Reviewerly/exHarmony huggingface dataset:
3D tracking using a multi-spectral 4th-generation phase plate

3D imaging and object tracking is critical for medical and biological research and can be achieved by multifocal imaging with diffractive optical elements (DOEs) converting depth (z) information into a modification of the 2D image. Recently, we introduced a simple system for achieving precise localization and tracking of point sources emitting fluorescent light at 525 nm using a spiral phase plate in a standard wide-field optical microscope with minor modification. We achieved a bead localization precision of <25 nm in the transverse dimensions and ≤70 nm along the axial dimension over an axial range of 6 μm. 3D diffusion constants of chromatin labelled with LacR-GFP arrays matched expected values. We are now pursuing chromatin tracking experiments to better understand how DNA damage affects chromatin motions and how chromatin dynamics impacts the DNA damage response. We are extending our chromatin tracking capabilities to simultaneously track chromatin and a DNA repair factor (53BP1) which has been fluorescently labelled at 610 nm with mCherry. To do this, we are testing a new 4th-generation waveplate that will enable 3D tracking of cellular objects labeled with two different fluorophores. Unlike 3rd-generation DOEs that are limited to tracking objects that fluoresce within a narrow wavelength range centered on their design wavelength, 4th-generation optics have the potential to perform 3D tracking with high efficiency over a broad wavelength range that can span the whole visible spectrum. We will present preliminary data on the performance of a microscope fitted with a multiwave vector vortex waveplate that can track particles fluorescing in a wide range of narrow wavelength channels, making this optical system ideal for single particle tracking in multispectral microscope systems.
Small Sample Fiber Full State Diagnosis Based on Fuzzy Clustering and Improved ResNet Network

The optical time domain reflectometer (OTDR) curve features of communication fibers exhibit subtle differences among their normal, subhealthy, and faulty operating states, making it challenging for existing machine learning-based fault diagnosis algorithms to extract these minute features. In addition, the OTDR curve field fault data are scarce, and data-driven deep neural network that needs a lot of data training cannot meet the requirements. In response to this issue, this paper proposes a communication fiber state diagnosis model based on fuzzy clustering and an improved ResNet. First, the pretrained residual network (ResNet) is modified by removing the classification layer and retaining the feature extraction layers. A global average pooling (GAP) layer is designed as a replacement for the fully connected layer. Second, fuzzy clustering, instead of the softmax classification layer, is employed in ResNet for its characteristic of requiring no subsequent data training. The improved model requires only a small amount of sample training to optimize the parameters of the GAP layer, thereby accommodating state diagnosis in scenarios with limited data availability. During the diagnosis process, the OTDR curves are input into the network, resulting in 512 features outputted in the GAP layer. These features are used to construct a feature vector matrix, and a dynamic clustering graph is formed using fuzzy clustering to realize the fiber state diagnosis. Through on-site data detection and validation, it has been demonstrated that the improved ResNet can effectively identify the full cycle of fiber states.
Open-ComBind: Harnessing unlabeled data for improved binding pose prediction

Accurate determination of a ligand binding pose is crucial for in silico drug discovery. Molecular docking is a widely used method for the prediction of non-covalent binding of a protein and ligand system. However, most molecular docking pipelines consider only one ligand at a time, even though ligands may share binding interactions when bound to the same receptor. Open-ComBind, an open-source version of the ComBind molecular docking pipeline, addresses this issue by incorporating information from multiple ligands, including those without known bound structures, to improve pose selection. Poses are simultaneously predicted for a docking ligand of interest as well as a set of helper ligands. First, we generate distributions of feature similarities between ligand pose pairs, comparing near-native poses with all sampled docked poses. These distributions capture the likelihood of observing similar features, such as hydrogen bonds or hydrophobic contacts, in different pose configurations. We then combine these similarity distributions with a per-ligand docking score to improve overall pose selection by 8.5% and 4.5% for high-affinity and congeneric series helper ligands, respectively. Open-ComBind reduces the average RMSD of ligands in our benchmark dataset by 8.2%. We investigate the significance of the inter-molecular features as well as the number of helper ligands to achieving accurate pose selection. We provide Open-ComBind as an easy-to-use command line and Python API to increase pose prediction performance at www.github.com/drewnutt/open-combind.
TonB raider: Discovering the secrets of the Ton system's order

Active nutrient uptake is fundamental for survival and pathogenicity of gram-negative bacteria, which operate a multi-protein Ton system to transport essential nutrients like metals and vitamins. This system harnesses the proton motive force at the inner membrane to energize the import through the outer membrane, but the mechanism of energy transfer remains enigmatic. Here, we study the periplasmic domain of ExbD, a crucial component of the proton channel of the Ton system. We show that this domain is a dynamic dimer switching between two conformations representing the proton channel’s open and closed states. By in vivo phenotypic assays we demonstrate that this conformational switch is essential for the nutrient uptake by bacteria. The open state of ExbD triggers a disorder to order transition of TonB, enabling TonB to supply energy to the nutrient transporter. We also reveal the anchoring role of the peptidoglycan layer in this mechanism. Herein, we propose a mechanistic model for the Ton system, emphasizing ExbD duality and the pivotal catalytic role of peptidoglycan. Sequence analysis suggests that this mechanism is conserved in other systems energizing gliding motility and membrane integrity. Our study fills important gaps in understanding bacterial motor mechanism and proposes novel antibacterial strategies.
Rational enzyme engineering via generative AI

Rational enzyme engineering necessitates an in-depth understanding of various enzyme mutations, a pursuit that poses significant difficulties. However, this obstacle could potentially be overcome by learning from nature's own mechanisms of evolving efficient enzymes. We employed generative AI to analyze natural protein sequences, thereby establishing a connection between enzyme evolution and catalysis. We found that the sequence information could be used to predict enzyme activity and stability in the enzyme active center and scaffold, respectively. The discovery paves the way for a fresh understanding of both enzyme catalysis and evolution. Moreover, the enzyme evolution-catalysis relationships provide a rational way for enzyme engineering, with a ∼50% success rate in enhancing enzyme performance in the experiment. Collectively, our research highlights the immense potential of the abundant natural protein sequences combined with generative AI for advancing enzymology and enzyme engineering.
Cyberbullying Detection: A Comparative Study of Classification Algorithms

In the realm of social media, cyberbullying's pervasive impact raises urgent concerns about its emotional and psychological toll on victims. This study addresses the imperative of effectively detecting cyberbullying. By leveraging ML and DL techniques, we aim to develop reliable methods that accurately identify instances of cyberbullying in social media data, enhancing detection efficiency and accuracy. This facilitates timely intervention and support for affected individuals. In this comprehensive analysis of existing systems, various ML and DL models are extensively texted for cyberbullying detection. The evaluated models include Random Forest, XgBoost, Naive Bayes, SVM, CNN, RNN, and BERT. Pre-processed datasets are utilized to train and evaluate the models. To evaluate the ability of each model to reliably identify cyberbullying in social media data, performance metrics such as F1 score, recall, precision, and accuracy are used. The findings of this study demonstrate the efficacy of different ML and DL models in monitoring cyberbullying in social media data. Among the models evaluated, the BERT model exhibits exceptional performance, achieving the highest accuracy rates of 88.8% for binary classification and 86.6% for multiclass classification.
Policy (In)Consistency and Sustainable Development Goals in Africa: A Systematic Literature Review

The attainment of Sustainable Development Goals hinges on the alignment of SDGs policies. This systematic literature review delves into the intricate relationship between policy inconsistency and sustainable development goals in Africa. Through this study, we unveil the challenges, implications, and the potential pathways that emerge at the intersection of policies and the quest for sustainable development in Africa. Our investigation takes a close look at the diverse dimensions of policy coherence, encompassing economic, social, and environmental considerations, and how these dimensions impact the progress of SDGs. Employing a systematic review approach, we meticulously filtered through 1745 results from databases, selecting 353 articles for a comprehensive analysis. Our findings underscore the significant role that policy inconsistency plays in impeding the attainment of SDGs in Africa. We propose an approach anchored on the alignment of SDGs policies and each goal of SDGS for the attainment of the 2030 SDGs agenda in Africa.
Website Quality Analysis Using Modified Webqual Method and Importance Performance Analysis on SITU TAK Website

Technological developments affect information services, such as website. Information services on the website make it easy to convey information widely. Therefore, the quality of the website can affect the information services. This research assessed one of Telkom University's academic service websites, namely SITU Student Activities Transcript (SITU TAK). The purpose of this study was to measure the quality of the website, user satisfaction, and the factors that can increase the user satisfaction. This study employed Webqual 4.0 method as the indicator and Importance-Performance Analysis (IPA) for grouping the factors based on the quadrant of IPA. Before grouping the data, the data first passed the validity test, reliability test, and gap analysis between user perceptions and expectations. Therefore, it can strengthen the conclusions and recommendations resulted from this study. After conducting this study, the final results were obtained, which stated that SITU TAK website still did not meet the expectations of its users. This can be seen in the results of the gap analysis calculation with a value of -0.63, which means that the level of importance or expectations of the users is still higher than the performance of the website.
Ring Oscillator Based True Random Number Generator

Abstract: A true random number generator (TRNG), also known as a hardware random number generator (HRNG), does not use a computer algorithm. Instead, it uses an external unpredictable physical variable such as stochastic models to generate random numbers. Here it gathers data from random electronic signals. Then, the data is converted into digital form and any patterns registered are removed to make it random. This data is used to create random numbers. It is mainly used in Cryptographic Security, authentication, secure communications, e-commerce transactions, Digital Signatures etc. In Existing Method, LFSR based TRNGs generate pseudo random numbers. They produce sequences of bits that appear random but are deterministic and repeat after a certain number of cycles known as the period. They are easy to predict and are not suitable for high security applications. This project aim is to overcome such circumstances, we use TERO(Three Edge Ring Oscillator) based TRNG. Three edges are simultaneously injected by each inverting NAND stage with enable signal Run. These edges will have an identical mean period since they propagate through the same stages. The TERO generates an oscillating signal with a frequency determined by the delay of inverters. TDC (Time to Digital Converter) is used to precisely measure the time intervals between the rising edges of TEROs output signal. Then, it converts time intervals into digital values, effectively generating truly random numbers. Power consumption depends on factors like operating frequency and load capacitance. The Software used is Xilinx Vivado/Xilinx ISE Tools. Here the Proposed Method exquisitely balances Low design effort and resource consumption with high throughput and high randomness.
Witnessing edge modes in trimerized circuit quantum electrodyamic lattice

Abstract We propose a scheme to investigate and witness edge modes of general one-dimensional (1D) photonic trimers in a circuit quantum electrodynamic (QED) lattice. These in-gap edge modes are strictly and analytically solved and the criteria for their emergence are indicated respectively. Moreover, the energy spectrum of the system shows two different regimes characterized by a discrepancy in the number of edge modes. Specifically, while there are always a couple of edge modes only present at one single boundary in both the regimes, one of the regimes also shows another pair of edge modes localized on the opposite boundary. Furthermore, these edge modes are witnessed with the aid of continuous-time quantum walks and average photon number measurements. Our scheme provides a comprehensive method for studying the edge modes of matter.
Implementasi Pengelolaan Bantuan Operasional Sekolah Reguler pada Peningkatan Sarana Prasarana dan Kualitas Pembelajaran di SD Yapis Kota Bogor

The purpose of this study was to describe the management of Reguler BOS at SD Yapis Bogor City and its impact on improving infrastructure and the quality of learning at SD Yapis Bogor City.The approach used in this study is a qualitative approach with descriptive methods. The resource person consists of 6 people with positions, Head of school, Deputy Head of Curriculum, Deputy Head of Student Affairs, treasurer, operator and teacher at fifth grade. The results showed that the management of Reguler BOS increased the infrastructure at SD Yapis. In 2023 Yapis already has a Computer Lab with 12 sets of computers along with tables and chairs. While in the previous year there were only 6 sets. This also affects the increase in ANBK results. ANBK is an indicator of improving the quality of learning for elementary school level students which is implemented in grade 5 students. From the 2023 Education Report Card, the proportion of students with numeracy skills reaching a minimum competency increased by 42.86%. Where in 2022 it will be 23.33 and in 2023 it will be 33.33.
Analyzing credit card fraud cases with supervised machine learning methods: logistic regression and Naive Bayes

Frauds involving credit cards are simple and effortless to target. With the rise of online payment credit cards have had a huge role in our daily life and economy for the past two decades and it is an important task for companies to identify fraud and non-fraud transactions. As the number of credit cards grows every day and the volume of transactions increases quickly in tandem, fraudsters who wish to exploit this market for illegitimate gains have come to light. Nowadays, it’s quite easy to access anyone’s credit card information, which makes it simpler for card fraudsters to do their crimes. Thanks to advances in technology, it is now possible to determine whether information gained with malicious intent has been used by looking at the costs and time involved in altering account transactions. The Credit Card Fraud analysis data set, which is obtained from the Kaggle database, is used in the modeling process together with The Logistic regression method and Naive Bayes algorithms. Using the Knime platform, we are going to apply machine learning techniques to practical data in this study. The goal of this study is to identify who performed the transaction by examining the periods when people use their credit cards. The Logistic regression approach and the Naive Bayes method both had success rates of 99.83%, which is the highest. The two methods’ results are based on Cohen’s kappa, accuracy, precision, recall, and other metrics.
Technologies for Designing Professional Educational Training Programs for the Digital Economy

In this article, the authors consider the issues of training qualified personnel for the digital economy at the present stage of higher education development. The article describes the relationship between the emergence of new technologies and high-tech industries and the emergence of new directions and specialties in the field of higher education, requiring students to master new competencies. The concept of "competence" and the problems of implementing the competence approach in the system of higher professional education are investigated. The main definitions and principles of the competence approach to the design of educational outcomes in higher education are outlined. The analysis of published studies and presented software products devoted to the design of curricula is given. An important analytical conclusion is that one of the important points in the development of educational programs is to focus on the needs of production in young professionals with a certain set of competencies, but now most programs of higher and secondary vocational education do not have time to take into account the latest achievements in the technological and industrial spheres. The development of students’ competencies is the goal of educational programs, therefore they should be structured in such a way as to ensure the consistent and systematic development of the necessary skills and knowledge. The authors note that the competence approach in higher professional education is implemented in the form of an approximate basic educational program that forms a model of a specialist whose competencies must meet production standards to perform specific labor functions. The article sets the task to form a set of disciplines that is optimal in terms of labor intensity and levels of competence formation, allowing the most effective implementation of the competencies of a student in the bachelor’s degree direction, taking into account the level of formation and limitations of the curriculum set by the employer. The mathematical model of solving the problem and the technology of designing the optimal competence model of the graduate, taking into account the requirements of the employer, are presented.
Recursion Algorithm for Highlighting the Maximum Clique of the Graph

The work considers the polynomial algorithm for calculating the maximum clique of the non orientable graph. The algorithm is based on the properties of the subspace of sections and the subspace of cycles belonging to the the space sugrapha of a non — unoriented graph. It is shown that the algorithm for the allocation of the set of maximum clicks of the graph has an exponential computational complexity. Examples of highlighting the maximum clique of the graph are considered.
Implementation of Archives Digitalization through Belajar.id Account in Improving Data Organization

The purpose writing this article is to understand the process of structuring digitized data at SDN 8 Rambang, due to archive services conventional which is ineffective and we are trying to change to a digital archive service. Due to the preservation of archives in the current era of Society 5.0 leads to how the public can access archival information that is capable of implementing technology in it. Archive arrangement includes several types of archives including textual, audio and audio-visual archives. The process of moving current archive contents saving it in different media, takes place continuously from old media to new media. Both documents and archives that are in digital format or still analog. Document digitization/archives are a process of change from conventional ones in various styles and forms to electronic archives.
Developing the Contribution of Information Processing Activities in Forming Persuasion

Research on persuasion or attitude change has been approached through singleprocess models. The focus of this research is to explain more clearly that persuasion only occurs through a process that is directly caused by social influence. However, the use of this approach produced contradictory results. On the one hand, social influence increases persuasion, but other research finds the opposite. This article discusses the possibility of using a dual-process model approach, which is expected to address the inconsistency of social influence on persuasion in single-process models. Dual process models are proposed to be able to provide new explanations regarding the possibility of multiple effects of social influence variables on persuasion by including information processing activity variables. Through the dual process model approach, it is possible to explain that a certain effect can be caused by different processes and that a certain variable can work differently and in different situations. Social influence has different mechanisms for determining persuasion, which can act as a simple cue when the thinking level is low and can have more than one role (multiple roles) when information processing activities are at a high thinking level.&#x0D; Keywords: elaboration likelihood models, information processing, persuasion, social influence
The Importance of Studying Spontaneous Speech in Computational Linguistics

This scientific work provides information on the importance of studying spontaneous speech in computational linguistics. Studying spontaneous speech has numerous practical implications. The ramifications of spontaneous speech analysis are extensive, ranging from improving voice assistants and speech-to-text systems to enhancing human-computer interaction. An examination of spontaneous speech in computational linguistics offers a more authentic depiction of language usage, poses difficulties for current models, and opens up fresh opportunities for enhancing the precision and adaptability of language processing systems. The integration of spontaneous speech analysis will be crucial in developing the discipline of computational linguistics as technology progresses.