Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction

Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variations with sudden usage of large household electrical appliances, existing forecasting models, majorly focused on lower volatile loads, may not work well. Moreover, abrupt operation of electrical appliances in a nanogrid, even for shorter durations, especially in “Peak Hours,” raises the energy cost substantially. In this paper, an ANN model with dynamic feature selection is developed to predict the hour-ahead load of nanogrids based on meteorological data and a load lag of 1 h (t-1). In addition, by thresholding the predicted load against the average load of previous hours, peak loads, and their time indices are accurately identified. Numerical testing results show that the developed model can predict loads of nanogrids with the Mean Square Error (MSE) of 0.03 KW, the Mean Absolute Percentage Error (MAPE) of 9%, and the coefficient of variation (CV) of 11.9% and results in an average of 20% daily energy cost savings by shifting peak load to off-peak hours.
Alinhamento de sequencias com rearranjos

Sequence comparison done by alignment algorithms is one of the most fundamental tasks in bioinformatics. The evolutive mutations considered in these alignments are insertions, deletions and substitutions of nucleotides. This work treats of generalizations introduced in alignment algorithms in such a way that other mutations known as rearrangements are also considered, more specifically, we consider inversions, duplications in tandem and duplications by transpositions. Alignment with inversions does not have a known polynomial algorithm and a simplification to the problem that considers only non-overlapping inversions were proposed by Schoniger and Waterman [56] in 1992. In 2003, independent works proposed algorithms with O(n) [20, 31, 21] time to align two sequences with non-overlapping inversions. We developed two algorithms to solve this problem: one in O(n log n) [4] time and other, considering some conditions in the scoring system, in O(n) [63] time, both in O(n) memory. In 1997, Benson proposed a model of alignment that also recognized tandem duplication. He proposed two exact algorithms to align two sequences with tandem duplication: one in O(n) time and O(n) memory, and other in O(n) time and O(n) memory. We propose one algorithm to align two sequences with tandem duplication in O(n) time and O(n) memory. We also propose one algorithm to align two sequences with transposons (a type of duplication more general than tandem duplication), in O(n) time and O(n) memory. In this case, n denotes the maximal length of the two aligned sequences.
The Influence of Traditional Culture Integration into Chinese Language and Literature Teaching on the Improvement of Mental Health of College Students

Contemporary college students not only face the pressure brought by their studies but also bear the pressure brought by society and their families. Helping college students correctly understand their mental health and effectively solve their' mental health diseases has become an important issue in education research. The cultivation of healthy psychological quality is inseparable from the cultivation of a humanistic spirit. Therefore, integrating excellent traditional culture into Chinese language and literature teaching and research and giving full play to and inheriting the educational role of excellent traditional culture can not only improve the literary quality of college students and benefit the construction of healthy mental of college students. The main purpose of this research is to analyze the effect of Chinese excellent traditional culture on the cultivation of college students' mental health, analyze the impact of traditional culture integration into Chinese language and literature teaching research on improving college students' mental health, and propose the valuable suggestion for integration of excellent traditional culture into Chinese language and literature teaching research. Firstly, we analyze the benefits of integrating traditional culture in literature to the construction of college students' healthy psychology; then, the current deficiencies of literature education are analyzed; next, through literature analysis and summary, the main factors affecting the mental health of college students were found and the importance of these factors was evaluated by questionnaire survey and mathematical statistics methods; finally, targeted opinions and suggestions are put forward. This research provides guidance and reference for inheriting excellent national culture through literature education and constructing university health psychology in the future.
Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace

Industry 4.0 transforms classical industrial systems into more human-centric and digitized systems. Close human–robot collaboration is becoming more frequent, which means security and efficiency issues need to be carefully considered. In this paper, we propose to equip robots with exteroceptive sensors and online motion generation so that the robot is able to perceive and predict human trajectories and react to the motion of the human in order to reduce the occurrence of the collisions. The dataset for training is generated in a real environment in which a human and a robot are sharing their workspace. An Encoder–Decoder based network is proposed to predict the human hand trajectories. A Model Predictive Control (MPC) framework is also proposed, which is able to plan a collision-free trajectory in the shared workspace based on this human motion prediction. The proposed framework is validated in a real environment that ensures collision free collaboration between humans and robots in a shared workspace.
Sensitivity of muscle force response of a two-state cross-bridge model to variations in model parameters

Muscle models based on the cross-bridge theory (Huxley-type models) are frequently used to calculate muscle forces for different contractile conditions. Dynamic and nonlinear characteristics of muscle forces produced during isometric, concentric, and eccentric contractions can be represented to a limited extent by using cross-bridge models. Cross-bridge models use various parameters to simulate force responses. However, there remains uncertainty as to the effect of changes in model parameters on force responses in Huxley-type models. In this study, we aimed to analyze the sensitivity of force response to changes in model parameters in Huxley-type models. A two-state Huxley model was used to determine the cross-bridge attachment distributions and forces for shortening and lengthening contractions. Sensitivity of muscle force to changes in attachment rate, detachment rate, and cross-bridge binding distance was examined within a range of ±20% of the nominal value using Monte Carlo simulations. Changes in the detachment rate influenced the predicted muscle forces the most for lengthening contractions, while changes in attachment rate and binding distance affected forces the most for shortening contractions. These results show once more the asymmetry between shortening and lengthening contractions and the difficulty in using a single cross-bridge model to predict forces during shortening and elongation accurately.
Modeling and simulation of heteroepitaxial growth

Modeling and Simulation of Heteroepitaxial Growth by Arvind Baskaran Co-Chairs: Peter S. Smereka and Krishnakumar R. Garikipati The objective of this work is to construct a model for simulation and study of strained Heteroepitaxial growth and use the same to study the nature of the surface instability in the system. This is done with the intent of studying the intermixing and its effects, on the surface instability and the critical thickness of island formation. A solid on solid kinetic Monte Carlo approach is adopted in order to model the discrete stochastic effects. The model uses simple surface atom hops to capture surface diffusion effectively. The hopping rates are modeled based on the energetics of the system. The energetics of the system are in turn modeled by bond counting (surface energy) and a ball and spring network (elastic energy). An efficient numerical method for the elastic computations is constructed. This approach involves the local estimation of the change in elastic energies using the Expanding Box method and upon failure the global evaluation using the Multigrid Fourier method. An efficient algorithm for evolution of the system based on a reduced rejection kinetic Monte Carlo is presented. In this method the hopping rates are replaced by computationally inexpensive upper bounds with a rejection step to compensate for the overestimate. The continuum limit of this model is shown to be equivalent to a classical phenomenological contin-
May Thousands of Miles Remain Green: How to Choose Between Carbon Sequestration and Social Benefits

: In order to mitigate the impacts of climate change on human development and reduce carbon levels in the atmosphere, we conduct research on how forests are managed. Specifically, we propose a carbon sequestration model, using the Gompertz model to study the change of tree diameter at breast height over time, and analyze the amount of forest carbon sequestration. In addition, based on the TOPSIS analysis and the evaluation method of InVEST, the most suitable forest management decision-making model for society is proposed, and the Person correlation coefficient method is used to calculate the harvesting intensity that maximizes the comprehensive benefit of the forest and minimizes the trade-off value. Finally, the carbon dioxide sequestration of the forest over the next 100 years was calculated and the optimal harvest interval of 10 years was determined.
Recent Advances in Silver Nanowire Based Flexible Capacitive Pressure Sensors: From Structure, Fabrication to Emerging Applications

Skin mountable flexible pressure sensors are crucial for the next generation technologies. Capacitive pressure sensors bring several advantages and hence, they have been preferred over piezoelectric and piezoresistive alternatives. Flexible electrode and dielectric material are the fundamental components of the capacitive flexible pressure sensors. Hence, solution processable silver nanowires (AgNWs) are highly promising as electrode material or filler material in dielectric layer, owing to their superior inherent conductivity and flexibility. This article reviews recent progress on fabrication of flexible capacitive pressure sensors with AgNWs used as electrode and dielectric material. Performance of the sensors based on AgNWs is comprehensively discussed and emerging applications, as well as future outlook, are presented in detail.
Estimation of antioxidant power of human milk from lactating mothers of Rajasthan, India

Human milk supplies all the nutrition and energy that supports the overall development of neonate. It has essential and predominant antioxidant capacity which has a capability to protect infant against all diseases. Present investigation is to analyze the antioxidant power and free radical scavenging activity status in human milk from lactating mothers in the initial period of lactation. Ferric Reducing Antioxidant Power (FRAP) assay and 1, 1-diphenyl- 2-picrylhydrazyl (DPPH) radical method were used for this analysis. In colostrum it was remarkably high level of antioxidant capacity and radical scavenging activity than transitional and mature milks. This study showed the significance of breast milk during the initial days of life and the importance of colostrum milk for neonates.
Ecophysiology of Urban Trees and Their Management—The North American Experience

Only a few species are capable of colonizing extreme environments, whereas more moderate environments can support many more. The diversity of species in a habitat is thus controlled by the environment, whether by soil fertility, altitude or any other factor. This diversity reflects the number of species adapted to grow in a habitat, and the nature of this adaptation must be examined in light of the physiological effect of the environmental factors making up the habitat, and their variation in time and space (11).
Bioefficacy of different formulations of Metarhizium anisopliae (Metchnikoff) Sorokin IOF strain (KF408075) against Oryctus rhinoceros (Linnaeus) in vitro condition

The laboratory experiments were carriedout at the Institute of Organic Farming (IOF), UAS, Dharwad, during 2014-2015 to study the bioefficacy of different formulations of Metarhizium anisopliae against coconut rhinoceros grubs, Oryctus rhinoceros (Linnaeus). Among different oil based formulation, rice bran oil (60%) + corn oil (40%) formulation found least LC 50 value (1.99 x 10 6 cfu/ml) at 432 h. In granular and wettable powder formulation, the granular formulation recorded least LC 50 value 0.44 x 10 8 cfu/g at 240 h. The medean lethal time (LT 50 ) of different oil based formulations, the rice bran oil (60%) + corn oil (40%) formulation @ 3 x 10 6 cfu / ml recorded lower LT 50 value of 319.89 h (13.32 days). Among granular and wettable powder formulation, the granular formulation @ 5 x 10 8 cfu / g, proved most effective exhibiting 50 per cent of mortality with least LT 50 value of 162.88 h (6.78 days).
Complex Analysis and its Several Applications

Complex analysis is important for science because it extends analytical methods from real variables to complex variables and complex numbers. Also, complex number has two independent components, one variable will not change when the other is changing, that are particularly useful when two variables must be dealt with simultaneously. In this essay, we are going to talk about the history chronologically such as who first introduced the idea of complex number, who first discovered the rule of complex number, and why complex analysis is important. Also, the essay includes some basics about the complex variable and complex analysis. For example, the definition of complex number, Cauchy-Riemann Equations, and Cauchy Goursat theorem can help us to get further known of the complex analysis and solve some basic analytic problems.
On Network Buzzwords From Meme Theory—“Mind”

In the era of network media, catchwords have become a discourse fact and cultural phenomenon with great influence in China in recent years. Language is in the sensitive dynamic change, common words develop into catchwords, and in addition to its own development, more important is often from the external cause of social events. This paper takes network buzzwords as the entry point to analyze the “Mind” of buzzwords. Although the word has always appeared in the public’s vision, it has gained a lot of attention after developing into buzzwords driven by hot social events. This paper explains the reasons for its popularity with the help of relevant theories of memetics, aiming to prove the inevitability of “Mind” of buzzwords becoming a powerful language on the Internet.
Frobenius-Schur theorem for $C^*$-categories

We generalize the Frobenius-Schur theorem to $C^*$-categories. From this category-theoretical point of view, we introduce the notions of real, complex and quaternionic representations of Hopf $C^*$-algebras. Based on these definitions, we give another type of the Hopf-algebraic analogue of the Frobenius-Schur theorem, originally due to Linchenko and Montgomery. Also given are similar results for weak Hopf $C^*$-algebras, table algebras and compact quantum groups.
Smart Health Consultation and Record Management

- There is currently a lot of study being done on the functioning of Electronic Health Records (EHR) software, and doctors are facing a lot of difficulties as they switch from a paper-based record management system to an electronic one. Our goal is to help healthcare providers embrace EHR software and improve the clinical and practise management aspects of their operations. The health reports of the individuals, which also include diagnostic data and medical prescriptions, are delivered in the form of papers, the subsequent hospital visit by the individual is blind to the individual's prior medical conditions and medications. However, this issue is lessened by storing a person's entire health record as a soft copy in the cloud. If they haven't already, healthcare providers are under increasing pressure to embrace an EHR. In order to avoid making a blind faith decision, providers should modulate their response to this pressure. In this study, examine ‘what trust means and present a metric of dependability’. Cloud based EHR Rank is used in this work. The measure of trustworthiness quantifies how successfully someone. EHR addresses provider needs and essential operational requirements. The certainty is used as the trustworthiness metric. The standard of the evidence used to assess the EHR is a consideration. The reliability model offers a framework for contextualizing and ranking crucial choices issues, as well as using risk-reduction techniques decision-making while choosing an EHR should be improved.
Software Architecture and Non-Fungible Tokens for Digital Twin Applications in the Built Environment

Blockchain technology (BCT) can enable distributed collaboration, enhance data sharing, and automate back-end processes for digital twin (DT) decentralized applications (dApps) in the construction industry (CI) 4.0. The aim of this paper was to propose a software architecture and to develop a framework of smart contracts for blockchain-based digital twin (BCDT) dApps throughout the lifecycle of projects in CI 4.0. This paper leveraged the existing literature and action research interviews to identify and validate the critical industry problems, functional requirements (FRs), and non-functional requirements (NFRs) to be addressed by BCDT dApps in CI 4.0. Basic use cases were developed to design a framework of smart contracts for BCDT dApps throughout the lifecycle of projects. The analysis of an online survey was used to identify the key requirements and enablers to propose a software architecture for BCDT applications and to validate the requirements for developing the framework of a smart contract for BCDTs. The findings were: (1) The identification of key problems in CI 4.0 for each BIM/BCDT dimension (3D, 4D, 5D, 6D, 7D, 8D, and contractual (cD)) and the related FRs and NFRs for BCDT applications. Additionally, key use cases were designed to address the problems identified. (2) The proposed BCDT architecture permitted us to narrow gaps in the literature on blockchain-based decentralized digital twins. Moreover, the proposed BCDT architecture and smart-contract framework addressed the main requirements in the literature on BCDTs. (3) The study leveraged the non-fungible token (NFT) standard to develop a framework for smart contracts that addressed the key use cases and the related industry problems and functional requirements that were identified. The study also considered the contractual dimension (cD) as an overarching dimension in relation to the other BCDT dimensions. (4) We also compared the costs of several public blockchains for executing the proposed smart-contract framework throughout the lifecycle of a medium-sized building project. The cost analysis permitted the development of criteria to evaluate the suitability of blockchain networks for BCDT applications in CI 4.0 depending on the principal blockchain networks’ properties (security, decentralization, scalability, and interoperability). Finally, this study resulted in a novel framework that included software architecture, smart-contract use cases, and selection criteria among blockchain networks for BCDT dApps in CI 4.0.
IoT enabled healthcare system for predicting the diseases using feature optimization, decision tree, neural network, and fuzzy temporal rules

Diabetes, cancer, and heart diseases are identified to be fatal for human beings due to the lack of awareness, erratic food habits, lack of physical work and so forth. Many disease prediction systems are introduced by various researchers. However, early diagnosis of the diseases and monitoring systems are not working effectively and it increases the death rate. Recently, Internet of Things (IoT) is incorporated to collect the instant data from patient and the system makes effective decisions. Many IoT enabled healthcare systems are available for predicting the specific diseases but such effective healthcare system for measuring the severity of diabetes, cancer and heart diseases is scarce. For this purpose, a new neuro‐fuzzy model is used in the proposed IoT enabled healthcare system for disease prediction, which combines fuzzy rules with temporal features. First, it categorizes the available features of the patient records. Second, a new temporal decision tree is introduced for learning the patient records carefully and predicting the record by using the available data. Third, the fuzzy temporal decision tree calculates the relationship between the available features and also selects them as contributed features. In addition, some of the features are identified as not useful for making a final decision, and these are to be removed. Fourth, the fuzzy rules are to be generated by using the Mamdani fuzzy membership function. Finally, a new framework is proposed which is the combination of neuro‐fuzzy temporal networks, fuzzy temporal decision trees, and fuzzy temporal rules. The proposed model is evaluated by conducting experiments and proved as better than the existing systems in terms of prediction accuracy, error rate, and efficiency.
On the angular distributions of tau- --> K_S pi-nu_tau decay

Based on experimental data by the Bell Collaboration, we present a phenomenological analysis of the angular distributions in tau- -->K_S pi-nu_tau decay. Our study shows that the angular analysis could lead to some interesting observables, and the future experimental investigation of these observables might be very helpful in revealing the nature of the scalar components of the decay. New physics contributions from the two-Higgs-doublet model to this decay have also been examined.
Non-Gaussian Error in Galaxy Survey (Part 1)

We propose a method to estimate non-Gaussian error bars on the matter power spectrum from galaxy surveys in the presence of non-trivial survey selection functions. The estimators are often obtained from formalisms like FKP and PKL, which rely on the assumption that the underlying field is Gaussian. The Monte Carlo method is more accurate but involves the tedious process of running and cross-correlating a large number of N-body simulations, in which the survey volume is embedded. From 200 N-body simulations, we extract a non-linear covariance matrix as a function of two scales and of the angle between two Fourier modes. All the non-Gaussian features of that matrix are then simply parametrized in terms of a few fitting functions and Eigenvectors. We furthermore develop a fast and accurate strategy that combines our parameterization with a general galaxy survey selection function, and incor- porate non-Gaussian Poisson uncertainty. We describe how to incorporate these two distinct non-Gaussian contributions into a typical analysis pipeline, and apply our method with the se- lection function from the 2dFGRS. We find that the observed Fourier modes correlate at much larger scales than that predicted by both FKP formalism or by pure N-body simulation in a "top hat" selection function. In particular, the observed Fourier modes are already 50 per cent correlated at k\sim0.1hMpc^(-1), and the non-Gaussian fractional variance on the power spectrum ({\sigma} /P(k))^2 is about a factor of 3.0 larger than the FKP prescription. At k~0.4hMpc^(-1), the deviations are an order of magnitude.