Miracle-dz/UniFAR-FTUs huggingface dataset:
RF Energy Harvesting Wireless Networks: Challenges And Opportunities

Energy harvesting wireless networks is one of the most researched topics in this decade, both in industry and academia, as it can offer self-sustaining sensor networks. With RF energy harvesting (RF-EH) embedded, the sensors can operate for extended periods by harvesting energy from the environment or by receiving it as an Energy signal from a hybrid base station (HBS). Thus, providing sustainable solutions for managing massive numbers of sensor nodes. However, the biggest hurdle of RF energy is the low energy density due to spreading loss. This paper investigates the RF-EH node hardware and design essentials, performance matrices of RF-EH. Power management in energy harvesting nodes is discussed. Furthermore, an information criticality algorithm is proposed for critical and hazardous use cases. Finally, some of the RF-EH applications and the opportunities of 5G technologies for the RF-EH are introduced.
Predictive Modelling using Capillary Pressure Models and Fluid Densities and their Effect on Reservoir Fluid Distribution

Capillary pressure has always been a point of concern for reservoir modeling. It describes the initial pressure at which the de-saturation will be initiated and at the other extreme; it defines the irreducible saturation of the displaced fluid. In this study, Brooks & Corey and Van Genuchten models are used to predict maximum displacement pressure data with reference to non-uniformity in pore-size distribution. Both models are compared and a correlation has been developed to predict maximum displacement pressure with reference to variation in pore-size distribution index for Van Genuchten model. Further, the fluid distribution and water-oil contact level with reference to free-water level has been calculated using both models for reservoir modeling. The obtained results show that Van Genuchten model gives higher thickness for transition zones and difference in water-oil contact level with reference to free water level, as compared to Brooks and Corey correlation. Furthermore, on comparative basis, these differences increase with the decrease in uniformity in pore size distribution. Thus, the degree of uncertainty and complexity in developing reservoirs and analyzing fluid dynamics increases, in case of tighter or heterogeneous formations. Moreover, this study also shows that density also plays an important role in fluid dynamics and distribution in the reservoir.
Towards a Resilient Model of Education in Cameroon

ARTICLE INFO ABSTRACT Article History: Received 07.07. 2020 Received in revised form 15.03. 2021 Accepted Available online 01.07. 2021 The emergence of Coronavirus herein referred to as COVID-19 shut down schools globally for a while. The closure of schools negatively impacted educational systems that had not developed a resilient or alternative model of instructional delivery such as the Cameroon Ministry of Basic Education. Tentative measures were taken to ensure the continuous delivery of teaching while hoping to build a resilient model of education that can guarantee continuous learning in times of emergencies. With the desire to develop an e-learning platform, the following research question: What are the factors that will foster the use of e-Learning Among Primary School Teachers in Cameroon? To respond to the research question, survey research design methodology was employed targeting n = 378 primary school teachers. The collected data was analysed using Pearson r correlation to examine the direct relationship between two variables. The findings revealed a positive relationship between the variables: relative advantage; compatibility; trialability; observability and attitude towards behaviour intention to use the e-learning platform. The relationship between attitude and behaviour intention was the strongest. An indication that attitude is a strong predictor of behaviour intention. It was concluded that relative advantage, compatibility, trialability and observability were predictors of teachers’ attitude towards a behaviour intention to adopt the e-learning platform. On the other hand, the relationship between complexity and attitude was negative indicating that teachers perceive the e-learning platform as complex. It was recommended that teachers should be: thoroughly sensitised on the advantages of adopting the e-learning platform to innovate teaching and learning; and provided with the opportunity to experiment with the platform before it is finally rollout. © IJERE. All rights reserved
AlwaysSafe: Reinforcement Learning without Safety Constraint Violations during Training

Deploying reinforcement learning (RL) involves major concerns around safety. Engineering a reward signal that allows the agent to maximize its performance while remaining safe is not trivial. Safe RL studies how to mitigate such problems. For instance, we can decouple safety from reward using constrained Markov decision processes (CMDPs), where an independent signal models the safety aspects. In this setting, an RL agent can autonomously find tradeoffs between performance and safety. Unfortunately, most RL agents designed for CMDPs only guarantee safety after the learning phase, which might prevent their direct deployment. In this work, we investigate settings where a concise abstract model of the safety aspects is given, a reasonable assumption since a thorough under-standing of safety-related matters is a prerequisite for deploying RL in typical applications. Factored CMDPs provide such compact models when a small subset of features describe the dynamics relevant for the safety constraints. We propose an RL algorithm that uses this abstract model to learn policies for CMDPs safely, that is without violating the constraints. During the training process, this algorithm can seamlessly switch from a conservative policy to a greedy policy without violating the safety constraints. We prove that this algorithm is safe under the given assumptions. Empirically, we show that even if safety and reward signals are contradictory, this algorithm always operates safely and, when they are aligned, this approach also improves the agent’s performance.
A new chameleon of the Trioceros affinis species complex (Squamata, Chamaeleonidae) from Ethiopia

A new species of chameleon, Trioceros wolfgangboehmei sp. nov., inhabiting the northern slopes of the Bale Mountains in Ethiopia, is described. It differs from its Ethiopian congeners by a combination of the following features: presence of a prominent dorsal crest with a low number of enlarged conical scales reaching along the anterior half of the tail as a prominent tail crest, a casque raised above the dorsal crest, heterogeneous body scalation, long canthus parietalis, rugose head scalation, high number of flank scales at midbody and unique hemipenial morphology. Based on morphological characteristics, phylogenetic discordances of previous studies and biogeographical patterns, this new species is assigned to the Trioceros affinis (Ruppell, 1845) species complex. An updated comprehensive key to the Trioceros found in Ethiopia is provided.
Quality analysis of Luzhou-flavor liquor and Fen-flavor liquor with IR spectroscopy

Quality of Luzhou-flavor liquor and Fen-flavor liquor was analyzed by IR spectroscopy.The findings indicated that freeze-dried samples of liquor had evident fingerprint and IR spectra of Luzhou-flavor liquor and Fen-flavor liquor were significantly different.High contents of ester and high contents of carboxylate were occurred in Luzhou-flavor liquor and Fen-flavor liquor,respectively.IR spectroscopy is a direct method for analysis and evaluation the quality of liqour.
Machine Learning Based Methods for Handling Imbalanced Data in Hepatitis Diagnosis

Introduction: Hepatitis C virus is the leading cause of mortality from liver disease. Also, diagnosis systems are usable tools for better disease control and management. The aim of this study was to design an HCV disease prediction system and classify its severity based on data mining methods. Method: This is an applied research that uses the hepatitis C dataset in the UCI library. The study was conducted in four steps including data preprocessing, data mining, evaluation and system design. In data pre-processing, data balancing techniques were performed. Then, three data mining algorithms (Multi-Layer Perceptron, Bayesian network, and decision tree) were implemented and 10-fold cross-validation method was used to evaluate data mining algorithms. Finally, user interface was designed in MATLAB programming language (version 2016) based on the best algorithm.Results:The results showed that the over-sampling method improved the performance measures of data mining algorithms in disease prediction, so that in the O-dataset the accuracy of the best method (random forest) was 99.9%. Also, the random forest for the O-dataset had the best performance measures in term of sensitivity, accuracy and f-measure (99.9%) and the 100% specificity amount.Conclusion: Considering that the presented approach has performed better than all suggested methods in previous studies, the proposed system in this study can be used well in HCV diagnosing and determining its severity.
Diagnostic of bone metastasis in patients with breast cancer

Breast cancer is the most commonly occurring cancer in women population. 70% of patients with breast cancer have evidence of skeletal involvement Early detection of bone metastasis is very important for accurate staging, optimal treatment as well as prognosis of patients with cancer. In the last several decades various radiologic and nuclear medicine tools have been developed and extensively employed in the detection of bone metastasis, including those of breast cancer. Owing to wide spread availability of these technologies and incidence of breast cancer patients, many of which develop bone metastasis during the course of the disease. This paper reviews major aspects of diagnostic methods of bone metastasis in patients with breast cancer. It describes the available imaging methods to diagnose bone metastasis and its utility without having formal comparison between these technologies. (TCM-GMJ April 2020; 5(1):P11-P14)
Tuning of graphene oxide composition by multiple oxidations for carbon dioxide storage and capture of toxic metals

Graphene oxide (GO) is a material used as a precursor for the synthesis of graphene and its derivatives. Chemical properties of graphene are strongly influenced by the chemical composition of the original GO. In this paper we would like to show that the amount as well as the type of functional groups can be significantly increased and controlled by multiple oxidations of GO. For this purpose we performed multiple oxidations using two chlorate methods (Staudenmaier and Hofmann) and a permanganate method (Hummers). The results show a possibility of tuning the composition of GO functionalities by multiple oxidations. The obtained results also show that the second and third subsequent reoxidation reactions significantly increase the amount of oxygen containing groups in GO, mainly carboxylic groups. The multiple oxidation of graphene oxide led to a significant increase of carbon storage capacity. The high concentration of oxygen functionalities led to an increase of sorption capacity by more than one order of magnitude.
Proniosomal Gel: A provesicular approach for transdermal drugdelivery

Over the last few years a comprehensive research has been done over proniosomal gel as a provesicular approach for transdermal drug delivery. However skin has a very tough diffusion barrier that is lipid bilayer in the stratum corneum inhibiting penetration of drug moiety which is rate limiting barrier for penetration of drugs. Vesicular systems (niosomes & liposomes) are promising systems to cross this permeation barrier. They may act as vehicles or as permeation enhancer for bioactive materials to enhance their penetration via stratum corneum. But their major drawback is their unstability, which can be overcome by utilizing provesicular approaches like proniosomes. Proniosomes (gel) are semisolid liquid crystal products of nonionic surfactants easily prepared by dissolving the surfactant in a minimal amount of an acceptable solvent (ethanol) and the least amount of aqueous phase (water). Proniosomal gel offers a great potential to reduce the side effects of drugs and increased therapeutic effectiveness. Proniosomes can entrap both hydrophilic and hydrophobic drugs. In this review we have covered proniosomal drug delivery along with preparation, formulation and evaluation parameters of proniosomes as drug carrier.
Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of latency-sensitive, data-heavy, and compute-intensive tasks. The robots, however, are constrained in the computational power and storage capacity. The concept of multi-agent cloud robotics enables robot-to-robot cooperation and creates a complementary environment for the robots in executing large-scale applications with the capability to utilize the edge and cloud resources. However, in such a collaborative environment, the optimal resource allocation for robotic tasks is challenging to achieve. Heterogeneous energy consumption rates and application of execution costs associated with the robots and computing instances make it even more complex. In addition, the data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-time interactions and impedes service performance guarantee. Taking all these issues into account, this paper comprehensively surveys the state-of-the-art on resource allocation and service provisioning in multi-agent cloud robotics. The paper presents the application domains of multi-agent cloud robotics through explicit comparison with the contemporary computing paradigms and identifies the specific research challenges. A complete taxonomy on resource allocation is presented for the first time, together with the discussion of resource pooling, computation offloading, and task scheduling for efficient service provisioning. Furthermore, we highlight the research gaps from the learned lessons, and present future directions deemed beneficial to further advance this emerging field.
ON THE MONOID OF COFINITE PARTIAL ISOMETRIES OF N WITH A BOUNDED FINITE NOISE

In the paper we study algebraic properties of the monoid IN ∞ of cofinite partial isometries of the set of positive integers N with the bounded finite noise j. For the monoids IN ∞ we prove counterparts of some classical results of Eberhart and Selden describing the closure of the bicyclic semigroup in a locally compact topological inverse semigroup. In particular we show that for any positive integer j every Hausdorff shift-continuous topology τ on IN ∞ is discrete and if IN g[j] ∞ is a proper dense subsemigroup of a Hausdorff semitopological semigroup S, then S \ IN ∞ is a closed ideal of S, and moreover if S is a topological inverse semigroup then S \ IN ∞ is a topological group. Also we describe the algebraic and topological structure of the closure of the monoid IN ∞ in a locally compact topological inverse semigroup.
The averaging principle for stochastic differential equations driven by a Wiener process revisited

We consider a one-dimensional stochastic differential equation driven by a Wiener process, where the diffusion coefficient depends on an ergodic fast process. The averaging principle is satisfied: it is wellknown that the slow component converges in distribution to the solution of an averaged equation, with generator determined by averaging the square of the diffusion coefficient. We propose a new version of the averaging principle, where the solution is interpreted as the sum of two terms: one depending on the average of the diffusion coefficient, the other giving fluctuations around that average. Both the average and fluctuation terms contribute to the limit, which illustrates why it is required to average the square of the diffusion coefficient to find the limit behavior. 2020 Mathematics Subject Classification. 60H10. Manuscript received 22nd July 2021, revised 26th October 2021, accepted 3rd November 2021.
PAM-6 Coded Modulation for IM/DD Channels with a Peak-Power Constraint

Coded modulation with probabilistic amplitude shaping (PAS) is considered for intensity modulation/direct detection channels with a transmitter peak-power constraint. PAS is used to map bits to a uniform PAM-6 distribution and outperforms PAM-8 for rates up to around 2.3 bits per channel use. PAM-6 with PAS also outperforms a cross-shaped QAM-32 constellation by up to 1dB and 0.65dB after bit-metric soft- and hard decoding, respectively. An alternative PAM-6 scheme based on a framed-cross-shaped QAM-32 constellation is proposed that shows similar gains.
How (Non-)Optimal is the Lexicon?

The mapping of lexical meanings to wordforms is a major feature of natural languages. While usage pressures might assign short words to frequent meanings (Zipf’s law of abbreviation), the need for a productive and open-ended vocabulary, local constraints on sequences of symbols, and various other factors all shape the lexicons of the world’s languages. Despite their importance in shaping lexical structure, the relative contributions of these factors have not been fully quantified. Taking a coding-theoretic view of the lexicon and making use of a novel generative statistical model, we define upper bounds for the compressibility of the lexicon under various constraints. Examining corpora from 7 typologically diverse languages, we use those upper bounds to quantify the lexicon’s optimality and to explore the relative costs of major constraints on natural codes. We find that (compositional) morphology and graphotactics can sufficiently account for most of the complexity of natural codes—as measured by code length.
Will the Winner Take All? Competing Influences in Social Networks Under Information Overload

Influence competition finds its significance in many applications, such as marketing, politics and public events like COVID-19. Existing work tends to believe that the stronger influence will always win and dominate nearly the whole network, i.e., "winner takes all". However, this finding somewhat contradicts with our common sense that many competing products are actually coexistent, e.g., Android vs. iOS. This contradiction naturally raises the question: will the winner take all? To answer this question, we make a comprehensive study into influence competition by identifying two factors frequently overlooked by prior art: (1) the incomplete observation of real diffusion networks;(2) the existence of information overload and its impact on user behaviors. To this end, we attempt to recover possible diffusion links based on user similarities, which are extracted by embedding users into a latent space. Following this, we further derive the condition under which users will be overloaded, and formulate the competing processes where users' behaviors differ before and after information overload. By establishing the explicit expressions of competing dynamics, we disclose that information overload acts as the critical "boundary line", before which the "winner takes all" phenomenon will definitively occur, whereas after information overload the share of influences gradually stabilizes and is jointly affected by their initial spreading conditions, influence powers and the advent of overload. Numerous experiments are conducted to validate our theoretical results where favorable agreement is found. Our work sheds light on the intrinsic driving forces behind real-world dynamics, thus providing useful insights into effective information engineering.
Multi three-body bound states in one dimensional Bose gases

Yanxia Liu, Yi-Cong Yu, 2, ∗ and Shu Chen 3, 4, † Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China Yangtze River Delta Physics Research Center, Liyang, Jiangsu 213300, China
Quasi-periodic Oscillations in the TESS Light Curve of TX Col, a Diskless Intermediate Polar on the Precipice of Forming an Accretion Disk

One of the fundamental properties of an intermediate polar is the dynamical nature of the accretion flow as it encounters the white dwarf’s (WD’s) magnetosphere. Many works have presumed a dichotomy between disk-fed accretion, in which the WD accretes from a Keplerian disk, and stream-fed accretion, in which the matter stream from the donor star directly impacts the WD’s magnetosphere without forming a disk. However, there is also a third, poorly understood regime in which the accretion flow consists of a torus of diamagnetic blobs that encircles the WD. This mode of accretion is expected to exist at mass-transfer rates below those observed during disk-fed accretion, but above those observed during pure stream-fed accretion. We invoke the diamagnetic-blob regime to explain the exceptional Transiting Exoplanet Survey Satellite light curve of the intermediate polar TX Col, which transitioned into and out of states of enhanced accretion during Cycles 1 and 3. Power-spectral analysis reveals that the accretion was principally stream fed. However, when the mass-transfer rate spiked, large-amplitude quasi-periodic oscillations (QPOs) abruptly appeared and dominated the light curve for weeks. The QPOs have two striking properties: they appear in a stream-fed geometry at elevated accretion rates, and they occur preferentially within a well-defined range of frequencies (∼10–25 cycle day−1). We propose that during episodes of enhanced accretion, a torus of diamagnetic blobs forms near the binary’s circularization radius and that the QPOs are beats between the white dwarf’s spin frequency and unstable blob orbits within the WD’s magnetosphere. We discuss how such a torus could be a critical step in producing an accretion disk in a formerly diskless system.
Detect opinion-based groups and reveal polarisation in survey data

Network visualisation, drawn from attitudinal survey data, exposes the structure of opinion-based groups. We make use of these network projections to identify the groups reliably through community detection algorithms and to examine social-identity-based polarisation.Our goal is to present a method for revealing polarisation in attitudinal surveys. This method can be broken down into the following steps: data preparation, construction of similarity-based net-works, algorithmic identification of opinion-based groups, and identification of item importance for community structure. We examine the method's performance and possible scope through applying it to empirical data and to a broad range of synthetic data sets. The empirical data application points out possible conclusions (i.e. social-identity polarization), whereas the synthetic data sets marks out the method's boundaries. Next to an application example on political attitude survey, our results suggest that the method works for various surveys but is also moderated by the efficacy of the community detection algorithms. Concerning the identification of opinion-based groups, we provide a solid method to rank the item's influence on group formation and as a group identifier. We discuss how this network approach to identifying polarization can classify non-overlapping opinion-based groups even in the absence of extreme opinions.