Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Design of Wide Tuning Range Low Phase Noise mm-wave LC-VCOs

The growing demand for higher data rate in wireless transceivers continues to consume available bandwidth and move towards multi-band/multi-channel mm-wave systems to satisfy latest specifications and be compatible with legacy standards. One of the critical components, the LC-voltage controlled oscillator (LC-VCO), is required to have low phase noise while maintaining wide tuning range to achieve low bit error rates (BERs). Due to the large parasitic capacitance and high losses in silicon based technologies, extending the tuning range and reducing phase noise is becoming very challenging. This dissertation studies and proposes novel architectures and circuit techniques to overcome the limitations of high frequency LC-VCOs. Different LC-VCO architectures are reviewed and the performance is summarized and compared. The components in LC-VCOs are theoretically analyzed and a prototype VCO is designed. An inductance redistribution technique to equally space sub-band coarse tuning characteristics is proposed and implemented. To predict the tuning range, a detailed analysis of the frequency dependent quality factor (Q) of the LC-tank is performed to characterize the tank loss. The frequency dependent Q is used to size the transconductance (gm) of the cross-coupled pair to satisfy the VCO startup condition. The relationship between the cross-coupled pair gm and the operating frequency is further derived.
Experimental Study on the Corrosion of Carbon Steel and Aluminum Alloy in Firefighting Protein Foam Concentrates

The corrosion of mild steel and Al alloy in Fomtec P 6% and 6% P Profoam 806 protein-based foam concentrates was investigated. Weight-loss data for steel showed corrosion penetration of 0.745 mipy in Fomtec and 2.269 mipy in Profoam, whereas for Al alloy the penetration levels were 0.474 and 1.093 mipy, respectively. Scanning electron microscopy and energy dispersive X-ray spectroscopy allowed characterization of the metallic surface covered or free from corrosion products. Values of corrosion potential, corrosion current density and corrosion penetration were calculated by using potentiodynamic polarization curves. Electrochemical impedance spectra illustrated the change in polarization resistance during anodic polarization. Data obtained by accelerated electrochemical methods confirm the greater aggressiveness of the Profoam concentrate compared to Fomtec concentrate.
Nanoscale Phase Separation of Incommensurate and Quasi-Commensurate Spin Stripes in Low Temperature Spin Glass of La2−xSrxNiO4

While spin striped phases in La2−xSrxNiO4+y for 0.25 < x < 0.33 are the archetypal case of a 1D spin density wave (SDW) phase in doped antiferromagnetic strongly correlated perovskites, few information is available on the SDW spatial organization. In this context, we have measured the spatial variation of the wave vector of the SDW reflection profile by scanning micro X-ray diffractions with a coherent beam. We obtained evidence of a SDW order–disorder transition by lowering a high temperature phase (T > 50 K) to a low temperature phase (T < 50 K). We have identified quasi-commensurate spin stripe puddles in the ordered phase at 50 < T < 70 K, while the low temperature spin glassy phase presents a nanoscale phase separation of T = 30 K, with the coexistence of quasi-commensurate and incommensurate spin stripe puddles assigned to the interplay of quantum frustration and strong electronic correlations.
Hierarchical Unsupervised Partitioning of Large Size Data and Its Application to Hyperspectral Images

In this paper, we propose a true unsupervised method to partition large-size images, where the number of classes, training samples, and other a priori information is not known. Thus, partitioning an image without any knowledge is a great challenge. This novel adaptive and hierarchical classification method is based on affinity propagation, where all criteria and parameters are adaptively calculated from the image to be partitioned. It is reliable to objectively discover classes of an image without user intervention and therefore satisfies all the objectives of an unsupervised method. Hierarchical partitioning adopted allows the user to analyze and interpret the data very finely. The optimal partition maximizing an objective criterion provides the number of classes and the exemplar of each class. The efficiency of the proposed method is demonstrated through experimental results on hyperspectral images. The obtained results show its superiority over the most widely used unsupervised and semi-supervised methods. The developed method can be used in several application domains to partition large-size images or data. It allows the user to consider all or part of the obtained classes and gives the possibility to select the samples in an objective way during a learning process.
Intraoperative Assessment of Coronary Resistances: A New Quality Marker and Potential Tool to Predict Early Graft Failure after Coronary Artery Bypass Grafting?

Intraoperative assessment of graft patency is pivotal for successful coronary revascularization. In the present study we aimed to propose a new, easy to perform tool to assess anastomotic quality intraoperatively, and to investigate its potential reliability in predicting early graft failure. Intraoperative graft flowmetry of 63 consecutive patients undergoing CABG were prospectively collected. Transit time flowmetry and its derivatives were recorded. Coronary resistances were calculated according to Hagen–Poiseuille equation both during cardioplegic arrest and after withdrawal from cardiopulmonary bypass. Angiographic evidence of graft occlusion at follow-up was cross-checked with intraoperative recordings. After a mean follow-up of 10.4 ± 6.0 months, 22 grafts were studied, and occlusion was documented in five (22.7%). Occluded grafts showed lower flows and higher resistances recorded during aortic cross-clamping. Coronary resistances, recorded during aortic cross-clamping, greater than 2.0 mmHg/mL/min, showed a sensitivity of 80% and a specificity of 100% to predict graft failure. We propose the routine recording of coronary resistances during aortic cross-clamping as an additional tool to overcome the acknowledged limitation of TTF to predict graft occlusion at 1 year.
Two-port higher mode circular microstrip antennas

For circular microstrip antennas, single-mode design techniques work well for the dominant mode (n=1, where n is the azimuthal dependence of the fields in the antenna), but this is not the case for zero-order (n=0) and higher order (n>1) modes, where a modal expansion is necessary. The design of higher order dominant mode circular microstrip antennas is addressed, with an example and measurements for the n=3 case. Measurements for the n=3 modes show reasonable agreement with theory in the forms of the impedance loci, but the frequency dependence is in error by the order of the bandwidth of the antenna. A design example is given for a roof-mounted circular patch for vehicular communications. The example indicates that an n=3 dominant mode patch can be used as a two-port antenna for diversity applications or with a 90 degrees phase shifter and adder as a single-port antenna for omnidirectional coverage with (essentially) circular polarization. >
Capacitor voltage balancing using redundant states of space vector modulation for five-level diode clamped inverters

A redundancy balancing technique for the five-level diode-clamped inverter is presented, which balances the four dc-link capacitor voltages at high modulation index and high power factor. The technique is based on dividing the vector space of the five-level inverter into six two-level vector spaces. Dwell times are calculated as for conventional two-level space vector modulation, and the switching sequence is determined depending on the four capacitor voltages, using a redundant state method. The double Fourier series is used to theoretically determine the resultant spectral components. The proposed technique maintains link capacitor balance for high modulation indices, including over modulation, irrespective of the power factor. The proposed algorithm is validated by simulation and practically.
Volatility and the Pricing Kernel

We use options and return data to show that negative stock market returns are significantly more painful to investors when they occur in periods of low volatility. In contrast, popular asset pricing theories imply that the pricing of stock market risk does not vary with volatility, or that it moves in the opposite direction. Our finding suggests that stock market volatility evolves largely independently from the pricing kernel. We embed this assumption into a consumption-based model with a disappointment averse investor. The model captures the dynamics of the pricing kernel and resolves four recent puzzles about stock market risk premia.
Tetrel Bonds between Phenyltrifluorosilane and Dimethyl Sulfoxide: Influence of Basis Sets, Substitution and Competition

Ab initio calculations have been performed for the complexes of DMSO and phenyltrifluorosilane (PTS) and its derivatives with a substituent of NH3, OCH3, CH3, OH, F, CHO, CN, NO2, and SO3H. It is necessary to use sufficiently flexible basis sets, such as aug’-cc-pVTZ, to get reliable results for the Si···O tetrel bonds. The tetrel bond in these complexes has been characterized in views of geometries, interaction energies, orbital interactions and topological parameters. The electron-donating group in PTS weakens this interaction and the electron-withdrawing group prominently strengthens it to the point where it exceeds that of the majority of hydrogen bonds. The largest interaction energy occurs in the p-HO3S-PhSiF3···DMSO complex, amounting to −122 kJ/mol. The strong Si···O tetrel bond depends to a large extent on the charge transfer from the O lone pair into the empty p orbital of Si, although it has a dominant electrostatic character. For the PTS derivatives of NH2, OH, CHO and NO2, the hydrogen bonded complex is favorable to the tetrel bonded complex for the NH2 and OH derivatives, while the σ-hole interaction prefers the π-hole interaction for the CHO and NO2 derivatives.
Monitoring Crack Propagation in Reinforced Concrete Shear Walls by Acoustic Emission

AbstractIn the last two decades, several efforts have been made to monitor the cracking behavior in RC structures. A technique that shows promise is acoustic emission (AE). This paper presents the results of an experimental study aimed at monitoring fracture processes in a large-scale RC shear wall using one of the most important AE parameters, that is, the b-value. The specimen was subjected to a displacement controlled reversed cyclic loading. A Gaussian filter is proposed to improve the interpretation of b-value data obtained during the test. In addition, a cluster analysis based on the k-means is presented to automatically classify the signal into tensile and shear cluster. Finally, a new algorithm called Sifted b-value (Sb) analysis is introduced to monitor the evolution of each crack mode. The proposed approach is capable to identify the initial yielding and eventually provide an early warning for the planning and implementation of remedial action to the structure at a point at which it is less expe.
Learning Thermographic Models for Optimal Image Processing of Decorated Surfaces

The use of infrared thermography presents unique perspectives in imaging of artifacts to help interrogate their surface and subsurface characteristics, highlight deviations and detect contrast. This research capitalizes on active and passive thermal imagery along with advanced machine learning-based algorithms for pre- and post-processing of acquired scans. Such codes operate efficiently (compress data) to help link the observed temperature variations and the thermophysical parameters of targeted samples. One such processing modality is dictionary learning, which infers a “frame dictionary” to help represent the scans as linear combinations of a small set of features, thus training data to show a sparse representation. This technique (along factorization and component analysis-based methods) was used in current research on ancient polychrome marquetries aimed at detecting aging anomalies. The presented research is unique in terms of the targeted samples and the applied approaches and should provide specific guidance to similar domains.
Travelling-wave photonic mixers for increased continuous-wave power beyond 1 THz

Travelling-wave designs for terahertz photonic mixers have been shown to surpass limitations of small-area designs and therefore have the potential to generate greatly increased continuous-wave (CW) powers at frequencies well above 1 THz and towards 2 THz. Still a lot of optimization process has to be done and new materials tested. Results with different geometries and materials made to date are compared.
The Role of Diet, Alcohol, BMI, and Physical Activity in Cancer Mortality: Summary Findings of the EPIC Study

Evidence on the impact of diet, alcohol, body-mass index (BMI), and physical activity on mortality due to cancer and other cancer-related outcomes is still scarce. Herein, we reviewed the contribution of the European Prospective Investigation into Cancer and Nutrition (EPIC) study to the current state of the art on the role of these factors in cancer mortality. We identified 45 studies using a rapid systematic review methodology. Dietary factors associated with reduced cancer mortality included raw vegetable intake; dietary fiber intake; the Mediterranean diet; other dietary scores; other diet patterns including low meat eaters, vegetarians/vegans, or fish eaters; dietary intake (or biomarkers) of some vitamins (e.g., vitamin D, vitamin K2, or Vitamin C); and intake of lignans. Physical activity and following healthy lifestyle recommendations also reduced cancer mortality risk. In contrast, dietary factors associated with higher cancer mortality risk included poor diet quality, consumption of alcohol and soft drinks including juice, and, to a lesser extent, intake of some fatty acids. Excess weight and obesity also increased the risk of cancer mortality. The EPIC study holds valuable information on diet and lifestyle factors and offers a unique opportunity to identify key diet-related factors for cancer mortality prevention.
Embedded-based Tomato Septoria Leaf Detection with Intel Movidius Neural Compute Stick

Tomatoes are a horticulture product with the potential to be developed since they have a high economic value and are in high demand by industry and consumers. Tomato plants, on the other hand, still need to be handled carefully in order to boost yield harvest. Furthermore, susceptibility of the tomato plants to Septoria leaf spot disease, which arises due to Septoria Lycopersici Speg fungal infection, is being one of the challenges in escalating tomato production itself. Regarding the problem, this study aims to detect Septoria leaf spot on tomato plants by developing a tool utilizing deep learning and Convolutional Neural Network (ConvNets or CNN) model. CNN model conducted in this work is a supervised learning technique that extensively operated for solving linear and non-linear problems. Moreover, Raspberry Pi microcontroller and Intel Movidius Neural Computing Stick (NCS) are employed in this work, in order to accelerate the computing process and to ease the detection process considering its portability, speed, and accuracy. The precise detection range is from 84.22 percent to 100 percent, with an average accuracy rate of 95.89 percent.
The Immunotherapy for Colorectal Cancer, Lung Cancer and Pancreatic Cancer

Immunotherapy is a novel anti-cancer method which employs a different mechanism to conventional treatment. It has become a significant strategy because it provides a better or an alternative option for cancer patients. Recently, immunotherapy has been increasingly approved for the treatment of cancer; however, it has various limitations; for instance, it is only suitable for specific patients, the response rate is still low in most cases, etc. Colorectal cancer, lung cancer and pancreatic cancer are known as three major death-causing cancers in most countries. In this review, we discuss immunotherapeutic treatment for these three cancers, and consider the option, prospects and limitations of immunotherapy. The development of immunotherapy should focus on the discovery of biomarkers to screen suitable patients, new targets on tumors, neoadjuvant immunotherapy and the combination of immunotherapy with conventional therapeutic methods. We can expect that immunotherapy potentially will develop as one of the best therapies for patients with advanced cancer or poor responses to traditional methods.
A Q-learning-based Automatic Heuristic Design Approach for Seru Scheduling

Seru production is a new mode of production with the advantages of quick response, high flexibility and high efficiency. It is well suited to the market that fluctuates frequently. The seru scheduling is an important issue for seru production system configuration problem because it reflects the management and control principle of seru production systems, which called just-in-time operation system. This paper studies a seru scheduling problem, which can be described as how to determine the sequence of serus in limited space for multiple orders considering worker overlapping. The objective is to minimize the maximum completion time. A Q-learning-based genetic programming algorithm is proposed to solve the above problem. Experimental results show the effectiveness of the proposed algorithm.
Is It Possible to Estimate Average Heart Rate from Facial Thermal Imaging?

The remote measurement of heart rate (HR) could have many applications, such as health and emotional conditions monitoring. Currently, methods based on visible cameras have been developed for HR estimation. However, the employment of such techniques with scarce illumination conditions could be challenging. Infrared Thermography (IRT) could be a valuable tool to overcome this limitation. This study investigated the possibility of estimating average HR with facial IRT through a cross-validated machine learning (ML) approach. The correlation coefficient between the estimated and the measured HR was 0.7. Although preliminary, these results demonstrate the feasibility of estimating HR with IRT.
Selection of transverse oscillation modes in tilted ridge DFB lasers

In this paper, the effects of tilt angle on multi-transverse moded ridge-waveguide DFB lasers is presented. A small tilt angle is introduced between the waveguide orientation and the gratings to control the lasing wavelength. The authors find that although each transverse mode experiences a wavelength shift that approximately varies as 1/cos /spl theta/, the accompanying variation in /spl kappa/ dictates which transverse mode will oscillate. At small tilt angles the fundamental E/sub x,0/ mode has the lowest threshold gain; at larger angles the higher order transverse modes will be sequentially selected while all other modes are attenuated. As a result, the wavelength versus tilt angle exhibits sudden jumps due to this transverse mode selection in addition to the 1/cos /spl theta/ dependence. For the analysis, the authors develop a two-dimensional finite-difference method for modeling the multilayered ridge waveguide modes. The effect of tilt angle on the intramode coupling coefficient is derived and the behavior of lasing wavelength, threshold gain and stop-band width are studied.
Growth of Black Soldier Fly Larvae Reared on Organic Side-Streams

Black soldier fly (BSF) larvae may play a role in a circular economy by upcycling low-value organic streams into high value biomass. In this paper, the capacity of BSF larvae to process 12 organic side-streams (mono-streams) and two standard substrates (chicken start mash and Gainesville diet) was investigated. Survival, larval mass, feed conversion ratio, and waste reduction were evaluated in relation to the proximate composition of the side-streams used. Survival rates larger than 80% were observed for 10 of the organic mono-streams and the two standard substrates. Maximum mean larval weight ranged from 38.3 mg up to 176.4 mg regardless of high survival and was highly correlated with substrate crude protein content. Feed conversion ratio (range 1.58–8.90) and waste reduction (range 17.0–58.9%) were similar to values reported in other studies in the literature. On low protein substrates (e.g., apple pulp), survival rates remained high, however, possibly due to protein deficiency, limited larval growth was observed. It is concluded that several low value organic side-streams can successfully be processed by BSF larvae, thereby opening the possibility of lowering the costs of BSF farming. Potentially mixing nutritionally distinct mono-streams into a mixed substrate might improve BSF performance. However, more research is needed for optimizing diets to guarantee production of BSF larvae of constant yield and quality.
Developing Hybrid Measures of Performance for Signalized Intersections

There are two methods for evaluating performance of signalized intersections – a volume based measure using the Critical Movement Summation (CMS) method and a delay based method, such as using simulation software like Synchro or the Highway Capacity Manual. Each method provides different indicators of the intersections performance. In general the two methods are used separately, which does not provide consistency in measuring intersection performance. This research highlights the differences between accepted methods and examines if there is a way to make the outputs from CMS sheets and Synchro models more comparable and preferably, interchangeable.
Biophysical Effects of Temperate Forests in Regulating Regional Temperature and Precipitation Pattern across Northeast China

The temperate forests in Northeast China are an important ecological barrier. However, the way in which temperate forests regulate the regional temperature and water cycling remains unclear. In this study, we quantitatively evaluated the role that temperate forests play in the regulation of the regional temperature and precipitation by combining remote sensing observations with a state-of-the-art regional climate model. Our results indicated that the forest ecosystem could slightly warm the annual air temperature by 0.04 ± 0.02 °C and bring more rainfall (17.49 ± 3.88 mm) over Northeast China. The temperature and precipitation modification function of forests varies across the seasons. If the trees were not there, our model suggests that the temperature across Northeast China would become much colder in the winter and spring, and much hotter in the summer than the observed climate. Interestingly, the temperature regulation from the forest ecosystem was detected in both forested regions and the adjacent agricultural areas, suggesting that the temperate forests in Northeast China cushion the air temperature by increasing the temperature in the winter and spring, and decreasing the temperature in the summer over the whole region. Our study also highlights the capacity of temperate forests to regulate regional water cycling in Northeast China. With high evapotranspiration, the forests could transfer sufficient moisture to the atmosphere. Combined with the associated moisture convergence, the temperate forests in Northeast China brought more rainfall in both forest and agricultural ecosystems. The increased rainfall was mainly concentrated in the spring and summer; these seasons accounted for 93.82% of the total increase in rainfall. These results imply that temperate forests make outstanding contributions to the maintainance of the sustainable development of agriculture in Northeast China.