A versatile strategy to activate self-sacrificial templated Li_(2)MnO_(3) by defect engineering toward advanced lithium storage

Despite the dazzling theoretical capacity,the devasting electrochemical activity of Li_(2)MnO_(3)(LMO)caused by the difficult oxidation of Mn4+impedes its practical application as the lithium-ion battery(LIB)cathode.The efficacious activation of the Li_(2)MnO_(3) by importing electrochemically active Mn3+ions or morphological engineering is instrumental to its lithium storage activity and structural integrity upon cycling.Herein,we propose a conceptual strategy with metal-organic frameworks(MOFs)as self-sacrificial templates to prepare oxygen-deficient Li_(2)MnO_(3)(O_v-LMO)for exalted lithium storage performance.Attributed to optimized morphological features,LMO materials derived from Mn-BDC(H_(2)BDC=1,4-dicarboxybenzene)delivered superior cycling/rate performances compared with their counterparts derived from Mn-BTC(H_(3)BTC=1,3,5-benzenetricarboxylicacid)and Mn-PTC(H_(4)PTC=pyromellitic acid).Both experimental and theoretical studies elucidate the efficacious activation of primitive LMO materials toward advanced lithium storage by importing oxygen deficiencies.Impressively,O_v-LMO derived from Mn-BDC(O_v-BDC-LMO)delivered intriguing reversible capacities(179.2 mA h g^(-1)at 20 mA g^(-1)after 200 cycles and 100.1 mA h g^(-1)at 80 mA g^(-1)after 300 cycles),which can be attributed to the small particle size that shortens pathways for Li+/electron transport,the enhanced redox activity induced by abundant oxygen vacancies,and the optimized electronic configuration that contributes to the faster lithium diffusivity.This work provides insights into the rational design of LMO by morphological and atomic modulation to direct its activation and practical application as an advanced LIB cathode.

Low-Brightness Object Recognition Based on Deep Learning

This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems.The primary goal is to tackle issues related to recognizing objects with low brightness levels.In this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images.The detection scenarios are categorized into normal brightness and low brightness situations.When the system determines a normal brightness environment,normal brightness images are recognized using deep learning methods.In low-brightness situations,three methods are proposed for recognition.The first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented image.The second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for recognition.The third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for recognition.In experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition system.This research outcome can be applied to nighttime surveillance systems or nighttime road safety systems.

Resource allocation for D2D-assisted haptic communications

Haptic communications is recognized as a promising enabler of extensive services by enabling real-time haptic control and feedback in remote environments,e.g.,teleoperation and autonomous driving.Considering the strict transmission requirements on reliability and latency,Device-to-Device(D2D)communications is introduced to assist haptic communications.In particular,the teleoperators with poor channel quality are assisted by auxiliaries,and each auxiliary and its corresponding teleoperator constitute a D2D pair.However,the haptic interaction and the scarcity of radio resources pose severe challenges to the resource allocation,especially facing the sporadic packet arrivals.First,the contentionbased access scheme is applied to achieve low-latency transmission,where the resource scheduling latency is omitted and users can directly access available resources.In this context,we derive the reliability index of D2D pairs under the contention-based access scheme,i.e.,closed-loop packet error probability.Then,the reliability performance is guaranteed by bidirectional power control,which aims to minimize the sum packet error probability of all D2D pairs.Potential game theory is introduced to solve the problem with low complexity.Accordingly,a distributed power control algorithm based on synchronous log-linear learning is proposed to converge to the optimal Nash Equilibrium.Experimental results demonstrate the superiority of the proposed learning algorithm.

Coexistence of antiferromagnetism and unconventional superconductivity in a quasi-one-dimensional flat-band system:Creutz lattice

We study the coexistence of antiferromagnetism and unconventional superconductivity on the Creutz lattice which shows strictly flat bands in the noninteracting regime.The famous renormalized mean-field theory is used to deal with strong electron-electron repulsive Hubbard interaction in the effective low-energy t-J model,the superfluid weight of the unconventional superconducting state has been calculated via the linear response theory.An unconventional superconducting state with both spin-singlet and staggered spin-triplet pairs emerges beyond a critical antiferromagnetic coupling interaction,while antiferromagnetism accompanies this state.The superconducting state with only spin-singlet pairs is dominant with paramagnetic phase.The A phase is analogous to the pseudogap phase,which shows that electrons go to form pairs but do not cause a supercurrent.We also show the superfluid behavior of the unconventional superconducting state and its critical temperature.It is proven directly that the flat band can effectively raise the critical temperature of superconductivity.It is implementable to simulate and control strongly-correlated electrons'behavior on the Creutz lattice in the ultracold atoms experiment or other artificial structures.Our results may help the understanding of the interplay between unconventional superconductivity and magnetism.

Blood Pressure Estimation with Phonocardiogram on CNN-Based Approach

Monitoring blood pressure is a critical aspect of safeguarding an individual’s health,as early detection of abnormal blood pressure levels facilitates timely medical intervention,ultimately leading to a reduction in mortality rates associated with cardiovascular diseases.Consequently,the development of a robust and continuous blood pressure monitoring system holds paramount significance.In the context of this research paper,we introduce an innovative deep learning regression model that harnesses phonocardiogram(PCG)data to achieve precise blood pressure estimation.Our novel approach incorporates a convolutional neural network(CNN)-based regression model,which not only enhances its adaptability to spatial variations but also empowers it to capture intricate patterns within the PCG signals.These advancements contribute significantly to the overall accuracy of blood pressure estimation.To substantiate the effectiveness of our proposed method,we meticulously gathered PCG signal data from 78 volunteers,adhering to the ethical guidelines of Suranaree University of Technology(Human Research Ethics number EC-65-78).Subsequently,we rigorously preprocessed the dataset to ensure its integrity.We further employed a K-fold cross-validation procedure for data division and alignment,combining the resulting datasets with a CNNfor blood pressure estimation.The experimental results are highly promising,yielding aMeanAbsolute Error(MAE)and standard deviation(STD)of approximately 10.69±7.23 mmHg for systolic pressure and 6.89±5.22 mmHg for diastolic pressure.Our study underscores the potential for precise blood pressure estimation,particularly using PCG signals,paving the way for a practical,non-invasive method with broad applicability in the healthcare domain.Early detection of abnormal blood pressure levels can facilitate timely medical interventions,ultimately reducing cardiovascular disease-related mortality rates.

Exciton-polaritons in a 2D hybrid organic-inorganic perovskite microcavity with the presence of optical Stark effect

This study investigates the properties of exciton-polaritons in a two-dimensional(2D)hybrid organic-inorganic perovskite microcavity in the presence of optical Stark effect.Through both steady and dynamic state analyses,strong coupling between excitons of perovskite and cavity photons is revealed,indicating the formation of polaritons in the perovskite microcavity.Besides,it is found that an external optical Stark pulse can induce energy shifts of excitons proportional to the pulse intensity,which modifies the dispersion characteristics of the polaritons.

Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

A New Perspective on Time and Gravity

This paper presents a hypothesis regarding the existence of time fused in spacetime, assuming that time possesses the properties of both a particle and a field. This duality is referred to as the field-particle of time (FPT). The analysis shows that when the FPT moves through matter, it causes time dilation. The FPT is also a significant element that appears in relativistic kinetic energy (KE = (γ - 1) · mc2). Accelerating matter to near the speed of light requires relativistic energy approaching infinity, which corresponds to the relativistic kinetic energy. Meanwhile, the potential energy (PE = mc2) from the rest mass remains constant. Then, the mass-energy equation can be rearranged in terms of PE and KE, as shown in E = (1 + (γ - 1)) · mc2. The relativistic energy of the FPT also directly affects the gravitational attraction of matter. It transfers energy to each other through spacetime. The analysis demonstrates that the gravitational force is inversely proportional to the distance squared, following Newton’s law of gravity, and it varies with the relative velocity of matter. The relationship equation between relative time and the gravitational constant indicates that a higher intensity of the gravitational field leads to a slower reference time for matter, in accordance with the general theory of relativity. A thought experiment presents a comparison of two atomic clocks placed in different locations. The first one is placed in a room temperature, around 25°C, on the surface of the Earth, and the second one is placed in high-density areas. The analysis, considering the presence of the FPT, shows that the reference time slows down in high-density areas. Therefore, the second clock must be noticeably slower than the first one, indicating the existence of the FPT passing through both atomic clocks at different speeds.

Reinforcing of Citizen’s Trust in E-Government: The Cameroon’s Case

The embracing of ICTs and related technologies has enhanced different approaches for governments worldwide to deliver services to their citizens in a smart way. However, the usage of e-government services by common citizens is recognized as one of the major setbacks of e-government development in both developed and developing countries. Moreover, government agencies in these countries are facing great challenges in keeping the citizens motivated enough to continue to use e-government services. This research aims to investigate the factors that influence citizens’ trust towards continue use of e-government services in Cameroon. The proposed research model consisted of three main constructs including technological, governmental, risk factors as well as six demographic characteristics (age, gender, educational level, income, internet experience and cultural perception). A five-point Likert scale questionnaire was designed to collect data physically and electronically, 352 valid questionnaires were retrieved. Simple and Multiple regression analysis methods were applied to build an adequate model based on the verification of hypotheses proposed. Based on results obtained, four demographic characteristics (age, education, occupation and income) have influence on citizens’ trust in e-government meanwhile gender and cultural affiliation have no influence. Furthermore, technological factors and governmental factors positively influence trust level in e-government, whereas risk factors have a negative influence on trust level. Deducing from the results, a list of recommendations is proposed to the government of Cameroon in order to reinforce citizens’ trust in e-government services.

An Effective Prediction Method for Supporting Decision Making in Real Estate Area Selection

Real estate has been a dominant industry in many countries. One problem for real estate companies is determining the most valuable area before starting a new project. Previous studies on this issue mainly focused on market needs and economic prospects, ignoring the impact of natural disasters. We observe that natural disasters are important for real estate area selection because they will introduce considerable losses to real estate enterprises. Following this observation, we first develop a self-defined new indicator named Average Loss Ratio to predict the losses caused by natural disasters in an area. Then, we adopt the existing ARIMA model to predict the Average Loss Ratio of an area. After that, we propose to integrate the TOPSIS model and the Grey Prediction Model to rank the recommendation levels for candidate areas, thereby assisting real estate companies in their decision-making process. We conduct experiments on real datasets to validate our proposal, and the results suggest the effectiveness of the proposed method.

Design and Implementation of Hand Gesture Detection System Using HM Model for Sign Language Recognition Development

Gesture detection is the primary and most significant step for sign language detection and sign language is the communication medium for people with speaking and hearing disabilities. This paper presents a novel method for dynamic hand gesture detection using Hidden Markov Models (HMMs) where we detect different English alphabet letters by tracing hand movements. The process involves skin color-based segmentation for hand isolation in video frames, followed by morphological operations to enhance image trajectories. Our system employs hand tracking and trajectory smoothing techniques, such as the Kalman filter, to monitor hand movements and refine gesture paths. Quantized sequences are then analyzed using the Baum-Welch Re-estimation Algorithm, an HMM-based approach. A maximum likelihood classifier is used to identify the most probable letter from the test sequences. Our method demonstrates significant improvements over traditional recognition techniques in real-time, automatic hand gesture recognition, particularly in its ability to distinguish complex gestures. The experimental results confirm the effectiveness of our approach in enhancing gesture-based sign language detection to alleviate the barrier between the deaf and hard-of-hearing community and general people.

A Compact Ultra-Wideband Circularly Polarized Antenna Array for Vehicular Communications

In this paper,a new compact ultrawideband(UWB)circularly polarized(CP)antenna array for vehicular communications is proposed.The antenna array consists of a 2×2 sequentially rotated T-shaped cross dipole,four parasitic elements,and a feeding network.By loading the T-shaped cross dipoles with parasitic rectangular elements with cut corners,the bandwidth can be expanded.On this basis,the radiation pattern can be improved by the topology with sequential rotation of four T-shaped cross-dipole antennas,and the axial ratio(AR)bandwidth of the antenna also can be further enhanced.In addition,due to the special topology that the vertical arms of all Tshaped cross dipoles are all oriented toward the center of the antenna array,the gain of proposed antenna is improved while the size of the antenna is almost the same as the traditional cross dipole.Simulated and measured results show that the proposed antenna has good CP characteristics,an impedance bandwidth for S11<-10 d B of about 106.1%(3.26:1,1.57-5.12 GHz)and the 3-d B AR bandwidth of about 104.1%(3.17:1,1.57-4.98 GHz),a wide 3-d B gain bandwidth of 73.3%as well as the peak gain of 8.6 d Bic at 3.5 GHz.The overall size of antenna is 0.56λ×0.56λ×0.12λ(λrefers to the wavelength of the lowest operating frequency in free space).The good performance of this compact UWB CP antenna array is promising for applications in vehicular communications.

Research on High Speed Communication Method of ELF-EM While Drilling Based on Adaptive Combined Filtering Algorithm

In terms of the electromagnetic wave measurement while drilling,the extremely low frequency electromagnetic(ELF-EM)signal below 20Hz is usually chosen as the downhole data carrier.To improve the transmission rate of ELF-EM signal and the signal to noise ratio(SNR)at the receiving end,the DQPSK modulation was proposed as the modulation method for the communication of electromagnetic wave system.Different from the traditional IQ orthogonal modulation and coherent demodulation methods,the proposed phase selection modulation and correlation algorithm demodulation are easier to implement and more practical.With regard to the communication synchronization,a fast algorithm,which based on the normalized cross-relation number,was used for waveform matching,and the maximum point of the correlation coefficient was used as the starting point of communication synchronization.The communication simulation results show that the proposed DQPSK modulation signal based on the adaptive combined filtering algorithm has better terminal error rate and transmission rate than the traditional modulation method.Under the same carrier frequency and code width,the transmission rate of DQPSK modulation is 4 to 5 times and 2 times that of PPM modulation and 2DPSK modulation respectively.The communication modulation and demodulation modes as well as the decoding algorithm with combined adaptive filter proposed in this paper can effectively solve practical engineering problems.

Coordinated Planning Transmission Tasks in Heterogeneous Space Networks:A Semi-Distributed Approach

This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite systems.Specifically,we first formulate the coordinated planning problem into a mixed integer liner programming(MILP)problem based on time expanded graph.Then,the problem is transferred and reformulated into a consensus optimization framework which can be solved by satellite systems parallelly.With alternating direction method of multipliers(ADMM),a semi-distributed coordinated transmission task planning algorithm is proposed,in which each satellite system plans its own tasks based on local information and limited communication with the coordination center.Simulation results demonstrate that compared with the centralized and fully-distributed methods,the proposed semi-distributed coordinated method can strike a better balance among task complete rate,complexity,and the amount of information required to be exchanged.

Investigation of the level spectra of nuclei in the northeast region of doubly magic^(40)Ca with intruder orbit g_(9/2)

This study utilizes large-scale shell model calculations with the extended pairing and multipole–multipole force model(EPQQM)to investigate low-lying states in the nuclei of^(42)Ca,^(42)Sc,and^(42−44)Ti.The model space in this study includes the fp shell as well as the intruder g_(9/2)orbit,which accurately reproduces the positive parity levels observed in the aforementioned nuclei and predicts high energy states with negative parity coupled with the intruder g_(9/2).The study further predicts two different configurations in^(43)Ti at around 6 MeV,specificallyπf_(7/2)^(2)νg_(9/2)andπf_(7/2)g_(9/2)νf_(7/2),both of which involve the intruder orbit g_(9/2).The levels coupled with the intruder g_(9/2)in^(44)Ti are predicted to lie between 7 and 11 MeV.The inclusion of the intruder orbit g_(9/2)is crucial for the exploration of high energy states in the northeast region of the doubly magic nucleus^(40)Ca.

CD-FL:Cataract Images Based Disease Detection Using Federated Learning

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over time.Automatic cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye treatment.In recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been exponential.Due to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field challenging.To address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this research.The study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient records.The significant features are extracted and normalized using the min-max normalization technique.In the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two clients.Before transferring the attributes to the global model,the suggested method trains the local model.The global model subsequently improves the technique after integrating the new parameters.Every client analyses the results in three rounds to decrease the over-fitting problem.The experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s data.The experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal.In India,around 15 million cases are diagnosed yearly.To mitigate the seriousness of the tumor it is essential to diagnose at the beginning.Notwithstanding,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor diagnoses.Similarly,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and location.Therefore,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit treatment.Several computer models adapted to diagnose the tumor,but the accuracy of the model needs to be tested.Considering all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception V3.Data augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and performance.An accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th epoch.With the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

在斜向非定常流动中由层流向湍流转变对模型尺度螺旋桨性能和压力脉动的影响

In this paper,after the successful applications to open water propeller performance estimations,the influence of transition sensitive and modified mass transfer models tuned to account for the laminar flow in the prediction of the cavitation inception of marine propulsors is investigated from the point of view of the unsteady functioning and induced pressure pulses.The VP1304(also known as PPTC)test case,for which dedicated data were collected during several workshops,is considered first.After preliminary analyses using RANS,also Detached Eddy Simulations(DES)are included to better account for the vortex dynamics and its influence on pressure pulses.Similarly to what observed in uniform inflow,results show a better agreement with the available measurements of propeller performances and confirm the reliability of the proposed approaches for unsteady,non-cavitating,model scale propeller predictions.The overall cavitation pattern is improved too by the application of the transition sensitive correction to the mass transfer model,but the complex dynamics of bubble cavitation observed in experiments prevents quantitatively better predictions in terms of thrust/torque breakdown and induced pressure pulses levels regardless the use of RANS or DES methods.

Blind Image Quality Assessment by Pairwise Ranking Image Series

Image quality assessment(IQA)is constantly innovating,but there are still three types of stickers that have not been resolved:the“content sticker”-limitation of training set,the“annotation sticker”-subjective instability in opinion scores and the“distortion sticker”-disordered distortion settings.In this paper,a No-Reference Image Quality Assessment(NR IQA)approach is proposed to deal with the problems.For“content sticker”,we introduce the idea of pairwise comparison and generate a largescale ranking set to pre-train the network;For“annotation sticker”,the absolute noise-containing subjective scores are transformed into ranking comparison results,and we design an indirect unsupervised regression based on EigenValue Decomposition(EVD);For“distortion sticker”,we propose a perception-based distortion classification method,which makes the distortion types clear and refined.Experiments have proved that our NR IQA approach Experiments show that the algorithm performs well and has good generalization ability.Furthermore,the proposed perception based distortion classification method would be able to provide insights on how the visual related studies may be developed and to broaden our understanding of human visual system.