Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Consumption and Maximum Number of Supported Nodes for BLE Biosensor Applications

There has been significant growth in wearable wireless patient physiological monitoring over the last decade. Many applications require a real-time, low-latency, small profile, and low-power-battery system. In this work, various Bluetooth low energy (BLE) configurations were tested in a multi-channel, wireless system to determine the lowest peripheral power configuration and the number of supported peripherals for each BLE configuration. Using nodes that continuously sampled at 1 kHz, connection intervals from 10-100 ms and event lengths of 2500, 5000 and 7500 μs were tested. The lowest current consumption, 2.39 mA, was measured for a connection interval of 100 ms, event length of 2500 μs, and maximum transmission unit (MTU) of 247 bytes. The maximum number of supported peripheral connections was observed to be 11 for a connection interval of 100 ms, event lengths of 5000 and 7500 μs, and MTU size of 247 bytes. We found that using longer connection intervals led to decreases in power consumption and shorter event lengths allowed for support of more peripheral sensors nodes for a given connection interval, assuming the event length is long enough to transmit the desired amount of data. Future work should investigate techniques to optimize power consumption further and to extend the number of supported peripheral nodes.
MS-YOLOv5: a lightweight algorithm for strawberry ripeness detection based on deep learning

The existing ripeness detection algorithm for strawberries suffers from low detection accuracy and high detection error rate. Considering these problems, we propose an improvement method based on YOLOv5, named MS-YOLOv5. The first step is to reconfigure the feature extraction network of MS-YOLOv5 by replacing the standard convolution with the depth hybrid deformable convolution (Ms-MDconv). In the second step, a double cooperative attention mechanism (Bc-attention) is constructed and implemented in the CSP2 module to improve the feature representation in complex environments. Finally, the Neck section of MS-YOLOv5 has been enhanced to use the fast-weighted fusion of cross-scale feature pyramid networks (FW-FPN) to replace the CSP2 module. It not only integrates multi-scale target features but also significantly reduces the number of parameters. The method was tested on the strawberry ripeness dataset, the mAP reached 0.956, the FPS reached 76, and the model size was 7.44M. The mAP and FPS are 8.4 and 1.3 percentage higher than the baseline network, respectively. The model size is reduced by 6.28M. This method is superior to mainstream algorithms in detection speed and accuracy. The system can accurately identify the ripeness of strawberries in complex environments, which could provide technical support for automated picking robots.
State of Health Estimation of Lithium-ion Battery Based On Improved Transfer Learning

Many existing methods of forecasting the state- of-health(SOH) assume that training and testing data follow the same distribution. The model based on dataset under one working condition may be ineffective for the dataset under another working condition due to the distribution discrepancy. In order to meet this challenge, this paper proposes an improved method Mutual Information Domain-Adversial Neural Networks (DANN) based on domain adaptation, which improves the domain discriminator to better extract domain invariant features. In addition, to avoid the loss of target information, the mutual information among target features, source features, and original target data is calculated to fix the features on the target site during the migration process. Different from the traditional methods, we only use 40% of the data sets for training, and the rest are used for prediction, so we can complete the prediction of more scenes. Experimental results show that this method can accurately and stably predict SOH.
Application of Framingham risk estimates to ethnic minorities in United Kingdom and implications for primary prevention of heart disease in general practice: cross sectional population based study

Abstract Objective: To compare the 10 year risk of coronary heart disease (CHD), stroke, and combined cardiovascular disease (CVD) estimated from the Framingham equations. Design:Population based cross sectional survey. Setting: Nine general practices in south London. Population: 1386 men and women, age 40-59 years, with no history of CVD (475 white people, 447 south Asian people, and 464 people of African origin), and a subgroup of 1069 without known diabetes, left ventricular hypertrophy, peripheral vascular disease, renal impairment, or target organ damage. Main outcome measures: 10 year risk estimates. Results: People of African origin had the lowest 10 year risk estimate of CHD adjusted for age and sex (7.0%, 95% confidence interval 6.5 to 7.5) compared with white people (8.8%, 8.2 to 9.5) and south Asians (9.2%, 8.6 to 9.9) and the highest estimated risk of stroke (1.7% (1.5 to 1.9), 1.4% (1.3 to 1.6), 1.6% (1.5 to 1.8), respectively). The estimate risk of combined CVD, however, was highest in south Asians (12.5%, 11.6 to 13.4) compared with white people (11.9%, 11.0 to 12.7) and people of African origin (10.5%, 9.7 to 11.2). In the subgroup of 1069, the probability that a risk of CHD 15% would identify risk of combined CVD 20% was 91% in white people and 81% in both south Asians and people of African origin. The use of thresholds for risk of CHD of 12% in south Asians and 10% in people of African origin would increase the probability of identifying those at risk to 100% and 97%, respectively. Conclusion: Primary care doctors should use a lower threshold of CHD risk when treating mild uncomplicated hypertension in people of African or south Asian origin.
Quantum Task Mapping for Distributed Heterogeneous Computing Systems

In this work, our aim is to determine the viability (feasibility) of quantum task mapping algorithms on distributed heterogeneous computing systems. To quantify viability, we compared the quantum algorithms with nine classical mapping algorithms: genetic algorithm (GA), simulated annealing (SA), opportunistic load balancing (OLB), minimum (task) completion time (MCT), minimum (task) execution time (MET), min-min, tabu, max-min, and duplex. Our analysis reveals that quantum mapping is feasible and can produce good-quality mappings comparable to the classical algorithms, but it is not dominant.
An improved stable motion control method for quadruped robot based on CPG and VMC

The gait control of a quadruped robot is the key to the robot's movement in complex terrain. In order to realize the stable motion of the quadruped robot on special terrains such as slopes, the hopf oscillator is used as the central mode generator to control the gait of the quadruped robot. The output of the oscillator is converted into the trajectory of the foot, the trajectory of the foot is adjusted by sensory feedback, the gait phase is switched by detecting the pressure of the foot, and the virtual model is used to control the movement of the foot along the given trajectory to achieve Stable motion of a quadruped robot. The simulated quadruped robot is built on the WEBOTS platform and verified by experiments. The results show that the improved gait controller can achieve smooth and compliant motion for the quadruped robot on flat ground and slopes.
Monocular Non-linear Photometric Transformation Visual Odometry Based on Direct Sparse Odometry

When no photometric calibration is available in Direct Sparse Odemetry (DSO), a direct sparse odometry with method of Monocular Non-linear Photometric Transformation Visual Odometry is proposed to improve tracking accuracy and robustness of DSO. Compared with DSO method, a new regularization item and a non-linear brightness transfer function are integrated in proposed method, so it can better simulate the photometric calibration process. The proposed method is evaluated on 50 different sequences of TUM MonoVO datasets containing several hours of video in total. The results show that the rms of aligned error of the proposed Monocular Non-linear Photometric Transformation Visual Odometry method is 11.4% lower than original Direct Sparse Odometry on average. Successful running times of the proposed method are 4.4% more than original Direct Sparse Odometry when the environment light source intensity changing.
Multi-Layer Features Based Self-Support Network for Few-Shot Segmentation

Few-shot semantic segmentation aims to make the network identify the object regions of new categories with only a few annotated samples. The key to the few-shot segmentation is to complete information interaction between support and query images, and avoid overfitting as much as possible. For combining the advantages of metric-based prototype learning and visual correspondence, we propose a Multi-Layer Features Based Self-Support Network (MFSNet). In MFSNet, we divide the interactive part of support and query features into two stages, multi-layer similarity feature stage and self-support prototype fusion stage. The multi-layer similarity feature stage focuses more on extracting the details of the segmented object, and the self-support prototype fusion stage refine the general feature information of the object. The two stages are linked together through self-support strategies. In addition, we propose a Fusion Block in MFSNet to establish a more robust semantic relationship between multi-layer features. MFSNet is tested on two benchmark semantic segmentation datasets, Pascal-5i and COCO-20i, and achieves new state-of-the-art performances on 1-shot segmentation tasks.
Optimal Filter for Sampled-data Systems with Multi-channel Markovian Packet Losses and Transmission Delay

This paper investigates the optimal filtering for the sampled-data systems with multiple Markovian packet losses and observation delays. A new Markov chain is introduced to tackle the packet loss phenomenon, and the reorganization observation technique is employed to deal with the transmission delay, by which the original sampled system is transformed into a sampled-data delay-free jumping parameter system. Based on the converted system, the optimal Markov jump linear filter is designed by the completion method and the stochastic approximation method, where the filter gains are given in terms of a piecewise continuous Riccati equation. Furthermore, the convergence analysis is supplied for the optimal filtering, and the stationary filter is obtained by solving a type of algebraic Riccati equation. Finally, an illustrative example is given to show the efficiency of the proposed filters.
TRAIN: A Reinforcement Learning Based Timing-Aware Neural Inference on Intermittent Systems

Intermittent systems become popular to be considered as the solutions of various application domains, thanks to the maturation of energy harvesting technology. Environmental monitoring is such an example and it is a time-sensitive application domain. In order to report the perceived environmental status in a timely manner, methods have been proposed to consider the freshness of the collected information on such systems with unstable power sources. Nevertheless, these methods cannot be applied to neural network workloads since these methods do not consider the delivered model accuracy. On the other hand, while there have been studies for deploying neural network applications on intermittent systems, they depend on branchy network architectures, each branch representing an energy-accuracy tradeoff, and do not take into account a time constraint, which tends to cause system failures because of the frequent generation of expired data. In this work, the first timing-aware framework TRAIN is proposed to deploy the neural network models on the intermittent systems by considering energy, time constraint, and delivered model accuracy. Compared with the prior studies that depend on branchy network architectures, TRAIN offers a broadened solution space representing various energy/time/accuracy tradeoffs. It is achieved by allowing to choose among different implementations of each model layer during the model inference at runtime, and the smart choices are made by the proposed reinforcement learning algorithm. Our results demonstrate TRAIN outperforms the prior study by 65%, regarding the delivered model accuracy. We believe that TRAIN paves the way for building complex applications on intermittent systems.
A Small-Area Fingerprint Recognition Algorithm Based on Inverted Residual Connection

Small-area fingerprints image contain less detailed point information. The traditional fingerprint recognition method is based on the detail points that cannot be applied to small-area fingerprints appropriately. To solve this problem, a small-area fingerprint recognition algorithm is proposed in this paper. The algorithm is built on a small-area fingerprint recognition network with inverse residual structure reducing the parameters and computation of the deep neural network. The network converts a fingerprint image to a 256-dimensional vector and judges the similarity of fingerprints by the Euclidean distance between the vectors. The experimental results show that the method achieves a correct rate of 96.4% on the small-area fingerprint dataset established on FVC2002 and self-collected fingerprints. And the model parameter is 4.34M satisfying the usage needs of mobile terminals.
Path-Based Processing using In-Memory Systolic Arrays for Accelerating Data-Intensive Applications

The next wave of scientific discovery is predicated on unleashing beyond-exascale simulation capabilities using in-memory computing. Path-based computing is a promising in-memory logic style for accelerating Boolean logic with deterministic precision. However, existing studies on path-based computing are limited to executing small combinational circuits. In this paper, we propose a framework called PSYS to accelerate data-intensive scientific computing applications using path-based in-memory systolic arrays. The approach leverages path-based computing for multiplying known constants with an unknown operand, which substantially reduces the computational complexity compared with general purpose multiplication of two unknown operands. The systolic arrays minimize data movement by storing the matrix elements using non-volatile memory and performing processing in-place. The framework decomposes unstructured computations to the systolic arrays while considering the non-regular computational patterns of the applications. Our experimental evaluations employ applications from the domains of engineering, physics, and mathematics. The experimental results demonstrate that compared with the state-of-the-art, the PSYS framework improves energy and latency by a factor of 101x and 23x, respectively.
A New Technique to Estimate the Cole Model for Bio-impedance Spectroscopy with the High-Frequency Characteristics Estimation

Bio-impedance spectroscopy (BIS) is a sophisticated testing technique used to analyze impedance changes at different frequencies. In this study, we investigated the estimation of the Cole Model for BIS measurements without the need for high-frequency resistance and reactance measurements, where they are inaccurate due to leakage capacitences. We employed a Texas Instruments evaluation kit (AFE4300) and compared the Cole plots of two different circuit models of tissue between the proposed configuration and a commercial impedance analyzer used as a reference. To enhance the performance of the AFE4300, we incorporated an external direct digital synthesis (DDS) to generate higher frequencies. The results demonstrated the reliability of the proposed theoretical estimation technique in accurately estimating the resistances and capacitance of the Cole Model.
of a rhizosphere communication molecule in tomato and potato

Potato cyst nematode (PCN) is a plant root sedentary endoparasite, specialized in the infection of solanaceous species such as potato ( Solanum tuberosum ) and tomato ( Solanum lycopersicum ). Earlier reports showed that solanoeclepin A (SolA), a triterpenoid metabolite that was isolated from the root exudate of potato, induces the hatching of PCN. Its low concentration in potato root exudate has hindered progress in fully understanding its hatching inducing activity and exploitation in the control of PCN. In order to further investigate the role of SolA in hatching of PCN, the establishment of a highly sensitive analytical method is a prerequisite. Here we present the efficient single-step extraction and UHPLC-MS/MS based analysis for rapid determination of SolA in sub-nanomolar concentrations in tomato root exudate. This method was used to analyze SolA production in different tomato cultivars and related solanaceous species, including the trap crop Solanum sisymbriifolium . Hatching assays with PCN, Globodera pallida , with root exudates of tomato genotypes revealed a significant positive correlation between SolA concentration and hatching activity. Our results demonstrate that there is natural variation in SolA production within solanaceous species and that this has an effect on PCN hatching. The analytical method we have developed can potentially be used to support breeding for crop genotypes that induce less hatching and may therefore display reduced infection by PCN.
Users ’ Guides to the Medical Literature

CLINICAL SCENARIO At a Monday morning meeting of your hospital’s continuous quality improvement committee, the last agenda item is an initiative to enhance patientclinician communication. The chair proposes that all medical charts include a form to record patient wishes about cardiopulmonary resuscitation and end-of-life care. The committee members agree in principle on the goals of enhanced communication and more accurate documentation of patient preferences. However, you raise potential concerns about how these forms might change the nature of end-of-life decision making and even impair communication. As the meeting draws to a close, you pose a fundamental question to the group for discussion the following week: Could life support preference forms unduly routinize and constrain dialogue between clinicians and patients or family members?
Users' Guides to the Medical Literature: XII. How to Use Articles About Health-Related Quality of Life

CLINICAL SCENARIO You are a physician following a 35-year-old man who has had active Crohn disease for 8 years. The symptoms were severe enough to require resectional surgery 4 years ago, and despite treatment with sulfasalazine and metronidazole, the patient has had active disease requiring oral steroids for the last 2 years. Repeated attempts to decrease the prednisone have failed, and the patient has required doses of greater than 15 mg per day to control symptoms. You are impressed by both the methods and results of a recent article1documenting that such patients benefit from oral methotrexate and suggest to the patient that he consider this medication. When you explain some of the risks of methotrexate, particularly potential liver toxicity, the patient is hesitant. How much better, he asks, am I likely to feel while taking this medication? INTRODUCTION There are 3 reasons we offer treatment to our patients.
Keys to Success: Mindfulness-Based Practices in the Classroom for Stress and Anxiety in Students With Disabilities

Abstract In the wake of COVID-19, the prevalence of stress and anxiety have become exceedingly widespread, and the cumulative effects of enduring multiple ongoing stressors altering every aspect of life have left many struggling to find suitable coping mechanisms. Students, in particular, appear to be operating in perpetual states of stress and anxiety within their constantly shifting school environments. Altering routines or restricting typical activities for students with disabilities can further induce mental stress and escalate challenging behaviors that often lead to poor academic performance. Mindfulness-based practices (MBPs) are one approach that educators can use to teach students with and without disabilities how to manage everyday stressors within the academic environment in more effective ways. Understanding how MBP’s can support student outcomes and best practices for meaningful participation by students with disabilities, educators can provide their students with useful strategies for managing stress and anxiety during school or in daily life. Educators can present traditional approaches, with adaptations as necessary, to facilitate simple classroom-based mindfulness activities for students with disabilities that enhance their ability to effectively navigate everyday stressors. Through relaxation training and self-management techniques, students gain tools to strengthen their emotional and psychological well-being in ways that can also enhance academic outcomes. While the practices are universal and beneficial for all students, they can be exceptionally valuable for students with disabilities. Future research should continue studying the effects of school-based mindfulness practices and strategies for implementation, especially for programs serving students with disabilities.
Double-difference traveltime tomography with edge-preserving regularization and a priori interfaces

S U M M A R Y Conventional traveltime seismic tomography methods with Tikhonov regularization (L2 norm) typically produce smooth models, but these models may be inappropriate when subsurface structure contains discontinuous features, such as faults or fractures, indicating that tomographic models should contain sharp boundaries. For this reason, we develop a doubledifference (DD) traveltime tomography method that uses a modified total-variation regularization scheme incorporated with a priori information on interfaces to preserve sharp property contrasts and obtain accurate inversion results. In order to solve the inversion problem, we employ an alternating minimization method to decouple the original DD tomography problem into two separate subproblems: a conventional DD tomography with Tikhonov regularization and a L2 total-variation inversion. We use the LSQR linear solver to solve the Tikhonov inversion and the split-Bregman iterative method to solve the total-variation inversion. Through our numerical examples, we show that our new DD tomography method yields more accurate results than the conventional DD tomography method at almost the same computational cost.
Codification of mangrove forests management strategies: Case study of Hara Protected Area, Iran

Protection of mangrove forests through the world as the most important centres of biodiversity has been emphasized from past to present, although nowadays these forests are threatened by various human activities. Mangrove forests of Hara Protected Area are not exempt from the mentioned situation. Multi-purpose land uses, existence of peculiar aquatic and terrestrial ecosystems, unique genetic resources and biodiversity of these forests need a particular management. Considering that in this type of coast, stability and survival of any of the human society’s activities (such as fishing, farming, and shipping) cannot be guaranteed without these forests, study ahead aims at suggesting of an appropriate strategy in order to management of the mangrove forests using SWOT analysis. For this purpose, all strengths and weaknesses of the region as well as opportunities and threats being faced these ecosystems, or will be faced in the future, were identified and applied for proposing effective management strategies. Environmental monitoring of human user units surrounding the protected area, preparing of twig harvesting program within the production capacity and harvest period, site selecting and development of mangrove forests located outside the protected area, preparation of environmental management of industries and factories located on the fringe of Hara Protected Area are five strategies considered as important strategies to protect such ecosystems. Enforcement measures should be planned based on them; and eventually, codification of development program was performed for healthcare and water infrastructure purposes in habitats located on the fringe and inside of the protected area.