River herring, collectively alewives (Alosa pseudoharengus) and blueback herring (A. aestivalis), have experienced substantial population declines over the past five decades due in large part to overfishing, combined with other sources of mortality, and disrupted access to critical freshwater spawning habitats. Anadromous river herring populations are currently assessed by counting adults in rivers during upstream spawning migrations, but no field-based assessment methods exist for estimating juvenile densities in freshwater nursery habitats. Counts of 4-year-old migrating adults are variable and prevent understanding about how mortality acts on different life stages prior to returning to spawn (e.g., juveniles and immature adults in lakes, rivers, estuaries, and oceans). This in turn makes it challenging to infer a link between adult counts and juvenile recruitment and to develop effective management policy. I used a pelagic purse seine to investigate juvenile river herring densities, growth, and mortality across 16 New England lakes. First, I evaluated the effectiveness and sampling precision of a pelagic purse seine for capturing juvenile river herring in lakes, since this sampling gear has not been systematically tested. Sampling at night in June or July resulted in highest catches. Precision, as measured by the coefficient of variation, was lowest in July (0.23) compared to June (0.32), August (0.38), and September (0.61). Simulation results indicated that the effort required to produce precise density estimates is largely dependent on lake size with small lakes (<50 >ha) requiring up to 10 purse seine hauls and large lakes (>50 ha) requiring 15–20 hauls. These results suggested that juvenile recruitment densities can be effectively measured using a purse seine at night in June or July with 10–20 hauls. Using juvenile fishes captured during purse seining in June–September 2015, I calculated growth and mortality rates from sagittal otoliths. Density, growth, and mortality were highly variable among lakes, and mixed-effects regression models explained 11%–76% of the variance in these estimates. Juvenile densities ranged over an order of magnitude and were inversely related to dissolved organic carbon. Juvenile growth rates were higher in productive systems (i.e., low secchi depth, high nutrients) and were strongly density-dependent, leading to much larger fish at age in productive lakes with low densities of river herring compared to high density lakes. Water temperature explained 56%–85% of the variation in juvenile growth rates during the first 30 days of life. Mortality was positively related to total phosphorous levels and inversely related to hatch date, with earlier hatching cohorts experiencing higher mortality. These results indicate the importance of water quality and juvenile densities in nursery habitats for determining juvenile growth and survival. This study encourages future assessments of juvenile river herring in freshwater and contributes to an understanding of factors explaining juvenile recruitment that can guide more effective and comprehensive management of river herring.

Arbetet kring mätfrekvenser, alltså hur ofta en producerad detalj ska mätas, inom produktionen på Volvo Cars följer i dagsläget inget standardiserat arbetssätt. Arbetet kring det bygger i stort på tidigare erfarenheter och vad liknande utrustningar har för mätfrekvens. Volvo Cars efterfrågar mer kunskap inom området för att få en mer kostnadseffektiv kvalitetssäkring. Arbetets huvudsyfte har innefattats av identifiering gällande vilka variabler som påverkar mätfrekvensen, samt uppbyggnad av en enklare modell där variablerna applicerats. Intervjuer har även genomförts på ett flertal företag, där några av de viktigaste slutsatserna är: Mätfrekvenser arbetas med retroaktivt, snarare än proaktivt. Duglighet är i dagsläget vanligast att använda vid arbete med mätfrekvenser. Arbete med mätfrekvenser sker inte standardiserat. Förbättring av mätfrekvenser jobbas med i låg grad och när det väl görs är det ofta triggat av en mantidsanalys. Arbetet har resulterat i identifiering av två huvudvariabler; duglighet och kvalitetskostnader. Även om verkligheten är mer komplicerad, kan dessa två variabler ses som huvudkategorier. Under duglighet och kvalitetskostnader finns sedan underkategorier. För duglighet finns verktygsrelaterade egenskaper såsom förslitning och dess material. Även detaljens material och dess termodynamiska egenskaper har inverkan på dugligheten. Slutligen återfinns felintensitet, vibrationer som uppstår och processens stabilitet. Gällande kvalitetsbristkostnader finns felkostnader som uppstår inom företagets väggar, interna felkostnader, och de felkostnader som uppstår när produkt levererats till kund, externa felkostnader. Utöver de två finns även kontrollkostnader och förebyggande kostnader. Arbetet har dessutom mynnat ut i en enklare modell där erfarenhet från intervjuer och data från Volvo Cars tagits i beaktande. Flera av de data som återfinns i modellen har tagits fram genom analysering av tre veckors produktionsdata från Volvo Cars. Data som används i modellen berörande kvalitet är duglighet och den procentuella fördelningen av den aktuella varianten. De data som har inverkan på kvalitetskostnaderna är hur många operationer flödet har och aktuell operations placering i relation till totala antalet. Även råämnets kostnad, allvarlighetsgraden för kvalitetsbristen hos aktuell egenskap och skrotkostnaden används. Modellen har sedan applicerat på en maskinerna som omfattats av arbetet för att kontrollera utfallet. Med data införd baserad på produktionsdata från Volvo Cars har en stickprovsfrekvens på 62 genererats. / Work on measuring frequencies, which is how often a produced detail is to be measured, within Volvo Cars’ production currently does not follow a standardized approach. The work around it basically builds on past experiences and what similar equipment has for measurement frequency. Volvo Cars requests more knowledge in the area to get more cost-effective quality assurance. The main objective of the work has contained identification of the variables that affect the measurement frequency, as well as construction of a simpler model where the variables are applied. Interviews have also been conducted on a number of companies, where some of the key conclusions are: Measuring frequencies are worked retrospectively, rather than proactively. Capability is currently the most common for work with measurement frequencies. Working with measurement frequencies does not occur standardized. Improving measurement frequencies occur to a low extent, and when done, it is often triggered by a man-time analysis. The work has resulted in the identification of two main variables; capability and quality costs. Although the reality is more complicated, these two variables can be seen as main categories. Under capability and quality costs, there are subcategories. For capability, tool-related properties such as wear and its material are available. The material of the detail and its thermodynamic properties also affect the capability. Finally, error intensity, vibrations and stability of the process are found. Regarding quality deficiency there are error costs arising within the company's walls, internal error costs, and the error costs that occur when the product is delivered to the customer, external error costs. In addition to the two, there are also control costs and prevention costs. In addition, the work has resulted in a simpler model, taking into account experience from interviews and data from Volvo Cars. Several of the data contained in the model have been developed by analyzing three-week production data from Volvo Cars. Data used in the model related to quality is the capability and the percentage distribution of the current variant. The data that impact on quality costs is how many operations the flow has and the current operation location in relation to the total number. The cost of the raw material, the severity of the quality lack of the current property and the scrap cost is also used. The model has then been applied to one of the machines covered by the work to check the outcome. With data imported based on production data from Volvo Cars, a sampling rate of 62 has been generated.

Kartotrak software allows optimal waste classification and avoids unnecessary remediation. It has been designed for those - site owners, safety authorities or contractors, involved in environmental site characterization projects - who need to locate and estimate contaminated soil volumes confidently.

Kartotrak software allows optimal waste classification and avoids unnecessary remediation. It has been designed for those - site owners, safety authorities or contractors, involved in environmental site characterization projects - who need to locate and estimate contaminated soil volumes confidently.