Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Induced Normothermia on Occurrence of Post Anesthetic Shivering and Wound Infection among Patients Undergoing Abdominal Surgeries

intraoperative hypothermia is a common complication. Many complications can be developed as a result of intraoperative hypothermia including postanesthesia shivering and wound infection, Therefore the aim of the current study was to evaluate the impact of induced normothermia on occurrence of post anesthetic shivering and wound infection among patients undergoing abdominal surgeries. A convenient sample consists of 60 adult male and female patients who was undergo abdominal surgery (hernia repair except inguinal hernia repair, open cholecystectomy) at one of the university hospitals in Cairo governorate was recruited to fulfill the aim. A quasi–experimental design (posttest control group design) was utilized to achieve the aim of the present study by using tool s I, Demographic and medical data Questioner II, Bedside shivering assessment scale (BSAS) III, Asepsis wound scoring system. The study result revealed that 70% of control group complain from hypothermia after 90 minute of anesthesia induction with mean (35.47° C ±.761 ° C). The current study revealed that the use of warm fluid is effective in keeping patient warmed in operation less than two hours with p value═ . 000.Study (warmed) group have less occurrence and severity of post anesthesia shivering with p ═ . 016 and wound infection with p═ . 035.
Hardware-accelerated join processing in large Semantic Web databases with FPGAs

The increasing amount of data to be processed by database systems asks for a continuous increase in processing power. While traditional system designs can hardly cope with these performance requirements, dedicated hardware accelerators provide the required processing power. However, dedicated hardware accelerators are inflexible and cannot be adapted to the requirements of a dedicated query. In this paper, a concept is introduced to improve the performance of a Semantic Web database by developing a flexible FPGA-based hardware accelerator. The feasibility of this approach is shown by implementing different types of join operators as one of the most important and most time consuming operators in query execution. The performance comparison between the proposed FPGA implementation and a software solution in C on a general-purpose processor shows a significant speed-up up to 10 times.
Bio-oil Derived from Indonesian Oil Palm Empty Fruit Bunch (EFB) using Middle-scale Slow Pyrolysis

Indonesia now is the largest producer of palm oil in the world. Over seven million hectares in the cultivation and more than 400 palm oil mills in operation. The major biomass byproduct from the palm oil industry is empty fruit bunches (EFB) that have a great potency as basic raw materials used for alternative energy. We have introduced a new technology for utilizing biomass waste for producing alternative energy (bio-gasoline, bio-kerosie and bio-solar) by combining pyrolysis-catalytic cracking-gasification processes in a whole system, namely as integrated autothermal technology. By using this technology, energy supplied to the system can be reduced significantly. In this study, pyrolysis of Indonesian oil palm empty fruit bunches (EFB) was investigated using a middle-scale slow pyrolysis. The products were bio-oil, char and gas. Then, bio-oil produced from this pirolysis can be used as feedstock for catalytic cracking. The effects of various pyrolysis temperatures, water content and different feedstock on the yields of the products were investigated. The temperature of pyrolysis and water content were varied in the range of 400-600°C and 13-18 %wt, respectively. The different feedstock was varied in the range of 500; 1,000; and 1,500 grams. The average heating rate was at 12oC min-1 to a final pyrolysis temperature. Product yields were found to be significantly influenced by the pyrolysis process conditions. Under the experimental conditions, the optimum bio-oil yield was 10.83 %wt obtained at 500°C, with water content of 18 %wt. While different feedstock has no significant effect on bio-oil yield. The maximum yield of char was 35.39 %wt, obtained at a pyrolysis temperature of 400°C, feedstock of 1000 grams. Meanwhile, the optimum yield of gas was 37.71 %wt, which could be achieved at a pyrolysis temperature of 450°C, feedstock of 1000 grams.
Polynomial Time Algorithms for Branching Markov Decision Processes and Probabilistic Min(Max) Polynomial Bellman Equations

We show that one can compute the least non-negative solution (a.k.a., least fixed point) for a system of probabilistic min (max) polynomial equations, to any desired accuracy > 0, in time polynomial in both the encoding size of the system and in log(1/ ). These are Bellman optimality equations for important classes of infinite-state Markov Decision Processes (MDPs), including Branching MDPs (BMDPs), which generalize classic multitype branching stochastic processes. We thus obtain the first polynomial time algorithm for computing, to any desired precision, optimal (maximum and minimum) extinction probabilities for BMDPs. Our algorithms are based on a novel generalization of Newton’s method which employs linear programming in each iteration. We also provide P-time algorithms for computing an -optimal policy for both maximizing and minimizing extinction probabilities in a BMDP, whereas we note a hardness result for computing an exact optimal policy. Furthermore, improving on prior results, we provide more efficient P-time algorithms for qualitative analysis of BMDPs, i.e., for determining whether the maximum or minimum extinction probability is 1, and, if so, computing a policy that achieves this. We also observe some complexity consequences of our results for branching simple stochastic games, which generalize BMDPs.
PsyBoG: Power spectral density analysis for detecting botnet groups

Botnets are widely used for acquiring economic profits, by launching attacks such as distributed denial-of-service (DDoS), identification theft, ad-ware installation, mass spamming, and click frauds. Many approaches have been proposed to detect botnet, which rely on end-host installations or operate on network traffic with deep packet inspection. They have limitations for detecting botnets which use evasion techniques such as packet encryption, fast flux, dynamic DNS and DGA. Sporadic botnet behavior caused by disconnecting the power of system or botnet's own nature also brings unignorable false detection. Furthermore, normal user's traffic causes a lot of false alarms. In this paper, we propose a novel approach called PsyBoG to detect botnets by capturing periodic activities. PsyBoG leverages signal processing techniques, PSD (Power Spectral Density) analysis, to discover the major frequencies from the periodic DNS queries of botnets. The PSD analysis allows us to detect sophisticated botnets irrespective of their evasion techniques, sporadic behavior and even the noise traffic generated by normal users. To evaluate PsyBoG, we utilize the real-world DNS traces collected from a /16 campus network including more than 48,046K queries, 34K distinct IP addresses and 146K domains. Finally, PsyBoG caught 19 unknown and 6 known botnet groups with 0.1% false positives.
Automatic snoring detection from nasal pressure data

This study presents a method for automatic snoring detection from a nasal pressure data. First, a spectrogram analysis was performed in order to obtain information about the spectral characteristic of nasal pressure data. The automatic method is based on a simple signal filtering and short-time energy technique. Fifteen patients were participated in order to evaluation the performance of the proposed method. Results are compared with manually labeled snoring events by watching video records. The sensitivity and positive predictivity value were 93.73% and 93.70%, respectively. The results in this study could provide sleep experts with the method to objectively monitor sleep-disordered breathing in CPAP system or PSG study.
Some Fixed Parameter Tractability Results for Planning with Non-Acyclic Domain-Transition Graphs

Bäckström studied the parameterised complexity of planning when the domain-transition graphs (DTGs) are acyclic. He used the parameters d (domain size), k (number of paths in the DTGs) and w (treewidth of the causal graph), and showed that planning is fixed-parameter tractable (fpt) in these parameters, and fpt in only parameter k if the causal graph is a polytree. We continue this work by considering some additional cases of non-acyclic DTGs. In particular, we consider the case where each strongly connected component (SCC) in a DTG must be a simple cycle, and we show that planning is fpt for this case if the causal graph is a polytree. This is done by first preprocessing the instance to construct an equivalent abstraction and then apply Bäckströms technique to this abstraction. We use the parameters d and k, reinterpreting this as the number of paths in the condensation of a DTG, and the two new parameters c (the number of contracted cycles along a path) and pmax (an upper bound for walking around cycles, when not unbounded).
Discovery of approximate concepts in clinical databases based on a rough set model

Rule discovery methods have been introduced to find useful and unexpected patterns from databases. However, one of the most important problems on these methods is that extracted rules have only positive knowledge, which do not include negative information that medical experts need to confirm whether a patient will suffer from symptoms caused by drug side-effect. This paper first discusses the characteristics of medical reasoning and defines positive and negative rules based on rough set model. Then, algorithms for induction of positive and negative rules are introduced. Then, the proposed method was evaluated on clinical databases, the experimental results of which shows several interesting patterns were discovered, such as a rule describing a relation between urticaria caused by antibiotics and food.
Investigating the performance, chemical, and microstructure properties of carbon nanotube-modified asphalt binder

Carbon nanotubes (CNT) hold the potential to enhance the performance of construction materials such as asphalt binders and mixtures. With the goal of designing long-life asphalt pavements, this research report investigates the laboratory performance of CNT-modified asphalt. Mixing combination conditions including shearing time and shearing rate are studied in the first step. Then modified asphalts with different CNT contents are evaluated by penetration, softening point, and ductility indexes. Storage stability of modified asphalt is accessed using the separation test. After that, aged asphalts are obtained by using the thin film oven test (TFOT). Samples of base and CNT-modified asphalts before and after ageing are characterised by running the rotational viscosity (RV) test, dynamic shear rheometer (DSR) test, and bending beam rheometer (BBR) test. Furthermore, Fourier transform infrared spectroscopy (FTIR) and atomic force microscopy (AFM) are employed to characterise the functional groups and microstructures of base and modified asphalt binders before and after ageing. It is shown that shearing rate and shearing rate*shearing time interaction have a profound influence on the property of modified asphalt whereas shearing time does not. Conventional test results indicate that increasing CNT content would decrease penetration and ductility while increasing the softening point. The separation test shows that CNT may settle down during the storage process. Rheological test results show that the addition of CNT can improve the high temperature performance and ageing resistance while decreasing the low temperature performance. The functional groups in base and modified asphalt are found to be similar which indicates that there are no chemical reactions during modification. It demonstrates that CNT-modified asphalt has better anti-ageing property with a lower increase in sulphoxide index after ageing. AFM characterisation provides details of the CNT modification mechanism. This research provides useful guidance and potential problems on the use of CNT as a high performance modifier.
Carbon Monoxide and Nitric Oxide: Interacting Messengers in Muscarinic Signaling to the Brain's Circadian Clock

Within the central nervous system, acetylcholine (ACh) functions as a state-dependent modulator at a range of sites, but its signaling mechanisms are yet unclear. Cholinergic projections from the brain stem and basal forebrain innervate the suprachiasmatic nucleus (SCN), the master circadian clock in mammals, and cholinergic stimuli adjust clock timing. Cholinergic effects on clock state require muscarinic receptor-mediated activation of guanylyl cyclase and cGMP synthesis, although the effect is indirect. Here we evaluate the roles of carbon monoxide (CO) and nitric oxide (NO), major activators of cGMP synthesis. Both heme oxygenase 2 (HO-2) and neuronal nitric oxide synthase (nNOS), enzymes that synthesize CO and NO, respectively, are expressed in rat SCN, with HO-2 localized to the central core of the SCN, whereas nNOS is a punctate plexus. Hemin, an activator of HO-2, but not the NO donor, SNAP, mimicked cholinergic effects on circadian timing. Selective inhibitors of HO fully blocked cholinergic clock resetting, whereas NOS inhibition partially attenuated this effect. Hemoglobin, an extracellular scavenger of both NO and CO, blocked cholinergic stimulation of cGMP synthesis, whereas l-NAME, a specific inhibitor of NOS, had no effect on cholinergic stimulation of cGMP, but decreased the cGMP basal level. We conclude that basal NO production generates cGMP tone that primes the clock for cholinergic signaling, whereas HO/CO transmit muscarinic receptor activation to the cGMP-signaling pathway that modulates clock state. In light of the recently reported inhibitory interaction between HO-2/CO and amyloid-beta, a marker of Alzheimer's disease (AD), we speculate that HO-2/CO signaling may be a defective component of cholinergic neurotransmission in the pathophysiology of AD, whose manifestations include disintegration of circadian timing.
Similarity Analysis of Korean Medical Literature and Its Association with Efforts to Improve Research and Publication Ethics

In the present study, the frequency of research misconduct in Korean medical papers was analyzed using the similarity check software iThenticate®. All Korean papers written in English that were published in 2009 and 2014 in KoreaMed Synapse were identified. In total, 23,848 papers were extracted. 4,050 original articles of them were randomly selected for similarity analysis. The average Similarity Index of the 4,050 papers decreased over time, particularly in 2013: in 2009 and 2014, it was 10.15% and 5.62%, respectively. And 357 (8.8%) had a Similarity Index of ≥ 20%. Authors considered a Similarity Index of ≥ 20% as suspected research misconduct. It was found that iThenticate® cannot functionally process citations without double quotation marks. Papers with a Similarity Index of ≥ 20% were thus individually checked for detecting such text-matching errors to accurately identify papers with suspected research misconduct. After correcting text-matching errors, 142 (3.5% of the 4,050 papers) were suspected of research misconduct. The annual frequency of these papers decreased over time, particularly in 2013: in 2009 and 2014, it was 5.2% and 1.7%, respectively. The decrease was associated with the introduction of CrossCheck by KoreaMed and the frequent use of similarity check software. The majority (81%) had Similarity Indices between 20% and 40%. The fact suggested that low Similarity index does not necessarily mean low possibility of research misconduct. It should be noted that, although iThenticate® provides a fundamental basis for detecting research misconduct, the final judgment should be made by experts.
Advantage Updating Applied to a Differrential Game

An application of reinforcement learning to a linear-quadratic, differential game is presented. The reinforcement learning system uses a recently developed algorithm, the residual gradient form of advantage updating. The game is a Markov Decision Process (MDP) with continuous time, states, and actions, linear dynamics, and a quadratic cost function. The game consists of two players, a missile and a plane; the missile pursues the plane and the plane evades the missile. The reinforcement learning algorithm for optimal control is modified for differential games in order to find the minimax point, rather than the maximum. Simulation results are compared to the optimal solution, demonstrating that the simulated reinforcement learning system converges to the optimal answer. The performance of both the residual gradient and non-residual gradient forms of advantage updating and Q-learning are compared. The results show that advantage updating converges faster than Q-learning in all simulations. The results also show advantage updating converges regardless of the time step duration; Q-learning is unable to converge as the time step duration grows small.
Techniques for in-band phase noise reduction in ΔΣ synthesizers

This paper reviews several techniques used to reduce the in-band phase noise contribution of /spl Delta//spl Sigma/ fractional-N frequency synthesizers. The paper develops several practical techniques for specifying the noise and linearity of components used in a /spl Delta//spl Sigma/ fractional-N synthesizer. As an example, it presents a synthesizer with an in-band phase noise floor of -97 dBc/Hz@10 KHz for an RF output frequency of 2.432 GHz and a reference frequency of 16 MHz. The synthesizer has a frequency resolution of 61 Hz and an on-chip crystal oscillator. The synthesizer was implemented in a 0.35-/spl mu/m SiGe process and consumes 6 mA from a 3 V supply. The in-band phase-noise, spurs, and power consumption of this synthesizer are each low and comparable to the state of the art.
Grass‐Shrub Riparian Buffer Removal of Sediment, Phosphorus, and Nitrogen From Simulated Runoff 1

Abstract: Riparian buffer forests and vegetative filter strips are widely recommended for improving surface water quality, but grass‐shrub riparian buffer system (RBSs) are less well studied. The objective of this study was to assess the influence of buffer width and vegetation type on the key processes and overall reductions of total suspended solids (TSS), phosphorus (P), and nitrogen (N) from simulated runoff passed through established (7‐year old) RBSs. Nine 1‐m RBS plots, with three replicates of three vegetation types (all natural selection grasses, two‐segment buffer with native grasses and plum shrub, and two‐segment buffer with natural selection grasses and plum shrub) and widths ranging from 8.3 to 16.1 m, received simulated runoff having 4,433 mg/l TSS from on‐site soil, 1.6 mg/l total P, and 20 mg/l total N. Flow‐weighted samples were collected by using Runoff Sampling System (ROSS) units. The buffers were very efficient in removal of sediments, N, and P, with removal efficiencies strongly linked to infiltration. Mass and concentration reductions averaged 99.7% and 97.9% for TSS, 91.8% and 42.9% for total P, and 92.1% and 44.4% for total N. Infiltration alone could account for >75% of TSS removal, >90% of total P removal, and >90% of total N removal. Vegetation type induced significant differences in removal of TSS, total P, and total N. These results demonstrate that adequately designed and implemented grass‐shrub buffers with widths of only 8 m provide for water quality improvement, particularly if adequate infiltration is achieved.
Semantic Technology Recommendation Based on the Analytic Network Process

Semantic technologies have become widely adopted in recent years, and choosing the right technologies for the problems that users face is often a difficult task. This paper presents an application of the Analytic Network Process for the recommendation of semantic technologies, which is based on a quality model for semantic technologies. Instead of relying on expert-based comparisons of alternatives, the comparisons in our framework depend on real evaluation results. Furthermore, the recommendations in our framework derive from user quality requirements, which leads to better recommendations tailored to users’ needs. This paper also presents an algorithm for pairwise comparisons, which is based on user quality requirements and evaluation results.
Drivetrain control strategy for wind turbines

This paper presents a new approach for the control of wind turbines. The goal is to maximise the output power while limiting the drivetrain fatigue load. The proposed strategy can be decomposed in two parts. In a first part, a nonlinear control strategy for rotor speed regulation is proposed as in [1]. It guarantees convergence of the rotor speed to its desired value. This first part only looks at the rotor dynamics and the aerodynamics. The drivetrain dynamics is not taken into account. However, drivetrain failure is a big contributor to the time a wind turbine spends out of commission. According to data gathered from existing offshore wind farms, drivetrain, generator, and gearbox failures account for around 14% of all down time for offshore turbines. Therefore, in a second part, a dedicated control action that minimize the drivetrain fatigue is proposed. The strategy is validated in simulation.
RhoB Acts as a Tumor Suppressor That Inhibits Malignancy of Clear Cell Renal Cell Carcinoma

This study aims to investigate the biological role of RhoB in clear cell renal cell carcinoma (ccRCC). The expression of RhoB was examined in specimens of patients and cell lines by Western blot and Immunohistochemistry. The correlation between RhoB expression and clinicopathologic variables was also analyzed. The effects of RhoB on cell proliferation, cell cycle, cell apoptosis, and invasion/migration were detected by over-expression and knockdown of RhoB level in ccRCC cells via plasmids and RNAi. The results showed that RhoB was low-expressed in ccRCC surgical specimens and cell lines compared with adjacent normal renal tissues and normal human renal proximal tubular epithelial cell lines (HKC), and its protein expression level was significantly associated with the tumor pathologic parameter embracing tumor size(P = 0.0157), pT stage(P = 0.0035), TNM stage(P = 0.0024) and Fuhrman tumor grade(P = 0.0008). Further, over-expression of RhoB remarkably inhibited the cancer cell proliferation, colony formation and promoted cancer cell apoptosis, and aslo reduced the invasion and migration ability of ccRCC cells. Interestingly, up-regulation of RhoB could induce cell cycle arrest in G2/M phase and led to cell cycle regulators(CyclineB1,CDK1) and pro-apoptotic protein(casp3,casp9) aberrant expression. Moreover, knockdown of RhoB in HKC cells promoted cell proliferation and migration. Taken together, our study indicates that RhoB expression is decreased in ccRCC carcinogenesis and progression. Up-regulation of RhoB significantly inhibits ccRCC cell malignant phenotype. These findings show that RhoB may play a tumor suppressive role in ccRCC cells, raising its potential value in futural therapeutic target for the patients of ccRCC.