Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust frequency estimation in three-phase power systems using correntropy-based adaptive filter

In this study, the authors propose a robust adaptive algorithm for frequency estimation in three-phase power systems when the voltage readings are corrupted by random noise sources. The proposed algorithm employs the Clarke's transformed three-phase voltage (a complex signal) and augmented complex statistics to deal with both of balanced and unbalanced system conditions. To derive the algorithm, a widely linear predictive model is assumed for the Clarke's transformed signal where the frequency of system is related to the parameters of this model. To estimate the model parameters with noisy voltage reading, they utilise the notions of maximum correntropy criterion and gradient-ascent optimisation. The proposed algorithm has the computational complexity of the popular complex least-mean-squares (CLMS) algorithm, along with the robustness that is obtained by using higher-order moments beyond just second-order moments. They compare the performance of the proposed algorithm with a recently introduced augmented CLMS (ACLMS) algorithm in different conditions, including the voltage sags and presence of impulsive noises and and higher-order harmonics. Their simulation results demonstrate that the proposed algorithm provides improved frequency estimation performance compared with ACLMS especially when the measured voltages are corrupted by impulsive noise.
Co-delivery of doxorubicin and hydroxychloroquine via chitosan/alginate nanoparticles for blocking autophagy and enhancing chemotherapy in breast cancer therapy

Breast cancer (BC) is the most common malignancy in women worldwide, and the standard treatment is chemotherapy or radiotherapy after surgery. In order to reduce the side effects of chemotherapy, various nanoparticles (NPs) have been discovered and synthesized, which has become a promising treatment for BC. In this study, a co-delivery nanodelivery drug system (Co-NDDS) was designed and synthesized with 2,3-dimercaptosuccinic acid (DMSA) coated Fe3O4 NPs as core encapsulated into chitosan/alginate nanoparticles (CANPs) shell, doxorubicin (DOX) and hydroxychloroquine (HCQ) as loading drugs. Smaller NPs carrying DOX (FeAC-DOX NPs) were loaded into larger NPs containing HCQ (FeAC-DOX@PC-HCQ NPs) by ionic gelation and emulsifying solvent volatilization methods. The physicochemical properties of this Co-NDDS were characterised, followed by in vitro studies of the anticancer effects and mechanisms using two different BC cell lines, MCF-7 cells and MDA-MB-231 cells. The results indicated that the Co-NDDS showcases exemplary physicochemical qualities and encapsulation capacity, facilitating accurate intracellular release through pH-sensitive attributes. Importantly, NPs can significantly increase the in vitro cytotoxicity of co-administered drugs and effectively inhibit the autophagy level of tumour cells. The Co-NDDS constructed in this study provides a promising strategy for the treatment of BC.
Frequency Response of Semi Independent Automobile Suspension System

__________________________________________________________________________________ Abstract: Suspension system is one of the important systems in automobile. Its helps to support the car body, engine, passengers, and at the same time it tries to isolate the vehicle body from the road bumps and absorbs the vibration while vehicle is moving on uneven surface of the road. In this paper, attention is focused towards a semi independent suspension vehicle. Describing the vehicle using seven independent coordinates, equations of motion are derived and the dynamic behaviour is studied through frequency response.
"My face, my rules": Enabling Personalized Protection Against Unacceptable Face Editing

Today, face editing is widely used to refine/alter photos in both professional and recreational settings. Yet it is also used to modify (and repost) existing online photos for cyberbullying. Our work considers an important open question: 'How can we support the collaborative use of face editing on social platforms while protecting against unacceptable edits and reposts by others? ' This is challenging because, as our user study shows, users vary widely in their definition of what edits are (un)acceptable. Any global filter policy deployed by social platforms is unlikely to address the needs of all users, but hinders social interactions enabled by photo editing. Instead, we argue that face edit protection policies should be implemented by social platforms based on individual user preferences. When posting an original photo online, a user can choose to specify the types of face edits (dis)allowed on the photo. Social platforms use these per-photo edit policies to moderate future photo uploads, i.e., edited photos containing modifications that violate the original photo's policy are either blocked or shelved for user approval. Realizing this personalized protection, however, faces two immediate challenges: (1) how to accurately recognize specific modifications, if any, contained in a photo; and (2) how to associate an edited photo with its original photo (and thus the edit policy). We show that these challenges can be addressed by combining highly efficient hashing based image search and scalable semantic image comparison, and build a prototype protector (Alethia) covering nine edit types. Evaluations using IRB-approved user studies and data-driven experiments (on 839K face photos) show that Alethia accurately recognizes edited photos that violate user policies and induces a feeling of protection to study participants. This demonstrates the initial feasibility of personalized face edit protection. We also discuss current limitations and future directions to push the concept forward.
Perbandingan Ketepatan Pemantauan Obyek Bergerak dengan Metode Mean Shift dan Expectation Maximization-Mean Shift

Pemantuan obyek bergerak dalam video visual sangat bermanfaat dalam kehidupan, seperti pengawas lingkungan, keamanan, robotika, multimedia, HCI (Human Computer Interfacing), dll. Untuk pencapaian hasil yang tepat diperlukan metode pematauan yang dinamis dan tidak tergantung pada satu unsur. Dalam pemantauan suatu obyek dapat dilakukan dengan metode pengelompokan warna, namun untuk mencapai ketepatan suatu pemantuan yang bervariasi maka diperlukan prosedur algoritma yang robust berdasarkan perluasan skala warna. Dengan memanfaatkan perkiraan dari obyek yang tidak dikenali maka diperlukan metode Expectation-Maximization untuk mencari lokal maksimum dari kesamaan pengukuran antara warna dari model dan target obyek. Sehingga didapatkan metode pemantauan dalam pencarian dari obyek yang efektif dan efisien dalam komputasi. Maka metode perluasan skala warna EM-Mean Shift lebih efektif digunakan untuk obyek yang bergerak cepat dengan berbagai variasi lingkungan baik perputaran maupun perbedaan skala obyek.
Fast and Transparent Adaptive Lens Based on Plasmonic Heating

In the current trend toward miniaturization, integrated micro-optical elements play a central role in the development of various applications including high-density data storage and imaging. In these operations, fine alignment and focus adjustment are usually performed mechanically, thus, limiting the accuracy, size and speed of devices. Here, we propose a novel reconfigurable microlens based on the engineering of the temperature distribution induced by a patterned plasmonic surface shined with a resonant near-infrared light. The refractive index change generated in a surrounding thermo-optical material acts as an effective lens whose parameters are remotely adjusted by the control near-infrared light. We demonstrate focal distance tunability of tens of microns with subnanometer accuracy along with time-responses down to 200 μs. The applicability of this photothermal lens is proved in the framework of optical microscopy and adaptive optics.
Explorer BRC 1 expression regulates bud activation potential but is not necessary or sufficient for bud growth inhibition in Arabidopsis

The degree of shoot branching in Arabidopsis is determined by the activation of axillary buds. Bud activity is regulated by diverse environmental and developmental signals, often mediated via plant hormones, including auxin, strigolactone and cytokinin. The transcription factor BRANCHED1 (BRC1) has been proposed to integrate these regulatory signals. This idea is based on increased branching in brc1 mutants, the effects of bud-regulating hormones on BRC1 expression, and a general correlation between BRC1 expression and bud growth inhibition. These data demonstrate the important role of BRC1 in shoot branching, but here we show that in Arabidopsis this correlation can be broken. Buds lacking BRC1 expression can remain inhibited and sensitive to inhibition by strigolactone. Furthermore, buds with high BRC1 transcript levels can be active. Based on these data, we propose that BRC1 regulates bud activation potential in concert with an auxin transport-based mechanism underpinning bud activity. In the context of strigolactonemediated bud regulation, our data suggest a coherent feed-forward loop in which strigolactone treatment reduces the probability of bud activation by parallel effects on BRC1 transcription and the shoot auxin transport network.
The Impact of Antimicrobial Therapy Duration in the Treatment of Prosthetic Joint Infections Depending on Surgical Strategies: A Systematic Review and Meta-analysis

Abstract The aim of this systematic review was to address the question if short antibiotic treatment (SAT; at least 4 but <12 weeks) versus long antibiotic treatment (LAT) affects outcomes in prosthetic joint infections (PJIs). Database research (Medline, Embase, Web of Science, Scopus, Cochrane) retrieved 3740 articles, of which 10 studies were included in the analysis. Compared to LAT, 11% lower odds of treatment failure in the SAT group were found, although the difference was not statistically significant (pooled odds ratio, 0.89 [95% confidence interval, .53–1.50]). No difference in treatment failure was found between SAT and LAT once stratified by type of surgery, studies conducted in the United States versus Europe, study design, and follow-up. There is still no conclusive evidence that antibiotic treatment of PJIs for 12 weeks or longer is associated with better outcomes, irrespective of the type of surgical procedure. Most recent, high-quality studies tend to favor longer antibiotic courses, making them preferable in most situations.
AN OBJECTIVISTACCOUNT OF PROBABILITIES IN STATISTICAL MECHANICS

Hoefer (2007, p. 550) offers the opinion that ‘the vast majority of scientists using non-subjective probabilities overtly or covertly in their research feel little need to spell out what they take their objective probabilities to be. ’ My experience (as a member of that group) accords with that view; indeed it goes further, in finding that most scientists are not even particularly interested in thinking about whether their view of probability is objective or subjective. The irritation expressed by Margenau (1950, p. 247) with ‘the missionary fervor with which everybody tries to tell the scientist what he ought [his italics] to mean by probability’ would not be uncommon. The relevant question here is, of course, the meaning of probability in statistical mechanics, and the approach most often used in textbooks on statistical mechanics (written for a scientific, as distinct from philosophically inclined, audience) is to either sidestep discussion of interpretations1 or offer remarks of a somewhat ambiguous nature. Compare, for example, the assertion by Tolman (1938, pp. 59–60) that in ‘the case of statistical mechanics the typical situation, in which statistical predictions are desired, arises when our knowledge of the condition of some system of interest [his italics] is not sufficient for a specification of its precise state’ with the remark two pages later that by ‘the
A Loop Parallelization Technique

In general, any nested loop can be parallelized as long as all dependence constraints among iterations are preserved by applying appropriate synchronizations. However, the perfor- mance is significantly affected by the synchronization overhead. In case of irregular and complex dependence constraints, it is very difficult to efficiently and systematically arrange synchronization primitives. In this paper, we propose a new method, data depen- dence uniformization, to overcome the difficulties in parallelizing a doubly nested loop with irregular dependence constraints. Our approach is based on the concept of vector decomposition. A sim- ple set of basic dependences is developed of which all dependence constraints can be composed. The set of basic dependences then will be added to every iteration to replace all original dependences so that the dependence constraints become uniform. Finally, an efficient synchronization method is presented to obey the uniform dependence constraints in every iteration.
Robust estimation of a dynamic spatio-temporal model with structural change

ABSTRACT We postulate a dynamic spatio-temporal model with constant covariate effect but with varying spatial effect over time and varying temporal effect across locations. To mitigate the effect of temporary structural change, the model can be estimated using the backfitting algorithm embedded with forward search algorithm and bootstrap. A simulation study is designed to evaluate structural optimality of the model with the estimation procedure. The fitted model exhibit superior predictive ability relative to the linear model. The proposed algorithm also consistently produced lower relative bias and standard errors for the spatial parameter estimates. While additional neighbourhoods do not necessarily improve predictive ability of the model, it trims down relative bias on the parameter estimates, specially for spatial parameter. Location of the temporary structural change along with the degree of structural change contributes to lower relative bias of parameter estimates and in better predictive ability of the model. The estimation procedure is able to produce parameter estimates that are robust to the occurrence of temporary structural change.
Impact of Tobacco Industry Wastewater on Germination and Seedling Growth of Mungbean

An experiment was conducted in the month of November, 2021 at Crop Physiology and Ecology Laboratory, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh to study the impact of tobacco industry wastewater on germination and early seedling traits of five mung bean accessions (BD-10022, BD-10023, BD-10024, BD-10026 and BD-10027) following factorial completely randomized design (CRD) with three replications. Mung bean accession, tobacco industry wastewater and their interaction significantly interacted on germination and seedling traits of mung bean. Tobacco industry wastewater reduced the germination percentage, germination rate and co-efficient of germination but insisted longer shoot and root as well as escalated the seedling dry weight of mung bean as compared to tap water. Among the five mung bean accessions, BD-10024 and BD-10027 performed comparatively better performance regarding germination and seedling traits, respectively under wastewater conditions. Bangladesh Agron. J. 2022, 25(2): 129-137
Analisis Ekstraksi Fitur dan Klasifikasi Data Keluarga Malware Menggunakan Convolutional Neural Network

Malware merupakan perangkat lunak berbahaya yang dapat mengganggu kinerja dari suatu sistem, dan telah menjadi salah satu cyber threat yang perlu mendapat perhatian khusus. Semakin hari perkembangan malware semakin berbagai macam dan mengalami evolusi semakin canggih, sehingga mempunyai kemampuan untuk melindungi diri dari suatu acaman baik itu antivirus atau sistem pengamanan yang lain, Salah satu upaya awal yang dapat dilakukan adalah melakukan analisis terhadap malware – malware yang ada, analisis dalam hal ini merupakan suatu proses untuk melakukan identifikasi terhadap perilaku malware, mulai dari apa yang dilakukan, apa yang diinginkan, dan apa tujuan utama dari malware tersebut, deep learning yang merupakan cabang ilmu dari kecerdasan buatan untuk melakukan penelitian terhadap karakteristik malware, dengan melakukan analisis terhadap karakteristik dari suatu varian malware, seperti menggunakan metode klasifikasi diharapkan hal tersebut dapat memberikan referensi untuk pembuatan sistem pengamanan terhadap malware yang lebih baik. Pada penelitian yang akan dilakukan peneliti mencoba untuk melakukan analisis terhadap data malware yang diambil dari Canadian Institute for Cybersecurity. Dalam hasil analisis tersebut didapatkan hasil precission dan recal 75%.
Lectures On Infinite Dimensional Lie Algebra

Thank you for downloading lectures on infinite dimensional lie algebra. As you may know, people have look hundreds times for their favorite readings like this lectures on infinite dimensional lie algebra, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some infectious virus inside their computer. lectures on infinite dimensional lie algebra is available in our digital library an online access to it is set as public so you can get it instantly. Our digital library saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the lectures on infinite dimensional lie algebra is universally compatible with any devices to read.
Complexity upper bounds using permutation group theory

Declaration I declare that the thesis entitled Complexity Upper Bounds using Permutation Group theory submitted for the degree of Doctor of Philosophy is the record of the work carried out by me during January 2003 to January 2006 under the guidance of V. Arvind and has not formed the basis for the award of any degree, diploma, associateship, fellowship, titles in this University or any other University or other Institution of Higher learning. Certificate I certify that the thesis entitled Complexity Upper Bounds using Permutation Group theory submitted for the degree of Doctor of Philosophy by Piyush P Kurur is the record of research carried out by him during January 2003 to January 2006 under my guidance and supervision, and that this work has not formed the basis for the award of any degree, diploma, associ-ateship, fellowship or other titles in this University or any other University or Institution of higher learning. Acknowledgements During my stay at IMSc, I was fortunate to be in the company of some wonderful people who have directly or indirectly contributed to this thesis. Firstly I thank Arvind for supervising my work. It was he who introduced me to many of the fascinating topic in Computer Science. Working with him was a great experience in itself. In him I found a great teacher, a friend and a research collaborator. His contribution to this thesis is far more than what I could express in this limited space. I had the privilege to learn Computer Science and Mathematics through some excellent lectures at IMSc. I thank the group at IMSc, especially (especially Eric Allender and Lance Fortnow) and ANTS VI for their funds that helped me attend these conferences. I thank the National Board of Higher Mathematics (NBHM) and the Indian Association for Research in Computer Sciences (IARCS) for funding some of my research visits. Finally I would like to thank my friends for making the life in IMSc memorable and entertaining.
Research on parameter extraction of thin‐film transistors based on swarm intelligence

The development of integrated circuits for displays and other applications requires semiconductor device models and appropriate parameter extraction techniques to predict and understand the circuit behavior. These techniques are paramount in reducing design errors and shortening the product development cycle. This paper presents an algorithm that employed swarm intelligence in exploring an automated and accurate parameter extraction technology. First, an automatic parameter extraction of Rensselaer Polytechnic Institute (RPI) Model for polysilicon thin‐film transistor (Poly‐Si TFT) is achieved by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. Compared with the best solution of the GA algorithm for automatic parameter extraction, the PSO outperformed the GA. However, it still prematurely converges to the suboptimal solution henceforth cannot obtain the expected solution accuracy. Second, the mutual learning particle swarm optimization (MLPSO) algorithm is proposed that introduces the concept of “mutual learning. ” The new algorithm aims to find the global optimum in getting suitable trade‐off between exploration and exploitation. In addition, the MLPSO algorithm implemented the novel random initialization and fitness function in simplifying the complex manual processes and the empirical calibration, and it led to achieving automatic and accurate parameters extraction.
A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis

Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time-spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog-leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA-SVM), and SFLA-SVM. The experiment results show that MSFLA-SVM achieves a much higher fault classification rate than BPNN, ACROA-SVM, and SFLA-SVM.