses of treatments, health resource utilization, and direct costs were performed in those who started an LAI antipsychotic. Results: Therapy modifications involved the antipsychotic medications in 84.8% of patients, mostly because of insufficient efficacy of prior regimen. Ninety-two (15.4%) patients started an LAI antipsychotic at recruitment. Of these, only 13 (14.1%) were prescribed with first-generation antipsychotics. During 1 year, 16.3% of patients who started and 14.9% of patients who did not start an LAI antipsychotic at recruitment relapsed, contrasting with the 20.9% who had been hospitalized only within the prior 6 months. After 1 year, 74.3% of patients who started an LAI antipsychotic continued concomitant treatment with oral antipsychotics. The mean (median) total direct health care cost per patient per month during the study year among the patients starting any LAI antipsychotic at baseline was 1,407 ( 897.7). Medication costs (including oral and LAI antipsychotics and concomitant medication) represented almost 44%, whereas nonmedication costs accounted for more than 55% of the mean total direct health care costs. Conclusion: LAI antipsychotics were infrequently prescribed in spite of a psychiatrist-perceived risk of nonadherence to oral antipsychotics. Mean medication costs were lower than nonmedication costs.

Improving resource utilization in carrier ethernet technologies

Ethernet está empezando a pasar de las redes de área local a una red de transporte. Sin embargo, como los requisitos de las redes de transporte son más exigentes, la tecnología necesita ser mejorada. Esquemas diseñados para mejorar Ethernet para que cumpla con las necesidades de transporte se pueden categorizar en dos clases. La primera clase mejora solo los componentes de control de Ethernet (Tecnologías basadas en STP), y la segunda clase mejora tanto componentes de control como de encaminamiento de Ethernet (tecnologías basadas en etiquetas). Esta tesis analiza y compara el uso de espacio en las etiquetas de las tecnologias basadas en ellas para garantizar su escalabilidad. La aplicabilidad de las técnicas existentes y los estudios que se pueden utilizar para superar o reducir los problemas de escalabilidad de la etiqueta son evaluados. Además, esta tesis propone un ILP para calcular el óptimo rendimiento de las technologias basadas en STP y las compara con las basadas en etiquetas para ser capaz de determinar, dada una específica situacion, que technologia utilizar.

Food resource utilization of the skates Rioraja agassizii (Müller & Henle, 1841) and Psammobatis extenta (Garman, 1913) on the continental shelf off Ubatuba, South-eastern Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Treatment-resistant depression increases health costs and resource utilization

Objective: Major Depressive Disorder (MDD) is a debilitating condition with a marked social impact. The impact of MDD and Treatment-Resistant Depression (TRD+) within the Brazilian health system is largely unknown. The goal of this study was to compare resource utilization and costs of care for treatment-resistant MDD relative to non-treatment-resistant depression (TRD-). Methods: We retrospectively analyzed the records of 212 patients who had been diagnosed with MDD according to the ICD-10 criteria. Specific criteria were used to identify patients with TRD+. Resource utilization was estimated, and the consumption of medication was annualized. We obtained information on medical visits, procedures, hospitalizations, emergency department visits and medication use related or not to MDD. Results: The sample consisted of 90 TRD+ and 122 TRD-patients. TRD+ patients used significantly more resources from the psychiatric service, but not from non-psychiatric clinics, compared to TRD-patients. Furthermore, TRD+ patients were significantly more likely to require hospitalizations. Overall, TRD+ patients imposed significantly higher (81.5%) annual costs compared to TRD-patients (R$ 5,520.85; US$ 3,075.34 vs. R$ 3,042.14; US$ 1,694.60). These findings demonstrate the burden of MDD, and especially of TRD+ patients, to the tertiary public health system. Our study should raise awareness of the impact of TRD+ and should be considered by policy makers when implementing public mental health initiatives.

Program planning document highway vehicle alternative fuels utilization program (AFUP).

April 1978.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

The interaction between master production scheduling and detailed capacity requirements planning : closing the loop in manufacturing resource planning (MRPII) systems

The widespread implementation of Manufacturing Resource Planning (MRPII) systems in this country and abroad and the reported dissatisfaction with their use formed the initial basis of this piece of research which concentrates on the fundamental theory and design of the Closed Loop MRPII system itself. The dissertation concentrates on two key aspects namely; how Master Production Scheduling is carried out in differing business environments and how well the `closing of the loop' operates by checking the capcity requirements of the different levels of plans within an organisation. The main hypothesis which is tested is that in U.K. manufacturing industry, resource checks are either not being carried out satisfactorily or they are not being fed back to the appropriate plan in a timely fashion. The research methodology employed involved initial detailed investigations into Master Scheduling and capacity planning in eight diverse manufacturing companies. This was followed by a nationwide survey of users in 349 companies, a survey of all the major suppliers of Production Management software in the U.K. and an analysis of the facilities offered by current software packages. The main conclusion which is drawn is that the hypothesis is proved in the majority of companies in that only just over 50% of companies are attempting Resource and Capacity Planning and only 20% are successfully feeding back CRP information to `close the loop'. Various causative factors are put forward and remedies are suggested.

Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots.

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

Simulated Annealing Approach Applied to the Energy Resource Management Considering Demand Response for Electric Vehicle

The aggregation and management of Distributed Energy Resources (DERs) by an Virtual Power Players (VPP) is an important task in a smart grid context. The Energy Resource Management (ERM) of theses DERs can become a hard and complex optimization problem. The large integration of several DERs, including Electric Vehicles (EVs), may lead to a scenario in which the VPP needs several hours to have a solution for the ERM problem. This is the reason why it is necessary to use metaheuristic methodologies to come up with a good solution with a reasonable amount of time. The presented paper proposes a Simulated Annealing (SA) approach to determine the ERM considering an intensive use of DERs, mainly EVs. In this paper, the possibility to apply Demand Response (DR) programs to the EVs is considered. Moreover, a trip reduce DR program is implemented. The SA methodology is tested on a 32-bus distribution network with 2000 EVs, and the SA results are compared with a deterministic technique and particle swarm optimization results.