Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of acupressure on constipation in patients undergoing hemodialysis: A randomized double-blind controlled clinical trial

Objective: Constipation is one of the most common digestive problems in patients undergoing hemodialysis. It has a negative effect on quality of life in these patients. As routine treatments are not effective in this regard, complementary therapies may help to overcome this condition. This study aimed to investigate the effect of acupressure on constipation in patients undergoing hemodialysis. Materials and Methods: This was a randomized double- blind placebo- controlled clinical trial conducted in 2014. A convenience sample of 70 patients undergoing hemodialysis was selected from hemodialysis units of three hospitals affiliated to Mazandaran University of Medical Sciences, Mazandaran, Iran. Patients were randomly assigned to intervention or control group. Intervention group received acupressure in acupressure points three times a week for four weeks during hemodialysis. In control group, acupressure was delivered in false points. We assessed the frequency of defecation in the two groups before and after the study. The study instruments consisted of a demographic questionnaire, and a data sheet for documenting constipation frequency. Results: The results indicated a significant difference between intervention group (13.73±3.63) and control group (10.06±3.77) in frequency of defecation during the fourth week of intervention (p<0.001). Regarding quality of stool, there was a meaningful difference between the groups in the fourth week in a way that the stool in the intervention group was more natural and in the control group, it was thicker and more adhesive. Conclusion: Acupressure seems to be an effective complementary treatment for constipation in patients undergoing hemodialysis.
OIL PRICE SHOCK: A NONLINEAR APPROACH *

Nowadays, the importance of crude oil goes beyond simple economic aspects and affects social life in general. As such, it is imperative that we should know what the relationship between GDP growth and oil price changes is like. This paper presents evidence of a nonlinear relationship between the two things. We also argue that this non-linearity is not merely due to the use of data from the mid-1980s onwards, as most authors, so far, seem to believe. In fact, we find the existence of non-linearity with the use of data earlier than 1984, and even before 1977. Furthermore, we question that the nonlinear transformations of oil prices proposed in the Literature are able to reflect such non-linearity. We therefore use a non-linear function that relates GDP growth to oil prices, and estimate this function by means of kernel methods, avoiding any assumptions about its form. This kernel estimation improves on the linear estimation, and also improves on both Hamilton? s (2001b) estimation and those of the nonlinear transformations. Hoy dia la importancia del petroleo sobrepasa los aspectos meramenteeconomicos, afectando de manera generalizada a nuestra vida social. Por ello, es muyimportante saber cual es la relacion existente entre el crecimiento del PIB y los cambiosen el precio del petroleo. Este articulo presenta evidencia de la existencia de unarelacion no-lineal entre ambos. Esta no-linealidad se debe no solamente al uso de datosdesde mitad de los anos ochenta, como la mayoria de los autores parecen creer. Dehecho, se puede encontrar la existencia de no-linealidad con el uso de datos anteriores a1984, e incluso anteriores a 1977. Este articulo adicionalmente cuestiona que lastransformaciones no-lineales propuestas en la literatura sean capaces de capturar dichano-linealidad. Por todo ello, se utiliza una funcion no-lineal que relaciona el crecimientodel PIB con el precio del petroleo, estimandola a traves de metodos ? kernel? , evitandoasi cualquier supuesto sobre su forma. Esta estimacion ? kernel? mejora la estimacionlineal, asi como aquellas derivadas de las transformaciones no-lineales y aquellapropuesta por Hamilton (2001b).
Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks

We study the effectiveness of various machine learning techniques, including artificial neural networks, support vector machine, logistic regression, K-nearest neighbors, decision tree and Naive Bayesian, for detecting and mitigating power jamming attacks in optical networks. Our study shows that artificial neural network is the most accurate in detecting out-of-band power jamming attacks in optical networks. To further mitigating the power jamming attacks, we apply a new resource reallocation scheme that utilizes the statistical information of attack detection accuracy, and propose a resource reallocation algorithm to lower the probability of successful jamming of lightpaths. Simulation results show that higher the accuracy of detection, lower is the likelihood of jamming a lightpath.
An Unstructured Grid Incompressible Navier-Stokes Algorithm for Convective Heat Transfer Based on Artificial Compressibility

The development of an explicit unstructured grid finite-volume scheme for solving the full incompressible Navier-Stokes equations along with the energy equation in a strongly coupled manner is presented. The Boussinesq approximation is utilized to account for thermal buoyancy. The method of artificial compressibility is used to solve the resulting equations in a time marching fashion. Roe’s approximate Riemann solver is used for the construction of the numerical flux. An eigensystem is derived for the flux Jacobian matrix, which is used in the evaluation of the numerical flux and the characteristic variable boundary conditions. The resulting algorithm is validated by simulating canonical test cases from the three regimes of convective heat transfer. The computed solutions are in close agreement with analytical solutions and other benchmark computations.
Stem Diameter (and Not Length) Limits Twig Leaf Biomass

The relationship between leaf and stem biomass as well as the relationship between leaf biomass and stem length and diameter are important to our understanding of a broad range of important plant scaling relationship because of their relationship to photosynthesis and thus growth. To understand how twig architecture (i.e., current year leaves, and stem diameter and length) affects stem diameter and length, and leaf number and biomass, we examined the twigs of 64 woody species collected from three forest types along an elevational gradient in the Wuyi Mountains, Jiangxi Province, China. We also compared the scaling relationships we observed with biomass allocation patterns reported at the whole tree level. Our results revealed isometric relationship between leaf and stem biomass on twigs despite differences in forest communities and despite changes in environmental factors along an elevational gradient. Across the 64 species, from twigs to individual trees, leaf biomass scaled approximately as the 2.0-power of stem diameter (but not for stem length or leaf number). These results help to identify a general rule that operates at two different levels of biological organization (twigs and whole trees). The scaling relationship between leaf biomass and stem diameter in twigs is insensitive to differences in species composition, elevation, or forest type. We speculate that this rule emerges because stem diameter serves as a proxy for the amount of resources supplied per unit cross section to developing leaves and for the flow of photosynthates from mature leaves to the rest of the plant body.
Angiotensin II-Induced Generation of Reactive Oxygen Species Is Regulated by a Phosphatidylinositol 3-Kinase/L-Type Calcium Channel Signaling Pathway

Angiotensin II (AngII) is an essential hormone that affects vascular physiology. For example, stimulation of vascular smooth muscle cells (VSMCs) rapidly induces vasoconstriction and results in the up-regulation of blood pressure. Chronic stimulation of VSMCs with AngII also results in hypertrophy. In this study, we confirmed an involvement of phosphatidylinositol 3-kinase (PI3K)-dependent calcium mobilization in AngII-induced generation of reactive oxygen species (ROS). Stimulation of rat aortic smooth muscle cells (RASMCs) with AngII significantly induced the generation of ROS in a dose- and time-dependent manner. AngII-induced generation of ROS was completely abolished by pharmacological inhibition of PI3K (with LY294002), but inhibition of the ERK signaling pathway had no effect. AngII-induced calcium mobilization was completely blocked by inhibition of PI3K, whereas inhibition of the ERK signaling pathway by PD98059 was ineffective. Depletion of extracellular calcium or inhibition of the L-type calcium channel by nifedipine completely blocked AngII-induced calcium mobilization. Depletion of extracellular calcium by EGTA and incubation of RASMCs with calcium- free medium both significantly blocked AngII-induced ROS generation. Inhibition of the L-type calcium channel also significantly blocked AngII-induced ROS generation. These results suggest that AngII-induced ROS generation is regulated by calcium mobilization, which, in turn, is modulated by a PI3K/L-type calcium channel signaling pathway.
The Population Ecology of Organizations

A population ecology perspective on organization-environment relations is proposed as an alternative to the dominant adaptation perspective. The strength of inertial pressures on organizational structure suggests the application of models that depend on competition and selection in populations of organizations. Several such models as well as issues that arise in attempts to apply them to the organization-environment problem are discussed.
Who should be my teammates: using a conversational agent to understand individuals and help teaming

We are building an intelligent agent to help teaming efforts. In this paper, we investigate the real-world use of such an agent to understand students deeply and help student team formation in a large university class involving about 200 students and 40 teams. Specifically, the agent interacted with each student in a text-based conversation at the beginning and end of the class. We show how the intelligent agent was able to elicit in-depth information from the students, infer the students' personality traits, and reveal the complex relationships between team personality compositions and team results. We also report on the students' behavior with and impression of the agent. We discuss the benefits and limitations of such an intelligent agent in helping team formation, and the design considerations for creating intelligent agents for aiding in teaming efforts.
Polynomial relations between matrices of graphs

We derive a correspondence between the eigenvalues of the adjacency matrix A and the signless Laplacian matrix Q of a graph G when G is ( d 1 , d 2 ) ‐biregular by using the relation A 2 = ( Q − d 1 I ) ( Q − d 2 I ) . This motivates asking when it is possible to have X r = f ( Y ) for f a polynomial, r > 0 , and X , Y matrices associated to a graph G . It turns out that, essentially, this can only happen if G is either regular or biregular.
Missing-Value Imputation of Continuous Missing Based on Deep Imputation Network Using Correlations among Multiple IoT Data Streams in a Smart Space

SUMMARY Data generated from the Internet of Things (IoT) devices in smart spaces are utilized in a variety of ﬁelds such as context recognition, service recommendation, and anomaly detection. However, the missing values in the data streams of the IoT devices remain a challenging problem owing to various missing patterns and heterogeneous data types from many di ﬀ erent data streams. In this regard, while we were analyzing the dataset collected from a smart space with multiple IoT devices, we found a continuous missing pattern that is quite di ﬀ erent from the existing missing-value patterns. The pattern has blocks of consecutive missing values over a few seconds and up to a few hours. Therefore, the pattern is a vital factor to the availability and reliability of IoT applications; yet, it cannot be solved by the existing missing-value imputation methods. Therefore, a novel approach for missing-value imputation of the continuous missing pattern is required. We deliberate that even if the missing values of the continuous missing pattern occur in one data stream, missing-values imputation is possible through learning other data streams correlated with this data stream. To solve the missing values of the continuous missing pattern problem, we analyzed multiple IoT data streams in a smart space and ﬁgured out the correlations between them that are the interdependencies among the data streams of the IoT devices in a smart space. To impute missing values of the continuous missing pattern, we propose a deep learning-based missing-value imputation model exploiting correlation information, namely, the deep imputation network (DeepIN), in a smart space. The DeepIN uses that multiple long short-term memories are constructed according to the correlation information of each IoT data stream. We evaluated the DeepIN on a real dataset from our campus IoT testbed, and the experimental results show that our proposed approach improves the imputation performance by 57.36% over the state-of-the-art missing-value imputation algorithm. Thus, our approach can be a promising methodology that enables IoT applications and services with a reasonable missing-value imputation accuracy (80 (cid:24) 85%) on average, even if a long-term block of values is missing in IoT environments.
Deep Modulation Embedding

Deep neural network has recently shown very promising applications in different research directions and attracted the industry attention as well. Although the idea was introduced in the past but just recently the main limitation of using this class of algorithms is solved by enabling parallel computing on GPU hardware. Opening the possibility of hardware prototyping with proven superiority of this class of algorithm, trigger several research directions in communication system too. Among them cognitive radio, modulation recognition, learning based receiver and transceiver are already given very interesting result in simulation and real experimental evaluation implemented on software defined radio. Specifically, modulation recognition is mostly approached as a classification problem which is a supervised learning framework. But it is here addressed as an unsupervised problem with introducing new features for training, a new loss function and investigating the robustness of the pipeline against several mismatch conditions.
Dataplant: In-DRAM Security Mechanisms for Low-Cost Devices

Low-cost devices are now commonplace and can be found in diverse environments such as home electronics or cars. As such devices are ubiquitous and portable, manufacturers strive to minimize their cost and power consumption by eliminating many features that exist in other computing systems. Unfortunately, this results in a lack of basic security features, even though their ubiquity and ease of physical access make these devices particularly vulnerable to attacks. To address the lack of security mechanisms in low-cost devices that make use of DRAM, we propose Dataplant, a new set of low-cost, high-performance, and reliable security primitives that reside in and make use of commodity DRAM chips. The main idea of Dataplant is to slightly modify the internal DRAM timing signals to expose the inherent process variation found in all DRAM chips for generating unpredictable but reproducible values (e.g., keys, seeds, signatures) within DRAM, without affecting regular DRAM operation. We use Dataplant to build two new security mechanisms: 1) a new Dataplant-based physical unclonable function (PUF) with high throughput and high resiliency to temperature changes, and 2) a new cold boot attack prevention mechanism based on Dataplant that automatically destroys all data within DRAM on every power cycle with zero run-time energy and latency overheads. These mechanisms are very easy to integrate with current DDR memory modules. Using a combination of detailed simulations and experiments with real DRAM devices, we show that our Dataplant-based PUF has 10x higher throughput than state-of-the-art DRAM PUFs while being much more resilient to temperature changes. We also demonstrate that our Dataplant-based cold boot attack protection mechanism is 19.5x faster and consumes 2.54x less energy when compared to existing mechanisms.
Metabolic Engineering of Bacillus subtilis Toward Taxadiene Biosynthesis as the First Committed Step for Taxol Production

Terpenoids are natural products known for their medicinal and commercial applications. Metabolic engineering of microbial hosts for the production of valuable compounds, such as artemisinin and Taxol, has gained vast interest in the last few decades. The Generally Regarded As Safe (GRAS) Bacillus subtilis 168 with its broad metabolic potential is considered one of these interesting microbial hosts. In the effort toward engineering B. subtilis as a cell factory for the production of the chemotherapeutic Taxol, we expressed the plant-derived taxadiene synthase (TXS) enzyme. TXS is responsible for the conversion of the precursor geranylgeranyl pyrophosphate (GGPP) to taxa-4,11-diene, which is the first committed intermediate in Taxol biosynthesis. Furthermore, overexpression of eight enzymes in the biosynthesis pathway was performed to increase the flux of the GGPP precursor. This was achieved by creating a synthetic operon harboring the B. subtilis genes encoding the 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway (dxs, ispD, ispF, ispH, ispC, ispE, ispG) together with ispA (encoding geranyl and farnesyl pyrophosphate synthases) responsible for providing farnesyl pyrophosphate (FPP). In addition, a vector harboring the crtE gene (encoding geranylgeranyl pyrophosphate synthase, GGPPS, of Pantoea ananatis) to increase the supply of GGPP was introduced. The overexpression of the MEP pathway enzymes along with IspA and GGPPS caused an 83-fold increase in the amount of taxadiene produced compared to the strain only expressing TXS and relying on the innate pathway of B. subtilis. The total amount of taxadiene produced by that strain was 17.8 mg/l. This is the first account of the successful expression of taxadiene synthase in B. subtilis. We determined that the expression of GGPPS through the crtE gene is essential for the formation of sufficient precursor, GGPP, in B. subtilis as its innate metabolism is not efficient in producing it. Finally, the extracellular localization of taxadiene production by overexpressing the complete MEP pathway along with IspA and GGPPS presents the prospect for further engineering aiming for semisynthesis of Taxol.
AFFINE-PERIODIC SOLUTIONS FOR DISCRETE DYNAMICAL SYSTEMS

The paper concerns the existence of affine-periodic solutions for discrete dynamicalsystems. This kind of solutions might be periodic, harmonic, quasi-periodic, even non-periodic.We prove the existence of affine-periodic solutions for discrete dynamical systems by using thetheory of Brouwer degree. As applications, another existence theorem is given via Lyapnovfunction.
Anaerobic removal of volatile organic compounds (VOC) from wastewater using methanol and ethylene glycol as co-substrate

Low concentration of chlorinated aliphatic compounds may be found in the wastewater, and has been accepted as toxic compounds for biological life. These volatile organic compounds (VOC) could be removed by different methods, but in this study, anaerobic decomposition was studied removal of VOC from the contaminated water. Aim of this work was treatment of VOC's by up-flow anaerobic blanket reactor (UASB) and to determine effects of different co-substrates (methanol and ethylene glycol) on VOC's removal at the anaerobic conditions. Furthermore, theoretical and practical investigation on the degradation of chlorinated hydrocarbons has importance in the water pollution. According to our results methanol was better co-substrate for VOC removal at anaerobic condition than ethylene glycol. Removal rates of DCM, CF, PCE and TCE in this investigation were approximately 65, 95, 100 and 90 % respectively with using co-substrate methanol.
Is There a Time and Place for Transformational Leadership? The Daily Impact of Leader Behaviors on Follower Task Performance

The purpose of this study was to weigh the value of Transformational Leadership (TFL) and Contingent-Reward Leadership (CRL) behaviors in determining followers' objective task performance. Past empirical evidence has suggested that TFL behaviors were relatively less important for influencing individual task performance in comparison to CRL behaviors. This study was designed to test the boundaries of this empirical conclusion. There were two main goals: 1) to investigate the daily effects of TFL and CRL behaviors on follower task performance given a "chaotic" work context, and 2) to address a temporal issue related to the measurement of TFL and its influence on individual task performance. Daily data were collected from a sample of 46 Canadian tree planters over a period of nine work days. The sample was composed of novice and experienced personnel. The data were hierarchically cross-classified, with days nested within individuals, and individuals nested between leaders. Hierarchical multiple regression and PROCESS moderation analysis results point to short-term effects of CRL and long-term effects of TFL for influencing task performance in both groups. Interestingly, the vision item from the inspirational motivation dimension of TFL demonstrated both a significant daily and lingering association to the task performance of novice and experienced tree planters. Implications of the results for leadership theory and practice are discussed, along with a review of the study strengths and limitations. To conclude, the author offers suggestions for the direction and focus of future leadership research given the changing nature of 21st century work environments.
Optimization of Mulch Mat Production from Oil Palm Empty Fruit Bunches and its Effects on Growth Performance of Acacia Hybrid Seedlings on Sandy Tailings

For forest plantation establishment on sandy soils such as tin tailings in Malaysia; mulching is important during transplanting of plants for moisture retention, protection of soil surface from erosion due to heavy rain and strong wind, regulating the soil surface temperature, reducing leaching of nutrients from fertilizers and weed control. EFB has been recycled as a mulch and source of nutrients in the oil palm fields and also has the potential for mulching forest species on sandy soils. However, the EFB in its natural form is heavy, thorny and bulky and cannot be stored. Therefore, the EFB need to be transformed into a form that is convenient and economical for mulching. Although the EFB is already commercially processed into a more convenient light form, it is too thin and has lost most of the EFB original beneficial characteristics. Thus, an improved thicker mats should be developed. Earlier studies had shown that it was possible to produce mulch mat from EFB fiber using binders that allow the mat to decompose. However, the optimum processing parameters to obtain the mats of targeted thickness were not determined and knowledge on its decomposition and effect on plant growth is greatly lacking. Therefore, the objectives of this study were to, (i) determine optimum parameters for making EFB mat which is thicker, capable of absorbing and retaining water and decomposes slowly, (ii) investigate its decomposition and (iii) determine its effect on the growth performance of Acacia hybrid planted on sandy tin tailings.