Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified perturb and observe MPPT with zero oscillation in steady-state for PV systems under partial shaded conditions

This paper proposes a modified perturb and observe (MP&O) to make the conventional P&O can track the maximum power point (MPP) even under the partial shaded conditions(PSC), and doesn't need to oscillation around the MPP. It can substantially lower the power loss. With the increasing growth of renewable energy, photovoltaic (PV) systems are increasingly used to generate electrical energy from solar irradiation. In order to make the PV system can output the maximum power at all times, we can use the maximum power point tracking (MPPT) algorithm more often to reach the achievement that the system can work at the highest efficiency under the different atmosphere. Among all the MPPT methods, the Perturb and Observe (P&O) is the most commonly used because of its simplicity, lower cost and easy to accomplish. It mainly uses P-V characteristic curve to make a judgment. When the slope is zero, it is said that the system works at the MPP. However, the conventional P&O has two primary issues: (1) When the PV system occurs the partial shaded conditions (PSC), it will make the P-V characteristic curve appear multiple peak point. It will lead the conventional P&O misjudge, and make the system work at local maximum power point (LMPP), not at global maximum power point (GMPP). (2) After tracking the MPP, the work point will oscillate around it. It will cause large and unnecessary power loss. This paper proposes two methods to solve the above problems: (1) Using a function to judge whether the PSC occurs. If it occurs, set a track direction flag to search the P-V characteristic curve. If there is any LMPP can output higher power, update the information of GMPP. (2) Stop the oscillation as soon as finding the maximum point. It will minimize the loss. Through detecting the solar panel output power, we can judge that if the irradiance changes or not. This modified Perturb and Observe (MP&O) can achieve superior overall performance while maintaining simplicity of implementation. At the last, this paper also proposes the simulation to prove the feasibility of this algorithm.
Evolution of Transfer Learning and Generalization in Machine Learning: Advancements Unveiled

In recent years, transfer learning has emerged as a pivotal technique in machine learning, enabling models to leverage insights gained from one domain to enhance their performance in distinct domains. This shift in approach has catalyzed a surge in research endeavors aimed at uncovering strategies to augment the effectiveness of transfer learning while also addressing the associated challenges. This study delves comprehensively into the multifaceted realm of transfer learning and generalization, exploring a spectrum of methodologies designed to amplify knowledge transfer across diverse contexts. From adapting to varying domains to exploring meta-learning principles, this investigation surveys an array of techniques aimed at bolstering models' capacity to extrapolate their learning to novel tasks and situations. Nevertheless, achieving seamless knowledge transfer encounters challenges such as domain shifts, dataset biases, and high-dimensional data complexities. By critically evaluating these hurdles, this research highlights the impediments that curtail transfer learning's full potential and discusses potential avenues for mitigation. Moreover, in the evolving landscape of machine learning, novel innovations like generative adversarial networks (GANs) and few-shot learning techniques have introduced new dimensions of adaptability and robustness to transfer learning. In summation, this comprehensive exploration underscores the intricate interplay between transfer learning and generalization, contributing to a deeper understanding of effective transfer learning strategies and their associated challenges, while guiding researchers and practitioners to pioneer innovative solutions that advance the frontiers of transfer learning and facilitate the development of more generalized machine learning models.
Evaluation of blood-brain barrier permeability in tryptophan hydroxylase 2-knockout mice

The blood-brain barrier (BBB) is critical to the health of the central nervous system (CNS). The possibility that 5-hydroxytryptamine (5-HT) participates in the alteration of the BBB has been previously demonstrated. Tryptophan hydroxylase 2 (TPH2) is a unique genetic enzyme isoform that catalyzes the rate-limiting step in the biosynthesis of 5-HT in the CNS; however, its role in the permeability changes of the BBB remains unclear. In the present study, TPH2-knockout mice were utilized in the assessment of BBB disruption, as measured by the Evans Blue (EB) extravasation or fluorescein isothiocyanate-albumin leakage assay in the brain. EB was not found to be retained in the brain in the TPH2-knockout mice or the wild-type controls. The results of the study demonstrate that TPH2 knockout has no effect on BBB permeability, indicating that TPH2 and the 5-HT system in the CNS are not sufficient to influence the BBB leakage.
BRISENT: An entropy-based model for bridge-pier scour estimation under complex hydraulic scenarios

The goal of this paper is to introduce the first clear-water scour model based on both the informational entropy concept and the principle of maximum entropy, showing that a variational approach is ideal for describing erosional processes under complex situations. The proposed bridge–pier scour entropic (BRISENT) model is capable of reproducing the main dynamics of scour depth evolution under steady hydraulic conditions, step-wise hydrographs, and flood waves. For the calibration process, 266 clear-water scour experiments from 20 precedent studies were considered, where the dimensionless parameters varied widely. Simple formulations are proposed to estimate BRISENT’s fitting coefficients, in which the ratio between pier-diameter and sediment-size was the most critical physical characteristic controlling scour model parametrization. A validation process considering highly unsteady and multi-peaked hydrographs was carried out, showing that the proposed BRISENT model reproduces scour evolution with high accuracy.
Subgradient methods and consensus algorithms for solving convex optimization problems

In this paper we propose a subgradient method for solving coupled optimization problems in a distributed way given restrictions on the communication topology. The iterative procedure maintains local variables at each node and relies on local subgradient updates in combination with a consensus process. The local subgradient steps are applied simultaneously as opposed to the standard sequential or cyclic procedure. We study convergence properties of the proposed scheme using results from consensus theory and approximate subgradient methods. The framework is illustrated on an optimal distributed finite-time rendezvous problem.
Protective immunogenicity and T lymphocyte specificity of a trivalent hybrid peptide containing NH2-terminal sequences of types 5, 6, and 24 M proteins synthesized in tandem

The protective immunogenicity of a hybrid peptide containing tandem copies of types 5, 6, and 24 M protein epitopes was investigated. An NH2-terminal peptide of type 24 M protein was chemically synthesized and then extended to include NH2-terminal peptides of types 6 and 5 M proteins yielding a 34-residue hybrid peptide containing a cysteine residue at its COOH-terminus. When conjugated via the cysteine residue to keyhole limpet hemocyanin (KLH), emulsified in CFA, and injected into rabbits, the synthetic hybrid evoked opsonic antibodies against types 5, 6, and 24 streptococci without stimulating tissue crossreactive immunity. The trivalent hybrid also was capable of priming T lymphocytes in vivo that responded to each of the native serotypes of M protein as well as to the synthetic hybrid peptide in vitro. The primed T cells failed to respond to the individual component peptides contained in the hybrid peptide, suggesting that the hybrid peptide confers conformations resembling the presentations of each of the subpeptides in the respective serotypes of M protein. The brisk immune responses to the type 6 peptide contained in the middle of the tandem hybrid indicates that with judicious placement between proline residues, potentially hidden peptides are readily accessible to the immune system. These results suggest that synthetic tandem peptides can be tailored in a fashion in which each of the component sets of protective epitopes can be made optimally immunoaccessible and immunogenic.
A comparison of movement direction-related versus load direction- related activity in primate motor cortex, using a two-dimensional reaching task

Shoulder joint-related motor cortex cells show continuously graded changes in activity, centered on a preferred movement direction, during active arm movements in 8 directions away from a central starting position (Georgopoulos et al., 1982). We demonstrate here that many of these cells show similar large continuously graded changes in discharge when the monkey compensates for inertial loads which pull the arm in 8 different directions. These load-dependent discharge variations are typically unimodal, centered on one load direction called the cell's load axis, and are often sufficiently continuous, symmetric, and broad as to show a good fit to a sinusoidal curve. A vectorial representation of cell activity indicates that the pattern of load-dependent activity changes in the population forms a signal whose direction is appropriate to compensate for the loads. The responses of single cells to different combinations of movement and load direction are often complex. Nevertheless, the mean activity of the sample population under any condition of movement direction and load direction can be described reasonably well by a simple linear summation of the movement-related discharge without any loads, and the change in tonic activity of the population caused by the load, measured prior to movement. The strength of the load-dependent discharge variation differs among cells. Cells can be sorted into 2 phasic and 2 tonic groups that show differing degrees of sensitivity to loads. In particular, it was found that the greater the degree of cell discharge variation associated with different actively maintained limb postures, the greater the activity changes caused by loads. No similar correlation was found for the degree of discharge variation during movement. Preliminary evidence suggests that phasic and tonic cell groups may be spatially segregated in the motor cortex. These observations are consistent with the idea that there exists in the motor cortex activity encoding aspects of movement kinematics, as well as movement dynamics. These observations are in agreement with studies of more distal arm joints, showing that the activity of certain motor cortex cells varies with the patterns of muscle activity and output forces required to produce a movement. These experiments extend the description of the control of the direction of movement of a multiple degree-of-freedom joint into the spatial (direction) domain to a greater extent than previously achieved.
STEM: species tree estimation using maximum likelihood for gene trees under coalescence

UNLABELLED STEM is a software package written in the C language to obtain maximum likelihood (ML) estimates for phylogenetic species trees given a sample of gene trees under the coalescent model. It includes options to compute the ML species tree, search the space of all species trees for the k trees of highest likelihood and compute ML branch lengths for a user-input species tree. AVAILABILITY The STEM package, including source code, is freely available at http://www.stat.osu.edu/~lkubatko/software/STEM/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Diversity and Coding Gains of Space-Time-Frequency Coded MIMO-OFDM

Space-time-frequency (STF) coding for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) over frequency-selective Rayleigh fading channels is considered. The maximum diversity order and coding gain of the system are derived when linear constellation preceding (LCP) is applied to OFDM symbols. It is also shown that with a proper subcarrier grouping of the OFDM symbols, the system implementation can be greatly simplified while maintaining the diversity order and coding gain. In particular, as long as the group size F is not less the number of effective resolvable channel taps L, the maximum diversity order can always be achieved. Moreover, if F ges L, and F is an Euler number or an integer power of 2, the maximum coding gain can be achieved. Otherwise, the achievable coding gain approximates 70% of the maximum one.
Bayesian recognition of safety relevant motion activities with inertial sensors and barometer

Activity recognition has been a hot topic in research throughout the last years. Walking, standing, sitting or lying have been detected with more or less confidence, in more or less suitable system designs. None of these systems however has entered daily life, neither in mass market, nor in professional environments. What is required is an unobtrusive system, requiring few resources and - most important - recognizing all important activities with high confidence. To this end, our research has focused on the professional market for safety related applications: first responders or also military use. Next to the classical motion related activities, our system supports motions in three dimensions that are necessary for all kinds of movements indoors as well as outdoors. These include falling, wriggling, crawling, climbing stairs up and down and using an elevator. We have proven this approach to run in real-time with only a single wireless sensor attached to the body while achieving robust and reliable recognition with a delay lower than two seconds.
An Overview of Performance Evaluation of MC-CDMA System

MC-CDMA is one of the most promising technique for future mobile communication, since it offers high bit rate and high capacity transmission. It is actually the combination two efficient techniques such as Code division Multiple Access (CDMA) and Orthogonal Frequency Division Multiplexing (OFDM) techniques. Consequently, it combines the benefits and limitation of both CDMA as well as OFDM techniques. MC-CDMA is an effective scheme that reduces the problems like spectral limitation and distortions due to multipath channels. The main limitation in MC-CDMA system is the high Peak to average power ratio (PAPR) which in turn reduces the system power efficiency. The studies shows that the use of efficient spreading codes with good correlation properties and PAPR reduction techniques, help to improve the system performance by reducing the PAPR. There are different PAPR reduction techniques such as signal scrambling techniques and signal distortion techniques, which will help to improve the system performance. However the technique should be chosen by considering the following factors such as, it should create only few harmful side effects such as in-band distortion, out-of band radiation, reduced BER degradation, low implementation complexity. This paper focusses on various problems and the possible solutions of MC-CDMA system.
Adaptive clustering with a variance-aware criterion: An alternative to k-means

This research investigates the effectiveness of a non-convex clustering criterion with the ability to discriminate clusters by means of quadratic boundaries that take into account cluster variances. Since no algorithms have been shown to work efficiently and effectively for this kind of criterion, we introduce and evaluate a generalized version of the incremental one-by-one clustering algorithm of MacQueen (1967) that is suitable for general variance-based criteria, whether convex or not. An experimental evaluation shows that the criterion performs remarkably well with a variety of heterogeneous data sets, both synthetic and real-world. Two novel applications of unsupervised learning to problems in the financial domain are then developed to test the method further. First, given a portfolio of investments with potentially hundreds of assets, we pose the problem of reducing the number of such assets while preserving its risk-return characteristics. Next, we tackle the problem of finding risk-reward opportunities to short-sell securities. For both problems, we offer novel clustering-based solutions and proceed to show that the clustering criterion addressed in the present research is an excellent choice. Defense Committee: Casimir Kulikowski (chair), Ahmed Elgammal and Apostolos Gerasoulis
Review of Machine Learning Models for Application in Adaptive Learning for Higher Education Student

The article reviews machine learning models and their application in the context of adaptive learning. The main focus is on the capabilities of adapting models for personalizing the educational process. The article also highlights issues related to the limitations of integrating machine learning into educational systems, including the problems of requiring large datasets and the need for specialized resources. Furthermore, it discusses the challenges in integrating and standardizing these technologies into existing educational systems and considers probable directions for future research. The problems of developing and implementing the technologies described in the article in higher education systems are noted.
Estimating the voice source in noise

Estimation of the glottal source has applications in many areas of speech processing. Therefore, a noise-robust automatic source estimation algorithm is proposed in this paper. The source signal is estimated using a codebook search approach. The glottal area waveforms extracted from high-speed recordings of the glottis is converted to the glottal flow signals in order to evaluate the performance of the proposed source estimation algorithm. Results in clean and noisy conditions, on average, show that the proposed algorithm provides more accurate estimation than the software toolkit Aparat [1] as well as an earlier approach [2].
Labeling of unique sequences in double-stranded DNA at sites of vicinal nicks generated by nicking endonucleases

We describe a new approach for labeling of unique sequences within dsDNA under nondenaturing conditions. The method is based on the site-specific formation of vicinal nicks, which are created by nicking endonucleases (NEases) at specified DNA sites on the same strand within dsDNA. The oligomeric segment flanked by both nicks is then substituted, in a strand displacement reaction, by an oligonucleotide probe that becomes covalently attached to the target site upon subsequent ligation. Monitoring probe hybridization and ligation reactions by electrophoretic mobility retardation assay, we show that selected target sites can be quantitatively labeled with excellent sequence specificity. In these experiments, predominantly probes carrying a target-independent 3′ terminal sequence were employed. At target labeling, thus a branched DNA structure known as 3′-flap DNA is obtained. The single-stranded terminus in 3′-flap DNA is then utilized to prime the replication of an externally supplied ssDNA circle in a rolling circle amplification (RCA) reaction. In model experiments with samples comprised of genomic λ-DNA and human herpes virus 6 type B (HHV-6B) DNA, we have used our labeling method in combination with surface RCA as reporter system to achieve both high sequence specificity of dsDNA targeting and high sensitivity of detection. The method can find applications in sensitive and specific detection of viral duplex DNA.
Chance and memory

Memory and chance are two properties or, more evocatively, two strategies that cells have evolved. Cellular memory is beneficial for broadly the same reasons that memory is beneficial to humans. How well could we function if we forgot the contents of the refrigerator every time the door was closed, or forgot where the children were, or who our spouse was? The inability to form new memories, called anterograde amnesia in humans, is tremendously debilitating, as seen in the famous psychiatric patient H.M. [1] and the popular movie “Memento” (Christopher Nolan, 2000). Cells have limited views of their surroundings. They are able to sense various chemical and physical properties of their immediate environment, but distal information can come only through stochastic processes, such as diffusion of chemicals. Cells lack the ability to directly sense distal parts of their environment, as humans can through vision and hearing. Thus, we might expect that cells have adopted memory of the past as a way of accumulating information about their environment and so gain an advantage in their behaviour. In a simple application, memory can be used to track how environmental signals change over time. For example, in the chemotaxis network of E. coli, cells use memory of previous measurements of chemical concentrations to estimate the time derivative of the chemical concentration and so to infer whether the organism is swimming up or down a chemical gradient [2]. Conversely, memory can be useful in enabling persistent behaviour despite changing environments. For example, differentiated cells in a multicellular organism must remain differentiated and not redifferentiate despite existing under a variety of conditions. If such cells become damaged or infected, they may undergo apoptosis, but we do not observe neurons becoming liver cells, or liver cells becoming muscle cells, and so on. Different mechanisms for achieving cellular memory operate at different time scales. For example, the chemotaxis machinery of E. coli can change response in seconds. Gene regulatory systems controlling sugar catabolism, such as the lac operon in E. coli [3] or the galactose network in Saccharomyces cerevisiae [4], take minutes or hours to switch state. Terminally differentiated cells can retain their state for years – essentially permanently, although scientists are learning how to reset their memories and return cells to a pluripotent status [5]. Of course, that cells
A Hierarchical Network for Multimodal Document-Level Relation Extraction

Document-level relation extraction aims to extract entity relations that span across multiple sentences. This task faces two critical issues: long dependency and mention selection. Prior works address the above problems from the textual perspective, however, it is hard to handle these problems solely based on text information. In this paper, we leverage video information to provide additional evidence for understanding long dependencies and offer a wider perspective for identifying relevant mentions, thus giving rise to a new task named Multimodal Document-level Relation Extraction (MDocRE). To tackle this new task, we construct a human-annotated dataset including documents and relevant videos, which, to the best of our knowledge, is the first document-level relation extraction dataset equipped with video clips. We also propose a hierarchical framework to learn interactions between different dependency levels and a textual-guided transformer architecture that incorporates both textual and video modalities. In addition, we utilize a mention gate module to address the mention-selection problem in both modalities. Experiments on our proposed dataset show that 1) incorporating video information greatly improves model performance; 2) our hierarchical framework has state-of-the-art results compared with both unimodal and multimodal baselines; 3) through collaborating with video information, our model better solves the long-dependency and mention-selection problems.