The production processes in the packaging materials industry has to be very efficient and cost-effective. These processes usually take place under extreme conditions and high speeds that requires a high level of reliability and efficiency. Rollers including their supporting bearings and motors are the most common components of production machines in the packaging materials industry. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore it is very important to take care of bearings during maintenance and detect their faults in an early stage in order to assure safe and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. After normalization and the wavelet transform of vibration signals, the standard deviation as a measure of average energy level and the logarithmic energy entropy as a measure of the degree of order/disorder are extracted in a few sub-bands of interest as representative features. Then the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection is performed by a quadratic classifier and the fault diagnosis by another two quadratic classifiers. Accuracy of the new technique was tested on the ball bearing data recorded at the Case Western Reserve University Bearing Data Center. In total four classes of the vibrations signals were studied, i.e. normal, with the fault of inner race, outer race and balls operation. An overall accuracy of 100% was achieved. The new technique can be used to increase reliability and efficiency by preventing unexpected faulty operation of machinery bearings.

Bosnia and Herzegovina (B&H) is faced with many challenges on its path of economic prosperity and suffers from many disappointments and setbacks of transition process. The social and economic activities have deteriorated and changed over time and B&H has passed through different political and economic changes. This paper aims to introduce the debate about the weakness of transitional model in order to explain the process of shift with the decline in production and demand, which increasingly affects the difficult socio-economic status of its citizens. The current concept of economic growth is based on consumption of private and public borrowing, as well as the increasing imports has been proven to be unsustainable. Accordingly, it provides a critical review of the effects that the current transition model left on the development of B&H. A comparative analysis of the characteristic of various important macroeconomic variables and graphical representations of the collected statistical data, on which it formulates proposals for overcoming the current situation in B&H, will be given.

Headache frequently occurs after spinal anesthesia or after craniotomy, especially after removal of acoustic neuroma. Headache after spinal anesthesia is caused by leakage of liquor through dural puncture and decrease of intracranial pressure, while pain after craniotomy is consequence of operative injury of peri-cranial muscles and soft tissues. Epidural administration of morphine and intravenous administration of cosyntropine or aminophylline at the end of a surgical intervention may prevent postoperative headache, while caffeine, gabapentin, pregabalin, theophylline, hydrocortisone or cosyntropine are efficient in the treatment. Drugs are not efficient for prevention of headache after craniotomy, while parenteral codeine and/or acetaminophen can terminate this type of pain. Non-steroid antiinflammatory drugs should be avoided for treatment of post-craniotomy headache, due to their extraand intra-cranial adverse effects. Timely administration of appropriate drugs for prevention or treatment of postoperative headache significantly decreases suffering, hastens recovery and prevent chronic headache.