Optimal Body Size and Energy Expenditure during Winter: Why are voles smaller in declining populations?

Winter is energetically challenging for small herbivores because of greater energy requirements for thermogenesis at a time when little energy is available. We formulated a model predicting optimal wintering body size, accounting for the scaling of both energy expenditure and assimilation to body size, and the trade-off between survival benefits of a large size and avoiding survival costs of foraging. The model predicts that if the energy cost of maintaining a given body mass differs between environments, animals should be smaller in the more demanding environments, and there should be a negative correlation between body mass and daily energy expenditure (DEE) across environments. In contrast, if animals adjust their energy intake according to variation in survival costs of foraging, there should be a positive correlation between body mass and DEE. Decreasing temperature always increases equilibrium DEE, but optimal body mass may either increase or decrease in colder climates depending on the exact effects of temperature on mass-specific survival and energy demands. Measuring DEE with doubly labeled water on wintering Microtus agrestis at four field sites, we found that DEE was highest at the sites where voles were smallest despite a positive correlation between DEE and body mass within sites. This suggests that variation in wintering body mass between sites was due to variation in food quality/availability and not adjustments in foraging activity to varying risks of predation.

Optimal Credit Rationing in Not-For-Profit Financial Institutions

We examine the dynamic optimization problem for not-for-profit financial institutions (NFPs) that maximize consumer surplus, not profits. We characterize the optimal dynamic policy and find that it involves credit rationing. Interest rates set by mature NFPs will typically be more favorable to customers than market rates, as any surplus is distributed in the form of interest rate subsidies, with credit rationing being required to prevent these subsidies from distorting loan volumes from their optimal levels. Rationing overcomes a fundamental problem in NFPs; it allows them to distribute the surplus without distorting the volume of activity from the efficient level.

Differences in Abdominal and Liver Fat Accumulation Between Obesity-Matched Adults With and Without Type 2 Diabetes

Despite attempts to identify the mechanisms by which obesity leads to the development of Type 2 Diabetes (T2D), it remains unclear why some but not all adults with obesity develop T2D. Given the established associations between visceral adipose tissue (VAT) and liver fat with insulin resistance, we hypothesized that compared to age and obesity matched adults who were non-diabetic (NT2D), adults with T2D would have greater amounts of VAT and liver fat. The International Study of Prediction of Intra-Abdominal Adiposity and Its Relationship with Cardiometabolic Risk/Intra-Abdominal Adiposity (INSPIRE ME IAA) aims to study the associations between VAT and liver fat and risk of developing T2D and cardiovascular disease. Four thousand, five hundred and four participants were initially recruited; from this, 2383 White and Asian adults were selected for this ancillary analysis. The NT2D and T2D groups were matched for age, body mass index (BMI) and waist circumference (WC). The T2D and NT2D groups were also compared to participants with either impaired fasting glucose (IFG) or impaired glucose tolerance (IGT; IFG/IGT)). Abdominal adipose tissue was measured by computed tomography; liver fat was estimated using computed tomography-derived mean attenuation. Secondary analysis determined whether differences existed between NT2D and T2D groups in VAT and liver fat accumulation within selected BMI categories for Whites and Asians. We report across sex and race, T2D and IFG/IGT groups had elevated VAT and liver fat compared to the NT2D group (p<0.05). VAT was not different between IFG/IGT and T2D groups (p>0.05), however liver fat was greater in the T2D group compared to the IFG/IGT group in both Whites and Asians (p<0.05). Within each BMI category, the T2D group had elevated VAT and liver fat compared to the age and anthropometrically matched NT2D group in both Whites and Asians (p<0.05). With few exceptions, abdominal subcutaneous adipose tissue was not different in the T2D or IFG/IGT groups compared to the NT2D group independent of sex and race. Compared to age and obesity-matched adults who are NT2D, we observe that White and Asian adults with T2D, and those with IFG/IGT, present with greater levels of both VAT and liver fat.

Allocating transmission loss to loads and generators through complex power flow tracing

This paper presents a new method for complex power flow tracing that can be used for allocating the transmission loss to loads or generators. Two algorithms for upstream tracing (UST) and downstream tracing (DST) of the complex power are introduced. UST algorithm traces the complex power extracted by loads back to source nodes and assigns a fraction of the complex power flow through each line to each load. DST algorithm traces the output of the generators down to the sink nodes determining the contributions of each generator to the complex power flow and losses through each line. While doing so, active- and reactive-power flows as well as complex losses are considered simultaneously, not separately as most of the available methods do. Transmission losses are taken into consideration during power flow tracing. Unbundling line losses are carried out using an equation, which has a physical basis, and considers the coupling between active- and reactive-power flows as well as the cross effects of active and reactive powers on active and reactive losses. The tracing algorithms introduced can be considered direct to a good extent, as there is no need for exhaustive search to determine the flow paths as these are determined in a systematic way during the course of tracing. Results of application of the proposed method are also presented.

A method for determining generators’ shares in loads, line flows and losses

This paper presents a new method for calculating the individual generators’ shares in line flows, line losses and loads. The method is described and illustrated on active power flows, but it can be applied in the same way to reactive power flows. Starting from a power flow solution, the line flow matrix is formed. This matrix is used for identifying node types, tracing the power flow from generators downstream to loads, and to determine generators’ participation factors to lines and loads. Neither exhaustive search nor matrix inversion is required. Hence, the method is claimed to be the least computationally demanding amongst all of the similar methods.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.