Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Fortifying Healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

A new era of data access and management has begun with the use of cloud computing in the healthcare industry.Despite the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity Operations.We use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional method.This suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

A Model Training Method for DDoS Detection Using CTGAN under 5GC Traffic

With the commercialization of 5th-generation mobile communications(5G)networks,a large-scale internet of things(IoT)environment is being built.Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service(DDoS)attacks across vast IoT devices.Recently,research on automated intrusion detection using machine learning(ML)for 5G environments has been actively conducted.However,5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data.If this data is used to train an ML model,it will likely suffer from generalization errors due to not training enough different features on the attack data.Therefore,this paper aims to study a training method to mitigate the generalization error problem of the ML model that classifies IoT DDoS attacks even under conditions of insufficient and imbalanced 5G traffic.We built a 5G testbed to construct a 5G dataset for training to solve the problem of insufficient data.To solve the imbalance problem,synthetic minority oversampling technique(SMOTE)and generative adversarial network(GAN)-based conditional tabular GAN(CTGAN)of data augmentation were used.The performance of the trained ML models was compared and meaningfully analyzed regarding the generalization error problem.The experimental results showed that CTGAN decreased the accuracy and f1-score compared to the Baseline.Still,regarding the generalization error,the difference between the validation and test results was reduced by at least 1.7 and up to 22.88 times,indicating an improvement in the problem.This result suggests that the ML model training method that utilizes CTGANs to augment attack data for training data in the 5G environment mitigates the generalization error problem.

Performance Evaluation of Composite Electrolyte with GQD for All-Solid-State Lithium Batteries

The use a stabilized lithium structure as cathode material for batteries could be a fundamental alternative in the development of next-generation energy storage devices.However,the lithium structure severely limits battery life causes safety concerns due to the growth of lithium(Li)dendrites during rapid charge/discharge cycles.Solid electrolytes,which are used in highdensity energy storage devices and avoid the instability of liquid electrolytes,can be a promising alternative for next-generation batteries.Nevertheless,poor lithium ion conductivity and structural defects at room temperature have been pointed out as limitations.In this study,through the application of a low-dimensional graphene quantum dot(GQD)layer structure,stable operation characteristics were demonstrated based on Li^(+)ion conductivity and excellent electrochemical performance.Moreover,the device based on the modified graphene quantum dots(GQDs)in solid state exhibited retention properties of 95.3%for 100 cycles at 0.5 C and room temperature(RT).Transmission electronmicroscopy analysis was performed to elucidate the Li^(+)ion action mechanism in the modified GQD/electrolyte heterostructure.The low-dimensional structure of theGQD-based solid electrolyte has provided an important strategy for stably-scalable solid-state lithium battery applications at room temperature.It was demonstrated that lithiated graphene quantum dots(Li-GQDs)inhibit the growth of Li dendrites by regulating the modified Li^(+)ion flux during charge/discharge cycling at current densities of 2.2–5.5 mA cm,acting as a modified Li diffusion heterointerface.A full Li GQDbased device was fabricated to demonstrate the practicality of the modified Li structure using the Li–GQD hetero-interface.This study indicates that the low-dimensional carbon structure in Li–GQDs can be an effective approach for stabilization of solid-state Li matrix architecture.

Development of hyaluronic acid-based edible film for alleviating dry mouth

For alleviating dry mouth symptoms,edible films based on hyaluronic acid(HA)with 3 different m(800,1200 and 2300 kDa)were prepared(800 F,1200 F and 2300 F,respectively),and the properties as well as effectiveness were compared.The concentration of each HA dispersion for film forming was set as 3.0%,1.5%or 1.0%,for the m800,1200 and 2300 kDa,respectively,based on the solubility.The 800 F showed the highest thickness,tensile strength,and water vapor transparency,whereas obtained the lowest transparency and elongation at break among samples.All of the HA films showed safety against microorganism during 28 storage day at 40℃with 60%humidity.The optimum site for film attachment in mouth was the palate,and800 F was the most effective for stimulating saliva secretion,eliciting a 38%increase compared to control(without film),tested by the elderly over 65 years old.By the sensory test,800 F was also the most acceptable.Based on above results,the edible films effectively stimulating saliva secretion could be produced with HA,and the physical,sensory characteristics as well as disintegration times of the film could be controlled by mand the dissolution concentration of HA.

Robust and Reusable Fuzzy Extractors from Non-Uniform Learning with Errors Problem

Afuzzy extractor can extract an almost uniformrandom string from a noisy source with enough entropy such as biometric data.To reproduce an identical key from repeated readings of biometric data,the fuzzy extractor generates a helper data and a random string from biometric data and uses the helper data to reproduce the random string from the second reading.In 2013,Fuller et al.proposed a computational fuzzy extractor based on the learning with errors problem.Their construction,however,can tolerate a sub-linear fraction of errors and has an inefficient decoding algorithm,which causes the reproducing time to increase significantly.In 2016,Canetti et al.proposed a fuzzy extractor with inputs from low-entropy distributions based on a strong primitive,which is called digital locker.However,their construction necessitates an excessive amount of storage space for the helper data,which is stored in authentication server.Based on these observations,we propose a new efficient computational fuzzy extractorwith small size of helper data.Our scheme supports reusability and robustness,which are security notions that must be satisfied in order to use a fuzzy extractor as a secure authentication method in real life.Also,it conceals no information about the biometric data and thanks to the new decoding algorithm can tolerate linear errors.Based on the non-uniform learning with errors problem,we present a formal security proof for the proposed fuzzy extractor.Furthermore,we analyze the performance of our fuzzy extractor scheme and provide parameter sets that meet the security requirements.As a result of our implementation and analysis,we show that our scheme outperforms previous fuzzy extractor schemes in terms of the efficiency of the generation and reproduction algorithms,as well as the size of helper data.

《follow a dream》

设计说明:本系列作品为女性包袋,灵感来源于梦境中模糊又迷离的感觉。造型中用不同的线条分割以及组合展现其现代简约的设计美感;花卉的造型强调了女性的审美特征;将竹节、植鞣革、金属等材料组合在一块展现不同的特性美。

Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification

Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.

Biomass-derived bifunctional electrocatalysts for oxygen reduction and evolution reaction: A review

Oxygen electrode catalysts are important as inter-conversion of O_(2) and H_(2)O is crucial for energy technologies.However,the sluggish kinetics of oxygen reduction and evolution reactions(ORR and OER)are a hindrance to their scalable production,whereas scarce and costly Pt and Ir/Ru-based catalysts with the highest electrocatalytic activity are commercially unviable.Since good ORR catalysts are not always efficient for OER and vice versa,so bifunctional catalysts on which OER and ORR occurs on the same electrode are very desirable.Alternative catalysts based on heteroatom-doped carbon nanomaterials,though showed good electrocatalytic activity yet their high cost and complex synthesis is not viable for scalable production.To overcome these drawbacks,biomass-derived heteroatom-doped porous carbons have recently emerged as low-cost,earth-abundant,renewable and sustainable environment-friendly materials for bifunctional oxygen catalysts.The tunable morphology,mesoporous structure and high concentration of catalytic active sites of these materials due to heteroatom(N)-doping could further enhance their ORR and OER activity,along with tolerance to methanol crossover and good durability.Thus,biomassderived heteroatom-doped porous carbons with large surface area,rich edge defects,numerous micropores and thin 2 D nanoarchitecture could be suitable as efficient bifunctional oxygen catalysts.In the present article,synthesis,N-doping,ORR/OER mechanism and electrocatalytic performance of biomassderived bifunctional catalysts has been discussed.The selected biomass(chitin,eggs,euonymus japonicas,tobacco,lysine and plant residue)except wood,act as both C and N precursor,resulting in N selfdoping of porous carbons that avoids the use of toxic chemicals,thus making the synthesis a facile and environment-friendly green process.The synthetic strategy could be further optimized to develop future biomass-based N self-doped porous carbons as metal-free high performance bifunctional oxygen catalysts for commercial energy applications.Recent advances and the importance of biomass-based bifunctional oxygen catalysts in metal-air batteries and fuel cells has been highlighted.The material design,perspectives and future directions in this field are also provided.

Improved VGG Model for Road Traffic Sign Recognition

Road traffic sign recognition is an important task in intelligent transportation system.Convolutional neural networks(CNNs)have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification.In this paper,it presents a road traffic sign recognition algorithm based on a convolutional neural network.In natural scenes,traffic signs are disturbed by factors such as illumination,occlusion,missing and deformation,and the accuracy of recognition decreases,this paper proposes a model called Improved VGG(IVGG)inspired by VGG model.The IVGG model includes 9 layers,compared with the original VGG model,it is added max-pooling operation and dropout operation after multiple convolutional layers,to catch the main features and save the training time.The paper proposes the method which adds dropout and Batch Normalization(BN)operations after each fully-connected layer,to further accelerate the model convergence,and then it can get better classification effect.It uses the German Traffic Sign Recognition Benchmark(GTSRB)dataset in the experiment.The IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning,and the spent time is also reduced greatly.

A Strategy of Signal Detection for Performance Improvement in Clipping Based OFDM System

In this paper,the supervised Deep Neural Network(DNN)based signal detection is analyzed for combating with nonlinear distortions efficiently and improving error performances in clipping based Orthogonal Frequency Division Multiplexing(OFDM)ssystem.One of the main disadvantages for the OFDM is the high Peak to Average Power Ratio(PAPR).The clipping is a simple method for the PAPR reduction.However,an effect of the clipping is nonlinear distortion,and estimations for transmitting symbols are difficult despite a Maximum Likelihood(ML)detection at the receiver.The DNN based online signal detection uses the offline learning model where all weights and biases at fully-connected layers are set to overcome nonlinear distortions by using training data sets.Thus,this paper introduces the required processes for the online signal detection and offline learning,and compares error performances with the ML detection in the clipping-based OFDM systems.In simulation results,the DNN based signal detection has better error performance than the conventional ML detection in multi-path fading wireless channel.The performance improvement is large as the complexity of system is increased such as huge Multiple Input Multiple Output(MIMO)system and high clipping rate.

Detecting IoT Botnet in 5G Core Network Using Machine Learning

As Internet of Things(IoT)devices with security issues are connected to 5G mobile networks,the importance of IoT Botnet detection research in mobile network environments is increasing.However,the existing research focused on AI-based IoT Botnet detection research in wired network environments.In addition,the existing research related to IoT Botnet detection in ML-based mobile network environments have been conducted up to 4G.Therefore,this paper conducts a study on ML-based IoT Botnet traffic detection in the 5G core network.The binary and multiclass classification was performed to compare simple normal/malicious detection and normal/threetype IoT Botnet malware detection.In both classification methods,the IoT Botnet detection performance using only 5GC’s GTP-U packets decreased by at least 22.99%of accuracy compared to detection in wired network environment.In addition,by conducting a feature importance experiment,the importance of feature study for IoT Botnet detection considering 5GC network characteristics was confirmed.Since this paper analyzed IoT botnet traffic passing through the 5GC network using ML and presented detection results,think it will be meaningful as a reference for research to link AI-based security to the 5GC network.

Developing a Model for Controlling Internal Corrosion in Water Supply System

A model was developed to generate charts that fit the conditions as diverse temperatures and ionic strengths, and that estimate the diversified state of water. The chart can be used as a tool for controlling corrosive waters resulted in internal corrosion and the model producing charts composed of a number of sub modules, and each module incorporated parameters including acidity, alkalinity, pH, and calcium ion. Utilizing the chart water quality of the raw water in G water purification works was estimated to be unsaturated and Langelier index becomes -1.4 which means that the water is highly corrosive and calcium carbonate would not be precipitated. Thus, the water requires treatment as the injection of water stabilizing chemicals to promote an oversaturated (protective) condition. As a result of adding 5 mg/L of lime, it is possible to be precipitated with 5 mg/L, and the water becomes noncorrosive. In addition, when 5 mg/L of caustic soda is added as a conditioning chemical, it signifies to be precipitated with 9 mg/L, and the water also turns out to be largely noncorrosive. Both chemicals are possible to use for the water to be favorable for the formation of a protective film. Optimum injection rate for controlling corrosion can be found by repeating the procedures until the well-conditioned water criteria are satisfied.

Flexible Memristive Devices Based on Graphene Quantum-Dot Nanocomposites

Artificial neural networks(ANNs)are attracting attention for their high performance in various fields,because increasing the network size improves its functioning.Since large-scale neural networks are difficult to implement on custom hardware,a two-dimensional(2D)structure is applied to an ANN in the form of a crossbar.We demonstrate a synapse crossbar device from recent research by applying a memristive system to neuromorphic chips.The system is designed using two-dimensional structures,graphene quantum dots(GQDs)and graphene oxide(GO).Raman spectrum analysis results indicate a D-band of 1421 cm^(−1) that occurs in the disorder;band is expressed as an atomic characteristic of carbon in the sp2 hybridized structure.There is also a G-band of 1518 cm^(−1) that corresponds to the graphite structure.The G bands measured for RGO-GQDs present significant GQD edge-dependent shifts with position.To avoid an abruptly-formed conduction path,effect of barrier layer on graphene/ITO interface was investigated.We confirmed the variation in the nanostructure in the RGO-GQD layers by analyzing them using HR-TEM.After applying a negative bias to the electrode,a crystalline RGO-GQD region formed,which a conductive path.Especially,a synaptic array for a neuromorphic chip with GQDs applied was demonstrated using a crossbar array.

Empathic Responses of Behavioral-Synchronization in Human-Agent Interaction

Artificial entities,such as virtual agents,have become more pervasive.Their long-term presence among humans requires the virtual agent’s ability to express appropriate emotions to elicit the necessary empathy from the users.Affective empathy involves behavioral mimicry,a synchronized co-movement between dyadic pairs.However,the characteristics of such synchrony between humans and virtual agents remain unclear in empathic interactions.Our study evaluates the participant’s behavioral synchronization when a virtual agent exhibits an emotional expression congruent with the emotional context through facial expressions,behavioral gestures,and voice.Participants viewed an emotion-eliciting video stimulus(negative or positive)with a virtual agent.The participants then conversed with the virtual agent about the video,such as how the participant felt about the content.The virtual agent expressed emotions congruent with the video or neutral emotion during the dialog.The participants’facial expressions,such as the facial expressive intensity and facial muscle movement,were measured during the dialog using a camera.The results showed the participants’significant behavioral synchronization(i.e.,cosine similarity≥.05)in both the negative and positive emotion conditions,evident in the participant’s facial mimicry with the virtual agent.Additionally,the participants’facial expressions,both movement and intensity,were significantly stronger in the emotional virtual agent than in the neutral virtual agent.In particular,we found that the facial muscle intensity of AU45(Blink)is an effective index to assess the participant’s synchronization that differs by the individual’s empathic capability(low,mid,high).Based on the results,we suggest an appraisal criterion to provide empirical conditions to validate empathic interaction based on the facial expression measures.

《虎韵》

设计说明:本系列作品为女性包袋,灵感来源于中国北方民间艺术。整体造型以老虎造型构成,运用解构再组合的构成法则将民间艺术呈现不同的现代艺术状态并且兼备实用价值。

RADIALLY SYMMETRIC SOLUTIONS FOR QUASILINEAR ELLIPTIC EQUATIONS INVOLVING NONHOMOGENEOUS OPERATORS IN AN ORLICZ-SOBOLEV SPACE SETTING

We investigate the following elliptic equations:⎧⎩⎨−M(∫R Nϕ(|∇u|2)dx)div(ϕ′(|∇u|2)∇u)+|u|α−2 u=λh(x,u),u(x)→0,as|x|→∞,in R N,where N≥2,1<p<q<N,α<q,1<α≤p∗q′/p′with p∗=NpN−p,ϕ(t)behaves like t q/2 for small t and t p/2 for large t,and p′and q′are the conjugate exponents of p and q,respectively.We study the existence of nontrivial radially symmetric solutions for the problem above by applying the mountain pass theorem and the fountain theorem.Moreover,taking into account the dual fountain theorem,we show that the problem admits a sequence of small-energy,radially symmetric solutions.

Steam activation of Fe-N-C catalyst for advanced power performance of alkaline hydrazine fuel cells

Alkaline hydrazine liquid fuel cells(AHFC) have been highlighted in terms of high power performance with non-precious metal catalysts.Although Fe-N-C is a promising non-Pt electrocatalyst for oxygen reduction reaction(ORR),the surface density of the active site is very low and the catalyst layer should be thick to acquire the necessary number of catalytic active sites.With this thick catalyst layer,it is important to have an optimum pore structure for effective reactant conveyance to active sites and an interface structure for faster charge transfer.Herein,we prepare a Fe-N-C catalyst with magnetite particles and hierarchical pore structure by steam activation.The steam activation process significantly improves the power performance of the AHFC as indicated by the lower IR and activation voltage losses.Based on a systematic characterization,we found that hierarchical pore structures improve the catalyst utilization efficiency of the AHFCs,and magnetite nanoparticles act as surface modifiers to reduce the interracial resistance between the electrode and the ion-exchange membrane.

A Complete and Simple Solution to a Discrete-Time Finite-Capacity BMAP/D/c Queue

We consider a discrete-time multi-server finite-capacity queueing system with correlated batch arrivals and deterministic service times (of single slot), which has a variety of potential applications in slotted digital telecommunication systems and other related areas. For this queueing system, we present, based on Markov chain analysis, not only the steady-state distributions but also the transient distributions of the system length and of the system waiting time in a simple and unified manner. From these distributions, important performance measures of practical interest can be easily obtained. Numerical examples concerning the superposition of certain video traffics are presented at the end.

Optimistic performance of carbon-free hydrazine fuel cells based on controlled electrode structure and water management

In this study, we first attempted to discover the optimal configuration of membrane-electrode assemblies(MEAs) used to achieve a high performance of direct hydrazine fuel cells(DHFCs). We have investigated the effect of water management and the electrode thickness on the performance of DHFCs, depending on the hydrophobicity of the gas diffusion layers in the cathode and the catalyst loading in the anode with the carbon-supported Ni, synthesized by a polyol process. With the optimal water management and electrode thickness, the MEA constructed using the as-prepared Ni/C anode catalyst containing the metallic and low oxidative state and ultra-low Pt loading cathode reduced the ohmic resistance and mass transfer limitation in the current-voltage curves observed for the alkaline DHFC, achieving an impressive power performance over 500 mW cm^(–2).