Determinants of the duration of sick leave due to occupational injuries: evidence from Spanish manufacturing

Introduction: Despite the significant economic impact of occupational injuries on companies and society, studies focused on analyzing the determinants of workdays lost due to sick leave remain scarce and incomplete. This paper contributes to this issue by (a) analyzing the drivers of sick leave duration, distinguishing factors that explain the health recovery time from those that could lead workers to a voluntary extension of the absence period, and (b) formulating and empirically testing the effect of gender, citizenship, temporary work, job tenure, amount of disability benefit, and size of the injured worker's firm on the number of days the employee is off work after the injury. Method: Hypotheses are tested on a comprehensive dataset that includes all nonfatal occupational injuries causing sick leave that occurred in the manufacturing sector in Spain during 2015¿2019, with more than 400,000 injuries. We conduct ordinary least squares and count data regression models in which the number of days off work is regressed on employees and work characteristics while accounting for a set of variables to control the injury's nature and severity. Results: The results show that after considering the intrinsic characteristics of the injury and the severity of the worker's injuries, women, native workers, workers with more seniority, workers with higher salaries, and those working in larger companies have longer periods of sick leave. The results suggest that moral hazard considerations significantly impact the time to return to work after an occupational injury. Practical applications: Based on the findings, several insights for company managers and public decision-makers are discussed. Specifically, interventions aimed at improving the organization of work and the working conditions of workers in manufacturing industries are highlighted, as well as the need to improve control and supervision mechanisms during the recovery process of injured workers.The authors acknowledge financial support from the Spanish Ministry of Science and Innovation, Grant PID2020-114460GB-C32 funded by MCIN/AEI/ 10.13039/501100011033. Open access funding provided by Universidad Pública de Navarra

An optimization tool to design the field of a Solar Power Tower plant allowing heliostats of different sizes

The design of a Solar Power Tower plant involves the optimization of the heliostat field layout. Fields are usually designed to have all heliostats of identical size. Although the use of a single heliostat size has been questioned in the literature, there are no tools to design fields with heliostats of several sizes at the same time. In this paper, the problem of optimizing the heliostat field layout of a system with heliostats of different sizes is addressed. We present an optimization tool to design solar plants allowing two heliostat sizes. The methodology is illustrated with a particular example considering different heliostat costs.MTM2013-41286-P (Spain) MTM2015-65915-R (Spain) P11-FQM-7603 (Andalucía) TD1207 (EU COST Action

Dual encoding of muscle tension and eye position by abducens motoneurons

Extraocular muscle tension associated with spontaneous eye movements has a pulse-slide-step profile similar to that of motoneuron firing rate. Existing models only relate motoneuron firing to eye position, velocity and acceleration. We measured and quantitatively compared lateral rectus muscle force and eye position with the firing of abducens motoneurons in the cat to determine fundamental encoding correlations. During fixations (step), muscle force increased exponentially with eccentric eye position, consistent with a model of estimate ensemble motor innervation based on neuronal sensitivities and recruitment order. Moreover, firing rate in all motoneurons tested was better related to eye position than to muscle tension during fixations. In contrast, during the postsaccadic slide phase, the time constant of firing rate decay was closely related to that of muscle force decay, suggesting that all motoneurons encode muscle tension as well. Discharge characteristics of abducens motoneurons formed overlapping clusters of phasic and tonic motoneurons, thus, tonic units recruited earlier and had a larger slide signal. We conclude that the slide signal is a discharge characteristic of the motoneuron that controls muscle tension during the postsaccadic phase and that motoneurons are specialized for both tension and position-related properties. The organization of signal content in the pool of abducens motoneurons from the very phasic to the very tonic units is possibly a result of the differential trophic background received from distinct types of muscle fibers

Variable selection for Naive Bayes classification

The Naive Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Naive Bayes' assumption of conditional independence, and may deteriorate the method's performance. Moreover, datasets are often characterized by a large number of features, which may complicate the interpretation of the results as well as slow down the method's execution. In this paper we propose a sparse version of the Naive Bayes classifier that is characterized by three properties. First, the sparsity is achieved taking into account the correlation structure of the covariates. Second, different performance measures can be used to guide the selection of features. Third, performance constraints on groups of higher interest can be included. Our proposal leads to a smart search, which yields competitive running times, whereas the flexibility in terms of performance measure for classification is integrated. Our findings show that, when compared against well-referenced feature selection approaches, the proposed sparse Naive Bayes obtains competitive results regarding accuracy, sparsity and running times for balanced datasets. In the case of datasets with unbalanced (or with different importance) classes, a better compromise between classification rates for the different classes is achieved.This research is partially supported by research grants and projects MTM2015-65915-R (Ministerio de Economia y Competitividad, Spain) and PID2019-110886RB-I00 (Ministerio de Ciencia, Innovacion y Universidades, Spain) , FQM-329 and P18-FR-2369 (Junta de Andalucia, Spain) , PR2019-029 (Universidad de Cadiz, Spain) , Fundacion BBVA and EC H2020 MSCA RISE NeEDS Project (Grant agreement ID: 822214) . This support is gratefully acknowledged. Documen

A cost-sensitive constrained Lasso

The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. Although the Lasso formulations are stated so that overall prediction error is optimized, no full control over the accuracy prediction on certain individuals of interest is allowed. In this work we propose a novel version of the Lasso in which quadratic performance constraints are added to Lasso-based objective functions, in such a way that threshold values are set to bound the prediction errors in the different groups of interest (not necessarily disjoint). As a result, a constrained sparse regression model is defined by a nonlinear optimization problem. This cost-sensitive constrained Lasso has a direct application in heterogeneous samples where data are collected from distinct sources, as it is standard in many biomedical contexts. Both theoretical properties and empirical studies concerning the new method are explored in this paper. In addition, two illustrations of the method on biomedical and sociological contexts are considered

A cost-sensitive constrained Lasso

The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. Although the Lasso formulations are stated so that overall prediction error is optimized, no full control over the accuracy prediction on certain individ- uals of interest is allowed. In this work we propose a novel version of the Lasso in which quadratic performance con- straints are added to Lasso-based objective functions, in such a way that threshold values are set to bound the prediction errors in the different groups of interest (not necessarily disjoint). As a result, a constrained sparse regression model is defined by a nonlinear optimization prob- lem. This cost-sensitive constrained Lasso has a direct application in heterogeneous samples where data are collected from distinct sources, as it is standard in many biomedical contexts. Both theoretical properties and empirical studies concerning the new method are explored in this paper. In addition, two illustrations of the method on biomedical and sociological contexts are considered

Case Report: Granular Cell Tumor In Breast

Granular cell tumor (GCT) of the breast is an unusual neoplasm, tipicallybenign, it represents between 5-6% of all GCT cases. These tumors aremore common in middle-aged premenopausal women with a greater predilection African American race [1]. Nevertheless, there are also cases described in men [2-4]. Almost all of them are favorable, the malignant casesare uncommon (only 1-3%). Sometimes it could be clinically and radiologically confused with a malignant breast tumor; so it's very importantto make a differential diagnosis. The choice therapy is an extensive localextirpation with free margins [5], without the need for adjuvant chemotherapy or radiotherapy. Our case is a 61-year-old woman with a GCT, andthree years ago a history of breast carcinoma in the same breast

Efecto del Locus CSN3 sobre la composición de la leche de cabra de raza murciano-granadina

CICYT AGL2002-04304-C03-01-02- 03 GA

Short communication: An association analysis between one missense polymorphism at the SREBF1 gene and milk yield and composition traits in goats

Sterol regulatory element binding transcription factor 1 (SREBF1) regulates the expression of genes involved in the biosynthesis of fatty acids and cholesterol. Herewith, we have sequenced the near-complete coding region and part of the 3?UTR of the goat SREBF1 gene. In doing so, we have detected a missense c.353CT polymorphism causing a proline to leucine substitution at position 118 (P118L). An association analysis with milk composition traits recorded in MurcianoGranadina goats only revealed a statistical tendency linking SREBF1 genotype and milk omega-3 fatty acid content. The lack of significant associations suggests that the P118L substitution does not involve a functional change.Le facteur de transcription de´nomme´ Sterol regulatory element binding transcription factor 1 (SREBF1) re´gule l’expression des ge`nes implique´s dans la biosynthe`se des acides gras et du choleste´rol. Dans cette e´tude, nous avons se´quence´ la quasi-totalite´ de la re´gion codante et une partie du la re´gion 3?UTR du ge`ne SREBF1 de la che`vre. Ce travail, nous a permis d’identifier un polymorphisme non-synonyme c.353CT causant la substitution d’une Proline en Leucine a` la position 118. L’e´tude d’association avec la composition du lait enregistre´e en che`vres Murciano-Granadina, a re´ve´le´ seulement une tendance statistique reliant SREBF1 ge´notype et l’acide gras ome´ga-3 du lait. L’absence d’associations significatives sugge`re que la substitution P118L n’implique pas un changement fonctionnel