Miracle-dz/UniFAR-FTUs huggingface dataset:
Nodal solutions of fourth-order Kirchhoff equations with critical growth in R^N

We consider a class of fourth-order elliptic equations of Kirchhoff type with critical growth in \(R^N\). By using constrained minimization in the Nehari manifold, weestablish sufficient conditions for the existence of nodal (that is, sign-changing) solutions. For more information see https://ejde.math.txstate.edu/Volumes/2021/19/abstr.html
Iterative Software Process Based Collaboration Model for Software Stakeholders

Software engineering is well known for its significance on software development complexities minimization. Due to software engineering significance researches were carried out to improve software engineering practices. However, there are some identified problems that lead to software development complexities like the lack of understandable collaboration or communication between software stakeholders during software development. And to this identified problem a research was carried out to solve this problem by proposing a collaboration model for software stakeholders’ collaboration during software development. However, the model proposed was restricted to a waterfall software process model. This study used segment of the framework used for developing the waterfall software process based collaboration model to develop an iterative software process based collaboration model for
The Relationship of Mindfulness, Self-Differentiation and Alexithymia With Borderline Personality Traits

Background: Psychological problems such as borderline personality traits can negatively affect students' behaviors, cognition, interpersonal communication and academic achievement. It is important to identify factors such as mindfulness, self-differentiation, and alexithymia and determine their relationship these traits. Objectives: This study was performed to investigate the relationship of mindfulness, self-differentiation and alexithymia with borderline personality traits. Methods: In this descriptive correlational study, 309 students from Shahid Bahonar University of Kerman (217 female and 92 male) were selected using the random cluster sampling method. They completed the Five Factor Mindfulness Questionnaire, the Self-Differentiation Scale, the Toronto Alexithymia Scale and the Borderline Personality Scale. After collecting the questionnaires, the data were analyzed using SPSS-24 and AMOS-24 software programs and path analysis method. Results: The analyses showed that the direct effect of mindfulness was significant only on fear of intimacy (p <0.05). Self-differentiation predicted three sub-scales of borderline personality including defense mechanisms, fear of intimacy (p <0.001) and reality testing (p <0.05) in a significant and negative manner. Alexithymia had a significant positive impact on all subscales of borderline personality including identity disturbance, primary defense mechanisms, fear of intimacy (p <0.001) and damaged reality testing (p <0.05). Conclusion: Alexithymia, self-differentiation and mindfulness were the most powerful predictors of students' borderline personality traits, respectively.
SMART WASTE MANAGEMENT SYSTEM USING IMAGE PROCESSING

ABSTRACT Waste management in big cities like Mumbai, Kolkata, Delhi, Chennai, Hyderabad, and other metropolitan cities is a very big challenge. It degrades the environmental conditions in those cities and its quality is spoiled by the residents. Over the last fewyears, there has been a several improvements towards this area by introducing home waste collection. The main idea is about achieving support from residents who are living in that particular area surrounding the waste. A digital equipment along with the components has been used to develop the waste collection from residents. An IoT has been used as a key solution to achieve this process. The regular waste management system can be replaced with number of sensors in the system along with AI (Artificial Intelligence) to achieve good waste classification. This project is to achieve smart waste classification system using IoT and deep learning model based on AI. Hardware sensors are used to resolve the data status of the bin and Image Processing program which is coded in python language is used to carry out the object detection and classification. Object detection and segregation of waste is performed by AI based framework with pre-trained object detection model. This model is trained with images of the waste of various types and it generates frozen interference graph. Object detection is done by using camera which is connected with the PC. PC serves as key processing unit. Ultrasonic sensor is placed on each waste bin to check the level of the waste filling available in the bin.
Identifying Knowledge Gaps Using a Graph-based Knowledge Representation

Schmidt, Daniel P. M.S., Department of Computer Science and Engineering, Wright State University, 2020. Identifying Knowledge Gaps Using a Graph-based Knowledge Representation. Knowledge integration and knowledge bases are becoming more and more prevalent in the systems we use every day. When developing these knowledge bases, it is important to ensure the correctness of the information upon entry, as well as allow queries of all sorts; for this, understanding where the gaps in knowledge can arise is critical. This thesis proposes a descriptive taxonomy of knowledge gaps, along with a framework for automated detection and resolution of some of those gaps. Additionally, the effectiveness of this framework is evaluated in terms of successful responses to queries on a knowledge base constructed from a prepared set of instructions.
Augmenting State Capacity for Child Development: Experimental Evidence from India

We use a large-scale randomized experiment to study the impact of augmenting staffing in the world’s largest public early childhood program: India’s Integrated Child Development Services (ICDS). Adding a half-time worker doubled net preschool instructional time and led to 0. 29σ and 0. 46σ increases in math and language test scores after 18 months for children who remained enrolled in the program. Rates of stunting and severe malnutrition were also lower in the treatment group. A cost-benefit analysis suggests that the benefits of augmenting ICDS staffing are likely to significantly exceed its costs even under conservative assumptions.
Optimization of binary decision diagram: Heuristics from reinforcement learning

Since nuclear power plants (NPPs) are safety critical infrastructure, enhancing safety of nuclear power plant is critical. To evaluate the degree of safety probabilistic safety assessment method is widely used. And to assess the probability, various techniques were invented. Fault tree (FT), reliability block diagram with general gate (RBDGG), universal generating function (UGF), and binary decision diagrams (BDD) are typical examples. However, none of the method is perfect for all circumstance. Each analysis method has their own pros and cons. For instance, fault tree has benefits when identifying the logical fault from effect. However, as system becomes complex, the tree structure becomes even more complex. RBDGG is firstly proposed by Kim [1]. RBDGG method is intuitive however, the method is hard to consider the system in logical loop and minimal cutsets. To solve the logical loop problem, Chae et. al. [2] suggested factor graph based approach. But the method is only suitable for the logical loop structure. Universal generating function which was developed by G. Levtin [3] has benefits when analyzing multi-state systems and degradation of the system. However, UGF requires prior definitions of various arithmetic functions. Binary decision diagram is the most intuitive method among them. However, the method has critical deficiency which is called as variable ordering problem. The complexity of the diagram depends on the order of the variable. And finding the optimal order of the variable is typical example of non-deterministic polynomial (NP) problem. In this paper, we propose reinforcement learning based variable ordering to find approximate optimal solution of the variable order of binary decision diagram. The organization of the paper is as follows. In section 2, a brief explanation about reinforcement learning will be provided. In section 3, general structure of the agent and reward function which is the most critical part in reinforcement learning will be discussed. Finally, section 4 presents conclusion.
Isoconversional Kinetic Analysis of Distributed Activation Energy Model Processes for Pyrolysis of Solid Fuels

Seven different distributed activation energy model (DAEM) processes, which were used to describe the pyrolysis kinetics of wood, algae, lignin, corn stalk skin, kerogen, cellulose, and coal, were analyzed by means of the Friedman differential isoconversional method. It has been shown that the activation energies obtained from the Friedman method are independent of the heating rate. For all DAEM processes considered, the effect activation energies evaluated by the Friedman isoconversional method showed significant dependence on the conversion. The kinetic parameters obtained by means of the Friedman isoconversional method were used to reconstruct the conversions and rates data. The agreement between the reconstructed data and the data calculated by the DAEM is excellent at the presimulated and extrapolated conditions for all the DAEM processes. This indicates the DAEM process can be taken as an example to check the accuracy of some integral isoconversional methods in the determination of the activation en.
Super-dosing of vitamin B2 induces beneficial hindgut transcriptome respons- es in weaned piglets

Super-dosing of vitamin B2 induces beneficial hindgut transcriptome responses in weaned piglets Jérôme Schmeisser*1, Britt Blokker1, Christelle Iaconis1, Stéphane Etheve2 and Estefania Perez Calvo1 DSM Nutritional Products, Centre de Recherche en Nutrition et Santé Animale, F-68305 Saint Louis, France DSM Nutritional Products, Nutrition Innovation Center, R&D, PO Box 2676, CH-4002 Basel, Switzerland Transcriptomics: Open Access Research Article
DESIGN OF A NOVEL FIRE DETECTION MODEL USING DEEP LEARNING ALGORITHM

Fire detection systems is paramount important for monitoring the buildings and surrounding environment. As the system is associated with preemptive warning, it is very much preferred technology for detecting fire in buildings and its surrounding environment. The existing applications are sensor enabled and they are limited in accuracy. These difficulties can be alleviated with the help of image processing and camera technology. In this paper, a deep learning based fire detection method is introduced to detect the fire so that the fire extinguishers system will be activated to put out the fire. The proposed system is incorporated with the family of CNN algorithm and the test results prove its efficiency compared with other implementations.
Flocculation and retention of fillers with nanocelluloses

The objective of this study was to examine the effect of different cellulose nanofibrils (CNF) on the flocculation and retention of precipitated calcium carbonate (PCC) and kaolin. Of particular interest was the study of how charge modifications of CNF affect its flocculation and retention capacity. The results clearly show that different CNFs can be used as flocculants and retention aids. However, CNFs have to be chemically modified (high charge density) in order to increase their electrostatic interactions with particles and other additives, and hence boost flocculation. Furthermore, the flocculation and retention efficiency is significantly improved when charge modified CNFs are combined with polyelectrolytes. It is probable that the presence of polyelectrolytes can strengthen and induce formation of CNF/polyelectrolyte bridges between filler particles, leading to efficient flocculation. Charge modified CNF could be used to increase filler retention up to 95% in both one and two component systems. The maximum retention was strongly dependent on the charge density of CNF in a two component system and on the polyelectrolyte concentration used.
Fundamental Factors and Small Equity Investor Behavior in Pakistan

This study examines the influence of fundamental factors on small equity investor behavior in the context of Pakistan. The fundamental factors considered in this study are social, political, environmental, regulatory, technological, economic and legal, known as SPERTEL factors. Primary data is collected from individual equity investors from all three stock exchanges of Pakistan. Confirmatory factor analysis (CFA) is used to test the validity of the survey instrument, and structural equation modeling (SEM) technique is used to test the hypotheses. The study documents that overall, small equity investor’s decision making is not influenced by fundamental factors. The study found significant influence of social and political factors on small equity investors’ behavior. However, economic, regulatory, technological, environmental and legal factors have no influence on individual equity investor’s decision making behavior in Pakistan. © 2014 Elixir All rights reserved ARTICLE INF O Articl e h istory: Received: 21June 2013; Received in revised form: 27 January 2014; Accepted: 27 January 2014;
Command Governor Supervisor Scheme Based On Region Of Attraction

Physical systems are subject to constraints, like input saturation or physical limits to the states, which also appears on each switched system mode. One way of dealing with them is to apply the Command Governor scheme, which changes the controller’s reference to enforce constraints. When the system is a switched one, the problem of stability arises. Switched systems are a common kind of system used to describe non-linear systems by dividing them into linear sections or different modes of operations of the same system, like the different phases an airplane goes through during take-off or landing. However, arbitrarily switching the modes of a switched system can cause instability, requiring a switching rule design. The most commonly used rule is the dwell-time switch, in which the system waits for a dwell-time to elapse after the references changes to switch modes. Seeing the possibility of speeding up such systems’ convergence, we propose a new rule based on the controller’s Region of Attraction, which requires the system’s state to be inside the mode’s controller’s Region of Attraction to switch, guaranteeing stability after a mode switch. With this technique, we also propose a hybrid switch technique, which can further speed up convergence and generate lower actuator effort in some cases. We present some simulations to illustrate the proposal’s potential and compare it with a scheme exploiting a dwell-time approach. The results suggest that our approach adds new CG and supervisor design possibilities, reducing the transition time between system modes and improving the closed-loop performance indexes.
A ( de ) ( post ) colonialist proposal of musical scene

This paper seeks to critically analyze the notion of ‘musical scene’ (Straw, 1991, 2006), in which, through its propagation in academia, as well as some of its methodological applicability, we find a discourse based upon a ‘modernist’ and ‘universalist’ ideal. Using decolonialist theory, we propose to rethink this notion, which in turn makes us critically review its archaeology and, at the same time, opens gaps through which we can apply this notion of musical scene and other musical forms which need to be anglophone. From that, we suggest to reconfigure the idea of musical scene by moving away, in a manner of speaking, from what is conventionally thought of as assumptions of scene, such as the anglo-saxon language and what is generally understood as cosmopolitanism.
Improving the capabilities of the Town Energy Balance model with up-to-date building energy simulation algorithms: an application to a set of representative buildings in Paris

Buildings’ energy systems release heat to the atmosphere that contributes to the urban heat island. In return, the energy demand from buildings depends on the meteorological conditions of their surroundings. Consequently, urban canopy models such as Town Energy Budget (TEB) have progressively included the representation of the main processes of building energetics: solar and internal heat gains, heat transmission through the enclosure and the heat exchange by infiltration and ventilation. The objective of this study is to extend the evaluation of the Building Energy Model (BEM) implemented in TEB. Five buildings representative of the morphological and thermal characteristics that can be encountered in European urban areas have been selected. The evaluation has been conducted with EnergyPlus building energy model and for two contrasted climates. The TEB model is able to estimate the heating and the cooling energy demand with an accuracy better than 5 kWh/m2/year for heating and 3 kWh/m2/year for cooling. This paper also discusses on the importance of computing the building’s surrounding surface temperature for energy demand calculations. TEB is able to account for this effect whereas EnergyPlus assumes that building surroundings are at air temperature. © 2014 Elsevier B.V. All rights reserved.
Design and Analysis of Compact MIMO Antenna with Enhanced Isolation Techniques for Wireless LAN applications

---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - A compact 2X2 and 4X4 Multiple-Input-Multiple-Output (MIMO) antennas are designed for Wireless Local Area Network (WLAN) applications with enhanced isolation. The frequency of operation is chosen to be 5GHz so that Multipath fading problem can be avoided by incorporating this into 802.11a standards that supports in Wi-Fi module. In this work three mutual reduction techniques are used. The orthogonal polarization method, parasitic element method and a novel neutralizing network method using hairpin filter is carried out. The isolation between the radiating patches is highly enhanced. The overall dimension of the proposed 2X2 and 4X4 WLAN MIMO antennas are 16 x 38 x 0.51 mm³ and 39 x 39 x 0.51 mm³, respectively. The measured bandwidth of 540MHz (4.71-5.25GHz) and 497MHz (4.69-5.19GHz) with better return loss of –43 dB and –42dB and isolation of -56dB and - 38dB for 2X2 and 4X4 antennas are, respectively, achieved. The measured bandwidth, realized gain, diversity gain (DG), Envelope correlation coefficient (ECC) and radiation efficiency for 2X2 and 4X4 MIMO antenna is analyzed and achieved at 5GHz frequency. This approach increases the isolation between the antennas as compared to the other methods
Optimizing Large-Scale Machine Learning over Groups

Many applications that use large-scale machine learning (ML) increasingly prefer different models for subgroups (e.g., countries) to improve accuracy, fairness, or other desiderata. We call this emerging popular practice learning over groups , analogizing to GROUP BY in SQL, albeit for ML training instead of SQL aggregates. From the systems standpoint, this practice compounds the already data-intensive workload of ML model selection (e.g., hyperparameter tuning). Often, thousands of models may need to be trained, necessitating high-throughput parallel execution. Alas, most ML systems today focus on training one model at a time or at best, parallelizing hyperparameter tuning. This status quo leads to resource wastage, low throughput, and high runtimes. In this work, we take the first step towards enabling and optimizing learning over groups from the data systems standpoint for three popular classes of ML: linear models, neural networks, and gradient-boosted decision trees. Analytically and empirically, we compare standard approaches to execute this workload today: task-parallelism and data-parallelism. We find neither is universally dominant. We put forth a novel hybrid approach we call grouped learning that avoids redundancy in communications and I/O using a novel form of parallel gradient descent we call Gradient Accumulation Parallelism (GAP). We prototype our ideas into a system we call Kingpin built on top of existing ML tools and the flexible massively-parallel runtime Ray. An extensive empirical evaluation on large ML benchmark datasets shows that Kingpin matches or is up to 14x faster than state-of-the-art ML systems, including Ray’s native execution and PyTorch DDP.
Systemic analysis of changes in protein modification and gene coexpression networks in cancer

Perturbed posttranslational modification (PTM) landscapes and gene expression networks commonly cause pathological phenotypes. To elucidate altered PTM landscapes on a large scale, disease-associated mutations from TCGA, Uniprot, and dbSNP were integrated with PTM sites from PhosphoSitePlus. We characterized each dataset individually, compared somatic with germline mutations, and analyzed PTM sites intersecting directly with disease variants. To assess the impact of mutations in the flanking regions of phosphosites, we developed DeltaScansite, a pipeline that compares Scansite predictions on wild type versus mutated sequences. Disease mutations are also visualized in PhosphoSitePlus. TCGA also curates gene expression data from tumor and normal tissues, enabling us to calculate gene coexpression networks and apply graph algorithms in Neo4j.
A FLEXIBLE BAYESIAN FRAMEWORK TO ESTIMATE AGE- AND CAUSE-SPECIFIC CHILD MORTALITY OVER TIME FROM SAMPLE REGISTRATION DATA* BY AUSTIN

*Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of Health under Award Number R21HD095451 and the National Institute Of Mental Health of the National Institutes of Health under Award Number DP2MH122405. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work was also supported in part by the Bill & Melinda Gates Foundation, Investment ID: OPP1172551
Characterization of raw and treated sugar beet shreds for copper ions adsorption

AbstractLignocellulosic materials have a great potential to be converted into different high value products including biofuels, value-added chemicals, and cheap energy sources for microbial fermentation and enzyme production. Their multifunctionality and ability to reuse contribute to their great importance in terms of environmental protection and sustainable development. Such materials are sugar beet shreds (SBS) which are usually used as animal feed but since they are produced in large amount in sugar industry, they can also be used as an adsorbent or for bioethanol production. Bioethanol production from SBS includes pretreatment processes in order to remove pectic substances and lignin, followed by enzymatic hydrolysis of cellulose in order to obtain the solution of fermented sugars. A novelty in this work is the investigation if solid residues from the pretreatment process of SBS for bioethanol production can be used as adsorbents, the same as raw SBS. Textural characteristics and capacities for coppe.