Miracle-dz/UniFAR-FTUs huggingface dataset:
A Conceptual Framework to Discover IT Project Risks in Developing Economies: An Application to Cambodia and Uganda Contexts

Background: The information technology (IT) project environment in developing economies is complex and dynamic due to their unique social, technological, cultural, and political contexts. However, the unique risk factors involved in IT projects in developing economies have seldom been discussed in the literature. This study proposes a conceptual framework for identifying risks in IT projects by considering the unique characteristics of project management in developing economies. Method: 17 project cases were examined via a series of expert interviews in Cambodia and Uganda. The interview results were coded into a total of 257 risk incidents, which were used to validate the proposed framework. Particularly, the risk incidents of each economy were matched with 16 risk categories under the proposed framework. The matched results were conceptualized into unique risk factors of each economy. Results: Our results reveal that the most critical risks in both economies involve the lack of structured and standardized project management (PM) processes, and misunderstanding and poor decision-making due to the lack of project experience and technical knowledge in local stakeholders. In addition to these shared risk factors, the two economies show distinct risk granularities. Particularly, IT projects in Cambodia are more vulnerable to cultural uniqueness while IT projects in Uganda suffer more with complex stakeholder structures. Conclusions: The proposed framework serves as guidance to identify IT project risks in developing economies. Furthermore, the identified risk factors using the framework proposed by this study help project managers or stakeholders recognize and mitigate the unique IT project risk factors in the two developing economies.
IoT Area Network Simulator For Network Dataset Generation

: With the development of information and communication technology (ICT), smart home technologies are expecting to be a potential solution for the population aging problem. Smart home simulators and testbeds have been introduced to provide materials for researchers in the ﬁeld of ambient assisted living to develop and evaluate their solutions. Since simulators reduce costs in terms of money and time, researchers are utilizing simulators to expand services to enhance user comfort, save energy, and detect abnormal behaviors in daily living activities, etc. However, available smart home simulators seem to focus on the operational aspects of simulated devices and environments. The network communication aspects have not been fulﬁlled so far. Following the development of the Internet of Things, smart home networks are more complex, and users in the smart home are ordinary people without knowledge about network management. Therefore, intelligent operation, administration, and maintenance (OAM) services for smart homes are desired. This paper proposes a smart home network simulator to generate a network dataset of a home network, which is essential to develop machine learning technologies for providing intelligent OAM services in smart homes. The simulator is implemented based on ECHONET Lite, a leading smart home protocol in Japan.
Risk assessment of logistics finance enterprises based on BP neural network and fuzzy mathematical model

Neural network is used to deal with the nonlinear relationship, usually there is a strong nonlinear relationship between input and output. Through the self-learning of neural network, the weight of data samples is determined after training, and the optimal solution is obtained according to the process steps. In this paper, thea authors analyze the risk assessment of logistics finance enterprises based on BP neural network and fuzzy mathematical model. For logistics companies, it is necessary to determine the ability of logistics companies to engage in logistics finance business, and then to make detailed and accurate grasp of relevant information. The difference between the actual output and the expected output of the training sample is small, so the fitting is completed well, and the parameters of the neural network are further adjusted. The results show that the model has a good ability of learning nonlinear function relations. To sum up, in order to reduce logistics financial risks, we must fully understand the factors that affect logistics financial risks, determine the proportion of risk factors, and then use the fuzzy evaluation method to analyze the financial business risks.
Elephant Genomes Elucidate Disease Defenses and Other Traits

Disease susceptibility and resistance comprise important factors in conservation, particularly in elephants. To determine genetic mechanisms underlying disease resistance and other unique elephant traits, we estimated 862 and 1,017 potential regulatory elements in Asian and African elephants, respectively. These elements are significantly enriched in both species with differentially expressed genes involved in immunity pathways, including tumor-necrosis factor which plays a role in the response to elephant endotheliotropic herpesvirus (EEHV). Population genomics analyses indicate that amplified TP53 retrogenes are maintained by purifying selection and may contribute to cancer resistance in elephants, including less malignancies in African vs. Asian elephants. Positive selection scans across elephant genomes revealed genes that may control iconic elephant traits such as tusk development, memory, and somatic maintenance. Our study supports the hypothesis that interspecies variation in gene regulation contributes to differential inflammatory responses leading to increased infectious disease and cancer susceptibility in Asian versus African elephants. Genomics can inform functional immunological studies which may improve both conservation for elephants and human therapies.
Measuring the Refractive Index and Its Two-Dimensional Distribution of Picoliter Microfluidics With the Scanning White Light Interference Microscopy Method

Measurements on refractive index and its distribution of microfluidics are widely applied in the field of biological detection, chemical analysis, material researches, medical diagnostics, and lab on a chip. Since the volume of the microfluidics is ultra-small, such as nanoliters or even picoliters, measuring the refractive index of microfluidics is challenging. We here presents an effective method to measure the refractive index and its two-dimensional distribution of picoliter microfluidics by the SWLI (scanning white light interference) microscopy technology. This method has the advantages of simplicity, low cost, non-contact, wide measurement range and high precision. We conduct the experiment to measure the refractive index of 2.4 picoliters liquid. The resolution is 10−4RIU (refractive index unit), and the relative error is 10−4 compared with the Abbe refractometer. We obtained the relationship between refractive index and concentration of glucose solution. In addition, we can measure the 2D (two-dimensional) distribution of RI (refractive index) of liquids. We can observe the liquid interface between deionized water and liquid paraffin clearly. Besides, we can observe the process of solute dissolution in the liquid and the change of refractive index.
Latency Constrained Non-Orthogonal Packets Scheduling With Finite Blocklength Codes

In this paper, we investigate the offline packets scheduling of a downlink system with two heterogeneous latency-constrained users. Specifically, user 1 is a latency-tolerant user which has one packet with long latency requirement, while user 2 is latency-sensitive user which has multiple packets with short latency requirements. To precisely characterize the transmission latency, the finite blocklength codes (FBC) capacity formula is used to measure the achievable date rates of the users. To cope with the heterogeneous latency requirements of users, a hybrid NOMA scheme is proposed. In the hybrid NOMA scheme, the packets of user 2 are non-orthogonally delivered with the packet of user 1 in a sequential way by taking the packets arrivals and deadlines into consideration. Due to complicate FBC capacity formula and the coupling effects of the variables, the formulated transmission energy minimization problem is challenging to solve. However, by properly utilizing the the fact that the transmission energy is monotonically decreasing with the blocklength, the optimal transmission blocklength and power of user 1 are derived in semiclosed-forms; while the remaining problem, for determining the transmission blocklength and power of user 2, is efficiently solved by using a successive upperbound minimization based algorithm. The simulation results demonstrate the efficacy of the proposed hybrid NOMA scheme.
Formating Reshape Control Strategy for Multiple Amphibious Spherical Robots

It is difficult for a rigid formation composed of multiple robots to pass through a narrow waterway, which needs to be realized by formation reshape. The virtual linkage structure is used to solve the problem that the conventional virtual structure is not easy to deform. In order to ensure the robustness of formation control, a discrete information consistency algorithm is added to provide the desire trajectory, then controller will be used to track it. At the same time, due to the difficulty of hydrodynamic modeling and the large overshoot of PID (proportional integral differential) controller, the double loop controller based on ADRC (Auto Disturbance Rejection Controller) include position loop and speed loop is used to adjust the position, and the single loop controller is used to adjust the heading angle and depth. The results of simulation show that the three combined methods of 3D virtual linkage structure, discrete consensus and ADRC realize the formation reshape.
Electromagnetic Radiation Reduction in 5G Networks and Beyond Using Thermal Radiation Mode

With the massive increase in the popularity of smartphones and mobile data applications demanding bandwidth requiring data rates of the order of Gb/s, exploration of untapped frequency spectrum such as mmWave has begun. Along with providing seamless connectivity and catering to achieving high QoS and QoE, investigations are ongoing to enhance our knowledge about the biological safety at high frequencies. There is a need to ensure safety and reliability for the exposed public and updating the government policies regarding safety standards and regulations. This article is consecrated to provide an insight into health effects pertaining to mmWave frequencies, addressing aspects such as thermal heating in the body tissues with temperature rise, specific absorption rate (SAR), power density. As a solution a proposal has been given for EM (Electromagnetic) radiation reduction for the mobile communication system in the form of a proposed mode i.e. “Thermal Radiation” (TR) mode endorsing its safe use, promoting Green WCN along with increased energy efficiency and reduced complexity for the future generations to come. The proposal also validates reduced power density, SAR and temperature elevation produced in the human tissue when compared to other models in the form of simulation results obtained. It can increase the safety and reliability of 5G and beyond i.e. 6G networks in future.
Performance Characterization of Simultaneous Multi-Threading and Index Partitioning for an Online Document Search Application

This work reports on the results of a performance characterization of Simultaneous Multi-Threading (SMT) and Index-Partitioning (IP) when executing an online document search application. One of the key paper findings is that SMT is suitable for a latency sensitive application. In particular, our analysis shows that while SMT degrades single-thread execution latency, it still improves overall response-latency (queueing plus execution latency) because the multiple SMT contexts increase available throughput and help decrease sufficiently queuing latency. SMT is particularly effective in reducing the queueing latency induced by IP. Additionally, we find that in every situation we have evaluated combining SMT and IP yields the same or better average and tail overall response-latency for the application, dataset and server type used in this study. This is true for both single and dual socket server configurations we have evaluated. Our analysis for the dual socket configuration reveals that IP benefits increase with higher utilization. This is in contrast to the typical belief that IP benefits diminish with higher utilization. This is a result of judicious pinning of index-threads and index-data to the hardware to reduce remote memory access.
Edge Intelligence for Energy-Efficient Computation Offloading and Resource Allocation in 5G Beyond

5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous capabilities of the end devices, edge servers, and the cloud and thus has the potential to enable computation-intensive and delay-sensitive applications via computation offloading. However, in multi user wireless networks, diverse application requirements and the possibility of various radio access modes for communication among devices make it challenging to design an optimal computation offloading scheme. In addition, having access to complete network information that includes variables such as wireless channel state, available bandwidth and computation resources, is a major issue. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information. In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption. We first present a multi-user end-edge-cloud orchestrated network where all devices and base stations have computation capabilities. Then, we formulate the joint computation offloading and resource allocation problem as a Markov Decision Process (MDP) and propose a new DRL algorithm to minimize system energy consumption. Numerical results based on a real-world dataset demonstrate that the proposed DRL-based algorithm significantly outperforms the benchmark policies in terms of system energy consumption. Extensive simulations show that learning rate, discount factor, and number of devices have considerable influence on the performance of the proposed algorithm.
SIG-CLA: A Significant Community Detection based on Cellular Learning Automata

Detecting community, as the fundamental task for the study of the network, reveals a hopeful approach to investigating the functional and topological properties of real networks. Recently, several algorithms for detecting community have been introduced from various perspectives. A typical algorithm which is noticed by many researchers in this scope is Modularity optimization. These algorithms significantly restricted in resolution limits that they may miss detecting communities that are less than a particular size. The paper presents an algorithm with the aid of ICLA (irregular cellular learning automata) for the detection of community structures (called SIG-CLA) in social networks. In the SIG-CLA, the ICLA is formed by the input network, and the communities are detected by communicating with both the local environment and the global environment via the significant function in the ICLA. Promising experimental results are presented to confirm the advantages of SIG-CLA with respect to Modularity and NMI.
Scheduling optimization of Automated Storage and Retrieval System Based on Four-way Shuttles

Automated storage and retrieval system is a very important research direction of industrial logistics. The emergence of four-way shuttle has significantly improved the automation of the entire system, and the related research on four-way shuttle storage and retrieval system is currently a hot issue. This paper introduces the automatic equipment composition of four-way shuttle storage and retrieval system and analyzes the operation flow. On this basis, the scheduling optimization model is established by using the genetic algorithm combined with the crossover mutation operator to study the effect of four-way shuttle and elevator on the operation efficiency of the storage system under different scheduling paths. Finally, the simulation results show that scheduling work according to the path obtained after model optimization can significantly improve the operating efficiency of the system. The research shows that the model can effectively obtain a better scheduling path, and has certain guiding significance for the actual operation of the industrial automated warehouse.
Multi-Beam NOMA for Millimeter-Wave Massive MIMO With Lens Antenna Array

Different from the conventional single-beam beamspace non-orthogonal multiple access (NOMA) scheme, where the users only within the same analog beam can be served by the NOMA technique, in this article, we propose a multi-beam NOMA scheme for the millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) with lens antenna array communication system. The proposed scheme can break the single-beam constraint and perform NOMA transmission within the different analog beams. Then, we design the power allocation for minimizing the total power consumption, which mainly includes two stages. In the first stage, after performing beam scanning and beam selection, we design the game-theory based user grouping method for minimizing the conditional total power consumption. In the second stage, with the achieved user grouping solution and the corresponding beamspace channel state information (CSI), we formulate and solve the power allocation problem for minimizing the total power consumption. In addition, given the user grouping solution for minimizing the total power consumption, for evaluating the sum rate performance of the proposed multi-beam beamspace MIMO-NOMA scheme, we formulate and solve the power allocation optimization problem for maximizing the sum rate. In simulations, compared with the single-beam beamspace MIMO-NOMA scheme, given the user grouping solution, the proposed scheme has inferior sum rate performance when the power budget is enough high. However, at the cost of a little higher transmit power consumption, the proposed scheme has lower total power consumption and can simultaneously serve more users with the same number of RF chains.
Soft Network Organisation Towards Future Distributed ML-based Sensing Systems

The article analyses efficient information detection by a network of sensors. It is assumed that every sensor collects local information, however, with the application of clustering in the network, it is possible to efficiently exchange knowledge within the network. In particular, the local knowledge about a licensed signal is acquired by sensors, and it is then exchanged and transferred to global knowledge. To this end, two types of clustering are analysed: hard K-means clustering and soft Kmeans. In the latter case, the crucial knowledge about stiffness is important to perform an efficient exchange of information. The analysis of desired stiffness values has been carried out with simulation experiments where the energy usage and reporting efficiency were compared for various sizes and types of clusters. Moreover, the quality of global knowledge is also shown in the simulation results.
Trajectory tracking control of hovercraft based on Shutting-Backstepping Sliding Model

Considering the problems of differential expansion and input saturation of the controller designed by the general backstepping method of the hovercraft during trajectory tracking, this paper proposes a Shunting-backstepping sliding mode control method. First, a three-degree-of-freedom hovercraft mathematical model is established. Then for the unknown environmental disturbances during actual navigation, an exponentially convergent disturbance observer is designed to estimate the environmental disturbances and compensate on the controller. A Shunting back-stepping sliding mode trajectory tracking controller is established. Finally, the simulation results compared with the tradition-al backstep sliding mode method show that the proposed algorithm can effectively avoid differential expansion and input saturation.
Solving the Network Shortest Path Problem on a Quantum Annealer

This article addresses the formulation for implementing a single source, single-destination shortest path algorithm on a quantum annealing computer. Three distinct approaches are presented. In all the three cases, the shortest path problem is formulated as a quadratic unconstrained binary optimization problem amenable to quantum annealing. The first implementation builds on existing quantum annealing solutions to the traveling salesman problem, and requires the anticipated maximum number of vertices on the solution path <inline-formula><tex-math notation="LaTeX">$|P|$</tex-math></inline-formula> to be provided as an input. For a graph with <inline-formula><tex-math notation="LaTeX">$|V|$</tex-math></inline-formula> vertices, <inline-formula><tex-math notation="LaTeX">$|E|$</tex-math></inline-formula> edges, and no self-loops, it encodes the problem instance using <inline-formula><tex-math notation="LaTeX">$|V||P|$</tex-math></inline-formula> qubits. The second implementation adapts the linear programming formulation of the shortest path problem, and encodes the problem instance using <inline-formula><tex-math notation="LaTeX">$|E|$</tex-math></inline-formula> qubits for directed graphs or <inline-formula><tex-math notation="LaTeX">$2|E|$</tex-math></inline-formula> qubits for undirected graphs. The third implementation, designed exclusively for undirected graphs, encodes the problem in <inline-formula><tex-math notation="LaTeX">$|E| + |V|$</tex-math></inline-formula> qubits. Scaling factors for penalty terms, complexity of coupling matrix construction, and numerical estimates of the annealing time required to find the shortest path are made explicit in the article.
Behavior Analysis of Soft Pneumatic Actuator Gripper by using Image Processing Technology

With the development of advance materials, soft robotics is being widely applied in various industrial manufacturing chains. One of the prominent applications of soft robotics is soft pneumatic actuator (SPA) gripper which is an essential part in the food industry due to their advantages like high compliance and human-friendly interaction. This study presents a systematic approach to analyze a SPA gripper used in the food industry. The behaviors of SPA gripper are analyzed by using image processing technology. Firstly, the structural geometry of soft gripper model controlled by pneumatic actuators is suggested. Moreover, the geometrical parameters like chamber thickness of the soft gripper is optimized by the combination of finite element method (FEM) modeling and experiment. The working characteristics of the chosen concept based on the chamber parameter of this soft SPA gripper are analyzed.
Bandgap Control of Two-Dimensional Coaxial Bragg Structures With Helically Corrugated Conductors

The untunable bandgap is a difficult problem for Bragg structures constructed from corrugated metallic waveguides, and becomes a major barrier for applications. Based on the coupled-mode theory, this paper presents the principle as well as a corresponding theoretical model of bandgap control of two-dimensional coaxial Bragg structures under the condition of a fixed ripple shape. It is shown that such control can be achieved by varying the angular deviation between the outer and inner corrugations, which can be done by rotating one of the conductors relative to the other. The effects of the proposed method on transmission bandgap control of two example structures were investigated by theoretical analysis and electromagnetic simulation. The results confirmed the validity of the proposal, and showed that such a control method can be used to manipulate the number and location of the working bands under multiple bandgap operation. These peculiarities provide potential applications of two-dimensional coaxial Bragg structures in constructing tunable passive and active high-power microwave devices.