Studies on serum macro and micro minerals status in repeat breeder and normal cyclic Nili-Ravi buffaloes and their treatment strategies

The present study was carried out with the objective to know the calcium (Ca), inorganic phosphorus (P), magnesium (Mg), copper (Cu), iron (Fe) and zinc (Zn) concentrations in serum of repeat breeder and normal cyclic buffaloes during oestrus. On the day of estrus, blood samples were collected from 35 buffaloes with a history of repeat breeding (RB) and 35 normal cycling (NC) buffaloes for mineral estimation. In the second part of the study, 35 repeat breeder (RBS) buffaloes were treated with a mineral mixture given orally for 10 days at the dosage rate of 150 g per day whereas other 35 repeat breeder buffaloes were given no mineral mixture (RBC). The overall pregnancy rate as well as 1st, 2nd and 3rd service pregnancy rate was calculated. The serum calcium, inorganic phosphorus, magnesium, copper, iron and zinc concentrations were significantly lower (P < 0.01) in RB buffaloes as compared to NC buffaloes. Sodium concentrations differed non-significantly between repeat breeder and normal cyclic buffaloes. Repeat breeder buffaloes (RBS) when fed orally 150 g per day of the mineral mixture for 10 days, the 1st, 2nd and 3rd service pregnancy rates were 42, 25 and 20%, while, overall pregnancy rate in these animals was 87%; whereas in repeat breeder control buffaloes, the overall pregnancy rate was 21%. In conclusion, the concentrations of macro and micro minerals were significantly lower in repeat breeder buffaloes and mineral mixtures should be added in the food stuff to improve reproductive efficiency of repeat breeder buffaloes. Keywords: Buffalo, repeat breeder, minerals, pregnancy rateAfrican Journal of Biotechnology, Vol. 13(10), pp. 1143-1146, 5 March, 201

A hybrid egocentric video summarization method to improve the healthcare for Alzheimer patients

Alzheimer patients face difficulty to remember the identity of persons and performing daily life activities. This paper presents a hybrid method to generate the egocentric video summary of important people, objects and medicines to facilitate the Alzheimer patients to recall their deserted memories. Lifelogging video data analysis is used to recall the human memory; however, the massive amount of lifelogging data makes it a challenging task to select the most relevant content to educate the Alzheimer’s patient. To address the challenges associated with massive lifelogging content, static video summarization approach is applied to select the key-frames that are more relevant in the context of recalling the deserted memories of the Alzheimer patients. This paper consists of three main modules that are face, object, and medicine recognition. Histogram of oriented gradient features are used to train the multi-class SVM for face recognition. SURF descriptors are employed to extract the features from the input video frames that are then used to find the corresponding points between the objects in the input video and the reference objects stored in the database. Morphological operators are applied followed by the optical character recognition to recognize and tag the medicines for Alzheimer patients. The performance of the proposed system is evaluated on 18 real-world homemade videos. Experimental results signify the effectiveness of the proposed system in terms of providing the most relevant content to enhance the memory of Alzheimer patients

Prediction of extreme price observations on Nord Pool

Dereguleringen av elektrisitetsmarkeder verden over har forsterket behovet for risikostyring i energimarkeder. I denne studien undersøker vi hvordan fundamentale drivere påvirker sannsynligheten for ekstreme prisobservasjoner på det nordiske kraftmarkedet. Vi utvikler modeller basert på fundamentale faktorer for å estimere sannsynligheten for ekstreme observasjoner på day-ahead prisen til Nord Pool. Resultatet viser at prisdynamikken er forskjellig for høye og lave priser under ulike tider av døgnet. Positive prishopp er forbundet med høy etterspørsel og lavt tilbud, samt høye priser i de foregående dagene og forekommer hovedsakelig om morgenen og på ettermiddagen. Negative prishopp forekommer hovedsakelig om natten, i samsvar med lav etterspørsel og høy vindproduksjon. Resultatet viser at positive og negative ekstrempriser har forskjellige drivere, hvorav vind- og konsumprognoser er viktige drivere bak ekstreme priser i begge retninger. Helhetlig sett har de fleste faktorene liten påvirkning på prisen, noe som trolig skyldes at det nordiske kraftmarkedet domineres av fleksibel og stabil vannkraft som jevner ut svingninger i de andre variabelene. Modellene fanger opp hoveddriverne bak ekstreme prisobservasjoner i begge retninger og prognoserer sannsynligheten for ekstremavvik med høy nøyaktighet. Resultatet i denne studien foreslår at logit modeller er godt egnet som et tilleggsverktøy i risikostyring i day-ahead elektrisitetsmarkeder. Funnene i denne studien bidrar med budsjett- og produksjonsplanlegging for strømprodusenter og økt forståelse av prisdynamikken for aktører som handler på Nord Pool Spot.The deregulation of electricity markets worldwide has reinforced the need for risk management in the energy market. In this study, we investigate how fundamental drivers affect the likelihood of extreme price observations on the Nordic power market. We develop models based on fundamental factors to estimate the probability of extreme prices on the day-ahead price on Nord Pool. The result shows that price dynamics are different for high and low prices at different times of the day. Positive spikes are associated with high demand and low supply, as well as high prices in the previous days and occurs mainly in the morning and in the afternoon. Low prices occur mainly at night, in accordance with low demand and high wind production. The results show that positive and negative extreme observations have different drivers, whereas forecasted wind and demand are important drivers in both directions. Overall, most factors have little impact on the price, due to the fact that the Nordic power market is mainly dominated by flexible and stable hydropower production that smooths fluctuations in the remaining variables. The models capture the main drivers behind extreme price observations in both directions and predict the probability of extreme deviations with high accuracy. The result of this study suggests that logit models are well suited as an additional tool in risk management in today's electricity markets. The findings in this study contribute budget and production planning for power producers and increased information to risk managers trading on Nord Pool Spot.M-Ø

IMPACT OF INTERNATIONAL MONETARY FUND BAILOUTS ON PAKISTAN STOCK EXCHANGE: AN EVENT STUDY

This study is to light the impact of the International Monetary Fund bailouts/ lending packages on Pakistan stock exchange because Pakistan's economy is having under influence of the International Monetary Fund by various packages with its terms and conditions. Therefore, it is essential to study the behavior of indices of Pakistan stock exchange upon the announcement of lending packages. To find the stock market behavior author uses five recent lending package in the last 20 years i.e. from 2000 to 2019 on all indices of Pakistan stock exchange. The event study methodology is employed through the mean adjusted model for the conduction of the study. Results suggested that there is a significant behavior of the stock market indices of Pakistan stock exchange. The positively significant impact of bailouts is recorded on event day, whereas, entire event windows were found to be negatively significant because there are positive and negative impacts recorded before and after the event date. Moreover, the post-event windows were found to be negatively significant for all of the events and indices. As results show that investor behaves abnormally upon announcement of the International Monetary Fund lending package. Key Words: IMF bailout, Stock Exchange, Event Stud

Egocentric Video Summarization Based on People Interaction Using Deep Learning

The availability of wearable cameras in the consumer market has motivated the users to record their daily life activities and post them on the social media. This exponential growth of egocentric videos demand to develop automated techniques to effectively summarizes the first-person video data. Egocentric videos are commonly used to record lifelogs these days due to the availability of low cost wearable cameras. However, egocentric videos are challenging to process due to the fact that placement of camera results in a video which presents great deal of variation in object appearance, illumination conditions, and movement. This paper presents an egocentric video summarization framework based on detecting important people in the video. The proposed method generates a compact summary of egocentric videos that contains information of the people whom the camera wearer interacts with. Our proposed approach focuses on identifying the interaction of camera wearer with important people. We have used AlexNet convolutional neural network to filter the key-frames (frames where camera wearer interacts closely with the people). We used five convolutional layers and two completely connected hidden layers and an output layer. Dropout regularization method is used to reduce the overfitting problem in completely connected layers. Performance of the proposed method is evaluated on UT Ego standard dataset. Experimental results signify the effectiveness of the proposed method in terms of summarizing the egocentric videos