Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Task Scheduling, Routing, and Charging for Multi-UAV Based Mobile Edge Computing

Unmanned aerial vehicles (UAVs) based mobile edge computing (MEC) systems have attracted increasing research attention recently. They can provide on-demand computing services for ground users (GUs) without relying on any communication infrastructures and have the potential to provide better computing services with lower latency, compared with the conventional ground-based MEC or cloud-based systems. Considering the limited battery capacity of the UAVs, existing studies on UAV-based MEC have focused on using UAVs to serve GUs over small areas so that all tasks can be completed during a single flight. In this paper, we aim to remove this restriction and expand the range of users the UAV-based MEC system can serve, by integrating charge stations into the system. A joint task scheduling, routing, and charging problem is then formulated with the objective to minimize the total energy consumption, total service time, and total energy charged simultaneously. To solve this problem, we develop a mixed-integer programming (MIP) model and an equivalent mixed-integer linear programming (MILP) model. Comparative numerical studies demonstrate the optimal solutions found by the proposed approaches.
Global Job Quality: Evidence from Wage Employment across Developing Countries

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. of Costs and Standards of Analysis of Private
Relationship Marketing and Customer Loyalty in Ghana’s Informal Economy: Does Customer Perceived Value Matter?

ABSTRACT In this study, we seek to understand the role of relationship marketing in achieving customer loyalty in Ghana’s informal economy. The study further investigated the role of customer perceived value as a pass-through mechanism for the relationship between relationship marketing and customer loyalty. We collected primary data from 229 customers of informal economy operators in Accra. The data was then analyzed using structural equation modeling utilizing the STATA 15 Package. Our findings indicate that commitment is the only fundamental relationship marketing factor investigated that has a direct influence on customer loyalty in the informal business context. Trust and conflict handling on the other hand benefit significantly from the presence of customer perceived value whereas communication seems to have negligible effects. Consequently, scholars and marketing practitioners seeking to deploy the relationship marketing concept in the informal economy need to be aware of these contextual interplays and how they shape further discussions of the relationship marketing theory.
Phase Noise Analysis of Separately Driven Ring Oscillators

In this paper, for the first time, the phase noise analysis of a Multi-loop Skew based Single Ended Oscillator (MSSROs) is derived and validated. Compared to the three stages of conventional ring oscillators (CROs), SDROs provide an equivalent oscillation frequency with improved phase noise with increasing stages. The primary distinction between these two designs (SDRO and three-stage CROs) is the inherent skew offset between the PMOS/NMOS gates caused by the unique connection. This skew offset is the fundamental cause of delay cell noise suppression; the SDROs have loosely coupled oscillators that run concurrently, forming multiple 3-stages of separately driven Ring Oscillators. As a result, a shaping function is derived in terms of skew offset, and simulating these with varying skew offset results in suppressing behavior. Additionally, we derived phase noise for a skew-based design and validated it in PDKs of 180nm and 65 nm. We plotted the thermal (flicker) noise contribution and found that increasing the number of stages leads to an approximately 1-2 dB reduction in phase noise while maintaining the same NMOS/PMOS size ratio. Finally, a 2-3 dB reduction in phase noise is achieved in MSSROs by incorporating the shaping function into phase noise equations.
Towards Distributed Communication and Control in Real-World Multi-Agent Reinforcement Learning

Multi-agent system investigates the problem of designing a complex system composed of multiple autonomous agents with limited ability and partial observability. As a milestone, AlphaStar has achieved remarkable success in StarCraft II, which is a significant breakthrough in the competitive environments with complex strategic spaces and real-time decisions. However, it poses new challenges for deploying these centralized control models in real-world environments because many of them in such competitive environments were not designed to accommodate the requirements of real-world communication networks, e.g., the problems of high latency and large traffic are inevitable when they are actually deployed. To alleviate this issue, we propose a distributed control paradigm that explicitly splits the control power between the centralized meta-agent and agent units through a combination of centralized and decentralized paradigms. The units can autonomously decide to follow the decisions of the meta-agent or adapt to environment variations immediately by themselves in a decentralized manner. We simulate real-world network environments based on the Mininet platform, experiments based on the StarCraft II Learning Environment (SC2LE) show that our approach achieves a better adaptation in real-world network environments.
Federated Learning for Anomaly Detection: A Case of Real-World Energy Storage Deployment

We have aspired as a green and intelligent future, where humans, the built environment, and the nature are interconnected as a cyber-physical system. To such an Internet of Things, the sustainability and robustness of the power system is crucial, and the reliable operation of the battery-backed energy storage systems is key because of their abilities in power smoothing and shifting. However, detecting battery failures at the early-deployment stage is challenging due to the unavailability of anomalous measurement data and privacy concerns. In this paper, we propose an anomaly detection scheme for the energy storage systems without using prior information. We train autoencoders on the normal measurement data. Instead of training autoencoders in a centralized way, we train a global autoencoder over many energy storage systems in a federated manner without compromising privacy. Experimental results show that the proposed algorithm effectively detects anomalous batteries instantly after the system is set up without sharing sensitive data.
Growth Inhibition of Rhizoctonia solani and Its Infection Inhibition on the Rice Seedling by Rice Endophytic Bacteria

Rhizoctonia solani merupakan salah satu patogen penting pada tanaman padi yang dapat menyebabkan hawar pada benih, daun, dan pelepah daun. Penyakit hawar pelepah ini menjadi masalah yang cukup serius di berbagai negara penanam padi, termasuk Indonesia. Pengendalian yang mengandalkan penggunaan pestisida sintetis dan varietas tahan sering kali menimbulkan permasalahan baru baik pencemaran lingkungan maupun munculnya populasi patogen resisten sehingga masih diperlukan pengendalian yang lebih aman dan ramah lingkungan. Penelitian ini melaporkan potensi bakteri endofit asal tanaman padi sehat dalam menghambat pertumbuhan R. solani secara in vitro dan kemampunnya dalam menekan infeksi R. solani pada perkecambahan padi. Percobaan menggunakan metode kultur ganda antara galur-galur bakteri endofit dan R. solani. Pengamatan dilakukan terhadap pertumbuhan koloni R. solani dan pertumbuhan miselium secara mikroskopis. Pengujian dilanjutkan dengan memperlakukan benih padi menggunakan bakteri endofit. Benih padi yang telah dilapisi dengan bakteri endofit kemudian ditumbuhkan pada medium agar-agar dekstrosa kentang yang telah ditumbuhi R. solani. Tingkat gangguan penyakit hawar pada benih menunjukkan bahwa hampir semua galur bakteri yang diuji dapat menghambat pertumbuhan koloni R. solani dan juga menyebabkan malformasi pada miseliumnya. Aplikasi bakteri endofit pada benih padi juga secara nyata menurunkan tingkat infeksi R. solani pada benih padi. Di antara bakteri yang diuji, OS7 berpotensi dikembangkan sebagai agens pengendali hayati hawar pelepah yang disebabkan oleh R. solani. Penelitian dalam skala rumah kaca dan skala lapangan perlu dilakukan lebih lanjut.
Subepithelial tumors: How does endoscopic full-thickness resection & submucosal tunneling with endoscopic resection compare with laparoscopic endoscopic cooperative surgery?

Background and study aims Endoscopic techniques are rapidly emerging for resection of subepithelial tumors (SETs). Submucosal tunneling for endoscopic resection (STER), endoscopic full-thickness resection (EFTR) and laparoscopic endoscopic cooperative surgery (LECS) are current alternatives to open surgery. In this study, we aimed to compare the three endoscopic techniques. Patients and methods Consecutive patients who underwent resection of a submucosal esophageal or gastric lesion at several tertiary care centers were included in a dedicated registry over 3 years. Demographics, size and location of resected lesion, histology of specimen, length of procedure, adverse events (AEs), duration of hospital stay, and follow-up data were collected. Results Ninety-six patients were included (47.7 % male, mean age 62): STER n = 34, EFTR n = 34, LECS n = 280. The lesions included leiomyoma, gastrointestinal stromal tumors (GISTs) and other. The mean lesion size was 28 mm (STD 16, range 20–72 mm). The majority of lesions in the EFTR and laparoscopic-assisted resection group were GISTs. There was no significant difference in clear resection margins, post-procedure complication rates, recurrence rate and total follow-up duration between the groups. However, the LECS group had a procedure time at least 30 minutes longer than STER or EFTR ( P < 0.01). Total hospital stay for the laparoscopic-assisted resection group was also longer when compared to STER (1.5) and EFTR (1.8) ( P < 0.01). Conclusions STER, EFTR, and laparoscopic-assisted resection are efficacious approaches for resection of SETs with similar R0 resection rates, complication rates, and AE rates. Laparoscopic assisted resection appears more time-consuming and is associated with a longer hospital stay.
Effect of pH, cationic inducer, and clam shells as bio-flocculant in the optimization of the flocculation process for enhanced microalgae harvesting using response surface methodology

ABSTRACT Flocculants agglomerate suspended microalgae cells, while cost and toxicity have led to the increased use of bio-flocculants. In this experiment, Chlorella pyrenoidosa was gathered by utilizing bio-flocculants from discarded clam shells. At pH 8, 0.2 mg/mL of bio-flocculant clam shell, 0.1 mg/mL of cationic inducer, and 240 rpm of mixing achieved 91.87 % flocculation efficiency and 458.1 mg of recovered biomass. Calcium ions in bio-flocculants are the main contributor to Chlorella pyrenoidosa flocculation, employing charge neutralization and sweeping as flocculation mechanisms. Zn2+ salt boosts flocculation by neutralizing the functional group's negative charge. The R2 values of 0.8969 and 0.8894 for harvesting efficiency and recovered biomass reflect the model's predictive power. XRD exhibited faint, indistinct peaks with considerable noise, indicating that the chitosan bioflocculant did not have a crystalline structure and that Chlorella had become fibrillar. Response Surface Methodology approach, which promotes flocculation, improves water reuse and microalgae harvesting.
Deep Learning and Biomedical Engineering

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. That is generally because of the increasing amount of data, input data sizes, and of course, greater complexity of objective real-world problems. Performed research studies in the associated literature show that the DL currently has a good performance among considered problems and it seems to be a strong solution for more advanced problems of the future. In this context, this chapter aims to provide some essential information about DL and its applications within the field of biomedical engineering. The chapter is organized as a reference source for enabling readers to have an idea about the relation between DL and biomedical engineering.
Jamming for Secrecy: Reinforcement Learning Based Anti-Eavesdropping Visible Light Communication

Due to the broadcast nature of visible light communication (VLC), the secrecy protection is a crucial issue. In this paper, aiming at the communication scenario of a point-to-point VLC transmission link, an anti-eavesdropping model utilizing friendly jamming is constructed, which is composed of a light-emitting diode (LED) transmitter, a legitimate receiver equipped with a photodiode (PD), and a potential eavesdropper trying to obtain the undisclosed information transmitted. In addition, the system is equipped with a friendly jammer composed of multiple LEDs. A friendly jamming scheme based on reinforcement learning (RL) is proposed, which adopts different friendly jamming actions with the dynamic change of the environment. The simulation results show that compared with the benchmark state-of-the-art method, the proposed scheme can significantly reduce the bit error rate (BER) of the legitimate receiver and improve the overall performance of the anti-eavesdropping VLC system.
Advances in real‐world applications for legged robots

This paper provides insight into the application of the quadrupedal robot ANYmal in outdoor missions of industrial inspection (autonomous robot for gas and oil sites [ARGOS] challenge) and search and rescue (European Robotics League (ERL) Emergency Robots). In both competitions, the legged robot had to autonomously and semiautonomously navigate in real‐world scenarios to complete high‐level tasks such as inspection and payload delivery. In the ARGOS competition, ANYmal used a rotating light detection and ranging sensor to localize on the industrial site and map the terrain and obstacles around the robot. In the ERL competition, additional real‐time kinematic–global positioning system was used to colocalize the legged robot with respect to a micro aerial vehicle that creates maps from the aerial view. The high mobility of legged robots allows overcoming large obstacles, for example, steps and stairs, with statically and dynamically stable gaits. Moreover, the versatile machine can adapt its posture for inspection and payload delivery. The paper concludes with insight into the general learnings from the ARGOS and ERL challenges.
Computation Throughput Maximization for UAV-Enabled MEC with Binary Computation Offloading

Mobile edge computing (MEC) has been considered to provide computation services near the edge of mobile networks, while the unmanned aerial vehicle (UAV) is becoming an important integrated component to extend service coverage. In this paper, we consider a UAV-enabled MEC with binary computation offloading, where a UAV serves as an aerial edge server and each task of devices is either executing locally or offloading to the aerial edge server as a whole. To provide fairness among different ground devices, we aim to maximize the minimum computation throughput for all devices via the joint design of computing mode selection and UAV trajectory as well as resource allocation. The optimization problem is formulated as a mixed-integer nonlinear problem consisting of binary variables, which is difficult to tackle. The influence of non-binary solutions is penalized with a penalty function, based on which we develop an efficient iteration algorithm to obtain a suboptimal solution via leveraging the penalty successive convex approximation (P-SCA) method and difference of two convex (D.C.) optimization framework, where the algorithm is guaranteed to converge. Extensive simulations are conducted and the results with different system parameters show the effectiveness of the proposed joint design algorithm compared with other benchmark schemes.
Study of the relative abundance for zooplankton community in the Euphrates River within the city of Samawah/ Iraq

This study, the first of its kind, was conducted on the Euphrates River in the city of Samawah. To identify and diagnose the environment of the diversity of zooplankton in the Euphrates River. This study was conducted from December of the year 2021 until February 2022. Three sites were chosen: The first site (enters The Euphrates River in the city of Samawah) The second site (city centre) and the third site (the exit of the river from the city) are 11 km away from each other. The study included a diagnosis of zooplankton, the study of total density and evidence of relative abundance. The study identified 50 zooplankton taxonomic units, 31 units belonged to Rotifera, 10 units belonged to Cladcera, and 9 units belonged to Copepoda. The Rotifera predominate in the Euphrates river, both quantitatively and qualitatively. The total density of zooplankton recorded values ​​ranging from 260 - 3110 ind/m3 recorded for Rotifera, the Rotifera recorded the highest density, which ranged between 220 - 2990 ind / m3, and the Cladcera recorded the lowest density in this study, which ranged between no-appearance - 160 ind / m 3 The density of the Copepoda ranged between 10 - 370 ind /m3.
Twitter Users’ Emotion, Emoticons and Scaling Metrics Based Categoric Interaction Analysis

The popularity and use of social networks has also begun to increase in parallel with the worldwide increasing accessibility and means of access to the Internet. As one of the world's most popular social networks, Twitter is a platform where users are interacting through follow-up, sharing, messaging and appreciation tools, sharing their ideas and emotions in a variety of individual and corporate contexts. Therefore, Twitter is intense, dynamic and always an up-to-date data source. Identifying and correlating the physical and emotional interaction of users can be valuable in political, social, academic and commercial aspects. Users' physical networking with each other and emotional analysis can be done with many tools and applications. The character, tendency and impact analysis of the users can be used in the development of business intelligence applications and in the determination of social strategies. In this study, a large Twitter user group is divided into four categories: political, Entertainment, Sports, Trade Marks. Then, the physical and emotional interaction of each category was revealed. The Physical interaction metrics determined as centrality, intensity, reciprocity and modularity while emotional interaction metrics were determined as resistance, passion, reach and emotionality. Positive, negative and neutral states of sharing were discussed in emotional measurement. Beside that, emoji-containing tweets have been transformed into texts and are especially included in emotion analysis. After all the metrics were calculated, physical and emotional interaction structures and overlap rates were revealed using "Interaction and Semantic Clustering Based Multinetwork Analysis" method.
Group-based WSN with the use of LoRa for long-range communications

Wireless Sensor Networks (WSNs) are widely spread to monitor all type of parameters with the aim of improving our every-day lives. Typical WSN are networks on which only one wireless technology is used, so applicability and restrictions are determined by it. In this paper, a WSN of WSNs with the configuration of multiple wireless technologies is proposed. A network discovery system is defined to explain how to build the topology of a network with different standards. In the same way, a routing protocol is defined to communicate devices with different wireless technologies. As part of the network, LoRa technology is intended to cover long distances so it can be used to interconnect WSNs located on the edge to the Core of the network. Low-cost Heltec LoRa WiFi v2 devices are employed to determine the performance of lowcost solutions for one hop. This study is done for both 433 MHz and 868 MHz devices, which are the available frequencies in Europe. The results show that the best coverage results are obtained for SF 7 in all cases. Furthermore, the use of these low-cost devices is not advised for distances above 1 Km.
Interrogative Belief Revision

This thesis deals with the theory of interrogative belief revision, a recent development in the subfield of philosophical logic known as belief revision theory. While classical belief revision theory deals with the dynamics of an agent’s beliefs, interrogative belief revision aims to describe the dynamics of the agent’s questions about the world, or research agenda, as well. Great emphasis is put on the interaction between belief change and changes in the research agenda. I develop both the technical framework for this theory by constructing and investigating modal logics for reasoning about the dynamics of research interests, as well as pursuing further the philosophical foundations and applications of the theory. Among the contributions of the thesis are: - a novel interpretation of the theory founded on a notion of epistemic strategies - a solution to the previously open problem of contraction and revision in this context - several systems of modal logic that can be used to reason about interrogative belief revision - completeness results and decidability results for these logics - a characterization of a class of relevant conditionals based on a notion of agenda relevance
QoS-Aware Uplink NOMA with Multi-Type Service Coexistence for LEO Satellite Constellation

With the advancement of low earth orbit (LEO) satellite constellation, the LEO satellite-based Internet of Things (S-IoT) has attracted extensive attentions due to its wide coverage and broadband access capability. Considering the mission critical communications (MCC) and massive machine-type communications (mMTC) requirements of terrestrial user equipments (UEs), we first propose a quality of service (QoS)-aware uplink non-orthogonal multiple access (NOMA) transmission scheme for LEO high-throughput satellite (HTS) constellation, where MCC and mMTC services can coexist. Then, we derive the closed-form expressions of ergodic capacity (EC) and outage probability (OP), and obtain the expression of system throughput (ST) for NOMA and OMA schemes. Further, we propose a reduced system outage probability (RSOP) algorithm to minimize the OP of MCC services, and an improved system throughput (IST) algorithm to enhance the ST of mMTC services, while guaranteeing the OP requirements for each UE. Finally, simulation results validate the accuracy of our theoretical derivations and show that both RSOP-NOMA scheme and IST-NOMA scheme can outperform the state-of-art ones.