Job control and ambulatory blood pressure

Objective: The effect of work on blood pressure (BP) in a general population with appropriate adjustment for confounders is not well defined. High job control has been found to be associated with lower BP and with nocturnal BP dipping. However, with older workers this may be compromised and has not been studied extensively. Methods: A cross-sectional study was carried out on a primary care-based sample (N=2047) aged 50–69 years. Data were collected on sociodemographic factors, medication, clinic, and ambulatory blood pressure. Job control was measured using two scales from the Copenhagen Psychosocial Questionnaire (COPSOQ) (possibility for development and influence at work). Nocturnal systolic BP (SBP) dipping was the reduction in SBP from day- to night-time using ambulatory SBP readings. Results: In general, BP increased with age, male gender, and higher body mass index. Workers with high influence at work and high possibility for development were more likely to have high asleep SBP [odds ratio (OR) 2.13, 95% confidence interval (95% CI) 1.05–4.34, P=0.04], (OR 2.27, 95% CI 1.11–4.66, P=0.03) respectively. Influence at work and awake BP were inversely associated: awake SBP (OR 2.44, 95% CI 1.35–4.41, P<0.01), awake DBP (OR 2.42, 95% CI 1.24–4.72, P=0.01). No association was seen between job control and nocturnal SBP dipping. Conclusion: Older workers with high job control may be more at risk of cardiovascular disease resulting from high day- and night-time BP with no evidence of nocturnal dipping.

Data-Aware Scheduling in Datacenters

Datacenters have emerged as the dominant form of computing infrastructure over the last two decades. The tremendous increase in the requirements of data analysis has led to a proportional increase in power consumption and datacenters are now one of the fastest growing electricity consumers in the United States. Another rising concern is the loss of throughput due to network congestion. Scheduling models that do not explicitly account for data placement may lead to a transfer of large amounts of data over the network causing unacceptable delays. In this dissertation, we study different scheduling models that are inspired by the dual objectives of minimizing energy costs and network congestion in a datacenter. As datacenters are equipped to handle peak workloads, the average server utilization in most datacenters is very low. As a result, one can achieve huge energy savings by selectively shutting down machines when demand is low. In this dissertation, we introduce the network-aware machine activation problem to find a schedule that simultaneously minimizes the number of machines necessary and the congestion incurred in the network. Our model significantly generalizes well-studied combinatorial optimization problems such as hard-capacitated hypergraph covering and is thus strongly NP-hard. As a result, we focus on finding good approximation algorithms. Data-parallel computation frameworks such as MapReduce have popularized the design of applications that require a large amount of communication between different machines. Efficient scheduling of these communication demands is essential to guarantee efficient execution of the different applications. In the second part of the thesis, we study the approximability of the co-flow scheduling problem that has been recently introduced to capture these application-level demands. Finally, we also study the question, "In what order should one process jobs?'' Often, precedence constraints specify a partial order over the set of jobs and the objective is to find suitable schedules that satisfy the partial order. However, in the presence of hard deadline constraints, it may be impossible to find a schedule that satisfies all precedence constraints. In this thesis we formalize different variants of job scheduling with soft precedence constraints and conduct the first systematic study of these problems.

A Component Based Heuristic Search Method with AdaptivePerturbations for Hospital Personnel Scheduling

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with adaptive perturbations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then mimic a natural evolutionary process on these components to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs a dynamic evaluation function which evaluates how well each component contributes towards the final objective. Two perturbation steps are then applied: the first perturbation eliminates a number of components that are deemed not worthy to stay in the current schedule; the second perturbation may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.

An evolutionary squeaky wheel optimisation approach to personnel scheduling

The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyse a technique called Evolutionary Squeaky Wheel Optimisation and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimisation improves the original Squeaky Wheel Optimisation’s effectiveness and execution speed by incorporating two additional steps (Selection and Mutation) for added evolution. In the Evolutionary Squeaky Wheel Optimisation, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The aim of the Analysis step is to identify below average solution components by calculating a fitness value for all components. The Selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The Mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repair is carried out by using the Prioritization step to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, improvements in the Evolutionary Squeaky Wheel Optimisation is achieved by selective solution disruption mixed with iterative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.

Work creativity as a dimension of job performance

To stay competitive, many employers are looking for creative and innovative employees to add value to their organization. However, current models of job performance overlook creative performance as an important criterion to measure in the workplace. The purpose of this dissertation is to conduct two separate but related studies on creative performance that aim to provide support that creative performance should be included in models of job performance, and ultimately included in performance evaluations in organizations. Study 1 is a meta-analysis on the relationship between creative performance and task performance, and the relationship between creative performance and organizational citizenship behavior (OCB). Overall, I found support for a medium to large corrected correlation for both the creative performance-task performance (ρ = .51) and creative performance-OCB (ρ = .49) relationships. Further, I also found that both rating-source and study location were significant moderators. Study 2 is a process model that includes creative performance alongside task performance and OCB as the outcome variables. I test a model in which both individual differences (specifically: conscientiousness, extraversion, proactive personality, and self-efficacy) and job characteristics (autonomy, feedback, and supervisor support) predict creative performance, task performance, and OCB through engagement as a mediator. In a sample of 299 employed individuals, I found that all the individual differences and job characteristics were positively correlated with all three performance criteria. I also looked at these relationships in a multiple regression framework and most of the individual differences and job characteristics still predicted the performance criteria. In the mediation analyses, I found support for engagement as a significant mediator of the individual differences-performance and job characteristics-performance relationships. Taken together, Study 1 and Study 2 support the notion that creative performance should be included in models of job performance. Implications for both researchers and practitioners alike are discussed.^

Entrepreneurship: a long way to go

Entrepreneurship education has emerged as one popular research domain in academic fields given its aim at enhancing and developing certain entrepreneurial qualities of undergraduates that change their state of behavior, even their entrepreneurial inclination and finally may result in the formation of new businesses as well as new job opportunities. This study attempts to investigate the Colombian student´s entrepreneurial qualities and the influence of entrepreneurial education during their studies.

Riesgo biomecánico y dolor lumbar en operarios y personal administrativo en una fábrica de jabón en Bogotá 2016

Introducción: El dolor lumbar y los desórdenes músculo esqueléticos comprometen la salud y la calidad de vida de los trabajadores, pueden poner en riesgo el futuro laboral de las personas. bjetivo: Estimar la prevalencia de dolor lumbar y los posibles factores biomecánicos asociados en el personal operativo y administrativo en una empresa manufacturera de jabón en Bogotá, en el año 2016 Metodología: Estudio de corte transversal donde se evaluó el riesgo biomecánico y la prevalencia del dolor lumbar en personal administrativo (138) y operativo (165); se utilizó como instrumento el ERGOPAR validado en España. Se revisó la asociación utilizando la prueba Chi Cuadrado de Pearson, con un nivel de significación α 0.05 Resultados: 303 trabajadores de una empresa manufacturera de jabón en Bogotá, donde predominó el género masculino (51,82%) y la población adulta media entre 30-39 años (57,42%). La prevalencia del dolor lumbar en la población fue de 61,39% (186). La edad no se asoció estadísticamente al dolor lumbar. Se encontró asociación estadística entre el síntoma dolor lumbar y extensión de cuello (p=0,05 OR1.95 IC 1.33-2.88), así como con agarrar o sujetar objetos (p= 0,036. OR 2.3 IC 1.59-3.51) y con las exigencias físicas laborales (p= 0.001 OR 1.99 IC 1.31-3.02). Conclusiones: La población estudiada presentó una alta prevalencia de dolor lumbar, con predominio en personal que realiza labores operativas, y del género femenino. La adopción de posturas de extensión del cuello y la sujeción o agarre de objetos son factores asociados directamente con la aparición de lumbalgia.

Constraint Programming-based Job Dispatching for Modern HPC Applications

A High-Performance Computing job dispatcher is a critical software that assigns the finite computing resources to submitted jobs. This resource assignment over time is known as the on-line job dispatching problem in HPC systems. The fact the problem is on-line means that solutions must be computed in real-time, and their required time cannot exceed some threshold to do not affect the normal system functioning. In addition, a job dispatcher must deal with a lot of uncertainty: submission times, the number of requested resources, and duration of jobs. Heuristic-based techniques have been broadly used in HPC systems, at the cost of achieving (sub-)optimal solutions in a short time. However, the scheduling and resource allocation components are separated, thus generates a decoupled decision that may cause a performance loss. Optimization-based techniques are less used for this problem, although they can significantly improve the performance of HPC systems at the expense of higher computation time. Nowadays, HPC systems are being used for modern applications, such as big data analytics and predictive model building, that employ, in general, many short jobs. However, this information is unknown at dispatching time, and job dispatchers need to process large numbers of them quickly while ensuring high Quality-of-Service (QoS) levels. Constraint Programming (CP) has been shown to be an effective approach to tackle job dispatching problems. However, state-of-the-art CP-based job dispatchers are unable to satisfy the challenges of on-line dispatching, such as generate dispatching decisions in a brief period and integrate current and past information of the housing system. Given the previous reasons, we propose CP-based dispatchers that are more suitable for HPC systems running modern applications, generating on-line dispatching decisions in a proper time and are able to make effective use of job duration predictions to improve QoS levels, especially for workloads dominated by short jobs.

Meta-learning for informed model searches in Automated Machine Learning

Day by day, machine learning is changing our lives in ways we could not have imagined just 5 years ago. ML expertise is more and more requested and needed, though just a limited number of ML engineers are available on the job market, and their knowledge is always limited by an inherent characteristic of theirs: they are humans. This thesis explores the possibilities offered by meta-learning, a new field in ML that takes learning a level higher: models are trained on other models' training data, starting from features of the dataset they were trained on, inference times, obtained performances, to try to understand the relationship between a good model and the way it was obtained. The so-called metamodel was trained on data collected by OpenML, the largest ML metadata platform that's publicly available today. Datasets were analyzed to obtain meta-features that describe them, which were then tied to model performances in a regression task. The obtained metamodel predicts the expected performances of a given model type (e.g., a random forest) on a given ML task (e.g., classification on the UCI census dataset). This research was then integrated into a custom-made AutoML framework, to show how meta-learning is not an end in itself, but it can be used to further progress our ML research. Encoding ML engineering expertise in a model allows better, faster, and more impactful ML applications across the whole world, while reducing the cost that is inevitably tied to human engineers.

Psychometric Properties Evaluation Of A New Ergonomics-related Job Factors Questionnaire Developed For Nursing Workers.

The objectives of this study were to develop a questionnaire that evaluates the perception of nursing workers to job factors that may contribute to musculoskeletal symptoms, and to evaluate its psychometric properties. Internationally recommended methodology was followed: construction of domains, items and the instrument as a whole, content validity, and pre-test. Psychometric properties were evaluated among 370 nursing workers. Construct validity was analyzed by the factorial analysis, known-groups technique, and convergent validity. Reliability was assessed through internal consistency and stability. Results indicated satisfactory fit indices during confirmatory factor analysis, significant difference (p < 0.01) between the responses of nursing and office workers, and moderate correlations between the new questionnaire and Numeric Pain Scale, SF-36 and WRFQ. Cronbach's alpha was close to 0.90 and ICC values ranged from 0.64 to 0.76. Therefore, results indicated that the new questionnaire had good psychometric properties for use in studies involving nursing workers.

Ecosystem Engineering Effects On Species Diversity Across Ecosystems: A Meta-analysis.

Ecosystem engineering is increasingly recognized as a relevant ecological driver of diversity and community composition. Although engineering impacts on the biota can vary from negative to positive, and from trivial to enormous, patterns and causes of variation in the magnitude of engineering effects across ecosystems and engineer types remain largely unknown. To elucidate the above patterns, we conducted a meta-analysis of 122 studies which explored effects of animal ecosystem engineers on species richness of other organisms in the community. The analysis revealed that the overall effect of ecosystem engineers on diversity is positive and corresponds to a 25% increase in species richness, indicating that ecosystem engineering is a facilitative process globally. Engineering effects were stronger in the tropics than at higher latitudes, likely because new or modified habitats provided by engineers in the tropics may help minimize competition and predation pressures on resident species. Within aquatic environments, engineering impacts were stronger in marine ecosystems (rocky shores) than in streams. In terrestrial ecosystems, engineers displayed stronger positive effects in arid environments (e.g. deserts). Ecosystem engineers that create new habitats or microhabitats had stronger effects than those that modify habitats or cause bioturbation. Invertebrate engineers and those with lower engineering persistence (<1 year) affected species richness more than vertebrate engineers which persisted for >1 year. Invertebrate species richness was particularly responsive to engineering impacts. This study is the first attempt to build an integrative framework of engineering effects on species diversity; it highlights the importance of considering latitude, habitat, engineering functional group, taxon and persistence of their effects in future theoretical and empirical studies.

Anxiety-like Effects Of Meta-chlorophenylpiperazine In Paradoxically Sleep-deprived Mice.

Chlorophenylpiperazines (CPP) are psychotropic drugs used in nightclub parties and are frequently used in a state of sleep deprivation, a condition which can potentiate the effects of psychoactive drugs. This study aimed to investigate the effects of sleep deprivation and sleep rebound (RB) on anxiety-like measures in mCPP-treated mice using the open field test. We first optimized our procedure by performing dose-effect curves and examining different pretreatment times in naïve male Swiss mice. Subsequently, a separate cohort of mice underwent paradoxical sleep deprivation (PSD) for 24 or 48h. In the last experiment, immediately after the 24h-PSD period, mice received an injection of saline or mCPP, but their general activity was quantified in the open field only after the RB period (24 or 48h). The dose of 5mgmL(-1) of mCPP was the most effective at decreasing rearing behavior, with peak effects 15min after injection. PSD decreased locomotion and rearing behaviors, thereby inhibiting a further impairment induced by mCPP. Plasma concentrations of mCPP were significantly higher in PSD 48h animals compared to the non-PSD control group. Twenty-four hours of RB combined with mCPP administration produced a slight reduction in locomotion. Our results show that mCPP was able to significantly change the behavior of naïve, PSD, and RB mice. When combined with sleep deprivation, there was a higher availability of drug in plasma levels. Taken together, our results suggest that sleep loss can enhance the behavioral effects of the potent psychoactive drug, mCPP, even after a period of rebound sleep.

Modeling The Effects Of Temperature And Ph On The Resistance Of Alicyclobacillus Acidoterrestris In Conventional Heat-treated Fruit Beverages Through A Meta-analysis Approach.

In this work, all publicly-accessible published findings on Alicyclobacillus acidoterrestris heat resistance in fruit beverages as affected by temperature and pH were compiled. Then, study characteristics (protocols, fruit and variety, °Brix, pH, temperature, heating medium, culture medium, inactivation method, strains, etc.) were extracted from the primary studies, and some of them incorporated to a meta-analysis mixed-effects linear model based on the basic Bigelow equation describing the heat resistance parameters of this bacterium. The model estimated mean D* values (time needed for one log reduction at a temperature of 95 °C and a pH of 3.5) of Alicyclobacillus in beverages of different fruits, two different concentration types, with and without bacteriocins, and with and without clarification. The zT (temperature change needed to cause one log reduction in D-values) estimated by the meta-analysis model were compared to those ('observed' zT values) reported in the primary studies, and in all cases they were within the confidence intervals of the model. The model was capable of predicting the heat resistance parameters of Alicyclobacillus in fruit beverages beyond the types available in the meta-analytical data. It is expected that the compilation of the thermal resistance of Alicyclobacillus in fruit beverages, carried out in this study, will be of utility to food quality managers in the determination or validation of the lethality of their current heat treatment processes.

Association Between Unilateral Tonsillar Enlargement And Lymphoma In Children: A Systematic Review And Meta-analysis.

Lymphoma is the most common head and neck malignancy in children, and palatine tonsils asymmetry is the most frequent clinical manifestation of tonsillar lymphoma. However, several studies with children with tonsillar asymmetry found no case of lymphoma, showing that the relationship of tonsillar asymmetry with lymphoma is unclear. In this review, we aimed to identify the association between tonsillar asymmetry and tonsillar lymphoma in children by conducting systematic reviews of the literature on children with palatine tonsil lymphoma and tonsillar asymmetry. Articles comprising the paediatric age group (up to 18 years) with information concerning clinical manifestations of tonsillar lymphoma or the diagnosis of the tonsillar asymmetry were included. The main cause of asymmetry of palatine tonsils was lymphoid hyperplasia, followed by lymphoma and nonspecific benign changes. The asymmetry of tonsils was present in 73.2% of cases of lymphoma. There was an association between asymmetric palatine tonsils and lymphoma, with a likelihood ratio of 43.5 for children with asymmetry of palatine tonsils and 8938.4 for children with asymmetry of tonsils and other signs of suspicion for malignancy. We also provide recommendations on the management of suspicious cases of palatine tonsil lymphoma.

Contribuições dos programas de pos-graduação stricto sensu na formação e atuação dos docentes de ensino superior : o caso da educação fisica

Universidade Estadual de Campinas . Faculdade de Educação Física