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This paper presents a novel method of smoke removal from the laparoscopic images. Due to the heterogeneous nature of surgical smoke, a two-stage network is proposed to estimate the smoke distribution and reconstruct a clear, smoke-free surgical scene. The utilization of the lightness channel plays a pivotal role in providing vital information pertaining to smoke density. The reconstruction of smoke-free image is guided by a hybrid embedding, which combines the estimated smoke mask with the initial image. Experimental results demonstrate that the proposed method boasts a Peak Signal to Noise Ratio that is $2.79\%$ higher than the state-of-the-art methods, while also exhibits a remarkable $38.2\%$ reduction in run-time. Overall, the proposed method offers comparable or even superior performance in terms of both smoke removal quality and computational efficiency when compared to existing state-of-the-art methods. This work will be publicly available on http://homepage.hit.edu.cn/wpgao, Comment: ISBI2024

We investigate how individuals use measures of apparent predictability from price charts to predict future market prices. Subjects in our experiment predict both random walk times series, as in the seminal work by Bloomfield & Hales (2002) (BH), and stock price time series. We successfully replicate the experimental findings in BH that subjects are less trend-chasing when there are more reversals in the first task. We find that subjects also overreact less to the trend when there is less momentum in the stock price in the second task, though the momentum factor that is significant is the autocorrelation instead of the number of reversals per se. Our subjects also appear to use other variables such as amplitude and volatility as measures of predictability. However, as random walk theory predicts, relying on apparent patterns in past data does not improve their prediction accuracy.

We investigate how individuals use measures of apparent predictability from price charts to predict future market prices. Subjects in our experiment predict both random walk times series, as in the seminal work by Bloomfield & Hales (2002) (BH), and stock price time series. We successfully replicate the experimental findings in BH that subjects are less trend-chasing when there are more reversals in the first task. We find that subjects also overreact less to the trend when there is less momentum in the stock price in the second task, though the momentum factor that is significant is the autocorrelation instead of the number of reversals per se. Our subjects also appear to use other variables such as amplitude and volatility as measures of predictability. However, as random walk theory predicts, relying on apparent patterns in past data does not improve their prediction accuracy.

Structural optimization of activated carbon (AC) mainly relies on experience, which depends on the intrinsic structure of biochar, processing conditions, and the interplay of both parties. A fundamental understanding of the pore structure evolution related to the intrinsic structure and composition remains a challenge. In this work, spent mushroom substrate, a rapidly growing byproduct of the mushroom cultivation industry, is used as model biomass to prepare AC under CO2 activation. The structure and composition of the AC products with different activation durations were systematically analyzed with several characterization techniques including N-2 adsorption-desorption, scanning electron microscopy, X-ray diffraction, and Raman spectroscopy. A multipeak separation method is developed that enables quantitative analysis of carbonized lignin and carbonized cellulose. A peak area ratio parameter is proposed to describe the retention of cellulose. It is revealed that higher retention of carbonized cellulose corresponds to a larger Brunauer-Emmett-Teller (BET) surface area, demonstrating the dominant role of cellulose in the pore structure development process. This work not only provides a qualitative correlation between cellulose and rich porous structure but also offers a new quantitative tool to understand the structure-composition relationship during the pore evolution process.

Though effective in the segmentation, conventional multilevel thresholding methods are computationally expensive as exhaustive search are used for optimal thresholds to optimize the objective functions. To overcome this problem, population-based metaheuristic algorithms are widely used to improve the searching capacity. In this paper, we improve a popular metaheuristic called cuckoo search using a ring topology based fully informed strategy. In this strategy, each individual in the population learns from its neighborhoods to improve the cooperation of the population and the learning efficiency. Best solution or best fitness value can be obtained from the initial random threshold values, whose quality is evaluated by the correlation function. Experimental results have been examined on various numbers of thresholds. The results demonstrate that the proposed algorithm is more accurate and efficient than other four popular methods.

The feature frame is a key idea of feature matching problem between two images. However, most of the traditional matching methods only simply employ the spatial location information (the coordinates), which ignores the shape and orientation information of the local feature. Such additional information can be obtained along with coordinates using general co-variant detectors such as DOG, Hessian, Harris-Affine and MSER. In this paper, we develop a novel method considering all the feature center position coordinates, the local feature shape and orientation information based on Gaussian Mixture Model for co-variant feature matching. We proposed three sub-versions in our method for solving the matching problem in different conditions: rigid, affine and non-rigid, respectively, which all optimized by expectation maximization algorithm. Due to the effective utilization of the additional shape and orientation information, the proposed model can significantly improve the performance in terms of convergence speed and recall. Besides, it is more robust to the outliers., Comment: arXiv admin note: text overlap with arXiv:0905.2635 by other authors

Partial deposition of carbon onto mesoporous TiO2 (C/TiO2) were prepared as supporting substrate for Pt catalyst development. Carbon deposition is achieved by in-situ carbonization of furfuryl alcohol. The hybrid catalysts were characterized by XRD, Raman, SEM and TEM and exhibited outstanding catalytic activity and stability in methanol oxidation reaction. The heterogeneous carbon coated on mesoporous TiO2 fibers provided excellent electrical conductivity and strong interfacial interaction between TiO2 support and Pt metal nanoparticles. Methanol oxidation reaction results showed that the activity of Pt-C/TiO2 is 3.0 and 1.5 times higher than that of Pt-TiO2 and Pt-C, respectively. In addition, the Pt-C/TiO2 exhibited a 6.7 times enhanced stability compared with Pt-C after 2000 cycles. The synergistic effect of C/TiO2 is responsible for the enhanced activity of Pt-C/TiO2, and its excellent durability could be ascribed to the strong interfacial interaction between Pt nanoparticles and C/TiO2 support.

Partial deposition of carbon onto mesoporous TiO2 (C/TiO2) were prepared as supporting substrate for Pt catalyst development. Carbon deposition is achieved by in-situ carbonization of furfuryl alcohol. The hybrid catalysts were characterized by XRD, Raman, SEM and TEM and exhibited outstanding catalytic activity and stability in methanol oxidation reaction. The heterogeneous carbon coated on mesoporous TiO2 fibers provided excellent electrical conductivity and strong interfacial interaction between TiO2 support and Pt metal nanoparticles. Methanol oxidation reaction results showed that the activity of Pt-C/TiO2 is 3.0 and 1.5 times higher than that of Pt-TiO2 and Pt-C, respectively. In addition, the Pt-C/TiO2 exhibited a 6.7 times enhanced stability compared with Pt-C after 2000 cycles. The synergistic effect of C/TiO2 is responsible for the enhanced activity of Pt-C/TiO2, and its excellent durability could be ascribed to the strong interfacial interaction between Pt nanoparticles and C/TiO2 support.

Partial deposition of carbon onto mesoporous TiO2 (C/TiO2) were prepared as supporting substrate for Pt catalyst development. Carbon deposition is achieved by in-situ carbonization of furfuryl alcohol. The hybrid catalysts were characterized by XRD, Raman, SEM and TEM and exhibited outstanding catalytic activity and stability in methanol oxidation reaction. The heterogeneous carbon coated on mesoporous TiO2 fibers provided excellent electrical conductivity and strong interfacial interaction between TiO2 support and Pt metal nanoparticles. Methanol oxidation reaction results showed that the activity of Pt-C/TiO2 is 3.0 and 1.5 times higher than that of Pt-TiO2 and Pt-C, respectively. In addition, the Pt-C/TiO2 exhibited a 6.7 times enhanced stability compared with Pt-C after 2000 cycles. The synergistic effect of C/TiO2 is responsible for the enhanced activity of Pt-C/TiO2, and its excellent durability could be ascribed to the strong interfacial interaction between Pt nanoparticles and C/TiO2 support.

Partial deposition of carbon onto mesoporous TiO2 (C/TiO2) were prepared as supporting substrate for Pt catalyst development. Carbon deposition is achieved by in-situ carbonization of furfuryl alcohol. The hybrid catalysts were characterized by XRD, Raman, SEM and TEM and exhibited outstanding catalytic activity and stability in methanol oxidation reaction. The heterogeneous carbon coated on mesoporous TiO2 fibers provided excellent electrical conductivity and strong interfacial interaction between TiO2 support and Pt metal nanoparticles. Methanol oxidation reaction results showed that the activity of Pt-C/TiO2 is 3.0 and 1.5 times higher than that of Pt-TiO2 and Pt-C, respectively. In addition, the Pt-C/TiO2 exhibited a 6.7 times enhanced stability compared with Pt-C after 2000 cycles. The synergistic effect of C/TiO2 is responsible for the enhanced activity of Pt-C/TiO2, and its excellent durability could be ascribed to the strong interfacial interaction between Pt nanoparticles and C/TiO2 support.

Devices resident in the stomach—used for a variety of clinical applications including nutritional modulation for bariatrics, ingestible electronics for diagnosis and monitoring, and gastric-retentive dosage forms for prolonged drug delivery—typically incorporate elastic polymers to compress the devices during delivery through the oesophagus and other narrow orifices in the digestive system. However, in the event of accidental device fracture or migration, the non-degradable nature of these materials risks intestinal obstruction. Here, we show that an elastic, pH-responsive supramolecular gel remains stable and elastic in the acidic environment of the stomach but can be dissolved in the neutral-pH environment of the small and large intestines. In a large animal model, prototype devices with these materials as the key component demonstrated prolonged gastric retention and safe passage. These enteric elastomers should increase the safety profile for a wide range of gastric-retentive devices., Bill & Melinda Gates Foundation (OPP1096734), United States. National Institutes of Health (EB000244), United States. National Institutes of Health (T32 5T32HL007604-29), United States. Department of Energy (DE-AC02-06CH11357)

Mesoporous carbon with embedded iron carbide nanoparticles was successfully synthesized via a facile impregnation–carbonization method. A green biomass resource, cotton fabric, was used as a carbon precursor and an iron precursor was implanted to create mesopores through a catalytic graphitization reaction. The pore structure of the nanocomposites can be tuned by adjusting the iron precursor loadings and the embedded iron carbide nanoparticles serve as an active component for magnetic separation after adsorption. The microstructure of the nanocomposites was carefully investigated by various characterization techniques including electron microscopy, X-ray diffraction, surface analyzer, magnetic property analyzer and etc. The newly created mesopores are demonstrated as a critical component to enhance the adsorption capacity of organic dyes and embedded iron carbide nanoparticles are responsible for the selective removal of heavy metal ions (Zn2+, Cu2+, Ni2+, Cr6+ and Pb2+). Isotherm adsorption, kinetic study at three different temperatures (25, 45 and 65 °C) and cycling retention tests were performed to understand the adsorptive behavior of the nanocomposites with organic dyes (methylene blue and methyl orange). Together with the preferable removal of more toxic heavy metal species (Cr6+ and Pb2+), these mesoporous nanocomposites show promising applications in pollutant removal from water. The facile material preparation allows convenient scale-up manufacturing with low cost and minimum environmental impact., Validerad; 2016; Nivå 2; 20160108 (andbra)

Mesoporous carbon with embedded iron carbide nanoparticles was successfully synthesized via a facile impregnation–carbonization method. A green biomass resource, cotton fabric, was used as a carbon precursor and an iron precursor was implanted to create mesopores through a catalytic graphitization reaction. The pore structure of the nanocomposites can be tuned by adjusting the iron precursor loadings and the embedded iron carbide nanoparticles serve as an active component for magnetic separation after adsorption. The microstructure of the nanocomposites was carefully investigated by various characterization techniques including electron microscopy, X-ray diffraction, surface analyzer, magnetic property analyzer and etc. The newly created mesopores are demonstrated as a critical component to enhance the adsorption capacity of organic dyes and embedded iron carbide nanoparticles are responsible for the selective removal of heavy metal ions (Zn2+, Cu2+, Ni2+, Cr6+ and Pb2+). Isotherm adsorption, kinetic study at three different temperatures (25, 45 and 65 °C) and cycling retention tests were performed to understand the adsorptive behavior of the nanocomposites with organic dyes (methylene blue and methyl orange). Together with the preferable removal of more toxic heavy metal species (Cr6+ and Pb2+), these mesoporous nanocomposites show promising applications in pollutant removal from water. The facile material preparation allows convenient scale-up manufacturing with low cost and minimum environmental impact., Validerad; 2016; Nivå 2; 20160108 (andbra)

A series of novel green lubricants with dissolved lignin in [choline][amino acid] ([CH][AA]) ionic liquids (ILs) have been synthesized in this work. The effect of lignin on the thermal and tribological properties of the lignin/[CH][AA] lubricants was systematically investigated by means of thermogravimetric analysis, differential scanning calorimetry, and a friction and wear tester. The lignin in [CH][AA] has been demonstrated to be an effective additive to improve thermal stability, reduce the wear rates and stabilize the friction coefficients of lignin/[CH][AA] lubricants. Density function theory calculations on the electronic structure of [CH][AA] ILs reveal the atomic natural charge of ILs and their hydrogen bonding capability with lignin. Moreover, these green lubricants show excellent anti-corrosive properties against commercial aluminum and iron boards. The strong physical adsorption of [CH][AA] ILs onto the steel surface and the reciprocal hydrogen bonding between [CH][AA] ILs and lignin synergistically contribute to the enhanced lubrication film strength and thus the tribological properties of these new lubricants. This work provides a new perspective on utilizing complete bio-products in advanced tribological lubrication systems. In addition, this will open a new application venue for lignin to improve product value in lignocellulosic biomass utilization., Validerad; 2015; Nivå 2; 20150821 (andbra)

Novel solvent systems composed of N-methyl-2-pyrrolidone and C1–C4 carboxylic acid exhibit unique physicochemical properties, e.g. large polarity, low viscosity and excellent hydrogen bonding capacity, which have demonstrated excellent lignin solubility that outperforms conventional solvents and ionic liquids., Validerad; 2015; Nivå 2; 20150821 (andbra)

A series of novel green lubricants with dissolved lignin in [choline][amino acid] ([CH][AA]) ionic liquids (ILs) have been synthesized in this work. The effect of lignin on the thermal and tribological properties of the lignin/[CH][AA] lubricants was systematically investigated by means of thermogravimetric analysis, differential scanning calorimetry, and a friction and wear tester. The lignin in [CH][AA] has been demonstrated to be an effective additive to improve thermal stability, reduce the wear rates and stabilize the friction coefficients of lignin/[CH][AA] lubricants. Density function theory calculations on the electronic structure of [CH][AA] ILs reveal the atomic natural charge of ILs and their hydrogen bonding capability with lignin. Moreover, these green lubricants show excellent anti-corrosive properties against commercial aluminum and iron boards. The strong physical adsorption of [CH][AA] ILs onto the steel surface and the reciprocal hydrogen bonding between [CH][AA] ILs and lignin synergistically contribute to the enhanced lubrication film strength and thus the tribological properties of these new lubricants. This work provides a new perspective on utilizing complete bio-products in advanced tribological lubrication systems. In addition, this will open a new application venue for lignin to improve product value in lignocellulosic biomass utilization., Validerad; 2015; Nivå 2; 20150821 (andbra)

Novel solvent systems composed of N-methyl-2-pyrrolidone and C1–C4 carboxylic acid exhibit unique physicochemical properties, e.g. large polarity, low viscosity and excellent hydrogen bonding capacity, which have demonstrated excellent lignin solubility that outperforms conventional solvents and ionic liquids., Validerad; 2015; Nivå 2; 20150821 (andbra)

Nowadays, the role water quality played has become more and more important, especially in the industrial field, which can directly impact the cost and quality of the products. Similarly, the quality of wastewater directly impacts the efficiency of the reuse and discharge of wastewater. To monitor the parameters during the process of waste water treatment, an exited Wireless Sensor Network service is proposed. The object of this article is to investigate the methods of wireless sensor on the main nods of the WSN system. The set of water quality standards is depended on the different requirements in different specials. This article was concerned about PH, dissolved oxygen, turbidity and chlorine which are the key parameters of water quality for industrial. Generally, there are two ways to measure the indicators, electrochemical and optical. In this article, the investigation is based on these two ways. The most suitable way to sense the PH value is using PH sensitive electrode. Two ways to monitor the dissolved oxygen, optical methods and electrode method, are introduced and compared. This thesis also introduced how to measure the turbidity with optical components. Two most common principals for chlorine are introduced and compared; one is called DPD colorimetric detection which is based on N, N-Diethyl-p-Phenylenediamine (DPD) reaction with active halogens, the other is called Amperometry which is en electrochemical technique that measures the change in current resulting from chemical reactions taking place as a function of the analytic concentration. Based on the principals introduced above, two experiments are proposed. One is for the PH measurement. The test circumstance consists of a relatively inexpensive PH electrode, a voltmeter and a reference PH meter. The other one is for the turbidity measurement which consists of two LEDs and a light-to-voltage sensor.

Nowadays, the role water quality played has become more and more important, especially in the industrial field, which can directly impact the cost and quality of the products. Similarly, the quality of wastewater directly impacts the efficiency of the reuse and discharge of wastewater. To monitor the parameters during the process of waste water treatment, an exited Wireless Sensor Network service is proposed. The object of this article is to investigate the methods of wireless sensor on the main nods of the WSN system. The set of water quality standards is depended on the different requirements in different specials. This article was concerned about PH, dissolved oxygen, turbidity and chlorine which are the key parameters of water quality for industrial. Generally, there are two ways to measure the indicators, electrochemical and optical. In this article, the investigation is based on these two ways. The most suitable way to sense the PH value is using PH sensitive electrode. Two ways to monitor the dissolved oxygen, optical methods and electrode method, are introduced and compared. This thesis also introduced how to measure the turbidity with optical components. Two most common principals for chlorine are introduced and compared; one is called DPD colorimetric detection which is based on N, N-Diethyl-p-Phenylenediamine (DPD) reaction with active halogens, the other is called Amperometry which is en electrochemical technique that measures the change in current resulting from chemical reactions taking place as a function of the analytic concentration. Based on the principals introduced above, two experiments are proposed. One is for the PH measurement. The test circumstance consists of a relatively inexpensive PH electrode, a voltmeter and a reference PH meter. The other one is for the turbidity measurement which consists of two LEDs and a light-to-voltage sensor.

Current-voltage (i-v) characteristics of dry poly(p-phenylenebenzobisthiazole) (PBZT) thin films are studied before and after various doping processes, Conductivity switching phenomenon is observed after doping with HCI and H2SO4 acidic solutions at different concentration levels. The conductivity is 10 folds higher in a relatively high sweeping voltage range (0.2-0.3 V) than that sweeping in lower range of 0-0.1V. Conductivity switching phenomenon becomes more obvious with an increase of acid concentration. HCI shows more favorable for a quick and efficient behavior than H2S04, Reduction of PBZT with metal (Fe, Cu, and Pd) doping process produces a linear conductivity along the whole sweeping range of voltage. A 30% electrical conductivity enhancement is observed after applying an external dc voltage on the acid soaked PBZT film and different voltages enhance the conductivity to the same level. The conductivity returns gradually after removing the applied voltage. Four-probe conductivity measurements, FT-IR, scanning electron microscopy, EDAX elemental analysis, x-ray diffraction and TGA thermal analysis were used for better understanding the doping processes and conductivity switching mechanisms. A molecular structure transformation during the doping process is proposed., Prepared in cooperation with Northrop Grumman Corporation, and with AFRL, Nonmetallic Materials Division.

We investigate how individuals use measures of apparent predictability from price charts to predict future market prices. Subjects in our experiment predict both random walk times series, as in the seminal work by Bloomfield & Hales (2002) (BH), and stock price time series. We successfully replicate the experimental findings in BH that subjects are less trend-chasing when there are more reversals in the first task. We find that subjects also overreact less to the trend when there is less momentum in the stock price in the second task, though the momentum factor that is significant is the autocorrelation instead of the number of reversals per se. Our subjects also appear to use other variables such as amplitude and volatility as measures of predictability. However, as random walk theory predicts, relying on apparent patterns in past data does not improve their prediction accuracy.

We investigate how individuals use measures of apparent predictability from price charts to predict future market prices. Subjects in our experiment predict both random walk times series, as in the seminal work by Bloomfield & Hales (2002) (BH), and stock price time series. We successfully replicate the experimental findings in BH that subjects are less trend-chasing when there are more reversals in the first task. We find that subjects also overreact less to the trend when there is less momentum in the stock price in the second task, though the momentum factor that is significant is the autocorrelation instead of the number of reversals per se. Our subjects also appear to use other variables such as amplitude and volatility as measures of predictability. However, as random walk theory predicts, relying on apparent patterns in past data does not improve their prediction accuracy.