Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Assessment of Teaching and Learning Methods for Secondary School Biology Subject Content as an Approach to Addressing Environmental Degradation in Malawi. Case Study of Karonga District

The purpose of this study was on assessing of teaching and learning methods for secondary school biology subject content as an approach to addressing environmental degradation in Malawi. Case study of Karonga district. The study was guided by the following research specific objectives: to assess how success criteria in secondary school Biology syllabi are framed to address environmental degradation in Malawi; to investigate the appropriateness of the methods of teaching and learning Biology that address environmental degradation and to examine how Biology teachers assess students’ fieldwork that is required by the syllabus. A mixed method approach (qualitative and quantitative methods) was used in this study. Sixty secondary school teachers were selected from 40 secondary schools in Northern Education Division in the Ministry of Education in Malawi as research participants. Schools and participants were selected through convenience and purposive sampling techniques respectively. In this study, data was collected through interviews, questionnaire and document analysis. Data analysis was done thematically and using statistics. The research study found out that the success criteria, content and teaching and learning methods in Biology would not address environmental degradation because success criteria were not framed well and had many action verbs such as ‘explain’, ‘describe’ and ‘state’. In addition, explanation was the main teaching and learning methods used by Secondary school Biology teachers which would not address environmental degradation since they did not encourage learning through practice, The findings also revealed that Biology teachers in secondary schools assessed learners with the aim of passing public examinations and not assisting learners to acquire skills to deal with issues of environmental degradation.
How Severe is Your COVID-19? Predicting SARS-CoV-2 Infection with Graph Attention Capsule Networks

Recent studies in machine learning have demonstrated the effectiveness of applying graph neural networks (GNNs) to single-cell RNA sequencing (scRNA-seq) data to predict COVID-19 disease states. In this study, we propose a graph attention capsule network (GACapNet) which extracts and fuses Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transcriptomic patterns to improve node classification performance on cells and genes. Significantly different from the existing GNN approaches, we innovatively incorporate a capsule layer with dynamic routing into our model architecture to combine and fuse gene features effectively and to allow those more prominent gene features present in the output. We evaluate our GACapNet model on two scRNA-seq datasets, and the experimental results show that our GACapNet model significantly outperforms state-of-the-art baseline models. Therefore, our study demonstrates the capability of advanced machine learning models to generate predictive features and evolutionary patterns of the SARS-CoV-2 pathogen, and the applicability of closing knowledge gaps in the pathogenesis and recovery of COVID-19.
Performance Analysis of Various Rectifier Topologies for 2.45 GHz RF Energy Harvesting Applications

This paper aims to evaluate and compare the performance of three basic configurations of RF energy harvesting rectifier, the series, the voltage doubler, and the voltage multiplier. To achieve this goal, different parameters affecting the performance of the rectifier were analyzed in the 2.45 GHz ISM band. When the simulated series rectifier, voltage doubler and voltage multiplier are perfectly matched, a conversion efficiency of 77.18 %, 75.29 % and 66.6 % is reached, respectively, at an input power of 15 dBm. While, the DC output voltage at the same input power level is 6.5 V, 5.29 V, and 7.59 V.
Enhanced Differential-Linear Attacks on Reduced Round ChaCha

We present numerous refinements to the previous differential-linear attacks on ChaCha in this study. Beierle et al. discovered a 3.5-round differential at CRYPTO 2020, which was based on the condition that suitable key-IV pairs are picked, which they termed as 'right pair'. They were able to refine their approach by doing so, but they also observed that the acquisition of a right pair requires an average of 25 iterations. In our work, we propose a method for achieving the right pairs with the help of listing, so that the extra multiplication of 25 in the overall complexity can be avoided. In addition, we present a tactical enhancement in 'Probabilistic Neutral Bit'- searching algorithm, a change in complexity computation and a novel attack strategy based on two input-output pairs. We employ them to lower the attack complexity from 2230.86 to 2218.95 for the 7-round ChaCha256. Furthermore, after almost ten years, we enhance the complexity of a 6-round 128-bit version of ChaCha (Shi et al: ICISC 2012) by more than 78 million times and for the first time, propose attacks on 7.25-round ChaCha256 and 6.5-round ChaCha128 with time complexities 2244.85 and 2121.40 respectively.
Complexity Science, Rules of the Whole Systems and the Qualitative Researches

For complex systems, rules of the whole systems should be investigated. Consequently, appropriate attention should pay to the qualitative researches. In this paper, a representation (E):Sni=11i=E(n) is given. It means: self-clustering, self-organizing and evolving to a new level. (E) is used to describe the formation of the structures of complex systems and their dynamic behavior. The concept ‘self-clustering’ is explained. The effect of clustering quantity to system ability is analyzed. Another representation (C):11+12+…+1nen 2 is followed. Many examples in different domains, such as in biology, natural world, social science, economics, engineering, etc, are given which can be explained by (E). From the standpoint of complex systems and with the aid of (E), the understanding of ‘from quantitative change to qualitative change’ is discussed. Finally, by comparing the self-similarity in the structures of complex systems with that in fractal, we can see that the complex structures are formed by repeating some simple rule again and again, so complexity is besides in simplicity. Moreover, self-clustering, self-organizing and repeating is the basic rule to form the structures of a kind of complex systems.
Effects of Interpolation Parameters in Inverse Distance Weighted Method on DEM Accuracy

Interpolation method is one of the most important factors which influence on the accuracy of DEM.However,there are few researches on how interpolation parameters influence interpolation method as well as the interpolation accuracy at last.The interpolation parameters of distance weighted interpolation method was considered as the research object,and respectively selected 6 kinds discrete data as the original data.Then metrics of RMSE was used to measure RMSE of the generated DEM in order to study that how interpolation parameters,including weighted index,search points,search directions,search radius in distance weighted interpolation method affected the accuracy of DEM interpolation.It was drawn: firstly,in the aspect of search directions,it was not obvious to use the four-direction and eight-direction to improve the accuracy of DEM interpolation;secondly,the best search points were 8~12 points;thirdly,when the weighted index was higher than 3,the impact of the change in weighted index on DEM accuracy tended to be not obvious,the best weighted index should be 2 or 3;finally,the degree of influence on the accuracy of DEM interpolation was "the power indexthe search pointsthe search direction".
Real-World Popularity Estimation from Community Structure of Followers on SNS

In this paper, we propose methods of estimating the offline real-world popularity of users of online social network services (SNSs). Because their followers on an SNS are biased sampling from their offline real-world fans, we cannot estimate their real-world popularity simply by the number of their online followers. Our methods are based on the following hypothesis: SNS users with followers more distributed over many communities are likely to have more real-world popularity. We developed four methods, three of which use variations of the clustering coefficients of the followers to measure how much they are distributed, and one of which uses a metric we newly designed. Through the evaluation of our methods on the data from nine Ms/Mr university competitions, we validated our hypothesis.
A Qualitative and Quantitative Study to Effect of El- Gharbia Main Drain Wastewater on The Surrounding Soils and Plant Life

El-Gharbia main drain poses a significant environmental threat, as it receives wastewater from three sources: sewage, agriculture, and industry. The objective of this study is to evaluate the impact of El-Gharbia main drain wastewater on the micronutrient and heavy metal content of surrounding soil and plant. Samples (soils and plants) were taken from fifteen sites along the drain in winter of 2020 and summer of 2021. Results showed that the content of soils and plants samples from micronutrients such as Fe, Mn, Zn, Cu and B and heavy metals such as Cd, Co, Cr, Ni and Pb) were differed from site to another, in some cases, the overall mean exceeded the permissible limit in both seasons, but the pollution degree was higher in the summer season. As for the bioaccumulation factor values (BCF) in plant samples were higher than 1. Application of risk assessment equations showed that the enrichment factor (EF) of micronutrients and heavy metals values were low level except for cadmium element values which varied from very significant to severe level and lead element values were moderate level in both seasons under study, contamination factor (CF) values were varied from low to moderate except for cadmium element were severe contamination level in both seasons, contamination degree (Cd) were varied from considerable to very high level in both seasons, pollution load index (PLI) values were high contamination level in both seasons except for site No. 1was low level at winter season.
Bearing Fault Diagnosis Based on an Advanced Method: ID-CNN-LSTM

In modern industry, ball bearings are not prone to failure, once a failure occurs, the production of the factory will be shut down, which will cause economic losses. Therefore, it's crucial to research how to diagnose ball bearings. This research proposed an advanced fault diagnosis method: 1D-CNN-LSTM to classify ball bearing faults and use the ball bearing faults data from Case Western Reserve University (CWRU) to execute experiments, which is the raw one-dimensional vibration sequential data. In the experiment, the raw vibration data is first split into multiple subsequences, and input to one-dimensional convolutional neural network (1D-CNN) wrapped by TimeDistributed layer to extract features. The output of 1D-CNN is a sequence, which is input to long short-term memory (LSTM) for sequential processing. Finally, the class of bearing fault is output for diagnosis. The results indicate a good model fit and outstanding generalization and robustness on new validation data. The assessment of the training dataset indicates that it has achieved a perfect accuracy of 100%, while the validation dataset has achieved an accuracy of 99.99%, which is an exceptional outcome.
The Dynamic Characteristics Analysis for the Large Bridge Based on the Improved Hilbert-Huang Transformation

To avoid the problem of modes mixing caused by the intermittent in the data,a method of the ensemble empirical mode decompostion(EEMD) is studied in this paper along with its error effects.In this method the white noise is used to improve the decomposion results.Then the free vibration response can by obtained by the random decrement technique(RDT) together with the improved decomposion results.This can provide the available data for Hilbert transformation to identify the modal parameters.Finally,this is applied on the GPS dynimic monitoring data of the Wuhan Baishazhou Bridge.The results show that the mode parameters can be identify by the EEMD together with RDT effectively.
An Exploratory Study of Architectural Style and Effort Estimation for Multi-Tenant Microservices-Based Software as a Service (SaaS)

Service Provider evolving to a multi-tenant Software as a Service (SaaS) model seeks to minimise effort and cost with a shared application environment. There are existing research techniques to fit the service in a shared environment; many require specific technology to be implemented. This requirement causes a challenge for Service Providers that already commit to a technology stack to adopt. The actual development efforts are also not revealed or discussed in existing works, posing another challenge for Service Providers to estimate efforts for decision-making. In this paper, we propose abstracting these multi-tenant designs in a technology-independent perspective as an architectural style to guide Service Providers in supporting multi-tenants on new or existing technology stacks. We start by reviewing existing works to understand the necessary properties of a multi-tenant architectural style. We then model the proposed architectural style using the UML web engineering approach for a case study of evolving single-tenant web application to support multiple tenants. We record our estimated and actual development efforts and validate them against an existing web effort prediction model. The evaluation results show that our effort estimations are consistent with the web effort prediction model trained from another set of 19 web projects. We hope that a validated web effort prediction model for multi-tenancy to estimate the development efforts early before actual implementation can help Service Providers make development decisions. This paper contributes a multi-tenant architectural style and a validated web effort prediction model for early effort estimation to help decision making. We hope this work can help software architects make an informed decision on designing for multi-tenancy in their web applications and encourage more widespread adoption of multi-tenant designs.
An Optimization Method for Measurement Matrix Based on Eigenvalue Decomposition

Measurement matrix is a very important part in compressive sensing.In order to decrease the mutual coherence between the measurement matrix and sparse transformed matrix and improve the quality of reconstruction,a Gram matrix was constructed based on the product of the measurement matrix and sparse transformed matrix.Then a new global mutual coherent coefficient was defined based on off-diagonal elements of the Gram matrix.After deriving the relationship between the global mutual coherent coefficient and the eigenvalues of the Gram matrix,we proposed an optimization model,which could minimize the global mutual coherent coefficient of the given matrices by adjusting the eigenvalues above zero to the average value of the sum of these eigenvalues without changing the sum.The speed of optimizing matrix is fast and the PSNR of the picture is improved with the optimized measurement matrix from the experimental results.These showed that our proposed method had some advantages in terms of reconstruction effect and optimization speed.
Impact of local versus global datasets on hydrological responses in Mahanadi River basin in India

ABSTRACT Global environmental changes are likely to have a large impact on water resources across the developing countries. However, most of these countries suffer from acute shortage of local data. New high-resolution global products are available, which can be integrated with large-scale hydrological models. Most of these global products, however, are rarely evaluated in developing regions. We propose a thorough evaluation of five new global products in Mahanadi River basin in India. We employ the variable infiltration capacity (VIC) model over the basin and perform model experiments to directly evaluate the impacts of the specific combinations of local and global datasets. Results suggest that the reference experiment, which uses all local datasets, most closely represents the observed discharge. However, some of the global datasets could be used as a viable alternative to local observations in this river basin and potentially in nearby basins where there is a lack of observations.
Depth and minimal slope for surface flows of cohesive granular materials on inclined channels

Abstract We present analytical predictions of the depth and onset slope of the steady surface flow of a cohesive granular material in an inclined channel. The rheology of Jop, Forterre & Pouliquen (Nature, vol. 441, 2006, pp. 727–730) is used, assuming co-axiality between the stress and strain-rate tensors, and a coefficient of friction dependent on the strain rate through the dimensionless inertial number $I$. This rheological law is augmented by a constant stress representing cohesion. Our analysis does not rely on a precise $\mu (I)$ functional, but only on its asymptotic power law in the limit of vanishing strain rates. Assuming a unidirectional flow, the Navier–Stokes equations can be solved explicitly to yield parametric equations of the iso-velocity lines in the plane perpendicular to the flow. Two types of channel walls are considered: rough and smooth, depicting walls whose friction coefficient is respectively larger or smaller than that of the flowing material. The steady flow starts above a critical onset angle and consists of a sheared zone confined between a surface plug flow and a deep dead zone. The details of the flow are discussed, depending on dimensionless parameters relating the static friction coefficient, cohesion strength of the material, incline angle, wall friction, and channel width. The depths of the flow at the centre of the channel and at the walls are calculated by a force balance on the flowing material. The critical angle for the onset of the flow is also calculated, and is found to be strongly dependent on the channel width, in agreement with experimental results on heap stability and in rotating drums. Our results predict the important conclusion that a cohesive material always starts to flow for an incline angle lower than 90° between smooth walls, whereas in a narrow enough channel with rough walls, it may not flow, even if the channel is inclined vertically.
Examining the Differential Effectiveness of Fear Appeals in Information Security Management Using Two-Stage Meta-Analysis

ABSTRACT Most of the information security management research involving fear appeals is guided by either protection motivation theory or the extended parallel processing model. Over time, extant research has extended these theories, as well as their derivative theories, in a variety of ways, leading to several theoretical and empirical inconsistencies. The large body of fragmented, and sometimes conflicting, research has muddied the broader understanding of what drives protection- and defensive motivation. We provide guidance to the security discourse by offering the first study in the literature to employ two-stage meta-analytic structural equation modeling (TSSEM), which combines covariance-based structural equation modeling and meta-analysis. Information systems (IS) researchers have traditionally used meta-analysis for structural equation modeling for such purposes—an approach that has several serious statistical flaws. Using 341 systematically selected empirical security articles (representing 383 unique studies) and TSSEM, we pool a large series of five datasets to test six models, from which we examine the effects of constructs and paths in the security fear-appeals literature. We compare and test six versions of models inspired by issues in the broader fear-appeals literature. We confirm the importance of both the threat- and coping-appraisal processes; establish the central role of fear and that it has greater importance than threat; show that efficacy is a stronger predictor of protection motivation than is threat; demonstrate that response costs as currently measured are ineffective but that maladaptive rewards have a strong negative effect on protection motivation and a positive effect on defensive motivation; and provide evidence that dual models of danger control and fear control should be used.
A systematic review of the adolescent‐directed marketing strategies of transnational fast food companies in low‐ and middle‐income countries

Fast food consumption is associated with excessive intake of energy‐dense foods; a major determinant of childhood obesity. The lack of data on the marketing strategies used to promote fast food to adolescents in low and middle‐income countries (LMICs) acts as a barrier to global efforts to reduce the marketing of unhealthy foods to young people around the world.
The Scuttle Fly, Megaselia scalaris (Loew, 1866) (Diptera: Phoridae): A New Threat on Laboratory Mass Production of Fruit Flies

The scuttle fly, Megaselia scalaris is an omnivorous species, capable of exploring a large variety of environments and ecological niches. It is known as an important detritivore species with maggots feeding on a variety of food of both animal and plant origin. M. scalaris was investigated as a severely-infesting pupal-adult parasitoid on the laboratory mass culture of the two tephritid species of the peach fruit fly, Bactrocera zonata (Saunders) and the Mediterranean fruit fly, Ceratitis capitata (Wiedemann) reared in laboratory of Horticultural Insects Research Department, Plant Protection Research Institute, Agricultural Research Center, Egypt. Pupae of the two species of fruit flies resulting from experiments to evaluate efficiency of certain essential oils (Clove, Lavender and Neem) as stomach poison against larval instars in treated artificial diet were separately collected till emergence. Three days post emergence, all emerged flies were investigated dead with empty body cavities and huge numbers of strange larvae and pupae were observed inside their abdominal cavity which were identified as, M. scalaris, Eurytoma martellii Domenichini, 1960 (Hymenoptera: Eurytomatidae) and Drosophila hydei Sturtevant,1921 (Diptera: Drosophilidae). The first species was major predominant, whereas the latest two species were rare. Thus, it is concluded that these species especially M. scalaris may form a threat factor for laboratory mass production of many species of insects such as tephritid fruit flies.
A Method of Line Tracking Based on Hough Transforms and Edge Histogram

An online combined two kinds of features method for visual object tracking was proposed. A feature set was built by combining object contour's linear parametric equation and edge histogram. Firstly, the Hough transform is used to detect line in picture and obtain the parameters of the line, meanwhile, get the histogram at the same position of the line. The resulting initial template contains the location and pixel distribution information of the target. Secondly, in the subsequent frames, pixels divided and clustered by gradient direction and parameter equation would form more than one line which was to be matched. At last, obtain the exact location of the straight edge using the histogram matching algorithm. The experimental results indicate that the algorithm can run effectively when occlusion and complicated backgrounds happened, and perform favorably on challenging sequences in terms of efficiency, accuracy and robustness.