Hans Rudolf Heinimann

A Computer Model to Differentiate Skidder and Cable-Yarder Based Road Network Concepts on Steep Slopes

Road spacing on slpes depends on the underlying off-road transportation technology. One major decision in road network planning is to determine under what terrain conditions ground- or cable based extraction systems should be applied. The present investigation aims to develop a road spacing model for steep slope conditions and to implement a total cost model for skidder and cableyarder based road network concepts. The study analyzes transportation and road geometry to specify the relationship between road density, slope gradient, and road spacing. Production functions for skidder and yarder-systems make it possible to derive transportation cost as a function of road density and slope gradient. A total cost function integrates road building cost, harvesting strategy, and production economics to derive optimal road density for the two network concepts. The difference between the cost levels at optimum road density is an indicator for differentiating cable and skidder-based extraction systems. The model was implemented as a Visual Basic add-in for Microsoft Excel spreadsheet software. This flexible approach makes future adaptations and changes very easy due to the modular concept. The validity of the model is limited to the production functions of the underlying off-road transportation technologies. Future work needs to develop production functions for the state-of-the-art technologies and to improve the road building cost model.

An Application of a Heuristic Network Algorithm to Cable Logging Layout Design

Abstract This paper describes a method for optimizing cable logging layouts using a heuristic network algorithm. A timber harvest unit layout is formulated as a network problem. Each grid cell containing timber volume to be harvested is identified as an individual entry node of the network. Mill locations or proposed timber exit locations are identified as destinations. Each origin will then be connected to one of the destinations through alternative links representing alternative cable corridors, harvesting equipment, landing locations, and truck road segments. A heuristic algorithm for network programming is used to solve the cost minimization network problem. A computerized model has been developed to implement the method. Logging feasibility and cost analysis modules are included in the model in order to evaluate the logging feasibility of alternative cable corridors and estimate yarding and transportation costs. The model was successfully applied to a harvest planning area to generate harvesting plans. This case study indicates that the planning method is best used for pre-planning since modeling assumptions with respect to tail spar availability and unconstrained road alignments may require modification of the plan before implementation.

Modeling spatial variability in the life-cycle costs of low-volume forest roads

Cost estimation is probably the most decisive factor in the process of computer-aided, preliminary planning for low-volume road networks. However, the cost of construction is normally assumed to be route-independent for a specific project area, resulting in sub-optimal layouts. This is especially true for mountainous terrain and in areas with unstable subsoil. Here, we present a model for more accurately estimating spatial variability in road life-cycle costs, based on terrain surface properties as well as geological properties of the subsoil. This parametric model incorporates four structural components: embankment, retaining structures, pavement, and drainage and stream-crossing structures. It is linked to a geo-database that allows users to derive location-specific parameter values as input. In applying this model, we have demonstrated that variability in costs ranges widely for mountainous areas, with the most expensive construction being approximately five times greater there than on more favorable sites. This variability strongly affects the optimal layout of a road network. First, when location-specific slope gradients are considered, costs are reduced by about 17% from those calculated via currently available engineering practices; when both slope gradient and geotechnical formations are included, those costs are decreased by about 20%. Second, the length of the road network is increased by about 4% and 10% respectively, compared with current practices.

Optimum geometric layout of a single cable road

Cable-based technologies have been a backbone for harvesting on steep slopes. The layout of a single cable road is challenging because one must identify intermediate support locations and heights that guarantee structural safety and operational efficiency while minimizing set-up and dismantling costs. Our study objectives were to (1) develop an optimization approach for designing the best possible intermediate support layout for a given ground profile, (2) compare optimization procedures between linearized and nonlinear analyses of a cable structure and (3) investigate the effect of simplifying a multi-span representation. Our results demonstrate that the computational effort is 30–60 times greater for an optimization approach based on nonlinear cable mechanical assumptions than when considering linear assumptions. Those nonlinear assumptions also stipulate lower heights for intermediate supports and a larger span length. Finally, compared with the unloaded case, tensile force in the skyline is increased by as much as 80% under load for a single-span skyline configuration. Our approach provides additional value for cable operations because it ensures greater structural safety at a lower cost for installation. Improvements are still needed in developing a stand-alone application that can be easily distributed. Moreover, our rather simple assumptions regarding set-up and dismantling costs must be refined.

Risky Decisions in a Lottery Task Are Associated with an Increase of Cocaine Use

Cocaine use disorder is associated with maladaptive decision-making behavior, which strongly contributes to the harmful consequences of chronic drug use. Prior research has shown that cocaine users exhibit impaired neuropsychological test performances, particularly with regard to attention, learning, and memory but also in executive functions such as decision-making and impulse control. However, to what extent cocaine users show impaired decision-making under risk without feedback has not yet been investigated systematically. Therefore, to examine risk-taking behavior, 31 chronic cocaine users and 26 stimulant-naïve healthy controls who were part of the Zurich Cocaine Cognition Study, performed the Randomized Lottery Task (RALT) with winning lotteries consisting of an uncertain and a certain prospect. Results revealed that risky decisions were associated with male sex, increased cocaine use in the past year, higher cocaine concentrations in the hair, and younger age. In addition, higher levels of cocaine in the hair and cumulative lifetime consumption were associated with risky decisions, whereas potentially confounding factors including cognition and psychiatric symptoms had no significant effect. Taken together, our results indicate that cocaine users who increased their consumption over a period of 1 year show deficits in the processing of risky information accompanied with increased risk-taking. Future research should analyse whether risky decisions could potentially serve as a prognostic marker for cocaine use disorder.

A resilience-oriented approach for quantitatively assessing recurrent spatial-temporal congestion on urban roads

Traffic congestion brings not only delay and inconvenience, but other associated national concerns, such as greenhouse gases, air pollutants, road safety issues and risks. Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart community. A considerable amount of works has made contributions to tackle the problem. Several methods, such as time-based approaches and level of service, can be effective for characterizing congestion on urban streets. However, studies with systemic perspectives have been minor in congestion quantification. Resilience, on the other hand, is an emerging concept that focuses on comprehensive systemic performance and characterizes the ability of a system to cope with disturbance and to recover its functionality. In this paper, we symbolized recurrent congestion as internal disturbance and proposed a modified metric inspired by the well-applied “R4” resilience-triangle framework. We constructed the metric with generic dimensions from both resilience engineering and transport science to quantify recurrent congestion based on spatial-temporal traffic patterns and made the comparison with other two approaches in freeway and signal-controlled arterial cases. Results showed that the metric can effectively capture congestion patterns in the study area and provides a quantitative benchmark for comparison. Also, it suggested not only a good comparative performance in measuring strength of proposed metric, but also its capability of considering the discharging process in congestion. The sensitivity tests showed that proposed metric possesses robustness against parameter perturbation in Robustness Range (RR), but the number of identified congestion patterns can be influenced by the existence of ϵ. In addition, the Elasticity Threshold (ET) and the spatial dimension of cell-based platform differ the congestion results significantly on both the detected number and intensity. By tackling this conventional problem with emerging concept, our metric provides a systemic alternative approach and enriches the toolbox for congestion assessment. Future work will be conducted on a larger scale with multiplex scenarios in various traffic conditions.

Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards

Transportation networks daily provide accessibility and crucial services to societies. However, they must also maintain an acceptable level of service to critical infrastructures in the case of disruptions, especially during natural disasters. We have developed a method for assessing the resilience of transportation network topology when exposed to environmental hazards. This approach integrates graph theory with stress testing methodology and involves five basic steps: (1) establishment of a scenario set that covers a range of seismic damage potential in the network, (2) assessment of resilience using various graph-based metrics, (3) topology-based simulations, (4) evaluation of changes in graph-based metrics, and (5) examination of resilience in terms of spatial distribution of critical nodes and the entire network topology. Our case study was from the city of Kathmandu in Nepal, where the earthquake on April 25, 2015, followed by a major aftershock on May 12, 2015, led to numerous casualties and caused significant damage. Therefore, it is a good example for demonstrating and validating the developed methodology. The results presented here indicate that the proposed approach is quite efficient and accurate in assisting stakeholders when evaluating the resilience of transportation networks based on their topology.

Modeling Stock Survivability Resilience in Signed Temporal Networks: A Study from London Stock Exchange

This paper examines the dynamic evolution process in London stock exchange and attempts to model stock survivability resilience in the financial networks. A big historical dataset of UK companies from London stock exchange for 40 years (1976–2016) was collected and conceptualized into weighted, temporally evolving and signed networks using correlation coefficients. Based on the legal definition of corporate failure, stocks were categorized into Continuing, Failed and Normal groups. Accordingly, we conducted analysis on (1) The long-term evolution process of the entire population with statistical inference and visualization. (2) Multivariate logistic modeling of survivability resilience using short-term network measures, degree ratio (\(r_{i}\)), node degree (\(k_{i}\)), and node strength (\(s_{i}\)). The results show an exponential market growth but with a “fission-fusion” behavior in network topologies, which indicates dynamic and complex characteristics of its expansion. On the other hand, regression and modeling outcomes show that the survivability resilience is correlated with \(k_{i}\) and \(s_{i}\). Moreover, the analysis of deviance suggests that the survivability resilience could be described, by and large, as a function of \(k_{i}\) since it contributes the most significant difference. The study provides a novel alternative to look at the bankruptcy in the stock market and is potentially helpful for shareholders, decision- and policy-makers.

Quantum Decision Theory in Simple Risky Choices

Quantum decision theory (QDT) is a recently developed theory of decision making based on the mathematics of Hilbert spaces, a framework known in physics for its application to quantum mechanics. This framework formalizes the concept of uncertainty and other effects that are particularly manifest in cognitive processes, which makes it well suited for the study of decision making. QDT describes a decision maker’s choice as a stochastic event occurring with a probability that is the sum of an objective utility factor and a subjective attraction factor. QDT offers a prediction for the average effect of subjectivity on decision makers, the quarter law. We examine individual and aggregated (group) data, and find that the results are in good agreement with the quarter law at the level of groups. At the individual level, it appears that the quarter law could be refined in order to reflect individual characteristics. This article revisits the formalism of QDT along a concrete example and offers a practical guide to researchers who are interested in applying QDT to a dataset of binary lotteries in the domain of gains.

Validity of GRE General Test scores and TOEFL scores for graduate admission to a technical university in Western Europe

ABSTRACT Graduate admission has become a critical process in tertiary education, whereby selecting valid admissions instruments is key. This study assessed the validity of Graduate Record Examination (GRE) General Test scores for admission to Master’s programmes at a technical university in Europe. We investigated the indicative value of GRE scores for the Master’s programme grade point average (GGPA) with and without the addition of the undergraduate GPA (UGPA) and the TOEFL score, and of GRE scores for study completion and Master’s thesis performance. GRE scores explained 20% of the variation in the GGPA, while additional 7% were explained by the TOEFL score and 3% by the UGPA. Contrary to common belief, the GRE quantitative reasoning score showed only little explanatory power. GRE scores were also weakly related to study progress but not to thesis performance. Nevertheless, GRE and TOEFL scores were found to be sensible admissions instruments. Rigorous methodology was used to obtain highly reliable results.