The perception of the illness and the self-efficacy in the management of emotions in cardiac patients

Cardiac rehabilitation is the sum of psychological, physical and social treatments that are offered to cardiac patients to maintain or regain an active position in society. This study wants to evaluate changes in the perception of the illness and in the self-efficacy of the management of positive and negative emotions in patients who went through cardiac rehabilitation. Sixty-seven patients (20 females, 47 males) were selected within the cardiac rehabilitation unit in the Hospital of Cittadella (Italy). Illness Perception Questionnaire - revised version and the Scale for the self-efficacy of the management of positive and negative emotions were submitted at the beginning and at the end of the rehabilitation program. One-way analyses-of-variance were performed to evaluate different answers in questionnaires between pre- and post-evaluation, and to explore gender differences. A significant change was found in the perception of duration of illness, perceived as permanent and longer after the cardiac rehabilitation program. Furthermore, at the end of the cardiac rehabilitation program men perceived the illness more chronic than women, even if they are less worried and anxious. Intensive cardiac rehabilitation has a great emotional impact on cardiac patients, influencing their perception and management of the illness. Working on emotions, through psychological groups, helps patients change their beliefs by offering them a different perspective to approach the illness

The measure of effectiveness of a short-term 2-week intensive Cardiac Rehabilitation program in decreasing levels of anxiety and depression

Research on heart disease have found a strong and consistent evidence of association between some psychosocial risk factors, including depression, anxiety, self-efficacy, lack of social support and outcome of disease. Depression increases the risk of cardiac death and is highly predictive of reduced adherence to recommended treatments; anxiety appears to be linked to adverse cardiac outcomes. It was demonstrated that Cardiac Rehabilitation (CR) leads to substantial improvements and positive outcomes because combines the prescription of physical activity with the modification of risk factors and aims to reduce symptoms related to the disease and the risk of new cardiovascular events. The main objective of this study is to determine if a short and intense CR program can produce a positive impact on anxious and depressive symptoms revealed in cardiac patients, confirming results of previous researches. The protocol was proposed to all patients referred for an outpatient CR after an acute event who attended the short 2-week intensive rehabilitation program. A total of 157 patients recruited at the operating unit of Cardiology, in the Hospital of Cittadella (Italy), was included in the analysis. The Beck Depression Inventory-II and the State-Trait Anxiety Inventory-Y were administered to the patients. SPSS 17.0 was used for statistical analysis. T-tests for paired samples were used to evaluate differences between the beginning and the end of the CR program. There was a statistically significant difference between the beginning and the end of the CR program. Results for paired samples showed significant differences in all factors of the BDI-II and in the total score. In addition, a statistically significant difference was found even in the state - anxiety subscale. No significant difference was detected for the trait anxiety. According to recent studies, this research shows that the CR program has a significant impact on levels of anxiety and depression, because all activities focus their commitment on changing the patient’s personal beliefs and perception of illness, promoting the exchange of information and sharing of concerns and fears, increasing the patient’s resilience with the aim of enabling him/her to reorganize positively his/her personal, family and professional sphere

model based control of intake air temperature and humidity on the test bench

Abstract Engine test benches are crucial instruments to perform tests on internal combustion engines. Possible purposes of these tests are to detect the engine performance, check the reliability of the components or make a proper calibration of engine control systems managing the actuations. Since many factors affect tests results in terms of performance, emissions and components durability, an engine test bench is equipped with several conditioning systems (oil, water and air temperature, air humidity, etc.). One of the most important systems is the HVAC (Heating, Ventilating and Air Conditioning), that is essential to control the conditions of the intake air. Intake air temperature, pressure and humidity should be controllable test parameters, because they play a key role on the combustion development. In fact, they can heavily affect the performance detected, such as power and specific consumption, and, in some cases, they may promote knock occurrence. This work presents an HVAC model-based control methodology, where each component of the air treatment system (humidifier, pre-heating and post-heating resistors, chiller and fan) is managed coupling open-loop and closed-loop controls. Each branch of the control model is composed of two parts, the first one to evaluate the target for the given HVAC component, based on the system physical model, the second one is a PID controller based on the difference between the set-point and the feedback values. The control methodology has been validated on an engine test bench where the automation system has been developed on an open software Real-Time compatible platform, allowing the integration of the HVAC control with all other functionalities concerning the test management. The paper shows the plant layout, details the control strategy and finally analyzes experimental results obtained on the test bench, highlighting the benefits of the proposed HVAC management approach

Analysis of the Process in Brief Psychotherapy Group: The Role of Therapeutic Factors

In all group therapeutic processes, there are interacting and interdependent mechanisms and changing conditions: the therapeutic factors (Corsini & Rosenberg, 1955; Yalom, 1995). These factors are intrinsic to the therapeutic process and unrelated to the type of group (Rorhbaugh & Bartels, 1975). The present study examines the factor structure of the questionnaire Factors Aspecific and Specific in the Group Therapy (FAT.A.S.-G.; Marogna, 2009), designed to investigate specific and non-specific therapeutic factors. The questionnaire was administered to 167 patients involved in a short-term psychotherapy group. The factor analysis identified two main dimensions: interdependence and the group as Object-Self. The Cronbach Alpha coefficients range from .88 to .93, showing high internal consistency between items

Analysis of the Process in Brief Psychotherapy Group: The Role of Therapeutic Factors

In all group therapeutic processes, there are interacting and interdependent mechanisms and changing conditions: the therapeutic factors (Corsini & Rosenberg, 1955; Yalom, 1995). These factors are intrinsic to the therapeutic process and unrelated to the type of group (Rorhbaugh & Bartels, 1975). The present study examines the factor structure of the questionnaire Factors Aspecific and Specific in the Group Therapy (FAT.A.S.-G.; Marogna, 2009), designed to investigate specific and non-specific therapeutic factors. The questionnaire was administered to 167 patients involved in a short-term psychotherapy group. The factor analysis identified two main dimensions: interdependence and the group as Object-Self. The Cronbach Alpha coefficients range from .88 to .93, showing high internal consistency between items

Antibiotics mastitis therapy: drug residue in lambs

Meat coming from suckling lambs (max 12 Kg BW) is a typical Sardinian taste dish, normally consumed during the religious linked feasts. The aim of this work is to evaluate drug residues in suckling lambs meat as consequence of antibiotics mastitis therapy in their mothers during lactation. The study was performed on twelve Sardinian ewes, that had lambed within two days of one another, with suckling lambs from a single flok of 150 animals. Milk bacteriological screening showed that 10 ewes out of 12 were positive and Coagulase-Negative Staphylococci were identified. The ten positive sheep were divided into two groups A and B, and each of them were given two successive intramuscularconsecutive intramuscolar injection of 6 ml of oxytetracycline OTC (Terralon 20% LA, Virbac) within 72 hours; the two negative sheep were used as control (C group). With the two ewes Group two trials were conducted: to the A treatment started 17 days after delivery, while group of 6 ewes, drug administration was given when lambs were 17 days old, to the B has been treated 28 days after delivery. During the experimental period milk was sampled twice a week; 5 milk samples/ewe for group A and 2 samples/ewe for group B were collected. Lambslambs were regularly slaughtered at about 35 days old and muscle tissue has been collected.sampled. All samples were immediately frozen until analysis. Oxytetracycline milk residues were measured by High Performance Liquid Chromatography with diode array detector while, for OTC tissue levels, LC/MS-MS technique was used. OTC concentration in milk, as observed in our own previous study, decreasedranged from 3,500 to 0,050 μ g/ml over three weeks. OTC residues were detected in both groups of lambs at levels below Maximum Residue Limit (MRL 0.100 μg/g). In order to avoid any drug residue in food chain, and an increase of drug resistance, national legislation should pay attention to avoid use of antibiotics in ewes feeding lambs that will be slaughtered

Mini-Batch Alignment: A Deep-Learning Model for Domain Factor-Independent Feature Extraction for Wi-Fi–CSI Data

Unobtrusive sensing (device-free sensing) aims to embed sensing into our daily lives. This is achievable by re-purposing communication technologies already used in our environments. Wireless Fidelity (Wi-Fi) sensing, using Channel State Information (CSI) measurement data, seems to be a perfect fit for this purpose since Wi-Fi networks are already omnipresent. However, a big challenge in this regard is CSI data being sensitive to ‘domain factors’ such as the position and orientation of a subject performing an activity or gesture. Due to these factors, CSI signal disturbances vary, causing domain shifts. Shifts lead to the lack of inference generalization, i.e., the model does not always perform well on unseen data during testing. We present a domain factor-independent feature-extraction pipeline called ‘mini-batch alignment’. Mini-batch alignment steers a feature-extraction model’s training process such that it is unable to separate intermediate feature-probability density functions of input data batches seen previously from the current input data batch. By means of this steering technique, we hypothesize that mini-batch alignment (i) absolves the need for providing a domain label, (ii) reduces pipeline re-building and re-training likelihood when encountering latent domain factors, and (iii) absolves the need for extra model storage and training time. We test this hypothesis via a vast number of performance-evaluation experiments. The experiments involve both one- and two-domain-factor leave-out cross-validation, two open-source gesture-recognition datasets called SignFi and Widar3, two pre-processed input types called Doppler Frequency Spectrum (DFS) and Gramian Angular Difference Field (GADF), and several existing domain-shift mitigation techniques. We show that mini-batch alignment performs on a par with other domain-shift mitigation techniques in both position and orientation one-domain leave-out cross-validation using the Widar3 dataset and DFS as input type. When considering a memory-complexity-reduced version of the GADF as input type, mini-batch alignment shows hints of recuperating performance regarding a standard baseline model to the extent that no additional performance due to weight steering is lost in both one-domain-factor leave-out and two-orientation-domain-factor leave-out cross-validation scenarios. However, this is not enough evidence that the mini-batch alignment hypothesis is valid. We identified pitfalls leading up to the hypothesis invalidation: (i) lack of good-quality benchmark datasets, (ii) invalid probability distribution assumptions, and (iii) non-linear distribution scaling issues

Mini-Batch Alignment: A Deep-Learning Model for Domain Factor-Independent Feature Extraction for Wi-Fi–CSI Data

Unobtrusive sensing (device-free sensing) aims to embed sensing into our daily lives. This is achievable by re-purposing communication technologies already used in our environments. Wireless Fidelity (Wi-Fi) sensing, using Channel State Information (CSI) measurement data, seems to be a perfect fit for this purpose since Wi-Fi networks are already omnipresent. However, a big challenge in this regard is CSI data being sensitive to ‘domain factors’ such as the position and orientation of a subject performing an activity or gesture. Due to these factors, CSI signal disturbances vary, causing domain shifts. Shifts lead to the lack of inference generalization, i.e., the model does not always perform well on unseen data during testing. We present a domain factor-independent feature-extraction pipeline called ‘mini-batch alignment’. Mini-batch alignment steers a feature-extraction model’s training process such that it is unable to separate intermediate feature-probability density functions of input data batches seen previously from the current input data batch. By means of this steering technique, we hypothesize that mini-batch alignment (i) absolves the need for providing a domain label, (ii) reduces pipeline re-building and re-training likelihood when encountering latent domain factors, and (iii) absolves the need for extra model storage and training time. We test this hypothesis via a vast number of performance-evaluation experiments. The experiments involve both one- and two-domain-factor leave-out cross-validation, two open-source gesture-recognition datasets called SignFi and Widar3, two pre-processed input types called Doppler Frequency Spectrum (DFS) and Gramian Angular Difference Field (GADF), and several existing domain-shift mitigation techniques. We show that mini-batch alignment performs on a par with other domain-shift mitigation techniques in both position and orientation one-domain leave-out cross-validation using the Widar3 dataset and DFS as input type. When considering a memory-complexity-reduced version of the GADF as input type, mini-batch alignment shows hints of recuperating performance regarding a standard baseline model to the extent that no additional performance due to weight steering is lost in both one-domain-factor leave-out and two-orientation-domain-factor leave-out cross-validation scenarios. However, this is not enough evidence that the mini-batch alignment hypothesis is valid. We identified pitfalls leading up to the hypothesis invalidation: (i) lack of good-quality benchmark datasets, (ii) invalid probability distribution assumptions, and (iii) non-linear distribution scaling issues