Miracle-dz/UniFAR-FTUs huggingface dataset:
PERSISTENCE OF MICROBIOLOGICAL AGENTS ON CORRODING BIOFILM IN A MODEL DRINKING WATER SYSTEM FOLLOWING INTENTIONAL CONTAMINATION

Persistence of vegetative and spore forming bacteria on corroded iron surfaces in drinking water was studied using biofilm annular reactors operated under oligotrophic conditions. Reactors were inoculated with either Klebsiella pneumoniae or Bacillus globigii in spore form, and persistence was monitored in the bulk and biofilm phases. In the first phase of experimentation using K. pneumoniae, an initial cell concentration of 10 MPN/ml in the bulk water phase resulted in significantly longer adhesion than initial concentrations one and two orders of magnitude lower. K. pneumoniae cultured in low nutrient growth medium persisted longer in dechlorinated tap water than those cultured in full strength medium. Cell surface charge was more negative under low nutrient conditions, and this influenced electrostatic attraction between the cells and the oxidized iron surface. Cells grown in full strength media persisted longer in water with both low (<0.2 mg/L) and high (>0.5 mg/L) free chlorine residuals. Growth media injected with the cells dechlorinated the water allowing adhesion without inactivation. Microelectrode measurements showed a 40-70% drop in free chlorine from the bulk to the coupon surface, which decreased disinfectant potency against adhered cells. Growth and injection conditions clearly influenced cell adhesion and persistence, but permanent colonization of the corroded iron surface by K. pneumoniae was not observed. The second phase of experimentation examined persistence of B. globigii (in spore form) on corroded iron. Spores were inoculated at 10 CFU/ml in the reactor bulk water, which was dechlorinated for the first experiment. Dechlorination allowed observation of the effect of shear and biofilm sloughing on persistence. Approximately 50% of the spores
Parameter Optimization of Adaptive Flux-Weakening Strategy for Permanent-Magnet Synchronous Motor Drives Based on Particle Swarm Algorithm

Operating in the high-speed range is necessary for high-performance permanent-magnet synchronous motor (PMSM) drives. However, due to the back electromotive force effect, the PMSM is approaching the voltage limit at field decreasing scope. This paper presents a new flux-weakening scheme along with an improved vector control strategy to alleviate the influence of this problem. Control parameters of the anti-windup proportional and integral (AWPI) controller are optimized off-line in relying on an adaptive velocity particle swarm optimization (AVPSO) algorithm. The AVPSO algorithm considers the summation of AWPI measurement error which is the objective function of the optimization problem without knowing the transfer function of the plant. Hence, the tuned flux-weakening controller with a filter is used to set the flux level without saturating the current controllers. Meanwhile, the other controllers of inner and outer loops award a great dynamic and steady-state performance for the PMSM. In the proposed scheme, the flux-weakening control is not dependent on machine parameters that adapts the flux level automatically and provide a fast transition between the constant torque region and the field-weakening region. Effectiveness and advantages of the proposed scheme are presented in this paper through both simulation and experimental results.
First Principles Characterization of $P_{n}V_{m}$ Clusters in Crystalline Silicon

We used ab initio calculations to characterize $P_{n} V_{m} (n= 1 - 6, m = 1, 2)$ clusters in crystalline Si by calculating their formation energy, dipole moment and local vibrational modes. This information served us to discuss which $P_{n}V_{m}$ complexes might be more relevant in doping during epitaxial growth or by ion implantation, and their possible behavior under microwave annealing treatments that was recently demonstrated as a promising process in technological nodes beyond 3 nm.
Transmission Design and Component Allocation for STAR-RIS Assisted NOMA Systems with Direct Link

This paper investigates a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink non-orthogonal multiple access (NOMA) system, consisting of a base station (BS), a STAR-RIS, a transmission user and a reflection user, where STAR-RIS uses mode switching protocols. Under the proposed transmission model, the base station serves the two users via the STAR-RIS and a direct link exists from the base station to the reflection user. By using the Beaulieu series, the statistical property of the Nakagami-m fading cascaded channel is characterized. Based on this, closed-from expressions of outage probability and diversity order for the reflection user and transmission user are obtained, respectively. In addition, to ensure the maximum gain in system throughput, a component allocation scheme is proposed, and the optimal allocation interval between the transmission and reflection components is obtained. Numerical results verify the theoretical analysis and demonstrate the superiority of the proposed scheme in terms of outage probability and system throughput compared to existing schemes.
Laser-light Visual Cueing Shoes with Foot Pressures and Inertial Sensing for Individuals with Parkinson’s Disease*

Gait disorder is a core problem in individuals with Parkinson's disease (PD), including bradykinesia, shuffling steps, festinating gait, and freeze of gait (FOG). Laser-light visual cueing has been demonstrated to be efficient in the mediation of gaits and the reduction in number of FOG episodes. However, previous approaches commonly adopted independent controls of visual cueing on left and right sides which was prone to produce two cues while individual was not in normal walking. In this study, we developed laser-light visual shoes which produced interlaced visual cues for left and right feet in a manner of one-side cueing at a time, solving the aforementioned problem. With parallel measurement of foot inertial data and foot pressures in each shoe, our results showed that the proposed visual cueing made PD individuals in the on-medication condition walk with a longer stance and swing times, that is, they walked more carefully and stable. The proposed approach can also be used to study kinematic and kinetic characteristics of gaits in the off-medication condition to clarify the mediation of visual cueing on motor control of PD individuals.Clinical Relevance— This demonstrates the effect of laser-light visual cueing on gaits in individuals with Parkinson’s disease.
Multiple Superimposed Pilots for Accurate Channel Estimation in Orthogonal Time Frequency Space Modulation

In this paper, we propose an accurate channel estimation scheme for orthogonal time frequency space modulation, in which we embed multiple superimposed pilots (SPs) instead of a single SP used in the conventional scheme. Multiple SPs are used to mitigate data-pilot interference efficiently and improve the accuracy of channel estimation. Our numerical simulations demonstrate that the proposed scheme outperforms the conventional SP-based scheme in terms of bit error rate and normalized mean squared error of channel estimates, indicating the potential for further improvement in spectral efficiency.
Gated CNN-Transformer Network for Automatic Cardiovascular Diagnosis using 12-lead Electrocardiogram

12-lead electrocardiogram (ECG) is a widely used method in the diagnosis of cardiovascular disease (CVD). With the increase in the number of CVD patients, the study of accurate automatic diagnosis methods via ECG has become a research hotspot. The use of deep learning-based methods can reduce the influence of human subjectivity and improve the diagnosis accuracy. In this paper, we propose a 12-lead ECG automatic diagnosis method based on channel features and temporal features fusion. Specifically, we design a gated CNN-Transformer network, in which the CNN block is used to extract signal embeddings to reduce data complexity. The dual-branch transformer structure is used to effectively extract channel and temporal features in low-dimensional embeddings, respectively. Finally, the features from the two branches are fused by the gating unit to achieve automatic CVD diagnosis from 12-lead ECG. The proposed end-to-end approach has more competitive performance than other deep learning algorithms, which achieves an overall diagnostic accuracy of 85.3% in the 12-lead ECG dataset of CPSC-2018.
Differential Corticospinal Excitability and Cortical Functional Connectivity Modulation by Spinal Cord Transcutaneous Stimulation-based Motor Training versus Motor Training alone in Able-bodied and SCI participants: A Multiple Case Study

Spinal cord transcutaneous stimulation (scTS) has shown its potential for boosting motor, sensory, and autonomic function recovery after a spinal cord injury. Despite the demonstrated benefits, little is known about the exact neuromodulatory mechanisms triggered by scTS and the cortex involvement in the beneficial effects observed. Here, we examine the effects of scTS-based motor training and motor training alone on sensorimotor cortical functional connectivity and corticospinal excitability in able-bodied and SCI participants.Clinical Relevance— The results show preliminary evidence of differential cortical involvement and modulation by scTS-based motor training in uninjured and spinal-cord injured individuals. A better understanding of scTS mechanisms of action could help optimize the intervention design and potentiate its benefits.
Language Guided Robotic Grasping with Fine-Grained Instructions

Given a single RGB image and the attribute-rich language instructions, this paper investigates the novel problem of using Fine-grained instructions for the Language guided robotic Grasping (FLarG). This problem is made challenging by learning fine-grained language descriptions to ground target objects. Recent advances have been made in visually grounding the objects simply by several coarse attributes [1]. However, these methods have poor performance as they cannot well align the multi-modal features, and do not make the best of recent powerful large pre-trained vision and language models, e.g., CLIP. To this end, this paper proposes a FLarG pipeline including stages of CLIP-guided object localization, and 6-DoF category-level object pose estimation for grasping. Specially, we first take the CLIP-based segmentation model CRIS as the backbone and propose an end-to-end DyCRIS model that uses a novel dynamic mask strategy to well fuse the multi-level language and vision features. Then, the well-trained instance segmentation backbone Mask R-CNN is adopted to further improve the predicted mask of our DyCRIS. Finally, the target object pose is inferred for the robotics grasping by using the recent 6-DoF object pose estimation method. To validate our CLIP-enhanced pipeline, we also construct a validation dataset for our FLarG task and name it RefNOCS. Extensive results on RefNOCS have shown the utility and effectiveness of our proposed method. The project homepage is available at https://sunqiang85.github. ioIFLarG/.
Assessment of the Interelectrode Distance Effect over the Omnipole with High Multielectrode Arrays

The development of high-density multielectrode catheters has significantly advanced cardiac electrophysiology mapping. High-density grid catheters have enabled the creation of a novel technique for reconstructing electrogram (EGM) signals known as "omnipole," which is believed to be more reliable than other methods, especially in terms of orientation independence. This study aims to evaluate how distance affects the omnipolar reconstruction of EGMs by comparing different configurations. Using an animal set up of perfused isolated rabbit hearts, recordings were taken using an ad hoc high-density epicardial multielectrode catheter. Inter-electrode distances ranging from 1 to 4 mm were analysed for their effect on the quality of resulting EGMs. Two biomarkers were computed to evaluate the robustness of the reconstructions: the areas contained within the bipolar loops and the amplitudes of the omnipoles. We hypothesised that both bipolar and omnipolar electrograms would be more robust at shorter inter-electrode distances. The results showed that an increase in distance triggers an increase in loop areas and amplitudes, which supports the hypothesis. This finding provides a more reliable estimate of wavefront propagation for the cross-omnipolar reconstruction method. These results emphasise the importance of distance in cardiac electrophysiology mapping and provide valuable insights into the use of high-density multielectrode catheters for EGM reconstruction.Clinical Relevance- The results of this study have direct clinical relevance in the application of the described techniques to recording systems in the cardiac electrophysiology laboratory, enabling clinicians to obtain more precise characterisation of signals in the myocardium.
Knowledge and Data Management: The Cornerstone of Effective Organizational Strategy

In the modern business landscape, organizations operate in highly complex and dynamic environments, facing constant challenges in maximizing efficiency, sustainability, and profitability. To achieve success, businesses must harness and leverage knowledge and data effectively. Knowledge and Data Management are important in shaping an organization's strategy. By maintaining a knowledge repository and implementing collaborative platforms, businesses can avoid duplicating efforts, reduce errors, and develop strategies with a long-term vision. The seamless transfer of knowledge from experienced employees to new hires also ensures the preservation of critical expertise, further improving an organization's competitive advantage. The effective utilization of data is crucial to optimize energy consumption, reduce costs, make processes more sustainable, and consequently minimize environmental impact. Energy-intensive industries must carefully monitor and analyse data related to energy consumption, production processes, and equipment efficiency to identify opportunities for improvement, energy management, and industrial management, serving as the base for informed decision-making and sustainable growth. This paper explores the interconnectedness of these key areas and how they can be improved through effective knowledge and data management practices.
Spatiotemporal Excitation Module-based CNN for Diagnosis of Hepatic Malignancy in Four-phase CT Images

Liver cancer is a part of the common causes of cancer death worldwide, and the accurate diagnosis of hepatic malignancy is important for effective next treatment. In this paper, we propose a convolutional neural network (CNN) based on a spatiotemporal excitation (STE) module for identification of hepatic malignancy in four-phase computed tomography (CT) images. To enhance the display detail of lesion, we expand single-channel CT images into three channels by using the channel expansion method. Our proposed STE module consists of a spatial excitation (SE) module and a temporal interaction (TI) module. The SE module calculates the temporal differences of CT slices at the feature level, which is used to excite shape-sensitive channels of the lesion features. The TI module shifts a portion of the channels in the temporal dimension to exchange information among the current CT slice and adjacent CT slices. Four-phase CT images of 398 patients diagnosed with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are used for experiments and five cross-validations are performed. Our model achieved average accuracy of 85.00% and average AUC of 88.91% for classifying HCC and ICC.Clinical Relevance— The proposed deep learning-based model can perform HCC and ICC recognition tasks based on four-phase CT images, assisting doctors to obtain better diagnostic performance.
Real-time Tracking of Handheld Object from Color or Depth Images

Object tracking during rehabilitation could help a therapist to evaluate a patient’s movement and progress. Hence, we present an image-based method for real-time tracking of handheld objects due to its ease of use and availability of color or depth cameras. We use an efficient projective point correspondence method and generalize the use of precomputed spare viewpoint information to allow real-time tracking of a rigid object. The method runs at more than 30 fps on a CPU while achieving submillimeter accuracy on synthetic datasets and robust tracking on a semi-synthetic dataset.Clinical relevance Real-time, accurate, and robust tracking of an object using an image-based method is a promising tool for rehabilitation applications as it is practical for clinical settings.
Acoustic emissions and age-related changes of the knee

Acoustic emission (AE) monitoring is currently being widely investigated as a diagnostic tool in orthopedics, in particular for osteoarthritis (OA) diagnostics. Considering that age is one of the main risk factors for OA, investigating age-related changes in joint AEs might provide an additional incentive for further studies and consequent translation to clinical practice. The aim of this study is to investigate age-related changes in knee AE and determine AE hit definition modes as well as AE hit parameters that allow for improved age group differentiation. Knee AEs were recorded from 51 participants in two age groups (18-35 and 50-75 years old) whilst cycling with 30 and 60 rpm cadence. Two AE sensors with 15-40 kHz and 100-450 kHz frequency ranges were used, and three AE event detection modes investigated. Additionally, participants’ Knee Osteoarthritis Outcome Scores (KOOS) were recorded. Low frequency sensors (15-40kHz) and hit modes with shortened hit and peak definition times showed the potential to distinguish between age groups. Moreover, a weak correlation was found between only three parameters (AE event median duration, rise time, and signal strength) and age, indicating that changes in joint AE are most likely associated with pathological changes rather than physiological ageing within the healthy norm.Clinical Relevance— the use of AE monitoring was examined in the context of age-related changes in knee health. The study indicates the potential for knee AE monitoring to be used as a quantitative measure of pathological changes in the knee status.
Risk-Sensitive Mobile Robot Navigation in Crowded Environment via Offline Reinforcement Learning

Mobile robot navigation in a human-populated environment has been of great interest to the research community in recent years, referred to as crowd navigation. Currently, offline reinforcement learning (RL)-based method has been introduced to this domain, for its ability to alleviate the sim2real gap brought by online RL which relies on simulators to execute training, and its scalability to use the same dataset to train for differently customized rewards. However, the performance of the navigation policy suffered from the distributional shift between the training data and the input during deployment, since when it gets an input out of the training data distribution, the learned policy has the risk of choosing an erroneous action that leads to catastrophic failure such as colliding with a human. To realize risk sensitivity and improve the safety of the offline RL agent during deployment, this work proposes a multipolicy control framework that combines offline RL navigation policy with a risk detector and a force-based risk-avoiding policy. In particular, a Lyapunov density model is learned using the latent feature of the offline RL policy and works as a risk detector to switch the control to the risk-avoiding policy when the robot has a tendency to go out of the area supported by the training data. Experimental results showed that the proposed method was able to learn navigation in a crowded scene from the offline trajectory dataset and the risk detector substantially reduces the collision rate of the vanilla offline RL agent while maintaining the navigation efficiency outperforming the state-of-the-art methods.
Analysis and Clustering of Upper Limb Motion during the Hand Dexterity Pegboard Test using Inertial Sensor Systems

Maintaining hand and upper limb mobility is important from the viewpoint of freedom in daily life and high performance in work. Few studies on the mobility and dexterity of the upper limb have focused on detailed hand and finger movements. Therefore, we measured the motion of the upper limbs during a general hand dexterity pegboard test using inertial sensor systems and our previous measuring method. To clarify the characteristics of each purpose of motion, we divided the peg-in-hole motion in the pegboard test into its three sections, focusing on two sections: the pinch section, and the carry and insert section. In addition, the obtained joint angles were grouped into arm group and finger group, and singular value decomposition was performed for each joint group in each section. By clustering the decomposition results across five subjects’ multiple right and left arm tests, and averaging the singular vectors in the same cluster, the joint distributions and combinations could be clarified. In addition, by recalculating joint angles from averaged SVD results and applying them to the rigid link model, we obtained motion animation with characteristics that made it possible to more clearly understand the requirements for greater dexterity. These results suggested high dexterity motion characteristics in the pinch section, and the carry and insert section of the pegboard test.
Effect of the Aperiodic Electrical Stimulation on the Visual Cortical Neuronal Response *

In patients with retinal degenerative illnesses such as retinitis pigmentosa and age-related macular degeneration, retinal prosthesis shows the potential to restore partial vision. The natural stimuli are the aperiodic events distributed across a short time span. However, most studies commonly used periodic stimulation. Even though some in vitro studies explored the effect of aperiodic retinal stimulation on the retina ganglion cells' membrane potential, it still needs to understand how the aperiodic electrical stimulation on the retina affects the response in visual cortex. This study investigated how aperiodic retinal stimulation affects the electrically evoked cortical response compared with periodic stimulation in Sprague Dawley (SD) rats. We found that the aperiodic retinal stimulation evoked a significantly higher spike rate than the periodic pattern, especially at high frequencies (10 and 20 Hz). The spike rates showed a more significant difference between the periodic and 10% noise stimulation (P = 0.0013 at 20 Hz, two-tailed paired t-test) at 20 Hz stimulation. Regarding the temporal precision of responses, the responses to aperiodic stimulation showed higher temporal precision compared to periodic stimulation. The response to some stimulation pulse numbers under 10 and 20 Hz 50% noise and Poisson pattern stimulation was higher than the response to the first pulse. However, at the same frequency, the response to some stimulation pulse numbers under periodic stimulation was lower than the response to the first pulse. These findings raised a possible way to increase the response level and the temporal precision of the electrically evoked response.Clinical Relevance— This suggests that using aperiodic stimulation in retinal prostheses can increase electrically evoked response levels and temporal precision.