Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Swarm Approach for Regulating Load Frequency in Two-Area Energy Systems

One of the most important strategies for running and controlling an electric power system is the load frequency controller. LFC can be used to solve a variety of issues, such as when a generating unit is rapidly turned off by protection equipment or when a heavy load is quickly connected or disconnected. When disturbances disrupt the natural power balance, the frequency deviates from what it should be. LFC is in charge of balancing the load and restoring the natural frequency to its proper level. In this case, load frequency control optimization techniques are used in the Multiple Connect Area System to provide reliable and quality operation on frequency and tie line power flow. The purpose of this paper is to demonstrate how optimising LFC in a two-area interconnected energy system with hydro, thermal plants, and a particle swarm optimization (PSO) method may improve power system stability and save revenue on power generation. A standard (PID) controller is used to control the system. The PSO optimization approach is utilised to determine the optimal gain values of the controllers kp, ki, and kd.
The Social Media Big Data Analysis for Demand Forecasting in the Context of Globalization: Development and Case Implementation of Innovative Frameworks

This paper aims to analyze the predictive effect of artificial intelligence on user demand in big data social media and to provide suggestions for developing enterprise innovation frameworks and implementing marketing strategies. In response to the inconsistency between the supply of enterprise products and services and market demand, deep learning algorithms have been introduced using social media big data analysis. This algorithm has been improved to construct a user demand prediction model in social media big data based on bidirectional long short-term memory (BiLSTM) fused with Word2Vec. The model uses data acquisition and pre-processing, Word2Vec algorithm to vectorization the data information, and BiLSTM network to model and train the sequence. Finally, the model is evaluated as an example.
Integrating Asymmetric Cryptographic Digital Wallet for Online Services in Nigeria

: Online stores have integrated one or more payment gateways on their website to facilitate the transaction of goods and services. In the Nigerian case, many physical stores are moving online. However, there is a gap in the payment methods which have slowed down business due to limited payment methods, charges incurred on gateways, and insecurity of websites. This paper is tailored towards the security of online transactions, reduced credit card usage and yet accentuation of cashless policy in Nigeria. Inline with the above stated, this paper proposed a secured digital wallet using cryptography to proffer a solution to exposing confidential information to different sites and curb fraudulent actions. By means of qualitative and quantitative research approach, data on user transactions were collected through google survey forms on how often they make transactions, 91.3% shop online but only 48.1% shop very often and 55% of sample questioned security of online payment, from the quantitative facts above, a larger percent of people who uses financial tools in their shopping activities have more security concern which this study solely aim at. In response to the corncern in the preceeding clauses, a mathematical model was formulated to generate keys for the wallet using an asymmetric cryptography scheme to improve the security process while using an online service. Furthermore, wallet referred in this paper uses the fund preload techniques which limits how often users needs to swipe their credit card for an handshake with the commercial banks, the end goal thus strengthen the Nigeria cashless policy. This paper generated encryption and decryption keys from the extension of the Euclid theorem and implemented a system that can generate keys for fund and asset transfer with the crypto service. The system was tested using unit testing and alpha and beta testing to assert the aims of the research. The result showed that digital wallet usage reduces the exposure of sensitive information on funds and allows using the account on different sites.
Enhancing Bone Grafting Outcomes: A Comprehensive Review of Antibacterial Artificial Composite Bone Scaffolds

Bone defects and dysfunctions are prevalent among patients, resulting from various causes such as trauma, tumors, congenital malformations, inflammation, and infection. The demand for bone defect repair materials is second only to blood transfusions. Artificial bone composites offer numerous advantages for bone damage repair, including their availability, absence of rejection or immune reactions, high malleability, exceptional mechanical strength, and outstanding biocompatibility. However, bacterial infections frequently occur during bone transplantation or on graft material structures, leading to severe complications such as osteomyelitis and osteoporosis. Moreover, existing osteogenic materials alone are inadequate to address the challenges posed by traumatic infections, presenting a significant hurdle for clinicians in reconstructing infectious bone defects. Consequently, it is crucial to functionalize artificial bone composites to facilitate effective bone repair and regeneration. Notably, antibacterial capabilities play a critical role in preventing and treating infectious bone defects, and current research is focusing on the interface between artificial bone composites and antibacterial treatments. This article provides an extensive review of the current state of artificial composite bone scaffolds with antibacterial properties for infection prevention in bone grafting.
On the spectral norm of inhomogeneous random matrices

Let $X$ be a $d\times d$ symmetric random matrix with independent but non-identically distributed Gaussian entries. It has been conjectured by Latal{a} that the spectral norm of $X$ is always of the same order as the largest Euclidean norm of its rows. A positive resolution of this conjecture would provide a sharp understanding of the probabilistic mechanisms that control the spectral norm of inhomogeneous Gaussian random matrices. This paper establishes the conjecture up to a dimensional factor of order $\sqrt{\log\log d}$. Moreover, dimension-free bounds are developed that are optimal to leading order and that establish the conjecture in special cases. The proofs of these results shed significant light on the geometry of the underlying Gaussian processes.
Flipping the Math Classroom for Non-Math Majors to Enrich Their Learning Experience

Abstract Students’ learning experiences in an introductory statistics course for non-math majors are compared between two different instructional approaches under controlled conditions. Two sections of the course (n = 52) are taught using a flipped classroom approach and one section (n = 30) is taught using a traditional lecture approach. All sections are taught by the same instructor in the same semester. General perceptions as well as students’ understanding and retention of the course material are measured and compared. The flipped classroom students outperform their traditional lecture peers on exams, especially in terms of their mathematical problem-solving skills. The flipped classroom students are also more confident than their traditional lecture peers about their abilities and their understanding of the course material, crediting their understanding primarily to the in-class activities, which are made possible because the flipped classroom design promotes an experiential, active-learning environment without compromising content.
Linking the solar cycle and Earth’s climate

In this paper, the authors have made an effort to investigate the impact of the solar cycle on Earth’s climate in the context of rainfall and temperature over a location, on El Nino/ La Nina, and world famines. The study shows that the peak sunspot number (SSN) often occurs in pairs. Multiple peaks are also seen frequently. The La Ninas follow multiple peaks, or sometimes associated with it. The El Ninos usually follow the solar minima, though not always. This study shows that the SSN trough will occur in 2020, thereby causing El Nino during 2019-2021. The multiple SSN peak is likely to occur during 2023-2028, predicting a La-Nina during this period. Multiple SSN peaks and very high SSN values bring about famines. The study shows that the total solar irradiance (TSI) bears a strong correlation with the SSN. Besides, the cosmic ray flux decreases as the SSN and the TSI increases. The monthly and yearly variations of SSN, TSI, and temperature show increasing trends over the years, indicating increased warming as the years advance. However, none of these parameters bears significant correlations with the temperature, either independently or together, implying that some other factors are also responsible for determining the temperature. The study shows no direct relationship between rainfall and the SSN. However, several years show a similar trend between the two. The investigation indicates a strong influence of the solar cycle on world climate.
The Factorization Method for Conducting Transmission Conditions

In this work we consider an inverse problem arising from electromagnetic scattering by a medium covered with a very thin and highly conducting layer. Our main objective is to show that the Factorization Method, which is an inverse problem solution algorithm, can be applied to detect the position and shape of such objects from the measurements of the scattered waves at large distances. Such problems originate from applications such as land-mine detection, radar or seismic imaging.
Blur Image Edge to Enhance Zernike Moments for Object Recognition

Zernike moments (ZMs) are a set of orthogonal moments which have been successfully used in the fields of image processing and pattern recognition. A combination of edge blurring with ZMs computation was introduced. In this study, several kinds of artificial binary stripe images were used to investigate the effects of edge blurring on the absolute mean error of reconstructed image from high-order ZMs. After the blurring process, the reconstruction errors were increased dramatically at edge pixels, but decreased on non-edge pixels. The experimental results demonstrated that 2-pixel blurring approach provided better performance for reducing reconstruction error. Finally, a template matching between two real images was simulated to illustrate the effectiveness of the proposed method.
A research competing model of organizational culture, organizational learning, management innovation and NPD: Evidence from technology firms

This study aims to develop a competing model to identify the best-fitting culture type in the relationship between organizational learning, management innovation, and New Product Development (NPD) performance, and it also aims to explore the moderator role of organizational culture in this relationship. The study collected data from 661 employees through survey methods from 234 firms in Turkey and tested with Structural Equation Modelling (SEM). The research shows that, despite the acceptable goodness of fit indices for each type in comparing four culture types, the market and adhocracy culture model is the best-fitting model and performs better with the best goodness of fit. In addition to the fact that the market and adhocracy cultures have partial moderator effects on the relationship between organizational learning, management innovation, and new product development, the study explains the relationship between organizational learning, new product development performance, and management innovation within a competing model based on organizational culture. It also provides a framework for firms to prioritize new product development to decide which cultural structure to choose.
Observed reduction in fracture toughness of AlN and AlN–Mo ceramic matrix composites with carbon additive

The fracture toughness of AlN ceramic matrix composites containing carbon (0.5 vol%), yttria (5.0 vol%), and Mo (0.0–4.0 vol%) was evaluated using Vickers indentation. The results of these measurements were compared to a carbon-free, commercial AlN (ST-200 ALN) as well as to AlN and AlN–Mo compositions from the literature. The presence of added carbon was found to correlate with a 21% reduction in fracture toughness of the 0.0 vol% Mo carbon-containing sample, compared to commercial ST-200 AlN, in both the A-orientation (indentation parallel to the pressing direction) and the B-orientation (indentation perpendicular to the pressing direction). Mo additions at small loading fractions (~ 0.25 vol%) were found to exhibit greater-than-expected increases in fracture toughness in the A-orientation, when compared to literature data on AlN–Mo composites. This increase in fracture toughness correlates to the removal of elemental carbon in the AlN matrix through reaction with the Mo additive, forming Mo2C, localized at the Mo particle sites. Further increasing Mo loading was observed to result in generally increasing fracture toughness values, as would be expected from literature data.
TCDetect: A New Method of Detecting the Presence of Tropical Cyclones Using Deep Learning

Tropical cyclones are high-impact weather events that have large human and economic effects, so it is important to be able to understand how their location, frequency, and structure might change in a future climate. Here, a lightweight deep learning model is presented that is intended for detecting the presence or absence of tropical cyclones during the execution of numerical simulations for use in an online data reduction method. This will help to avoid saving vast amounts of data for analysis after the simulation is complete. With run-time detection, it might be possible to reduce the need for some of the high-frequency high-resolution output that would otherwise be required. The model was trained on ERA-Interim reanalysis data from 1979 to 2017, and the training was concentrated on delivering the highest possible recall rate (successful detection of cyclones) while rejecting enough data to make a difference in outputs. When tested using data from the two subsequent years, the recall or probability of detection rate was 92%. The precision rate or success ratio obtained was that of 36%. For the desired data reduction application, if the desired target included all tropical cyclone events, even those that did not obtain hurricane-strength status, the effective precision was 85%. The recall rate and the area under curve for the precision–recall (AUC-PR) compare favorably with other methods of cyclone identification while using the smallest number of parameters for both training and inference.
Antibiotic Susceptibility Surveillance in the Punjab Province of Pakistan: Findings and Implications

Background and Objectives: The increase in antimicrobial resistance (AMR) across countries has seriously impacted the effective management of infectious diseases, with subsequent impact on morbidity, mortality and costs. This includes Pakistan. Antimicrobial surveillance activities should be mandatory to continually assess the extent of multidrug-resistant bacteria and the implications for future empiric prescribing. The objective of this retrospective observational study was to monitor the susceptibility pattern of microbes in Pakistan. Materials and Methods: Clinical samples from seven laboratories in Punjab, Pakistan were collected between January 2018 and April 2019, with Punjab being the most populous province in Pakistan. The isolates were identified and their antimicrobial susceptibility was tested using the Kirby-Bauer disc diffusion assay and micro broth dilution methods. The antibiotics assessed were those typically prescribed in Pakistan. Results: In total, 2523 bacterial cultural reports were studied. The most frequently isolated pathogens were Staphylococcus aureus (866, 34.3%), followed by Escherichia coli (814, 32.2%), Pseudomonas aeruginosa (454, 18.0%) and Klebsiella pneumoniae (269, 10.7%). Most pathogens were isolated from pus (1464, 58.0%), followed by urine (718, 28.5%), blood (164, 6.5%) and sputum (81, 3.2%). Conclusions: The findings suggest that current antimicrobial options are severally restricted in Pakistan due to the emergence of multidrug-resistant pathogens. This calls for urgent actions including initiating antimicrobial stewardship programs to enhance prudent prescribing of antibiotics. This includes agreeing on appropriate empiric therapy as part of agreed guidelines, in line with the WHO EML and AWaRe book, whilst awaiting culture reports. This is alongside other measures to reduce inappropriate antimicrobial prescribing and reverse the threat of rising AMR.
Thermal and Dielectric Investigations of Polystyrene Nanoparticles as a Viable Platform—Toward the Next Generation of Fillers for Nanocomposites

Nanoparticles are often used as fillers for enhancing various properties of polymer composites such as mechanical, electrical, or dielectric. Among them, polymer nanoparticles are considered ideal contenders because of their compatibility with a polymer matrix. For this reason, it is important that they are synthesized in a surfactant-free form, to obtain predictable surface and structural properties. Here, we synthesized a series of polystyrene nanoparticles (PS NPs), by emulsion polymerization of styrene, using varying amounts of divinylbenzene as a crosslinking agent and sodium 4-vinylbenzenesulfonate as a copolymerizing monomer surfactant—“surfmer”. Using “surfmers” we obtained surfactant-free nanoparticles that are monodisperse, with a high degree of thermal stability, as observed by scanning electron microscopy and thermogravimetric investigations. The prepared series of NPs were investigated by means of broadband dielectric spectroscopy and we demonstrate that by fine-tuning their chemical composition, fine changes in their dielectric and thermal properties are obtained. Further, we demonstrate that the physical transformations in the nanoparticles, such as the glass transition, can be predicted by performing the first derivative of dielectric permittivity for all investigated samples. The glass transition temperature of PS NPs appears to be inversely correlated with the dielectric permittivity and the average diameter of NPs.
Ratio of the jet power to the bolometric luminosity of the disk during accretion onto a black hole

Disk accretion onto black holes is accompanied by collimated outflows (jets). In active galactic nuclei (AGN), the kinetic energy flux of the jet (jet power or kinetic luminosity) may exceed the bolometric luminosity of the disk by a few orders of magnitude. This may be explained in the framework of the so-called “cold” disk accretion when the only source of the AGN energy is the energy released by accretion. The radiation from the disk is suppressed because the disk wind carries out almost all the angular momentum and the gravitational energy of the accreting material. In this paper, we calculate the “unavoidable” radiation from the “cold” disk and the ratio of the kinetic energy power of the outflow to the bolometric luminosity of the accretion disk around a super massive black hole in the framework of the Shakura and Sunyaev paradigm of an optically thick [Formula: see text]-disk. The exploration of the Fundamental Plane of Black Holes allows us to obtain equations that define the bolometric luminosity and the ratio of the luminosities as functions of the black hole mass and accretion rate. The application of our equations in the case of the M87 jet demonstrates good agreement with observations. In the case of Sgr A*, these equations allow us to predict the kinetic energy flux from the disk around the Galactic supermassive black hole.
A multi-model ensemble method based on convolutional neural networks for aircraft detection in large remote sensing images

ABSTRACT Aircraft detection is one of hot issues of remote sensing (RS) image interpretation. Different from existing methods which aim at detecting objects in regions of interest (ROI) of airports, in this work, we propose a novel and effective aircraft detection framework based on convolutional neural networks (CNN) to detect multi-scale targets in extremely large and complicated scenes. In particular, we design a constrained EdgeBoxes (CEdgeBoxes) approach to generate a modest number of target candidates quickly and precisely. Then, in order to overcome the drawbacks of using handcrafted features and traditional classifiers, a modified GoogLeNet combined with Fast Region-based CNN (R-CNN) is designed to extract useful features from RS images and then detect various kinds of multi-scale aircrafts. Most importantly, we propose a new multi-model ensemble method to decrease the false alarm rate (FAR) caused by the imbalanced distribution of complicated targets and backgrounds. Thorough experiments, which are conducted on the dataset acquired by QuickBird with a resolution of 0.6 m, demonstrate our method can effectively detect aircrafts in large scenes with low FARs.
Thermal stratification hinders gyrotactic micro-organism rising in free-surface turbulence

Thermal stratification in water bodies influences the exchange of heat, momentum, and chemical species across the air-water interface by modifying the sub-surface turbulence characteristics. Turbulence modifications may in turn prevent small motile algae (phytoplankton, in particular) from reaching the heated surface. We examine how different regimes of stable thermal stratification affect the motion of these microscopic organisms (modelled as gyrotactic self-propelling cells) in a free-surface turbulent channel flow. This archetypal setup mimics an environmentally plausible situation that can be found in lakes and oceans. Results from direct numerical simulations of turbulence coupled with Lagrangian tracking reveal that rising of bottom-heavy self-propelling cells depends strongly on the strength of stratification, especially near the thermocline where high temperature and velocity gradients occur: Here hydrodynamic shear may disrupt directional cell motility and hamper near-surface accumulation. For al.
Comparativo de Estratégias para Resposta a Emergências usando Redes Ad Hoc Veiculares e Computação em Névoa

Em acidentes de trânsito extremos, vítimas frequentemente não conseguem entrar em contato com as equipes de resposta a emergência a tempo. Visando automatizar o processo de detecção e relato de acidentes, este artigo apresenta resultados parciais da comparação de diferentes estratégias para resposta a emergências que usam as capacidades de comunicação de Redes Ad Hoc Veiculares (VANETs) assistidas por computação na névoa para relatar emergências com a ajuda de uma unidade de resposta a emergência. Experimentos de simulação mostraram que algumas estratégias podem reduzir o tempo total de resposta a emergências em até 60,13%. O software desenvolvido está disponível como software livre permitindo a reprodução dos experimentos.
Comparative Analysis of Unmanned Aerial Vehicles Used in Photogrammetric Surveys

: There are many manufacturers on the market offering various types of Unmanned Aerial Vehicles (UAV). The multitude of drones available on the market means that the choice of a UAV for a specific application appears to be a decision problem to be solved. The aim of this article is a comparative analysis of drones used in photogrammetric