Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrode potential monitoring and electrolytic control in anaerobic digestion.

INCREASED BIOLOGICAL SLUDGE PRODUCTION that results from higher treatment re quirements and new ocean and land dis posal restrictions has created sludge han dling and disposal problems in many areas. Anaerobic digestion is one of the most ex tensively used sludge stabilization methods, and the digestion units account for a sub stantial portion of the existing wastewater treatment plant investment. In addition to the sludge stabilization, the digestion process produces a methane rich gas that can be used as a fuel. Present methods of anaerobic digestion monitoring and control have resulted in conservative operation at lower rates and loadings than the maximum possible. The present increased activity toward tighter restrictions on the level of toxic metals in sewers is a favorable indication for in creased use of this process. It is therefore necessary to find better process monitoring and control methods so that the advantages of the process can be realized. This paper deals with electrode potential measurement as a digestion monitoring parameter and reports on the relationship of electrode potential measurements to the methane-forming phase of the digestion process. It also reports on research that demonstrates that the pH of a digesting sludge can be controlled by direct current electrolysis with a resulting stability in the electrode potential and an increase in al kalinity.
Microstructures and Mechanical Properties of Mullite/SiC Composite Ceramics Prepared by Gelcasting

Mullite/SiC composite ceramics were fabricated by gelcasting process using SiC and mullite powder as raw materials.The effect of mullite content on microstructure and mechanical properties was investigated.With the increase of mullite content,the po-rosity firstly decline and then increase,mechanical properties are contrary to porosity.When the mullite content is 20%(volume frac-tion),the porosity reach the lowest value of 9.4%,but linear shrinkage,Young's modulus of mullite/SiC composite ceramic reach the highest values,which are 26.5% and 148.6 GPa,respectively.When mullite content reaches 30%,the flexural strength and fracture toughness are higher than others,which are 397.7 MPa and 4.17 MPa·m1/2,respectively.The right amount of mullite addition can sig-nificantly improve the mechanical properties of composite ceramics.But more mullite addition will cause a lot of defects due to the volatilization of mullite during the sintering process,and then the performance of composite ceramics decrease.
Bupivacaine application reduces post thyroidectomy pain: Cerrahpasa experience.

BACKGROUND We aimed to evaluate the impact of bupivacaine administration into the surgical field after total thyroidectomy on post-operative pain and analgesic requirement with a double-blind, prospective, clinical and randomized study. METHODS The study was performed between 2010 and 2011. Pain assessment was performed with the visual analog score (VAS). Patients were pre-operatively, randomly divided into two groups to receive either bupivacaine or saline. One group received a 10-mL of bupivacaine solution while the other group was treated with the same volume of 0.9% NaCl through the drain after completion of total thyroidectomy procedure. All patients were anesthetized and operated with the same anesthesia and surgical team. RESULTS Ninety-one patients (20 males) were included in the study. No patient dropped out of the study during the procedures. No mortality was seen. The VAS scores were significantly lower in the bupivacaine administered group at post-operative minute 30 (3.7±3.2 vs. 5±2.9; P=0.03), hour one (3.04±2.4 vs. 4.2±2.8; P=0.04), and hour eight (1.8±2.04 vs. 3.2±2.1; P=0.005). Thirteen patients required analgesia during their hospital stay in the bupivacaine group while this number was twenty-two in the saline group (P=0.005). CONCLUSIONS Local bupivacaine administration into the surgical field after total thyroidectomy reduces pain and analgesic requirement during the hospital stay.
Characteristics of tasks and technology as a driver of task-technology fit and the use of the hotel reservation information system

Purpose This study aims to examine the effect of task and technology characteristics on the compatibility of technology and tasks, as well as examine the reciprocal effect between the task-technology fit and the use of information systems. Design/methodology/approach The study took place in 36 star hotels from one-star to four-star hotels in some cities and districts in South Kalimantan Province. There were 24 hotels in Banjarmasin, 7 hotels in Banjarbaru and 1 hotel in each area of Banjar, Tanah Bumbu, Tabalong, Hulu Sungai Utara and Barito Kuala. The hotels chosen were those implemented the information and communication technology as supporting administrative activities to serve hotel customers. The population was the front office staff in the existing hotels as the users of the information technology. The sampling technique used in this research was the questionnaire distribution in accordance with the number of population. Data were collected from the filled questionnaires. From the 239distributed questionnaires, 164 (68.62 per cent) were returned and used as the research data. Findings Task characteristics and technology characteristics have a significant and positive effect on task-technology fit, in which the higher the task characteristics and technology characteristics, the higher the task-technology fit. The task-technology fit and the use of information systems are positive and reciprocal. This means that the higher the task-technology fit, the higher the use of information systems. Originality/value The originality of this study is reciprocal relationship between the variables of use with the task-technology fit. Some researchers have found the compatibility of technological tasks affecting the use of information systems, namely, Lin and Huang (2008), Norzaidi and Salwani (2009), Larsen et al. (2009), McGill and Klobas (2009), D’Ambra and Wilson (2013), Im (2014) and Chang et al. (2015). On the other hand, in task-technology fit theory, Goodhue and Thompson (1995) state that use affects the task-technology fit.
Evaluating Machine Learning Methods for City Bike Demand Prediction in Oslo

A bike-sharing system is a service in which a fleet of bicycles is made available to the public on a short-term basis through self-served docking stations. These stations are limited in capacity and are often depleted or saturated with bikes due to sudden spikes in demand. These spikes are hard to avoid and are both detrimental to the user experience and the effectiveness of the system. Machine learning methods have been used to forecast demand spikes at station level in similar systems successfully and would likely be a valuable tool in proactively counteracting the effect of demand spikes in the Oslo bike-sharing system. The goal of this thesis is to evaluate common machine learning methods for demand prediction modeling at individual bike-sharing stations in Oslo. To accomplish this, four machine learning methods which have successfully predicted station-level demand in similar systems are evaluated through a set of experiments. The methods are based on a random forest, gradient boosting tree and recurrent neural networks with either long short-term memoryor gated recurrent unit units. Based on the experimental results, the recurrent neural network with the long short-term memory unit is deemed to be the most suitable for the Oslo bike-sharing system, both due to performance and future potential. However, all methods achieved good performance and made accurate predictions. The results pave the road for developing a full-scale prediction system in the Oslo bikesharing system, by highlighting the most promising prediction method.
Stochastic Modeling of Impulse Responses in Reverberating Environments

Propagation of waves within media supporting reverberation is usually regarded as a direct extension of the case of multi-path propagation, where a set of independent paths, equivalent to plane-wave contributions, can be drawn between a transmitter and a receiver. This paper adopts an alternative approach, based on modal theory, in order to derive models of the stochastic behavior of impulse responses (IRs) measured within such media. IRs can be represented as stationary Gaussian random processes whose amplitude is modulated by a decay function that converges to an exponential only if the time constants of each mode are similar, otherwise displaying a decay rate slowing with time as the modal time constants become more diverse. The asymptotic convergence to a Gaussian process is controlled by the number of available modes, which modal theory predicts to increase linearly with the bandwidth, but quadratically with the frequency. Modal theory implies that groups of typically more than eight propagation paths must be coherently related in order to give rise to reverberation. As a result, fewer degrees of freedom may be available than expected from the number of propagation paths involved, thus leading to a slower convergence to Gaussian propagation models. The stochastic model introduced is further applied in order to understand how far IRs can locally fluctuate away from their root-mean-square amplitude profiles. All theoretical predictions are supported by experimental results.
Mass change and erosion mechanism of the polyimide film during atomic oxygen exposure

The variations of mass changes of polyimide films during exposure in different flux of atomic oxygen (AO) beams which was a ground-simulated low earth orbit (LEO) space environment, were in-situ studied using QCM. The results show that the mass of the polyimide film increases at the initial stage of the erosion under the condition of low AO flux, subsequently begins to decrease linearly. However, under the condition of high AO flux, the polyimide film shows a steady-state mass loss in the whole exposure process. The computer simulation based on Langmuir adsorption theory suggests that the major reaction path of carrying the mass away from the surface occurs at the oxidized surface, The mass gain results from AO adsorption under an insufficient AO flux on the un-oxidized surface.
EFECT OF FYM AND POTASSIUM ON YIELD, NUTRIENT UPTAKE AND ECONOMICS OF WHEAT IN ALLUVIAL SOIL

An experiment was conducted at Bichpuri (Agra) during rabi seasons of 2008-09 and 2009-10 to study the impact of FYM and potassium on yield, nutrient uptake and economics of wheat in alluvial soil of Agra.The experiment was laid out in factorial randomized block design with four levels of FYM (0,2.5,5.0 and 10 t ha -1 ) and four levels of potassium (0,30,60 and 90 kg K2O ha -1 ). The grain and straw yield increased significantly up to 10 t FYM ha -1 and 90 kg K2O ha -1 over absolute control. The increased in grain yield with 2.5, 5.0 and 10 t FYM ha -1 were recorded to the tune of 8.6, 14.0 and 15.7% and 4.1, 8.5 and 13.6%, over control in first and second year, respectively. The corresponding increases in yield due to 30, 60 and 90 kg K2O ha -1 over control were 5.3, 14.9 and 25.2%, 11.0 and 19.9 and 31.4%. The levels of FYM and K applied together in different combinations increased the yield significantly more than those with their individual application. Significantly higher nitrogen, phosphorus and potassium uptake n wheat crop was recorded with FYM and potassium application. Magnesium uptake increased with the lower levels of potassium followed by decrease with each higher levels of K. The maximum net returns (`. 47997.7 ha -1 ) and B:C ratio (2.42) were obtained with 90 kg K2O ha -1 .
Energy-Aware Computation Offloading and Transmit Power Allocation in Ultradense IoT Networks

To meet the surging demands on network throughput and spectrum resources arising with billions of Internet-of-Things mobile devices (IMDs), ultradense networks are envisioned to be a promising technology, which gives rise to the so-called ultradense Internet-of-Things (IoT) networks. Meanwhile, with the constant emergence of new IoT applications, the conflict between computing-intensive applications and resource-constrained IMDs is increasingly prominent. By offloading computing-intensive tasks to the edge servers in close proximity, mobile-edge computing is expected as an effective solution to address this issue. However, computation offloading research in ultradense IoT networks is still scarce until now. Toward this end, we provide this paper to study the energy-aware task offloading problem with multiple edge servers in ultradense IoT networks, where diverse kinds of computation tasks are randomly requested by the IMDs and the computing resources at the edge servers change dynamically. An iterative searching-based task offloading scheme is proposed as our solution, which jointly optimizes task offloading, computational frequency scaling, and transmit power allocation. Extensive numerical results demonstrate the superior performance of conducting task offloading among multiple edge servers, and corroborate the advantages of our scheme over existing works which either fixed computational frequency and transmit power, or neglected the impact of the IMDs’ residual battery.
Structural Changes of Adhesive Discs during Attachment of Boston Ivy

This study investigates the developmental pattern of adhesive discs (ADs) to highlight the ontogeny and structural changes that occur during the growth of Boston ivy. Initiation to postmortem features of ADs were examined through light and scanning electron microscopy. The study also reveals a new finding of the dislocation of peripheral tissues of adaxial origin. Four phases of attachment are suggested with regards to its climbing behavior: 1) pre-attachment, 2) upon attachment, 3) after attachment, and 4) final attachment. During initiation, several ADs originate from tendril primordia without epidermal differentiation. However, different growth rates in the epidermis results in completely different ADs. ADs were discerned by size, shape, and color during expansion, but cells in the adaxial surface remained alive longer than the other side. Upon contact, the ADs demonstrate simultaneous growth and deterioration, but once attachment is established the latter process subdues to final stages. Epidermal transformation, adhesive secretion, cellular disruption, and mechanical stress were essential for the self-clinging nature of Boston ivy. The post-attachment sequence is also believed to be critical in achieving maximum mechanical strength to provide extensive support. The developmental process of ADs is prompted by tactile stimulation but in a highly organized and systematic manner.
Predicting clinical variables from neuroimages using fused sparse group lasso

Predictive models in which neuroimage features serve as predictors and a clinical variable is modeled as the outcome are good candidates for clinical application because (1) they can exploit dependencies between predictor variables and thus potentially explain more variability in the outcome than a mass univariate approach, and (2) they allow inference at the individual level, such that a prediction can be obtained for a new individual whose data was not used to train the model. This dissertation proposes methods for neuroimaging prediction models that not only aim for predictive accuracy, but also seek interpretability and potential insight into the underlying pathophysiology of neuropsychiatric disorders. In the first part of this dissertation we propose the fused sparse group lasso penalty, which encourages structured, sparse, interpretable solutions by incorporating prior information about spatial and group structure among voxels. We derive optimization steps for fused sparse group lasso penalized regression using the alternating direction method of multipliers algorithm. With simulation studies, we demonstrate conditions under which fusion and group penalties together outperform either of them alone. We then use fused sparse group lasso to predict continuous measures from resting state magnetic resonance imaging data using the Autism Brain Imaging Data Exchange dataset. In the second part of this dissertation we use fused sparse group lasso to predict age from multimodal neuroimaging data in a sample of cognitively normal adults aged 65 and older. In general, we show how the incorporation of prior information via the fused sparse group lasso penalty can enhance the interpretability of neuroimaging predictive models while also yielding good predictive performance. Public health significance: Psychiatric disorders and neurological diseases such as Alzheimer's present a large public health burden. As of yet, there have been relatively few translations of basic neuroscience findings to clinical applications in psychiatry. Prediction models using neuroimaging data can potentially help clinicians with diagnosis and prediction of prognosis and treatment response. Establishing interpretable neuroimaging-based biomarkers can improve our understanding of the neurobiological mechanisms underlying neuropsychiatric disorders and suggest approaches for prevention and treatment.
A phase field modeling of dynamic brittle fracture at finite strains

Fracture is the total or partial separation of an initially intact body through the propagation of one or several cracks. Computational methods for fracture mechanics are becoming increasingly important in dealing with the nucleation and propagation of these cracks. One method is the phase field approach, which approximates sharp crack discontinuities with a continuous scalar field, the so-called phase field. The latter represents the smooth transition between the intact and broken material phases. The evolution of the phase field due to external loads describes the fracture process. An original length scale is used to govern the diffusive approximation of sharp cracks. This method further employs a degradation function to account for the loss of the material stiffness during fracture by linking the phase field to the body’s bulk energy. To prevent the development of unrealistic crack patterns and interpenetration of crack faces under compression, this study uses the anisotropic split of the bulk energy, as proposed by Amor et al. [5], to model the different fracture behavior in tension, shear and compression. This research is part of a larger project aimed at the modeling of Antarctic sea ice dynamics. One aspect of this project is the modeling of the gradual break-up of the consolidated ice during spring. As a first step, this study reviews a phase field model used for dynamic brittle fracture at finite strains. Subsequently, this model is implemented into the in-house finite element software SESKA to solve the benchmark tension and shear tests on a single-edge notched block. The implementation adopts the so-called monolithic scheme, which computes the displacement and phase field solutions simultaneously, with a Newmark time integration scheme. The results of the solved problems demonstrate the capabilities of the implemented dynamic phase field model to capture the nucleation and propagation of cracks. They further confirm that the choice of length-scale and mesh size influences the solutions. In this regard, a small value of the length-scale converges to the sharp crack topology and yields a larger stress value. On the other hand, a large length-scale parameter combined with a too coarse mesh size can yield unrealistic results.
SIGNATURE RECOGNITION AND VERIFICATION USING ( SURF ) ALGORITHM WITH SVM KERNELS

One important way to identify the signer is the personal signature. The operation of recognize the signature and identify it, is very important. This process may be done either offline or online. This paper explains the online technique. Features extraction, pattern matching and Images processing are techniques used for signature confirmation. Speed up robust features (SURF) is an algorithm that uses the image of local feature with ability for matching images. SURF recognizes, describes and extracts the local feature of forged signature from the image. SURF algorithm provides fast and accurate comparison tool that can work under different lights, visions and rotation situations to check if the person signature is original or forged. The features extracted from the SURF algorithm are entered into Bag-of-word features. The features of bagof-word are used inside multiclass Support Vector Machine (SVM) classifies. SURF with SVM kernels gives an accuracy of 98.75%.
Photocatalytic degradation of reactive black 5 using Photo-Fenton and ZnO nanoparticles under UV irradiation

Due to their extensive range of applications, the existence of notable amounts of reactive dyes in wastewater raises the need for more efficient treatment techniques. The present work investigates the removal of Reactive Black 5 (RB5) azo dye by homogeneous advanced oxidation processes (photo-Fenton) and heterogeneous advanced oxidation processes (ZnO nanoparticle / UV). The Synthesized ZnO nanoparticles (NPs) were characterized using Fourier transforms infrared (FTIR), transmission electron microscopy (TEM), Scanning electron microscope (SEM), X-ray diffraction (XRD), UV–Vis spectroscopy, and the photoluminescence (PL). The formation of ZnO NPs was confirmed by FTIR with a broad band at 420.50 cm−1. TEM analysis shows that the synthesized ZnO NPs particle size ranged between 19.1 and 33.1 nm. The ZnO NPs hexagonal phase was observed from XRD analysis. Photo-Fenton (FeSO4.7H2O/ UV/H2O2) showed high decolorization rate of RB5 reached 98% within the first 5 min. While ZnO NPs showed lower decolorization rate during the same period. The effects of FeSO4.7H2O initial dosage, RB5 initial concentrations, ZnO initial dosage and contact time were investigated as well.
Rapid and Noninvasive Typing and Assessment of Nutrient Content of Maize Kernels Using a Handheld Raman Spectrometer

To thrive as a global civilization, food production must meet the demands of our ever-growing population. There are more than a billion people on the planet suffering from malnutrition through poor quality or lack of food. Nutrient content of food can be determined by a variety of methods, which have issues such as slow analysis or sample destruction. Near-infrared (NIR) spectroscopy is a long-standing alternative to these methods. In this work, we demonstrated that Raman spectroscopy (RS), another spectroscopic method, can also be used to assess the nutrient content of maize (Zea mays), one of the most widely cultivated grains in the world. Using a handheld Raman spectrometer, we predicted the content of carbohydrates, fibers, carotenoids, and proteins in six different varieties of maize. This analysis requires only a single maize kernel and is fast (1s), portable, noninvasive, and nondestructive. Moreover, we showed that RS in combination with chemometric methods can be used for highly accurate (approximately 90%) spectroscopic typing of maize, which is important for plant breeders and farmers. Finally, we demonstrate that Raman-based approach is as accurate as NIR analysis. These findings suggest that portable Raman systems can be used on combines and grain elevators for autonomous control of grain quality.
Influence of tilted surface alignment on flexoelectric domains in twisted nematic layers

The small deformations induced by electric field in twisted flexoelectric nematic layers were simulated numerically. The one-dimensional (i.e. homogeneous over the whole area of the layer) and two-dimensional (i.e. spatially periodic flexoelectric domains) deformations were considered. It was shown that the periodic deformations do not arise if sufficiently high pretilt angle is imposed by the boundary conditions. The value of pretilt angle necessary for elimination of domains increases with the flexoelectric coefficient e 33 . This result is of practical meaning because the flexoelectric domains are undesirable from an applicative point of view since they destroy the homogeneous appearance of the area of an excited pixel of a display.