A paper waste prediction model

The aim of this paper is to develop a model predicting the collected amount of waste paper at the regional level of municipalities. Leaming about the factors that influence the amount of collected paper is a prerequisite for the evaluation and reorganization of collection systems. We hypothesize that the amount of collected paper depends on both, the waste potential and factors which influence the convenience such as the density of collection sites. For this study, we use a sample of 649 municipalities. The data show a high variance in terms of the collected waste paper per person and year between the municipalities. We develop a multivariate regression model providing valuable insights about the relationship between demographic parameters and the amount of collected waste paper. Furthermore, in this novel approach we found a significant positive impact of the convenience of the collection system

An Integration of Predictive Coding and Phenomenological Approaches

Current theories in the framework of hierarchical predictive coding propose that positive symptoms of schizophrenia, such as delusions and hallucinations, arise from an alteration in Bayesian inference, the term inference referring to a process by which learned predictions are used to infer probable causes of sensory data. However, for one particularly striking and frequent symptom of schizophrenia, thought insertion, no plausible account has been proposed in terms of the predictive-coding framework. Here we propose that thought insertion is due to an altered experience of thoughts as coming from “nowhere”, as is already indicated by the early 20th century phenomenological accounts by the early Heidelberg School of psychiatry. These accounts identified thought insertion as one of the self-disturbances (from German: “Ichstörungen”) of schizophrenia and used mescaline as a model-psychosis in healthy individuals to explore the possible mechanisms. The early Heidelberg School (Gruhle, Mayer-Gross, Beringer) first named and defined the self- disturbances, and proposed that thought insertion involves a disruption of the inner connectedness of thoughts and experiences, and a “becoming sensory” of those thoughts experienced as inserted. This account offers a novel way to integrate the phenomenology of thought insertion with the predictive coding framework. We argue that the altered experience of thoughts may be caused by a reduced precision of context-dependent predictions, relative to sensory precision. According to the principles of Bayesian inference, this reduced precision leads to increased prediction-error signals evoked by the neural activity that encodes thoughts. Thus, in analogy with the prediction-error related aberrant salience of external events that has been proposed previously, “internal” events such as thoughts (including volitions, emotions and memories) can also be associated with increased prediction-error signaling and are thus imbued with aberrant salience. We suggest that the individual’s attempt to explain the aberrant salience of thoughts results in their interpretation as being inserted by an alien agent, similarly to the emergence of delusions in response to the aberrant salience of sensory stimuli

Potential Performance Enhancement of a Solar Combisystem with an Intelligent Controller

Solar thermal systems in residential buildings are generally controlled by two-level controllers, which activate solar thermal or at times with low solar radiation auxiliary energy supply into a thermal storage. Simple controllers do not have any information on actual or expected solar radiation. This leads to interference of auxiliary- and solar heat supply, which reduces the share of solar thermal energy fed into the thermal storage. Increasing accuracy of weather forecast data suggests incorporation of this information in the control algorithm. This work analyzes the maximum potential performance enhancement when applying such an intelligent predictive control. Two solar thermal systems with one auxiliary source respectively are designed in TRNSYS - these systems represent the base case. Further, a number of simulations are conducted with minor variations for the plant parameters - this gives generic results for different system configurations. In addition, each system configuration is altered to mimic the behavior of a plant with intelligent predictive control. Comparison of results indicates an improvement potential up to 10% for annual solar fractions and up to 30% for monthly solar fractions. The performance bound with respect to the annual auxiliary energy savings is approximately 8%

Estimating the ice thickness of mountain glaciers with a shape optimization algorithm using surface topography and mass-balance

We present a shape optimization algorithm to estimate the ice thickness distribution within a two-dimensional, non-sliding mountain glacier, given a transient surface geometry and a mass-balance distribution. The approach is based on the minimization of the surface topography misfit at the end of the glacier's evolution in the shallow ice approximation of ice flow. Neither filtering of the surface topography where its gradient vanishes nor interpolation of the basal shear stress is involved. Novelty of the presented shape optimization algorithm is the use of surface topography and mass-balance only within a time-dependent Lagrangian approach for moving-boundary glaciers. On real-world inspired geometries, it is shown to produce estimations of even better quality in smaller time than the recently proposed steady and transient inverse methods. A sensitivity analysis completes the study and evinces the method's higher susceptibility to perturbations in the surface topography than in surface mass-balance or rate facto

SmartRegio – Employing Spatial Data to Provide Decision Support for SMEs and City Administrations

When decisions have to be made which are based on the characteristics and expected developments in specific spatial environments (such as finding the best place for a new production site or for a new shop), geo data and the information that can be derived from it plays a crucial role. While larger companies typically can afford the setup of the required organisational units as well as the access to relevant data from commercial providers, smaller organisations such as SMEs or city administrations are at a disadvantage. The aim of the SmartRegio project was to develop solutions for such organisations that combine freely available (mass) spatial data from many different sources as a decision-making basis focusing on governmental and private actors operating with a focus on a specific region. The data sources include data from infrastructures like energy and mobility, data from public entities, and also data from social media and media channels. The SmartRegio project successfully identified and tackled major technical and legal challenges when aiming to exploit such data, while at the same time realising a generic infrastructure that supports the required processes within the given context

Is It Always Unethical to Use a Placebo in a Clinical Trial?

Background to the debate: Placebos are used in trials to conceal whether a treatment is being given or not and hence to control for the psychosomatic effects of offering treatment. Placebo-controlled trials are controversial. Critics of such trials argue that if a proven effective therapy exists, a placebo should not be used. But proponents argue that placebo trials are still crucial to prove the efficacy and safety of many treatments

Antipsychotic Withdrawal Symptoms: A Systematic Review and Meta-Analysis

Objective Avoiding withdrawal symptoms following antipsychotic discontinuation is an important factor when planning a safe therapy. We performed a systematic review and meta-analysis concerning occurrence of withdrawal symptoms after discontinuation of antipsychotics. Data Sources We searched the databases CENTRAL, Pubmed, and EMBASE with no restriction to the beginning of the searched time period and until October 1, 2019 (PROSPERO registration no. CRD42019119148). Study Selection Of the 18,043 screened studies, controlled and cohort trials that assessed withdrawal symptoms after discontinuation of oral antipsychotics were included in the random-effects model. Studies that did not implement placebo substitution were excluded from analyses. The primary outcome was the proportion of individuals with withdrawal symptoms after antipsychotic discontinuation. We compared a control group with continued antipsychotic treatment in the assessment of odds ratio and number needed to harm (NNH). Data Extraction We followed guidelines by the Cochrane Collaboration, PRISMA, and MOOSE. Results Five studies with a total of 261 individuals were included. The primary outcome, proportion of individuals with withdrawal symptoms after antipsychotic discontinuation, was 0.53 (95% CI, 0.37–0.70; I2 = 82.98%, P < 0.01). An odds ratio of 7.97 (95% CI, 2.39–26.58; I2 = 82.7%, P = 0.003) and NNH of 3 was calculated for the occurrence of withdrawal symptoms after antipsychotic discontinuation. Conclusion Withdrawal symptoms appear to occur frequently after abrupt discontinuation of an oral antipsychotic. The lack of randomized controlled trials with low risk of bias on antipsychotic withdrawal symptoms highlights the need for further research

Acute stress alters probabilistic reversal learning in healthy male adults

Behavioural adaptation is a fundamental cognitive ability, ensuring survival by allowing for flexible adjustment to changing environments. In laboratory settings, behavioural adaptation can be measured with reversal learning paradigms requiring agents to adjust reward learning to stimulus–action–outcome contingency changes. Stress is found to alter flexibility of reward learning, but effect directionality is mixed across studies. Here, we used model-based functional MRI (fMRI) in a within-subjects design to investigate the effect of acute psychosocial stress on flexible behavioural adaptation. Healthy male volunteers (n = 28) did a reversal learning task during fMRI in two sessions, once after the Trier Social Stress Test (TSST), a validated psychosocial stress induction method, and once after a control condition. Stress effects on choice behaviour were investigated using multilevel generalized linear models and computational models describing different learning processes that potentially generated the data. Computational models were fitted using a hierarchical Bayesian approach, and model-derived reward prediction errors (RPE) were used as fMRI regressors. We found that acute psychosocial stress slightly increased correct response rates. Model comparison revealed that double-update learning with altered choice temperature under stress best explained the observed behaviour. In the brain, model-derived RPEs were correlated with BOLD signals in striatum and ventromedial prefrontal cortex (vmPFC). Striatal RPE signals for win trials were stronger during stress compared with the control condition. Our study suggests that acute psychosocial stress could enhance reversal learning and RPE brain responses in healthy male participants and provides a starting point to explore these effects further in a more diverse population

Disentangling the Roles of Approach, Activation and Valence in Instrumental and Pavlovian Responding

Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility