Miracle-dz/UniFAR-FTUs huggingface dataset:
Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.
Expression of the cold-induced wheat gene Wcsf20 and its homologs in related species and interspecific

Low-temperature response was measured at the whole plant and at the molecular level in wheat-rye amphiploids and in other interspecific combinations. Cold tolerance of interspecifics whose parents diverged widely in hardiness levels resembled the less hardy higher ploidy level wheat parent. Expression of the low-temperature induced Wcsl20 gene of wheat (Triticum aestivum L. em. Thell.) has been associated with freezing tolerance and was used here to study mRNA and protein accumulation in interspecific and parental lines during cold acclimation. Northern and Western analyses showed that homologous mRNAs and proteins were present in all the related species used in the experiments. Cold-tolerant rye (Secale cereale L.) produced a strong mRNA signal that was sustained throughout the entire 49-day cold-acclimation period. The wheats produced a mRNA signal that had diminished after 49 days of low-temperature exposure. The wheat-rye triticales did not exhibit the independent accumulation kinetics of the cold-tolerant rye parent but, rather, more closely resembled the wheat parent in that the mRNA signal was greatly diminished after 49 days of low-temperature exposure. The influence of the rye genome was manifest in slightly greater mRNA and protein accumulation in earlier stages of acclimation. Protein accumulations in the triticales were also maintained to a somewhat greater extent than found in the wheats at the end of the 49-day acclimation period. Protein accumulations in the wheat-crested wheatgrass (Agropyron cristatum L. Gaertner) interspecific resembled that of the wheat parent. The influence of the higher ploidy level wheats of the expression of homologous gene families from wheat-related hardy diploids in interspecific combinations may in part explain the poor cold tolerance observed.
Striking Nonlinear Dynamics of Mode-Locked Fibre Lasers

We report on the real-time observation of various remarkable nonlinear phenomena in mode-locked fibre lasers. These include the build-up of dissipative solitons and soliton molecules, collision-induced soliton explosions, and the excitation and dynamics of breathing dissipative solitons and breather molecular complexes. Numerical simulations of the laser model support our experimental findings.
Mode Locked Laser Phase Noise Reduction Under Optical Feedback for Coherent DWDM Communication

Single section quantum dash (QDash) mode locked lasers (MLL) can provide a flat and broadband optical frequency comb with low energy consumption, operational simplicity and large-scale low-cost production possibilities. MLL longitudinal modes can be employed as single carriers with a regular spectral spacing in a dense wavelength division multiplexing (DWDM) link, making them promising components for next generation DWDM transceivers. However, individual modes of a MLL suffer from relatively high phase noise while high capacity coherent transmission requires carriers with low phase noise. Optical feedback, which is a well-known method to reduce the linewidth of a single mode laser, can be used to stabilize comb lasers. This article reports on the investigation of phase noise properties of comb lines delivered from a single section QDash MLL under optical feedback and shows that each MLL longitudinal mode optical linewidth can be drastically narrowed. It enables coherent transmission with extended link budget over 50 km at Tbit/s line rates.
Analysis of Various Types of Fiber Dispersion for Fiber Optical Communication

An optical fiber is an important communication channel as it proposes a high bandwidth and less attenuation, and can be easy challenging assistances such as huge-quality data transmission and others in computer networks. This proposed paper shows the simulation and good design of digital optic fiber with single mode and multimode fiber dispersion is presented. Additional this project shows the comparison of single mode and multimode fiber dispersion values such as single mode fiber with dispersion value of 0, 3, 17 and multimode fiber dispersion with FBG & without FBG is used to obtain a efficient result. The result is attained using Optisystem 7 software.
Preliminary Study on Deep-learning for Retinal Vessels Segmentation

The retina is an internal part of the human eye that plays a crucial role in vision. Retinal vessel segmentation is an important basis for the identification and classification of Diabetic Retinopathy (DR), Age related macular degeneration (AMD)，Retinal Detachment, and other retinal diseases. At the same time, the morphological structure of retinal blood vessels can also be used to diagnose cardiovascular diseases, which is important for early diagnosis and prevention of exacerbation of the disease. There are already advanced state-of-the-art methods to help automatically segment and identify retinal vessel diseases. This paper presents the methods of the principle and applications of deep learning in retinal image analysis. We described various segmentation methods based on deep learning. Analyzing along with the limitations of each method. Finally, we proposed some suggestions for the improvement of retinal image analysis.
Laboratory Studies of the Saimiri-Haemagogus Cycle of Jungle Yellow Fever.

VII. Summary 1. A local strain of yellow fever virus was carried through 5 cycles in the laboratory using saimiri monkeys as hosts and the mosquito Haemagogus capricornii as vector. 2. This is a laboratory reproduction of what may be a local transmission mechanism of jungle yellow fever, as both saimiri monkeys and haemagogus mosquitoes have been implicated in the epidemiology of the disease by field studies. 3. The extrinsic incubation period in Haemagogus capricornii under the conditions of these experiments was 20 to 24 days at room temperature (24°–27°, mean 25.4°C.); and 13 to 15 days at a constant temperature of 30°C. 4. A virus strain was used that had been subject to a minimum of laboratory manipulation. Adult white mice of the standard strain used in yellow fever studies were found to show an irregular susceptibility to intracerebral inoculation of small amounts of this virus, but 3- and 5-day-old mice were highly susceptible. The strain was highly virulent for saimiri monkeys: 8 of the 10 animals infected by mosquito bite died in the course of acute infections. 5. Only a certain percentage of specimens of Haemogogus capricornii became infected after feeding on monkeys with virus in circulation. The percentage infected in a particular experiment appeared to depend on (a) the amount of virus originally ingested by the mosquitoes and (b) the environmental temperature at which the mosquitoes were kept. The possible effect of virus strain and of the genetic constitution of the mosquitoes was not studied. 6. The correlation between factors governing haemagogus infection in the laboratory and the habits of the mosquito judged by field studies, is discussed. The mosquito seems to require a high environmental temperature to become infected, and its habit of flying in the canopy zone of the forest means that it is subject to relatively high temperatures in nature. The correspondence in habits between this species of mosquito and saimiri monkeys in the Villavicencio area is close.
Learning from informative losses boosts the sense of agency

Sense of agency, the feeling of having control over one’s actions, is modulated by whether one’s choices lead to desired or undesired outcomes. Learning similarly depends on outcome values from previous experience. In the current study, we evaluate a possible link between the sense of agency and learning, by investigating how intentional binding, an implicit measure of agency, changes during a probabilistic learning task. In two experiments, we show increased intentional binding in trials that follow losses, compared with trials that follow wins. Experiment 1 demonstrated that this post-error agency boost (PEAB) effect is rule-specific, as it did not occur if the trial following an error involved different action–outcome contingencies. Furthermore, PEAB was not modulated by the type of outcome presentation (monetary vs. affective). Experiment 2 showed that the PEAB effect can also occur when the current action involves a forced (as opposed to free) choice, but only when the previous, loss-provoking action was chosen freely. Thus, PEAB occurs when current actions are informed by outcomes of one’s own previous action choices. Electroencephalography (EEG) data linked these effects to two event-related potential components, namely, the Feedback Related Negativity and the P300. Taken together, these results support the notion that PEAB reflects an adaptive property of human sense of agency, facilitating effective learning about the action–outcome structure of a specific task, to optimise future performance. By clarifying the conditions for enhancing the sense of agency through learning, this work adds to our understanding of human learning and agency.
End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm

Changing lanes while driving requires coordinating the lateral and longitudinal controls of a vehicle, considering its running state and the surrounding environment. Although the existing rule-based automated lane-changing method is simple, it is unsuitable for unpredictable scenarios encountered in practice. Therefore, using a deep deterministic policy gradient (DDPG) algorithm, we propose an end-to-end method for automated lane changing based on lidar data. The distance state information of the lane boundary and the surrounding vehicles obtained by the agent in a simulation environment is denoted as the state space for an automated lane-change problem based on reinforcement learning. The steering wheel angle and longitudinal acceleration are used as the action space, and both the state and action spaces are continuous. In terms of the reward function, avoiding collision and setting different expected lane-changing distances that represent different driving styles are considered for security, and the angular velocity of the steering wheel and jerk are considered for comfort. The minimum speed limit for lane changing and the control of the agent for a quick lane change are considered for efficiency. For a one-way two-lane road, a visual simulation environment scene is constructed using Pyglet. By comparing the lane-changing process tracks of two driving styles in a simplified traffic flow scene, we study the influence of driving style on the lane-changing process and lane-changing time. Through the training and adjustment of the combined lateral and longitudinal control of autonomous vehicles with different driving styles in complex traffic scenes, the vehicles could complete a series of driving tasks while considering driving-style differences. The experimental results show that autonomous vehicles can reflect the differences in the driving styles at the time of lane change at the same speed. Under the combined lateral and longitudinal control, the autonomous vehicles exhibit good robustness to different speeds and traffic density in different road sections. Thus, autonomous vehicles trained using the proposed method can learn an automated lane-changing policy while considering safety, comfort, and efficiency.
600 nm-Wide Band ASE Optical Source Exploiting a Tm:Er:Yb:Ho Co-Doped Germanate Fiber

In this paper, the numerical model of a 980-nm pumped optical fiber source exploiting the amplified spontaneous emission (ASE) phenomenon is illustrated. The device is based on a Tm:Er:Yb:Ho co-doped germanate fiber and allows incoherent light emission in the 1500–2100 nm wavelength range. The model exploits the rate equations approach and takes into account the energy transfer phenomena between different rare earths. Several simulations are performed in order to identify the best dopant concentrations for extending the output ASE spectrum as much as possible. By employing a 1 m-long optical fiber, with an input pump power of 0.3 W, an ASE spectrum covering a band of about 600 nm with a total output power of over 15.8 mW is predicted.
Performance Comparison of Operational Models for Media Channel Provisioning with Reduced Margins

Real-time monitoring of optical performance has been shown to enable hardware savings by operating the transport network closer to the performance limits. Despite this, most studies forgo operational considerations, such as the procedures to be enforced when an active media channel (MCh) is too close to the performance limit and it is no longer acceptable to use it in the present configuration. Importantly, the operational models that can be adopted to perform this task are dependent on factors such as the transport node architecture and the service level agreement (SLA). Hence, this work describes three main strategies to exploit reduced margin provisioning in a transport network, comparing their performance over reference networks in terms of key metrics such as the extent of hardware savings and the number of, potentially traffic affecting, re-routing events.
Electric Vehicles Scale Evolution Model Considering Social Attributes

Predicting the scale of electric vehicles at different time is the basis for the power grid to effectively accept charging loads. The development of electric vehicles is not only affected by economy, technology and policy, but also affected by the factors from social networks. This paper proposes the electric vehicle purchase decision model considering the influences of technology, economy and society. A two-dimensional space is used to characterize the random influences of related economic and social groups on consumer decisions, which can dynamically simulate the interaction between social environment and consumer decisions. Thus, based on such two-dimensional social space, an electric vehicle scale evolution model is established, so that it can be used to analyze the impact of the structural characteristics of social networks on the scale evolution. A large community in Changsha is taken as an example to analyze the scale development trend of electric vehicles under different social networks. Case studies demonstrate the feasibility and effectiveness of the proposed methodology.
A Preoperative Nomogram for Predicting Chemoresistance to Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Squamous Carcinoma Treated with Radical Hysterectomy

Purpose This study aimed to investigate the factors associated with chemoresistance to neoadjuvant chemotherapy (NACT) followed by radical hysterectomy (RH) and construct a nomogram to predict the chemoresistance in patients with locally advanced cervical squamous carcinoma (LACSC). Materials and Methods This retrospective study included 516 patients with International Federation of Gynecology and Obstetrics (2003) stage IB2 and IIA2 cervical cancer treated with NACT and RH between 2007 and 2017. Clinicopathologic data were collected, and patients were assigned to training (n=381) and validation (n=135) sets. Univariate and multivariate analyses were performed to analyze factors associated with chemoresistance to NACT. A nomogram was built using the multivariate logistic regression analysis results. We evaluated the discriminative ability and accuracy of the model using a concordance index and a calibration curve. The predictive probability of chemoresistance to NACT was defined as > 34%. Results Multivariate analysis confirmed menopausal status, clinical tumor diameter, serum squamous cell carcinoma antigen level, and parametrial invasion on magnetic resonance imaging before treatment as independent prognostic factors associated with chemoresistance to NACT. The concordance indices of the nomogram for training and validation sets were 0.861 (95% confidence interval [CI], 0.822 to 0.900) and 0.807 (95% CI, 0.807 to 0.888), respectively. Calibration plots revealed a good fit between the model-predicted probabilities and actual probabilities (Hosmer-Lemeshow test, p=0.597). Furthermore, grouping based on the nomogram was associated with progression-free survival. Conclusion We developed a nomogram for predicting chemoresistance in LACSC patients treated with RH. This nomogram can help physicians make clinical decisions regarding primary management and postoperative follow-up of the patients.
Power Quality Disturbance Classification via Deep Convolutional Auto-Encoders and Stacked LSTM Recurrent Neural Networks

Power quality disturbances are one of the main problems in electric power systems. The perturbations or disturbances occur due to many factors such as non-linear loads, lighting controls, power electronic equipment, system faults and switching events. In this sense, if monitoring and control actions are not developed to mitigate properly these disturbances, an overall interruption of the power transmission and distribution networks could be generated, causing an important social impact and huge economic losses. To date, several strategies have been proposed in the literature, including signal processing based feature extraction approaches, machine learning based classifiers and heuristic optimization techniques to identify and detect power quality disturbances. However, limitations in terms of computational load, efficiency and performance are still important issues to solve. In this paper, a novel technique of automatic classification of power quality disturbances is presented. The proposed ensemble algorithm consists of a Convolutional Auto-Encoder compression architecture, which is included to reduce the signal size, and a stacked Long Short Term Memory (LSTM) Recurrent Neural Network (RNN), to conduct the classification process keeping high accuracy metrics. The performance of the proposed algorithm is evaluated through the process and analysis of a synthetic database created with the characteristic mathematical models formulated for nine disturbances. The results show a significant reduction of time processing and a higher accuracy rate compared with traditional LSTM RNNs.
A Novel Superconducting Interconnection for Superconducting MCM Using Wire Bonding Method with Pb-In Alloy

In this paper, a novel superconducting interconnection method in SFQ MCM is introduced. The lead- indium alloy solder wire (Pb25In75) was used to achieve wire bonding on the niobium pad by ultrasonic bonding. Then the tensile test was carried out and superconducting properties was measured by means of Kelvin Four-terminal method. The results showed that it has high tensile strength and superconducting properties at 4.98K, achieving a superconducting interconnection, and that a good contact between lead-indium wire and niobium pad is realized.
A low cost 60GHz antenna in Fan-Out Panel Level Package for millimeter-wave radar application

A linear series-fed patch array (SFPA) antenna is designed based on a low cost Fan-Out Panel Level Packaging technology. The presented antenna operates from 60GHz to 64GHz and is targeted for millimeter-wave radar application. The antenna achieves more than 10dBi radiation gain with good return loss in the entire operating bandwidth. The antenna is investigated to be less sensitive to the thickness variation of the air gap caused by the solder balls. The antenna-inpackage (AiP) is a promising solution to produce a low cost high performance system-in-package (SiP) module for MMW radar system.
Using Bayesian Regression for Stacking Time Series Predictive Models

The paper describes the use of Bayesian inference for stacking regression of different predictive models for time series. The models ARIMA, Neural Network, Random Forest, Extra Tree were used for the prediction on the first level of model ensemble. On the second level, time series predictions of these models on the validation set were used for stacking by Bayesian regression. This approach gives distributions for regression coefficients of these models. It makes it possible to estimate the uncertainty contributed by each model to stacking result. The information about these distributions allows us to select an optimal set of stacking models, taking into account the domain knowledge. A probabilistic approach for stacking predictive models allows us to make risk assessment for the predictions that is important in a decision-making process.
Enabling Warp Speed Using the Hypervelocity Innovation Model: A Blue Print for Drug Development in Pandemics

Pharmaceutical R&D is organized around prioritized investments, delivering on assets with the greatest return of investments. Linear sequencing of trials and financial risks are determinants of speed in an enterprise. There is less flexibility in leveraging expertise around priorities that are thrust on the system by external forces. This commentary provides an insight into enabling warp speed using a hypervelocity mindset for mobilizing resources and scientific creativity, for accelerated timelines in response to pandemics.
Operating Cost Reduction of DC Microgrids Under Real-Time Pricing Using Adaptive Differential Evolution Algorithm

Virtual resistance-based droop control is widely adopted as secondary-layer control for grid-connected converters in DC microgrids. This paper presents an alternative usage of the virtual resistances to minimize the total operating cost of DC microgrids under real-time pricing. The total operating cost covers the running cost of utility grids, renewable energy sources (RES), energy storage systems (ESS), fuel cells, and power loss on the distribution lines. An adaptive Differential Evolution (ADE) algorithm is adopted in this paper to optimize the virtual resistances of the droop control for the grid-connected converters of dispatchable units, such that the power flow can be regulated. The performances of the proposed strategy are evaluated by the case studies of a 12-bus 380 V DC microgrid using Matlab and a 32-bus 380 V DC microgrid using a Real-Time Digital Simulator (RTDS). Both results validate that the ADE can significantly reduce the operating cost of DC microgrids and outperform the conventional Genetic Algorithm (GA) in terms of cost saving. Comparisons among the microgrids with different numbers of dispatchable units reveal that the cost saving is more prominent when the expansion of dispatchable units.