Investigation of Academic Procrastination and its Dimensions and its Relationship with Academic Performance among Students of Tabriz University of Medical Sciences, Iran

Background & Objective: Procrastination is often described as a deliberate and irrational delay. It is prevalent among students all over the world. The aim of this was study to determine the prevalence of academic procrastination among the students of Tabriz University of Medical Sciences, Iran, in the academic year 2014-2015 and its correlation with academic performance. Methods: This cross-sectional study was carried on 150 medical, nursing, and midwifery students living in the dormitory of Tabriz University of Medical Sciences. The subjects were selected through non-probability sampling. The data collection tool used was the Academic Procrastination Scale (APS) designed by Solomon and Rothblum. The total reliability of the PAS and the reliability of its subscales were determined using Cronbach’s alpha (range: 0.41-0.78). The data were analyzed using chi-square test and multiple regression analysis. Results: According to the results, the prevalence of procrastination and its dimensions were 35.3%, 28.0%, 30.7%, and 31.3%, respectively. The chi-square test also showed that procrastination in assignments, articles and essays preparation, and the total score of procrastination had a meaningful negative correlation with the academic performance of the nursing and obstetric students (P < 0.01). Furthermore, the total score of procrastination and its dimensions had significant negative correlation with the academic performance of the whole sample (P < 0.01). The stepwise multiple regression reveald analysis that the total score of procrastination was the best factor for predicting academic performance. Conclusion: The present study results indicated procrastination as the most common cause of reduction of academic performance among students. Therefore, complementary studies are needed to investigate the causes of academic procrastination. Key Words: Prevalence, Academic procrastination, Academic performanc

The Effect of Stress Inoculation Training on Breastfeeding Self-Efficacy and Perceived Stress of Mothers With Low Birth Weight Infants: A Clinical Trial

Objective: Mothers with low birth weight infants experience more stress, which results in reduced breastfeeding self-efficacy and exclusive breastfeeding; In this regard, stress Inoculation Training (SIT) is one of the effective ways for inoculation against stress and psychological distress; Therefore, this study aimed to investigate the effect of SIT on breastfeeding self-efficacy and perceived stress of mothers with low birth weight infants. Materials and methods: This clinical trial study was conducted from October to December 2017 on 100 mothers with low birth weight infants; the infants had been hospitalized in the neonatal intensive care unit (NICU) in Kermanshah, Iran. The eligible mothers were randomly divided into two groups, i.e., intervention (n = 50) and control (n = 50) groups. Results: The mean score of breastfeeding self-efficacy, before SIT (33.82 ± 8.92) compared to after SIT (42.02 ± 8.83), significantly increased (p 0.05). The mean score of perceived stress was significantly reduced after SIT (26.29 ± 6.49) compared to values before SIT (31.25 ± 5.82) (p < 0.001). Conclusion: The present study showed that on the one hand, SIT can effectively increase the breastfeeding self-efficacy in mothers with low birth weight infants; on the other hand, it can reduce their perceived stress. Therefore, the need for holding in-service training courses is felt in order to train the caregiving personnel, especially nurses, in applying the SIT technique

Early and non-intrusive lameness detection in dairy cows using 3-dimensional video

ABSTRACTLameness is a major issue in dairy herds and its early and automated detection offers animal welfare benefits together with high potential commercial savings for farmers. Current advancements in automated detection have not achieved a sensitive measure for classifying early lameness. A novel proxy for lameness using 3-dimensional (3D) depth video data to analyse the animal’s gait asymmetry is introduced. This dynamic proxy is derived from the height variations in the hip joint during walking. The video capture setup is completely covert and it facilitates an automated process. The animals are recorded using an overhead 3D depth camera as they walk freely in single file after the milking session. A 3D depth image of the cow’s body is used to automatically track key regions such as the hooks and the spine. The height movements are calculated from these regions to form the locomotion signals of this study, which are analysed using a Hilbert transform. Our results using a 1-5 locomotion scoring (LS) system on 22 Holstein Friesian dairy cows, a threshold could be identified between LS 1 and 2 (and above). This boundary is important as it represents the earliest point in time at which a cow is considered lame, and its early detection could improve intervention outcome thereby minimising losses and reducing animal suffering. Using a linear Support Vector Machine (SVM) binary classification model, the threshold achieved an accuracy of 95.7% with a 100% sensitivity (detecting lame cows) and 75% specificity (detecting non-lame cows)

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Assessing walking posture with geometric morphometrics: Effects of rearing environment in pigs

International audienceRearing social animals like pigs in isolation from conspecifics can have consequences on behaviour and physiology. The aim of this experiment was to determine whether rearing conditions affect body postures. We adapted a method for quantitative evaluation of postures based on geometric morphometrics, developed in horses, for pigs and applied it in different conditions. Forty eight 75-day old females were reared either alone in 2.25 m2 pens (IH, N = 24 animals and 4 groups) or in groups of four in 4.64 m2 pens (GH, N = 24) for two weeks. They were habituated to human handling (stroking, speaking) and marking on their backs every day, and tested individually once a day for 10 min in a corridor outside the home pen during the two subsequent weeks. We observed their behaviour and posture during the first exposure to the test (novelty), and the fourth and fifth (after habituation). On the sixth and seventh tests, a familiar stockperson was present in the corridor (human presence). Before each test, the animals were marked with seven landmarks along their length, corresponding to anatomical points and easily located. An experimenter took pictures of the animals walking along the corridor, and these pictures were transferred to tps software for analysis. GH animals were more often active in the rearing pen than IH (median (IQ) 15% of observations [12-20%] versus 2% [0-13%]; P &lt; 0.05). All animals except one IH initiated contact with the handler during the last sessions of handling (Fisher's exact test, ns). Principal Component Analyses revealed significant effects of rearing and testing conditions on pigs’ behaviour and posture. Novelty led to fewer vocalisations and more exploration for IH than GH animals (P &lt; 0.05), but there were no differences between treatments after habituation to the testing situation. The backs of IH animals were more rounded than those of GH (P &lt; 0.05; dimension 1 of PCA), independently of the test condition. Human presence had no effect on posture. In conclusion, the method based on geometric morphometrics that we developed to study pig posture detected variations in walking posture in pigs associated with rearing conditions. Postures might reflect affective states in pigs, as shown in other species, but further studies are needed to verify thi

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.

This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow's gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5