Jose Humberto Gonzalez Gomez · Open MIND · 2026
This paper develops a conceptual and analytical framework for the study of Dependent MSME Households (HMD), defined as households whose economic well-being is structurally conditioned by the performance of the micro, small and medium-sized enterprise (MSME) sector. The central contribution is the introduction of the household sectoral dependence index (δ), which allows households to be classified according to their degree of exposure to income originating from the MSME sector, regardless of the occupational category of their members (employer, employee, or self-employed). The framework is extended to the concepts of HMD according to main or primary income (HMDP) and HMD with female main income earner (HMDPF), with particular emphasis on the transmission of productive shocks to household well-being.
Cited by 0Open access
Jose Humberto Gonzalez Gomez · Open MIND · 2026
This paper develops a conceptual and analytical framework for the study of Dependent MSME Households (HMD), defined as households whose economic well-being is structurally conditioned by the performance of the micro, small and medium-sized enterprise (MSME) sector. The central contribution is the introduction of the household sectoral dependence index (δ), which allows households to be classified according to their degree of exposure to income originating from the MSME sector, regardless of the occupational category of their members (employer, employee, or self-employed). The framework is extended to the concepts of HMD according to main or primary income (HMDP) and HMD with female main income earner (HMDPF), with particular emphasis on the transmission of productive shocks to household well-being.
Cited by 0Open access
Jose Humberto Gonzalez Gomez · Open MIND · 2026
This paper develops a conceptual and analytical framework for the study of Dependent MSME Households (HMD), defined as households whose economic well-being is structurally conditioned by the performance of the micro, small and medium-sized enterprise (MSME) sector. The central contribution is the introduction of the household sectoral dependence index (δ), which allows households to be classified according to their degree of exposure to income originating from the MSME sector, regardless of the occupational category of their members (employer, employee, or self-employed). The framework is extended to the concepts of HMD according to main or primary income (HMDP) and HMD with female main income earner (HMDPF), with particular emphasis on the transmission of productive shocks to household well-being.
Cited by 0Open access
Jacopo Bono (6449294), Claudia Clopath (324706) · Figshare · 2019
<p>(a) Schematic of presynaptic multiplied by postsynaptic firing rates after monocular deprivation, and assuming different ODI distributions of input neurons. If the ODI distribution is too narrow, it is harder for the threshold <i>θ</i> to separate more closed-eye dominated inputs from more open-eye dominated inputs. (b) Schematic of presynaptic multiplied by postsynaptic firing rates after monocular deprivation, and assuming heterogeneous postsynaptic firing. Postsynaptic neurons that receive weaker inputs and fire at lower rates, lead to a depression of open-eye dominated inputs while leaving closed-eye inputs unaltered. These neurons show a counter-intuitive shift towards the closed eye. (c) Examples of the product of presynaptic and postsynaptic firing rates for normally active neurons during MD, BD and MI. For red and blue traces, the product is taken with the most contralaterally dominated and ipsilaterally dominated presynaptic neuron respectively. (d) Ocular dominance index of the postsynaptic neuron at the beginning of the simulation versus the end. The neurons are normally activated. (e) Evolution of synaptic weights over time, in the case of monocular deprivation of the contralateral eye. The star denotes the onset of deprivation, the double dagger denotes the onset of reduced inhibition. Corresponding neuronal activations are shown in panel c.</p>
Cited by 0Open access
Benjamin Djulbegović, Iztok Hozo · Cancer treatment and research · 2023
Cited by 2
Sushil Jajodia, Pierangela Samarati, Moti Yung · 2025
Cited by 0
Matteo Modena, Matteo Modena · 2007
The expectations hypothesis implies that rational investors can predict future changes in interest rates by simply observing the yield spread. According to Mishkin (1990) the expectations theory can also be reformulated in terms of the ability of the spread to predict future inflation. Unfortunately, although appealing, the theory has found little empirical support. Time-varying term premia and changing risk perception have been advocated to rationalize the aforementioned weak empirical evidence. In this work we suggest that the time-varying nature of term premia makes single-equation models inappropriate to analyse the informative content of the term structure. In particular, when the deviations between the expected and the actual spread are large, which occurs in times of soaring term premia volatility, linear models fail to support the expectations theory. Within a threshold model for term premia, we provide evidence that the yield spread contains valuable information to predict future interest rates changes once the risk-averse attitude of economic agents is appropriately considered. Empirical results show that the predictive ability of the yield spread is contingent on the level of uncertainty as captured by the size of monetary policy surprise. JEL classification: C01, C30, E43, G12.
Cited by 0
Santiago Alonso-Diaz (5066648), Jessica F. Cantlon (106670), Steven T. Piantadosi (162556) · Figshare · 2018
<p>The model reproduces human accuracy and response times (bottom panels). The mean response times for the hard ratios (red an green) are slightly faster than human participants, but the quantiles, including the median are well fitted (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195188#pone.0195188.s011" target="_blank">S11 Fig</a>). Reaching (top right panel), however, was different from human subjects. Best fit parameters: thr = 0.62; dft = 0.28; ndt = 0.19 s; sig2 = 0.14; k = 3.56.</p>
Cited by 0Open access
Gaute Grønstøl · 2022
Cited by 0
Gaute Grønstøl · 2017
Cited by 2
Jacopo Bono (6449294), Claudia Clopath (324706) · Figshare · 2019
<p>(a) Schematic of the model. One postsynaptic neuron receives inputs from a population of presynaptic neurons, each with different ODI. We use a grey-scale color scheme where darker colors denote contralateral-eye dominated neurons and brighter colors denote ipsilateral-eye dominated neurons. (b) Schematic of pre- and postsynaptic firing rates under normal rearing (left), monocular deprivation of the contralateral eye (second from left), monocular deprivation with a reduced inhibition (second from right) and monocular deprivation with high contrast inputs (right). The bottom row depicts the product between pre- and postsynaptic firing rates, and the threshold <i>θ</i> separating LTD from LTP. (c) Example traces of the product of presynaptic and postsynaptic firing rates of the neurons during monocular deprivation of the contralateral eye (MD-CL), binocular deprivation (BD), monocular inactivation of the contralateral eye (MI) and monocular deprivation of the ipsilateral eye (MD-IL). For red and blue traces, the product is taken with the most contralaterally dominated and ipsilaterally dominated presynaptic neuron respectively. (d) Ocular dominance index of the postsynaptic neuron at the beginning of the simulation versus the end. (e) Evolution of synaptic weights over time, in the case of monocular deprivation of the contralateral eye. The star denotes the onset of deprivation, the double dagger denotes the onset of reduced inhibition. Corresponding neuronal activations are shown in panel c.</p>
Cited by 0Open access
CUHK Digital Repository (Chinese University of Hong Kong) · 2005
Cited by 0
Iosr Journals, Prof. M. Padmavathamma C. Phani Ramesh · Figshare · 2014
Cited by 0Open access
Benjamin Djulbegović, Iztok Hozo, Thomas Mayrhofer et al. · Journal of Evaluation in Clinical Practice · 2018
Calculation of the threshold depends on careful disease versus utility definitions and a decision-maker's values and preferences.
Cited by 35Open access
Bobby R. Scott · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · 2010
This paper introduces a novel microdosimetric model for metallic nanomaterial-parti- cles (MENAP)-induced cytotoxicity. The focus is on the engineered insoluble MENAP which represent a significant breakthrough in the design and development of new products for consumers, industry, and medicine. Increased production is rapidly occurring and may cause currently unrecognized health effects (e.g., nervous system dysfunction, heart disease, cancer); thus, dose-response models for MENAP-induced biological effects are needed to facilitate health risk assessment. The stochastic threshold microdose (STM) model presented introduces novel stochastic microdose metrics for use in constructing dose- response relationships for the frequency of specific cellular (e.g., cell killing, mutations, neoplastic transformation) or subcellular (e.g., mitochondria dysfunction) effects. A key metric is the exposure-time-dependent, specific burden (MENAP count) for a given critical target (e.g., mitochondria, nucleus). Exceeding a stochastic threshold specific burden triggers cell death. For critical targets in the cytoplasm, the autophagic mode of death is triggered. For the nuclear target, the apoptotic mode of death is triggered. Overall cell survival is evaluated for the indicated competing modes of death when both apply. The STM model can be applied to cytotoxicity data using Bayesian methods implemented via Markov chain Monte Carlo.
Cited by 0
Richard Dawkins · Animal Behaviour · 1969
Cited by 20
Yue Pan, Jiazhu Pan · Journal of Time Series Analysis · 2024
Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its variations have been widely adopted in the study of financial volatilities, while the extension of GARCH‐type models to high‐dimensional data is always difficult because of over‐parameterization and computational complexity. In this article, we propose a multi‐variate GARCH‐type model that can simplify the parameterization by utilizing the network structure that can be appropriately specified for certain types of high‐dimensional data. The asymmetry in the dynamics of volatilities is also considered as our model adopts a threshold structure. To enable our model to handle data with extremely high dimension, we investigate the near‐epoch dependence (NED) of our model, and the asymptotic properties of our quasi‐maximum‐likelihood‐estimator (QMLE) are derived from the limit theorems for NED random fields. Simulations are conducted to test our theoretical results. At last we fit our model to log‐returns of four groups of stocks and the results indicate that bad news is not necessarily more influential on volatility if the network effects are considered.
Cited by 0Open access
Benjamin Wei, Robert K. Shepherd, Roy M. Robins‐Browne et al. · Otology & Neurotology · 2006
A minimal threshold of bacteria is required to induce meningitis in healthy animals and is different for three different routes of infection (intraperitoneal, middle ear, and inner ear). Cochleostomy performed 4 weeks before the inoculation did not reduce the threshold of bacteria required for meningitis in all three infectious routes. This threshold model will also serve as a valuable tool, assisting clinicians to quantitatively analyze if the presence of a cochlear implant or other CNS prostheses alter the risk of meningitis.
Cited by 23Open access
2022
Cited by 0
López-Contreras González, Elena, Karmali, Faisal, King, Susan · Figshare · 2022
These files are a companion to our paper which shows that vestibular threshold measures are affected by cognitive biases. Specifically, we found that perceptual responses are influenced by the preceding trial, and this causes the threshold to be overestimated. We developed an enhanced model that accounts for these biases when fitting thresholds. The file "allsubjectdata.mat" contains the deidentified data used for all analyses in the paper. The file "EnhancedModelFunction.m" contains the code to analyze the data presented in the paper.
Cited by 0Open access