bluuebunny/crossref_metadata_2025 huggingface dataset:
The potential of using museum podcasts for educational purposes for intercultural exchange
Authors: Kalizhanova A.N.
Published: February 2024

Digital technologies take museums beyond the academic framework and expand their capabilities in the educational sphere. Web 2.0 tools allow museums to target the current generation of young people as well as transfer the basic values of humanity to develop an all-sided, harmonious personality capable of lifelong education. Museum podcasting for educational purposes seems to be authentic intercultural content that helps to establish efficient intercultural communication between students from Kazakhstan and other countries, aimed at popularizing cultural heritage and promoting social inclusion and civic engagement. Eleven EFL students agreed to take part in recording five museum podcasts for educational purposes to improve listening and speaking skills, work on interactive student problems, and create a dialogic space that affords student-teacher agency. Student involvement resulted in the development of a meaningful strategy for creating educational museum podcasting, where each episode deals with the art-historical findings of the researchers from the Museum of Archeology and Ethnography of Buketov Karaganda University. We think that by using this digital content during curatorial hours, university teachers will help spread awareness of Central Kazakhstan's historical and cultural heritage among students and young people both domestically and abroad.
Особенности развития гибких навыков у студентов высшего учебного заведения
Authors: Махметова Д.Т.
Published: February 2024

За последнее десятилетие научные исследования в области образования подчеркивали важность развития ряда неакадемических качеств, таких как способность сотрудничать и выстраивать конструктивные взаимоотношения с другими людьми, решать проблемы, часто их называют унифицированными навыками или гибкими навыками. Группа гибких навыков (Soft skills) включает в себя большую группу навыков, которые не зависят от учебных программ, но оказывают большое влияние на профессиональное становление личности и успешное развитие твердых (профессиональных) навыков. В отличие от академических знаний, которые являются предметными и имеют формальное оценивание, гибкие навыки напрямую не зависят от учебных программ, но развиваются в них. Таким образом, гибкие навыки рассматриваются как навыки, которые могут быть использованы в широком диапазоне рабочей среды. На основании изложенного выше, необходимо определить направление деятельности в условиях образовательного процесса в высшем учебном заведении, которое будет способствовать комплексному развитию гибких навыков обучающихся. В работе обоснована приоритетность развития гибких навыков как процессуальная характеристика образовательного процесса университета. Развитие гибких навыков обучающихся в рамках в университете, с одной стороны, рассматривается как компонент учебных дисциплин, а с другой — в качестве самостоятельного процесса с целью развития спектра навыков, необходимых для различных видов деятельности. Таким образом, изучая проблему развития комплекса гибких навыков обучающихся университета, названы технологии развития гибких навыков, которые станут структурными элементами модели.
Mekanisme Pemanfaatan Gelombang Mikro Navigasi Kapal dalam Pendayagunaan Sinar Radar Gelombang Elektromagnetik
Authors: Triwulandari Ike , Sudarti Sudarti , Yushardi Yushardi
Published: February 2024

Gelombang mikro merupakan gelombang elektromagnetik super tinggi yang terjadi antara 3 GHz sampai 300 GHz. Keunggulan gelombang mikro adalah panjang gelombangnya yang pendek sehingga dapat mengalir dengan lebih cepat dari pada yang lain.  Radar adalah contoh utama penggunaan gelombang mikro. Radiasi gelombang mikro digunakan dalam navigasi kapal untuk mengukur kedalaman laut dan mengidentifikasi ancaman seperti terumbu karang, benturan, gelombang, dan tubrukan dengan kapal lain. Selain itu, navigasi kapal menggunakan teknologi radar untuk memastikan perjalanan kapal aman. Dengan demikian, para awak kapal dapat melihat situasi yang dapat mengancam keselamatan saat kapal berlayar karena gelombang radar dapat mendeteksi sinyal peta sebagai penunjuk arah dengan media Maps (GPS).Tujuan dibuatnya artikel ini yaitu agar dapat dijadikan sumber pengetahuan bagi semua orang mengenai pemanfaatan gelombang mikro navigasi kapal dalam pendayagunaan sinar radar gelombang elektromagnetik. Penelitian ini dilakukan menggunakan metode studi literatur yaitu dengan mencari dan mengumpulkan  informasi melalui beberapa jurnal, buku, dan internet untuk memperoleh informasi yang akurat dan relavan.
Mekanisme Kinerja Pemanfaatan Instalasi Pengolahan Air Limbah (Ipal) Sebagai Solusi Pengelolaan Air Sungai
Authors: Pitaloka Ami Widya , Sudarti Sudarti , Yushardi Yushardi
Published: February 2024

Salah satu sumber daya alam yang penting bagi ekosistem dan kehidupan manusia adalah air. Beberapa variabel, termasuk limbah rumah tangga dan industri, dapat berdampak pada kualitas air sungai. Praktik pengelolaan yang baik sangat penting untuk menjaga kualitas air sungai, termasuk pengendalian air limbah sebelum dibuang ke sungai. Infrastruktur bernama Instalasi Pengolahan Air Limbah (IPAL) digunakan untuk mengolah air limbah sebelum dibuang ke sungai. IPAL memiliki beberapa tahapan pengolahan yang melibatkan berbagai mekanisme untuk memastikan kualitas air limbah yang dihasilkan memenuhi standar yang ditentukan. Salah satu tahapan penting dalam IPAL adalah kolam sedimentasi akhir. Mekanisme kerja kolam sedimentasi akhir melibatkan proses pengendapan, dimana partikel-partikel padat yang lebih berat akan turun ke dasar kolam dan membentuk lumpur atau endapan. Dengan mengendapkan partikel-partikel padat tersebut, kekeruhan air limbah dapat dikurangi sehingga kualitas air menjadi lebih baik. Dengan adanya IPAL, kualitas air limbah dapat ditingkatkan sebelum dibuang ke lingkungan. Hal ini penting untuk menjaga kesehatan masyarakat yang menggunakan air sungai untuk minum dan keperluan lainnya, serta untuk kelestarian sungai dan ekosistem sekitarnya.
The Global State of Democracy Indices: Technical Procedures Guide, Version 7 (2023)
Authors: Tufis Claudiu D. , Hudson Alexander
Published: February 2024

The Global State of Democracy is a report that aims to provide policymakers with an evidence-based analysis of the state of global democracy, supported by the Global State of Democracy Indices (GSoD Indices), in order to inform policy interventions and identify problem-solving approaches to trends affecting the quality of democracy around the world. This document revises and updates the conceptual and measurement framework that guided the construction of Version 7 of the GSoD Indices, which depicts democratic trends at the country, regional and global levels across a broad range of different attributes of democracy in the period 1975–2022. The data underlying the GSoD Indices is based on a total of 157 indicators developed by various scholars and organizations using different approaches, including expert surveys, standards-based coding by research groups and analysts, observational data and composite measures.
Bimbingan dan Konseling Menurut M. Quraish Shihab dalam Tafsir Al-Mishbah Qur’an Surat Al-Ashr Ayat 3
Authors: Angraeni Desi , Ibnudin Ibnudin , Rufaedah Evi Aeni , Himmawan Didik
Published: February 2024

Di era zaman modern ini manusia banyak sekali mengalami permasalahan yang sulit dan kompleks, terkadang manusia ingin keluar dari permasalahan, tetapi muncul permasalahan yang baru sehingga butuh seseorang untuk memberikan arahan, bimbingan, acuan agar dapat menyelesaikan permasalahan didalam kehidupannya. Menurut M.Quraish Shihab dalam Tafsir Al-Mishbah Qur’an Surat Al-Ashr Ayat 3 menjelaskan bahwa sesama manusia harus saling  menasehati dalam hal kebaikan, maka dari itu bimbingan dan konseling sangatlah dibutuhkan dalam hal tersebut. Kajian ini mengkaji tentang bimbingan dan konseling menurut Perspektif Al-Qur’an dalam Tafsir Al-Mishbah Surat Al-Ashr Ayat 3. Pada penelitian ini bimbingan dan Konseling Menurut Perspektif  Al-Qur’an Surat Al-Ashr Ayat 3 adalah berarti suatu aktifitas memberikan bimbingan, pelajaran, dan pedoman kepada individu yang meminta bimbingan (klien)untuk dapat mengembangkan potensi akal pikiran, kejiwaan, keimanan, keyakinan, serta dapat menanggulangi problematika hidup dan kehidupannya dengan baik yang berpandangan kepada Al-Quran dan As-Sunnah.
Diseño de estrategias de comercio electrónico para aumentar la competitividad en las microempresas de artesanías de San Juan de Pasto
Authors: Gustín Bolaños Juan Sebastián , Díaz Genoy Ángela María , Benavides Pupiales Luis Eduardo
Published: February 2024

El comercio electrónico hoy en día es muy importante para las empresas, ya que permite conocer al cliente sin necesidad de la presencialidad, puesto que la virtualidad se volvió un método de vida que ha facilitado el crecimiento del comercio, así como ha hecho posible el envío, el conocimiento y el pago del producto, ya sea para el comprador o, en el mejor caso el vendedor, impulsando en el mercado.

Las artesanías en la ciudad de San Juan de Pasto son de las más sobresalientes a nivel nacional; la ejecución de los artistas y sus diseños diferentes con distintos materiales han tenido un gran auge por no solamente estar bien hechos, sino también, por ser de muy buena calidad. Cada material, sea cuero o barniz, todos están calificados de la mejor manera, y este es un trabajo que ha transcurrido de generación en generación, pasando este hermoso arte a través de los años.

La mayoría de la economía de estos empresarios se basa en la venta de sus productos; sin embargo, por medio del comercio electrónico podrían generar mayores ganancias, dado que no hay catálogos virtuales de todas sus creaciones y no dan a conocer su trabajo; también podrían vender y recibir pagos virtuales; así su economía crecería.

Con el diseño de estrategias del comercio electrónico en las microempresas de artesanías de la ciudad de San Juan de Pasto, se busca que estas conozcan las distintas fuentes de comercio, no tan solo físico ni únicamente a nivel nacional, sino a nivel mundial. Distintas causas como la pandemia, han hecho crecer la manera virtual, de suerte que, el conocimiento de esta modalidad hará que crezca la empresa; por ese medio se dará a conocer plataformas como páginas web, para que distintas personas puedan conocer el producto y todo el proceso de producción, generando finalmente una empresa competitiva.
Caracterización de los artesanos del Carnaval de Negros y Blancos de la ciudad de Pasto
Authors: Villota Ángel Jisel , Gamboa Guerrero Sofía , Casanova Guerrero Ana Lucía
Published: February 2024

Desde hace tiempo, el Carnaval de Negros y Blancos se ha caracterizado por dar a conocer el trabajo y talento de cada grupo que le conforma, representando así, el patrimonio cultural de la población pastusa; sin embargo, para llevar a cabo esta festividad se requiere de unos elementos que estén disponibles para la elaboración y presentación, tales como: tiempo, presupuesto económico, materiales, talleres de trabajo y, sobre todo, un equipo que aporte todo su compromiso y el amor que se percibirá en la exposición del carnaval.
Analisis Spektogram Sinyal Suara Asli dan Suara Hasil Konversi Berbasis Derivative Gelombang Glotal
Authors: Hartono Sriwijanaka Yudi , Suyuti Saidah , Asmara Bambang Panji , Ashad Bayu Adrian , Hamzidah Nurul Khaerani
Published: February 2024

Ucapan manusia yang dihasilkan oleh suatu sistem produksi yang dibentuk oleh alat â€“ alat ucap manusia. Proses ini dimulai dengan formulasi pesan dalam otak pembicara (penutur), pesan tersebut selanjutny akan diubah menjadi perintah-perintah yang diberikan kepada alat-alat ucap manusia sehingga dihasilkan ucapan yang sesuai dengan pesan yang ingin disampaikan. Transformasi sinyal suara (speech) berbasis derivative gelombang glottal digunakan dengan metode Liljenctranct fant. Pada metode ini parameter yang berperang penting adalah open Quotient (OQ), Speed Quotiont (SQ), formant dan pitch.Â  Keempat parameter tersebut sangat berpengaruh dalam menentukan kualitas konversi suara.Â  Spektogram adalah suatu representasi dari warna suara atau spektral yang bervariasi terhadap waktu yang menunjukkan tingkat insitas energi spektral (density Spectral). Berdasarkan dari data base sinyal suara yang diproses secara pengolahan sinyal digital diperoleh adanya perbedaan ketajaman spektral pada spektogram antara sinyal asli dengan sinyal konversi, dimana tingkat ketajaman spektral warna hasil konversi lebih tinggi dari pada spektral suara asli.Â  Hal ini dikarenakan adanya penguatan intensitas lebih dari spektral akibat transformasi derivative dari gelombang glottal akibat parameter OQ, SQ, formant dan pitch.Â  Sinyal suara yang dihasilkan tidak berubah baik bentuk sinyal maupun durasinya, yang berubah hanyalah warna suaranya. Untuk membedakan suara hasil konversi darri suara pria lain dan suara wanita yaitu dengan merubah besarnya nilai keempat parameter tersebut secara tepat.
Perancangan Aplikasi Sistem Informasi Inventarisasi Aset Menggunakan QR Code (Studi Kasus Dinas Pendidikan Dan Kebudayaan Kota Parepare)
Authors: Ciptadi Chandra , Ilyas Muh. , Syahrir Syahrir
Published: February 2024

Penelitian ini bertujuan untuk membantu pihak pengelola barang inventaris aset pada Dinas Pendidikan dan Kebudayaan Kota Parepare dalam melakukan pencatatan barang inventarisasi secara cepat dan akurat. Selain itu, sistem yang dibangun dapat melakukan monitoring terhadap barang menggunakan scanner barcode QR serta dapat mengatur jumlah minimal stok, sehingga dapat melakukan pemesanan tepat waktu. Metode pengembangan sistem yang digunakan dalam penelitian ini yaitu Waterfall yang dimulai dengan mengalisa kebutuhan dari sistem, kemudian membuat perancangan sistem, setelah itu proses pembuatan sistem dilakukan dengan menyusun kode program. Untuk minimal stok metode yang digunakan yaitu Min-Max Stock. Setelah semua komponen sistem selesai selanjutnya adalah dengan melakukan pengujian terhadap sistem. Hasil dari penelitian ini didapatkan sebuah sistem informasi inventarisasi aset yang dapat melakukan monitoring barang masuk dan keluar menggunakan scanner QR code serta dapat mengatur jumlah minimal stok barang dan melakukan pencatatan data-data barang. Pengujian yang dilakukan menggunakan blackbox kepada tiga aktor yaitu kepala dinas, staff, dan operator terhadap fitur sistem, semua fitur yang ada dapat berjalan dengan baik sesuai dengan apa yang diharapkan.
Prototype Elevator 5 Lantai Berbasis PLC
Authors: Yudin Ayudila , Habib Muh. Asnan , Hamdani Hamdani , Alfira WS Wisna Saputri
Published: February 2024

Penelitian ini melibatkan perancangan dan pengujian prototype sistem elevator 5 lantai yang berbasis pada PLC. Dalam tahap perancangan, menggambarkan struktur utama elevator, termasuk kerangka elevator, sangkar elevator dengan sistem pendeteksi beban dan pemberat, serta komponen kunci dalam pintu elevator seperti gear, rel, dan dudukan motor DC. Selain itu, merancang rangkaian kontrol yang kompleks yang melibatkan PLC, tombol push button, sensor proximity, sensor infrared (IR), serta motor AC dan motor DC. Tombol pengendalian dapat memindahkan sangkar naik dan turun sesuai dengan tujuan yang dipilih oleh pengguna, dengan sensor proximity yang secara otomatis menghentikannya di lantai yang dituju. Pintu elevator juga berfungsi dengan baik, membuka secara otomatis ketika mendeteksi keberadaan objek di depannya dan menghentikan motor ketika pintu mencapai limit switch. Tujuan dari penelitian ini adalah untuk menciptakan prototype elevator yang mendekati fungsi lift yang digunakan di gedung bertingkat, dengan menggunakan PLC sebagai pengendali utama dan motor oriental 3 phasa sebagai komponen penggerak utama yang handal. serta memasukkan sensor load cell dengan kapasitas 1 kg untuk mengukur beban yang dimasukkan ke dalam lift, serta sistem pintu otomatis untuk meningkatkan keamanan dan kenyamanan. Penelitian ini bertujuan untuk memenuhi kebutuhan akan alat peraga dan pengujian sistem elevator yang canggih
Feature Extraction of Motor Imagery EEG Signals Based on PSD CSP Fusion
Authors: Hu Weixin , Geng Xiaozhong , Yue Mengzhe , Wang Linen , Zhang Xi
Published: February 2024

Aiming at the current problems of single feature extraction of motor imagery EEG signals and low accuracy of classification and recognition, a feature extraction method of motor imagery EEG signals based on the fusion of PSD and CSP (PSD-CSP) is proposed. Firstly, the FastICA algorithm is employed for artifact signal removal from the raw EEG data. Subsequently, features are extracted from the Power Spectral Density (PSD) and Common Spatial Pattern (CSP), followed by their serial fusion. Finally, a Support Vector Machine (SVM) classifier is used for classification. In binary motor imagery classification experiments on the BCI Competition IV Dataset I, the average classification accuracy reaches 91.43%, and comparative analysis with other methods demonstrates the feasibility of the proposed fusion algorithm.
Research on Electrical Equipment Overheating Fault Identification Based on Infrared Technology
Authors: Xu Zuowei , Zhu Dan , Zhang Zhong’ao
Published: February 2024

In order to improve the accuracy of identifying overheating faults in electrical equipment and shorten the response time, a method for identifying overheating faults in electrical equipment based on infrared technology is proposed. Firstly, infrared images of electrical equipment are collected through an infrared scanner. Secondly, in order to improve the quality of infrared images, feature extraction is performed on the collected infrared images. Finally, the infrared image is enhanced through nonlinear NSCT transformation, and the identification of overheating faults and defects in electrical equipment is completed by calculating the relative temperature difference of the tested electrical equipment. The experimental results show that compared to existing fault identification methods, this method can accurately identify overheating faults of various electrical equipment, and the identification time response is significantly shortened.
Optimization Method of Energy Storage Capacity of New Energy Vehicle Power Battery Based on Fuzzy Control
Authors: Wang Zhe
Published: February 2024

In order to meet the mileage requirements of new energy vehicles, it is necessary to improve the capacity of power batteries. Therefore, this paper proposes a new optimization method of energy storage capacity of power batteries of new energy vehicles based on fuzzy control. Firstly, according to the control block diagram of power battery, the fuzzy control method is used to control the power of power battery, and the relationship between the sum of power fluctuation out of limit and the change of battery rated energy storage capacity is obtained. Secondly, the objective function of energy storage capacity optimization of new energy vehicle power battery is constructed, and the constraints of energy storage capacity optimization of new energy vehicle power battery are constructed based on the constraints of battery charge and discharge power and SOC. The optimization of energy storage capacity can be realized by solving the objective function under constraints. Finally, a comparative verification experiment is carried out. The experimental results show that this method can effectively control the power fluctuation of power battery and improve the energy storage capacity of battery.
Design of Intelligent Underwater Cable Inspection and Control System
Authors: Feng Fang , Du Qing , Yang Qian
Published: February 2024

Underwater cables play an important role in marine energy development, island economic development, and national territorial security. Regular testing of them is a necessary means to ensure the safe and reliable operation of underwater cables. This article designs an intelligent underwater cable inspection control system by simulating the motion trajectory of an underwater robot through the motion of a small car. This system uses Raspberry Pi as the intelligent control core, and the computer can display real-time underwater cable images and related environmental data collected by the camera; Underwater intelligent devices use sensors to detect the magnetic field around the cable to determine whether there is a break point in the cable. The positioning module can determine the location of the cable fault. The intelligent device can also monitor the underwater environment temperature of the cable in real-time, collect image information of the cable in real-time, and facilitate intelligent interaction with the computer.
A Methodology for Generating and Optimizing Chain-of-Thought Based on Knowledge Graphs
Authors: Luan Qiushi
Published: February 2024

One of the critical indicators for assessing the practical applicability of large language models is their competency in vertical domain question-answering tasks. However, in real-world applications, fine-tuning these large models often compromises their inherent capabilities. Moreover, fine-tuning does not offer precise control over the model’s generated outputs.Consequently, enhancing the question-answering performance of large models in specialized domains has become a focal concern in the field. To address these challenges, this paper introduces a novel approach for generating and optimizing a “Chain-of-Thought”(CoT), leveraging domain-specific knowledge graphs. Specifically, we propose a Knowledge Graph-generated Chain of Thought (KGCoT) method that utilizes graph search algorithms to generate a chain of thought. This chain guides the injection of specialized knowledge into large language models and adapts the weightings based on user feedback, thereby optimizing subsequent graph searches.Heuristic searches are performed on the knowledge graph based on edge weights, culminating in the amalgamation of discovered entities and knowledge into a chain of thought. This KGCoT serves as a prompt to stimulate the large language model’s contemplation of domain-specific knowledge. Additionally, an adaptive weight optimization formula refines the chain’s weights in response to output feedback, thereby continually enhancing the quality of future search results and ensuring real-time optimization capabilities for the model.Through empirical evaluations conducted on publicly available datasets, the large language model ChatGLM, when prompted with a KGCoT, exhibited a 72.8% improvement in its BLEU score compared to its baseline performance. This outperformed other models like LLaMA and RWKV, unequivocally substantiating the efficacy of the proposed KGCoT method.
Sound Source Localization System Based on TDOA Algorithm
Authors: Wang Kefei , Zhang Mingxue
Published: February 2024

Sound source localization holds significant value in various fields such as drone navigation, speech recognition, audio surveillance, and more. This paper presents a sound source localization system based on TDOA algorithm. The system is designed to meet the requirements of low-cost, simplicity, and reliability in sound localization devices, and it has been experimentally validated. Initially, the TDOA algorithm is employed to estimate the time delays of the target sound source arriving at different microphones, which are then used to determine the source’s direction. The calculation of the source’s direction relies on spatial geometric relationships, enabling precise triangulation using TDOA measurements. To enhance system performance, we introduce Kalman filtering to correct the state estimates of the sound source target, further refining the estimated source position. Through the operation and fine-tuning of Kalman filtering, we successfully improve the system’s localization accuracy, achieving rapid and stable tracking of the sound source target. Experimental results demonstrate that the sound source localization system based on the TDOA algorithm offers significant advantages in terms of low cost and reliability, making it suitable for a wide range of applications.
Design of Financial Early-Warning Model Based on Decision Tree
Authors: Lu Zhankuan , Chen Yan , Zheng Fukang , He Ke , Zhang Yan , Zhang Weishan
Published: February 2024

To find out the key financial indicators of risks in the business process, a financial early warning model based on improved decision tree is proposed. The scheme improves the traditional ID3 algorithm and establishes a simplified financial index system. A decision tree model is constructed on multiple training subsets as a basic learner. Then the formal gain criterion is used to rank and reduce the dimensions of the financial indicators of the enterprise, thus avoiding the impact of redundant information. Finally, data mining and analysis technology is applied to establish the financial budget and early warning model. The experimental analysis results show that the financial early warning model based on the decision tree established in this paper has a high accuracy, and the overall accuracy of early warning reaches 75%. It can judge financial failures and financial risks, so that enterprises can do a good job in prevention as early as possible to avoid financial crisis.
Pharmaceutical Laboratory Course Scheduling System Optimization Based on Genetic Algorithm
Authors: Chen Shaohua
Published: February 2024

This article analyzes and assumes the construction of laboratory platforms in response to the specific requirements for intelligent management of pharmaceutical experiments and the experimental teaching requirements of pharmaceutical concepts. Then, the influencing factors and main constraints in the course scheduling problem are systematically discussed, a mathematical model of the course scheduling process is provided, and an overall framework for the solution method is also proposed. On this basis, the improvements are proposed to the algorithm, which mixed simulated annealing algorithm to improve the accuracy and convergence speed of the algorithm. Finally, the improved adaptive genetic algorithm is applied to the intelligent course scheduling system in universities, and the usability of the system is tested through empirical analysis. The comparison and analysis results between the improved genetic algorithm and the classical genetic algorithm indicate that our strategy can achieve good global optimal solution search ability, which provides better service for teaching management system of pharmaceutical laboratories.
A Project Risk Prediction Model Based on Bayesian Networks
Authors: Han Zehua , Yang Jian , Wan Xiang , Kang Junsheng , Wang Feihu
Published: February 2024

Contrast to the actual needs of risk assessment for large-scale engineering construction projects, a project risk prediction model based on Bayesian networks (BN) is proposed to predict the risks which affect progress. Firstly, the definition of a Bayesian network for construction project progress is provided, and relevant nodes such as work nodes and time nodes are introduced into the network model. Then, the principle of BN application in engineering progress is presented, and a specific risk management model is proposed. Finally, the Bayesian network model for schedule risk is improved by measuring the impact of interaction effects on project risk and calculating the conditional probability table for each node in the network. Empirical analysis is combined with engineering examples, which quantifies the probability of occurrence of various risk factors and conducts risk sensitivity prediction analysis. The results show that it can identify the degree of influence of relevant risk factors affecting the progress of complex projects, improve the accuracy of risk models, and reduce the degree of loss of engineering project risks, using BN models to study the advantages of risk uncertainty.
Business Credit Evaluation Model Based on Neural Network Under Financial Sharing Mode
Authors: Zhang Yan
Published: February 2024

To establish a scientific and effective enterprise credit evaluation method under the financial sharing mode, this paper proposes a commercial credit evaluation model based on neural network optimization. We analyze the implementation process of enterprise credit evaluation under the financial sharing model, as well as the common functional requirements of enterprise financial sharing service centers. On this basis, a corresponding indicator system is established using a commercial enterprise as a case study. The quantification method of discrete data and the normalization processing method of indicator data in the indicator system are provided. During the process of neural network processing, an additional momentum training algorithm is adopted to improve the slow convergence or long-term non convergence of the network during training. The case analysis verifies that the credit evaluation model is trained and tested using 100 sets of sample data. The experimental results also show that such scheme can effectively reduce human factors in determining weights in traditional qualitative methods of enterprise credit evaluation, and the sample testing error is very small, significantly improving the ability to control fund risk and the objectivity of credit evaluation.
Electronic Bill Fast Recognition Based on OCR and CNN
Authors: Zheng Fukang , Chen Yan , Lu Zhankuan , Liu Lingling , Chen Xiaoyang , Xin Xiao
Published: February 2024

The existing methods used for bill recognition need specific equipment scanning or a large number of bill labels to achieve a good recognition effect. To solve above problems, a bill recognition scheme based on OCR technology is proposed. In the part of image preprocessing, the methods of binarization, filtering and denoising, image rectification are used. Then, we use deep learning as the basic model of the bill text detection and recognition phase, and use ResNet to complete the detection of specific fields in the way of image classification. Finally, on the basis of the rapid identification method designed in this paper, the feasibility and effectiveness of the scheme are verified through the demand analysis and functional test of the bill identification system. The results show that it has achieved good results in both simulation data sets and practical applications, and it has more advantages than traditional recognition technologies in text and image recognition.
Evaluation of College Teaching Quality Based on Association Data Mining
Authors: Guo Jinghong
Published: February 2024

To find the valuable information hidden behind the teaching evaluation data, a scheme of applying the data mining technology based on association rules to teaching evaluation is proposed. We analyze the basic principle of association data mining and the needs of teaching quality evaluation in colleges, and establish a multidimensional comprehensive evaluation online analysis mining model. After data standardization, to reduce the generation of candidate item sets, Apriori algorithm is optimized, and the running speed of association rule mining is increased by an order of magnitude. Finally, the concrete construction methods of mining environment and association rules are provided through experimental analysis, which verifies the effectiveness of the teaching evaluation scheme proposed in this paper.
A Cost Prediction Model for Construction Projects Based on Bayesian Networks
Authors: Xu Ping , Liu Chengming
Published: February 2024

Due to the complex calculation process of traditional methods in the entire process cost prediction of construction projects, it doe not consider the complex interaction relationship between multiple influencing factors during the cost formation process, so a construction project cost prediction model based on Bayesian networks is proposed. This method integrates project experience, historical information, and actual cost performance data sequences observed during project implementation, to determine the degree of influence and interrelationship of each influencing factor on the cost components. Then, an explanatory structural model and Bayesian network are used to establish a cost prediction model for construction projects, and the entire process cost of the construction project are calculated and output through analysis functions. Finally, the application effect of the model in an example is explored, and the results show that the various costs and total costs acquired by Bayesian network conditional probabilities are basically consistent with the budgeted costs of original plan. The model not only has feasibility and high accuracy, but also has a positive impact on the probability and degree of low project cost risk events.
A Shared Financial Risk Warning Strategy Based on Deep Learning
Authors: Yang Le , Han Zehua , Qi Zhanxin , Yuan Xuejun , Ni Hongyan
Published: February 2024

The financial sharing center also carries certain service risks while providing high-quality accounting services. To address such issue, this article proposes a financial sharing risk warning model based on deep learning. We have adopted a new perspective to construct a shared financial risk indicator system for enterprises, integrating important information in financial indicator data with gain information in annual report text. Then, an optimized algorithm is introduced to optimize the parameters of the deep learning model, and the model is used to mine historical financial data from multiple periods. Finally, using the relevant financial data of a listed company in Tai’ an DBS, as the testing sample, a neural network model is constructed by deep learning as a research tool. The selected early warning indicator data is imported into Matlab to train a deep learning neural network, to predict whether a company is in financial crisis. The experimental results show that the risk prediction algorithm based on deep learning can accurately predict the financial data trends of enterprises, which significantly improves the model’s generalization ability, and its prediction performance is better than traditional single objective optimization algorithms.
Enterprise Financial Management and Decision System Based on Improved Decision Tree
Authors: Ding Meng
Published: February 2024

The use of traditional financial statements and historical experience by enterprise managers cannot meet the needs of informationization in university financial management. Based on the research and analysis of enterprise financial data, and integrated with data mining technology this article designs a decision tree model based enterprise financial management and decision-making system. In the intelligent decision-making system, certain optimization improvements are proposed for the continuous attribute discretization method in C4.5 algorithm, adjusting the range of attribute metric information gain rate. Then, using the mean value theorem and Maclaurin approximation, the modified information gain rate equation is logarithmically simplified, and a improved decision tree model is constructed to achieve financial management and decision-making in universities. Finally, the feasibility and effectiveness of the solution are verified through the specific implementation and testing process of the system. The experimental results also indicate that the improved C4.5 decision tree greatly improves execution efficiency and reduces demand space compared to traditional classification algorithms, and it also has high comprehensive performance.
A Big Data Analysis Model for College Employment Based on Decision Tree and Apriori
Authors: Sun Xiaowei
Published: February 2024

To assist managers in the decision-making analysis of employment’ employment data, this paper performs the research object of college employment, and applies big data mining to employment information data analysis. A new algorithm based on Apriori association rules is used to mine the association rules between big data of student achievement and employment flow. The scheme also uses the decision tree constructed on the basis of FP-Tree Algorithm and the cost sensitive filter matrix to Apriori, and provides the implementation process of the algorithm block. The comparison results of the test and analysis class with the same algorithm can be seen that the algorithm reduces the number of traversal and scanning records, and has better performance in readability and execution efficiency.
Optimization of Artistic Architecture Modeling Based on Genetic Algorithm
Authors: Zhang Junzhuo , Wang Feng
Published: February 2024

To optimize the modeling design in art building modeling, this paper proposes a scheme of applying interactive genetic algorithm(GA) to architectural modeling innovation. Firstly, the principle of traditional GA and its improvement scheme are discussed, and the basic process of applying interactive IGA to architectural modeling design is provided. Aiming at the fatigue defect of users in the evaluation quantity, the constraint condition is added to the fitness function to speed up the convergence speed of the algorithm, and the specific implementation scheme is also described. Finally, the optimization process of the structural optimization design program is tested through a three-storey and three-span art building frame designing example. The analysis results show that our method to the innovative design of artistic architectural modeling has the advantages of simple design principle and short calculation time, and it constructs a multi-disciplinary collaborative reference platform in architecture design.
Combining jumping knowledge into traffic forecasting: An attention-based spatial-temporal adaptive integration gated network
Authors: Zhou Rucheng , Zhang Dongmei , Zhu Jiabao , Min Geyong
Published: February 2024

Traffic forecasting has become a core component of Intelligent Transportation Systems. However, accurate traffic forecasting is very challenging, caused by the complex traffic road networks. Most existing forecasting methods do not fully consider the topological structure information of road networks, making it difficult to extract accurate spatial features. In addition, spatial and temporal features have different impacts on traffic conditions, but the existing studies ignore the distribution of spatial-temporal features in traffic regions. To address these limitations, we propose a novel graph neural network architecture named Attention-based Spatial-Temporal Adaptive Integration Gated Network (AST-AIGN). The originality of AST-AIGN is to obtain a spatial feature that more accurately reflects the topological structure of the road networks by embedding Graph Attention Network (GAT) into Jumping Knowledge Net (JK-Net). We propose a data-dependent function called spatial-temporal adaptive integration gate to process the diversity of feature distribution and highlight features in road networks that significantly affects traffic conditions. We evaluate our model on two real-world traffic datasets from the Caltrans Performance Measurement System (PEMS04 and PEMS08), and the extensive experimental results demonstrate the proposed AST-AIGN architecture outperforms other baselines.
Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm
Authors: Al-Jumaili Ahmed Hadi Ali , Muniyandi Ravie Chandren , Hasan Mohammad Kamrul , Singh Mandeep Jit , Paw Johnny Koh Siaw , Al-Jumaily Abdulmajeed
Published: February 2024

Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout’s distributed machine-learning environment. The study taps into Apache Hadoop’s robust tools for data storage and processing, including HDFS and MapReduce, to effectively manage and analyze big data challenges. The preprocessing phase utilizes Canopy clustering to expedite the initial partitioning of data points, which are subsequently refined by K-means to enhance clustering performance. Experimental results confirm that incorporating the Canopy as an initial step markedly reduces the computational effort to process the vast quantity of parallel power load abnormalities. The Canopy clustering approach, enabled by distributed machine learning through Apache Mahout, is utilized as a preprocessing step within the K-means clustering technique. The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. Data vectors are generated based on the time needed, sequential and parallel candidate feature data are obtained, and the data rate is combined. After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. The results showed that using canopy as a preprocessing step cut the time it proceeds to deal with the significant number of power load abnormalities found in parallel using a fast density peak dataset and the time it proceeds for the k-means algorithm to run. Additionally, tests demonstrate that combining canopy and the K-means algorithm to analyze data performs consistently and dependably on the Hadoop platform and has a clustering result that offers a scalable and effective solution for power system monitoring.
The Relationship Between Fermented Dairy Consumption with Cognitive Function Among Older US Adults: Data from the NHANES 2011–2014
Authors: Han Yinlian , Yang Mu , Tian Min , Yang Yang , Liu Wen , Liu Yiming
Published: February 2024

Background:

The aging global population has led to an increased burden of cognitive impairment in older adults.

Objective:

This study examined the relationship between fermented dairy intake and cognitive function in this population.

Methods:

Yogurt, cheese, and fermented dairy consumption were assessed through two 24-hour dietary recall interviews, categorized into low, medium, and high intake groups. Multivariate linear regression was employed to examine the relationship between fermented dairy intake and cognitive tests, including the Alzheimer’s Disease Word Learning Immediate Recall Test (CERAD-IRT), CERAD Delayed Recall Test (CERAD-DRT), Animal Fluency Test (AFT), Digit Symbol Substitution Test (DSST), and global cognitive z-scores, adjusting for potential confounding factors.

Results:

The study comprised 2,462 participants (average age 69.34±6.75 years, 52.07% female). Among yogurt consumers, global cognition and AFT z-scores are notably higher than non-consumers. Conversely, individuals who consume cheese display significantly lower CERAD-DRT z-scores. Compared to participants not intake fermented dairy, consumers of fermented dairy show significantly higher AFT and DSST z-scores and lower CERAD-DRT z-scores. Moreover, when categorizing individuals based on their intake of fermented dairy, those with low and medium consumption show significantly higher AFT and DSST z-scores, as well as significantly lower CERAD-DRT z-scores compared to non-consumers.

Conclusions:

Our study suggests that moderate consumption of fermented dairy products is associated with better executive function and verbal fluency in the elderly.
Blood Pressure Variability and Plasma Alzheimer’s Disease Biomarkers in the SPRINT Trial
Authors: Sible Isabel J. , Nation Daniel A.
Published: February 2024

Background:

Recent observational studies suggest higher blood pressure (BP) variability (BPV) is associated with Alzheimer’s disease (AD) biomarkers amyloid-beta (Aβ) and tau. Less is known about relationships in interventional cohorts with strictly controlled mean BP levels.

Objective:

Investigate the longitudinal relationship between BPV and change in plasma AD biomarkers under standard versus intensive BP treatment.

Methods:

In this post hoc analysis of the SPRINT trial, 457 participants ( n = 206 in standard group, n = 251 in intensive group) underwent repeated BP measurement between baseline and 12-months follow-up, and venipuncture at baseline and median (IQR) 3.5 (3.0–4.0) years later to determine plasma AD biomarkers total tau and Aβ1–42:Aβ1–40 ratio. BPV was calculated as tertiles of variability independent of mean. Linear mixed models investigated the effect of BPV×time on AD biomarker levels.

Results:

Higher BPV was associated with increased levels of total tau in the standard group (β [95% CI] 1st versus 3rd tertiles of BPV: 0.21 [0.02, 0.41], p = 0.035), but not in the intensive group (β [95% CI] 1st versus 3rd tertiles of BPV: −0.02 [−0.19, 0.16], p = 0.843). BPV was not associated with Aβ 1–42:Aβ 1–40 ratio in either group. Mean BP was not associated with biomarkers.

Conclusions:

Higher BPV was associated with increased plasma total tau under standard BP treatment. Findings add new evidence to prior observational work linking BPV to AD pathophysiology and suggest that, despite strict control of mean BP, BPV remains a risk for pathophysiological change underlying risk for AD.
Traumatic Brain Injury Alters the Trajectory of Age-Related Mitochondrial Change
Authors: Harris Janna L. , Wang Xiaowan , Christian Sarah K. , Novikova Lesya , Kalani Anuradha , Hui Dongwei , Ferren Sadie , Barbay Scott , Ortiz Judit Perez , Nudo Randolph J. , Brooks William M. , Wilkins Heather M. , Chalise Prabhakar , Michaelis Mary Lou , Michaelis Elias K. , Swerdlow Russell H.
Published: February 2024

Background:

Some epidemiologic studies associate traumatic brain injury (TBI) with Alzheimer’s disease (AD).

Objective:

To test whether a TBI-induced acceleration of age-related mitochondrial change could potentially mediate the reported TBI-AD association.

Methods:

We administered unilateral controlled cortical impact (CCI) or sham injuries to 5-month-old C57BL/6J and tau transgenic rTg4510 mice. In the non-transgenics, we assessed behavior (1–5 days, 1 month, and 15 months), lesion size (1 and 15 months), respiratory chain enzymes (1 and 15 months), and mitochondrial DNA copy number (mtDNAcn) (1 and 15 months) after CCI/sham. In the transgenics we quantified post-injury mtDNAcn and tangle burden.

Results:

In the non-transgenics CCI caused acute behavioral deficits that improved or resolved by 1-month post-injury. Protein-normalized complex I and cytochrome oxidase activities were not significantly altered at 1 or 15 months, although complex I activity in the CCI ipsilesional cortex declined during that period. Hippocampal mtDNAcn was not altered by injury at 1 month, increased with age, and rose to the greatest extent in the CCI contralesional hippocampus. In the injured then aged transgenics, the ipsilesional hippocampus contained less mtDNA and fewer tangles than the contralesional hippocampus; mtDNAcn and tangle counts did not correlate.

Conclusions:

As mice age their brains increase mtDNAcn as part of a compensatory response that preserves mitochondrial function, and TBI enhances this response. TBI may, therefore, increase the amount of compensation required to preserve late-life mitochondrial function. If TBI does modify AD risk, altering the trajectory or biology of aging-related mitochondrial changes could mediate the effect.
Magnetic memory testing towards fatigue crack propagation of Q235 steel for remanufacturing
Authors: Ye Jianhua , Guo Ze , Zeng Shoujin , Xu Mingsan
Published: February 2024

A crack propagation trial was performed on a Q235 steel M(T) sample to investigate the modifications in magnetic memory signals throughout the crack propagation procedure of ferromagnetic substances. Various detection lines were employed to gather and scrutinize magnetic memory signals under two-stage fatigue loading. The interrelation between the gradient value K max, the peak-to-peak value [Formula: see text], the stress intensity factor K a, the extension of the sample 𝜀, crack length a, and the cyclic N was established. The findings indicate that the H p( y) curves present a varied peak at the crack tip and in the notch, whereas the H p( x) curve is linear. The magnetic signals display similar changes during two-stage fatigue loading, but the post-break state deviates. The fatigue process’s [Formula: see text] shows three phases of fluctuation, escalation, and decline. Throughout the period of fatigue crack growth, K max and 𝜀 rise exponentially, K a ascends linearly, and a shows linear changes. The characteristics of magnetic memory signals can measure harm after varying service periods and offer robust foundations for remanufacturing.