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La spalla dolorosa è una delle principali cause di visita e di trattamento in rieducazione. Per molte patologie, la rieducazione delle spalle dolorose ha dimostrato la sua efficacia. Essa permette di recuperare le ampiezze del movimento articolare fisiologiche delle spalle rigide nel quadro delle capsulopatie con rigidità (capsulite adesiva) e di sostituire l’azione dei tendini lacerati nel quadro delle spalle degenerative per ripristinare una funzione persa. Essa è raccomandata come trattamento di prima intenzione e può rappresentare un modo per ritardare o, addirittura, evitare l’intervento chirurgico. Il fisioterapista, dopo aver valutato le varie articolazioni del complesso articolare della spalla nonché della regione cervicotoracica, inizia una rieducazione che potrà includere delle tecniche di terapia manuale, degli esercizi attivi e di autorieducazione e l’uso di agenti fisici.

El hombro doloroso es una de las causas principales de consulta y tratamiento en rehabilitación. La rehabilitación de los hombros dolorosos se ha revelado eficaz en numerosas patologías. Permite recuperar las amplitudes articulares fisiológicas de los hombros rígidos en el contexto de las capsulopatías asociadas a rigidez (capsulitis retráctil) y suplir la acción de tendones rotos en el contexto de los hombros degenerativos con el fin de recuperar una función perdida. Se recomienda como tratamiento de primera elección y puede ser una manera de retrasar, o incluso de evitar, la intervención quirúrgica. El kinesiterapeuta, después de evaluar el complejo articular del hombro y de la región cervicotorácica, emprende una rehabilitación que puede consistir en técnicas de terapia manual, ejercicios activos y de autorrehabilitación y en la utilización de agentes físicos.

The article presents a comparison of traditional optimization and agent-based modeling (ABM) simulation techniques. It explores qualitative aspects of the two tools, such as their similarities and differences and strengths and weaknesses based on interviews of experts in areas such as artificial intelligence, operations research and computer science. Among the ten interviewees include V.S. Subrahmanian at the University of Maryland, Rob Zuidwijk at Erasmus University and Kafui Monu, a doctorate degree candidate at the University of British Columbia.

Freight planning is an increasingly visible topic in the arena of transportation planning. Freight planning metrics and benchmarks can help state agencies select and assess the impact of improvement projects with respect to freight mobility. Given the unique patterns of freight flow, freight metrics are most valuable when derived from freight-specific data. Extraction of timely freight-specific data to support freight planning efforts is not, however, always straightforward. Washington State, the primary focus of this study, has been developing potential methodologies for extracting and applying freight-specific data in benchmarking freight projects. The Freight Mobility Strategic Investment Board (FMSIB) of Washington State deployed intelligent transportation systems (ITS) technologies [Global Positioning Systems (GPS) and automated vehicle identification (AVI)] with the stated goal of collecting freight-specific data. An examination of the data collected for the FMSIB benchmarking test reveals that both sources of data—GPS and AVI—can support the freight planning process. This paper highlights results of the FMSIB study, the comparative accuracy of the two data sources, and the uses of each for planning purposes.

Freight planning is an increasingly visible topic in the arena of transportation planning. Freight planning metrics and benchmarks can help state agencies select and assess the impact of improvement projects with respect to freight mobility. Given the unique patterns of freight flow, freight metrics are most valuable when derived from freight-specific data. Extraction of timely freight-specific data to support freight planning efforts is not, however, always straightforward. Washington State, the primary focus of this study, has been developing potential methodologies for extracting and applying freight-specific data in benchmarking freight projects. The Freight Mobility Strategic Investment Board (FMSIB) of Washington State deployed intelligent transportation systems (ITS) technologies [Global Positioning Systems (GPS) and automated vehicle identification (AVI)] with the stated goal of collecting freight-specific data. An examination of the data collected for the FMSIB benchmarking test reveals that both sources of data—GPS and AVI—can support the freight planning process. This paper highlights results of the FMSIB study, the comparative accuracy of the two data sources, and the uses of each for planning purposes.