Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Controller for Stability Robustness, Performance Robustness, and Disturbance Attenuation of a Maglev System

A HYBRID CONTROLLER FOR STABILITY ROBUSTNESS, PERFORMANCE ROBUSTNESS, AND DISTURBANCE ATTENUATION OF A MAGLEV SYSTEM Feng Tian, B.S., M.S. Marquette University, 2015 Devices using magnetic levitation (maglev) offer the potential for friction-free, high-speed, and high-precision operation. Applications include frictionless bearings, high-speed ground transportation systems, wafer distribution systems, high-precision positioning stages, and vibration isolation tables. Maglev systems rely on feedback controllers to maintain stable levitation. Designing such feedback controllers is challenging since mathematically the electromagnetic force is nonlinear and there is no local minimum point on the levitating force function. As a result, maglev systems are open-loop unstable. Additionally, maglev systems experience disturbances and system parameter variations (uncertainties) during operation. A successful controller design for maglev system guarantees stability during levitating despite system nonlinearity, and desirable system performance despite disturbances and system uncertainties. This research investigates five controllers that can achieve stable levitation: PD, PID, lead, model reference control, and LQR/LQG. It proposes an acceleration feedback controller (AFC) design that attenuates disturbance on a maglev system with a PD controller. This research proposes three robust controllers, QFT, H , and QFT/ H , followed by a novel AFC-enhanced QFT/ H (AQH) controller. The AQH controller allows system robustness and disturbance attenuation to be achieved in one controller design. The controller designs are validated through simulations and experiments. In this research, the disturbances are represented by force disturbances on the levitated object, and the system uncertainties are represented by parameter variations. The experiments are conducted on a 1 DOF maglev testbed, with system performance including stability, disturbance rejection, and robustness being evaluated. Experiments show that the tested controllers can maintain stable levitation. Disturbance attenuation is achieved with the AFC. The robust controllers, QFT, H , QFT/ H , and AQH successfully guarantee system robustness. In addition, AQH controller provides the maglev system with a disturbance attenuation feature. The contributions of this research are the design and implementation of the acceleration feedback controller, the QFT/ H , and the AQH controller. Disturbance attenuation and system robustness are achieved with these controllers. The controllers developed in this research are applicable to similar maglev systems.
A proportional Dvoretzky-Rogers factorization result

If X is an n -dimensional normed space and " 2 (0 ; 1), there exists m (cid:21) (1 − " ) n , such that the formal identity i 2 ; 1 : l m 2 ! l m 1 can be written as i 2 ; 1 = (cid:11) (cid:14) (cid:12);(cid:12) : l m 2 ! X;(cid:11) : X ! l m 1 , with k (cid:11) k (cid:1) k (cid:12) k (cid:20) c=" . This is proved as a consequence of a Sauer-Shelah type theorem for ellipsoids.
DCTCN: Deep Complex Temporal Convolutional Network for Long Time Speech Enhancement

Recently, with the rapid development of deep learning, the performance of Monaural speech enhancement (SE) in terms of intelligibility and speech quality has been signiﬁcantly improved. In time-frequency (TF) domain, we generally use convolutional neural networks (CNN) to predict the mask from the noisy amplitude spectrum to the pure amplitude spectrum. Deep complex convolution recurrent network (DCCRN) uses the algo-rithm of complex numbers to process convolutional networks and long short-term memory (LSTM), and has achieved good results. However, LSTM can only model short time frames, and its performance is often not good enough when processing information on longer time frames. The single convolution kernel size of encoder-deocder also limits the ability of model to extract and restore features. In this paper, we design a new network to handle these problems, called Deep Complex Temporal Convolutional Network (DCTCN), where temporal convolution network (TCN) using the rule of complex calculation. The En-coder and Decoder use selective kernel network (SkNet) to capture multi-scale receptive ﬁeld in the encoding and decoding phase. Compared with DCCRN, the proposed DCTCN can be more effective in modeling long time series, and SKNet can extract and restore more ﬁne-grained features. On the TIMIT and VoiceBank+DEMAND datasets, our model obtains very competitive results compared with previous models.
Challenges in the Future of Restorative Youth Justice in Ireland: Minimising Intervention, Maximising Participation

While restorative approaches encompass a small proportion of youth justice practices in Ireland, the new Youth Justice Strategy (2021–2027) aims to include more victims in restorative justice, expand family conferencing and train youth justice professionals in restorative practices. This article discusses the legal, policy and practice contexts of these developments, considering how Ireland has defined, used and researched restorative youth justice to date. It situates the ongoing efforts to meet victims’ and children’s needs, and to change criminal justice cultures, in the international criminological and legal literature on minimum intervention and child participation, analysing the possibilities and challenges facing restorative youth justice in Ireland.
PREVALENCE OF GASTROINTESTINAL HELMINTHS OF AFRICAN CATFISH (Clarias gariepinus BURCHELL 1822) IN ZOBE RESERVOIR, KATSINA STATE, NIGERIA

A six months study was carried out to examine the gastrointestinal helminth of wild Clarias gariepinus from Zobe reservoir, Dutsin-Ma, Katsina State, Nigeria. A total of 162 pieces of Clarias gariepinus were purchased from fishermen at three (Raddawa, Tabobi and Makera) of the landing sites on Zobe reservoir between March and August 2021. Fish samples were identified at each of the three landing sites. The fish were transported alive to the fish biology laboratory, Federal University Dutsin-Ma, for the helminths examination. Experimental fish samples were measured for length and weighed. Stomach and intestinal parasites were, identified and counted. The helminths recovered include, one Cestode, Monibothrium sp, four nematodes, Procamallanus sp, Contracaecum sp, Capilaria sp and Camallanus sp and one Metacercariae of Trematode, with the Nematodes as the dominant helminths. The result showed higher number of the fish parasites in the intestine of C. gariepinus. The prevalence in female specimens of C. gariepinus 77 (88.50%) was higher than that of the males (84.00%). The Chi-square result was not significant showing there is no association between the sex and prevalence of helminths. It is not significant also for length and weight, but the sampling station had a P-value of 0.001, showing that there is a relationship between the sampling station and the prevalence of helminths in the sampled C. gariepinus from Zobe reservoir. The highest prevalence was from the Makera sampling station. African catfish from the Zobe reservoir were highly infested with gastrointernal parasites, and this cut across the sexes, ages and landing sites. Hence, Clarias gariepinus from the reservoir should be cooked properly to avoid the transfer of the parasites to the consumers. It is strongly advised that more studies should be carried out to examine parasites from other fish species from the reservoir, to ascertain the safety of consumers that relied on the reservoir for fish. It may also be important to put up regulations such as restrictions on waste disposal and grazing, that will limit potential activities that may encourage an increase in parasites around the water body.
Working Paper 40: Developing anti-corruption interventions addressing social norms

This Working Paper provides guidance on developing anti-corruption interventions based on a Social Norms and Behaviour Change (SNBC) approach. Still a relatively nascent field, SNBC interventions typically address social norms that make corruption acceptable or expected, and attempt to influence behaviours away from corrupt practices. The guidance is based on lessons learned from a largely successful pilot project in Tanzania that targeted social norms fuelling bribery ("gift giving") in health facilities and attempted to change the behaviours of both health care providers and users away from exchanging gifts. Survey results showed a 14–44% decrease in gift-giving intentions, attitudes and positive beliefs among hospital users following the pilot intervention. The guidance covers: How to identify when a SNBC approach is suitable Essential background research needed to design anti-corruption SNBC interventions Frameworks to formulate theories of change Specific elements to build into SNBC interventions What practitioners should expect when embarking on an SNBC intervention Ways they can help build evidence and understanding of SNBC approaches in the anti-corruption field. About and acknowledgements This publication was supported by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Federal Ministry for Economic Cooperation and Development (BMZ). The contents of this publication do not represent the official position of either BMZ or GIZ. The pilot intervention that serves as the basis for most of the reflections included in this document was funded by the Global Integrity Anti-Corruption Evidence Programme (GI-ACE), funded with UK aid from the UK government.
REMAP: A Spatiotemporal CNN Accelerator Optimization Methodology and Toolkit Thereof

Designing convolutional neural network (CNN) accelerators is getting more difficult owing to the fast-increasing types of CNN models. Some approaches use constant dataflow and microarchitecture that have lower design complexity. However, these accelerators are difficult to adapt with the highly-diverse CNN models and often suffer from low process element utilization. Some other accelerators resort to reconfigurable devices, such as field-programmable gate array (FPGA) and coarse-grained reconfigurable array to support flexible dataflows in order to fit diverse CNN layers. However, layer-by-layer processing may require more energy for frequent reconfiguration and off-chip DDR access. In this work, we introduce a reconfigurable pipeline accelerator (RPA) that can reduce the latency and DDR access by pipelining the compuptation of CNN layers. Although there have been several researches that try to speedup the design process by automatically exploring subset of the accelerator design space, identifying an available automated design tool that can effectively find the complete and optimal design scheme remains a problem, especially for the novel RPA architecture type. Unfortunately, comprehensive exploration of the whole design space faces an excessive large searching space. To tackle this problem, we propose REMAP, a toolkit for designing CNN accelerators based on the Monte Carlo tree search (MCTS) method. To efficiently search the huge design space, we propose several methods to improve searching efficiency. Evaluations show that REMAP significantly outperforms some state-of-the-art approaches; compared with GAMMA, it achieves an average speed increase of $14.75\times $ , and an energy reduction of 45.45%; it also achieves a speed increase of $32.6\times $ against ConfuciuX on MobileNetV2 and ResNet50. We also show an FPGA accelerator implementation which is based on REMAP’s search result, and it achieves high performance in real-time CNN tasks. This indicates that REMAP can provide high-quality design exploration with valuable insights and useful architecture design guidances.
Theory & Methods: An Analysis of Two‐Dimensional Agricultural Field Trials in the Presence of Outliers and Fertility Jumps

This paper proposes a model for outliers and fertility jumps in agricultural field trials when the fertility is correlated in two directions. Uncertainty concerning the occurrence of these anomalies can be substantial, especially when outliers occur near a fertility jump. The proposed estimation technique can take this uncertainty into account rather than condition on a particular set of anomalies. Diagnostics for the detection of these anomalies are also discussed. The procedures are illustrated with a wheat trial.
Strategies for developing a Conversational Speech Dataset for Text-To-Speech Synthesis

There have been many efforts to improve the quality of speech synthesis systems in conversational AI. Although state-of-the-art systems are capable of producing natural-sounding speech, the generated speech often lacks prosodic variation and is not always suited to the task. In this paper, we examine dialogue data collection methods to use as training data for our acoustic models. We collect speech using three different setups: (1) Random read-aloud sentences; (2) Performed dialogues; (3) Semi-Spontaneous dialogues. We analyze prosodic and textual properties of the data collected in these setups and make some recommendations to collect data for speech synthesis in conversational AI settings.
Clinical efficacy of norepinephrine combined with cimetidine in treatment of neonatal upper gastrointestinal hemorrhage and its adverse reactions

Objectives: To investigate the clinical efficacy of norepinephrine combined with cimetidine in the treatment of neonatal upper gastrointestinal hemorrhage and its adverse reactions. Methods: A total of 68 cases of neonatal upper gastrointestinal hemorrhage in Huangshi Maternal and Child Health Care Hospital from please mention dates October 2018 to February 2020 were selected and randomly divided into treatment group and control group by coin tossing, with 34 infants in each group. The control group received conventional therapy, and the treatment group was additionally treated with norepinephrine combined with cimetidine. The efficacy and safety were compared between the two groups. Results: The time when the bleeding stops, the time of fecal occult blood turning negative and hospital stay of the treatment group were shorter than those of the control group (P < 0.05). Superoxide dismutase (SOD) level increased while malondialdehyde (MDA) level decreased in both groups after treatment compared with those before treatment (P < 0.05). After treatment, the SOD level was higher while the MDA level was lower in the treatment group than those in the control group (P < 0.05). The effective rate of the treatment group was higher than that of the control group (P < 0.05). However, no significance was found in adverse reactions between the two groups (P > 0.05). Conclusion: Norepinephrine combined with cimetidine in the treatment of neonatal upper gastrointestinal hemorrhage can shorten the recovery time of symptoms, improve efficacy and reduce stress reaction. It is safe, effective and worthy of use in clinical practice.
Forty years of invasion research: more papers, more collaboration...bigger impact?

Scientific research has become increasingly collaborative. We systematically reviewed invasion science literature published between 1980 and 2020 and catalogued in Clarivate Analytics Web of Science to examine patterns of authorship and the relationship between co-authorship and annual citation rates. This study analysed 27,234 publications across 1,218 journals and demonstrated that, as the number of publications in invasion science has exponentially increased, the number of authors publishing per year and the average number of authors per paper have also increased. The rising number of authors per paper coincides with a marked decline of single-authored publications; approximately 92% of publications in this dataset were multi-authored, with single-authored papers comprising less than 4% of all papers published in 2020. The increase in multi-authored papers is likely driven by multiple factors, including the widespread perception that collaboration increases scientific quality. The number of authors is positively correlated with perceived research impact; papers with two or more authors produce research that is more frequently cited compared to single-authored papers, and papers with five or more authors have annual citation rates almost double that of single-authored papers. The complexity, context-dependence and urgency of biological invasions contributed to the rise of the highly collaborative field of modern invasion science.
Anticipating the Climate Change Impacts on Madeira’s Agriculture: The Characterization and Monitoring of a Vine Agrosystem

Climate—Madeira Strategy (CMS) foresees two models to describe the climate scenarios for the Madeira region in 2050 and 2070. These scenarios anticipate an average temperature rise of 1.4 to 3.7 °C and a decrease in precipitation by 30 to 40%. Consequently, Madeira’s agriculture will suffer the impacts of climate change. To understand these impacts, a baseline of major agrosystem components needs to be established, with the ultimate goal to monitor its consequences in its functioning. CASBio project used the 1961–1991 and 2010–2020 meteorological data series to modulate climate conditions and characterize and monitor six agrosystems for 2 years. One of them was a vineyard, Quinta das Vinhas, representing a typical agrosystem in the Mediterranean climate. The annual and seasonal variation in climatic parameters, soil conditions, microbiological communities, floristic and insect diversity, and crop production was assessed, using a total of 50 parameters. The results were used to establish a baseline of the agrosystem components and their seasonal and annual variation. The major findings are: (i) winter and summer extreme events show a trend in temperature and precipitation supporting a fast change in climate; (ii) a critical imbalance between nitrogen-fixing and denitrifying bacteria was identified, especially in summer, that could be determined by the rise in temperature and drought; (iii) among floristic diversity, the therophytes and geophytes confirm to be the most suitable indicators for the rise in temperature and reduction in precipitation in the agrosystems; (iv) an imbalance in favor of C. capitata plague was observed, associated with the summer rise in temperature and decrease in precipitation; (v) despite an increase in most of the grape varieties production, the Madeiran wine local varieties were shown to be less stable in productivity under observed climate conditions. The agrosystem baseline is a starting point for long-term monitoring and allows for further quantifying the influence of climate change on agrosystem productivity, resilience, and sustainability.
A compact transformer-based GAN vocoder

Recent work has shown that self-attention module in Trans-former architecture is an effective way of modeling natural languages and images. In this study, we propose a novel approach for waveform synthesis utilizing Self-Attention Network (SAN). To the best of our knowledge, Transformer architectures or SANs have not yet been successfully applied in high-fidelity waveform generation. The main challenge in applying SANs to waveform generation tasks lies in its quadratic growth of the computational complexity with respect to the input sequence length, which makes it unsuitable for high sample-rate wave-form generation. To solve this problem, we apply dilated sliding window to vanilla SAN. This technique enables our model to have a large receptive field while maintaining linear computational complexity and a small footprint. We experimentally show that the proposed model achieves smaller model size, while producing audio samples with comparable speech quality in comparison with the best publicly available model. In particular, our small footprint model has only 0.57M parameters and can generate 22.05kHz high-fidelity audio 113 × faster than real-time on a NVIDIA V100 GPU without engineered inference kernels.
Wireless Temperature Measurement Validation Method for PCR Machines by Magnetic Hall-Effect Sensor

The temperature validation controlled by temperature indication has a vital role in the polymerase chain reaction (PCR) test machine or thermal cycler. However, the validation process is complicated for several types of thermal cycler. Some PCR test machines must close the lid tightly while running. It makes the probe’s cable of the temperature sensor might be pinched or break when the thermal cycler lid is closed. Opening the lid (open-air condition) makes the measurement will not accurate. To solve this problem, wireless temperature measurement and validation methods for PCR machines are developed based on magnetic field measurements. The magnetic field of the object will respond to any changes in temperature. The hall-effect sensor, which is validated by gauss meter, detects any magnetic response a certain material covers even the object. This detection yields output data processed to find the thermal cycler's appropriate temperature wireless validation method. The experiment used a Neodymium magnet as a wireless probe. The position of the Neodymium magnet pole significantly affected the relation between magnetic flux and temperature in experimental results. The reversed pole toward sensors had better linearity (R2= 0.8062) than the unreversed pole (R2= 0.7794). The annealing step commonly achieved the optimum measurement uncertainty. However, the measurement uncertainty and signal sensitivity investigation recommended employing the beneficial combination of pole magnet position to design the temperature validator based on magnetic induction for a closed lid thermal cycler (PCR machine). Overall, the experimental yields can be used to build a wireless temperature validator for a sealed PCR machine based on magnetic induction.
The Implementation of Tembang Macapat Learning as A Means of Primary School Character Education

Javanese culture has become unfamiliar to the millennial generation, even though character values in Javanese culture are excellent. Javanese culture is mainly introduced traditionally, one of which is the tembang macapat. The research aimed to describe character education, including planning, implementation, and evaluation in tembang macapat, which is taught in primary school, particularly in high grades, in grades four, five, and six. This research applied qualitative research with phenomenological methods. The data collection techniques used interviews, observation, and documentation. Triangulation was used to test the validity of the data. The data analysis techniques consisted of data collection, data reduction, data display, verification, and conclusion. Then it presented and concluded from the data that has been analyzed. The findings were as follows: 1) Planning for tembang macapat learning by analyzing the initial conditions of Primary School character education activities; 2) The tembang macapat learning was implemented once a week in the morning assembly and created tembang macapat poster; 3) Evaluations of tembang macapat learning instilled in students were the character of brotherhood, togetherness, affection, courtesy, respectfulness, and humility. Therefore, it concluded that the tembang macapat of gambuh, mijil, and dhandanggula can be used to strengthen primary school character education.