Miracle-dz/UniFAR-FTUs huggingface dataset:
PERAMALAN HARGA KOMODITAS BERAS DI KALIMANTAN TIMUR MENGGUNAKAN ALGORITMA NEURAL NETWORK

Beras merupakan salah satu bahan makanan yang sangat penting bagi sebagian besar penduduk Indonesia. Selain menjadi makanan pokok, kebutuhan beras juga semakin meningkat, terutama di Provinsi Kalimantan Timur yang jumlah penduduknya terus bertambah setiap tahun. Kenaikan dan penurunan harga beras sangat mempengaruhi petani karena berdampak pada produksi yang lebih tinggi. Oleh karena itu, diperlukan solusi untuk mengatasi kekhawatiran terkait perubahan tersebut. akan terjadinya naik turunnya harga beras dengan memprediksi harga beras di Kalimantan timur yang bernilai numerik. Penelitian ini bertujuan memprediksi harga beras harian yaitu beras premium, menggunakan sample dataset harga beras. Dengan himpunan data selama 3 tahun terakhir, dari tanggal 25 mei 2020 sampai 20 Desember 2022, yang diperoleh dari Komunitas di Kalimantan Timur yang berjumlah 1001. Metode yang digunakan dalam penelitian ini menggunakan algoritma neural network dengan windowing salah satu cara untuk mengetahui status penjualan beras adalah dengan melakukan prediksi menggunakan algoritma neural network dengan menggunakan aplikasi rapidminer. Hasil eksperimen memprediksi harga beras berhasil. Hasil dari prediksi harga beras model menggunakan algoritma neural network menunjukkan hasil kinerja yang diukur dengan Root Mean Square Error (RMSE) beras premium menghasilkan RMSE sebesar 52,846.
Silicon-on-Insulator-Based Narrowband Microwave Photonic Filter With Widely Tunable Bandwidth

We propose and experimentally demonstrate sub-gigahertz microwave photonic filters (MPFs) with widely tunable bandwidths and ultra-high rejection ratios based on a silicon add-drop Mach–Zehnder interferometers (MZI) coupled ring array. To realize a ring filter with a widely tunable bandwidth and a flexibly adjustable extinction ratio, the two coupling waveguides of the silicon resonator are designed as bending waveguides and are both fabricated with microheaters. Consequently, the effective coupling coefficients between the add-drop ring and the coupling waveguides could be significantly manipulated, resulting in tunable bandwidths and extinction ratios. Capitalizing the designed resonators with broadly adjustable bandwidth, the experimental results show that the 3dB-bandwidth of the MPF could be widely tuned from 0.178 GHz to 22.7 GHz. What's more, owing to the ring flexibly tunable extinction ratios and the complete interference cancellation technology, the maximum MPF rejection ratios could realize beyond 75 dB. To the best of our knowledge, it is the first time to realize the notch MPFs with widely tunable 3dB-bandwidths ranging from sub-gigahertz to tens of gigahertz while maintaining the ultra-high rejection ratios. The proposed scheme paves a way for the notch MPFs with the dominant advantages of narrow bandwidths, wide bandwidth tuning ranges, ultra-high rejection ratios and complementary metal-oxide semiconductor (CMOS)-compatibility, which is competent to process dynamic radio frequency (RF) signals in on-chip microwave systems.
Délégation de lots de tâches pour la réduction de la durée moyenne de réalisation

Résumé. — Nous étudions le problème de la réallocation de tâches pour l’équilibrage de charge dans les modèles distribués de traitement de données massives. Nous proposons une stratégie qui repose sur des agents coopératifs pour optimiser le réordonnancement de tâches dans un ensemble de jobs devant être exécutés le plus tôt possible. Elle permet aux agents de déterminer localement les prochaines tâches à exécuter, à déléguer, voire à échanger grâce à leurs connaissances, leurs croyances et leur modèle des pairs. La nouveauté réside dans la capacité des agents à identifier les opportunités et les agents limitants pour réallouer efficacement des lots de tâches à travers des négociations bila-térales concurrentes. La stratégie mise en œuvre par les agents permet de garantir une améliorationcontinuedudélaideréalisation. Nosexpérimentationsmontrentqueladurée moyennederéalisationatteinteparnotrestratégieestmeilleurequecelleobtenueavecunerésolutionDCOPetresteprochedecelleobtenueavecuneheuristiqueclassique, avecdanstouslescasuntempsderéordonnancementsignificativementréduit. Mots-clés
Control and Estimation Oriented Model Order Reduction for Linear and Nonlinear Systems

Optimization based controls are advantageous in meeting stringent performance requirements and accommodating constraints. Although computers are becoming more powerful, solving optimization problems in real-time remains an obstacle because of associated computational complexity. Research efforts to address real-time optimization with limited computational power have intensified over the last decade, and one direction that has shown some success is model order reduction. This dissertation contains a collection of results relating to openand closed-loop reduction techniques for large scale unconstrained linear descriptor systems, constrained linear systems, and nonlinear systems. For unconstrained linear descriptor systems, this dissertation develops novel gramian and Riccati solution approximation techniques. The gramian approximation is used for an openloop reduction technique following that of balanced truncation proposed by (Moore, 1981) for ordinary linear systems and (Stykel, 2004) for linear descriptor systems. The Riccati solution is used to generalize the Linear Quadratic Gaussian balanced truncation (LQGBT) of (Verriest, 1981) and (Jonckheere and Silverman, 1983). These are applied to an electric machine model to reduce the number of states from >100000 to 8 while improving accuracy over the state-of-the-art modal truncation of (Zhou, 2015) for the purpose of condition monitoring. Furthermore, a link between unconstrained model predictive control (MPC) with a terminal penalty and LQG of a linear system is noted, suggesting an LQGBT reduced model as a natural model for reduced MPC design. The efficacy of such a reduced controller is demonstrated by the real-time control of a diesel airpath. Model reduction generally introduces modeling errors, and controlling a constrained plant subject to modeling errors falls squarely into robust control. A standard assumption of
Detection of Abnormal electrocardiograms Based on Various Feature Extraction methods

Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart obtained by placing various electrodes on specific areas of the subject's body surface. Abnormalities in a patient's ECG signal may indicate cardiac diseases that require immediate medical attention. As a result, detecting an abnormal ECG is critical for the patient's benefit. This work develops a method for classifying ECG signals as normal or abnormal. In this paper, we propose a method for detecting cardiac arrhythmias in electrocardiograms (ECG). In the first stage, the proposal focuses on various feature extractor methods. The Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and Improved complete ensemble empirical mode decomposition with adaptive noise were the feature extraction techniques evaluated (ICEEMDAN). The PCA method is then used to reduce the number of features. Finally, for classification, the Support Vector Machine (SVM) was used, which was trained using the features extracted in the first stage. The proposed models are tested using datasets from MIT-BIH arrhythmia and PTB Diagnostics. The experimental results show that using 3-PCs with the DWT method produces better results than the other methods, which achieve 98.7% in terms of accuracy.
Broadband laser-driven electromagnetically induced transparency in three-level systems with a double Fano continuum

We discuss the electromagnetically induced transparency (EIT) phenomenon for a model in which a structured continuum is described by a so-called double Fano structure; instead of one autoionizing (AI) state, two such states are embedded in a flat continuum. Such a Fano structure is the upper level of a Λ-like three-level system and is coupled to two lower ones by an external laser field involving δ-correlated, Gaussian, Markov process (white noise), simulating realistic conditions of the experiment. For such a system we derived and solved a set of coupled stochastic integrodifferential equations in the stationary regime, obtaining exact formulas determining the electric susceptibility of the system. Dispersion and absorption spectra of the medium susceptibility were calculated and compared with those already discussed in the literature. We have shown that the presence of an additional AI level considerably changes the structure of transparency windows and how noisy excitation influences the EIT processes.
Complementarity Model for Steady-State Analysis of Resonant LLC Power Converters

In recent years, the complementarity formalism has been shown to be an interesting framework for the mathematical representation of a large class of power converters. In this brief, the complementarity model of a resonant LLC power converter is presented. The model usefulness is demonstrated for the identification of the circuit parameters and for the numerical analysis of the stationary behavior. The comparison between experimental measurements and numerical results shows the efficacy of the proposed approach.
Parameter estimation and the statistics of nonlinear cosmic fields

The large scale distribution of matter in the universe contains valuable information about fundamental cosmological parameters, the properties of dark matter and the formation processes of galaxies. The best hope of retrieving this information lies in providing a statistical description of the matter distribution that may be used for comparing models with observation. Unfortunately much of the important information lies on scales below which nonlinear gravitational effects have taken hold, complicating both models and statistics considerably. This thesis deals with the distribution of matter mass and galaxies on such scales. The aim is to develop new statistical tools that make use of the nonlinear evolution for the purposes of constraining cosmological models. A new derivation for the 1 -point probability distribution function (PDF) for density inhomogeneities is presented first. The calculation is based upon an exact statistical treatment, using the Chapman -Kolmogorov equation and second order Eulerian perturbation theory to propagate the initial density field into the nonlinear regime. The analysis yields the generating function for moments, allowing for a straightforward derivation of the skewness. A new dependance upon the perturbation spectrum is found for the skewness at second order. The results of the analysis are compared against other methods for deriving the 1 -point PDF and against data from numerical N -body simulations. Good agreement is found in both cases. The 1 -point PDF for galaxies is derived next, taking into account nonlinear biasing of the density field and the distorting effects associated with working in redshift space. Once again perturbation theory is used to evolve the density field into the nonlinear regime and the Chapman -Kolmogorov equation to propagate the initial probabilities. Transformation of the dark matter density to a biased galaxy distribution is done through an Eulerian biasing prescription, expanding the nonlinear bias function to second order. An advantage of the Chapman-
On the Crossing Number of Complete Graphs: Growing Minimal Kn From Minimal Kn 1

This dissertation addresses the generation of minimal complete graphs. A complete graph, denoted Kn, is a graph in which each vertex is connected to every other vertex of the graph with an edge. A minimal graph is a representation of a graph that exhibits its crossing number. The crossing number of a graph is the smallest number of edge crossings over all planar representations of the graph. Presented is a constructive framework for iteratively generating minimal Kn from minimal Kn−1. The process described allows for complete enumeration of all minimal Kn derivable from minimal Kn−1, including those graphs that are only locally minimal. A graph is locally minimal if it reflects the smallest number of crossings possible for a given region placement of vertex n in minimal Kn−1. All graphs are classified into isomorphic families, allowing for efficient iteration to the next set of complete graphs of order n+1 due to needing only one representative from each Kn isomorphic family for complete results. In addition to determining the crossing number for Kn generated by Kn−1, all minimal graphs are available for exploration of their underlying structure. This allows for substructure isomorphic analysis and localized region neighborhood analysis, both of which will lead to yet more efficient iteration of Kn for increasing n. Information culled from these graphs may also shed light on new or improved lower bound estimation techniques. A seven-step process is presented that allows for iterative growth. The main module in the process, Star Analysis, takes a global view of growing minimal Kn by focusing on optimizing the enumeration of all star graphs, K1,n−1, centered at vertex n placed into initial minimal Kn−1 at all possible different region locations. This research introduces new conjectures and opens some new questions for exploring the structure of minimal Kn. Though this work focuses on minimal complete graphs, the process appears to be applicable to an assortment of other graph families. An example is presented of its application to complete bipartite graphs.
Scratchpad Memory Unit in Hybrid Control-flow and Dataflow Architectures

: Dataflow architectures offer superior performance compared to control-flow architectures under certain conditions. This paper focuses on memory organization of a hybrid control-flow and dataflow architecture which guaranties that memory allocation can be accomplished in a predictable time. Mapping logical addresses into physical ones and accessing local memory in constant time is achieved using special address translation hardware. The memory organization is based on Buddy-system. It allows allocating arbitrary amounts of memory and prefetching data while it is being accessed by control-flow and dataflow hardware.
On simultaneous confidence intervals based on rank-estimates with application to analysis of gene expression data

Abstract Inferential methods based on ranks present robust and powerful alternative methodology for testing and estimation. In this article, two objectives are followed. First, develop a general method of simultaneous confidence intervals based on the rank estimates of the parameters of a general linear model and derive the asymptotic distribution of the pivotal quantity. Second, extend the method to high dimensional data such as gene expression data for which the usual large sample approximation does not apply. It is common in practice to use the asymptotic distribution to make inference for small samples. The empirical investigation in this article shows that for methods based on the rank-estimates, this approach does not produce a viable inference and should be avoided. A method based on the bootstrap is outlined and it is shown to provide a reliable and accurate method of constructing simultaneous confidence intervals based on rank estimates. In particular it is shown that commonly applied methods of normal or t-approximation are not satisfactory, particularly for large-scale inferences. Methods based on ranks are uniquely suitable for analysis of microarray gene expression data since they often involve large scale inferences based on small samples containing a large number of outliers and violate the assumption of normality. A real microarray data is analyzed using the rank-estimate simultaneous confidence intervals. Viability of the proposed method is assessed through a Monte Carlo simulation study under varied assumptions.
Cross-Drone Transformer Network for Robust Single Object Tracking

Drones have been widely used in a variety of applications, e.g., aerial photography and military security, because of their high maneuverability and broad views compared with fixed cameras. Multi-drone tracking systems can provide rich information about targets by collecting complementary video clips from different views, especially when targets are occluded or disappear in some views. However, it is challenging to handle cross-drone information interaction and multi-drone information fusion in multi-drone visual tracking. Recently, Transformer has shown significant advantages in automatically modeling the correlation between templates and search regions for visual tracking. To leverage its potential in multi-drone tracking, we propose a novel cross-drone Transformer network (TransMDOT) for visual object tracking tasks. The self-attention mechanism is used to automatically capture the correlation between multiple templates and the corresponding search region to achieve multi-drone feature fusion. During the tracking process, a cross-drone mapping mechanism is proposed by using the surrounding information of the drone with promising tracking status as reference, assisting drones that lost targets to re-calibrate, which implements real-time cross-drone information interaction. As the existing multi-drone evaluation metrics only consider spatial information while ignore temporal information, we further present a system perception index (SPFI) that combines both temporal and spatial information to evaluate the tracking status of multiple drones. Experiments on the MDOT dataset prove that TransMDOT greatly surpasses the state-of-the-art methods in both single-drone performance and multi-drone system fusion performance. Our code will be available on https://github.com/cgjacklin/transmdot.
Investigation of the Effectiveness of the Solution-Focused Group Counseling Program to Increase Self-Control in University Students

The purpose of this research is to examine the effectiveness of the solution-focused group counseling program aimed at increasing the self-control levels of university students. The research is an experimental study with experimental-control group with pre-test, post-test and follow-up measurement design. The study group of the research consisted of 26 university students. Within the scope of this research, 13 of the students were randomly assigned to the experimental group and 13 to the control group. In the research, university students in the experimental group were given 6 sessions (each session is between 90-120 minutes and one day a week) of solution-focused group psychological counseling once a week. No studies were conducted on the control group. In this study, “Self-Control Scale” and “Personal Information Form” were used as data collection tools. Mann Whitney U Test and Wilcoxon Signed Rank Test were used in the analysis of the data. As a result of the research, it was determined that the solution-focused group was more effective than the control group in increasing self-control. In addition, it was determined that this effectiveness was maintained in the follow-up test performed at the end of three months.
Huygens’ metasurfaces from microwaves to optics: a review

Abstract In this article, the basic principles and the main applications of Huygens’ metasurfaces (HMSs) are reviewed from microwaves to optics. In general, HMSs comprise a thin layer of orthogonal electric and magnetic dipoles, which form an array of Huygens’ sources. In a refraction setting, these sources radiate mostly in the forward direction and can be used to manipulate an incident electromagnetic wave at will. In the case of passive HMSs, the Huygens’ sources are induced by an incident electromagnetic field. Examples of passive manipulations include reflectionless refraction, perfect anomalous reflection, and arbitrary antenna beam forming. In the case of active HMSs, the Huygens’ sources are impressed active sources. Active HMS manipulations include cloaking and subwavelength spot formation in a cavity environment.
The Effects of Social Networking Sites Needs and Academic Stressors on Academic Motivation of College Students

In recent years, social networking sites (SNSs) have experienced a surge in popularity, particularly among college students. These online platforms offer diverse functionalities that facilitate connections with friends and family, information sharing, and resource exchange. This study aimed to determine the effects of SNSs needs and academic stressors on the academic motivation of college students in one private schools in Iligan City, Lanao del Norte, Philippines during the second semester of the academic year 2022-2023. Using a simple random sampling procedure, 511 first, second, and third-year college students participated and responded to the adapted Likert-type instruments assessing the study variables. The result showed that most college students used Facebook, Facebook Messenger, and YouTube daily. It was also revealed that both SNSs and academic stressors positively affect extrinsic motivation. However, only SNSs have a positive impact on intrinsic motivation. Furthermore, academic stressors positively affect amotivation. Hence, educators and school administrators should consider incorporating SNSs to enhance motivation and engagement among college students. Specifically, educators can leverage the features of SNSs to promote collaboration, communication, and information sharing among students. Additionally, educators may consider designing academic activities encouraging students to use SNSs to explore and apply course content.
Analysis on the characteristics for upper bound of [1,2]-domination in trees

In this paper, we propose a theoretical model for characterization and upper bounds of [1,2]-domination set of network which has tree structure. In detail, we propose a theoretic model for upper bounds on [1,2]-domination set of a tree network which has some typical constrains. To that purpose, we introduce a graph theory to model and analyze the characteristics of tree structure networks. We assume a node subset D of a graph G=(V,E). We define that D is a [1,2]-dominant set if for any node v in set V which is not an element of a set D is adjacent to a node or two nodes of an element in a set D (that is, 1 ≤ ∩ ≤ 2 for every node v ∈  ). The minimum cardinality of a [1,2]-dominating set of  , which is denoted by    , is called the [1,2]-domination number of  . In this paper, we show new upper bounds and characteristics about the [1,2]-domination number of tree.
Identification of Babesia capreoli in an
 Ixodes nipponensis tick obtained from a Korean water
 deer

Babesiosis is a tick-borne disease caused by intraerythrocytic protozoa. Despite the increas-ing acknowledgement that babesiosis represents a threat to animal and human health, to date there have been few studies focusing on the disease in the Republic of Korea (ROK). In the present study, we report a Babesia capreoli infection in an Ixodes nipponensis tick obtained from a Korean water deer ( Hydropotes inermis argyropus ). The tick was identified with polymerase chain reaction analysis as I. nipponensis (Japanese hard tick). A phylogenetic analysis based on the 18S rRNA gene sequences revealed that the isolate found in I. nipponensis belonged to the B. capreoli lineage and was distinct from the Asian, European, and North American lineages of Babesia divergens . Although our isolate belonged to the B. capreoli lineage it did not form a cluster with others isolates in the same lineage; this may be due to differences in the tick species that transmit B. capreoli or in the host species. We were unable to identify the reservoir host for our case of B. capreoli transmission, though regional ticks may be the primary vector. This study confirms the presence of B. capreoli in the ROK, and its presence suggests that further study is warranted to determine its prevalence and pathogenicity in wild and domesticated animals.