bluuebunny/crossref_metadata_2025 huggingface dataset:
MODULAR DATA ANALYTICS AS A TOOL FOR CITIZEN DATA SCIENTISTS IN QUALITY MANAGEMENT
Authors: Pähler Sebastian , Syberg Marius , Deuse Jochen
Published: February 2024

The vision of Industrial Data Science to link data along the entire value chain with data from the product life cycle contains great potential for holistic quality improvements. In order to be able to exploit this increasing potential, tools and evaluations as well as the qualification of experts in quality management in the field of Industrial Data Science are essential. A promising approach is the generalisation and modularisation of data analysis processes, which can be reused in Quality Management for similar analytics tasks of different data sources in this discipline. With an appropriate integration into the company’s processes and easy accessibility of the generalized analytics modules, this approach promises to reduce the complexity and difficulty of data analysis. In this paper a total of seven different exemplary analysis modules that quality management experts can use to handle, visualise and analyse new data in Field Quality Surveillance. These are provided as part of a research project via a browser-based platform that enables users to perform data analyses without the use of other software or programming knowledge. The analysis modules are a central building block for supporting Quality Management experts to act as Citizen Data Scientists. In addition, skills training and general change management for organisational restructuring must go hand in hand with the use of the modules in order to achieve valid analytics results quickly.
Geometrical analysis of naturally grown timber for the design of load-bearing structures
Authors: Moreno Gata Kevin , Spahn Florian , Klinkel Sven , Trautz Martin
Published: February 2024

Combining tradition and innovation, timber plays essential roles in building structures for architecture and engineering. Tree branching geometries and timber in natural state often serve as sources of inspiration. However, the mechanical properties of naturally grown timber, inherently inconsistent and geometrically varied, remain insufficiently studied, particularly for construction and simulations. This knowledge gap perpetuates the prevalent use of straight, uniformly harvested timber, while neglecting curved and bifurcated elements with smaller cross-sections.
This research investigates the potential of naturally grown timber in structural design, emphasizing the importance of understanding natural characteristics and growth patterns of trees to optimize timber use. The developed methodology leverages non-invasive technologies, such as Computerized Tomography (CT), to precisely capture geometrical and material properties of wood. These data sources are then integrated to visualize cross-sectional geometries and material properties, forming the basis for our analytical approach. Utilizing Generalized Scaled Boundary Isogeometric Analysis, the methodology enhances the accuracy and efficiency of simulations, aligning structural design with natural growth principles. This approach not only fosters sustainable resource practices by promoting the use of major tree parts but also transforms discarded materials into valuable resources. The paper concludes with a demonstration of this methodology applied in a practical construction scenario.
Unmasking the Dark Triad: A data fusion machine learning approach to characterize the neural bases of narcissistic, Machiavellian, and psychopathic traits.
Authors: Bakiaj Richard , Muñoz Clara Isabel Pantoja , Bizzego Andrea , grecucci alessandro
Published: February 2024

The Dark Triad (DT), encompassing narcissism, Machiavellianism, and psychopathy traits, poses significant societal challenges. Understanding the neural underpinnings of these traits is crucial for developing effective interventions and preventive strategies. Our study aimed to unveil the neural substrates of the DT by examining brain scans from 201 individuals (mean age: 32.43, 105 females) using the unsupervised learning algorithm transposed Independent Vector Analysis (tIVA). tIVA, known for identifying complex patterns in neuroimaging data, detected fifteen joint gray matter (GM) and white matter (WM) networks. Of these networks, four were associated with the DT. The first component comprises areas within the reward network, including the thalamus, caudate, anterior cingulate, and prefrontal regions. The second component encompasses regions within the executive network, predominantly involving prefrontal and posterior areas. The third component includes regions within the default mode network (DMN), such as the angular gyrus, the precuneus, and the posterior cingulate cortex. Lastly, the fourth component overlaps with areas of the visual network, primarily located in the occipital and temporal lobes. Within these networks, the reward-related component correlated with narcissism, suggesting an association with the need for constant interpersonal rewards to enhance self-esteem and grandiosity in narcissistic individuals. Conversely, the DM-related component correlated with Machiavellianism, potentially reflecting the heightened strategic thinking employed by Machiavellian individuals for manipulation purposes. In line with established trends, sex differences emerged, with males displaying notably higher DT scores. Our findings offer insights into the intricate neurobiological bases of the DT personality and hold implications for future research and interventions.
Constraints on novel word learning in heritage speakers
Authors: Ge Yuxin , Rato Anabela Alves dos Santos , Rebuschat Patrick , Monaghan Padraic
Published: February 2024

Recent research on word learning has found that adults can rapidly learn novel words by tracking cross-situational statistics, but learning is greatly influenced by the phonological properties of the words and by the native language of the speakers. Mandarin-native speakers could easily pick up novel words with Mandarin tones after a short exposure, but English-native speakers had specific difficulty with the tonal components. It is, however, unclear how much experience with Mandarin is needed to successfully use the tonal cue in word learning. In this study, we explored this question by focusing on the heritage language population, who typically are exposed to the target language at an early age but then develop and switch to another majority language. Specifically, we investigated whether heritage Mandarin speakers residing in an English-speaking region and speaking English as a dominant language would be able to learn novel Mandarin tonal words from statistical tracking. It helps us understand whether early exposure to the target feature is sufficient to promote the use of that feature in word learning later in life. We trained 30 heritage Mandarin speakers with Mandarin pseudowords via a cross-situational statistical word learning task (CSWL). Heritage Mandarin speakers were able to learn the pseudowords across multiple situations, but similar-sounding words (i.e., minimal pairs) were more difficult to identify, and words that contrast only in lexical tones (i.e., Mandarin lexical tone) were distinguished at chance level throughout learning. We also collected information about the participants’ heritage language experience and usage. We did not observe a relationship between HL experience/usage and performance in tonal word learning, suggesting that HL exposure does not necessarily lead to an advantage in learning the target language.
Grammar Learning Strategies Employed by Pre-Service English Teachers of a University
Authors: Fauziah Fauziah , Daud Afrianto , Dahnilsyah Dahnilsyah
Published: February 2024

This study aims to find out what kinds of grammar learning strategies are mostly employed by a group of pre-service English teachers. The subjects of this study were second-year students of the English Study Program, Faculty of Teacher Training and Education of a university in Indonesia. This study used a total population sampling technique which means the entire population is used as the sample (n= 90 students). Data were collected using a set of questionnaires consisting of three types of grammar learning strategies, namely cognitive strategies, metacognitive strategies and socio-affective strategies. This study found that the pre-service English teachers employed various strategies when they learn and use grammar. The three types of grammar learning strategies appeared to be the commonly used strategies, with cognitive strategies as the most frequently employed by the pre-service English teachers as indicated by the average score of 4.12 and the lowest strategy was metacognitive strategy indicated by the average score of 3.86.
Nepal’s National Security in Changing Political Circumstances
Authors: Bagale Sarmila
Published: February 2024

This article explores the several security challenges faced by small and landlocked countries which seem to be harmful for peace, prosperity, security and development. Small and landlocked countries, like Nepal, in the world are experiencing different security challenges at present in the backdrop of different political power. The national security in twenty first century is meant for peace, prosperity and economic progress and development. In the changing dynamics of Nepal, especially after the promulgation of the Constitution in 2015, the security issue has been a major concern for political stability and international relations. Though the security issue has a very long history, since the beginning of human civilization it was important, its goal is not ended yet. Nonetheless, the internal and external dynamics of national security have been shifted in the changing contexts of regional, and global political power. It has diverse sensitivity in different circumstances of political regime. The classical agro-based state-constructed security is relocated into human-centric notion for peace, stability and development. Contrary the small and landlocked states are always in threat in different ways which is undoubtedly different things to different people. The sense of national security in the academia involves mitigation of physical threat and psychological fear. As a result, the cherished values collapsed and affect the conditions of peace, stability and development.
Restructuring Nepalese Societies Through Residential Relocation and it's Social Effects
Authors: Silwal Keshav Raj
Published: February 2024

This paper focuses to voluntary relocation of residents of Nepalese society and its effects on social relationship which unfolds the restructuring and transforming old residential structure of Nepalese society. Some scholars attribute the individual factors to the residential relocation and exemplify the life-cycle changes of household heads or household composition such as the age, employment or marital status of the household head and the number of children (Rossi, 1955, p. 24).  They believe that the increase of marriage rate and fertility rate are determinative conditions for the residential relocation and rapid expansion of suburban areas. Besides these arguments, many scholars have embraced various facets of social change with residential relocation and find that most residents move to significantly higher income and less racially segregated suburban neighbourhoods with access to better infrastructure, services, and amenities (Deluca et al., 2005; Diewald and Mayer, 2009). Suburban growth is propelled by transportation and technology development such as mass-produced automobiles and highways. It is interesting social phenomenon in Nepal that the residents on hilly side and remote areas are relocating slightly downward and near to market areas. This paper follows field work on Lele valley, Lalitpur district which is emerging suburban area. It finds that people prefer to relocate their residents near to market and transport facilities creating a new social relationship. . It is found that they relocate their residents because of income sent from their family members who are in foreign employment or they sell their land. They introduce a new type of nexus and relationship with new neighbors who are not among their kinship and start bounding in every occasion.
Mid-Day Meals Programs in the School of Pokhara through Child Friendly School Manual
Authors: Tripathi Krishna Prasad
Published: February 2024

This study, titled “Assessing School Mid-Day Meals Programs in the Schools of Pokhara through Child-Friendly School Manual,” aimed to identify modalities and assess school facilities related to mid-day meals in both institutional and public schools. The research design employed was explanatory sequential. The population comprised schools, headmasters, and class five students, and a multistage sampling technique was utilized. Five diverse schools each from institutional (preprimary, primary, upper primary, secondary, and higher secondary) and public schools were randomly selected for comparison. During data collection, students were assured that their responses would be confidential, and teachers were not present in the classrooms to encourage free expression of opinions. The findings revealed that more than half of the students in institutional schools carried homemade lunches, while public schools provided meals in canteens. Observations indicated that insufficient seating in canteens resulted in students having to eat meals on the ground or in classrooms. Moreover, many schools lacked soap in wash bins, potentially impacting students’ hygiene, and health. It is recommended to improve school facilities related to mid-day meals in accordance with child-friendly school guidelines.
Uses of Medicinal Plants Against Common Skin Problems in the Pokhara Valley
Authors: Dwa Om Prasad
Published: February 2024

The present paper explores the different applications of medicinal plants to cure different skin diseases like boils, cuts, dandruff, eczema, herpes, inflammation, itching, leprosy, pimples, scabies, sores, syphilis, and wounds in rural wards in Pokhara Valley. Based on scientific studies and traditional knowledge, the present work identifies several potential medicinal plants, such as Achyranthus aspera, Ageratum conyzoides, Aloe vera, Amaranthus spinosus, Artemisia indica, Berberis aristate, Calotropis gigantea, Cassia tora, Curcuma longa, Oxalis corniculate, etc. The result of this execution is based on the user’s experience when they are suffering from such skin problems and the use of medicinal plants against those problems. The paper also discusses the advantages of utilizing medicinal plants with their minimum side effects, cost-effectiveness, and sustainable use.
Negotiating Contractual Penalties in the Employment Relationships
Authors: Tintěra Tomáš
Published: February 2024

The paper examines the existing legal regulation and case law regarding the imposition of monetary and other sanctions, particularly contractual penalties, in labour law relations in the Czech Republic. The text analyses the initial state as well as the development and raises concerns about the compatibility of the current stringent regulation with the ever-changing work environment. The paper also discusses in detail the imposition of contractual penalties on the employees, the employers and their nature in the non-compete clause.
Capacidad antioxidante de la pulpa de Vasconcellea candicans “Mito”
Authors: Gutiérrez-Román Ana Isabel Flor , Santa Cruz-Carpio Carlos Marco , Velarde-Vilchez Mónica , Nolasco-Cárdenas Oscar
Published: February 2024

Debido al potencial antioxidante de los frutos del “mito” (Vasconcellea candicans (A. Gray) A. DC 1864) y la posibilidad de ser utilizado como aditivo natural en la industria de alimentos, planteamos como objetivo evaluar la concentración de polifenoles, la capacidad antioxidante y la concentración media inhibitoria (IC50) de la pulpa de este fruto en dos estados de madurez y en dos extractos, acuoso y etanólico al 70% (etanol 70: agua 30). Determinamos la concentración de polifenoles totales, la capacidad antioxidante como % de Captura radicales libres y el coeficiente de inhibición (IC50). Nuestros resultados indican que el extracto acuoso de la pulpa de los frutos de mito inmaduros tiene la mayor concentración de polifenoles totales, mientras que la capacidad antioxidante fue mayor en los extractos etanólicos, siendo éstos, mayores que los encontrados en papaya. El IC50 fue mayor en el extracto etanólico maduro de mito vs. papaya. No encontramos correlación entre polifenoles totales, capacidad antioxidante y el IC50. Concluimos que el estado de madurez influye en la concentración de los polifenoles totales, mientras que la captación de radicales libres y coeficiente de IC50, varía según el tipo de extractante utilizado, demostrándose que la pulpa de “mito” es una fuente promisoria para utilizar como antioxidante natural.
Cuantificación de la alcalinidad del agua natural aplicando los números de Bernoulli y la integral aproximada de Simpson
Authors: Marín-Machuca Olegario , Alvarado-Zambrano Fredy Aníbal , Vargas-Ayala Jessica Blanca , Ortiz-Guizado Julia Iraida , Jáuregui-del-Águila Luis Germán , Alvarado-Zambrano Ricardo Arnaldo , Marín-Sánchez Ulert , Candela-Díaz José Eduardo , Quispe-Quispe Alexander , Marín-Sanchez Obert
Published: February 2024

El cálculo de los números de Bernoulli se fundamenta en que existen numerosas expresiones matemáticas y fenómenos de la ingeniería ambiental en las que se pueden aplicar. Sí bien es cierto que, con ayuda de la computadora y el uso de programas, existen gran cantidad de ellos; en las referencias actuales se publican en un número reducido y, por lo tanto, al emplearlas, se hacen aproximaciones no satisfactorias. El propósito fue calcular los veinte primeros números de Bernoulli, aplicarlo a la matemática (∫ ln(cosx)dx) y a la ingeniería ambiental, cuantificando la alcalinidad del agua natural en forma de carbonato de calcio (CaCO3)(mg/l ), midiendo el dióxido de carbono (CO2). Existe una gran variedad de relaciones que contienen a los números de Bernoulli presentándose en varias aplicaciones prácticas de la matemática. Las mejores relaciones utilizadas en esta oportunidad son desarrollos binomiales. Empleando las ecuaciones planteadas y adecuadas, los números de Bernoulli fueron calculados mecánicamente, llegando a determinar el número de números planteados como objetivo. Finalmente, los números de Bernoulli contribuyen de forma significativa a cálculos matemáticos como para calcular las integrales del tipo ln(cosx), así como las a aplicaciones a la ingeniería ambiental, evaluando la alcalinidad del agua como carbonato de calcio (CaCO3).
Efecto biocida del extracto de semillas de higuerilla (Ricinus communis) en Ulomoides dermestoides (Coleoptera: Tenebrionidae)
Authors: De la Cruz – Leytón Camila , Romero-Cabello Candy Lizet , Lozano – Lévano César , Zavaleta – Rengifo Alejandrina
Published: February 2024

Ulomoides dermestoides Fairmaire, 1893 (Coleoptera: Tenebrionidae), es una plaga de los granos almacenados, que afecta a la germinación y al valor comercial de las semillas. Ricinus communis (Linneo, 1753) (Euphorbiaceae) que posee compuestos con propiedades fitotóxicas como la ricinina (alcaloide tóxico), y, la ricina (albúmina fitotóxica). El objetivo de esta investigación es evaluar el efecto biocida del aceite esencial de R. communis extraído por los métodos de maceración y Soxhlet sobre larvas de los últimos estadios y adultos de U. dermestoides. El diseño experimental consistió en un grupo control (C) y tres grupos experimentales E1 (33%), E2 (60%) y E3 (70,4%), con cuatro repeticiones por tratamiento. El periodo de evaluación fue de 120 horas en intervalos de 24 horas. Los resultados obtenidos indican que los tratamientos E2 y E3 tienen el mayor efecto biocida sobre larvas y adultos de U. dermestoides, registrando el mayor número de individuos muertos a las 24 h del inicio del tratamiento. Se concluye que el aceite esencial de R. communis obtenido por el método de maceración y Soxhlet tiene un efecto biocida sobre larvas de los últimos estadios y adultos de U. dermestoides.
Болашақ мұғалімдердің желілік коммуникативтік мәдениетін жетілдірудің педагогикалық негіздері
Authors: Керимбаева Б.Т.
Published: February 2024

Білім беру үдерісін цифрлық түрлендіру жағдайында мұғалімнің желілік коммуникативтік мәдениетін қалыптастыру мәселесі қазіргі уақытта білім беруде өте өзекті болып отырған маңызды бағыт ретінде қарастырылады. Сонымен қатар болашақ мұғалімдердің желілік коммуникативтік мәдениетін жетілдірудің педагогикалық негіздері зерттелген. Мақалада желілік коммуникативтік мәдениеттің өзгеріске ұшырауы, қарым-қатынас тілі мен стилінің өзгерісі жөнінде, қазіргі өмірдің жылдам қарқынына сәйкес келетін қарым-қатынастың жаңа формалары мен арналарының пайда болуы туралы айтылған, бұл көп нәрсені алмасуға мүмкіндік береді. Яғни ақпарат алу, жылдам кері байланыс алу, нәтижелі диалог пен ынтымақтастық құру. Осылайша, дамып келе жатқан желілік коммуникативтік мәдениет трансформациялық процестерді түсінуді, қоғамның цифрлық трансформациясының жаңа жағдайында оның даму деңгейін бағалаудың қолданыстағы үлгілері мен критерийлерін әзірлеуді талап етеді. Қазіргі кезде болашақ мұғалімдердің желілік коммуникативтік мәдениетін дамытуда тек сыртқы факторлар жүйесінің әсер етуінен емес, сонымен қатар өзін-өзі дамыту әдістемесі арқылы да іске асыруға болады. Педагогикада білім беру өнімділігінің дәрежесі оқу-танымдық іс-әрекеттің белсенділік деңгейіне тікелей байланысты.
Sistemas agropecuarios resilientes: piloto de evaluación de resiliencia en tres sistemas productivos del municipio de Chinú, Córdoba
Authors: Quintero-Ferrer Catalina , Solano-Peña Juan Manuel
Published: February 2024

El presente artículo muestra los resultados obtenidos de la implementación de la metodología de autoevaluación y de valoración holística de la resiliencia climática de agricultores y pastores (SHARP, por sus siglas en inglés), desarrollada por la Oficina de las Naciones Unidas para la Agricultura y la Alimentación FAO, en tres sistemas agropecuarios del municipio de Chinú, Córdoba, Colombia. El estudio se enmarca en las estrategias de adaptación y de mitigación al cambio climático del Plan Departamental de Mitigación y adaptación al Cambio Climático del Departamento de Córdoba.
Pembahasan Kata Ummy Dalam Al-Qur’an (Studi Analisis Surat Al-Jumu’ah)
Authors: Zafirah Mufidah
Published: February 2024

Penelitian ini  bertujuan untuk memenuhi tugas mata kuliah Tafsir Pendidikan yakni menganalisis surat Al-Jumu’ah ayat 2, mengenai pembahasan kata Ummiy dalam Al-Qur’an. Penelitian ini menggunakan pendekatan studi pustaka. Hasil penelitian menunjukan bahwa  kata ummiy dalam Al-Qur’an surat Al-jumu’ah ayat 2, didapatkan dalam Tafsir Para Mufassir terhadap ayat-ayat ummi dalam beberapa kitab tafsir diantaranya, Tafsir al-Mishbah, Tafsir Ibnu Katsir, Tafsir al-Maraghi, Tafsir Jalalayn, dan Tafsir al-Muyassar. Dan berbagai sumber yang jelas. Tafsir Al-Mishbah dan Al-Maraghi dalam kata ummiyyīna dengan ayat tersebut tidak bisa membaca dan menulis. Berbeda dengan Ibnu Katsir beliau berpendapat bahwa kata ummi tersebut adalah Allah Swt membangkitkan Nabi Muhammad Saw dari kalangan mereka sendiri, disini beliau berbeda pendapat dengan dua mufassir sebelumnya yang menyebutkan bahwa nabi tidak bisa membaca dan menulis.
INFOGRAFIS KEPENDUDUKAN KOTA MAKASSAR MENGGUNAKAN ALGORITMA K-MEANS
Authors: Nur Dahlia , Raharjo Muhammad Fajri , Chaerul Muhammad Fikriansyah
Published: February 2024

Makassar City is the fourth-largest city in Indonesia and the largest in Eastern Indonesia with an area of 175.77 square km and a population of Â±1.5 people in 2019, divided into 15 districts and 143 sub-districts. Uneven population density in each sub-district has an impact on the emergence of social problems in society such as: unhealthy environment, chaotic building management, population registration information that is not updated. To overcome this problem, it is necessary to have population info graphics that show the population, age of the population, education, etc. With info graphic data on the population of Makassar city that can be accessed online, people looking for data can use it as a reference. To achieve the objectives of this research, the K-means algorithm method is used to divide observations into K clusters where each observation is a member of the cluster with the closest average value, through an iterative process until the grouping converges. The K-means algorithm can be used on population data. For example, the grouping of population data is divided into three clusters, namely (cluster 1) dense, (cluster 2) medium and (cluster 3) not dense. The results obtained show that five districts: Mariso, Mamajang, Makassar, Bontoala and Tallo are the districts with the densest populations.
Analisis Sistem Security Information and Event Management (SIEM) Aplikasi Wazuh pada Dinas Komunikasi Informatika Statistik dan Persandian Sulawesi Selatan
Authors: Nas Mardhiyah , Ulfiah Farchia , Putri Ulya
Published: February 2024

The South Sulawesi Communication Informatics Statistics and Standardization Office is an implementer of government affairs that assists in carrying out government affairs in the fields of communication, informatics, statistics, and signage. Currently, agencies are utilizing technological developments to maximize their performance, such as the use of web servers to provide information and provide services. But of course this can cause problems such as data theft. Because of the many threats that can attack at any time. Therefore, an application is needed that can prevent this from happening. In order to overcome this, a monitoring system is implemented using the Wazuh application which is an application of SIEM. to find out how this application works in the event of an attack, testing will be carried out using 2 types of attacks, namely Distributed Denial of Service (DDoS) Slowloris and Brute Force. In this test, data will be taken in the form of application responses, namely the response time of the Wazuh application and the classification of the Wazuh application level against DDoS and Brute Force attacks which will be displayed on the Wazuh application Dashboard. Based on the test results, the wazuh application successfully detects DDoS Slowloris and Brute force attacks and can classify these two attacks at levels 3 to 10.
Alat Bantu Jalan Tunanetra Menggunakan Sensor Light Detection And Ranging (LiDAR) berbasis Arduino
Authors: Aryani Dharma , Gunawan Arya Irfan , Reski Reski , Nisa Khairun , Zubair Achmad
Published: February 2024

Walking stick is very important for the visually impaired person. It can help the visually impaired to walk more easily and to navigate the environment feeling safe and confident. Therefore, a cane that can detect object or obstacle is designed using Arduino microcontroller combined with a Light Detection And Ranging (LiDAR) sensor as an obstacle or object detector. Lidar sensors are utilized in visually impaired walkers equipped with notifications and warnings in the form of buzzer sounds and vibrations when obstacles and objects are detected. The walking stick is functioned in three conditions based on the distance of the stick and the object. For distance of 0 to 45 cm, the stick vibrates and emit a sound that reminds the user for the presence of an object. In medium distance, which is a range of 45 to 75 cm, the vibration exist with additional buzzer sound every second will occur i. Furthermore, the stick only vibrates if the object is detected in a distance of 75 to 100 cm. This walking stick can help the visually impaired in carrying out activities more safely and reduce the risk of accidents to users
Aplikasi Grey Level Coocurent Matrix(Glcm) Menggunakan Matlab Gui Dan Ann Dalam Identifikasi Penyakit Pada Tanaman Porang
Authors: Marwan Fayyadh Ats Tsaqib , Ramadhanti Hauraisya Nayla , Wahid Neyla Cahyaningrum , Rimantho Dedi
Published: February 2024

Porang is an Indonesian export product, its export potential still has enormous value opportunities. Diseases in porang are caused by several things, including pathogenic bacteria which damage the tubers. Caterpillars and grasshoppers also become diseases in people. The GLCM method provides speed of initial identification of diseases in porang. ANN (Artificial Neural Network) helps classify diseases in porang plants. The Matlab GUI is used as an graphical interface for the porang disease classification application.
A Video Resource Recommendation System Based on Deep Learning
Authors: Zhao Yunxuan
Published: February 2024

To recommend video resources in education for better results, this article attempts to use a deep learning model to construct a system suitable for mobile devices. According to the specific design principles and requirements analysis, the overall logical framework of the system and the main technology of development are provided. Then the composition of server and client is explained by B/S system architecture, and the function realization scheme of key modules of the system is provided by video coding and compression technology, MSSQL database technology and ASP.NET. The recommendation module adopts the deep learning lightweight model MobileNet to improve detection speed and accuracy, and reduce memory, which achieves real-time detection and pushing effects. The empirical analysis of the system shows that it has the characteristics of simple and fast interface, safe and reliable. It also realizes the mining and matching of massive potential information in videos, so it has an important auxiliary role for the ideological and political teaching.
Fast Estimation Model of Green Building Project Cost Based on Support Vector Regression Machine
Authors: Wang Li
Published: February 2024

In order to solve the problems of low estimation accuracy and long estimation time of traditional construction project cost estimation methods, a new fast estimation method of green construction project cost based on support vector regression machine is proposed in this paper. Firstly, according to the principle of support vector regression machine, the nonlinear classification function of green building cost data is constructed to complete the classification of cost data. Secondly, based on the classification results, the principal components of the sample data series of cost estimation are calculated, and the constraint parameters of cost estimation are constructed. Finally, according to the relationship between green building construction cycle and overall project cost, a fast estimation model of green building project cost is constructed. The experimental results show that compared with the traditional estimation model, the estimation accuracy of this method is higher and the estimation time is shorter.
Intelligent Remote Search and Rescue Vehicle
Authors: Feng Fang , Yang Hua , Du Qing
Published: February 2024

This article proposes a design scheme for intelligent equipment for remote search and rescue in the event of a mine disaster. This design uses STM32 and ESP32 as the core for data processing and transmission. Multiple sensors are used to intelligently detect harmful gases, distinguish the presence of human voices, and automatically avoid obstacles. Remote control is achieved through Bluetooth technology, and MIME technology is used for real-time image transmission on site. By detecting signal strength, relative positioning is achieved using hyperbolic positioning method. The intelligent remote search and rescue vehicle based on STM32 can detect the harmful gas content and presence of vital signs at the rescue site while ensuring normal rescue, and collect real-time images of the scene.
A Study on Plant Classification Based on SE-ResNext
Authors: Guo Huangbin , Yan Hui , Xie Fahuan , Liu Hupeng , Yu Ping , Long Duo
Published: February 2024

The classification of weeds and crops is key to precision agriculture. To achieve the challenge of recognizing and classifying weeds and crops with high accuracy, this paper uses a deep learning approach. We propose the SE-ResNext network to optimize ResNext using the channel attention mechanism. Embedding the channel attention mechanism in the backbone network of ResNext, this deep learning model achieves 97.51% classification accuracy on the V2 Plant Seedlings dataset. The training accuracy of the SE-ResNext model is 98.49% and the MSE of the SE-ResNext model is 0.044, which are the best values. Compared to AlexNet and GoogleNet, this model has higher classification accuracy. The results can provide technical support for the autonomous operation of field-weeding robots. Enables accurate classification of weeds and crops.
A Fast Fault Diagnosis Method for Power Transformers Based on Convolutional Neural Networks
Authors: Pan Jiawei , Xu Qiushi , Ning Jian
Published: February 2024

To improve the efficiency and accuracy of power transformer fault diagnosis, a fast fault diagnosis method for power transformer operation based on convolutional neural network is proposed. Firstly, wavelet analysis method is used to denoise the operation data of power transformers. Secondly, after completing the denoising process, a convolutional neural network model is constructed to extract features from the operating data of power transformers. Finally, the Softmax function is used to quickly classify and diagnose the operational faults of power transformers. The experimental results show that the method proposed in this paper can improve the accuracy of fault diagnosis, shorten the time of fault diagnosis, and improve the efficiency of fault diagnosis.
Weather Prediction Based on Multivariate LSTM Neural Network Model
Authors: Xie Fahuan , Yan Hui , Long Yunxin , Guo Huangbin , Liu Hupeng , Yu Ping
Published: February 2024

Traditional weather forecasting methods are often difficult to cope with the complex relationships and temporal dependencies between multiple meteorological factors. Therefore, this study proposes a neural network model based on multivariate short term memory (LSTM) to explore an innovative approach to improve the accuracy of weather forecasting. We use a multivariable LSTM model with multiple meteorological variables as inputs to better capture the timing characteristics of weather systems. After training and verification on the actual meteorological data, the results showed that the accuracy of the multivariate LSTM model improved significantly: RMSE decreased from 0.0543 to 0.0137; MSE decreased from 0.0544 to 0.0140. Compared with traditional methods, this model can predict weather conditions more accurately and capture the complex relationship between different meteorological factors.
Identification of Pests and Diseases Based on Cascaded Convolutional Neural Network
Authors: Hu Haiyan , Su Chang , Ju Jiaqi
Published: February 2024

Corn is one of the main food crops in China, and its diseases and pests directly affect China’s food security. In order to carry out real-time and accurate detection and recognition of corn pests and diseases in complex agricultural environments, this paper proposes a corn pests and diseases recognition method based on cascaded convolutional neural networks. Due to the differences in different deep learning models, two different convolutional neural backbone networks, AlexNet and Inception, were used to construct a double voting network model for corn common pests and diseases image classification, and accurate corn pests and diseases categories were calculated. Then, the siamese network model is used to calculate and measure the Euclidean distance between different degrees of pests and diseases, and the corresponding degree of corn pests and diseases is given. Finally, the output results of the fusion of the double voting network and the siamese network were used to calculate the categories and severity of corn pests and diseases in complex environments. Through a large number of experiments, it has been shown that the proposed method can effectively improve the accuracy of corn pest identification and pest severity assessment.
An Optimized UniLM Model of Traditional Chinese Medicine Intelligent Question Answering
Authors: Long Yunxin , Gao Peng , Guo Huangbin , Fu Jichuan , Liu Hupeng , Yan Hui
Published: February 2024

The scale of datasets and knowledge bases is relatively limited in the field of Traditional Chinese Medicine (TCM). To efficiently construct and supplement these knowledge repositories while expanding the scale of datasets, this study proposes an optimized UniLM natural language processing model. Initially, we developed a UniLM model based on transfer learning. Secondly, During the training process, we introduced minor perturbations into the embedding layer to generate adversarial samples, thereby enhancing the model’s generalization capabilities through adversarial training. Finally, we employed a five-fold cross-validation method to validate and evaluate the model. The model achieved an average recall of 0.977, an average accuracy of 0.973, and an average F1 score of 0.975. The optimized UniLM model exhibits high robustness and strong generalization capabilities. These attributes contribute to improved performance in generating questions in the TCM field as well as in the comprehensive utilization of textual data.
An Intelligent Defense Method for IPv6 Networks Based on Deep Neural Networks
Authors: Zhang Yanan , Wang Xuanzhong , Bao Yimeng
Published: February 2024

Contraposing to the poor performance of IDS and firewall collaboration under IPv6, this article designs and implements an intelligent defense strategy for exit firewalls based on deep neural networks. The solution involves setting up intrusion detection agents and centralized control servers. Then, after negotiation, the encrypted data packet is passed through the firewall, and a convolutional neural network is adopted to perform feature selection on the traffic data, and a Softmax classifier is used for network attack behavior recognition. The detection output is further protected against illegal intrusion through the IDS interface, enabling the firewall to have both encryption and authentication functions. On this basis, the algorithm is tested by establishing a practical environment. The experimental results show that such method improves the efficiency of combining firewall and IDS. Compared to CNN and LSTM models, it not only converges faster, but also reduces the error detection rate by an average of 2%. Therefore, it provides an effective approach for the research field of network intrusion detection in the IPv6 environment.
A Building Energy Consumption Prediction Model Based on Data Mining and Cluster Analysis
Authors: Zhang Yunli , Zhang Liang
Published: February 2024

To predict the building energy consumption data more effectively and scientifically, an energy consumption prediction model based on data mining and clustering analysis is proposed. We first apply data mining to the benchmark evaluation of building energy consumption and propose the process of building energy consumption benchmark evaluation. The energy consumption monitoring model identifies building operating energy consumption patterns through clustering, which mines and matches real-time collected energy consumption data. Then, k-means algorithm is adopted to extract typical daily energy patterns, and the cumulative frequency distribution method is also used to determine the energy consumption baseline value for each type of building. The implementation of the proposed data preprocessing system and method in the case analysis verify that the scheme can accurately identify the relevant factors affecting energy consumption. It improves the accuracy of building energy consumption prediction and has high operational efficiency, thus providing managers with auxiliary decision-making for building energy conservation.
Enterprise Green Construction Cost Control Strategy Based on BP Neural Network
Authors: Li Xiaolu
Published: February 2024

Most enterprises face the problem of insufficient environmental costs due to high investment and low profits in green construction. Therefore, this paper utilizes the advantages of neural networks to establish a green building construction cost estimation model based on BP neural networks. We analyze the problems in environmental cost accounting and control during the implementation of green construction, and obtain the engineering characteristic factors that affect cost control. Then, based on these characteristic factors, a suitable measure project cost prediction neural network model was constructed, which automatically extracts the regular relationship between engineering features and cost data from a large amount of past calculation data. Finally, a case study was conducted to estimate the environmental cost accounting system of a certain steel enterprise based on BP neural network. The analysis results indicate that the cost prediction model proposed in this article has better stability and can accurately estimate the cost of green buildings.
Hybrid Teaching Platform Design for Ideological and Political Courses Based on K-Means Clustering
Authors: He Tian , Wei Shujun
Published: February 2024

In order to cluster based on the obtained user basic information and interest characteristics to improve traditional online teaching systems, a design scheme for a mixed teaching mobile platform for ideological and political courses based on K -means algorithm is proposed. In terms of software design, the K-means algorithm optimized by GA is used to cluster student information, and the hierarchical method is used to set up knowledge point structure modules, and the specific implementation method is provided. It also adopts MVC+JSP as the script framework, and determines the overall workflow through demand analysis. Finally, with the assistance of Eclipse, the experiments and software tests prove the feasibility and effectiveness of the platform, and the clustering results are relatively stable and the running time is significantly reduced. Therefore, it provides a new way to strengthen and improve the teaching of college students’ ideological and political courses in the new era.
Foreign Language Teaching Resources Recommendation Approach Based on Knowledge Map
Authors: Niu Xiaoqing
Published: February 2024

To improve the utilization of foreign language online teaching resources, a resource recommendation model based on knowledge map is proposed. The scheme constructs the ontology of knowledge map in Python by analyzing the data of online users’ learning behaviors. Then Neo4j is adopted to store the map database of agricultural knowledge map. In the process of resource recommendation, a new kind of knowledge map representation learning algorithm is introduced into collaborative filtering algorithm to extract structured information in the knowledge map. Finally, a foreign language teaching resource recommendation system based on knowledge map is designed and implemented through an example. Through comparative experimental analysis, it is proved that the method is superior to the traditional recommendation method in terms of accuracy index and it can improve the satisfaction of teachers and students with different demand for foreign language studying.
Feature Extraction and Attack Correlation Detection in Power Communication Networks Based on Convolutional Neural Networks
Authors: Wang Xuanzhong , Zhang Yanan , Li Zhutian , Gao Jinzhao , Guo Shan
Published: February 2024

To solve the problem of low feature recognition in existing feature extraction methods in power communication networks, a CNN model is adopted for network data feature extraction and attack correlation detection. Firstly, a basic framework for data preprocessing and further system analysis is proposed for the communication protocol and link basic structure of intelligent substations, as well as general network communication flow collection and analysis methods. Then, detailed design will be performed for different functional modules, including data collection, traffic extraction, and SVM based classification and recognition. Then, convolutional neural networks are used to classify the associated and non associated traffic feature maps, in order to determine whether they are related. Finally, an experiment is conducted with a real power communication network. The results show that the data feature extraction method proposed in the article can achieve reliability prediction from actual operating data of the power communication network, greatly improving feature identification and reducing storage and calculation costs while maintaining accuracy.
Financial Data Anomaly Recognition Model Based on Improved Support Vector Machine
Authors: Zhang Xiang
Published: February 2024

In order to achieve unsupervised classification of enterprise financial transaction data and identify suspicious abnormal financial data, a financial data anomaly recognition model based on support vector machine is proposed. This article adopts sample testing and independence testing to reduce the dimensionality of the company’s indicators, and selects principal component indicators that can accurately measure the financial condition of the enterprise. When the SVM model is introduced to solve the financial anomaly identification problem, the parameter optimization module are improved. Then, a mixed kernel function through linear combination and optimized parameters using PSO is established. The experiment is performed in MATLAB environment to construct an improved SVM model for feature vector training based on training dataset algorithms. The results indicate that our scheme can detect suspicious values in actual financial account data and effectively avoid overfitting and underfitting phenomena. Compared to similar algorithms, the recognition accuracy and robustness have been improved, making it more suitable for financial crisis warning.
A Grey Forecasting Model for Energy Demand Based on Optimized BP Neural Networks
Authors: Cheng Baihua , Lv Yixin
Published: February 2024

In order to accurately evaluate regional energy consumption and demand, the prediction model based on optimized Grey Neural Network was discussed in this paper. Considering the advantages of grey prediction model and artificial neural network model, BP neural network model was introduced to analyze the model, and genetic algorithm was used to optimize the network weight design of series model. Based on the consumption data of urban residents in a province from 2009 to 2019, the case analysis adopted the improved prediction model to test the actual demand trend of energy in the future. The results showed that the combined prediction model can reflect the development trend of energy consumption and demand in a period of time, and it had better prediction accuracy than the single prediction model. Therefore, it was feasible to apply it to complex nonlinear systems.
Psoas muscle index and psoas muscle density are associated with functional status in patients with degenerative lumbar spinal stenosis
Authors: Hou Xiaofei , Hu Hailiang , Kong Chao , Zhang Sitao , Wang Wei , Lu Shibao
Published: February 2024

BACKGROUND: The factors affecting lumbar spinal function in patients with degenerative lumbar spinal stenosis (DLSS) are still unclear. OBJECTIVE: This study explored psoas major muscle morphology in patients with DLSS and its association with their functional status. METHODS: A retrospective study was conducted on 288 patients with DLSS and 260 control subjects. Psoas major muscle evaluation included three morphometric parameters at the L3/4 disc level: psoas major index (PMI), muscle attenuation, and psoas major morphological changes (MPM). The association between psoas major morphology and functional status was assessed using the Oswestry disability index (ODI). RESULTS: Both female and male patients with DLSS had a higher PMI and lower muscle attenuation. PMI and muscle attenuation were inversely correlated with age in the DLSS group. After multivariable analyses, the PMI and psoas major muscle attenuation were positively correlated with patients’ functional status. CONCLUSION: The PMI and muscle attenuation were positively correlated with functional status in patients with DLSS. These findings have important implications for physiotherapy programs of postoperative rehabilitation and conservative management of DLSS.