Experiences of COPD patients with existing smoking cessation programs and their preferences for improvement - a qualitative analysis

BACKGROUND: Smoking is a major risk factor for chronic obstructive pulmonary disease (COPD). For current smokers who are diagnosed with COPD, their first treatment option is to stop smoking. Motivation is necessary for long-term smoking cessation; therefore, when designing smoking cessation programs, the patients' needs and preferences should be considered. We focused on COPD patients' experiences with existing smoking cessation programs and evaluated their preferences for the improvement of these programs. METHODS: We conducted 18 guideline-based interviews with COPD patients between April and June 2014 in Germany. Each patient with COPD, who was a current or past smoker and had made at least one attempt to quit smoking in the past 5 years, was included in the study. We audiotaped, verbatim transcribed, and evaluated the interviews, using content analysis. RESULTS: The patients had broad and different experiences with pharmaceutical, behavioral, and alternative approaches that supported or negatively influenced the smoking cessation process. Pharmaceuticals were viewed as an expensive alternative with many side effects although they helped to stop cravings for a few moments. Furthermore, the bad structure and impersonal content of the seminars for smoking cessation negatively influenced group cohesion, and therefore degrading the patients' motivation to stop smoking. Alternative methods, such as acupuncture and hypnosis were mostly ineffective in smoking cessation, but in some cases, served as motivational strategies. CONCLUSION: Negative experiences with smoking cessation were explained by the patients' lack of motivation or resolution. Other negative experiences, such as the structure of seminars for smoking cessation and the high price of pharmaceuticals should be addressed through policy changes to increase the patients' motivation to quit smoking.

Comprehensive economic and financial analysis of the enterprise activity: approaches, methods and implementation

This thesis intends to analyse the performance and the efficiency of companies and to identify the key factors that may explain it. A comprehensive analysis based on a set of economic and financial ratios was studied as an instrument which provides information on enterprise performance and its efficiency. It was selected a sample with 15 enterprises: 7 Portuguese and 8 Ukrainian ones, belonging to several industries. Financial and non-financial data was collected for 6 years, during the period of 2009 to 2014. Research questions that guided this work were: Are the enterprises efficient/profitable? What factors influence enterprises’ efficiency/performance? Is there any difference between Ukrainian and Portuguese enterprises’ efficiency/performance, which factors have more influence? Which industrial sector is represented by more efficient/profitable enterprises? The main results showed that in average enterprises were efficient; comparing by states Ukrainian enterprises are more efficient; industries have similar level of efficiency. Among factors that influence ATR positively are fixed and current assets turnover ratios, ROA; negatively influencing are EBITDA margin and liquidity ratio. There is no significant difference between models by country. Concerning profitability, enterprises have low performance level but in comparison of countries Ukrainian enterprises have better profitability in average. Regarding the industry sector, paper industry is the most profitable. Among factors influencing ROA are profit margin, fixed asset turnover ratio, EBITDA margin, Debt to equity ratio and the country. In case of profitability both countries have different models. For Ukrainian enterprises is suggested to pay attention on factors of Short-term debt to total debt, ROA, Interest coverage ratio in order to be more efficient; Profit margin and EBITDA margin to make their performance better. For Portuguese enterprises for improving efficiency the observation and improvement of fixed assets turnover ratio, current assets turnover ratio, Short-term financial debt to total debt, Leverage Ratio, EBITDA margin is suggested; for improving higher profitability track fixed assets turnover ratio, current assets turnover ratio, Debt to equity ratio, Profit margin and Interest coverage ratio is suggested.

Architectural-Physical Co-Design of 3D CPUs with Micro-Fluidic Cooling

The performance, energy efficiency and cost improvements due to traditional technology scaling have begun to slow down and present diminishing returns. Underlying reasons for this trend include fundamental physical limits of transistor scaling, the growing significance of quantum effects as transistors shrink, and a growing mismatch between transistors and interconnects regarding size, speed and power. Continued Moore's Law scaling will not come from technology scaling alone, and must involve improvements to design tools and development of new disruptive technologies such as 3D integration. 3D integration presents potential improvements to interconnect power and delay by translating the routing problem into a third dimension, and facilitates transistor density scaling independent of technology node. Furthermore, 3D IC technology opens up a new architectural design space of heterogeneously-integrated high-bandwidth CPUs. Vertical integration promises to provide the CPU architectures of the future by integrating high performance processors with on-chip high-bandwidth memory systems and highly connected network-on-chip structures. Such techniques can overcome the well-known CPU performance bottlenecks referred to as memory and communication wall. However the promising improvements to performance and energy efficiency offered by 3D CPUs does not come without cost, both in the financial investments to develop the technology, and the increased complexity of design. Two main limitations to 3D IC technology have been heat removal and TSV reliability. Transistor stacking creates increases in power density, current density and thermal resistance in air cooled packages. Furthermore the technology introduces vertical through silicon vias (TSVs) that create new points of failure in the chip and require development of new BEOL technologies. Although these issues can be controlled to some extent using thermal-reliability aware physical and architectural 3D design techniques, high performance embedded cooling schemes, such as micro-fluidic (MF) cooling, are fundamentally necessary to unlock the true potential of 3D ICs. A new paradigm is being put forth which integrates the computational, electrical, physical, thermal and reliability views of a system. The unification of these diverse aspects of integrated circuits is called Co-Design. Independent design and optimization of each aspect leads to sub-optimal designs due to a lack of understanding of cross-domain interactions and their impacts on the feasibility region of the architectural design space. Co-Design enables optimization across layers with a multi-domain view and thus unlocks new high-performance and energy efficient configurations. Although the co-design paradigm is becoming increasingly necessary in all fields of IC design, it is even more critical in 3D ICs where, as we show, the inter-layer coupling and higher degree of connectivity between components exacerbates the interdependence between architectural parameters, physical design parameters and the multitude of metrics of interest to the designer (i.e. power, performance, temperature and reliability). In this dissertation we present a framework for multi-domain co-simulation and co-optimization of 3D CPU architectures with both air and MF cooling solutions. Finally we propose an approach for design space exploration and modeling within the new Co-Design paradigm, and discuss the possible avenues for improvement of this work in the future.

A new plant growth system rig based on thermodynamic solar energy: a study for energy efficiency assessment

At the present there is a high pressure toward the improvement of all production processes. Those improvements can target distinct factors along the production chain. In particular, and due to recent tight energy efficiency policies, those that involve energy efficiency. As can be expected, agricultural processes are not immune to this tendency. Even more when dealing with indoor productions. In this context, this work presents an innovative system that aims to improve the energy efficiency of a trees growing platform. This improvement in energy consumption is accomplished by replacing an electric heating system by one based on thermodynamic panels. The assessment of the heating fluid caudal and its temperature was experimentally obtained by means of a custom made scaled prototype whose actuators status are commanded by a Fuzzy-based controller. The obtained results suggest that the change in the heating paradigm will lead to overall savings that can easily reach 60% on the energy bill.

Economic efficiency of members of protected designations of origin: sharing reputation indicators in the experience goods of wine and cheese

The aim of this paper is to analyse the economic efficiency of members of protected designations of origin (PDO). For the first time we analyse the value of PDO labels from the point of view of economic efficiency. The central hypothesis is that a PDO has a positive impact on the economic efficiency of its member companies and that this is because a PDO label is a collective reputation indicator that foments efficient investment in quality in terms of member returns. The methodology applied to test this hypothesis is based on data envelopment analysis to estimate economic efficiency, and econometric models to explain company efficiency through both the PDO label, as an indicator of collective reputation, and the characteristics of the company. The results obtained in the experience goods of wine and cheese in Spain show that PDO labels have a positive impact on economic efficiency. Additionally, the age and size of the company have a positive effect while the wage level of the company has a different influence on efficiency depending on the sector considered. Overall, the results reveal the importance of PDOs in industries in which the signal of reputation is not only reliant on the individual brands.

Quantum of Vision

Visual inputs to artificial and biological visual systems are often quantized: cameras accumulate photons from the visual world, and the brain receives action potentials from visual sensory neurons. Collecting more information quanta leads to a longer acquisition time and better performance. In many visual tasks, collecting a small number of quanta is sufficient to solve the task well. The ability to determine the right number of quanta is pivotal in situations where visual information is costly to obtain, such as photon-starved or time-critical environments. In these situations, conventional vision systems that always collect a fixed and large amount of information are infeasible. I develop a framework that judiciously determines the number of information quanta to observe based on the cost of observation and the requirement for accuracy. The framework implements the optimal speed versus accuracy tradeoff when two assumptions are met, namely that the task is fully specified probabilistically and constant over time. I also extend the framework to address scenarios that violate the assumptions. I deploy the framework to three recognition tasks: visual search (where both assumptions are satisfied), scotopic visual recognition (where the model is not specified), and visual discrimination with unknown stimulus onset (where the model is dynamic over time). Scotopic classification experiments suggest that the framework leads to dramatic improvement in photon-efficiency compared to conventional computer vision algorithms. Human psychophysics experiments confirmed that the framework provides a parsimonious and versatile explanation for human behavior under time pressure in both static and dynamic environments.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

Optimization of Pattern Matching Algorithms for Multi- and Many-Core Platforms

Image and video compression play a major role in the world today, allowing the storage and transmission of large multimedia content volumes. However, the processing of this information requires high computational resources, hence the improvement of the computational performance of these compression algorithms is very important. The Multidimensional Multiscale Parser (MMP) is a pattern-matching-based compression algorithm for multimedia contents, namely images, achieving high compression ratios, maintaining good image quality, Rodrigues et al. [2008]. However, in comparison with other existing algorithms, this algorithm takes some time to execute. Therefore, two parallel implementations for GPUs were proposed by Ribeiro [2016] and Silva [2015] in CUDA and OpenCL-GPU, respectively. In this dissertation, to complement the referred work, we propose two parallel versions that run the MMP algorithm in CPU: one resorting to OpenMP and another that converts the existing OpenCL-GPU into OpenCL-CPU. The proposed solutions are able to improve the computational performance of MMP by 3 and 2:7 , respectively. The High Efficiency Video Coding (HEVC/H.265) is the most recent standard for compression of image and video. Its impressive compression performance, makes it a target for many adaptations, particularly for holoscopic image/video processing (or light field). Some of the proposed modifications to encode this new multimedia content are based on geometry-based disparity compensations (SS), developed by Conti et al. [2014], and a Geometric Transformations (GT) module, proposed by Monteiro et al. [2015]. These compression algorithms for holoscopic images based on HEVC present an implementation of specific search for similar micro-images that is more efficient than the one performed by HEVC, but its implementation is considerably slower than HEVC. In order to enable better execution times, we choose to use the OpenCL API as the GPU enabling language in order to increase the module performance. With its most costly setting, we are able to reduce the GT module execution time from 6.9 days to less then 4 hours, effectively attaining a speedup of 45 .

Natural ventilation and indoor air quality in educational buildings: experimental assessment and improvement strategies

Indoor environmental conditions in classrooms, in particular temperature and indoor air quality, influence students’ health, attitude and performance. In recent years, several studies regarding indoor environmental quality of classrooms were published and natural ventilation proved to have great potential, particularly in southern European climate. This research aimed to evaluate indoor environmental conditions in eight schools and to assess their improvement potential by simple natural ventilation strategies. Temperature, relative humidity and carbon dioxide concentration were measured in 32 classrooms. Ventilation performance of the classrooms was characterized using two techniques, first by fan pressurization measurements of the envelope airtightness and later by tracer gas measurements of the air change rate assuming different envelope conditions. A total of 110 tracer gas measurements were made and the results validated ventilation protocols that were tested afterward. The results of the ventilation protocol implementation were encouraging and, overall, a decrease on the CO2 concentration was observed without modifying the comfort conditions. Nevertheless, additional measurements must be performed for winter conditions.

Functional engineering of hybrid heterostructures for application in electronic, optical and optoelectronic nanostructured devices

Interfacing materials with different intrinsic chemical-physical characteristics allows for the generation of a new system with multifunctional features. Here, this original concept is implemented for tailoring the functional properties of bi-dimensional black phosphorus (2D bP or phosphorene) and organic light-emitting transistors (OLETs). Phosphorene is highly reactive under atmospheric conditions and its small-area/lab-scale deposition techniques have hampered the introduction of this material in real-world applications so far. The protection of 2D bP against the oxygen by means of functionalization with alkane molecules and pyrene derivatives, showed long-term stability with respect to the bare 2D bP by avoiding remarkable oxidation up to 6 months, paving the way towards ultra-sensitive oxygen chemo-sensors. A new approach of deposition-precipitation heterogeneous reaction was developed to decorate 2D bP with Au nanoparticles (NP)s, obtaining a “stabilizer-free” that may broaden the possible applications of the 2D bP/Au NPs interface in catalysis and biodiagnostics. Finally, 2D bP was deposited by electrospray technique, obtaining oxidized-phosphorous flakes as wide as hundreds of µm2 and providing for the first time a phosphorous-based bidimensional system responsive to electromechanical stimuli. The second part of the thesis focuses on the study of organic heterostructures in ambipolar OLET devices, intriguing optoelectronic devices that couple the micro-scaled light-emission with electrical switching. Initially, an ambipolar single-layer OLET based on a multifunctional organic semiconductor, is presented. The bias-depending light-emission shifted within the transistor channel, as expected in well-balanced ambipolar OLETs. However, the emitted optical power of the single layer-based device was unsatisfactory. To improve optoelectronic performance of the device, a multilayer organic architecture based on hole-transporting semiconductor, emissive donor-acceptor blend and electron-transporting semiconductor was optimized. We showed that the introduction of a suitable electron-injecting layer at the interface between the electron-transporting and light-emission layers may enable a ≈ 2× improvement of efficiency at reduced applied bias.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

Ikkε Is Key To Induction Of Insulin Resistance In The Hypothalamus, And Its Inhibition Reverses Obesity.

IKK epsilon (IKKε) is induced by the activation of nuclear factor-κB (NF-κB). Whole-body IKKε knockout mice on a high-fat diet (HFD) were protected from insulin resistance and showed altered energy balance. We demonstrate that IKKε is expressed in neurons and is upregulated in the hypothalamus of obese mice, contributing to insulin and leptin resistance. Blocking IKKε in the hypothalamus of obese mice with CAYMAN10576 or small interfering RNA decreased NF-κB activation in this tissue, relieving the inflammatory environment. Inhibition of IKKε activity, but not TBK1, reduced IRS-1(Ser307) phosphorylation and insulin and leptin resistance by an improvement of the IR/IRS-1/Akt and JAK2/STAT3 pathways in the hypothalamus. These improvements were independent of body weight and food intake. Increased insulin and leptin action/signaling in the hypothalamus may contribute to a decrease in adiposity and hypophagia and an enhancement of energy expenditure accompanied by lower NPY and increased POMC mRNA levels. Improvement of hypothalamic insulin action decreases fasting glycemia, glycemia after pyruvate injection, and PEPCK protein expression in the liver of HFD-fed and db/db mice, suggesting a reduction in hepatic glucose production. We suggest that IKKε may be a key inflammatory mediator in the hypothalamus of obese mice, and its hypothalamic inhibition improves energy and glucose metabolism.

[yoga And The Promotion Of Health].

The scope of this paper is to analyze the self-declared symptoms and state of well-being of participants in the Yoga and Promotion of Health program, which consisted of hatha yoga lessons. It includes body exercises and breathing techniques, as well as ethical and philosophical content, administered to two groups of lecturers, workers and students of a public university in the State of São Paulo from August to December 2011 and March to June 2012. The participants filled out the adapted version of the Measure Yourself Medical Outcome Profile form at the beginning and end of the program. Of the 20 participants in Group 1, eight filled out the form and half of them reported the improvement of self-declared symptoms; as regards the state of well being, three of them felt they had improved. In Group 2, which also had 20 participants, nine completed the program and all of them reported improvements of self-declared symptoms and well-being. In conclusion, yoga is a mind-body practice which exerts an important therapeutic effect on most practitioners and also promotes health for the majority of them, expanding their capacity of self perception and self care. However, it should be noted that it doesn't achieve the same positive effect for all practitioners as some yoga traditions advocate.

Diet Carotenoid Lutein Modulates The Expression Of Genes Related To Oxygen Transporters And Decreases Dna Damage And Oxidative Stress In Mice.

Lutein (LT) is a carotenoid obtained by diet and despite its antioxidant activity had been biochemically reported, few studies are available concerning its influence on the expression of antioxidant genes. The expression of 84 genes implicated in antioxidant defense was quantified using quantitative reverse transcription polymerase chain reaction array. DNA damage was measured by comet assay and glutathione (GSH) and thiobarbituric acid reactive substances (TBARS) were quantified as biochemical parameters of oxidative stress in mouse kidney and liver. cDDP treatment reduced concentration of GSH and increased TBARS, parameters that were ameliorated in treatment associated with LT. cDDP altered the expression of 32 genes, increasing the expression of GPx2, APC, Nqo1 and CCs. LT changed the expression of 37 genes with an induction of 13 mainly oxygen transporters. In treatments associating cDDP and LT, 30 genes had their expression changed with a increase of the same genes of the cDDP treatment alone. These results suggest that LT might act scavenging reactive species and also inducing the expression of genes related to a better antioxidant response, highlighting the improvement of oxygen transport. This improved redox state of the cell through LT treatment could be related to the antigenotoxic and antioxidant effects observed.

The Effect Of Pelvic Floor Muscle Training Alone Or In Combination With Electrostimulation In The Treatment Of Sexual Dysfunction In Women With Multiple Sclerosis.

Sexual dysfunction (SD) affects up to 80% of multiple sclerosis (MS) patients and pelvic floor muscles (PFMs) play an important role in the sexual function of these patients. The objective of this paper is to evaluate the impact of a rehabilitation program to treat lower urinary tract symptoms on SD of women with MS. Thirty MS women were randomly allocated to one of three groups: pelvic floor muscle training (PFMT) with electromyographic (EMG) biofeedback and sham neuromuscular electrostimulation (NMES) (Group I), PFMT with EMG biofeedback and intravaginal NMES (Group II), and PFMT with EMG biofeedback and transcutaneous tibial nerve stimulation (TTNS) (Group III). Assessments, before and after the treatment, included: PFM function, PFM tone, flexibility of the vaginal opening and ability to relax the PFMs, and the Female Sexual Function Index (FSFI) questionnaire. After treatment, all groups showed improvements in all domains of the PERFECT scheme. PFM tone and flexibility of the vaginal opening was lower after the intervention only for Group II. All groups improved in arousal, lubrication, satisfaction and total score domains of the FSFI questionnaire. This study indicates that PFMT alone or in combination with intravaginal NMES or TTNS contributes to the improvement of SD.