Miracle-dz/UniFAR-FTUs huggingface dataset:
Nigeria’s Bilateral Trade in Goods and Services with Selected International Trading Partners

Over the period, Nigeria’s major export commodities are agricultural goods, solid minerals, energy goods manufactured goods, crude oil and other oil products of which petroleum crude accounted for about 79% of total export while imports consisted of refined petroleum, raw materials, cars, agricultural goods, energy goods and other manufacture products. Using the augmented gravity model approach, this study analysed Nigeria’s bilateral trade in goods and services with selected international partners namely Belgium, China, France, India, Italy, Netherlands, South Africa, Spain, United Kingdom, and United States for the period 1995–2020. This study estimated the pooled ordinary least square and the fixed effect estimation in a balanced panel model. It was found that trade flow in Nigeria is positively and significantly determined by gross domestic product, population, and common colony in both models. The result further showed that distance negatively and significantly affects trade flow in Nigeria while relative price was positive and significant in the pooled effect model and negatively significant in the fixed effect model. Common language was positively significant in the pooled effect model but was statistically insignificant in the fixed effect model. Lastly, real effective exchange rate was not statistically significant in explaining bilateral trade flow in Nigeria within the study period in both models. Based on the results and the implications it holds, the study recommends amongst others, that policies aimed at diversifying the economy away from oil towards a technological, service and export driven economy should be earnestly pursued. This study further concludes that policy actions and human capital development programmes are therefore required to convert the teeming population into active and effective labour force which will in turn improve the gross domestic product and consequently improve trade flow
Verbal Linguistics Intelligence and Self-Directed Learning in EFL Writing Problem Solving: A Study of Grit's Mediating Influence

: In EFL writing, the interplay of Verbal linguistic intelligence and self-directed learning, influenced by grit, is crucial for enhancing proficiency. This research aims to explore the intricate relationship between linguistic intelligence, independent learning, and the pivotal role of grit. A cross-sectional design was instituted, encompassing 228 participants, aged 20-25, from a renowned private university in Malang, Indonesia. The Grit Scale by Duckworth et al. (2007) gauged participants' perseverance, while tools grounded in Hayes and Flower's paradigms assessed self-directed Learning. Verbal-linguistic Intelligence was appraised using the MIDAS tool. The findings illuminate the intricate dynamics of Verbal linguistics lntelligence, self-directed learning, and grit in academic writing. Verbal Intelligence strongly correlated with the constructs, underscoring its cardinal role in academic writing and analytical capabilities. Grit emerged as a crucial mediator between Self-Directed Learning, Verbal Intelligence, and writing problem-solving strategies, presenting a promising avenue for targeted pedagogical interventions. This investigation bolsters the foundation of resilient educational assessment tools and magnifies the transformative potential of grit and linguistic prowess in sharpening analytical abilities. Further studies should expound upon these relationships across diverse landscapes, remaining mindful of inherent biases.
MALIMG2022: DATA AUGMENTATION AND TRANSFER LEARNING TO SOLVE IMBALANCED TRAINING DATA FOR MALWARE CLASSIFICATION

Data augmentation is creating new images by transforming old ones. It’s used to solve imbalanced image classification problems in many domains. Usually, data augmentation is used when we are unable to get more data for underrepresented classes. So, data augmentation techniques help us to increase the size of training data in order to avoid any bias in the classifier. This paper’s main contribution is to develop a balancing tool for any imbalanced multiclass database. Then, we use this approach in application to the Malimg database to improve its effectiveness and speed to solve imbalanced data problems. As a result, we generated 2 versions of the Malimg database namely Malimg2022 (Large and XXLarge), also we make the “Balance Me” application that can balance any database using augmentation techniques. These new versions are balanced, having the same number of samples per class using data augmentation with different transformation techniques. From a technical point of view, Zero-day malwares are none than old ones with few modifications, so data augmentation could be seen as a simulation of new malware variants that should be detected effectively. Finally, the new balanced data were evaluated using transfer learning models. So, the generated databases didn’t show an overall improvement in the whole classification task, however, we have found some improvements related to some malware classes. Here, we can say that the use of data augmentation to balance data isn’t always a good choice.
How Well Do the Rosgen Classification and Associated "Natural Channel Design" Methods Integrate and Quantify Fluvial Processes and Channel Response?

Over the past 10 years the Rosgen classification system and its associated methods of natural channel design have become synonymous (to many without prior knowledge of the field) with the term stream restoration and the science of fluvial geomorphology. Since the mid 1990s, this classification approach has become widely, and perhaps dominantly adopted by governmental agencies, particularly those funding restoration projects. For example, in a request for proposals for the restoration of Trout Creek in Montana, the Natural Resources Conservation Service required experience in the use and application of a stream classification system and its implementation. Similarly, classification systems have been used in evaluation guides for riparian areas and U.S. Forest Service management plans. Most notably, many highly trained geomorphologists and hydraulic engineers are often held suspect, or even thought incorrect, if their approach does not include reference to or application of a classification system. This, combined with the para-professional training provided by some involved in natural channel design empower individuals and groups with limited backgrounds in stream and watershed sciences to engineer wholesale re-patterning of stream reaches using 50-year old technology that was never intended for engineering design. At Level I, the Rosgen classification system consists of eight or nine major stream types, based on hydraulic-geometry relations and four other measures of channel shape to distinguish the dimensions of alluvial stream channels as a function of the bankfull stage. Six classes of the particle size of the boundary sediments are used to further sub-divide each of the major stream types, resulting in 48 or 54 stream types. Aside from the difficulty in identifying bankfull stage, particularly in incising channels, and the issue of sampling from two distinct populations (beds and banks) to classify the boundary sediments, the classification provides a consistent and reproducible means for practitioners to describe channel morphology although difficulties have been encountered in lower-gradient stream systems. Use of the scheme to communicate between users or as a conceptual model, however, has not justified its use for engineering design or for predicting river behavior; its use for designing mitigation projects, therefore, seems beyond its technical scope.
FxD: a functional debugger for dysfunctional spreadsheets

Recent enhancements to the spreadsheet formula language and intelligent spreadsheet interfaces allow spreadsheet users to build more complex spreadsheets in systematic ways (e.g., via functional abstractions). However, users have been slow to adopt such features, partly due to the absence of corresponding improvements in tools such as editors and debuggers. In this paper, we present FxD, a novel spreadsheet debugging interface, which provides structured information needed for spreadsheet users to debug formulas in systematic ways through affordances such as the ability to step into the execution of dependencies and provide contextual information to users based on the current context. An in-vitro, within-subject (n=12) experiment revealed that, even though using FxD did not lead to faster debugging, participants reported qualitative improvements (e.g., feelings of efficiency and capability) when debugging with it. Further, participants were more satisfied with the amount of information provided by FxD and felt that it would enhance their existing debugging workflows. Our results have implications for the design of debuggers for spreadsheets and for functional programming languages in general.
Crack Severity Classiﬁcation from Timber Cross-Sectional Images Using Convolutional Neural Network

Featured Application: Application of convolutional neural network (CNN) for automatic evaluation of internal crack severity in building square timbers. In our previous article, we constructed a simple CNN and employed cross-sectional pictures covered with silver paint to make the cracks easier to recognize. However, in this article, we conﬁrmed that the severity of the internal cracks could be evaluated with high accuracy by a ResNet50-CNN (Residual Neural Network 50 CNN), even in the case of unpainted images. Given that quality managers must evaluate numerous timbers, this automation process reduces the workload. Abstract: Cedar and cypress used for wooden construction have high moisture content after harvesting. To be used as building materials, they must undergo high-temperature drying. However, this process causes internal cracks that are invisible on the outer surface. These defects are serious because they reduce the strength of the timber, i.e., the buckling strength and joint durability. Therefore, the severity of internal cracks should be evaluated. A square timber was cut at an arbitrary position and assessed based on the length, thickness, and shape of the cracks in the cross-section; however, this process is time-consuming and labor-intensive. Therefore, we used a convolutional neural network (CNN) to automatically evaluate the severity of cracks from cross-sectional timber images. Previously, we used silver-painted images of cross-sections so that the cracks are easier to observe; however, this task was burdensome. Hence, in this study, we attempted to classify crack severity using ResNet (Residual Neural Network) from unpainted images. First, ResNet50 was employed and trained with supervised data to classify the crack severity level. The classiﬁcation accuracy was then evaluated using test images (not used for training) and reached 86.67%. In conclusion, we conﬁrmed that the proposed CNN could evaluate cross-sectional cracks on behalf of humans.
Going beyond the Literal: A Longitudinal Study of Metaphorical 1 Conceptualization in Sustainability Reports

7 Language expressions are primarily shaped by conceptual metaphors. By adopting Conceptual 8 Metaphor Theory as a tool, this study explores the communicative strategies adopted by a 9 company’s outlook on social and environmental responsibility from a longitudinal perspective. 10 Conceptual Metaphor theory, also known as Cognitive Metaphor Theory, is an expression used 11 in cognitive linguistics to describe the understanding of one notion or conceptual domain in 12 terms of another. In more specific terms, the article discusses how Structural, Orientational, 13 and Ontological conceptual metaphors are employed in the communicative strategies adopted 14 by Chevron Co. pre-and during the COVID-19 pandemic crisis. Chevron is an American 15 joint-stock company that operates in the field of power generation and oil refining.The 16 qualitative and quantitative linguistic analysis conducted using the Sketch Engine program 17 aimed to pinpoint and clarify the ways in which Chevron and Co’s use of language creates and 18 communicates metaphors in its Sustainability Reports.
The In�uence of Direct Radiative Forcing Versus Indirect Sea Surface Temperature Warming on Southern Hemisphere Subtropical Anticyclones Under Global Warming

Southern hemisphere subtropical anticyclones are projected to change in a warmer climate during both austral summer and winter. A recent study of CMIP 5 & 6 projections found a combination of local diabatic heating changes and static-stability-induced changes in baroclinic eddy growth as the dominant drivers. Yet the underlying mechanisms forcing these changes still remain uninvestigated. This study aims to enhance our mechanistic understanding of what drives these Southern Hemisphere anticyclones changes during both seasons. Using an AGCM, we decompose the response to CO 2 -induced warming into two components: (1) the fast atmospheric response to direct CO 2 radiative forcing, and (2) the slow atmospheric response due to indirect sea surface temperature warming. Additionally, we isolate the in�uence of tropical diabatic heating with AGCM added heating experiments. As a complement to our numerical AGCM experiments, we analyze the Atmospheric and Cloud Feedback Model Intercomparison Project experiments. Results from sensitivity experiments show that slow subtropical sea surface temperature warming primarily forces the projected changes in subtropical anticyclones through baroclinicity change. Fast CO 2 atmospheric radiative forcing on the other hand plays a secondary role, with the exception of the South Atlantic subtropical anticyclone in austral winter, where it opposes the forcing by sea surface temperature changes resulting in a muted net response. Lastly, we �nd that tropical diabatic heating changes only signi�cantly in�uence Southern Hemisphere subtropical anticyclone changes through tropospheric wind shear changes during austral winter.
Design, fabrication and preliminary testing of plasmonic luminescent solar concentrator devices

This paper details the design process, fabrication, optimisation and early-stage performance testing of Luminescent and Plasmonic Luminescent Solar Concentrator (LSC & PLSC) devices. A PLSC is a novel approach to solar concentrator technologies that utilizes the principles of luminescence and plasmonics to enhance the devices' solar energy conversion efficiency. This research analyses various mould dimensions, materials and lightguide fabrication methodologies to ensure equivalent LSC/PLSC devices were created in a reproducible method. The optimisation was an iterative process throughout the production and testing stages after which a 100 × 100 × 5 mm PLSC was identified as the optimal for a rooftop installation. To ensure consistency in production as well as assessing the practicality of PLSC installations for building integration, performance testing has been conducted in both indoor and outdoor environments. Additionally, the lifespan of the devices are currently being investigated through ongoing performance evaluations. The incorporation of a reflective backplate has resulted in device efficiency improvements between 14–18% during indoor tests and was consequently included for all devices during outdoor performance analysis. Power conversion efficiencies of 2.3% and 1.7% have been recorded in sub-optimal conditions as well as concentration ratios of 11 and 9 for the PLSC and LSC devices respectively.
Corona emission thresholds for three types of hydrometeor interaction in thunderclouds

Laboratory studies have been conducted of the conditions under which glancing collisions, at relative velocities V characteristic of those occurring in thunderstorms, of three types of hydrometeor pairs of precipitation dimension (such as (1) warm drop pairs, (2) supercooled drop pairs, and (3) a supercooled drop and a graupel pellet) produce a corona discharge in an electric field E. In each case, the observed corona is emitted at the tip of an ephemeral liquid filament, of length greater than the dimensions of the interacting hydrometeors, drawn out during each interaction. For each type of hydrometeor pair, the probability f that corona was produced during an interaction increased steadily from zero for increasing values of E above about 150 kV/m, and was significantly in excess of 50% for values of E = 400 kV/m, which is probably about the maximum ambient value occurring in a thunderstorm. We conclude that if the associated hydrometeors are present in strongly electrified regions of thunderstorms (unlikely for interaction type 1 but manifestly possible for types 2 and 3, corona initiation leading to the production of lightning could result from each of the three types of interaction studied.
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF

Mesoscale modeling can be used to analyze key parameters for wind turbine load assessment in a large variety of tropical cyclones. However, the modeled wind structure of tropical cyclones is known to be sensitive to the boundary layer scheme. We analyze modeled wind speed, shear, and wind veer across a wind turbine rotor plane in the eyewall and rainband region. We further assess the sensitivity of wind speed, shear, and veer to the boundary layer parameterization. Three model realizations of typhoon Megi over the open ocean using three frequently used boundary layer schemes in the 5 Weather Research and Forecasting model are analyzed. All three typhoon simulations reasonably reproduce the cyclone track and structure. The boundary layer parametrization causes up to 21 % differences in median hub height wind speed between the simulations. The simulated wind speed variability is further dependent on the boundary layer scheme. The modeled wind shear is smaller or equal to the current IEC standard regardless of the boundary layer scheme for the eyewall and rainband region. While the surface inflow angle is sensitive to the boundary layer simulation, wind veer in the lowest 400 m of the 10 atmospheric boundary layer is less affected by the boundary layer parametrization. Simulated wind veer reaches values up to 1 . 8 × 10 − 2 ◦ m − 1 ( 1 . 1 × 10 − 2 ◦ m − 1 ) in the eyewall region (rainband region) and is relatively small compared to moderate wind speed regimes. On average, simulated wind speed shear and wind veer are highest in the eyewall region. Yet strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads, due to rapid coherent wind profile changes at the turbine location.
MIT Open Access Articles Modeling cache performance beyond LRU

Modern processors use high-performance cache replacement policies that outperform traditional alternatives like least-recently used ( LRU ). Unfortunately, current cache models do not capture these high-performance policies as most use stack distances, which are inherently tied to LRU or its variants. Accurate predictions of cache performance enable many optimizations in multicore systems. For example, cache partitioning uses these predictions to divide capacity among applications in order to maximize performance, guarantee quality of service, or achieve other system objectives. Without an accurate model for high-performance replacement policies, these optimizations are unavailable to modern processors. We present a new probabilistic cache model designed for high-performance replacement policies. It uses absolute reuse distances instead of stack distances, and models replacement policies as abstract ranking functions . These innovations let us model arbitrary age-based replacement policies. Our model achieves median error of less than 1% across several high-performance policies on both synthetic and SPECCPU2006 benchmarks. Finally, we present a case study showing how to use the model to improve shared cache performance.
Constructing place specific measures of health and socio-economic inequality for metropolitan Vancouver

In recent decades researchers have used Census-based socio-economic models to explain why some people are healthier than others. Little attention has been directed to survey-based methods for quantifying socio-economic inequality due to the inherent subjectivity of individual responses in characterizing data. This thesis argues that this very subjectivity allows us to better understand and evaluate social gradients in health status. A survey instrument was developed and distributed to British Columbia's Medical Health Officers (MHOs) as part of a methodology for identifying the socio-economic variables characteristic of local health outcomes specific to urban areas in British Columbia, Canada. A weighted Kappa test statistic and a GIs-based Multicriteria Analysis are used to evaluate the strength of the variables selected by the MHOs. This research demonstrates that place-specific and survey-based methods of constructing deprivation indices are effective strategies for identifying spatial distributions of health and socio-economic outcomes within urban areas in British Columbia.
A HYBRID DEEP CNN-LSTM NETWORK (HDCLN) FOR SHOULDER IMPLANT CLASSIFICATION USING X-RAY IMAGES

Today, prosthesis manufactured from polyethelene and metallicconstituents have been widely used to replace the impaired ball and sockets of the human shoulder. Years after the replacement, there may be need for reoperation and revision due todesecration in the quality of the prosthesis. This process requires the information about the prototype as well as the corresponding manufacturer of the prosthesis. In some situations, the patient and the primary doctor may not have the information about the prototype and manufacturer of the prosthesis. Usually, manual recognition of the prototype and manufacturer of the prosthesis is carried out during the preoperative planning. However, the manual identification of the model and manufacturer is time-consuming and prone to error. An automatic model identification and manufacturer classification system can speed up the treatment process and reduce the operation risk associated with the manual identification system.In this paper, we introduce a hybrid deep CNN-LSTM network for identification and classification of implant manufacturer. In this network, we used the CNN layer to extract features from the radiographic images and the LSTM layer for identification and classification of implants. A collection of 597 implant images from four manufacturers, including 83 images from Cofield, 294 from Depuy, 71 from Tornier, and 149 from Zimmer, were used as dataset to train and test the model. Based on experimental results, our model achieved the accuracy of 98.7%, precision of 98%, F1-score of 98.2% and recall of 98%. Based on the performance of the model, we believe that this model will be a useful tool in preoperative planning and can be applied in the identification and classification of implants from other manufacturers.