Miracle-dz/UniFAR-FTUs huggingface dataset:
Runtime leakage control in deep sub-micron cmos technologies

MOSFET scaling into deep sub-micro realm has resulted in significant increase in leakage power consumption. In 45nm technology generation and beyond, leakage power consumption will catch up with, and may even dominate, dynamic power consumption. This makes leakage power reduction an indispensable component for low power designs in deep sub-micro technologies. Many leakage control techniques have been introduced and studied so far. They can be characterized into two classes: runtime techniques and design-time techniques. Design-time techniques only modify the circuit and thus have limited capability of leakage reduction. On the other hand, runtime techniques tune the circuit into low-leakage mode according to the variation of circuit workload. When a circuit or a system has substantial slackness in its workload, runtime techniques can yield significant leakage saving. Hence runtime techniques, such as power gating and reverse body biasing, are widely used in industrial practices, and extensively studied in current researches as well. Since the invention of runtime leakage control techniques (RTLC), most of they have been applied in a very crude manner. Several key questions regarding the design methodologies of RTLC remain unanswered, including how to design the optimum control policy, what is the optimum granularity of applying RTLC, and how to reduce leakage in circuit active mode. Before these questions are answered, RTLC can only be of an ad-hoc style. On top of these major questions, several other common problems in deep sub-micro technologies need to be considered before RTLC converges to a practical technique. These common problems include temperature and process variation, robustness issues in deep sub-micro technologies etc. They make the design of RTLC even more complicated and challenging. In response to all these questions and challenges, our research aims at answering the major open questions of RTLC, tackling the practical problems in deep sub-micro technologies and finally forging a systematical solution for RTLC. To this end, this thesis studies the corresponding modeling, optimization, design methodology and design automation issues. Our whole study is driven by the following two main ideas. First, we consider that aggressive idleness exploitation is the key to achieve maximal leakage control. Second, we consider that applying RTLC in a finer manner is the key to enable aggressive idleness exploitation. Our study is based on two leakage control techniques: power gating and reserve body biasing. During our analysis, one type of RBB technique, Vth hopping, turns out to be more effective to control leakage in a finer manner. Therefore it becomes the center of our final solution.
Gabor-GLCM-Based Texture Feature Extraction Using Flame Image to Predict the O2 Content and NOx

Flame image feature extraction is the basis for boiler combustion monitoring and control. The flame video images of recent research are mainly derived from experimental burners in the laboratory, and few pay attention to the flame images in industrial boilers. The actual industrial boiler flame images differ significantly from the laboratory flame images. Additionally, certain flame image features cannot be captured in the laboratory owing to the limitations of the camera installations. Therefore, a flame image texture feature extraction algorithm based on an industrial boiler is proposed in this paper. The texture features were enhanced using a Gabor filter for the RGB channels of the flame images, and then, the statistics of the texture features were scalarized by a gray-level co-occurrence matrix (GLCM). The data were filtered and downscaled by a data compressor consisting of Gaussian-weighted mean and principal component analysis (PCA) to obtain eight key variables. The extracted eight variables were verified to be effective in characterizing the O2 and NOx contents of flue gas using the mutual information method. The combustion process regression model was constructed using a gated recurrent unit (GRU) on the 8 h combustion data of the boiler, and the predicted mean absolute percentage error (MAPE) for O2 and NOx content in the test set reached 7.5 and 10.2%, respectively. Compared to the conventional methods of direct PCA on images and GLCM plus PCA on images, the MAPE for O2 content prediction was reduced by 12.3 and 7.3%, and the MAPE for NOx content prediction was reduced by 10.5 and 6.1%, respectively. The advantage of the new flame feature based on Gabor-GLCM is suitable for the subsequent analysis and control of an industrial combustion system.
Forgetful Students Use Facebook

Students always seem to "forget" about the one assignment they have for study hall- grade checks. Since this is a non-graded class, there is no real penalty besides detention. To encourage and remind students about their grade checks, I have used various methods from email to rewards, but all seem to have the same result of forgotten grade checks. The purpose of this action research project is to examine Facebook's impact on student's perceived attitudes and frequency of turning in grade checks in a sophomore study hall class at a private high school on Oahu, Hawai'i. Students were asked to complete a short survey about their Facebook usage and normal day-to-day activities. Next, students were asked to join a closed Facebook group where I posted grade check reminders, positive reinforcement, and study tips. Lastly, I posted interview questions to the group wall for students to answer. Some students were also interviewed about their participation during class. Overall, there were mixed results in grade check submission by the students but an increase in communication. The group page turned into an efficient communication tool for the students and, especially, the study hall supervisor.
Peripheral Blood Transcriptome in Breast Cancer Patients as a Source of Less Invasive Immune Biomarkers for Personalized Medicine, and Implications for Triple Negative Breast Cancer

Simple Summary Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous breast cancer (BC) type which is difficult to treat and accompanied by disease recurrence. A better understanding of TNBC and the identification of novel biomarkers is needed to facilitate clinical decisions. Immune-related biomarkers are of particular interest, since immune responses play an important role in BC outcome. Transcriptome studies of peripheral blood cells can help us to understand the systemic immune responses to cancer cells and the mechanisms underlying cancer initiation and progression. They enable the identification of novel immune biomarkers for early cancer detection and personalized BC management and may bring forward new immunotherapy options. Recent transcriptome analyses of peripheral blood cells have delineated novel BC-patient immune subgroups. This categorization has implications for cancer prognosis, the identification of patients likely to benefit from immunotherapy, and treatment efficacy monitoring. Additionally, transcriptome studies have identified TNBC-enriched blood transcriptional signatures that can differentiate TNBC from other classical BC subtypes. Abstract Transcriptome studies of peripheral blood cells can advance our understanding of the systemic immune response to the presence of cancer and the mechanisms underlying cancer onset and progression. This enables the identification of novel minimally invasive immune biomarkers for early cancer detection and personalized cancer management and may bring forward new immunotherapy options. Recent blood gene expression analyses in breast cancer (BC) identified distinct patient subtypes that differed in the immune reaction to cancer and were distinct from the clinical BC subtypes, which are categorized based on expression of specific receptors on tumor cells. Introducing new BC subtypes based on peripheral blood gene expression profiles may be appropriate, since it may assist in BC prognosis, the identification of patients likely to benefit from immunotherapy, and treatment efficacy monitoring. Triple-negative breast cancer (TNBC) is an aggressive, heterogeneous, and difficult-to-treat disease, and identification of novel biomarkers for this BC is crucial for clinical decision-making. A few studies have reported TNBC-enriched blood transcriptional signatures, mostly related to strong inflammation and augmentation of altered immune signaling, that can differentiate TNBC from other classical BC subtypes and facilitate diagnosis. Future research is geared toward transitioning from expression signatures in unfractionated blood cells to those in immune cell subpopulations.
Brain-Derived Neurotrophic Factor Levels in Cord Blood from Growth Restricted Fetuses with Doppler Alteration Compared to Adequate for Gestational Age Fetuses

Background and Objectives: Fetal growth restriction (FGR) is a severe obstetric disease characterized by a low fetal size entailing a set of undesired consequences. For instance, previous studies have noticed a worrisome association between FGR with an abnormal neurodevelopment. However, the precise link between FGR and neurodevelopmental alterations are not yet fully understood yet. Brain-derived neurotrophic factor (BDNF) is a critical neurotrophin strongly implicated in neurodevelopmental and other neurological processes. In addition, serum levels of BDNF appears to be an interesting indicator of pathological pregnancies, being correlated with the neonatal brain levels. Therefore, the aim of this study is to analyze the blood levels of BDNF in the cord blood from fetuses with FGR in comparison to those with weight appropriate for gestational age (AGA). Materials and Methods: In this study, 130 subjects were recruited: 91 in group A (AGA fetuses); 39 in group B (16 FGR fetuses with exclusively middle cerebral artery (MCA) pulsatility index (PI) < 5th percentile and 23 with umbilical artery (UA) PI > 95th percentile). Serum levels of BDNF were determined through ELISA reactions in these groups. Results: Our results show a significant decrease in cord blood levels of BDNF in FGR and more prominently in those with UA PI >95th percentile in comparison to AGA. FGR fetuses with exclusively decreased MCA PI below the 5th percentile also show reduced levels of BDNF than AGA, although this difference was not statistically significant. Conclusions: Overall, our study reports a potential pathophysiological link between reduced levels of BDNF and neurodevelopmental alterations in fetuses with FGR. However, further studies should be conducted in those FGR subjects with MCA PI < 5th percentile in order to understand the possible implications of BDNF in this group.
Direct Printing for Next Generation High Density Packaging

Next generation micro-packaging will feature a number of advances in high density techniques, including more components in a smaller volume. New techniques for metallization, passives, antennas, solders, and adhesives will be required. The key factor for these will be size constraints. Micro-dispensing can print a number of materials to include dielectrics, ruthenates, metallics, solder, and adhesives. Those with experience in micro-dispensing recognize the size limitation using a dispensing approach and understand that the hope of achieving smaller line widths, smaller pad sizes, and precise placement of the material is not feasible. nScrypt has demonstrated the ability to considerably shrink the line widths and dots from traditional micro-dispensing, thus enabling the next generation packaging and increasing the component count in the same or less volume. Micro-dispensing solder dots, precisely placing narrow adhesive lines and potentially directly printing resistors and antennas to replace standard pic.
The Coherence Challenge Between Policy Makers and School Leaders: Exploring a National Pedagogical Reform

Purpose It has been suggested that coherency between primary stakeholders involved in an educational reform is a key determinant of its success. The “Meaningful Learning Reform” is defined as an open reform, serving as a bottom-up solution to promote school autonomy. This reform allows school leaders to exercise considerable discretion regarding the broad policy’s goals of reaching 21st century learning skills in public schools. The purpose of this study is to examine the variations in perceptions of policy makers and principals while implementing this generally outlined “Meaningful Learning Reform. ” Methodology In this qualitative phenomenological study, interviews were conducted with principals (N = 30), that were involved in the implementation of the reform examined, and policy makers (N = 2) that served as national initiators of the reform. Findings The data analysis of the stakeholders conveys the coherency of interpretations in three main themes: (a) misalignments in the nature of reform’s meaning—including differences in the perceptions of the name given to the reform, the reform’s purpose, and its foundation tenets; (b) contradictions regarding the authority leading the reform—various outlooks regarding the direction of the leading authority; and (c) difficulties in the process of implementation—including initiation and implementation phases of the reform. Originality/Value The exploration of the variations of interpretations between policy makers and principals contributes to a deeper understanding of coherency in the educational context. The data provides new insights regarding the dynamics between top-down and bottom-up leadership while implementing a generally outlined pedagogical reform.
Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research—photovoltaic and wind energy, atmospheric physics and processes; in climate research—parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting.
Improved control of photovoltaic interfaces

Photovoltaic (solar electric) technology has shown significant potential as a source of practical and sustainable energy; this study focuses on increasing the performance of photovoltaic systems through the use of improved control and power interfaces. The main objective is to find an effective control algorithm and topology that are optimally suited to extracting the maximum power possible from photovoltaic modules. The thesis consists of the following primary subjects: photovoltaic modelling, the topological study of photovoltaic interfaces, the regulation of photovoltaic voltage, and maximum power tracking. In photovoltaic power systems both photovoltaic modules and switching mode converters present non-linear and time-variant characteristics, resulting in a difficult control problem. This study applies in-depth modelling and analysis to quantify these inherent characteristics, specifically using successive linearization to create a simplified linear problem. Additionally, Youla Parameterisation is employed to design a stable control system for regulating the photovoltaic voltage. Finally, the thesis focuses on two critical aspects to improve the performance of maximum power point tracking. One improvement is to accurately locate the position of the maximum power point by using centred differentiation. The second is to reduce the oscillation around the steady-state maximum power point by controlling active perturbations. Adopting the method of steepest descent for maximum power point tracking, which delivers faster dynamic response and a smoother steady-state than the hill climbing method, enables these improvements. Comprehensive experimental evaluations have successfully illustrated the effectiveness of the proposed algorithms. Experimental evaluations show that the proposed control algorithm harvests about 1% more energy than the traditional method under the same evaluation platform and weather conditions without increasing the complexity of the hardware. 11
Quantitative real-time PCR assays for the concurrent diagnosis of infectious laryngotracheitis virus, Newcastle disease virus and avian metapneumovirus in poultry

Newcastle disease (ND), infectious laryngotracheitis (ILT) and avian metapneumovirus (aMPV) can be similar making it critical to quickly differentiate them. Herein, we adapted pre-existing molecular-based diagnostic assays for NDV and ILTV, and developed new assays for aMPV A and B, for use under synchronized thermocycling conditions. All assays performed equivalently with linearity over a 5 log10 dynamic range, a reproducible (R2 > 0.99) limit of detection of ≥ 10 target copies, and amplification efficiencies between 86.8%–98.2%. Using biological specimens for NDV and ILTV showed 100% specificity. Identical amplification conditions will simplify procedures for detection in diagnostic laboratories.
Power-function expansion of the nondimensional complementary relationship of evaporation: the emergence of dual attractors

The polynomial form of the nondimensional complementary relationship (CR) follows from an isenthalpic process of evaporation under a constant surface available energy and unchanging wind. The exact polynomial expression results from rationally derived first and second-order boundary conditions (BC). By keeping the BCs, the polynomial can be extended into a two-parameter (a and b) power function for added flexibility. When a = b = 2 it reverts to the polynomial version. With the help of Australian FLUXNET data it is demonstrated that the power-function formulation excels among CR-based two-parameter models considered, even when a = 2 is prescribed to reduce the number of parameters to calibrate to two. The same powerfunction approach (a = 2) is then employed with a combination of different gridded monthly potential evaporation terms across Australia, while calibrating b against the multiyear simplified water-balance evaporation rate on a cell-by-cell basis. The resulting bi-modal histogram of the b values peaks first near b = 2 and then at b 1 (secondary modus), confirming earlier findings that occasionally a linear version (i.e., b = 1) of the CR yields the best estimates. It is further demonstrated that the linear form emerges when regional-scale transport of moist air is negligible toward the study area during its drying, while the more typical nonlinear CR version prevails otherwise. A thermodynamic-based explanation is yet to be found as to why the flexible power function curves (i.e., b [? ] 2) converge to the polynomial one (b = 2) in such cases.
Osteopenia in a Mouse Model of Spinal Cord Injury: Effects of Age, Sex and Motor Function

Simple Summary In the first two years following spinal cord injury, people lose up to 50% of bone below the injury. This injury-induced bone loss significantly affects rehabilitation and leaves people vulnerable to fractures and post-fracture complications, including lung and urinary tract infections, blood clots in the veins, and depression. Unfortunately, little is known about the factors driving this bone loss. In fact, even though we know that injury, age, and sex independently increase bone loss, there have been no studies looking at the cumulative effects of these variables. People with spinal injury are aging, and the age at which injuries occur is increasing. It is essential to know whether these factors together will further compromise bone. To examine this, we assessed bone loss in young and old, male and female mice after spinal injury. As expected, we found that aging alone decreased motor activity and bone volume. Spinal injury also reduced bone volume, but it did not worsen the effects of age. Instead, injury effects appeared related to reduced rearing activity. The data suggest that although partial weight-bearing does not reduce bone loss after spinal cord injury, therapies that put full weight on the legs may be clinically effective. Abstract After spinal cord injury (SCI), 80% of individuals are diagnosed with osteopenia or osteoporosis. The dramatic loss of bone after SCI increases the potential for fractures 100-fold, with post-fracture complications occurring in 54% of cases. With the age of new SCI injuries increasing, we hypothesized that a SCI-induced reduction in weight bearing could further exacerbate age-induced bone loss. To test this, young (2–3 months) and old (20–30 months) male and female mice were given a moderate spinal contusion injury (T9–T10), and recovery was assessed for 28 days (BMS, rearing counts, distance traveled). Tibial trabecular bone volume was measured after 28 days with ex vivo microCT. While BMS scores did not differ across groups, older subjects travelled less in the open field and there was a decrease in rearing with age and SCI. As expected, aging decreased trabecular bone volume and cortical thickness in both old male and female mice. SCI alone also reduced trabecular bone volume in young mice, but did not have an additional effect beyond the age-dependent decrease in trabecular and cortical bone volume seen in both sexes. Interestingly, both rearing and total activity correlated with decreased bone volume. These data underscore the importance of load and use on bone mass. While partial weight-bearing does not stabilize/reverse bone loss in humans, our data suggest that therapies that simulate complete loading may be effective after SCI.
A 14-bit 500-MS/s DAC with digital background calibration

The linearity of current-steering digital-to-analog converters (DACs) at low signal frequencies is mainly limited by matching properties of current sources, so large-size current source arrays are widely used for better matching. This, however, results in large gradient errors and parasitic capacitance, which degrade the spurious free dynamic range (SFDR) for high-frequency signals. To overcome this problem, calibration is an effective method. In this paper, a digital background calibration technique for current-steering DACs is presented and verified by a 14-bit DAC in a 0.13 μm standard CMOS process. The measured differential nonlinearity (DNL) and integral nonlinearity (INL) are 0.4 LSB and 1.2 LSB, respectively. At 500-MS/s, the SFDR is 70 dB and 50.3 dB for signals of 5.4 MHz and 224 MHz, respectively. The core area is 0.69 mm2 and the power consumption is 165 mW from a mixed power supply with 1.2 V and 3.3 V.