Skuggpriser på åkermark i Nyland baserat på LIR-data

Lantbrukets lönsamhet är ett ständigt återkommande diskussionsämne. Åkermarkens pris är i sin tur en av de främsta faktorerna som påverkar jordbrukets lönsamhet. Målsättningen med den här magistersavhandlingen var att undersöka hur mycket det lönar sig att betala för åkermarken som man brukar och med betoning på arrende. Frågesättningen var vilka odlingsväxter det lönar sig att odla och ifall det finns skillnad mellan de olika CAP-perioderna som varit i kraft under åren 2011–2020, innan 2015 och efter. Data som används i avhandlingen är LIR-data från Nylands svenska lantbrukssällskap från åren 2011–2020. LIR står för lantbrukets individuella rådgivning och är en pakettjänst för växtodlingsgårdar som omfattar bland annat växtodlingsrådgivning och uppföljning av de ekonomiska resultaten som sedan utges i form av en publikation för medlemmarna. De ekonomiska nyckeltalen består av riktiga värden i mån av möjlighet och därtill bestämda kalkylvärden. Det fanns stora variationer mellan gårdarna i både ekonomiskt resultat samt i skördemängd. Bland annat präglades åren 2013 och 2018 av dåliga skördar. Optimeringen har gjorts med hjälp av linjär programmering. Genom att optimera växtföljden har så kallade skuggpriser tagits fram, det vill säga ett marginalvärde vad ytterligare en hektar åkermark skulle avkasta. Skuggpriset innebär i det här fallet täckningsbidrag c (TB C), det vill säga vinsten innan åkerkostnader dragits av. Som jämförelse till optimeringsmodellen gjordes också en modell som skulle motsvara den nuvarande växtodlingen på LIR-gårdarna. Avhandlingen har endast koncentrerats till odlingsväxter som kan odlas med de vanligaste jordbruksmaskinerna, således har potatis och sockerbeta lämnats bort, dessutom har kummin medvetet inte tagits i beaktande. Täckningsbidrag C maximerades genom att välja ut den kombinationen grödor som ger den högsta vinsten på en modellgård på 150 hektar med restriktionerna att det max fick vara 20 % oljeväxter, max 20 % proteingrödor samt max 40 % höstsäd, därtill måste gården ha minst 20 % vårspannmål för att vara representativ samt för perioden 2015–2020 ha minst 5 % i gröngödslingsvall för att simulera EFA-kravet. Det genomsnittliga skuggpriset för perioden 2011–2014 blev 449 euro per hektar efter maximeringen samt 413 euro per hektar för perioden 2015–2020. Motsvarande resultat för LIR-modellen var 383 euro per hektar respektive 299 euro per hektar. Den utarbetade modellen gav således 66 euro bättre resultat i medeltal för perioden 2011–2014 samt 116 euro per hektar för perioden 2015–2020. Den andra CAP-perioden (2015–2020) verkade ge ett något sämre resultat än den första, en möjlighet är att EFA-arealen har en negativ inverkan på vinsten. Gemensamt var att det överlag lönade sig att maximera arealen med höstsäd samt odla ryps och ärter eller bondböna. I den här avhandlingen har det använts medeltal för optimeringarna, vilket är ett riktgivande svar men förslag på vidare forskning är att på detaljnivå undersöka vinsten för enskilda gårdar i Nyland. En ekonometrisk forskningsmetod kunde således övervägas för att reda ut de bakomliggande faktorerna som påverkar betalningsmöjligheten

High-Capacity Conductive Nanocellulose Paper Sheets for Electrochemically Controlled Extraction of DNA Oligomers

Highly porous polypyrrole (PPy)-nanocellulose paper sheets have been evaluated as inexpensive and disposable electrochemically controlled three-dimensional solid phase extraction materials. The composites, which had a total anion exchange capacity of about 1.1 mol kg−1, were used for extraction and subsequent release of negatively charged fluorophore tagged DNA oligomers via galvanostatic oxidation and reduction of a 30–50 nm conformal PPy layer on the cellulose substrate. The ion exchange capacity, which was, at least, two orders of magnitude higher than those previously reached in electrochemically controlled extraction, originated from the high surface area (i.e. 80 m2 g−1) of the porous composites and the thin PPy layer which ensured excellent access to the ion exchange material. This enabled the extractions to be carried out faster and with better control of the PPy charge than with previously employed approaches. Experiments in equimolar mixtures of (dT)6, (dT)20, and (dT)40 DNA oligomers showed that all oligomers could be extracted, and that the smallest oligomer was preferentially released with an efficiency of up to 40% during the reduction of the PPy layer. These results indicate that the present material is very promising for the development of inexpensive and efficient electrochemically controlled ion-exchange membranes for batch-wise extraction of biomolecules

Evaluation of Memory Prefetching Techniques for Modem Applications

Processor performance has increased far faster than memories have been able to keep up with, forcing processor designers to use caches in order to bridge the speed difference. This can increase performance significantly for programs that utilize the caches efficiently but results in significant performance penalties when data is not in cache. One way to mitigate this problem is to to make sure that data is cached before it is needed using memory prefetching. This thesis focuses on different ways to perform prefetching in systems with strict area and energy requirements by evaluating a number of prefetch techniques based on performance in two programs as well as metrics such as coverage and accuracy. Both data and instruction prefetching are investigated. The studied techniques include a number of versions of next line prefetching, prefetching based on stride identification and history as well as post-increment based prefetching. While the best increase in program performance is achieved using next 2 lines prefetching it comes at a significant energy cost as well as drastically increased memory traffic making it unsuitable for use in energy-constrained applications. RPT-based prefetching on the other hand gives a good balance between performance and cost managing to improve performance by 4% and 7% for two programs while keeping the impact on both area and energy minimal

Evaluation of Memory Prefetching Techniques for Modem Applications

Processor performance has increased far faster than memories have been able to keep up with, forcing processor designers to use caches in order to bridge the speed difference. This can increase performance significantly for programs that utilize the caches efficiently but results in significant performance penalties when data is not in cache. One way to mitigate this problem is to to make sure that data is cached before it is needed using memory prefetching. This thesis focuses on different ways to perform prefetching in systems with strict area and energy requirements by evaluating a number of prefetch techniques based on performance in two programs as well as metrics such as coverage and accuracy. Both data and instruction prefetching are investigated. The studied techniques include a number of versions of next line prefetching, prefetching based on stride identification and history as well as post-increment based prefetching. While the best increase in program performance is achieved using next 2 lines prefetching it comes at a significant energy cost as well as drastically increased memory traffic making it unsuitable for use in energy-constrained applications. RPT-based prefetching on the other hand gives a good balance between performance and cost managing to improve performance by 4% and 7% for two programs while keeping the impact on both area and energy minimal

Towards MR contrast independent synthetic CT generation

The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics. To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, T1 and T2 maps (i.e. contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only T2w MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose. On T2w images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on T1w images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model. Using a dataset of T2w MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available

Ultrafast All-Polymer Paper-Based Batteries

Conducting polymers for battery applications have been subject to numerous investigations during the last two decades. However, the functional charging rates and the cycling stabilities have so far been found to be insufficient for practical applications. These shortcomings can, at least partially, be explained by the fact that thick layers of the conducting polymers have been used to obtain sufficient capacities of the batteries. In the present letter, we introduce a novel nanostructured high-surface area electrode material for energy storage applications composed of cellulose fibers of algal origin individually coated with a 50 nm thin layer of polypyrrole. Our results show the hitherto highest reported charge capacities and charging rates for an all polymer paper-based battery. The composite conductive paper material is shown to have a specific surface area of 80 m² g⁻¹ and batteries based on this material can be charged with currents as high as 600 mA cm⁻² with only 6% loss in capacity over 100 subsequent charge and discharge cycles. The aqueous-based batteries, which are entirely based on cellulose and polypyrrole and exhibit charge capacities between 25 and 33 mAh g⁻¹ or 38−50 mAh g⁻¹ per weight of the active material, open up new possibilities for the production of environmentally friendly, cost efficient, up-scalable and lightweight energy storage systems

The Impact of COVID-19 on Parkinson's Disease : A Case-Controlled Registry and Questionnaire Study on Clinical Markers and Patients' Perceptions

Introduction: Parkinson's disease (PD) is a neurodegenerative disease with motor and nonmotor symptoms. Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Objectives: To explore how COVID-19 affects motor, nonmotor, and general health aspects of PD and to map how PD patients perceive their change in symptoms since falling ill with COVID-19. Method: The study was descriptive, case-controlled, and based on both registry and questionnaire data. At baseline, the controls were matched on age, sex, and disease severity. Information on the severity of the disease, nonmotor symptoms, motor symptoms, and general health was retrieved from the Swedish Registry for PD. Registry data from a COVID-19 group (n=45) and a control group (n=73), as well as questionnaires from a COVID-19 group (n=24) and a control group (n=42), were compared. Results: We did not find that SARS-CoV-2 infection affects any major aspect of nonmotor symptoms, motor symptoms, general health, and perception of change in PD patients' post-COVID-19. Compared to controls, the COVID-19 group reported a more positive subjective experience of pain and quality of life and a perception of change post-COVID-19 regarding general motor function, sleep quality, and mood (all p<0.05). Conclusion: Although SARS-CoV-2 infection does not seem to affect PD symptoms in any major respect, the subjective experience of several aspects of life in PD patients might be slightly improved post-COVID-19 compared to a control group. The findings warrant further investigations due to the small sample size and possible survivorship bias