Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Modeling and Identification of Wearable Lower Limb Rehabilitation Exoskeleton Robots

Wearable lower limb rehabilitation exoskeleton robots play a positive role in lower limb rehabilitation training and assistance walking for patients with lower limb disorders. Firstly, the 3 degrees of freedom link-based dynamic model with friction is established by the Lagrange method. Secondly, a parameter identification experiment is designed based on a lower limb exoskeleton prototype. It contains three parts: static experiment of discrete controlled by specified position, dynamic experiment of uniform speed motion controlled by linear excitations, and dynamic experiment of continuous motion controlled by sinusoidal excitations. During the process of experiment, several terms in joint output torque expression are set to zero for simplicity of calculation, and leave the parameters to be identified. Furthermore, based on the acquired actuator torque data, nine parameters are identified by plotting and curves fitting with the least square method, including inertial parameters, static friction and Coulomb viscous friction. Finally, the parameter identification results are verified through comparing the torque measured by experiment and estimated by model.
Machine learning-enabled real-time detection of cloud and aerosol layers using airborne lidar

Lidar profiling of the atmosphere provides information on existence of cloud and aerosol layers and the height and structure of those layers. Knowledge of feature boundaries is a key input to assimilation models. Moreover, identifying feature boundaries with minimal latency is essential to impact operational assimilation and real-time decision making. Using advanced convolution neural network algorithms, we demonstrate real-time determination of atmospheric feature boundaries using an airborne backscatter lidar. Results are shown to agree well with traditional processing methods and are produced with higher horizontal resolution than the traditional method. Demonstrated using airborne lidar, the algorithms and process are extendable to real-time generation of data products from a future spaceborne sensor.
The phylogeny and distribution of Wolbachia in two pathogen vector insects, Asian citrus psyllid and Longan psyllid

Background Wolbachia is the most abundant bacterial endosymbiont among insects. It can play a prominent role in the development, reproduction and immunity of its given insect host. To date, Wolbachia presence is well studied within aphids, whiteflies and planthoppers, but relatively few studies have investigated its presence in psyllids. Methods Here, the infection status of Wolbachia in five species of psyllid, including Asian citrus psyllid Diaphorina citri and longan psyllid Cornegenapsylla sinica was investigated. The phylogenetic relationships of different Wolbachia lines and their infection density and patterns in D. citri and C. sinica from different countries was also examined. Results The infection rates of Wolbachia in D. citri and C. sinica were both 100%, and their sequencing types are ST173 and ST532 respectively. Phylogenetic analysis revealed that the Wolbachia lines in D. citri and C. sinica both belong to the Con subgroup of Wolbachia supergroup B. In addition, Wolbachia displayed a scattered localization pattern in the 5th instar nymphs and in the reproductive organs of both D. citri and C. sinica but differed in other tissues; it was highest in the midgut, lowest in the salivary glands and medium in both the testes and ovaries. Conclusion Our findings assist in further understanding the coevolution of Wolbachia and its psyllid hosts. Given that Wolbachia could play an important role in insect pest control and pathogen transmission inhibition, our findings may also provide new insights for development of control strategies for D. citri and C. sinica.
The acoustics of “black smoker” hydrothermal plumes

High‐temperature “black smoker” hydrothermal plumes occur when seawater that has penetrated into the oceanic crust and assimilated heat from magma is discharged from vents located at the axis of a mid‐ocean ridge. The acidic, metal‐rich, discharge mixes with alkaline, oxidizing seawater, and a fine suspension of sulfide particles is precipitated and convected by the flow. Vents fields have now been found at both fast and slow seafloor spreading centers and may be a ubiquitous feature of mid‐ocean ridges. A review of the progress made in using underwater acoustics to study black smoker plumes is presented. Both active and passive techniques are being investigated. Active techniques involve a high‐frequency monostatic sonar mounted on a submersible. Analysis of the amplitude and phase of the signal backscattered from the plume provides information about the three‐dimensional shape of the plume as well as estimates of the flow‐velocity field of the discharging fluid. Passive techniques use bottom‐mounted hydrophones to listen to the very low‐frequency, hydrodynamic noise generated by a plume. These noise signatures have potential use in locating, characterizing, and monitoring plume sites and in determining the contribution plume noise makes to the overall ambient noise field in the ocean.
Espousing AI to Enhance Cost-Efficient and Immersive Experience for Human Computer Interaction

—Because of recent technological and interface advancements in the field, the virtual reality (VR) movement has entered a new era. Mobility is one of the most crucial behaviours in virtual reality. In this research, popular virtual reality mobility systems are compared, and it is shown that gesture control is a key technology for allowing distinctive virtual world communication paradigms. Gesture based movements are very beneficial when there are a lot of spatial restrictions. With a focus on cost-effectiveness, the current study introduces a gesture-based virtual movement (GVM) system that eradicates the obligation for expensive hardware/controllers for virtual world mobility (i.e., walk/ jump/ hold for this research) using artificial intelligence (AI). Additionally, the GVM aims to prevent users from becoming dizzy by allowing them to change the trajectory by simply turning their head in the intended direction. The GVM was assessed on its interpreted realism, presence, and spatial drift in the actual environment in comparison to the state-of-the-art techniques. The results demonstrated how the GVM outperformed the prevailing methodologies in a number of common interaction components. Additionally, the empirical analysis showed that GVM offers customers a real-time experience with a latency of ~65 milliseconds.
X-ray-Fluorescence-Based Screening Method for Uranium in Contaminated Brackish Water Using Graphene Oxide Nanosheets

In the event of uranium release into the environment due to an accident, confirming the presence of uranium contamination is difficult because uranium is a naturally occurring element. In this study, we developed a method based on X-ray fluorescence (XRF) for the facile screening of uranium in brackish water samples in the event of an accident in a coastal area. Graphene oxide nanosheets were added to uncontaminated brackish water sampled from different sites to adsorb the uranium present in the samples, if any. The graphene oxide nanosheets were then collected using a membrane filter and analyzed using XRF. The results revealed that the signal intensity of the U Lα peak was proportional to the salinity. Hence, uranium contamination could be confirmed when the intensity of the U Lα peak was significantly greater than that derived from the background uranium content, as estimated from the salinity value. Thus, in the event of an accident, the salinity of the collected brackish water should be measured, and XRF analysis should be performed using our developed method. This method is useful for screening brackish water for uranium contamination.
Multi-Agent Reinforcement Learning Reward Engineering via Stochastic Game Evaluation

With the proliferation of Reinforcement Learning (RL) algorithms across multiple applications, the design of appropriate reward mechanisms that elicit desired behaviours becomes crucial. Reward setting is made more difficult in the multi-agent case where cooperative, competitive or mixed interactions between agents may lead to differing outcomes. In this paper, we formulate the reward engineering of multi-agent reinforcement learning approaches via game theoretic models. This approach is used to analyze the overall team reward when choosing one game theoretic structure over another. An empirical analysis is provided over game theoretic simulators that demonstrate co-operative game rewards improve the rewards by upto 30%. This formulation may be applied to a variety of use cases within logistics, transport and telecommunications domains that employ multi-agent techniques.
Convex linear combinations of compositions

SUMMARY When a sampled target composition is suspected of being a mixture of different compositions from a number of independent sources the question of the nature of the mixing mechanism arises. For the resolution of this question several models involving convex linear mixtures of compositions are considered and in particular the distributional problem of describing the pattern of variability of the target compositions, given information about the source distributions, is resolved in terms of approximations involving logistic normal and logistic skew normal distributions. The quality of these approximations is shown to be satisfactory through a series of simulations briefly reported. The modelling and subsequent statistical inference are motivated by an illustrative application to investigating the nature of pollution at three fishing locations in a Scottish loch.
Self-energy effects of the optical phonons of heavily doped p − G a A s and p − G e

We have studied, using first-order Raman scattering, the self-energy of the long-wavelength transverse-optical phonons of $p\ensuremath{-}\mathrm{G}\mathrm{a}\mathrm{A}\mathrm{s}$ as well as the optical phonons of $p\ensuremath{-}\mathrm{G}\mathrm{e}$. The phonon Raman lines of $p\ensuremath{-}\mathrm{G}\mathrm{a}\mathrm{A}\mathrm{s}$ are shifted to lower energies and become broadened compared with those of the pure material. They also show an asymmetrical line shape characteristic of a Fano-type discrete-continuum interference. The electronic continuum interacting with the transverse-optical phonons of $p\ensuremath{-}\mathrm{G}\mathrm{a}\mathrm{A}\mathrm{s}$ has been characterized as indirect intraband transitions with initial and final states in the heavy-hole valence band. The phonon Raman lines of $p\ensuremath{-}\mathrm{G}\mathrm{e}$ display effects similar to those of $p\ensuremath{-}\mathrm{G}\mathrm{a}\mathrm{A}\mathrm{s}$. However, the frequency shifts and broadening depend on the wavelength of the laser light used to excite the spectra. Intra- and inter-valence-band transitions provide the continuum responsible for the Fano asymmetry. The dependence of the self-energy of the phonons on the incident photon energy is attributed to the dispersive nature of the intraband electron-phonon coupling with the heavy-hole band. By fitting the frequency shifts of the phonon Raman lines measured for different laser wavelengths to the theory a value of 32 \ifmmode\pm\else\textpm\fi{} 6 eV is obtained for the deformation-potential constant ${d}_{0}$ of Ge.
A Chinese Calligraphy-Writing Robotic System Based on Image-to-Action Translations and a Hypothesis Generation Net

This paper attempts to use a delta robot’s structure and reliable coordinates to develop a self-learning Chinese calligraphy-writing system that requires precise control. Ideally, to achieve human-like behavior, a delta robot can learn stroke trajectories autonomously and present the stroke beauty of calligraphy characters. Unfortunately, state-of-the-art approaches have not yet considered the presentation of stroke beauty resulting from angles of rotation and tilt of the brush. This paper presents an integrated system consisting of a stroke processing module, a hypothesis generation net (HGN) learning model with self-learning capability, a delta robot, and an image capture module. Our approach utilizes both the stroke trajectories from the stroke processing module and angles information from the HGN learning model to automatically produce five degrees of freedom action instructions. Based on the instructions, the delta robot completes calligraphy writing. Then, the image capture module provides feedback to the writing system for error calculation and coordinate correction. We utilize the mean absolute percentage error to verify the performance of the writing results. A correction algorithm and linear regression were used to improve the error correction results (less than 2% error). After several cycles, the written results approached the target sample finally. Consequently, the written results produced by the delta robot prove that our proposed system with learning ability can write Chinese calligraphy aesthetically.
EDXRS study of a new collimator for enhanced scattering techniques

This paper explores the possibilities offered by basic photon scattering phenomenology in the study of a new collimator devised to enhance the collection of photons undergoing a single scattering in a volume of interest. In order to maximize the spatial resolution, a 90° scattering geometry was considered. The collimator was composed of two parallel plates perpendicular to both the detector surface and the plane of first scattering defined by the trajectories of the incident and scattered photons. In this way, photons whose azimuthal angle ranges over a wide interval but with the same scattering angle could reach the detector. Experimental comparison between this collimation and the usual narrow one was performed at a photon energy of 59.54 keV under the same sample and irradiation geometry. The first results showed that an enhancement of about 10-fold could be reached with regard to the main Compton and coherent peaks. On the other hand, the multiple scattering fraction only rose to a maximum of 20% in the worst case, as compared with that observed in classical highly collimated geometry. The usual analytical capacity offered by the scattering techniques was demonstrated to be still valid in this situation.
A whole cell fluorescence quenching-based approach for the investigation of polyethyleneimine functionalized silver nanoparticles interaction with Candida albicans

The antimicrobial activity of metal nanoparticles can be considered a two-step process. In the first step, nanoparticles interact with the cell surface; the second step involves the implementation of the microbicidal processes. Silver nanoparticles have been widely explored for their antimicrobial activity against many pathogens. The interaction dynamics of functionalized silver nanoparticles at the biological interface must be better understood to develop surface-tuned biocompatible nanomaterial-containing formulations with selective antimicrobial activity. Herein, this study used the intrinsic fluorescence of whole C. albicans cells as a molecular probe to understand the cell surface interaction dynamics of polyethyleneimine-functionalized silver nanoparticles and antifungal mechanism of the same. The results demonstrated that synthesized PEI-f-Ag-NPs were ~ 5.6 ± 1.2 nm in size and exhibited a crystalline structure. Furthermore, the recorded zeta potential (+18.2 mV) was associated with the stability of NPS and shown a strong electrostatic interaction tendency between the negatively charged cell surface. Thus, rapid killing kinetics was observed, with a remarkably low MIC value of 5 μg/mL. PEI-f-Ag-NPs quenched the intrinsic fluorescence of C. albicans cells with increasing incubation time and concentration and have shown saturation effect within 120 min. The calculated binding constant (Kb = 1 × 105 M−1, n = 1.01) indicated strong binding tendency of PEI-f-Ag-NPs with C. albicans surface. It should also be noted that the silver nanoparticles interacted more selectively with the tyrosine-rich proteins in the fungal cell. However, calcofluor white fluorescence quenching showed non-specific binding on the cell surface. Thus, the antifungal mechanisms of PEI-f-Ag-NPs were observed as reactive oxygen species (ROS) overproduction and cell wall pit formation. This study demonstrated the utility of fluorescence spectroscopy for qualitative analysis of polyethyleneimine-functionalized silver nanoparticle interaction/binding with C. albicans cell surface biomolecules. Although, a quantitative approach is needed to better understand the interaction dynamics in order to formulate selective surface tuned nanoparticle for selective antifungal activity.
Platelet pannexin-1 channels modulate neutrophil activation and migration but not the progression of abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a common disease and highly lethal if untreated. The progressive dilatation of the abdominal aorta is accompanied by degradation and remodeling of the vessel wall due to chronic inflammation. Pannexins represent anion-selective channels and play a crucial role in non-vesicular ATP release to amplify paracrine signaling in cells. Thus, pannexins are involved in many (patho-) physiological processes. Recently, Panx1 channels were identified to be significantly involved in abdominal aortic aneurysm formation through endothelial derived Panx1 regulated inflammation and aortic remodeling. In platelets, Panx1 becomes activated following activation of glycoprotein (GP) VI. Since platelets play a role in cardiovascular diseases including abdominal aortic aneurysm, we analyzed the contribution of platelet Panx1 in the progression of abdominal aortic aneurysm. We detected enhanced Panx1 plasma levels in abdominal aortic aneurysm patients. In experimental abdominal aortic aneurysm using the pancreatic porcine elastase (PPE) mouse model, a major contribution of platelet Panx1 channels in platelet activation, pro-coagulant activity of platelets and platelet-mediated inflammation has been detected. In detail, platelets are important for the migration of neutrophils into the aortic wall induced by direct cell interaction and by activation of endothelial cells. Decreased platelet activation and inflammation did not affect ECM remodeling or wall thickness in platelet-specific Panx1 knock-out mice following PPE surgery. Thus, aortic diameter expansion at different time points after elastase infusion of the aortic wall was unaltered in platelet-specific Panx1 deficient mice suggesting that the modulation of inflammation alone does not affect abdominal aortic aneurysm formation and progression. In conclusion, our data strongly supports the role of platelets in inflammatory responses in abdominal aortic aneurysm via Panx1 channels and adds important knowledge about the significance of platelets in abdominal aortic aneurysm pathology important for the establishment of an anti-platelet therapy for abdominal aortic aneurysm patients.
Green finance: current status, development, and future course of actions in China

This study explores China’s implementation of a green financial system to combat the urgent climate emergency and achieve sustainable development amid rapid industrial and urban development. The research examines the current state of green finance in China, including its development, challenges, and future directions. The study finds that China has made significant progress in establishing green finance, being a leader in green financial products like green bonds and credits. Moreover, as suggested by some literature, green finance products could lead to CO2 reductions. However, challenges still remain, such as promoting green insurance, establishing a unified green financial system, and addressing the lack of asymmetry in green financial information. This research provides an essential contribution to the growing body of knowledge on green finance, offering useful insights into China’s unique approach to sustainable development through green finance.
Experimental evidence on the productivity effects of generative artificial intelligence

We examined the productivity effects of a generative artificial intelligence (AI) technology, the assistive chatbot ChatGPT, in the context of midlevel professional writing tasks. In a preregistered online experiment, we assigned occupation-specific, incentivized writing tasks to 453 college-educated professionals and randomly exposed half of them to ChatGPT. Our results show that ChatGPT substantially raised productivity: The average time taken decreased by 40% and output quality rose by 18%. Inequality between workers decreased, and concern and excitement about AI temporarily rose. Workers exposed to ChatGPT during the experiment were 2 times as likely to report using it in their real job 2 weeks after the experiment and 1.6 times as likely 2 months after the experiment. Description Editor’s summary Automation has historically displaced human workers in factories (e.g., automotive manufacturing) or in performing routine computational tasks. Will generative artificial intelligence (AI) tools such as ChatGPT disrupt the labor market by making educated professionals obsolete, or will these tools complement their skills and enhance productivity? Noy and Zhang examined this issue in an experiment that recruited college-educated professionals to complete incentivized writing tasks. Participants assigned to use ChatGPT were more productive, efficient, and enjoyed the tasks more. Participants with weaker skills benefited the most from ChatGPT, which carries policy implications for efforts to reduce productivity inequality through AI. —EEU The assistive chatbot ChatGPT raises productivity in professional writing tasks and reduces productivity inequality.
A Multicomponent Iterative Method for Efficient Source Reconstruction Based on Magnitude-Only and Single-Plane Near-Field Scanning

A multicomponent iterative method is proposed for efficient source reconstruction based on magnitude-only and single-plane near field scanning. Compared with the conventional method filling the multicomponent data into a whole transformation matrix, the multicomponent iterative method can save huge calculation time by evenly reducing the size of the transformation matrix into magnetic group and electric group, without losing solution accuracy of the equivalent dipole moments. The accuracy and efficiency of the proposed source reconstruction is verified on a simulated patch antenna model. The proposed method is also applied to efficiently find the equivalent sources and reconstruct the surface electromagnetic field of a practical serpentine line. For a larger transformation matrix (depending on the factors such as the scanned field components, scanned points, and dipole numbers), the proposed iteration method shows higher efficiency.
Allowed and forbidden light in near-field optics. II. Interacting dipolar particles

Near-field optical imaging mechanisms are investigated on an elementary level with the use of a coupled dipole formalism. Both optical probe and sample particles are considered as single dipolar cells. The sample particles are located on the surface of a layered substrate, and the optical probe is guided over the sample at constant height. The theory of the coupled dipole formalism with the use of Green's functions of a layered reference system is outlined, and asymptotic forms for far-field radiation are derived. Depending on the direction of observation, the recorded far-field radiation contains different information. It is shown that radiation emitted into the lower half-space at angles within the critical angle of total internal reflection (allowed light) provides reliable images that are only weakly correlated to the topography of the sample. Higher resolution is achieved with the radiation emitted at supercritical angles (forbidden light), but the high sensitivity to topographical variations makes image interpretation difficult. In order to image the optical properties of the sample, it is shown to be unfavorable to include the forbidden light in the optical signal.