Miracle-dz/UniFAR-FTUs huggingface dataset:
VALUE RELEVANCE AND USERS CONFIDENCE IN POST INTERNATIONAL FINANCIAL REPORTING STANDARDS ADOPTION ERA OF NIGERIAN BANKS OLADEJO M.O Department of Management and Accounting, Faculty of Management Sciences

Conclusions from the existing studies on IFRS adoption and value relevance were characterized by conflicting results and mixed findings. This study attempts to evaluate the impact of adoption of International Financial Reporting Standards (IFRS) on the value relevance of financial information of Nigerian Deposit Money Banks. A mixed research approach was adopted for this study. To analyze the secondary data correlational research design was employed using Edward, Bells and Olhson (EBO) model. Primary data through structured questionnaire administered on a sample of 200 randomly selected preparers and users of banks financial statements was also used to provide alternative evidence. Data were analyzed using descriptive and econometric approaches. General least square (GLS) multiple regression analyses were employed to analyze the panel data based on random effect model using SPSS 17 statistical tool.The results from data analysis showed r calculated of 0.69 indicating a significant relationship between adoption of IFRS variables and value relevance indexes in the NDMBs, from the analysis of questionnaire responses. The relationship, using EBO model, between the variables used for post IFRS adoption analysis shows that share price (SP) is positively correlated with earning per share (EPS), change in earnings per share (CEPS) and book value per share (BVPS) at 0.368, 0.096 and 0.103 respectively meaning that SP and EPS have higher positive relationship but lower with CEPS and BVPS. Invariably, higher earnings attract higher SP which automatically enhances the confidence of investors in the financial statements after the adoption. The pre-IFRS R-squared is 0.35 while post-IFRS R-squared is 0.16 showing that post IFRS financial information has a weak value relevance. However, the elements that made up financial information are not value relevant in post IFRS adoption thereby giving a low cumulative relationship to share price. The study suggests concerted efforts by banks to strengthen the value relevance and improve users’ confidence of their financial reports since IFRS adoption has been made mandatory
Method of pedestrian detection ahead of vehicle based on linear SVM

The pedestrians ahead of vehicle in complex transportation background were detected by using the pedestrian detection method based on linear SVM.According to the characteristics of pedestrian non-rigid,trilinear interpolation algorithm was used to extract the histograms of oriented gradient features of images.Linear support vector machine(SVM) was used to carry on the multi-scale fusion examination of the video image in order to adapt to the requirement of pedestrian detection in complex transportation background and to effectively improve the detection accuracy.The experiment results show that the algorithm can effectively identify the different scales and poses pedestrian in mixed traffic videos.
Graphene Oxide Application in Cement-Bound Materials—State of the Art

In recent years, the incorporation of nanoparticles in the cementitious matrix has been regarded as a prominent strategy in the research effort. This paper reviews the influences of the nanomaterial GO (graphene oxide) in cement bound materials, as GO has become one of the most well-known inclusion nanoparticles in the cementitious matrix in the last few years. This study describes (i) the influences of the GO on enhancing hydration mechanism in the cementitious materials; and (ii) the improved performance of the cementitious materials, which are highly dependent on the functionalizing behavior of the GO. All this improves the properties of the matrix. The paper reports the influences of GO on the workability of the mixtures and the durability enhancement of the cementitious composites. Therefore, it demonstrates promising results of employing GO in the fabrication of novel building materials. However, tremendous progress in utilizing GO in scientific domains like materials technology has achieved a significant level. Remarkable progress has been achieved by applying the GO in different fields such as solar cells and super capacitors. Hence, more intensive research is required to achieve comparable improvement in building materials which constitutes a significant future challenge.
Super-resolution image reconstruction network based on dual-channel pixel attention mechanism

In the field of image processing, image super-resolution reconstruction technology based on deep learning is becoming more and more mature. Relying on stacking convolutional layers to expand the depth of the reconstruction network can improve the similarity of the reconstructed image, but as the depth of the convolutional layer continues increase, the learning rate of the reconstruction network will also become lower. In this paper, the dual-channel pixel attention mechanism PA combined with the ESPCN network model can not only retains the high reconstruction rate of ESPCN, but also improves the image reconstruction effect. The experimental results show that the network model proposed in this paper is better than SRCNN, FSRCNN, ESPCN for single image reconstruction, and the peak signal-to-noise ratio (PSNR) and structural similarity ratio (SSIM) are significantly improved.
Performance Enhancement of Finned Tube Heat Exchanger using New Vortex Generator

The present work aims to investigate a performance enhancement in an H-shaped finned tube heat exchanger using a new design of winglet vortex generator. The new design has an aerodynamic shape that reduces the pressure drop and enhances the heat transfer, resulting into an improvement in the overall thermo-hydraulic performance. A three-dimensional model is developed using ANSYS fluent to carry out simulations for Reynolds number varying between 5000 ≤ Re ≤ 11000 and to predict the net enhancement in the heat exchanger performance. The validation of the model is done using experimentally developed correlations of dimensionless pressure drop and heat transfer characteristics. Comparative results for the finned tube heat exchanger with and without vortex generator are presented. Results indicate a significant enhancement in the heat transfer characteristics of the finned tube heat exchanger with the new design of vortex generator. Volume goodness ratio and Colburn j-factor found to be 19.63% and 30.33% higher than the baseline finned tube heat exchanger without any vortex generator.
Unmanned helicopter flight control actuator specification through mission profile analysis

Helicopter dronization is expanding, as for example with the VSR700 project, and leads to the design and the integration of electromechanical actuators (EMA) into the primary flight control system (PFCS). The PFCS is in charge of controlling the helicopter flight over its 4 axis (roll, pitch, yaw, vertical). It controls the blade pitch through dedicated mechanical kinematics and actuators. The hydraulic technology has been conventionally used in actuators for more than 60 years. On the other hand, the introduction of the EMA technology requires the reconsideration of design practices right at development start. Indeed, the establishment and synthesis of the specification need to deal with – new design drivers (high performance points, wear, fatigue) and - new inherent technological imperfections (friction, inertia and reduction ratio). To address these topics, this paper draws a list of the main EMA design drivers to focus on along with a brief description of the main EMA components. Then, it proposes indicators evaluated over a complete mission profile in time coming from measurement on a given applicative helicopter flight. These indicators are chosen and elaborated to provide an image of the design drivers responsible for rapid and gradual degradations of the actuator components. Also, they give an idea of the importance taken by the actuator imperfections into the global performance. Furthermore, we explain how mission profiles are processed depending of data sources. Finally, through a comparison with a standard aircraft mission profile, we emphasize the specificity of the helicopter application use case.
Highly iterative concept development for requirement-compliant air taxis

The increasing demand for individual mobility worldwide is leading to the development of new forms of mobility. Individual air mobility (e.g., air taxis) could be one innovative approach to meet this increasing demand. To successfully realise such innovative approaches, interested companies must know the technical, economic, and ecological requirements of all relevant stakeholders and integrate them in their air taxi developments. Based on these diverse requirements, a resilient air taxi concept must first be created that describes the essential characteristics of future air taxis and enables a well-founded assessment. However, in practice, it is often difficult for companies to outline such a concept and then develop it consistently and highly iteratively. To face this challenge, a process has been set up which creates a deep link between the requirements and concept development phase to assure the development of requirement-compliant air taxis in an agile manner. The process is divided into two parts. The first part connects the market analysis, requirements definition and concept creation, thus linking the requirements and concept development phase. It is based on a double diamond model consisting of two phases. In contrast, the second part is focusing on the pre-development until the end of the concept development phase by adapting the SCRUM methodology to derive a sound air taxi concept. Both parts are executed in a highly iterative manner to reflect the significant amount of uncertainty at this early process stages.
Nonsingular fast terminal sliding mode control for manipulator based on combinatorial reaching law

Aiming at the problems of external disturbance, modeling errors, tracking speed and chattering in manipulator trajectory tracking control, a nonsingular fast terminal sliding mode control strategy for manipulator based on combinatorial reaching law was proposed. First, the nonlinear disturbance observer was used to observe the system’s external disturbance and modeling errors. Then, on the basis of the nonsingular fast terminal sliding mode surface, an improved nonsingular fast terminal sliding mode surface was proposed, which improves the convergence speed in the sliding motion process of sliding mode control. At the same time, a combinatorial reaching law combining the power reaching law and the variable speed reaching law was proposed, which improved the approach speed of the system under the premise of weakening the chattering. The simulation results verified the effectiveness of this control strategy.
Short-term electricity consumption forecasting method based on empirical mode decomposition of long-short term memory network

Accurate electricity consumption forecasting can improve the efficiency of grid dispatching and effectively guarantee the stable operation of the power system. Electricity consumption forecasting is important for the analysis of customer-side electricity consumption behavior, but the instability of electricity consumption sequences poses difficulties for forecasting. Therefore, an improved combination of integrated empirical modal decomposition (EMD) and long short-term memory network (LSTM) is proposed for customer-side electricity consumption forecasting. This paper starts from the idea of blind source separation and independent prediction, and firstly decomposes the original electricity consumption data into several inherent mode functions (IMFs) with different frequencies and amplitudes by empirical modal decomposition (EMD), and then uses LSTM to extract features and make temporal prediction for each IMF component one by one with machine learning intelligent algorithm. , and finally obtains end-user-side short-term electricity consumption prediction results by accumulating multiple target prediction results. Compared with direct forecasting, the proposed EMD-LSTM independent forecasting model is able to identify the characteristics of each frequency component of electricity consumption data, and its error is reduced by about 15% on average, thus achieving the goal of improving the accuracy of load forecasting in short-term electricity consumption forecasting scenarios.
Thermal Management of Multi Core Processor Using U Configured Parallel Microchannel Cooling System

Thriving technologies in the electronics industry demands effective cooling systems for proper thermal management of the devices. To propose an effective cooling system, real-time challenges need to be taken into examination. One of the challenges is the non-uniform heat load emitted by the device. In this paper, a multicore processor has mimicked for the heat load and examined with U configured parallel microchannel cooling system (PMCS) for its capacity to cool the processor effectively. A detailed numerical examination has been conceded out by mimicking the AMD Ryzen-7 octa-core processor for its size, shape and heat load. To make it convenient for applying non-uniform heat load, the shape of the PMCS is divided into 4*4 array of a total of 16 heaters. The present has witnessed that, there is an uneven distribution of fluid flow across the channels. The initial channels do get more quantity of fluid whereas the end channels do get a very less quantity of fluid. Why because of the flow maldistribution, it formed the hot spots (high-temperature zones), which will be further intensified due to the non-uniform heat load emitted by the processor. It perceived from the present numerical analysis, there is a formation of hotspots not only due to the random location of active cores but also due to the flow maldistribution across the channels. The flow arrangement of U configuration having worse flow distribution among other existing configuration. The present work conclusively demonstrates that high maldistribution leads towards dropping the uniformity of cooling. But the existing maldistribution can be effectively exploited to tackle the non-uniform heat load released by the processor when the active core location falls in the place of initial channels.
Experimental study on instantaneous plume characteristics based on flame 3D reconstruction

3D flame can directly represent the combustion status of combustible and fire scale, but flame pulsation makes it difficult for existing 3D reconstruction technology to achieve flame reconstruction. In response to this situation, we use computer image processing technology to propose a flame model based on the cylindrical hypothesis to realize the 3D reconstruction of the flame in this paper. On this basis, we measured the volume and height of flames with different fuel flow rates for 6 types of burners. The results show that the average flame height and volume increase with the increase of fuel flow rate, but the average flame height decreases with the increase of the burner size, and there is no obvious correlation between the average volume and burner size.
Machine Learning classification techniques applied to static air traffic conflict detection

This article evaluates Machine Learning (ML) classification techniques applied to air-traffic conflict detection. The methodology develops a static approach in which the conflict prediction is performed when an aircraft pierces into the airspace. Conflict detection does not evaluate separation infringements but a Situation of Interest (SI). An aircraft pair constitutes a SI when it is expected to get with a horizontal separation between both aircraft closer than 10 Nautical Miles (NM) and a vertical separation closer than 1000 feet (ft). Therefore, the ML predictor classifies aircraft pairs between SI or No SI pairs. Air traffic information is extracted from The OpenSky Network that provides ADS-B trajectories. ADS-B trajectories do not offer enough SI samples to be evaluated. Hence, the authors performed simulations varying the entry time of the trajectories to the airspace within the same time period. The methodology was applied to a portion of Switzerland airspace, and simulations reached a 5% rate of SI samples. Cost-sensitive techniques were used to handle the strong imbalance of the database. Two experiments were performed: the Pure model considered the whole database, and the Hybrid model considered aircraft pairs that intersect horizontally closer than 20 NM and vertically lower than 1000 ft. The Hybrid model provided the best results achieving 72% recall, representing the success rate of Missed alerts and 82% accuracy, which means the whole predictions’ success rate.
Design and control of optical steady image system of aerial camera

In order to solve the problem of aerial photography imaging blur, an aerial camera optical image stabilization system is designed. Based on the principle of optical image stabilization, a forward image stabilization system of aerial camera was designed by analyzing the imaging blur of aerial camera. The equivalent resolution of the system is 500-1000 l p/mm, and the corresponding pixel size is 1-2 μm. Aiming at the optical image stabilization system to establish its PID and corresponding adaptive PID control model, establish its closed-loop system transfer function, and use MATLAB to simulate and analyze the control system. The results show that the whole image stabilization system becomes stable after 0.01s and can achieve the goal of image stabilization. It solves the problem of forward image motion of aerial camera, and greatly improves the imaging effect of aerial camera when it works.
Responses of evapotranspiration to changing climate for the past six decades in the upper catchment of the Olifants River basin

In river basins such as the Olifants, the evapotranspiration has been documented as the biggest water user and therefore one of the most important hydrological processes in the basin. Thus, using the Soil and Water Assessment Tool (SWAT) model, this study attempted to assess the hydrological responses of evapotranspiration (ET) to climate change in the Olifants River basin over a period of six decades, focusing mainly on the upper catchment (UC) of the basin. The study showed, on average, a general decline of seasonal ET with a decrease of 10.5% in spring, 29.8% in summer and 17.3% in winter. However, autumn ET showed an average increase of 12.5%. With (1960 – 1970) decade considered as the baseline, the study also found that annual ET in the UC, has been on the decline consistently since the 1960s with the most recent decade (2010 – 2018) taking the biggest share of the drop (12.8%). Here, the average annual decline of ET of 1.75 mm/yr was observed with most of the decline happening in the last decade. This general decadal trend of annual ET mortality in the area coincides with the temperature rise and precipitation decrease of the area, signifying the impact climate change may have had on the evapotranspiration for the past six decades.
Research on the Design of Intelligent Chatbot Based on Deep Learning

At present, chat robots still have the problems of insufficient utilization of multi-modal information, weak emotion expression ability, and lack of model fusion mechanism. With the application of deep learning in the fields of natural language understanding, word vector representation, machine translation, sentiment analysis, and Chinese word segmentation, people have begun to study the key technologies of chat robots and apply deep learning to chat robots. In view of the problem that the traditional chat robot Seq2Seq model training will produce too safe, common, and repetitive answers to the speech corpus, which affects the interactive experience with users, this paper combines the idea of mutual information to improve the objective function of the model so that the model can produce richer and more diverse answers and give users a better experience. Experiments show that the model has improved many indicators compared with the single-modal model in the microblog data set experiment.
Association plant leaf longevity to the increasing warm and arid environment

The longevity of plant leaves is an important factor relating to their growth strategies and responding to global climate change. The focus of this study is to present a meta-analysis of leaf life spans through an extensive literature survey. As to our knowledge, no comprehensive synthesis has yet been presented to reveal the response of leaf longevities to changing external abiotic factors, including elevated levels of carbon dioxide, increasing temperatures and shorter availability of moisture. We screened 146 abstracts from the Web of Science and summarized the results of 78 specific studies dealing with plants leaf longevity including herbaceous, shrub and tree species. The reaction of tree species was assessed in field studies while shrub and herb species were studied under experimental or field conditions. The leaf longevities of tree species are most affected by elevated levels of CO2, but not by the other two factors. Shorter leaf longevities of herbaceous species are observed in response to rising temperatures and greater moisture levels. Shrub species did not show significant changes in leaf longevity responding to any of the studied external factors. Herbaceous species were more affected by combined changes in temperature, moisture, and CO2 than shrub and tree species. In view of the whole plant biome, there are no significant responses, neither.
Food Cart: The Importance of Brand Recognition, Support, and Retention to Franchise Business

The purpose of this study is to collect data from the owners of food cart businesses. This study benefits the franchisors in a way of learning the importance of the support, brand recognition, and retention of a franchise business. This may also convince young or future entrepreneurs to consider that franchising is a starting business amidst pandemic. Descriptive research is used in the study. The main instrument used of gathering data was questionnaire. In this study, the Pearson’s correlation shows that the dependent and independent variables are highly correlated in which the brand recognition and perceived franchise support have an effect to the intention of retaining the franchise system positively and significantly. Furthermore, franchisor considers the opinion of the franchisee that they care about the general satisfaction and does not take advantage of the franchisee. The brand recognition is strong, as well as respected and considered it as an asset of the franchise business. The study also shows that the franchisee has the intention of retaining the franchise system and consider of introducing the franchise system to others as they consider it as their first priority.