Simple Interface Research Articles

Designing a Chatbot Application Using the Flask Framework and Rule-Based Algorithm

This research aims to develop a website based chatbot application implemented at PT. International Hardware Indo using the Flask framework and Rule-Based algorithm. The application is designed to improve customer service efficiency by providing quick answers to frequently asked questions, reducing the workload of customer service, and addressing operational time constraints. The chatbot development process follows the Waterfall methodology, which includes requirements analysis, design, implementation, testing, and maintenance. The Rule-Based algorithm is employed to map user input to the company dataset through logical "IF-THEN" rules. The research results demonstrate a chatbot accuracy rate of 90%, as measured through black box testing, despite limitations in handling input outside the trained dataset. The chatbot is designed with hybrid navigation, a simple interface, and cross-platform device compatibility. This research contributes to the development of chatbot based information systems that are widely accessible to customers, thereby enhancing user experience and improving operational efficiency for the company.

Backward stepwise ridge regression and the Lasso: how do they do as finders of true regressors and their coefficients?

For regressor subset selection, three algorithms are introduced. Two use ridge regression (RR), with Lawless and Wang’s parameter ( k LW ). The third (RLasso) is Lasso, but with k LW or Hoerl, Kennard, and Baldwin’s tuning parameter ( k HKB ). Backward stepwise elimination (BSE) first fits RR to the full data. Then, the most insignificant regressor is dropped; and RR is fit to the remaining regressors. This repeats until no insignificant regressor remains. Backward stepwise group elimination (BSGE) RR works exactly like BSE RR except that all insignificant regressors are dropped each time. It is practical to do signless coefficient t -testing with a p -value of 15%. Lasso estimation uses Mallows C p criterion-based parameter ( k Cp ). Accuracy in predicting and finding coefficients are assessed by simulation because the assessment is mathematically intractable. BSE RR, BSGE RR and RLasso surpass Lasso. BSE RR predicts better than BSGE RR. When k Cp exceeds k LW or k HKB , RLasso prediction accuracy increases even more. RLasso with k HKB predicts better than RLasso with k LW . Napier’s profound idea is invoked, by introducing computer-simulated “regression tables” to locate input-data regions for maximizing the chances for model discovery. Championing the oracular property in finite samples is cautioned. RR uses “elementary” arithmetic to surpass Lasso, advancing human-machine interface simplicity.

АНАЛИЗ ПЕРСПЕКТИВНОСТИ ТЕРРИТОРИИ СИХОТЭ-АЛИНСКОГО ПОДВИЖНОГО ПОЯСА (ХАБАРОВСКИЙ КРАЙ) НА ОБНАРУЖЕНИЕ РОССЫПЕЙ МЕТОДОМ БАЗИСНЫХ ПОВЕРХНОСТЕЙ С ПОМОЩЬЮ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ ENOE И MAXENT

The application of certain research methods to determine the potential of the area for certain types of minerals requires specialty software that must be compatible with common types of data and supported by major operating systems, as well as have a simple graphical interface for comfortable work of a wide range of users. In addition to research tools, the software program should have the ability to save results in common file formats. The method of base erosion surfaces is based on topographic computation using a detected erosion network. It facilitates outlining of the local zones of Neotectonic movements and computing of their amplitudes. The compiled maps of different order base erosion surfaces and their differences enable the buried intrusions of potentially ore bearing nature and placer sources to be revealed. The paper examines the ENOE 0.6a software and its application to the analysis of ore-bearing structures of the Sikhote-Alin mobile belt using as an example gold placers in the zone adjacent to the Bekchiul multiphase pluton that hosts the large Mnogovershinnoe gold-silver deposit. ENOE allowed us to combine datasets describing Neotectonic processes and development of the erosion network. Based on the obtained datasets, geological data included, a predictive machine learning model developed by the maximum entropy method was trained and tested which made it possible to delineate the known hard-rock and placer targets. Model validation and testing have shown its applicability for preliminary territory analysis.

The use of Google web applications to create a learning environment in war conditions

Introduction: In the modern world the digitalization of educational trends offers many mechanisms for implementation. At the same time their use depends on many conditions. The purpose of this article is to study the peculiarities of using Google web applications to create an accessible and interactive learning environment in wartime. The main objective are using Google tools to implement education in wartime, describe the results of their use, finding its efficiency. Method: The realization of this goal involves the use of certain scientific methods. In particular, we are talking about the use of a survey and the method of content analysis of scientific literature, which made it possible to compare the Ukrainian experience with the existing paradigms of perception of Google tools in education. Results: The study results show that Google web applications are an important part of the modern educational process. All applications have a simple and user-friendly interface. The measurements showed that Google Classroom (33.33%) and Google Meet (26.67%) are the most used applications. This proves their important role in organizing online classes both at the organizational and direct learning levels. Teachers also use Google Drive, Google Docs, and Google Forms, which fulfill their function in organizing the modern educational process. Conclusion: The conclusions note that the importance of further research is dictated by the need to take into account new changes and capabilities of the tools.

A simple interface circuit for edge inference kernels based on SC-LNO memristor crossbar

A simple interface circuit for edge inference kernels based on SC-LNO memristor crossbar

DOG BREED CLASSIFICATION USING TRANSFER LEARNING

“Dog Breed Classification using Transfer Learning “ the project focus on accurately classifying dog breeds is a challenging computer vision task due to the subtle differences between breeds and the high variability within breeds. This project addresses these complexities using transfer learning with the Inception V3 model, a state-of-the-art convolutional neural network (CNN). Traditional machine learning approaches often struggle with subtle inter-breed differences and require large labeled datasets and significant computational resources. Training deep learning models from scratch is typically infeasible, which makes transfer learning an ideal solution. By using a pre-trained Inception V3 model, this approach leverages learned features from large-scale datasets like ImageNet and fine-tunes them for dog breed classification, reducing trainingtime while enhancing accuracy. The fine- tuned Inception V3 model undergoes transfer learningwhere initial layers remain frozen to retain general features, and fine-tuning optimizes the later layers for specific breed classification. To make the solution accessible and user-friendly, a Flask web application is developed. Users can upload dog images and receive breed predictions in real time, with the backend integrating the trained model to process the images, extract features, and output predictions. The application is scalable and practical for diverse deployment scenarios, such as pet identification, educational purposes, or veterinary applications. This project not only highlights the efficiency of transfer learning in reducing training time but also demonstrates how AI can be applied effectively in real-world challenges. Combining advanced deep learning with a simple web interface bridges the gap between complex machine learning models and practical applications, emphasizing accuracy, efficiency, and accessibility in pet care and animal research. Key Words: Inception v3, fine-tuning , CNN , flask

Desain dan Implementasi Aplikasi Kasir Rental Mobil Menggunakan Visual Studio

This study presents an analysis of the development of a Visual Studio-based Car Rental application that aims to simplify the online car rental process. By increasing the need for more efficient transportation solutions, the app is designed to overcome obstacles in car rental, such as distance and information limitations. The methods used in this study include interviews with users and service providers, observation of existing rental practices, and literature related to information technology. The development results show that this application has a simple and user-friendly interface, is able to manage customer and vehicle data in a structured manner, and improves operational efficiency for service providers. In conclusion, the Car Rental application not only provides convenience for users but also supports the growth of the car rental business in the digital era.

THE LEGAL NATURE OF E-COMMERCE IN THE REPUBLIC OF UZBEKISTAN

In modern realities, the vast majority of consumers prefer to receive and pay for goods, works and/orservices without leaving home. Commercial banks and other organizations actively provide assistance, creatingconvenience for the public, in the development, development and application of electronic payment infrastructure.Digitalization of financial services is designed to reduce costs and improve the efficiency of the system,digitalization allows you to increase the number of people served, even in rural regions, due to greater speed andease of use of a simple interface. The steady advancement of the economy into the virtual space – its rapiddigitalization around the world causes an immediate demand for all market participants to make the necessarymodifications to the content of their activities. Of course, commercial banks, which are the building blocks of thematerial development of society, did not stand aside either.

Student Perceptions of ChatGPT Integration in the Digital Age

The digital era presents new opportunities in education, including the integration of artificial intelligence (AI) technologies such as ChatGPT. This study aims to explore students' perceptions of using ChatGPT in face-to-face learning through a literature review approach. The analysis draws on various reliable sources, including scientific journals, books, and relevant research reports.The findings indicate that ChatGPT is considered beneficial for assisting students in understanding materials, completing tasks, and enhancing learning motivation. This technology is also regarded as user-friendly due to its simple interface and quick responses. However, several challenges were identified, such as limited digital literacy, the risk of inaccurate information, and potential misuse as a shortcut without deeper engagement with the material. The study concludes that ChatGPT holds great potential to support face-to-face learning, especially when used wisely under teacher guidance. To maximize its effectiveness, efforts should be made to improve students' digital literacy, establish clear usage policies, and provide guidance during the learning process.

DensToolKit2: A comprehensive open-source package for analyzing the electron density and its derivative scalar and vector fields.

In this article, we provide details of the suite DensToolKit-v2, which consists of a set of cross-platform, optionally parallelized programs for analyzing the molecular electron density (ρ), as well as different fields and chemical indices derived from it. Notably, with this version, the user can compute the Non-Covalent Interaction index, the Density Overlap Regions Index, and fields related to single-spin-type molecular orbitals, such as the spin density. In addition, DensToolKit-v2 includes several programs for analyzing other less-known fields, such as the Density Matrix of order 1, the two-electron pair density function, and the Fourier transforms of these fields, that is, functionals in momentum space. A new sub-program to compute integrated properties of each of the fields released in the suite is included. A simple graphical user interface is released, which eases the visualization of ρ critical points topology. Most interestingly, this version includes a program that renders estimations of pKa's of carboxylic acids and pKb's of amines (primary, secondary, and tertiary) through refined relations between experimental data and the molecular electrostatic potential computed at isosurfaces of ρ. Details related to the speed of the programs and a few examples of how to use the program in workflows are discussed, and the source code is released through a git repository under the GPLv3 terms.

Exploring the role of adamantane core in the mesophase formation of trimers

ABSTRACT We report the synthesis and phase behaviour of liquid crystalline trimers containing a 1,3-adamantane unit in the central core with 4-n-hexylbiphenyl and 4-cyanobiphenyl as wing mesogens. These materials with even parity spacers form hexagonal smectic phases (B and E). As shown by conformational simulation using a simple graphical user interface tool, their liquid crystal behaviour appears to be primarily driven by their molecular shape. The presence 1,3-adamantane unit in the central core of these trimers leads to narrow distribution of possible conformers with domination of elongated forms that drive the formation of hexagonal smectic phases. The odd parity trimers have a broad distribution of possible conformers that govern the formation of nematic phase or prevent liquid crystalline behaviour.

Setonix: Blockchain-Based Hierarchy Domain Name System for Web3

DNS is an essential component for internet access, but the traditional centralized structure is not suitable for the rapidly growing Web3 ecosystem. Currently, Web3 services rely on Web2-based access paths, revealing limitations such as the Single Point of Failure (SPoF) issue and the compromise of decentralization principles. To address these issues, this paper proposes Setonix, a blockchain-based decentralized DNS. Experimental results of the implemented Setonix showed that it had only a marginal performance difference of up to approximately 5% compared to Google and Cloudflare DNS. Additionally, it demonstrated over 95% lower latency compared to Handshake DNS, proving its high efficiency. Setonix eliminates dependency on centralized access paths and brings the hierarchical address structure of existing domain names into the blockchain network, adhering to legacy systems to minimize user resistance. Additionally, it provides a blockchain-based architecture that supports cross-compatibility between traditional domain names and Web3 domain names through a simple interface. This design significantly enhances the scalability and accessibility of Web3, presenting a new standard for decentralized internet services. By doing so, it helps to establish a foundation that contributes to the growth and innovation of the Web3 ecosystem.

Open-source Electrophilic Fragment Screening Platform to Identify Chemical Starting Points for UCHL1 Covalent Inhibitors

Target-based screening of covalent fragment libraries with mass spectrometry has emerged as a powerful strategy to identify chemical starting points for small molecule inhibitors or find new binding pockets on proteins of interest. These libraries span diverse chemical space with a modest number of compounds. Screening covalent fragments against purified protein targets reduces the demands on the mass spectrometer with respect to absolute throughput, detection limit, and dynamic range. Given these relaxed analytical requirements, we sought to develop an open-source, medium-throughput mass spectrometry system for target-based covalent fragment screening. Our platform comprises automated, dual LC desalting columns integrated with electrospray ionization for rapid sample introduction and mass spectrometry detection. The system is operated through a simple python graphical user interface running on commodity microcontroller boards which allow integration with diverse liquid chromatography and mass spectrometry instruments. We provide scripts for fragment pooling, construction of sample batches, along with routines for data processing and visualization. The system enables primary screening of ∼10,000 covalent fragments per day in pooled format. In a proof-of-concept study we executed primary and secondary screens to identify 27 hit fragments against UCHL1, a deubiquitinating enzyme that is emerging as a drug target of interest across multiple clinical indications. We validated and triaged these covalent compounds through a series of orthogonal biochemical and chemoproteomic assays. The most promising chloroacetamide covalent fragment inhibited UCHL1 activity in vitro (IC50 <5 µM) and exhibited dose-dependent binding along with good selectivity against 57 cellular DUBs as quantified by activity-based protein profiling.

Fingerprint-Authenticated Voting System with Real- Time Data Sharing via Android Application

Abstract: This system presents a novel electronic voting solution tailored for college-level elections, integrating biometric authentication with real-time data transmission. The system is built around an ESP32 microcontroller, a fingerprint sensor for secure user authentication, and an OLED display for interaction. Upon successful authentication, the ESP32 activates Bluetooth to connect seamlessly with an Android application, providing users with a simple interface for voting, polling, or submitting feedback. This system improves security and transparency by ensuring only authorized individuals can vote, while real-time data transmission fosters immediate result processing and accountability. By combining biometric security, ease of use, and instantaneous communication, the system promotes an inclusive and participatory governance environment within educational institutions

SeqLengthPlot v2.0: an all-in-one, easy-to-use tool for visualizing and retrieving sequence lengths from FASTA files.

Accurate sequence length profiling is essential in bioinformatics, particularly in genomics and proteomics. Existing tools like SeqKit and the Trinity toolkit provide basic sequence statistics but often fall short in offering comprehensive analytics and plotting options. For instance, SeqKit is a very complete and fast tool for sequence analysis, delivering useful metrics (e.g. number of sequences, average, minimum, and maximum lengths) and can return sequences either shorter or longer (but not both at once) for a given length. Similarly, Trinity's Perl-based scripts provide detailed contig length distributions (e.g. N50, median, and average lengths) but do not include the total number of sequences or offer graphical representations of the data. Given that key sequence analysis tasks are often distributed across multiple tools, we introduce SeqLengthPlot v2.0, an all-in-one, easy-to-use Python-based tool. Through a simple command-line interface, this straightforward tool enables users to split input FASTA files (nucleotide and protein) into two distinct files based on a customizable sequence length cutoff. It also automatically retrieves the resulting FASTA files, generates length distribution plots, and provides comprehensive statistical summaries. SeqLengthPlot_v2.0.2 can be accessed at https://github.com/danydguezperez/SeqLengthPlot/releases/tag/v2.0.2.

Silent Speech Eyewear Interface: Silent Speech Recognition Method Using Eyewear and an Ear-Mounted Microphone with Infrared Distance Sensors.

As eyewear devices such as smart glasses become more common, it is important to provide input methods that can be used at all times for such situations and people. Silent speech interaction (SSI) has the potential to be useful as a hands-free input method for various situations and people, including those who have difficulty with voiced speech. However, previous methods have involved sensor devices that are difficult to use anytime and anywhere. We propose a method for SSI that involves using an eyewear device equipped with infrared distance sensors. The proposed method measures facial skin movements associated with speech from the infrared distance sensor mounted on an eyewear device and recognizes silent speech commands by applying machine learning to time series sensor data. The proposed method was applied to a prototype system including a sensor device consisting of eyewear and ear-mounted microphones to measure the movements of the cheek, jaw joint, and jaw. Evaluations 1 and 2 showed that five speech commands could be recognized with an F value of 0.90 and ten longer speech commands with an F value of 0.83. Evaluation 3 showed how the recognition accuracy changes with the combination of sensor points. Evaluation 4 examined whether the proposed method can be used for a larger number of speech commands with 21 commands by using deep learning LSTM and a combination of DTW and kNN. Evaluation 5 examined the recognition accuracy in some situations affecting recognition accuracy such as re-attaching devices and walking. These results show the feasibility of the proposed method for a simple hands-free input interface, such as with media players and voice assistants. Our study provides the first wearable sensing method that can easily apply SSI functions to eyewear devices.

TDS Simulator: A MATLAB App to model temperature-programmed hydrogen desorption

We present TDS Simulator, a new software tool aimed at modelling thermal desorption spectroscopy (TDS) experiments. TDS is a widely used technique for quantifying key characteristics of hydrogen-material interactions, such as diffusivity and trapping. However, interpreting the output of TDS experiments is non-trivial and requires appropriate post-processing tools. This work introduces the first software tool capable of simulating TDS curves for arbitrary choices of material parameters and hydrogen trap characteristics, using the primary hydrogen diffusion and trapping models (Oriani, McNabb–Foster). Moreover, TDS Simulator contains a specific functionality for loading experimental TDS data and conducting the inverse calibration of a selected transport model, providing automatic estimates of the density and binding energy of each hydrogen trap type in the material. In its first version, TDS Simulator is provided as a MATLAB App, which is made freely available to the community and provides a simple graphical user interface (GUI) to make use of TDS Simulator straightforward. As reported in the present manuscript, the outputs of TDS Simulator have been extensively validated against literature data. Demonstrations of automatic determination of trap characteristics from experimental data through the optimization tool are also provided. The present work enables an efficient and straightforward characterization of hydrogen-material characteristics relevant to multiple applications, from nuclear fusion to the development of hydrogen-compatible materials for the hydrogen economy. TDS Simulator can be downloaded from https://mechmat.web.ox.ac.uk/codes.

Gaze Zone Classification for Driving Studies Using YOLOv8 Image Classification.

Gaze zone detection involves estimating where drivers look in terms of broad categories (e.g., left mirror, speedometer, rear mirror). We here specifically focus on the automatic annotation of gaze zones in the context of road safety research, where the system can be tuned to specific drivers and driving conditions, so that an easy to use but accurate system may be obtained. We show with an existing dataset of eye region crops (nine gaze zones) and two newly collected datasets (12 and 10 gaze zones) that image classification with YOLOv8, which has a simple command line interface, achieves near-perfect accuracy without any pre-processing of the images, as long as a model is trained on the driver and conditions for which annotation is required (such as whether the drivers wear glasses or sunglasses). We also present two apps to collect the training images and to train and apply the YOLOv8 models. Future research will need to explore how well the method extends to real driving conditions, which may be more variable and more difficult to annotate for ground truth labels.

IAMSAM: image-based analysis of molecular signatures using the Segment Anything Model

Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.

Enhancing Healthcare with Personalized Recommendations: A Machine Learning Approach to Symptom-Driven Diagnosis

This paper introduces the Personalized Medical Recommendation System, a machine learning-based platform that supports users in symptom analysis and personalized healthcare management. Users input symptoms through a simple interface, and machine learning models predict potential diseases. The system offers tailored recommendations, including suggested medications, prescription details, and exercise routines. Built on a Flask web application, it provides accessible healthcare support with a strong focus on data privacy and security. Continuous improvement through user data refines predictive accuracy, advancing personalized medicine. This study outlines the system's design, implementation, and performance, underscoring its potential to enhance health outcomes. Key Words: personalized healthcare, machine learning, disease prediction, medical recommendation system, symptom analysis, data privacy.