Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilevel Fault-Tolerance Aware Scheduling Technique in Cloud Environment

In cloud computing, the resources are delivered to the users on demand at a considerable cost. Due to low maintenance and high scalability services, enterprises wish to deploy their newly developed application towards the computing environment. For large scale applications, fault tolerance is an essential task that guarantees the reliability and availability of computing services. In this paper, a multi-level fault tolerance scheduling mechanism is proposed that overcomes the real-time failure in the system. In the first phase, non-functional testing and decision making algorithm is used to find the reliability of virtual machines. Here, the reliability criterion is achieved by Reliable Decision K-Nearest Neighbor (RDK-NN) algorithm that considers only the best reliable virtual machine. In the second phase, high availability is achieved using a scheduling algorithm. For this purpose, a Teaching-Learning Based Optimization (TLBO) scheduling is proposed that provides a better-scheduled set of tasks for the corresponding users. The evaluation of the proposed approach is carried out under Cloudsim platform. The performance is determined in terms of makespan time, failure ratio, performance improvement rate, response time and rejection ratio to estimate the scheduling task. The result shows that the system achieves high reliability and availability of data with a multi-level format in the cloud environment.
Do campaign contributions have an influence on the price of public works?

The objective of this article is to verify empirically how the campaign contributions have an influence on the price of public works. To achieve this objective, we used panel data of public works contracts bid between the years of 2008 and 2013, besides data from the accountability of the election campaigns occurred in 2008, 2010 and 2012. The results of the econometric models estimation indicated that companies that contributed to campaigns were able to increase the value of their contracts in 8 percentage points through contract amendments, in comparison with the amendments of the companies that did not contribute. They also indicated that bids won by companies that contributed to campaigns presented a discount lower than the bids won by non-contributing companies in 1 percentage point. We can conclude that it is essential to evaluate the legality of the contract amendments in the scope of the public works audits when companies that contract with the public authority contributed to campaigns.
Dual‐Targeting Heparin‐Based Nanoparticles that Re‐Assemble in Blood for Glioma Therapy through Both Anti‐Proliferation and Anti‐Angiogenesis

The efficient and specific delivery of nanoparticles (NPs) to brain tumors is crucial for successful glioma treatment. Heparin‐based polymers decorated with two peptides self‐assemble into multi‐functional NPs that specifically target glioma cells. These NPs re‐self‐assemble to a smaller size in blood, which is beneficial for in‐vivo brain drug delivery. The hydrodynamic size of one type of these NPs is 63 ± 11 nm under blood‐mimic conditions (10% fetal bovine serum), but it is 164 ± 16 nm in water. Additionally their zeta potential is more neutral in the blood‐mimic conditions. Transmission electron microscopy reveals the morphology of the spherical NPs. In vitro experiments demonstrate that the NPs exhibit a high cellular uptake and the ability to efficiently discourage proliferation, endothelial‐lined vessels, and vasculogenic mimicry. In vivo studies demonstrate that the NPs can by‐pass the normal blood–brain and blood–(brain tumor) barriers and specifically accumulate in glioma tissues; moreover, they present an excellent anti‐glioma effect in subcutaneous/intracranial glioma‐bearing mice. Their superiority is due to their appropriate size in blood and the synergic effect arising from their targeting of two different receptors. The data suggests that these NPs are ideal for anti‐glioma therapy.
A Edge Master Computing for Pineapple Monitoring System with Drone and Data-management

This paper describes the pineapple monitoring system based on the edge computing which consists of the sensing drone and database. A huge sensing data transferred to the computer system will be changed from the cloud to edge, near future. The merits of this change are low power dissipation, reducing data transfer, real time operation, and security issues. To enjoy these merits, effective compaction of sensing data, energy efficient networking, and edge oriented data management architecture has been proposed. We design the pineapple monitoring system using above core technologies and evaluates the enhancement of system performance.
Multi-View Fine-Grained Vehicle Classification with Multi-Loss Learning

A computer vision solution applied to an automatic toll collection (ATC) with a subscription/membership is proposed in this paper. In this application, a unique identifier (ID) is related to a concrete vehicle and a membership. A camera system is put in place to verify that for each transaction the vehicle and the ID correspond with the actual membership data. The visual system extracts different vehicle characteristics including license plate number, make, model, color, number of axles, etc. The system then compares the extracted characteristics with those found in the membership. We focus on solving the vehicle make classification and for that, we propose a fine-grained vehicle classification that exploits the multi-camera composition of the system by feeding a convolutional neural network (CNN) with multiple views of the vehicle. Our multi-view (MV) network extracts features from the different views of the vehicle for vehicle’s make classification. The presented evaluations show that using information from different views of a vehicle improves the make classification performance in particular in more challenging toll collection scenarios.
Wideband Beamforming for Hybrid Massive MIMO Terahertz Communications

The combination of large bandwidth at terahertz (THz) and the large number of antennas in massive MIMO results in the non-negligible spatial wideband effect in time domain or the corresponding beam squint issue in frequency domain, which will cause severe performance degradation if not properly treated. In particular, for a phased array based hybrid transceiver, there exists a contradiction between the requirement of mitigating the beam squint issue and the hardware implementation of the analog beamformer/combiner, which makes the accurate beamforming an enormous challenge. In this paper, we propose two wideband hybrid beamforming approaches, based on the virtual sub-array and the true-time-delay (TTD) lines, respectively, to eliminate the impact of beam squint. The former one divides the whole array into several virtual sub-arrays to generate a wider beam and provides an evenly distributed array gain across the whole operating frequency band. To further enhance the beamforming performance and thoroughly address the aforementioned contradiction, the latter one introduces the TTD lines and propose a new hardware implementation of analog beamformer/combiner. This TTD-aided hybrid implementation enables the wideband beamforming and achieves the near-optimal performance close to full-digital transceivers. Analytical and numerical results demonstrate the effectiveness of two proposed wideband beamforming approaches.
Characterization of bacteria, antibiotics of the fluoroquinolone type and their biological targets DNA and gyrase utilizing the unique potential of vibrational spectroscopy

Im Rahmen dieser Arbeit wurden verschiedene Schwingungsspektroskopische Techniken (IR-Absorptions-, Mikro-Raman-, UV-Resonanz-Raman-, oberflachenverstarkte und spitzenverstarkte Raman-Spektroskopie) dazu verwandt, Bakterien zu charakterisieren. Einen besonderen Einblick in die Zusammensetzung und Dynamik der auseren Bakterienschicht bietet die erstmalige Anwendung der spitzenverstarkte Raman- Spektroskopie auf komplexe biologische Systeme wie Bakterien. Die Technik erlaubt die Gewinnung detaillierter chemischer Informationen mit hoher Ortsauflosung (wenige 10 nm). Ebenso konnten die Anderungen der chemischen Zusammensetzung wahrend des Bakterienwachstums in Abwesenheit und in Gegenwart von Antibiotika aus der Gruppe der Fluorochinolone mit Hilfe schwingungsspektroskopischer Methoden verfolgt werden. Die Fluorochinolone greifen als biologische Zielstrukturen das Enzym Gyrase und die bakterielle DNA an, was schlieslich zum Zelltod fuhrt. Die am Wirkmechanismus beteiligten Komponenten Wirkstoff, DNA und Gyrase wurden zunachst in In-vitro-Experimenten schwingungsspektroskopisch charakterisiert und die Ergebnisse schlieslich zur Interpretation der In-vivo-Experimente mit Bakterien verwandt. Mit Hilfe statistischer Auswertemethoden konnten die durch das Antibiotikum hervorgerufenen Anderungen in den Bakterienspektren auf Veranderungen an den Protein- und DNA-Bausteinen zuruckgefuhrt werden, was den angenommenen Wirkmechanismus der Fluorochinolone unterstutzt.
Optimization-Based Strategies for Optimal Inverse Parameters Estimation for Heat Transfer Systems

Thermal design for electronic devices approached through the solution analysis of the Inverse Heat Transfer Problem (IHTP) has not been extensively explored. This article proposes an alternative strategy and a contrasting approach for the optimal inverse parameters’ estimation of heat transfer systems, particularly in designing heat sinks. A framework to tackle a Rectangular Microchannel Heat Sink (RMCHS) design modeled by the Entropy Generation Minimization (EGM) criterion is developed. This framework comprises two strategies to be compared. The serial proposal works sequentially depending on the parameters’ sensitivity into the RMCHS model, backpropagating estimated parameters to all processes. The parallel strategy processes all parameters simultaneously. Instead of focusing efforts on a typical optimization process, a sequential procedure takes advantage of the most influential parameters in the heat sink model and the excellent exploration-exploitation rate of Metaheuristic Optimization Algorithms (MOAs). The most sensitive design variables are prioritized in the serial strategy. The implemented estimation-optimization strategies are addressed through an IHTP’s inverse analysis. Thereby, global MOAs are implemented to solve the specific application and become an alternative to the gradient-based methods when their efficiency and effectiveness are at stake. MOAs show overall relative errors related to minimal entropy generation rate inferior to 0.07% for data with 30 dB of SNR and less than 7.63% of error for data with 10 dB of SNR compared with the Levenberg-Marquardt method. Numerical results show that serial strategy provided a stable and reliable design solution even with contaminated data, obtaining better performances than the multiparametric strategy. Additionally, parametric and nonparametric statistical tests were used to validate the appropriate optimization algorithm and the most reliable strategy. The statistical tests confirmed the optimal-inverse problem estimation and optimization improvement by combining the serial strategy and analyzed MOAs to design the RMCHS based on the EGM criterion.
Learning Mathematics Among University Engineering Students During The Pandemic: A Didactic Experience

The study evaluates engineering students' didactic experience and group learning appreciation of mathematics in 28 virtual groups using a teamwork and scientific debate strategy. The results show that the didactic experience supports the development of collaborative work and participation in decision-making. Besides, it establishes that collaboration is essential in problem-solving strategies and the various forms of reasoning and argumentation. Finally, it shows that the experience of the students is very positive.
Greener Physical Layer Technologies for 6G Mobile Communications

Energy efficiency (EE) has been regarded as an essential design metric in 5G mobile communications. However, the massive antenna numbers, larger bandwidths, and higher density of base stations have been severely deteriorating the power consumption of 5G networks. In this article, the fundamental EE-spectrum efficiency (SE) joint design issues are thoughtfully considered. The relationships between EE and SE in single-input single-output (SISO), multiple-input multiple-output, and hybrid beamforming structures are first reviewed, where the increase of SE (EE) will unfortunately bring a reduction of EE (SE). To resolve this contradiction, a non-orthogonal multiple access (NOMA) scheme with energy-efficient user scheduling and resource allocation is investigated, which promises a simultaneous increase of EE and SE. Furthermore, a precoded waveform overlapping multiplexing scheme is presented, with significant improvements in EE and SE over the traditional Nyquist-criterion-based SISO systems. The waveform overlapping multiple access scheme is further proposed, which promises large performance improvements over the NOMA schemes discussed in 5G New Radio. The application of the waveform overlapping scheme in multiple-antenna systems is also investigated. Finally, the challenges of the waveform overlapping scheme and several other research topics are discussed. For sustainable development of the wireless communications industry, greener design in physical layer technologies should be second to none in the future 6G networks and beyond.
Semantic Compositional Network with Top-Down Attention for Product Title Generation from Image

E-commerce is currently one of the most widely used forms of transactions. C2C e-commerce is one type of e-commerce where the seller can be anyone, thus the product data input is done manually. Product title generation from image system will be very useful for sellers in C2C e-commerce. In this work, we propose the usage of image captioning method for generating product title from the image, by combining two recent works: Semantic Compositional Networks and top-down attention from Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. Applying this approach, our result improves ROUGE-L and BLEU-1 - BLEU-4 score by 1.5887% −9.6275% against the Semantic Compositional Networks and by 0.5593% −4.9356% against the image captioning with top-down attention from the second work except for the BLEU-2.
Differential Evolution for VNE-5G Scenarios

Network slicing is a key component of the envisioned 5G network. The slicing process is mathematically known as a virtual network embedding problem (VNE). Embedding a large number of virtual networks to a real physical network over time is an NP-Hard problem, and it becomes more complex when regarding different requirements of distinct types of network slices. Thus, we propose a new VNE algorithm based on Differential Evolution called VNE-DE to map virtual networks to a physical substrate. VNE-DE seeks better allocation of resources taking into account the most common QoS parameters of slice requirements: bandwidth, delay and reliability. Furthermore, we compare it with two Genetic Algorithm approaches, and its outcomes demonstrate improvement in the accuracy of the allocation of resources regarding.
Simulation Analysis of the Electromagnetic Force Distribution and Formability Parameters for Sheet Metal Electromagnetic Bulging Using a New Magnetic Field Shaper

While electromagnetic forming of aluminum alloy sheet has gained several advantages, the uneven radial deformation of the sheet is still one of the main drawback of this technology that calls for reliable and cost-effective solution. Aiming at this point, this paper presents an electromagnetic bulging system equipped with a new magnetic field shaper placed between the driving coil and the object sheet to modulate the distribution of the induced eddy current in the sheet, and hence regulating the distribution of the electromagnetic force to achieve more uniform deformation. A finite element electromagnetic-structure 2D coupling model simulating this process is developed and the effects of workpiece geometry and deformation speed on the circuit, magnetic field and structure field are considered. The coupling between the electric circuit and magnetic field; magnetic field and workpiece deformation is realized to simulate the actual system of thin plate electromagnetic bulging. The performance of the proposed system is compared with that of the traditional system through investigating the profiles of the induced eddy current, electromagnetic force and forming effect. Simulation results show that the uniformity of sheet metal forming is improved by using the proposed magnetic field shaper, and the maximum uniform deformation area is increasing from 32.1 mm to 73.8 mm, which stimulates more industrial applications of sheet metal electromagnetic forming.
Grit as a Determinant of Success in the Teacher Recruitment Examination

Grit, which concerns perseverance and passion to achieve long-term goals, predicts success in a wide range of areas. As a preliminary step towards the application of grit in institutional research (IR), this study longitudinally analyzes the impact of individual differences regarding level of grit on results in teacher recruitment examinations. In this study, undergraduate students at a Japanese teacher-training university (N = 629) responded to a questionnaire survey. The results showed that students with higher grit were more likely to take the teacher recruitment exam, and also to pass the second part of this exam. This effect persisted even after controlling for self-control, a noncognitive skill that is strongly associated with goal achievement. It was also found that perseverance of effort and consistency of interest, which are subfactors of grit, had differing effects on the results of teacher recruitment exams respectively: while consistency of interest had an effect on the probability of taking the teacher recruitment exam, perseverance of effort had an effect on the probability of passing the second part of the exam. We concluded that consideration of grit can help teacher-training universities resolve the issue of producing high numbers of high-quality teachers.
Multi-Level Energy Management Systems Toward a Smarter Grid: A Review

Home Energy Management Systems (HEMSs) may not be able to solve network issues, especially in the presence of high penetration level of Electric Vehicles (EVs) and decentral renewable energy. To solve the problem, Grid Energy Management Systems (GEMSs) were introduced. However, because of the contradictory nature of the main objectives of HEMS which are economical oriented on end-users, e.g., cost minimization, and GEMS which are technical oriented on system operators, e.g., maximization of system stability and power quality cannot be satisfied simultaneously. Hence, a multi-level energy management system seems to be necessary to improve the techno-economic performance of the distribution system while satisfying end-users, electricity retailers, and the system operator. Because of the significance of the subject, this paper presents the state-of-the-art regarding different energy management systems at home, aggregator, and network levels. The advantages and disadvantages of each system are discussed and compared, considering their main elements such as objective functions, constraints, optimization algorithms, communication protocols, and impact of EVs. The challenges and limitations in hierarchical energy management are explained. Finally, some future research directions are suggested to improve the multi-level energy management system.
Determination of Optimal Parametric Distribution and Technical Evaluation of Wind Resource Characteristics for Wind Power Potential at Jhimpir, Pakistan

The objective of this research work is to analyze wind characteristics and to assess wind power potential by selecting the best fit probability distribution function of Jhimpir Sindh Pakistan. This type of detailed investigation helps wind power generation companies in selecting suitable wind turbine and provides information of wind characteristics of potential site. Eight probability distribution functions are tested on the wind speed data from January 2015 to July 2018. Frequency bins of Weibull and Rayleigh distribution with maximum probabilities of 0.1210 and 0.1143 are most closest representation of our data. In order to, quantitatively analysis which distribution function is best fitting the local wind regime, we have applied the coefficient-of-determination, Kolmogorov-Smirnov, Chi square, Cramer-von Mises, Anderson-Darling tests along with Akaike information and Bayesian information criterion. These statistical test are used to rank the empirical distribution functions in order to identify two distribution function better fitting the actual wind speed data. After selecting two best fitted distribution functions, we analyze wind power potential and compare the error of wind power density based on these distribution functions (Weibull and Rayleigh). The power densities reported varied from 73.67 to 648.73W/m2. Results indicate that power densities of Weibull and Rayleigh for the candidate site are 84.67–698.65W/m2 and 83.67–1021.4W/m2, respectively. The highest error for Weibull and Rayleigh are 0.1850 and 0.5745, respectively. Whereas lowest error are 0.0178 and 0.0180, respectively. Complete analysis suggested that Weibull distribution function is the most suitable for Jhimpir Sindh Pakistan and the studied site is suitable for wind power production. In addition, comprehensive analysis of wind direction at the candidate site suggested that Eastern and Southeastern wind directions are predominant with 38.52% and 33.24% of the total time.
Surface Electromagnetic Performance Analysis of a Graphene-Based Terahertz Sensor Using a Novel Spectroscopy Technique

In this paper, a novel terahertz (THz) spectroscopy technique and a new graphene-based sensor is proposed. The proposed sensor consists of a graphene-based metasurface (MS) that operates in reflection mode over a broad range of frequency band (0.2 – 6 THz) and can detect relative permittivity of up to 4 with a resolution of 0.1 and a thickness ranging from 5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> to 600 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> with a resolution of 0.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>. To the best of author’s knowledge, such a THz sensor with such capabilities has not been reported yet. Additionally, an equivalent circuit of the novel unit cell is derived and compared with two conventional grooved structures to showcase the superiority of the proposed unit cell. The proposed spectroscopy technique utilizes some unique spectral features of a broadband reflection wave including Accumulated Spectral power (ASP) and Averaged Group Delay (AGD), which are independent to resonance frequencies and can operate over a broad range of spectrum. ASP and AGD can be combined to analyse the magnitude and phase of the reflection diagram as a coherent technique for sensing purposes. This enables the capability to distinguish between different analytes with high precision which, to the best of author’s knowledge, has been accomplished for the first time.
Academic Success Prediction based on Important Student Data Selected via Multi-objective Evolutionary Computation

This paper proposes an academic success prediction modeling approach that can be used for student advising, in which a multi-objective evolutionary computation approach is applied that automatically selects important explanatory variables suitable to predict academic success and construct multiple predictive models based on machine learning. Numerical experiments using actual student data suggest that it is possible to construct predictive models in considering the trade-off of prediction performance and model interpretability.