SummaryBackgroundTrial findings show cognitive behaviour therapy (CBT) and graded exercise therapy (GET) can be effective treatments for chronic fatigue syndrome, but patients' organisations have reported that these treatments can be harmful and favour pacing and specialist health care. We aimed to assess effectiveness and safety of all four treatments.MethodsIn our parallel-group randomised trial, patients meeting Oxford criteria for chronic fatigue syndrome were recruited from six secondary-care clinics in the UK and randomly allocated by computer-generated sequence to receive specialist medical care (SMC) alone or with adaptive pacing therapy (APT), CBT, or GET. Primary outcomes were fatigue (measured by Chalder fatigue questionnaire score) and physical function (measured by short form-36 subscale score) up to 52 weeks after randomisation, and safety was assessed primarily by recording all serious adverse events, including serious adverse reactions to trial treatments. Primary outcomes were rated by participants, who were necessarily unmasked to treatment assignment; the statistician was masked to treatment assignment for the analysis of primary outcomes. We used longitudinal regression models to compare SMC alone with other treatments, APT with CBT, and APT with GET. The final analysis included all participants for whom we had data for primary outcomes. This trial is registered at http://isrctn.org, number ISRCTN54285094.FindingsWe recruited 641 eligible patients, of whom 160 were assigned to the APT group, 161 to the CBT group, 160 to the GET group, and 160 to the SMC-alone group. Compared with SMC alone, mean fatigue scores at 52 weeks were 3·4 (95% CI 1·8 to 5·0) points lower for CBT (p=0·0001) and 3·2 (1·7 to 4·8) points lower for GET (p=0·0003), but did not differ for APT (0·7 [−0·9 to 2·3] points lower; p=0·38). Compared with SMC alone, mean physical function scores were 7·1 (2·0 to 12·1) points higher for CBT (p=0·0068) and 9·4 (4·4 to 14·4) points higher for GET (p=0·0005), but did not differ for APT (3·4 [−1·6 to 8·4] points lower; p=0·18). Compared with APT, CBT and GET were associated with less fatigue (CBT p=0·0027; GET p=0·0059) and better physical function (CBT p=0·0002; GET p<0·0001). Subgroup analysis of 427 participants meeting international criteria for chronic fatigue syndrome and 329 participants meeting London criteria for myalgic encephalomyelitis yielded equivalent results. Serious adverse reactions were recorded in two (1%) of 159 participants in the APT group, three (2%) of 161 in the CBT group, two (1%) of 160 in the GET group, and two (1%) of 160 in the SMC-alone group.InterpretationCBT and GET can safely be added to SMC to moderately improve outcomes for chronic fatigue syndrome, but APT is not an effective addition.FundingUK Medical Research Council, Department of Health for England, Scottish Chief Scientist Office, Department for Work and Pensions.

Historic Building Information Modelling (HBIM) is a new approach for modelling historic buildings which develops full Building Information Models (BIMs) from remotely sensed data. HBIM consists of a novel library of reusable parametric objects, based on historic architectural data and a system for mapping theses library objects to survey data. This chapter describes the development of a library of parametric objects for HBIM that can be used to model classical architectural elements. Steps towards automating the HBIM process are also described in this chapter. Using concepts from procedural modelling, a new set of rules and algorithms have been developed to automatically combine HBIM library objects and generate different building arrangements by altering parameters. This is a semi-automatic process where the required building structure and objects are first automatically generated and then refined to match survey data. The use of procedural modelling techniques with HBIM library objects introduces automation and speeds up the slow process of plotting library objects to survey data.

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-This paper outlines a two stage approach for digitally recording cultural heritage sites. This approach involves a 3D modeling stage and the integration of the 3D model into a 3D GIS for further management and analysis. The modeling stage is carried out using a new concept; Historic Building Information Modeling (HBIM) which has been developed at the Dublin Institute of Technology [12]. Historic Building Information Modeling is a system for modeling historic structures from laser scan and photogrammetric data using Building Information Modeling (BIM) software. The HBIM process involves a reverse engineering solution whereby parametric objects representing architectural elements are mapped onto laser scan or photogrammetric survey data. A library of parametric architectural objects has been designed from historic manuscripts and architectural pattern books. These parametric objects were built using an embedded scripting language within the BIM software called Geometric Descriptive Language (GDL). These objects are combined and mapped onto the survey data to build the entire model. After the 3D model has been created the next stage involves integrating the 3D model into a 3D GIS for further analysis. The international framework for 3D city modeling, CityGML has been adopted for this purpose. CityGML provides an interoperable framework for modeling 3D geometries, semantics, topology and appearance properties [13]. The aim of this research is to bridge the gap between parametric CAD modeling and 3D GIS while using benefits from both systems to help document and analyze cultural heritage sites.

ABSTRACT:In an extensive review of existing literature a number of observations were made in relation to the current approaches for recording and modelling existing buildings and environments: Data collection and pre-processing techniques are becoming increasingly automated to allow for near real-time data capture and fast processing of this data for later modelling applications. Current BIM software is almost completely focused on new buildings and has very limited tools and pre-defined libraries for modelling existing and historic buildings. The development of reusable parametric library objects for existing and historic buildings supports modelling with high levels of detail while decreasing the modelling time. Mapping these parametric objects to survey data, however, is still a time-consuming task that requires further research. Promising developments have been made towards automatic object recognition and feature extraction from point clouds for as-built BIM. However, results are currently limited to simple and planar features. Further work is required for automatic accurate and reliable reconstruction of complex geometries from point cloud data. Procedural modelling can provide an automated solution for generating 3D geometries but lacks the detail and accuracy required for most asbuilt applications in AEC and heritage fields.