Hubungan Tingkat Kecemasan Dengan Kecenderungan Insomnia Pada Pasien Rawat Inap Di Rsud Dr. Soehadi Prijonegoro Sragen

Anxiety or anxiety arises as a response to stress, both physiological stress and psychological stress. Anxiety is considered as mental tension accompanied by bodily disorders that cause a feeling of being wary of threats, anxiety associated with physiological and psychological stress. Sleep disturbance or insomnia is one sign of overall anxiety disorder. Insomnia experienced by anxious people is difficult to get to sleep, a frightening dream, often surprised when you wake up, and not sleeping soundly. This study aims to analyze the relationship between the level of anxiety and the tendency of insomnia in inpatients at the dr. Soehadi Prijonegoro SragenRegional Hospital. This research is a quantitative study with a descriptive correlational design. The population in this study were all adolescent and adult patients who were hospitalized in dr. Soehadi Prijonegoro SragenRegional Hospital in February 2019. The study sample consisted of 112 patients using the total sampling technique. Data analysis using Product Moment correlation test. The results of this study indicate that patients as many as 82 people (73.22%) included in the level of moderate anxiety and as many as 80 people (71.43%) included in the category of severe insomnia. Product Moment correlation test results showed p-value 0.00 <0.05 which means that there is a significant relationship between the level of anxiety and the tendency of insomnia in hospitalized patients. Suggestions for hospitals to be able to create a comfortable environment, especially to limit boundaries or curtains in the patient's room and to limit the patient's visiting time so that the patient's sleep is not disturbed. Keywords: Anxiety, Tendency to Insomnia, Hospitalized Patients

Formal Analysis of Linear Control Systems using Theorem Proving

Control systems are an integral part of almost every engineering and physical system and thus their accurate analysis is of utmost importance. Traditionally, control systems are analyzed using paper-and-pencil proof and computer simulation methods, however, both of these methods cannot provide accurate analysis due to their inherent limitations. Model checking has been widely used to analyze control systems but the continuous nature of their environment and physical components cannot be truly captured by a state-transition system in this technique. To overcome these limitations, we propose to use higher-order-logic theorem proving for analyzing linear control systems based on a formalized theory of the Laplace transform method. For this purpose, we have formalized the foundations of linear control system analysis in higher-order logic so that a linear control system can be readily modeled and analyzed. The paper presents a new formalization of the Laplace transform and the formal verification of its properties that are frequently used in the transfer function based analysis to judge the frequency response, gain margin and phase margin, and stability of a linear control system. We also formalize the active realizations of various controllers, like Proportional-Integral-Derivative (PID), Proportional-Integral (PI), Proportional-Derivative (PD), and various active and passive compensators, like lead, lag and lag-lead. For illustration, we present a formal analysis of an unmanned free-swimming submersible vehicle using the HOL Light theorem prover.Comment: International Conference on Formal Engineering Method

A comprehensive review of wireless body area network

Recent development and advancement of information and communication technologies facilitate people in different dimensions of life. Most importantly, in the healthcare industry, this has become more and more involved with the information and communication technology-based services. One of the most important services is monitoring of remote patients, that enables the healthcare providers to observe, diagnose and prescribe the patients without being physically present. The advantage of miniaturization of sensor technologies gives the flexibility of installing in, on or off the body of patients, which is capable of forwarding physiological data wirelessly to remote servers. Such technology is named as Wireless Body Area Network (WBAN). In this paper, WBAN architecture, communication technologies for WBAN, challenges and different aspects of WBAN are illustrated. This paper also describes the architectural limitations of existing WBAN communication frameworks. blueFurthermore, implementation requirements are presented based on IEEE 802.15.6 standard. Finally, as a source of motivation towards future development of research incorporating Software Defined Networking (SDN), Energy Harvesting (EH) and Blockchain technology into WBAN are also provided

Classification of Foetal Distress and Hypoxia Using Machine Learning Approaches

© 2018, Springer International Publishing AG, part of Springer Nature. Foetal distress and hypoxia (oxygen deprivation) is considered as a serious condition and one of the main factors for caesarean section in the obstetrics and Gynecology department. It is the third most common cause of death in new-born babies. Many foetuses that experienced some sort of hypoxic effects can develop series risks including damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe the foetal well being. Foetal surveillance by monitoring the foetal heart rate with a cardiotocography is widely used. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. In this paper, machine-learning algorithms are utilized to classify foetuses which are experiencing oxygen deprivation using PH value (a measure of hydrogen ion concentration of blood used to specify the acidity or alkalinity) and Base Deficit of extra cellular fluid level (a measure of the total concentration of blood buffer base that indicates the metabolic acidosis or compensated respiratory alkalosis) as indicators of respiratory and metabolic acidosis, respectively, using open source partum clinical data obtained from Physionet. Six well know machine learning classifier models are utilised in our experiments for the evaluation; each model was presented with a set of selected features derived from the clinical data. Classifier’s evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as the confusion matrix. Our simulation results indicate that machine-learning algorithms provide viable methods that could delivery improvements over conventional analysis

A versatile synthesis method of dendrites-free segmented nanowires with a precise size control

We report an innovative strategy to obtain cylindrical nanowires combining well established and low-cost bottom-up methods such as template-assisted nanowires synthesis and electrodeposition process. This approach allows the growth of single-layer or multi-segmented nanowires with precise control over their length (from few nanometers to several micrometers). The employed techniques give rise to branched pores at the bottom of the templates and consequently dendrites at the end of the nanowires. With our method, these undesired features are easily removed from the nanowires by a selective chemical etching. This is crucial for magnetic characterizations where such non-homogeneous branches may introduce undesired features into the final magnetic response. The obtained structures show extremely narrow distributions in diameter and length, improved robustness and high-yield, making this versatile approach strongly compatible with large scale production at an industrial level. Finally, we show the possibility to tune accurately the size of the nanostructures and consequently provide an easy control over the magnetic properties of these nanostructures

A comparison of univariate, vector, bilinear autoregressive, and band power features for brain–computer interfaces

Selecting suitable feature types is crucial to obtain good overall brain–computer interface performance. Popular feature types include logarithmic band power (logBP), autoregressive (AR) parameters, time-domain parameters, and wavelet-based methods. In this study, we focused on different variants of AR models and compare performance with logBP features. In particular, we analyzed univariate, vector, and bilinear AR models. We used four-class motor imagery data from nine healthy users over two sessions. We used the first session to optimize parameters such as model order and frequency bands. We then evaluated optimized feature extraction methods on the unseen second session. We found that band power yields significantly higher classification accuracies than AR methods. However, we did not update the bias of the classifiers for the second session in our analysis procedure. When updating the bias at the beginning of a new session, we found no significant differences between all methods anymore. Furthermore, our results indicate that subject-specific optimization is not better than globally optimized parameters. The comparison within the AR methods showed that the vector model is significantly better than both univariate and bilinear variants. Finally, adding the prediction error variance to the feature space significantly improved classification results

Solution Behavior and Activity of a Halophilic Esterase under High Salt Concentration

Background: Halophiles are extremophiles that thrive in environments with very high concentrations of salt. Although the salt reliance and physiology of these extremophiles have been widely investigated, the molecular working mechanisms of their enzymes under salty conditions have been little explored. Methodology/Principal Findings: A halophilic esterolytic enzyme LipC derived from archeaon Haloarcula marismortui was overexpressed from Escherichia coli BL21. The purified enzyme showed a range of hydrolytic activity towards the substrates of p-nitrophenyl esters with different alkyl chains (n = 2−16), with the highest activity being observed for p-nitrophenyl acetate, consistent with the basic character of an esterase. The optimal esterase activities were found to be at pH 9.5 and [NaCl] = 3.4 M or [KCl] = 3.0 M and at around 45°C. Interestingly, the hydrolysis activity showed a clear reversibility against changes in salt concentration. At the ambient temperature of 22°C, enzyme systems working under the optimal salt concentrations were very stable against time. Increase in temperature increased the activity but reduced its stability. Circular dichroism (CD), dynamic light scattering (DLS) and small angle neutron scattering (SANS) were deployed to determine the physical states of LipC in solution. As the salt concentration increased, DLS revealed substantial increase in aggregate sizes, but CD measurements revealed the maximal retention of the α-helical structure at the salt concentration matching the optimal activity. These observations were supported by SANS analysis that revealed the highest proportion of unimers and dimers around the optimal salt concentration, although the coexistent larger aggregates showed a trend of increasing size with salt concentration, consistent with the DLS data. Conclusions/Significance: The solution α-helical structure and activity relation also matched the highest proportion of enzyme unimers and dimers. Given that all the solutions studied were structurally inhomogeneous, it is important for future work to understand how the LipC's solution aggregation affected its activity

Fractional anisotropy in white matter tracts of very-low-birth-weight infants

Background: Advances in neonatal intensive care have not yet reduced the high incidence of neurodevelopmental disability among very-low-birth-weight (VLBW) infants. As neurological deficits are related to white-matter injury, early detection is important. Diffusion tensor imaging (DTI) could be an excellent tool for assessment of white-matter injury. Objective: To provide DTI fractional anisotropy (FA) reference values for white-matter tracts of VLBW infants for clinical use. Materials and methods: We retrospectively analysed DTI images of 28 VLBW infants (26-32 weeks gestational age) without evidence of white-matter abnormalities on conventional MRI sequences, and normal developmental outcome (assessed at age 1-3 years). For DTI an echoplanar sequence with diffusion gradient (b = 1,000 s/mm2) applied in 25 non-collinear directions was used. We measured FA and apparent diffusion coefficient (ADC) of different white-matter tracts in the first 4 days of life. Results: A statistically significant correlation was found between gestational age and FA of the posterior limb of the internal capsule in VLBW infants (r = 0.495, P<0.01). Conclusion: Values of FA and ADC were measured in white-matter tracts of VLBW infants. FA of the pyramidal tracts measured in the first few days after birth is related to gestational age