Silvicultural decisions based on simulation-optimization systems

Forest management is facing new challenges under climate change. By adjusting thinning regimes, conventional forest management can be adapted to various objectives of utilization of forest resources, such as wood quality, forest bioenergy, and carbon sequestration. This thesis aims to develop and apply a simulation-optimization system as a tool for an interdisciplinary understanding of the interactions between wood science, forest ecology, and forest economics. In this thesis, the OptiFor software was developed for forest resources management. The OptiFor simulation-optimization system integrated the process-based growth model PipeQual, wood quality models, biomass production and carbon emission models, as well as energy wood and commercial logging models into a single optimization model. Osyczka s direct and random search algorithm was employed to identify optimal values for a set of decision variables. The numerical studies in this thesis broadened our current knowledge and understanding of the relationships between wood science, forest ecology, and forest economics. The results for timber production show that optimal thinning regimes depend on site quality and initial stand characteristics. Taking wood properties into account, our results show that increasing the intensity of thinning resulted in lower wood density and shorter fibers. The addition of nutrients accelerated volume growth, but lowered wood quality for Norway spruce. Integrating energy wood harvesting into conventional forest management showed that conventional forest management without energy wood harvesting was still superior in sparse stands of Scots pine. Energy wood from pre-commercial thinning turned out to be optimal for dense stands. When carbon balance is taken into account, our results show that changing carbon assessment methods leads to very different optimal thinning regimes and average carbon stocks. Raising the carbon price resulted in longer rotations and a higher mean annual increment, as well as a significantly higher average carbon stock over the rotation.

Opioid dependence: Brain structure and function : a magnetic resonance imaging, neuropsychological, and electromagnetic study

Background: Opiod dependence is a chronic severe brain disorder associated with enormous health and social problems. The relapse back to opioid abuse is very high especially in early abstinence, but neuropsychological and neurophysiological deficits during opioid abuse or soon after cessation of opioids are scarcely investigated. Also the structural brain changes and their correlations with the length of opioid abuse or abuse onset age are not known. In this study the cognitive functions, neural basis of cognitive dysfunction, and brain structural changes was studied in opioid-dependent patients and in age and sex matched healthy controls. Materials and methods: All subjects participating in the study, 23 opioid dependents of whom, 15 were also benzodiazepine and five cannabis co-dependent and 18 healthy age and sex matched controls went through Structured Clinical Interviews (SCID) to obtain DSM-IV axis I and II diagnosis and to exclude psychiatric illness not related to opioid dependence or personality disorders. Simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) measurements were done on 21 opioid-dependent individuals on the day of hospitalization for withdrawal therapy. The neural basis of auditory processing was studied and pre-attentive attention and sensory memory were investigated. During the withdrawal 15 opioid-dependent patients participated in neuropsychological tests, measuring fluid intelligence, attention and working memory, verbal and visual memory, and executive functions. Fifteen healthy subjects served as controls for the MEG-EEG measurements and neuropsychological assessment. The brain magnetic resonance imaging (MRI) was obtained from 17 patients after approximately two weeks abstinence, and from 17 controls. The areas of different brain structures and the absolute and relative volumes of cerebrum, cerebral white and gray matter, and cerebrospinal fluid (CSF) spaces were measured and the Sylvian fissure ratio (SFR) and bifrontal ratio were calculated. Also correlation between the cerebral measures and neuropsychological performance was done. Results: MEG-EEG measurements showed that compared to controls the opioid-dependent patients had delayed mismatch negativity (MMN) response to novel sounds in the EEG and P3am on the contralateral hemisphere to the stimulated ear in MEG. The equivalent current dipole (ECD) of N1m response was stronger in patients with benzodiazepine co-dependence than those without benzodiazepine co-dependence or controls. In early abstinence the opioid dependents performed poorer than the controls in tests measuring attention and working memory, executive function and fluid intelligence. Test results of the Culture Fair Intelligence Test (CFIT), testing fluid intelligence, and Paced Auditory Serial Addition Test (PASAT), measuring attention and working memory correlated positively with the days of abstinence. MRI measurements showed that the relative volume of CSF was significantly larger in opioid dependents, which could also be seen in visual analysis. Also Sylvian fissures, expressed by SFR were wider in patients, which correlated negatively with the age of opioid abuse onset. In controls the relative gray matter volume had a positive correlation with composite cognitive performance, but this correlation was not found in opioid dependents in early abstinence. Conclusions: Opioid dependents had wide Sylvian fissures and CSF spaces indicating frontotemporal atrophy. Dilatation of Sylvian fissures correlated with the abuse onset age. During early withdrawal cognitive performance of opioid dependents was impaired. While intoxicated the pre-attentive attention to novel stimulus was delayed and benzodiazepine co-dependence impaired sound detection. All these changes point to disturbances on frontotemporal areas.

Electromagnetic signatures of lightning near the HF frequency band

The dissertation deals with remote narrowband measurements of the electromagnetic radiation emitted by lightning flashes. A lightning flash consists of a number of sub-processes. The return stroke, which transfers electrical charge from the thundercloud to to the ground, is electromagnetically an impulsive wideband process; that is, it emits radiation at most frequencies in the electromagnetic spectrum, but its duration is only some tens of microseconds. Before and after the return stroke, multiple sub-processes redistribute electrical charges within the thundercloud. These sub-processes can last for tens to hundreds of milliseconds, many orders of magnitude longer than the return stroke. Each sub-process causes radiation with specific time-domain characteristics, having maxima at different frequencies. Thus, if the radiation is measured at a single narrow frequency band, it is difficult to identify the sub-processes, and some sub-processes can be missed altogether. However, narrowband detectors are simple to design and miniaturize. In particular, near the High Frequency band (High Frequency, 3 MHz to 30 MHz), ordinary shortwave radios can, in principle, be used as detectors. This dissertation utilizes a prototype detector which is essentially a handheld AM radio receiver. Measurements were made in Scandinavia, and several independent data sources were used to identify lightning sub-processes, as well as the distance to each individual flash. It is shown that multiple sub-processes radiate strongly near the HF band. The return stroke usually radiates intensely, but it cannot be reliably identified from the time-domain signal alone. This means that a narrowband measurement is best used to characterize the energy of the radiation integrated over the whole flash, without attempting to identify individual processes. The dissertation analyzes the conditions under which this integrated energy can be used to estimate the distance to the flash. It is shown that flash-by-flash variations are large, but the integrated energy is very sensitive to changes in the distance, dropping as approximately the inverse cube root of the distance. Flashes can, in principle, be detected at distances of more than 100 km, but since the ground conductivity can vary, ranging accuracy drops dramatically at distances larger than 20 km. These limitations mean that individual flashes cannot be ranged accurately using a single narrowband detector, and the useful range is limited to 30 kilometers at the most. Nevertheless, simple statistical corrections are developed, which enable an accurate estimate of the distance to the closest edge of an active storm cell, as well as the approach speed. The results of the dissertation could therefore have practical applications in real-time short-range lightning detection and warning systems.