Miracle-dz/UniFAR-FTUs huggingface dataset:
Conservation laws for coding

This work deals with coding systems based on sparse graph codes. The key issue we address is the relationship between iterative (in particular belief propagation) and maximum a posteriori decoding. We show that between the two there is a fundamental connection, which is reminiscent of the Maxwell construction in thermodynamics. The main objects we consider are EXIT-like functions. EXIT functions were originally introduced as handy tools for the design of iterative coding systems. It gradually became clear that EXIT functions possess several fundamental properties. Many of these properties, however, apply only to the erasure case. This motivates us to introduce GEXIT functions that coincide with EXIT functions over the erasure channel. In many aspects, GEXIT functions over general memoryless output-symmetric channels play the same role as EXIT functions do over the erasure channel. In particular, GEXIT functions are characterized by the general area theorem. As a first consequence, we demonstrate that in order for the rate of an ensemble of codes to approach the capacity under belief propagation decoding, the GEXIT functions of the component codes have to be matched perfectly. This statement was previously known as the matching condition for the erasure case. We then use these GEXIT functions to show that in the limit of large blocklengths a fundamental connection appears between belief propagation and maximum a posteriori decoding. A decoding algorithm, which we call Maxwell decoder, provides an operational interpretation of this relationship for the erasure case. Both the algorithm and the analysis of the decoder are the translation of the Maxwell construction from statistical mechanics to the context of probabilistic decoding. We take the first steps to extend this construction to general memoryless output-symmetric channels. More exactly, a general upper bound on the maximum a posteriori threshold for sparse graph codes is given. It is conjectured that the fundamental connection between belief propagation and maximum a posteriori decoding carries over to the general case.
Research on UAV Task Assignment Based on Fireworks Algorithm

: From military, industry, agriculture to artificial intelligence and other fields, unmanned aerial vehicles (UAVs) have been widely used. Task allocation and path planning are the two most basic technologies in all the application research of UAV. As the core of multi-aircraft tactical deployment and cooperative control, the task allocation scheme largely determines the operational efficiency and execution effect of multi-UAVs. Due to the influence of the task environment and its own resources, the multi-machine cooperative task allocation problem is a multi-objective optimization and decision-making problem limited by many constraints. In recent years, many methods of task assignment and path planning have been put forward, and some progress has been made. According to the overall control of the application scenario, the task planning makes a reasonable task allocation scheme for the UAV to help it complete the task better. Multi-UAV task assignment is a complex multi-constraint multi-objective nonlinear optimization problem. Aiming at the respective defects of common intelligent algorithms, this paper proposes to use fireworks algorithm to deal with this problem.
Study and optimisation of WEDM parameters of AISI P20+Ni using RSM and hybrid deep neural network

ABSTRACT The main task in Wire Electrical Discharge Machining (WEDM) is to select the proper tool material for machining the workpiece to get better surface finishing and high cutting speed. To overcome these challenges, nowadays, coated electrodes are used. The main objective of this research work is to study the impact of certain input parameters such as pulse off-time, pulse on-time, servo voltage, and peak current in WEDM on Material Removal Rate (MRR), Recast Layer Thickness (RLT), and Surface Roughness (SR) output parameters by using zinc-coated brass wire as an electrode and AISI P20+Ni (DIN 1.2738) as the workpiece. One of the hardest materials used to make die for plastic moulds. The study is carried out experimentally and then compared with the design done by response surface methodology (RSM) using ANOVA and hybrid deep neural network (DNN) predicted values, which are optimised based on Manta ray foraging optimisation (MRFO) to obtain more precise outputs. The developed RSM and hybrid DNN+MRFO were designed in Design Expert 12 and MATLAB R2020a platforms and achieved 90% average prediction accuracy in the DNN+MRFO method. The experimentally obtained maximum MRR and minimum RLT and SR are 2.657 mm3/min, 7.9 µm, and 0.351 µm, respectively. The regression results showed that DNN+MRFO outperforms the RSM model. The achieved desirability at confirmatory analysis for RSM and DNN+MRFO are 0.844 and 0.921, respectively.
Community wellbeing in the built environment: towards a relational building assessment

ABSTRACT Community Wellbeing (CW) in the built environment is acknowledged as being ‘greater than the sum of its parts’, a process that emerges when residents negotiate understandings of community within shared spaces. However, methods of evaluation have not caught up. In practice, evaluation methods and frameworks measure CW as an aggregate of individual wellbeing, and operate as a ‘pick and mix’ of dimensions and indicators. Such an approach fails to capture the resident experience as it emerges from (and is shaped by) the local community context. To bridge the gap between theory and practice, we advance participatory mixed methods to integrate CW theory with current building performance assessment methods in support of the development of a novel, evidence-based tool. The first section covers the shift from an aggregate to relational conceptual framework, employing a social practice theory lens to acknowledge how CW emerges from individual and collective interactions. We then situate CW within current building performance assessment methods by reviewing seven CW frameworks. Finally, we recommend improvements to CW assessment based on our own research, including participatory methods, local community engagement, and Photovoice.
EFFECT OF DIFFERENT NUTRIENT MEDIA ON THE GROWTH OF SOME ARACEAE FAMILY PLANTS DURING IN VITRO ROOTING AND ADAPTATION STAGES

This work aims to study the effect of in vitro (tissue culture media ) and ex vitro (planting media) conditions on growth of some Araceae plants ( Syngonium podophyllum, Spathiphyllum wallisii and Philodendron scandens) The obtained results indicated that MS basal medium at full salt strength supplemented with 30 g/l sucrose produced the highest shoot length, fresh weight and number of roots for Syngonium podophyllum . While MS at quarter salt strength supplemented with 10g/l sucrose was suitable to Spathiphyllum wallisii, which gave the highest shoot length and number of leaves. The best medium for root formation was MS basal medium supplemented with 10 g/l sucrose. For Philodendron scandens plants, the results pointed out that shoots cultured in MS at quarter salt strength with 30g/l sucrose gave the highest values of fresh weight, root length and number of roots. For the effect of planting media, the data indicated that MS at full salt strength with 30g/l sucrose during rooting stage and planting medium containing peat: sand at 3:1 (v/v) gave the highest survival percentage for Syngonium podophyllum plants. The best growth and development of Spathiphyllum wallisii and Philodendron scandens plants during acclimatization stage were recorded for plantlets produced from MS at quarter salt strength with 10g/l or 20g/l sucrose during rooting stage and acclimatized in the previous planting medium (peat : sand 3:1 v/v), The present work aims to study factors affecting rooting and adaptation stages. This study included the: effect of MS salt strength and sucrose concentrations on growth and development of some Araceae plants growing in vitro (rooting stage) and effect of planting media on survival percentage of these plants grown ex vitro (adaptation stage). mg/L nicotinic, 0.1 mg/L thiamine, 2 mg/L glycine and 2 mg/L BA (Jambor – Benczur and Marta – Riffer 1990). The media were solidified with agar, (7 gm/L) and pH was adjusted to 5.7 and media were distributed 60 ml in each 350 mm. culture jar. The media were incubated at 26 + 2 0 C under 16 hrs illumination of 2000 lux from cool white fluorescent lamps. Shoots were transferred to the rooting media for the following study.
Magnetohydrodynamic Rayleigh Problem with Hall Effect and Rotation

This paper gives very significant analytical and numerical results to the magnetohydrodynamic flow version of the classical Rayleigh problem including Hall effect and Rotation. An exact solution of the MHD flow of incompressible, electrically conducting, viscous fluid past a uniformly accelerated and insulated infinite plate has been presented. Numerical values for the effects of the Hall parameter , N Hartmann number M and the Rotation parameter 2 K (i.e.,) the reciprocal of Ekman number on the velocity components u and v are tabulated and their profiles are shown graphically. The numerical results show that the velocity components u and v decreases with the increase of both M and , 2 K whereas the velocity component u decreases for fixed values of y and M for different values of N and t along with the increase of 2 K but v gets unstable.
Long non-coding RNA MALAT1 promotes cell proliferation, migration and invasion by targeting miR-590-3p in osteosarcoma

Osteosarcoma (OS) is a common malignant bone cancer and commonly occurs in adolescents and children. Long non-coding RNAs (lncRNAs) play major roles in cancer cell proliferation and metastasis. The present study aimed to investigate the potential molecular mechanism of lncRNA MALAT1 in OS. The levels of lncRNA MALAT1 and microRNA-590-3p were detected by reverse transcription-quantitative PCR in OS tissues and cells. Cell Counting Kit-8 and flow cytometry assays were conducted to assess cell proliferation and apoptosis. Cell migration and invasion were examined by Transwell assay. The levels of E-cadherin, N-Cadherin, Vimentin and Snail were measured by western blotting. The target of MALAT1 was predicted using online software and confirmed by luciferase reporter, RNA immunoprecipitation and RNA pull-down assays. The results indicated that MALAT1 was highly expressed in OS tissues and cell lines. MALAT1 knockdown promoted apoptosis and suppressed proliferation, migration, invasion and epithelial- mesenchymal transition (EMT) of OS cells. Overexpression of miR-590-3p increased cell apoptosis and hampered cell proliferation, migration, invasion and EMT in OS cells. In addition, MALAT1 knockdown upregulated the expression of miR-590-3p in OS cells. In conclusion, MALAT1 was demonstrated to suppress cell apoptosis and induce cell proliferation, migration, invasion and EMT by inhibiting miR-590-3p in OS, which indicated that MALAT1 has potential value in the diagnosis and treatment of OS.
Plasma lipids, lipid peroxides and antioxidant system in osteoarthritic patients underwent spa therapy

Objective: The molecular processes underlying degenerative cartilage disease ''osteoarthritis, OA'' are not fully known. Although oxidative stress causes cell damage in various tissues, there is not enough evidence for the involvement of oxidative stress in degenerative joint diseases. On the other hand, various spa therapies such as balneotherapy, mud, mineral water, and sulfur bath have long been used for treating osteoarthritis. This study aimed to investigate the effect of spa therapy with balneotherapy on oxidant/antioxidant status and lipid levels. Material and Methods: This prospective cross-sectional study was conducted on 28 osteoarthritis patients who had spa therapy at Bursa Military Hospital, Turkey. Osteoarthritis patients between 45-70 years who had no contraindications to spa therapy were eligible for inclusion in the study. Spa therapy included balneotherapy with acratothermal water, physical therapy modalities, and mild exercise for 15 days. Blood samples were obtained before and after the treatment cycle to determine the patients' possible changes in oxidant/antioxidant status and lipid profiles. Serum malondialdehyde (MDA), total thiol (T-SH) levels, total antioxidant capacity (TAC), superoxide dismutase (SOD), glutathione peroxidase (GPx) activity, and plasma lipids were measured. Results: We found a statistically nonsignificant decrease in MDA levels and a significant increase in GPx activity. Whereas plasma lipids, T-SH levels, TAC, and SOD activity remained unchanged. Conclusion: We may suggest that different mechanisms may play a role in the beneficial effects of spa therapy with balneotherapy in OA besides stimulation of GPx activity.
The Shape of Your Cloud: How to Design and Run Polylithic Cloud Applications

Nowadays the major trend in IT dictates deploying applications in the cloud, cutting the monolithic software into small, easily manageable and developable components, and running them in a microservice scheme. With these choices come the questions: which cloud service types to choose from the several available options, and how to distribute the monolith in order to best resonate with the selected cloud features. We propose a model that presents monolithic applications in a novel way and focuses on key properties that are crucial in the development of cloud-native applications. The model focuses on the organization of scaling units, and it accounts for the cost of provisioned resources in scale-out periods and invocation delays among the application components. We analyze dis-aggregated monolithic applications that are deployed in the cloud, offering both Container-as-a-Service (CaaS) and Function-as-a-Service (FaaS) platforms. We showcase the efficiency of our proposed optimization solution by presenting the reduction in operation costs as an illustrative example. We propose to group similarly low scale components together in CaaS, while running dynamically scaled components in FaaS. By doing so, the price is decreased as unnecessary memory provisioning is eliminated, while application response time does not show any degradation.
Palynofossils from the Tertiary (Barail Group) of Nagaland Palaeoecological interpretation and age

The palynofloral assemblage from the sediments of Barail Group exposed on the Moriani-Mokokchung Road, Nagaland contains 43 genera and 52 species. Spores of Parkeriaceae and Polypodiaceae dominate the assemblage, whereas Tricolporopilites, Lanagiopollis, Lakiapollis and palm pollen are the major angiospermic elements. The assemblage compares well with that of the Kopili Formation of Assam and Bengal Basin and Harudi Formation of Kutch Basin. The palynoassemblage indicates an Upper Eocene age. The presence of Barringtonia and Parkeriaceae indicates swampy environment of deposition.
Data-caring in digital democracies: Brazilian politics and a pedagogical experience with conversations

ABSTRACT Drawing from feminist contributions in data studies and pedagogy, this article develops the notion of data-caring as an ethico-political intervention. It interrogates the way the ‘digital architecture of neoliberalism’ reduces the complexity of social relations and looks at the absence of conversations and narratives. Against the background of political polarization in Brazil, the article discusses how datafying practices in the digital world enact categories that identify Self and Other without context or complexity. In opposition, data-caring is based on the understanding that the kind of dialogue necessary to live together in polarized times requires conversations that can decompress narrative arcs. As we approach new elections, I detail the experience of teaching an experimental course after the 2018 elections in Brazil and offer insights about what constituted an exploratory study in data-caring, where we explored how we can care about data, care with data and datafy with care.
Road object recognition method based on improved YOLOv3

Abstract: Based on the emergence and development of autonomous driving technology, the identification of obstacles on the road is a very important and challenging task. And there are many difficulties in the realization of this task, for example, there are many types of road targets, and the scale span of the road obstacles is large. In view of these problems, this experiment proposes three improvement directions for the YOLOv3 algorithm to perform the task of road target prediction: one is to improve the up-sampling multiple to use more shallow spatial information to improve the accuracy of small target detection. The second is to change the way of feature fusion of the feature pyramid. Thirdly, the convergence direction of the model is changed by clustering learning. Experiments on the BDD100K data set show that the yolov3_10cls_tiny algorithm proposed in this paper has the best detection performance and better average accuracy than YOLOv3.
The role of CAM ecophysiology in the Anthropocene

Human impact on the planet is such that geologists have acknowledged the start of a new geological epoch, the Anthropocene, which is characterized by an environmental emergency with multiple open fronts such as climate change, biodiversity loss, decreased fresh water availability, alterations to the nitrogen cycle, and chemical pollution. Activities such as food production and the increasing aggregation of people in urban areas are simultaneously major drivers and vulnerable points of such environmental change. For example, climate change represents a major threat to food production, considering that a reduction of annual precipitation, in addition to temperature increases, is likely to occur especially in tropical agricultural regions. The use of CAM crops has been recognized as a useful strategy for climate change adaptation, owing to their tolerance of high temperatures and their inherently high water use efficiency. The potential performance of Opuntia ficus-indica and Agave tequilana is modeled as an application of the environmental productivity index for identifying potential areas for cultivation under climate change. Regarding alterations to the nitrogen biogeochemical cycle, CAM epiphytes can be useful to characterize environmental pollution in tropical environments, especially when electrochemical monitoring networks are lacking. Indeed, CAM epiphytes are particularly promising to characterize the prevalent levels of heavy metals, persistent organic pollutants and, especially, nitrogen deposition. This is illustrated with the use of the orchid Laelia speciosa and the bromeliad Tillandsia recurvata as biomonitors of atmospheric nitrogen deposition. From fundamental research on the mechanisms behind plant responses to environmental change to applications in agriculture and biomonitoring, CAM ecophysiology will be essential in the Anthropocene
Two Stage Prediction Model of Sunspots Monthly Value Based on CEEMDAN and Particle Swarm Optimization ELM

Sunspot number is the most basic parameter to describe the level of solar activity. The accurate prediction of sunspot number can reflect the electromagnetic disturbance level of the electromagnetic layer, ionosphere and the middle and high layers in the future in advance, so as to provide important reference information for navigation, positioning, communication and the prediction of orbital attenuation of LEO satellites. Aiming at the characteristics of sunspot time series such as non-stationary, chaotic and difficult to predict, this paper proposed a two-stage combined prediction model based on complete Ensemble Empirical mode decomposition with adaptive noise (CEEMDAN), particle swarm optimization (PSO) and extreme learning machine(ELM) network. In the first stage of prediction: firstly, the original sunspot monthly mean series is decomposed by CEEMDAN to reduce the non-linearity and non-stationary of the original series. Then, an ELM prediction model is established for the sub-sequences decomposed by CEEMDAN, and the input layer dimension, hidden layer dimension, input layer weight and hidden layer bias of ELM are optimized by PSO algorithm. Finally, the prediction results of the first stage are obtained by superimposing the prediction results of each sub-sequence. The second stage is the error self-correction stage. Firstly, the prediction error sequence of the first stage is obtained. Then, the CEEMDAN-PSO-ELM prediction model is used to self-correct the prediction error of the first stage. Finally, the prediction results of the first stage and the self-correction results of the second stage are superimposed to obtain the final prediction value of the monthly sunspot number. In this paper, CEEMDAN is used to reduce the non-linearity and non-stationary of the sunspot series, and PSO is used to determine the best parameters of ELM network, and the useful information in the prediction error is fully considered, which effectively improves the prediction accuracy of sunspot monthly mean series. The prediction experiment is carried out by using the measured sunspot monthly mean series, and the proposed model is compared with wavelet neural network (WNN), back propagation neural network (BPNN), ELM, CEEMDAN-ELM and CEEMDAN-PSO-ELM. The experimental results show that the prediction accuracy of the proposed two-stage prediction model has been significantly improved, and has better prediction stability.
Scale‐dependent environmental effects on phenotypic distributions in Heliconius butterflies

Abstract Identifying the relative importance of different mechanisms responsible for the emergence and maintenance of phenotypic diversity can be challenging, as multiple selective pressures and stochastic events are involved in these processes. Therefore, testing how environmental conditions shape the distribution of phenotypes can offer important insights on local adaptation, divergence, and speciation. The red‐yellow Müllerian mimicry ring of Heliconius butterflies exhibits a wide diversity of color patterns across the Neotropics and is involved in multiple hybrid zones, making it a powerful system to investigate environmental drivers of phenotypic distributions. Using the distantly related Heliconius erato and Heliconius melpomene co‐mimics and a multiscale distribution approach, we investigated whether distinct phenotypes of these species are associated with different environmental conditions. We show that Heliconius red‐yellow phenotypic distribution is strongly driven by environmental gradients (especially thermal and precipitation variables), but that phenotype and environment associations vary with spatial scale. While co‐mimics are usually predicted to occur in similar environments at large spatial scales, patterns at local scales are not always consistent (i.e., different variables are best predictors of phenotypic occurrence in different locations) or congruent (i.e., co‐mimics show distinct associations with environment). We suggest that large‐scale analyses are important for identifying how environmental factors shape broad mimetic phenotypic distributions, but that local studies are essential to understand the context‐dependent biotic, abiotic, and historical mechanisms driving finer‐scale phenotypic transitions.
USING LOGIC TO DETERMINE KEY ITEMS IN MATH EDUCATION

Due to the COVID-19 restrictions, high-schools in Spain are having both online and in-class lectures. As a result, the students can use not only the information provided by the teachers in class, but they can also use several other methods such as videos and online examples that allow the students to have materials from different places. In this paper, we analyse the Mathematics results of the first two terms in a secondary school from Andalusia. This analysis can help find the central units of the subject found, that is, giving enough background knowledge to keep up with the module. When these are found, the teachers can improve the learning and the results in the following years. © 15th International Conference e-Learning, EL 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021. All rights reserved.