Power Assistance Research Articles

Ru/Pt@BSA nanoparticles for efficient photo-catalytic oxidation of NAD(P)H and targeted cancer treatment under hypoxic conditions

Photo-catalytic oxidation of intracellular nicotinamide adenine dinucleotide (2′-phosphate) (NAD(P)H) has attracted much attention for cancer therapy. However, the general oxygen-dependent mechanism heavily depresses the efficacy in hypoxic tumors. To solve this problem, herein platinum nanoparticles (Pt NPs) with catalase-like (CAT-like) and catalytic H2 evolution activities were introduced as a powerful assistant to enhance the photo-catalytic NAD(P)H oxidation of Ru1 ([Ru(phen)2(PIP-OCH3)]2+, phen = 1,10-phenanthroline, PIP-OCH3 = 2-(4‑methoxy phenyl)-1H-imidazo[4,5-f][1,10]phenanthroline) under hypoxic and even oxygen-free conditions. Firstly, Pt NPs can transform the original and in situ formed H2O2 once again into O2 by the CAT-like activity, thus relieving tumor hypoxia and realizing cyclic utilization (at least in part) of the precious oxygen in hypoxia. Secondly, Pt NPs can also be served as H2 evolution catalysts while using Ru1 as the photosensitizer and NAD(P)H as the electron and proton donor. In this process, NAD(P)H is oxidized without the participation of oxygen, which can provide an effective way even under oxygen-free conditions. Via co-encapsulation of Ru1 and Pt NPs in bovine serum albumin (BSA) with tumor targeting ability, the resultant Ru/Pt@BSA could photo-catalyze intracellular NAD(P)H oxidation under hypoxic conditions (3% O2), and exhibited an efficient and selective anticancer activity both in vitro and in vivo. Our results may provide new sights for efficient and targeted cancer treatment under hypoxic conditions.

UKF-Based Observer Design for the Electric Brake Booster in Situations of Disturbance

The motor-driven electric brake booster (E-Booster) can replace the traditional vacuum booster to realize the braking power assistance and active braking. Independent of extra sensors, this paper proposes a full-state observer for E-Booster based on Unscented Kalman Filter (UKF) in the presence of a driver’s input force disturbance. The electro-hydraulic system is first modeled, which includes a nonlinear hydraulic model and the reaction disk’s rubber model. The pre-compression is designed to produce linear power assistance based on the properties of rubber material. With the existence of the disturbance, the linear quadratic regulator (LQR) algorithm is used to track the pre-compression of the reaction disk so that E-Booster is developed into a closed-loop system to achieve power assistance. The proposed UKF observer can online estimate the states considering the controller input and disturbance input. To reduce the process error, the hydraulic p-V characteristic is fitted using the recursive least squares (RLS) method before observation. Furthermore, the simulation test and vehicle tests are performed to validate the observation effect. In the closed-loop test, UKF decreases residual error by 16% when compared to the typical Extended Kalman Filter (EKF). The simulation results remain consistent with the experimental results, demonstrating the effectiveness of the proposed method.

Shared e-scooter micromobility: review of use patterns, perceptions and environmental impacts

ABSTRACT Recently, a new shared micromobility service has become popular in cities. The service is supplied by a new vehicle, the e-scooter, which is equipped with a dockless security system and electric power assistance. The relatively unregulated proliferation of these systems driven by the private sector has resulted in numerous research questions about their repercussions. This paper reviews scientific publications as well as evaluation reports and other technical documents from around the world to provide insights about these issues. In particular, we focus on mobility, consumer perception and environment. Based on this review, we observe several knowledge needs in different directions: deeper comprehension of use patterns, their function in the whole transport system, and appropriate policies, designs and operations for competitive and sustainable shared e-scooter services.

Difficulties and Solutions of Intelligent Fitness Assistance Device to Help Students with Weak Physical Fitness

Intelligent fitness assistance device is an intelligent (electromechanical or other power) assistance device. According to the actual situation of students with weak physical fitness, the device gives intelligent help. In order to reduce the difficulty of fitness and improve the fitness efficiency, this study introduced the relevant theories and technologies in the field of intelligent electromechanical into students' fitness. We collected and sorted out corresponding theories and technologies from the field of physical fitness and the field of intelligent electromechanical assistance, sought the integration point of the two interdisciplinary disciplines, took electromechanical assistance as the main technical support, take students with weak physical fitness as the service object, and sorted out and innovated intelligent fitness assistance devices mainly under the guidance and help of experts in physical education and physics. Literature research method, interdisciplinary research method, simulation method (model method) and other methods were used to collect and sort out corresponding technologies based on students' fitness needs and innovation. Aiming at the problems of weak physical fitness and insufficient strength and endurance of students, an intelligent booster device was designed to scientifically improve the fitness level and effect. In the current sports environment of weak national physique, declining physique of students of all ages, and extensive restrictions on students' fitness, sports intelligent fitness assistance device is time-saving and efficient and has strong discipline and technology integration. However, there are integration difficulties between the two. The sports intelligent fitness assistance device promotes the subjective development of students' fitness in multi-dimensional and multi-way from the objective level of hardware, in terms of fitness willingness, operation feasibility Safety controllability and long-term guarantee, which has strong practical significance. The test design is a push up intelligent electromechanical booster device, which is trial manufactured by using electromechanical theory and mature ready-made machines (this time, due to limited funds and technical level, the variable frequency lifting motor that is easy to purchase and test is selected); Computer programming based on single chip microcomputer is used to realize the integration of computer knowledge and sports knowledge. Generally speaking, the concept of this study is basically realized, and the device basically runs normally. In the follow-up research and exploration, the scientific and technological level of research, design and manufacturing will be improved, and more scientific, efficient, and safe devices or equipment will be provided for students' fitness.

An Origami-Inspired Negative Pressure Folding Actuator Coupling Hardness with Softness

Soft actuators have a high potential for the creative design of flexible robots and safe human–robot interaction. So far, significant progress has been made in soft actuators’ flexibility, deformation amplitude, and variable stiffness. However, there are still deficiencies in output force and force retention. This paper presents a new negative pressure-driven folding flexible actuator inspired by origami. First, we establish a theoretical model to predict such an actuator’s output force and displacement under given pressures. Next, five actuators are fabricated using three different materials and evaluated on a test platform. The test results reveal that one actuator generates a maximum pull force of 1125.9 N and the maximum push force of 818.2 N, and another outputs a full force reaching 600 times its weight. Finally, demonstrative experiments are conducted extensively, including stretching, contracting, clamping, single-arm power assistance, and underwater movement. They show our actuators’ performance and feature coupling hardness with softness, e.g., large force output, strong force retention, two-way working, and even muscle-like explosive strength gaining. The existing soft actuators desire these valuable properties.

Control Strategy for Power Assist Upper Limb Rehabilitation Robot with the Therapist’s Motion Intention Prediction

Currently, fully automated rehabilitation robots can assist therapists in providing rehabilitation therapy, hence the patients could get hurt. On the other hand, manual treatment may cause less patient injury but it is tiresome, and there are not enough therapists in most countries. Power assist rehabilitation robots can support the therapists in conducting the treatment and may help to alleviate this problem. The goal of this study is to develop a control strategy for the robot to assist the therapist’s movement in a power assist upper limb rehabilitation treatment. The system combines the advantages of robotic and manual rehabilitation therapy. Torque and position sensors fitted on the power assist upper limb rehabilitation robot arm are used for motion intention estimation. The amount of angular velocity necessary to be delivered to the feedback controller will be determined by predicting the therapist‘s motion intention using the impedance control method. The resulting velocity from the motion intention estimator is incorporated into the Sliding Mode Control - Function Approximation Technique (SMC-FAT) based adaptive controller. The SMC-FAT based adaptive controller in the feedback loop, overcomes the uncertain parameters in the combination of the robot and the human arm. The motion intention estimator forecasts the movement of therapists. The proposed controller is used to regulate elbow flexion and extension motion on a power assist upper limb rehabilitation robot with one degree of freedom (DOF). The proposed control system has been tested using MATLAB simulation and hardware experimental tests. The outcomes demonstrate the effectiveness of the proposed controller in directing the rehabilitation robot to follow the desired trajectory based on the therapist's motion intention, with maximum errors of 0.002rad/sec, 0.005rad/sec and 0.02rad/sec for sinusoidal, constant torque values, and hardware experiment respectively. ABSTRAK: Pada masa ini, robot rehabilitasi automatik sepenuhnya dapat membantu ahli terapi dalam menyediakan terapi pemulihan, tetapi pesakit berkemungkinan tercedera. Sebaliknya, rawatan manual berkemungkinan mengurangkan kecederaan pesakit tetapi ia memenatkan, dan terdapat kurang ahli terapi yang mencukupi di kebanyakan negara. Robot pembantu rehabilitasi dapat membantu ahli terapi dalam menjalankan pemulihan dan mengurangkan masalah ini. Sistem ini menggabungkan kelebihan terapi pemulihan robotik dan manual. Alat pengesan tork dan kedudukan diletakkan pada anggota atas lengan robot rahabilitasi yang digunakan bagi mengesan anggaran jarak pergerakan ahli terapi. Anggaran halaju sudut diperlukan bagi kawalan gerak balas dan dapat diketahui melalui anggaran niat gerakan ahli terapi menggunakan kaedah kawalan impedans. Halaju yang terhasil daripada anggaran niat gerakan diadaptasi ke dalam pengawal adaptif berasaskan Kawalan Mod Gelongsor - Teknik Anggaran Fungsi (SMC-FAT). Pengawal penyesuaian berasaskan SMC-FAT dalam gelung maklum balas, mengatasi parameter yang tidak pasti dalam gabungan robot dan lengan manusia. Penganggar niat gerakan meramalkan gerakan ahli terapi. Pengawal yang dicadangkan digunakan bagi mengawal lenturan siku dan gerakan lanjutan pada robot rehabilitasi dengan satu darjah kebebasan (DOF). Sistem kawalan yang dicadangkan telah diuji menggunakan simulasi MATLAB dan ujian eksperimen perkakasan. Dapatan kajian menunjukkan keberkesanan pengawal yang dicadangkan dalam mengarahkan robot rehabilitasi mengikut trajektori yang dikehendaki berdasarkan niat gerakan ahli terapi, dengan ralat maksimum masing-masing 0.002rad/s dan 0.005rad/s bagi sinusoidal, nilai tork malar, dan eksperimen perkakasan masing-masing.

Эффекты мощного ультразвукового воздействия при прессовании на структурные и люминесцентные свойства YAG : Ce-=SUP=-3+-=/SUP=- керамики

Yttrium aluminum garnet (YAG) ceramics doped with Ce3+ was prepared by dry compaction using the common uniaxial pressing under powerful ultrasound assistance (PUA). The structure and luminescent properties of sintered ceramics were investigated. It was demonstrated that the powerful ultrasonic assistant during the dry pressing has a positive effect on the structural and luminescent characteristics of YAG: Ce3+ ceramics. The sintered material consolidation is very intensive accompanied by a decrease in the number of pores and grain growth at a sintering temperature of 1650 ºC. The luminous efficiency of YAG:Ce3+ ceramics vary from 120 to 219 lm/W (without ultrasound treatment) and from 120 to 250 lm/W (prepared with ultrasound conditions) closely related with the phase purity and implicitly with sintering temperature. It was demonstrated the positive effect of PUA on the thermal properties of ceramics.

The Validation of the Improvement Effect of Maneuverability on Power Assist System using Guidance Force of Toward the Planned Trajectory Considering Approachability

The Validation of the Improvement Effect of Maneuverability on Power Assist System using Guidance Force of Toward the Planned Trajectory Considering Approachability

Machine Learning Models for Predicting the Type and Outcome of Ureteral Stones Treatments

AbstractBackground:Kidney and ureter stones are the third pathologies in urological diseases. Less invasive treatments such as transureteral lithotripsy and extracorporeal shock wave lithotripsy are used to treat ureteral stones. Data mining has provided the possibility of improving decision-making in choosing the optimal treatment. In this paper predictive models for the detection of ureter stone treatment (first model) and its outcome (second model) is developed based on the patient’s demographic, clinical, and laboratory factors.Methods and Material:In this cross-sectional study a questionnaire was used to identify the most effective features in the predictive models, and Information on 440 patients was collected. The models were constructed using machine learning techniques (Multilayer perceptron, Classification, and regression tree, k-nearest neighbors, Support vector machine, Naïve Bayes classifier, Random Forest, and AdaBoost) in the Bigpro1 analytical system.Results:Among the Holdout and K-fold cross-validation methods used, the Holdout method showed better performance. From the data-based balancing methods used in the second model, the Synthetic Minority oversampling technique showed better performance. Also, the AdaBoost algorithm had the best performance. In this algorithm, accuracy, sensitivity, specificity, precision, F- measure, and Area under the carve in the first model were 89%, 87%, 91%, 90%, 89%, and 94% respectively, and in the second model were 81%, 81%, 82%, 84%, 82%, and 85% respectively.Conclusions:The results were promising and showed that the data mining techniques could be a powerful assistant for urologists to predict a surgical outcome and also to choose an appropriate surgical treatment for removing ureter stones.

Development of the Power Assist Exosuit for Agricultural Work Fild

Development of the Power Assist Exosuit for Agricultural Work Fild

Research on logistics service recommendation model and application under mobile cloud environment

Today's personal computer applications are mainly cloud computing services, but it is usually inevitable to apply cloud computing to mobile cloud environments. Computing in the mobile cloud environment has the following advantages: more resources, free access to the "cloud" through any device anytime and anywhere, and provides powerful functions for computer storage and computing, while meeting the needs of more convenient software services. It has become the focus of attention in the last two years. Logistics service quality is an important indicator to measure service quality and reflects customer satisfaction with service. This is very important for service users and logistics providers. However, at present, there is a lack of unified judgment standards for the measurement of logistics service quality, and different scoring systems have different points of emphasis, so users can't get effective recommendation when choosing products. Therefore, this paper applies the principle of mobile cloud environment, combines the evaluation of logistics service quality with the big data system, selects the appropriate logistics service according to the needs of users, and recommends it to users, so as to provide more powerful assistance for users to choose products and improve the efficiency of product selection.This article introduces the recommendation model design, analyzes the complete update problem of the traditional recommendation model, proposes the incremental update recommendation model design, and implements it in a distributed manner on the basis of the Hadoop cloud computing platform.

Exoskeleton design utilizing foot-strike energy for enhancing the climbing ability of the wearer.

In this paper, a passive exoskeleton is developed to strengthen the motion ability of the knee joint bearing four times the body weight during climbing mountains or stairs. With the novel design, an inverted cam mechanism is designed to transform foot-strike energy to joint positive work of the knee joint, a type-selection mechanism for spring steel bars, and further their application model based on the linear elastic theory of metal materials are established, and to attach the exoskeleton assistance torque to the energy consumption characteristics of the knee joint, the moment-arm variation and the cam profile of the inverted cam mechanism are investigated. In addition, this paper proposes the potential link between the utilization of foot-strike energy and discrete power assistance for the knee joint, which creates simplifying conditions for exoskeleton designs with clutch mechanisms. Finally, to verify the effectiveness of the exoskeleton design, two exoskeleton prototypes were used for respiratory metabolism tests, in which 75% of the test results obtained an average power-assistant efficiency of 14.15%.

Improving frequency response for AC interconnected microgrids containing renewable energy resources

Interconnecting two or more microgrids can help improve power system performance under changing operational circumstances by providing mutual and bidirectional power assistance. This study proposes two interconnected AC microgrids based on three renewable energy sources (wind, solar, and biogas). The wind turbine powers a permanent magnet synchronous generator. A solar photovoltaic system with an appropriate inverter has been installed. In the biogas generator, a biogas engine is connected to a synchronous generator. M1 and M2, two interconnected AC microgrids, are investigated in this study. M2 is connected to a hydro turbine, which provides constant power. The distribution power loss, frequency, and voltage of interconnected AC microgrids are modeled as a multi-objective function (OF). Minimizing this OF will result in optimal power flow and frequency enhancement in interconnected AC microgrids. This research is different from the rest of the research works that talk about the virtual inertia control (VIC) method, as it not only improves frequency using an optimal controller but also achieves optimal power flow in microgrids. In this paper, the following five controllers have been studied: proportional integral controller (PI), fractional-order PI controller (FOPI), fuzzy PI controller (FPI), fuzzy fractional-order PI controller (FFOPI), and VIC based on FFOPI controller. The five controllers are tuned using particle swarm optimization (PSO) to minimize the (OF). The main contribution of this paper is the comprehensive study of the performance of interconnected AC microgrids under step load disturbances, the eventual grid following/forming contingencies, and the virtual inertia control of renewable energy resources used in the structure of the microgrids, and simulation results are recorded using the MATLAB™ platform. The voltages and frequencies of both microgrids settle with zero steady-state error following a disturbance within 0.5 s with less overshoots/undershoots (3.7e-5/-0.12e-3) using VIC. Moreover, the total power losses of two interconnected microgrids must be considered for the different controllers to identify which one provides the best optimal power flow.

South Sudanese Crisis and Its Foreign Intervention, 2013-2021

The purpose of this study was to explore the dynamics of South Sudanese crisis from 2013 to 2021. Descriptive research design that anchored on the judgmental sampling technique was adopted. The secondary sources (books, journal articles, conference papers, internet materials and monographs) were sourced from Nigerian libraries and internet, subjected to content analysis, before qualitatively analyzed for the study. Findings revealed that political exclusion, proliferation of small arms and light weapons, corruption and weak institutions triggered grievances that led to December 2013 South Sudanese crisis. The crisis had impacted negatively on security, humanitarian needs, human rights, as well as economy. United Nations Mission in South Sudan, Inter Governmental Authority on Development, African Union Mission in South Sudan, United States, Canada, Uganda, Sudan, Kenya, Ethiopia and Eritrea had intervened. Though these findings have deepened the understanding of Group theory, the fear of South Sudanese crisis reoccurring immediately after general election in 2024 is looming. The study the recommends, rotational presidency, devolution of power, mental disarmament and development assistance to avert the incoming crisis.

Research on College English Micro Course Teaching under the Guidance of Second Language Acquisition Theory

Under the background and premise of the continuous development of modern information technology, micro course plays an important role in teaching and has become a powerful assistant for educators. This informationbased teaching channel has attracted extensive attention in the educational field. Nowadays, micro class platform has become a powerful helper of College English teaching. The rise of micro class provides a stage for teachers to realize their own ideas. This new educational idea and method can open a new window for College English teaching, bring new reform and upgrading, and play a good role in reforming and alleviating the disadvantages of traditional teaching. Under the influence of second language acquisition theory, the application of micro class will embark on a new road, which is more in line with the development trend and needs of education in China. This paper focuses on the research of College English micro course teaching under the guidance of second language acquisition theory, hoping to provide some references and suggestions for relevant workers.

A systematic review of predictive models for hospital-acquired pressure injury using machine learning.

To summarize the use of machine learning (ML) for hospital-acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high-quality ML predictive models. As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study. Systematic review. Relevant articles published between 2010-2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool. Twenty-three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149-75353; the prevalence of pressure injuries ranged from 0.5%-49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre-processing and model validation. ML, as a powerful decision-making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre-processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice. This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre-processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision-making tool. A complete and rigorous model construction process should be followed in future studies to develop high-quality ML models that can be applied in clinical practice.

Understanding the Dynamic Mechanism of Urban Land Use and Population Distribution Evolution from a Microscopic Perspective

With the advancement of urbanization, the contradiction in the man–land relationship becomes more and more difficult to ignore. Investigation of the change in urban land use, population distribution and its mechanism can provide powerful assistance for urban planning. Since the changes in urban land use and population distribution is a complex process with spatial heterogeneity, the current methods for describing them are still lacking in both interpretability and spatial differences. In this paper, we combine the expansion phenomena of urban land use and population distribution with the heat equation to understand the mechanism. The particle swarm optimization (PSO) algorithm is used to identify the diffusion coefficient to obtain the diffusion law in the city’s development. In this way, the diffusion coefficient identified from the data is directly associated with urban changes. The mechanism of changes in urban land use and population distribution can be explained with the diffusion equation and the diffusion coefficient. Our model is first validated on land use and land cover data, followed by further refinement of the spatial differences in the artificial impervious surface data. The experiment’s results imply that by applying the model to the population data, the model’s generalization ability has been significantly improved.

Pitchfork-bifurcation-based competitive and collaborative control of an E-bike system

This paper is concerned with the design of a human-in-the-loop system for deployment on a smart pedelec (e-bike). From the control-theoretic perspective, the goal is not only to use the power assistance of the e-bike to reject disturbances along the route but also to manage the possibly competitive interactions between a human and the motor intervention. Managing the competitive/cooperative nature of the interactions is crucial for applications in which we wish to control physical aspects of the cycling behavior (e.g. heart rate and breathing rate). The basis of the control is a pitchfork bifurcation system, modeling the interactions, augmented using ideas from gain-scheduling. In vivo experiments have been conducted, showing the effectiveness of the proposed control strategy.

Porphyrin‐Based Covalent Organic Framework for Imaging‐Guided Cancer Combinatorial Immuno‐Sonodynamic Therapy

AbstractThe rational design of tumor microenvironment (TME)‐activated nanomedicine is driving a new direction in tumor immunology. Furthermore, the novel therapeutic mode of ultrasound‐triggered sonodynamic therapy (SDT) has been proven to specifically activate the immune response. Herein, a well‐defined covalent organic framework (COF) with sonosensitive properties is synthesized through experimental and theoretical verification, followed by the efficient loading of the toll‐like receptor agonist (Poly(I:C)) and in situ growth of paramagnetic transitional metallic oxide of manganese bioxide (MnO2). The MnO2‐Poly(I:C)@COF shell can reverse the reductive TME by consuming glutathione (GSH) to release Mn2+, simultaneously generating marked magnetic resonance imaging signals for real‐time guidance. Importantly, the MnO2 acts as an enzyme‐like nano‐catalyst to promote TME‐overexpressed hydrogen peroxide (H2O2) and produce oxygen, facilitating SDT‐induced reactive oxygen species production, and inducing immunogenic cell death, thereby boosting immune engine and triggering abundant neoantigen exposure. With the powerful assistance of immunological agents Mn2+ and Poly(I:C), the triggered immune engine is amplified by refueling the engine (stepping on the accelerator) to reduce the immunosuppressive state. Overall, this study improves the synthesis of multifunctional COF and expands its application. The developed nano‐sonosensitizer system provides a paradigm to enhance SDT‐based high‐performance multifunctional sonosensitizers, and this SDT‐mediated strong tumor‐suppressive efficiency and activated immune effect represent a promising combinatorial therapeutic strategy for cancer therapy.

Artificial intelligence in lung cancer: Application and future thinking.

The availability of medical big data and the rapid development of computer software and hardware have greatly promoted the advancement of intelligent medical healthcare. Artificial intelligence (AI) has been successfully applied in many fields of medicine, especially in lung cancer. The performance of AI in some specific tasks has surpassed that of humans. Several AI software has been deeply used in clinical practice to help decision-making, which is producing a profound influence on clinicians. At present, the application of AI in the field of lung cancer mainly includes detection, segmentation, classification, prognosis prediction, efficacy evaluation, and so on. AI faces certain challenges and opportunities in the era of big data in terms of data acquisition, annotation and interpretability. Researchers have conducted deep and extensive studies using AI in the field of lung cancer, and AI is expected to become a powerful assistant in the prevention and treatment of lung cancer. AI is bringing an unprecedented revolution to radiologists, but the role of radiologists is crucial in the development of AI.