Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

Congestion control is one of the main obstacles in cyberspace traffic.Overcrowding in internet traffic may cause several problems;such as high packet hold-up,high packet dropping,and low packet output.In the course of data transmission for various applications in the Internet of things,such problems are usually generated relative to the input.To tackle such problems,this paper presents an analytical model using an optimized Random Early Detection(RED)algorithm-based approach for internet traffic management.The validity of the proposed model is checked through extensive simulation-based experiments.An analysis is observed for different functions on internet traffic.Four performance metrics are taken into consideration,namely,the possibility of packet loss,throughput,mean queue length and mean queue delay.Three sets of experiments are observed with varying simulation results.The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.

Water Pollution and Environmental Governance of the Tai and Chao Lake Basins in China in an International Perspective

The Tai and Chao Lake basins are currently facing a serious water pollution crisis associated with the absence of an effective environmental governance system. The water pollution and the water governance system of the two basins will be compared. The reasons for water pollution in both basins are similar, namely the weak current water environmental governance system cannot deal with the consequences of the rapidly growing economy. China’s water governance system is a complicated combination of basin management with both departmental management and regional management. There is an absence of legal support and sound coordination mechanisms, resulting in fragmented management practices in the existing water environmental governance system. A comparison is made for the Tai and Chao Lake basins and Canada, France, the United Kingdom and the United States. Based on China’s present central-local governance structure and departmental system, an integrated reform of basin level and water environmental governance in China should learn from international experiences. The reforms could consist of improved governance structures, rebuilding authoritative and powerful agencies for basin management, strengthening the organizational structure of the basin administrations, improving legislation and regulatory systems for basin management and enhancing public participation mechanisms.

Behavioral Effects of Colorful Lights with Different Spectra on Siberian Rubythroat Luscinia calliope

To investigate the activities of typical night-migrating birds under the light with various spectral compositions during autumn migration,23 Siberian Rubythroat Luscinia calliope were tested under colorful LED lights with different wavelengths and mixed white at 100 lx.Using behavior response as an indicator,the quantitative relationship between the activity level of test birds and illumination stimulation duration was acquired.It was found that the visible light sensitivity of Siberian Rubythroat was the highest for the 478 nm turquoise and decreased towards the 622 nm red.

Outsourcing model city selection in China from the offshore outsourcer's perspective based on the additive SE-DEA model

More and more enterprises are outsourcing activities that are neither cost efficient if done in-house nor central to their businesses. Most of the studies in outsourcing decision making focus on vendor selection. However, little research has been done about location selection, which is also a critical step in offshore service outsourcing. The purpose of this paper is to offer a new method to deal with the destination selection problem in China. We employed the additive SE-DEA model to overcome the drawbacks of traditional DEA and SE-DEA methods, and calculated the relative efficiency of 20 service outsourcing model cities(excluding Xiamen). Based on two years of longitudinal study, we made a comparison of the 20 cities. Finally we classified the model cities by combining them with the service outsourcing ability dimension and also gave some selection suggestions and development suggestions for outsourcers' outsourcing service and the model cities, respectively.

Problematic of Accounting Evolutivity: An Answer Through the Analysis of SYSCOHADA in the Cameroonian Context

This article describes the conceptual, practical, and organizational difficulties of evolutivity of a new uniform act. Besides the problem of stock market listing of most African countries enterprises seen among many other things as a prerequisite to the internationalization of accountancy, particularly, to the emergency of the full or total or not at all implementation of an innovation, it is a matter of bringing the foundations into focus. The most expected heated debates on the SYSCOHADA accounting evolutivity in view of the implementation of its new uniform act, of the quality of information provided and the informational pertinence are therefore collected through semi-direct interviews from accountants and auditors. A situational analysis is carried out in the Cameroonian context. From a qualitative approach, the potential constraints are explained. The interest of the analysis of this evolutivity is that, despite its temporal status, it is intimately linked to organizational and social realities.

Integrating digital twins and deep learning for medical image analysis in the era of COVID-19

Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and tech-nologies.Digital twins may allow healthcare organizations to determine methods of improving medical processes,enhancing patient experience,lowering operating expenses,and extending the value of care.During the present COVID-19 pandemic,various medical devices,such as X-rays and CT scan machines and processes,are constantly being used to collect and analyze medical images.When collecting and processing an extensive volume of data in the form of images,machines and processes sometimes suffer from system failures,creating critical issues for hospitals and patients.Methods To address this,we introduce a digital-twin-based smart healthcare system in-tegrated with medical devices to collect information regarding the current health condition,configuration,and maintenance history of the device/machine/system.Furthermore,medical images,that is,X-rays,are analyzed by using a deep-learning model to detect the infection of COVID-19.The designed system is based on the cascade recurrent convolution neural network(RCNN)architecture.In this architecture,the detector stages are deeper and more sequentially selective against small and close false positives.This architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the other.At each stage,the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different stages.In this manner,the arrangement of detectors is adjusted to increase the intersection over union,overcoming the problem of overfitting.We train the model by using X-ray images as the model was previously trained on another dataset.Results The developed system achieves good accuracy during the detection phase of COVID-19.The experimental outcomes reveal the efficiency of the detection architecture,which yields a mean average precision rate of 0.94.

Survey on <i>Essential and Accidental Real-Time Issues</i>in Software Engineering

Software product lines have recently been presented as one of the best promis-ing improvements for the efficient software development. Different research works contribute supportive parameters and negotiations regarding the prob-lems of producing a perfect software scheme. Traditional approaches or recy-cling software are not effective to solve the problems concerning software competence. Since fast developments with software engineering in the past few years, studies show that some approaches are getting extensive attention in both industries and universities. This method is categorized as the software product line improvement;that supports reusing of software in big organizations. Different industries are adopting product lines to enhance efficiency and reduce operational expenses by way of emerging product developments. This research paper is formed to offer in-depth study regarding the software engineering issues such as complexity, conformity, changeability, invisibility, time constraints, budget constraints, and security. We have conducted various research surveys by visiting different professional software development organizations and took feedback from the professional software engineers to analyze the real-time problems that they are facing during the development process of software systems. Survey results proved that complexity is a most occurring issue that most software developers face while developing software applications. Moreover, invisibility is the problem that rarely happens according to the survey.

Labor Transfer:A Catalyst or Anticatalyst for Poverty Alleviation?--A study based on a survey of 237 rural households in Yi communities in Liangshan

This paper takes income and“two no worries and three guarantees”(rural poor people have no worry about food and clothing and have access to compulsory education,basic medical services,and safe housing)as two essential indexes,and is based on data collected from a survey of 237 rural households living below the poverty level in Yi communities in Liangshan.Through case studies and positive analysis,we explored the impact of labor transfer on poverty alleviation.We found that the following factors contribute to a double paradox of labor service-enabled poverty alleviation:An income growth trend which prioritizes“external income”(from urban work)over“internal income”(from an agricultural business),restrictions by performance indexes,as well as a lack of public services in rural areas.Although labor transfer can help rural households increase household income and non-agricultural income and reduce poverty within a short period of time,it can hardly promote agricultural businesses through non-agricultural employment and thereby help poverty-stricken rural households permanently alleviate poverty.Although labor transfer can exacerbate various issues facing poverty-stricken households(such as a delay in housing construction,school drop-out rates and health risks for the elderly),it can also play a positive role in raising the profile of farmers and improving rural education and living environments.Based on this study,we propose continuing the supply-side reform in the labor market,implementing differentiated poverty alleviation policies,improving the existing mechanism for poverty alleviation assessment,establishing rural public services,and launching a corresponding multi-channel financing mechanism.

Overall Planning of Public Power and Expression of Private Rights in Cross-regional Trading of Construction Land Quotas

In China, the trading of construction land quotas has undergone an institutional evolution process characterized by gradual deregulation.In 2021, the Central Committee of the Communist Party of China(CPC) resolved to develop a national cross-regional trading mechanism for construction land quotas.Construction land quotas, which have attributes of both public power and private rights, share certain common grounds with the rights of land development, dumping and carbon emission.To build a national trading market for construction land quotas, it is necessary to make clarifications and innovations in macro-level ideas, meso-level mechanisms, and micro-level designs.

Towards Collaborative Robotics in Top View Surveillance:A Framework for Multiple Object Tracking by Detection Using Deep Learning

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.

Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate.Early diagnosis may increase the chances of treatment and survival however the process is time-consuming(reliability and complexity involved to extract the hand-crafted features)and largely depends on pathologists’experience.Convolutional Neural Network(CNN—an end-to-end model)is known to be an alternative to overcome the aforesaid problems.Therefore,this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet(a high-class imbalanced dataset).Though,during training,class-imbalanced data can negatively affect the performance of CNN.Therefore,an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance.In this process,a hierarchical CNN model is designed,in which the former model is non-regularized(due to dense architecture)and the later one is regularized,specifically designed for small histopathology images.Moreover,the regularized model is integrated with CNN’s basic architecture to reduce overfitting.Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training.The training and testing accuracies of the non-regularized CNN model are 98%&78%with an imbalanced dataset and 96%&81%with a balanced dataset,respectively.The regularized CNN model training and testing accuracies are 84%&75%for an imbalanced dataset and 87%&86%for a balanced dataset.

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe care to patients.This work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively detected.To do this,the overhead view patients’data set has been recorded.For testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based model.The model uses confidence maps and detects 18 different key points at various locations of the body of the patient.Applying association rules and part affinity fields,the detected key points are later converted into six main body organs.Furthermore,the distance of subsequent key points is measured using coordinates information.Finally,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions.The accuracy of the proposed system is assessed on various test sequences.The experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.

Q-Learning Based Routing Protocol for Congestion Avoidance

The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods.However,these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically.To overcome this drawback,we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources.In a proposed routing protocol,either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state,Q-value,and reward function to set the desired path toward the destination.A new reward function that consists of a buffer occupancy,link reliability and hop count is considered.Moreover,look ahead algorithm is employed to update the Q-value with values within two hops simultaneously.This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account,accordingly.Finally,the simulation results presented approximately 20%higher packet delivery ratio and 15%shorter end-to-end delay,compared to those with the existing scheme by avoiding congestion adaptively.

Efcient Algorithms for Cache-Throughput Analysis in Cellular-D2D 5G Network

In this paper,we propose a two-tiered segment-based Device-toDevice(S-D2D)caching approach to decrease the startup and playback delay experienced by Video-on-Demand(VoD)users in a cellular network.In the S-D2D caching approach cache space of each mobile device is divided into two cache-blocks.The rst cache-block reserve for caching and delivering the beginning portion of the most popular video les and the second cacheblock caches the latter portion of the requested video les‘fully or partially’depending on the users’video watching behaviour and popularity of videos.In this approach before caching,video is divided and grouped in a sequence of xed-sized fragments called segments.To control the admission to both cacheblocks and improve the system throughput,we further propose and evaluate three cache admission control algorithms.We also propose a video segment access protocol to elaborate on how to cache and share the video segments in a segmentation based D2D caching architecture.We formulate an optimisation problem and nd the optimal cache probability and beginning-segment size that maximise the cache-throughput probability of beginning-segments.To solve the non-convex cache-throughout maximisation problem,we derive an iterative algorithm,where the optimal solution is derived in each step.We used extensive simulations to evaluate the performance of our proposed S-D2D caching system.

Triple Key Security Algorithm Against Single Key Attack on Multiple Rounds

In cipher algorithms,the encryption and decryption are based on the same key.There are some limitations in cipher algorithms,for example in polyalphabetic substitution cipher the key size must be equal to plaintext otherwise it will be repeated and if the key is known then encryption becomes useless.This paper aims to improve the said limitations by designing of Triple key security algorithm(TKS)in which the key is modified on polyalphabetic substitution cipher to maintain the size of the key and plaintext.Each plaintext character is substituted by an alternative message.The mode of substitution is transformed cyclically which depends on the current position of the modified communication.Three keys are used in the encryption and decryption process on 8 or 16 rounds with the Exclusively-OR(XOR)of the 1st key.This study also identifies a single-key attack on multiple rounds block cipher in mobile communications and applied the proposed technique to prevent the attack.By utilization of the TKS algorithm,the decryption is illustrated,and security is analyzed in detail with mathematical examples.

Urdu Ligature Recognition System:An Evolutionary Approach

Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex characteristics.Evolutionary approaches like genetic algorithms have been used in the past for various optimization as well as pattern recognition tasks,reporting exceptional results.The proposed Urdu ligature recognition system uses a genetic algorithm for optimization and recognition.Overall the proposed recognition system observes the processes of pre-processing,segmentation,feature extraction,hierarchical clustering,classification rules and genetic algorithm optimization and recognition.The pre-processing stage removes noise from the sentence images,whereas,in segmentation,the sentences are segmented into ligature components.Fifteen features are extracted from each of the segmented ligature images.Intra-feature hierarchical clustering is observed that results in clustered data.Next,classification rules are used for the representation of the clustered data.The genetic algorithm performs an optimization mechanism using multi-level sorting of the clustered data for improving the classification rules used for recognition of Urdu ligatures.Experiments conducted on the benchmark UPTI dataset for the proposed Urdu ligature recognition system yields promising results,achieving a recognition rate of 96.72%.

Rural Land System Reform Pilot Experiment in Pidu District: Background, Debate and Enlightenment

By combining top-level design with local pilot practices,rural land system reform,as an important priority on the agenda of deepening comprehensive reform,has accumulated a series of replicable and applicable experiences that are conducive to amendments to relevant laws.This paper takes stock of the basic experience of Pidu District in addressing typical problems arising in its rural land system reform and then investigates the inner link between the reform and the persistence of high housing prices in cities,the capital flowing to the countryside,and the rural revitalization program.Based on this analysis,the author puts forward three basic directions of the rural land system reform:remain committed to marketization,balance the interests of all parties and promote the development of rural industries.

Adaptive Flower Pollination Algorithm Based on Chaotic Map

Flower pollination algorithm (FPA) is one of the well-known evolutionary techniques used extensively to solve optimization problems. Despite its efficiency and wide use, the identical search behaviors may lead the algorithm to converge to local optima. In this paper, an adaptive FPA based on chaotic map (CAFPA) is proposed. The proposed algorithm first used the ergodicity of the logistic chaos mechanism, and chaotic mapping of the initial population to make the initial iterative population more evenly distributed in the solution space. Then at the self-pollination stage, the over-random condition of the gamete renewal was improved, the traction force of contemporary optimal position was given, and adaptive logarithmic inertia weight was introduced to adjust the proportion between the contemporary pollen position and disturbance to improve the performance of the algorithm. By comparing the new algorithm with three famous optimization algorithms, the accuracy and performance of the proposed approach are evaluated by 14 well-known benchmark functions. Statistical comparisons of experimental results show that CAFPA is superior to FPA, PSO, and BOA in terms of convergence speed and robustness.

Enhanced Butterfly Optimization Algorithm for Large-Scale Optimization Problems

To solve large-scale optimization problems,Fragrance coefficient and variant Particle Swarm local search Butterfly Optimization Algorithm(FPSBOA)is proposed.In the position update stage of Butterfly Optimization Algorithm(BOA),the fragrance coefficient is designed to balance the exploration and exploitation of BOA.The variant particle swarm local search strategy is proposed to improve the local search ability of the current optimal butterfly and prevent the algorithm from falling into local optimality.192000-dimensional functions and 201000-dimensional CEC 2010 large-scale functions are used to verify FPSBOA for complex large-scale optimization problems.The experimental results are statistically analyzed by Friedman test and Wilcoxon rank-sum test.All attained results demonstrated that FPSBOA can better solve more challenging scientific and industrial real-world problems with thousands of variables.Finally,four mechanical engineering problems and one ten-dimensional process synthesis and design problem are applied to FPSBOA,which shows FPSBOA has the feasibility and effectiveness in real-world application problems.