Sampling for quality inspection and correction: AOQL performance criteria (Version 3)

Sampling;mathematische statistiek

Developing dwelling as an approach to landscape and place: The cases of long-distance transhumance and Easter processions

Approaches to place and landscape have concerned geographers, at least throughout Modern history. In geographical place and landscape writings, notions of dwelling have been taken up and developed since the 1970s to indicate the lived and practised character of the relationships between human beings and their environment. Dwelling has experienced a controversial history in geography, and bears some negative or limiting connotations: it would be backward-looking, exclusionary, static, nostalgic, and hindered by the idea of rootedness and the authentic / non-authentic life split.This thesis critically considers in what ways seminal dwelling literatures (those written by Martin Heidegger and Tim Ingold) might be problematical and / or enriching for place and landscape writing. In this thesis I argue that the theoretical complexity of seminal dwelling literatures is often overlooked while I also argue that some understandings of the relational, the incomplete, and the contingent are largely missing or problematically conceptualised in seminal dwelling literatures.Taking into account this reflection on the theoretical background of dwelling, the thesis explores possibilities for integrating dwelling in a framework inspired by non-representational theory (NRT). Such links are made in the thesis’ case studies: communities practising (a) long-distance transhumant herding in rural Spain (in which herders and herd journey biennially for about four weeks in response to environmental changes caused by the seasonal cycle), and (b) Easter processions in central Seville (in which brotherhoods celebrate the Resurrection of Jesus Christ). Place and landscape practices are accessed through ethnographical engagements, in which herding - and processional landscapes become the lived contexts for reflection. In the case studies, dwelling is redeveloped through a framework that prioritises posthumanism, relationality and openness, as well as issues of rhythmicity, and nearness (as people care for - and attune to happenings). In long-distance transhumance the rural, the ecological and the practical are privileged, whereas in Easter processions the urban, the spiritual, and to the sheer beauty of life are emphasised. As such, the case studies offer distinct perspectives on the possibilities for developments of dwelling in place and landscape writing, while the case studies share denominators such as journeying, seasonality, embodiment, and practice richness

Sustainable Competitive Advantage through CLSC-strategies

The empiric results reveal that sustainable competitive advantage is highly influenced by remanufacturing strategies and product acquisition strategies of remanufacturing firms. A strategic positioning and the CLSC resources and capabilities are most important in this regard. Remarketing strategies predominantly influence the value creation of the firm. Most frequently mentioned are remanufacturing strategies related to improving the product quality, the output and flexibility of production and the sourcing of components. Production acquisition strategies are most often mentioned in relation to increasing availability of used products and lowering the costs of used products by developing own collect systems. Finally remarketing has a medium impact on value creation. Value is mainly created by targeting the right customer with the right product offering and delivering marketing support to resellers combined with marketing activities focused on end-users. CLSC strategies should not only focus on cost reductions. From this research it can be concluded that remanufacturers attempt a great deal in order to stay ahead of their competition. The data analyses reveal that a majority of the remanufacturers in this research use a strategic position that is based on delivering high quality products that can compete with OEM products in the market. Differentiation strategies should therefore focus on quality, additional service and price

Detection and Prediction of Freezing of Gait in Parkinson’s Disease using Wearable Sensors and Machine Learning

Freezing of gait (FOG), is a brief episodic absence of forward body progression despite the intention to walk. Appearing mostly in mid-late stage Parkinson’s disease (PD), freezing manifests as a sudden loss of lower-limb function, and is closely linked to falling, decreased functional mobility, and loss of independence. Wearable-sensor based devices can detect freezes already in progress, and intervene by delivering auditory, visual, or tactile stimuli called cues. Cueing has been shown to reduce FOG duration and allow walking to continue. However, FOG detection and cueing systems require data from the freeze episode itself and are thus unable to prevent freezing. Anticipating the FOG episode before onset and supplying a timely cue could prevent the freeze from occurring altogether. FOG has been predicted in offline analyses by training machine learning models to identify wearable-sensor signal patterns known to precede FOG. The most commonly used sensors for FOG detection and prediction are inertial measurement units (IMU) that include an accelerometer, gyroscope and sometimes magnetometer. Currently, the best FOG prediction systems use data collected from multiple sensors on various body locations to develop person-specific models. Multi-sensor systems are more complex and may be challenging to integrate into real-life assistive devices. The ultimate goal of FOG prediction systems is a user-friendly assistive device that can be used by anyone experiencing FOG. To achieve this goal, person-independent models with high FOG prediction performance and a minimal number of conveniently located sensors are needed. The objectives of this thesis were: to develop and evaluate FOG detection and prediction models using IMU and plantar pressure data; determine if event-based or period of gait disruption FOG definitions have better classification performance for FOG detection and prediction; and evaluate FOG prediction models that use a single unilateral plantar pressure insole sensor or bilateral sensors. In this thesis, IMU (accelerometer and gyroscope) and plantar pressure insole sensors were used to collect data from 11 people with FOG while they walked a freeze provoking path. A custom-made synchronization and labeling program was used synchronize the IMU and plantar pressure data and annotate FOG episodes. Data were divided into overlapping 1 s windows with 0.2 s shift between consecutive windows. Time domain, Fourier transform based, and wavelet transform based features were extracted from the data. A total of 861 features were extracted from each of the 71,000 data windows. To evaluate the effectiveness of FOG detection and prediction models using plantar pressure and IMU data features, three feature sets were compared: plantar pressure, IMU, and both plantar pressure and IMU features. Minimum-redundancy maximum-relevance (mRMR) and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or Non-FOG states, wherein the Total-FOG class included windows with data from 2 s before the FOG onset until the end of the FOG episode. The plantar-pressure-only model had the greatest sensitivity, and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, freeze windows, and transition windows between Pre-FOG and FOG). The best model, which used plantar pressure and IMU features, detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Models using both plantar pressure and IMU features performed better than models that used either sensor type alone. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect FOG detection and prediction model performance, especially with respect to multiple FOG in rapid succession. This research examined the effects of defining FOG either as a period of gait disruption (merging successive FOG), or based on an event (no merging), on FOG detection and prediction. Plantar pressure and lower limb acceleration data were used to extract a set of features and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging had little effect on FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession. Despite the known asymmetry of PD motor symptom manifestation, the difference between the more severely affected side (MSS) and less severely affected side (LSS) is rarely considered in FOG detection and prediction studies. The additional information provided by the MSS or LSS, if any, may be beneficial to FOG prediction models, especially if using a single sensor. To examine the effect of using data from the MSS, LSS, or both limbs, multiple FOG prediction models were trained and compared. Three datasets were created using plantar pressure data from the MSS, LSS, and both sides together. Feature selection was performed, and FOG prediction models were trained using the top 5, 10, 15, 20, 25 or 30 features for each dataset. The best models were the MSS model with 15 features, and the LSS and bilateral features with 5 features. The LSS model reached the highest sensitivity (79.5%) and identified the highest percentage of FOG episodes (94.9%). The MSS model achieved the highest specificity (84.9%) and the lowest false positive (FP) rate (2 FP/walking trial). Overall, the bilateral model was best. The bilateral model had 77.3% sensitivity, 82.9% specificity, and identified 94.3% of FOG episodes an average of 1.1 s before FOG onset. Compared to the bilateral model, the LSS model had a higher false positive rate; however, the bilateral and LSS models were similar in all other evaluation metrics. Therefore, using the LSS model instead of the bilateral model would produce similar FOG prediction performance at the cost of slightly more false positives. Given the advantages of single sensor systems, the increased FP rate may be acceptable. Therefore, a single plantar pressure sensor placed on the LSS could be used to develop a FOG prediction system and produce performance similar to a bilateral system

First cannabis use: does onset shift to younger ages? Findings from 1988 tot 2003 from the Dutch National School Survey on Substance Use

Aims To investigate the hypothesis that changes in cannabis prevalence among Dutch secondary school students (aged 12-17 years) were paralleled by shifts in the age of Krst cannahis use. Design and participants Data were derived from five waves (1988. 1992. 1996.1999 and 2003) of the Dutch National School Survey on Substance Use. a nationally representative cross-sectional study, with a total of i2 777 respondents. Measurements Written questionnaires on cannabis. tobacco, alcohol, other drug use and soclo-dcmographic and behavioural variables were administered in classroom settings. Findings Survival analysis showed a strong increase in cumulative incidences hy age of lirsl cannabis use Troin 1988 to 1992, a further increase in 1996 and stabilization in 1999. continuing into 2003. From 1992 to 1996. age of onset shifted towards younger ages. Onset peaked at age 15 in 1992 and age 14 in 1996, The proportion of life-time cannabis users starting at age 1 3 or younger increased from 26% in 1992 to 41% in 1996. The overall trend was similar for boys and girls. Conclusions The study largely confirmed the expectation that the increase in cannabis use from 1988 to 1996 was paralleled by a decrease in the age of first cannabis use. From 1996 to 2003 age of first cannabis use and prevalence stabilized, possibly occasioned by a change in cannabis policy in the mid-1990s. KEYWORDS Age of onset, cannabis use. secondary school students. trends