New methodology for optimizing transit priority at the network level

A new methodology for optimizing transit road space priority at the network level is proposed. Transit vehicles carry large numbers of passengers within congested road space efficiently. This aids justification of transit priority. Almost all studies that have investigated transit priority lanes focus at a link or an arterial road level, and no study has investigated road space allocation for priority from a network perspective. The aim of the proposed approach is to find the optimum combination of exclusive lanes in an existing operational transport network. Mode share is assumed variable, and an assignment is performed for both private and transit traffic. The problem is formulated by using bilevel programming, which minimizes the total travel time. The approach is applied to an example network and the results are discussed. The approach can identify the optimal combination of transit priority lanes and achieve the global optimum of the objective function. Areas for further development are discussed

Transgenesis in Animal Agriculture: Addressing Animal Health and Welfare Concerns

The US Food and Drug Administration’s final Guidance for Industry on the regulation of transgenesis in animal agriculture has paved the way for the commercialization of genetically engineered (GE) farm animals. The production-related diseases associated with extant breeding technologies are reviewed, as well as the predictable welfare consequences of continued emphasis on prolificacy at the potential expense of physical fitness. Areas in which biotechnology could be used to improve the welfare of animals while maintaining profitability are explored along with regulatory schema to improve agency integration in GE animal oversight

Strategic logistics management principles in urban public transport

One copy microfiche.Thesis (M.Ing.) -- University of Stellenbosch, 1998.Full text to be digitised and attached to bibliographic record

A computerised decision support system for the implementation of strategic logistics management optimisation principles in the planning and operation of integrated urban public transport

Thesis (PhD)--University of Stellenbosch, 2001.ENGLISH ABSTRACT: Public and private transport system planning and operation have tended to be fragmented functions. In particular, public transport is often planned and operated independently of the "private" transport system. South African government policy now requires that comprehensive, strategic transport plans be prepared by metropolitan transport authorities. These plans are expected to conform to national strategic objectives as well as including local current and longterm objectives. This planning is required in the environment of a multi-modal, multi-operator, public-private partnership scenario that is new for most of the role players. The lack of experience is accompanied by a lack of any existing model for dealing with this scenario. This dissertation describes such a model. The model is based on the principles of strategic logistics management commonly employed in commerce and industry, including service-oriented industries. The modelling process is thus based on achieving a combination of customer service and long-term objectives. The model comprises a number of separate components and steps: • A transport network model (Emme/2). ~ A multi-class, generalised-cost assignment of private and public transport demand onto a network, modified to be modeless to the public transport users, is performed. This assignment allows for the imposition of generalised-cost reflecting urban-planning objectives in addition to more conventional costs such as travel cost. In this assignment, the interaction of public and private transport is accounted for and results in an associated modal choice. ~ A series of single-class generalised-cost assignments is then used to "focus" public transport demand to create corridors of demand adequate to justify public transport routes. This process can be enhanced to develop a design promoting switching from private to public transport. It also allows for multi-period route design. ~ The results of this modelling process are output to a text file and then subject to the processes described below. The results of these processes are then input into the network model where a standard transit assignment is performed and used to modify the proposed lines and update the network design data with respect to boardings and alightings at nodes. This information is used to design fixed infrastructure. • A Microsoft Access database and route extraction program. );> The network model data is drawn into the database where it is subject to a route extraction program that converts the assignment results from the network model into a set of mode specific potential public transport route definitions. These route definitions are based on paths of maximum demand. The extraction process is controlled by parameters specified by the planner, such as minimum route lengths and the demand level for various categories of service. );> After route extraction, vehicle allocation, and transit assignment, the database provides details of the boardings and alightings and number and details of transit lines using each node and link in the network. This data is used to design fixed infrastructure. • A Microsoft Excel spreadsheet vehicle operating cost model. );> For each vehicle type, the operating cost given the anticipated vehicle mileage and operating speed is determined. This is used to guide the choice of vehicles for different routes. • A Lingo goal-programming model. );> The potential routes and the available or potential fleet are subjected to a goalprogramme in which the optimum choice of vehicle allocation is determined. The allocation parameters can be controlled by the planner. These parameters may include costs, energy, fuel consumption, and vehicle and route limitations amongst others. Multiperiod design is included in the modelling process so that the optimum design may be for the operating period, daily, or weekly cycle. The modelling process provides two main outputs: • A set of fully described and costed transit lines ill terms of both routing and vehicle allocation. These transit line definitions can be output to the level of driver instructions if necessary. • Details of the type and location of infrastructure to be provided on the network.AFRIKAANSE OPSOMMING: Openbare en private vervoerstelsel-beplanning en -bedryf IS geneig om gefragmenteerde funksies te wees. Dit is veral waarneembaar in die openbare vervoerstelsels waarvan die beplanning en bedryf onafhanklik van die "private" vervoerstelsels plaasvind. Die beleid van die Suid-Afrikaanse regering vereis dat omvattende strategiese vervoerplanne deur die metropolitaanse vervoer owerhede voorberei word. Daar word van hierdie planne verwag om aan die nasionale strategiese doelwitte, asook die plaaslike bestaande en langtermyn doelwitte te voldoen. Hierdie beplanning word vereis deur 'n omgewing wat nuut is vir die meeste rolspelers en bestaan uit multi-modale, multi-operateur en openbare-private vennootskap scenario's. Die tekort aan ondervinding gaan gepaard met 'n tekort aan 'n bestaande model wat gebruik kan word om hierdie scenario's te hanteer. So 'n model word deur hierdie verhandeling beskryf. Die model is gebasseer op die beginsels van strategiese logistieke bestuur wat algemeen gebruik word in die handel en industrie, insluitende die diens-georïenteerde industrieë. Die modelleringsproses wil dus 'n kombinasie van diens aan kliënte en langtermyn doelwitte bereik. Die model bestaan uit onderskeie komponente en stappe: • 'n Vervoernetwerkmodel (Emmel2) }i;> 'n Multi-klas, veralgemeende-koste toedeling van private en openbare vervoeraanvraag op 'n netwerk, aangepas om modusloos te wees vir die openbare vervoergebruiker, word uitgevoer. Hierdie toedeling laat nie net die heffing van meer konvensionele kostes, soos reiskoste toe nie, maar ook veralgemeende kostes wat staatsbeplarmingsdoelwitte reflekteer. In hierdie opdrag word die interaksie van openbare- en private vervoer ondersoek waarvan die uiteinde 'n geassosieerde modale keuse is. }i;> 'n Reeks enkelklas veralgemeende koste toedelings word dan gebruik om op openbare vervoeraanvraag te fokus en daardeur korridors van aanvraag, wat gepas is om openbare vervoerroetes te regverdig, te skep. Hierdie proses kan verfyn word om 'n plan te ontwikkel wat die verskuiwing van private vervoer na openbare vervoer sal bevorder. Dit laat ook die ontwerp van multi-periode roetes toe. }i;> Die resultate van hierdie modelleringsproses word uitgevoer na 'n tekslêer en dan aan die prosesse, wat hier onder beskryf word, onderwerp. Die resultate van hierdie prosesse word dan ingevoer in die netwerkmodel waar 'n standaard publieke vervoertoedeling uitgevoer word. Dit word dan gebruik om die voorgestelde roetes te wysig en die netwerk data, met betrekking tot die aantal persone wat op en af klim by nodes, op te dateer. Hierdie inligting word gebruik vir die ontwerp van infrastrukture. • 'n Microsoft Access databasis en roete-ontrekkingsprogram );> Die netwerkmodel data word in die databasis ingetrek waar dit aan 'n roeteontrekkingsprogram onderwerp word. Hierdie program skakel die toedelingsresultate van die netwerkmodel om na 'n stel potensiële modus spesifieke openbare vervoerroete definisies. Hierdie roete definisies word gebasseer op paaie van maksimum aanvraag. Die ontrekkingsproses word deur parameters, soos minimum lengte van roetes en die vlak van aanvraag van verskeie kategorieë van diens, wat deur die beplanner gespesifiseer word, gekontroleer. );> Na die ontrekking van roetes, voertuigtoekenning en vervoertoedeling, voorsien die databasis besonderhede van die aantal persone wat op en af klim asook die aantal en details van vervoerroete wat elke node en skakel in die netwerk gebruik. Hierdie data word gebruik om infrastrukture te ontwerp. • 'n Microsoft Excel sigblad voertuig bedryfskoste model )i> Vir elke tipe voertuig word die bedryfskoste, volgens die verwagte afstand en spoed van die spesifieke voertuig, bepaal. Die resultate word gebruik om die keuse van voertuie vir verskillende roetes te bepaal. • 'n Lingo doelprogrameringsmodel );> Die potensiële roetes en die beskikbare of potensiële vloot word onderwerp aan 'n doelprogram waarin die optimum keuse van voertuigtoekenning bepaal word. Die toekenningsparameters kan deur die beplanner gekontroleer word. Die parameters kan onder andere kostes, energie, brandstofverbruik en voertuig- en roete beperkings, insluit. Multi-periode ontwerp is ingesluit in die modelleringsproses sodat die optimum ontwerp vir die bedryfsperiode, daaglikse of weeklikse siklusse, kan wees. Die modelleringsproses lewer twee hoofuitkomste: • 'n Stel volledig beskrywende en koste berekende vervoerroete wat, indien nodig, na die vlak van bestuurder instruksies, uitgevoer kan word. • Details van die tipe en plek van infrastruktuur wat benodig word deur die netwerk

Thigh-length compression stockings and DVT after stroke

Controversy exists as to whether neoadjuvant chemotherapy improves survival in patients with invasive bladder cancer, despite randomised controlled trials of more than 3000 patients. We undertook a systematic review and meta-analysis to assess the effect of such treatment on survival in patients with this disease

Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Background Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatory actions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19. Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospital with COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients were randomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once per day by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatment groups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment and were twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants and local study staff were not masked to the allocated treatment, but all others involved in the trial were masked to the outcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treat population. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936. Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) were eligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was 65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomly allocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall, 561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days (rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median 10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days (rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, no significant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilation or death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24). Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or other prespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restricted to patients in whom there is a clear antimicrobial indication. Funding UK Research and Innovation (Medical Research Council) and National Institute of Health Research