bluuebunny/crossref_metadata_2025 huggingface dataset:
Geo-environmental Suitability Assessment for Shallow Foundition Pit in Urban Zhengzhou City Based on GIS and AHP Methods
Authors: Lin Guo
Published: April 2024

With the objective of the security and sustainability of underground space, the geo-environment suitability assessment for shallow foundation pit was conducted by GIS and AHP methods combining with the characteristics of the study area on the basis of the investigation of the current exploitation of underground space and the geological environment conditions in Zhengzhou City and the advanced experiences at home and abroad. Computer technology provides powerful tools and methods to the geo-environmental suitability assessment of underground space, and supports decision-making and planning. In the research, the suitability of geo-environment was assessed using the multi-objective linear weighted function model. The results indicate that the elements influencing the geo-environment suitability of shallow foundation pit in the study area mainly include stability of foundation pit, active earth pressure of slope, and shallow groundwater depth and water enrichment of aquifer according to the impact of evaluation factors on the foundation pit engineering. The results showed that the suitable area with an area of about 96.2 km2 is located to the west of Beijing–Guangzhou Railway, the medium-suitable area with an area of about 174.4 km2 is located to the east of Beijing–Guangzhou Railway and to the west of the Beijing-Guangzhou high-speed railway, the poor-suitable area with an area of about 111.3 km2 is located to the east of Dongfeng canal-Jingguang high-speed Railway and we should particularly focus on the impact of surface water, groundwater, soft soil and land subsidence during construction. The application of GIS and AHP method provided key technical support for the assessment. The geo-environment suitability evaluation of the foundation pit engineering by GIS and AHP methods has a strong pertinence and can offer technical assistance for the exploitation of underground space in Zhengzhou City.
An Algorithm Course Grade Warning Model Based on Ensemble Learning
Authors: Wei Zhang, Xiaohong Hu
Published: April 2024

This paper analyzes the massive multidimensional teaching process data generated in the blended teaching of algorithm design and analysis courses. In response to the problems of uneven data set categories and distribution differences in curriculum data among students at different levels and majors, the data is processed unbiasedly to construct new curriculum ability features. A model based on ensemble learning is constructed to provide intelligent early warning for students' learning status. Teaching application found that the model can effectively identify students with learning difficulties and provide effective assistance for teachers' differentiated teaching and guiding students' curriculum learning
Point Cloud Reduction Method Based on Curvature Grading and Voxel Filtering
Authors: Zhiheng Ding, Renfu Li
Published: April 2024

In the context of automated assembly engineering based on reverse engineering in the aerospace field, redundant data will make it impossible to complete 3D model reconstruction efficiently and quickly, and point cloud data must be streamlined. A single simplification method cannot accurately retain the feature information of the scanned point cloud. Aiming at this background, a point cloud simplification method combining curvature grading and octree voxel filtering is introduced. The obtained curvature is divided into different levels by using the properties of logarithmic function, and the strong and weak feature points are distinguished. The weak feature points are filtered by octree voxel, and the filtered results are merged with the strong feature points to obtain the final streamlined results. This method is compared with the random sampling method and the point cloud simplification method combining 3D-SIFT feature extraction and voxel filtering. The experimental results show that this method not only greatly reduces the amount of data, but also preserves the local details of the original point cloud data, so as to achieve the purpose of efficiently compressing point cloud data.
Multi-Function Collaborative Flexible Source-Load Intelligent Interactive Operation Control Technology for Industrial Communities
Authors: Zhi Tan
Published: April 2024

Different from the traditional power supply mode of only distribution network, the new energy distribution network gradually presents a strong coupling and strong correlation between the "source - network - load - energy storage" multi-energy system structure. Its various types of resources have the characteristics of wide regional distribution of "multiple points and wide areas". According to the characteristics of distribution network, this project studies the voltage self-optimizing control strategy in a wide range of distribution network with multi-source coordination. Through the coordinated regulation of multiple decentralized systems such as new energy power generation, energy storage and flexible load, the voltage quality in a large range is improved under the premise of guaranteeing energy storage capacity. Through the analysis of an urban distribution network, the results show that the proposed algorithm is feasible and advanced. Compared with the conventional regulation mode, this mode is more suitable for the regulation requirements of distribution network.
Research on the Development Status and Influence of Zhengzhou Primitive Celadon in the Context of Artificial Intelligence
Authors: Yi Zhang, Liangjie Zhang Kexiang Ma, Hui Chen,
Published: April 2024

In today's environment where the development of artificial intelligence is clearly accelerating, the research on traditional Chinese ceramics has also been influenced by it. Through artificial intelligence technology, we delve into exploring the developmental status and influence of Zhengzhou primitive celadon in Chinese ceramic culture, and conduct analysis and research through big data screening. At the same time, this provides a theoretical basis for the protection and inheritance of Zhengzhou primitive celadon. Zhengzhou primitive celadon. Through artificial intelligence means, we obtain relevant data from literature and archaeological discoveries related to China's primitive ceramics, and then conduct systematic research to deeply explore the cultural connotations of Zhengzhou primitive celadon. As a part of ancient Chinese ceramic culture, although the history of Zhengzhou primitive celadon can be traced back to the Shang and Zhou dynasties, due to geographical and historical reasons, its status and influence are relatively low. Under the conditions of artificial intelligence, the in-depth analysis of its unique value through big data analysis helps to better understand and inherit this unique variety of celadon, Zhengzhou primitive celadon. It provides theoretical support and guidance for the protection and inheritance of Zhengzhou primitive celadon. By utilizing artificial intelligence means to participate in the revival and development process of Zhengzhou celadon, it has positive significance for promoting the greater development of Zhengzhou primitive celadon in the field of ceramic art.
Research on Risk Assessment Model for Brushing Type Telecom Network Fraud Victim Based on Bayesian Network
Authors: Tuo Shi, Zijun Liu Qi Zhang, Xuechen Li,
Published: April 2024

Assessing the risk level and exploring the risk characteristics of victims of brushing type telecom network fraud is of practical significance for crime risk prevention and control. Starting from the demographic characteristics of victims and case characteristics, this paper establishes a Bayesian network model with a tuple composed of loss amount and contact duration as the evaluation index of victim risk level, aiming to provide ideas for police to implement precise anti-fraud propaganda. The research shows that the tuple victim risk assessment model has a high prediction accuracy and can take into account both the loss amount and contact duration, which is feasible as a victim risk assessment model; there is no significant single influencing factor characteristic that affects the victim risk level; among the key victim groups of women who are commerce service personnel and use social media platforms, police should focus on highly educated groups, and among people with the same educational background, police should focus on young people under 28 years old.
Research on the Innovation and Implementation Pathway of the Integrated Aesthetic Education Model in Research Universities within the Context of Digital Information and Intelligence Strategies
Authors: Yaode Wang Meng Ge,
Published: April 2024

In the context of the digital revolution and the increasing integration of intelligence technologies, the innovation and implementation of an integrated aesthetic education model at research universities emerge as crucial. This study aims to explore the intersection of artificial intelligence (AI) and aesthetic education, examining how AI can enhance traditional aesthetic teaching methods and promote the development of critical thinking, creativity, and cultural understanding. The research explores the potential of AI in areas such as data analytics, personalized learning, and virtual reality to transform aesthetic education and its implementation in research universities. By integrating AI into aesthetic education, this study aims to contribute to the advancement of educational practices in the digital age, focusing on the enhancement of learning experiences and outcomes.
Exploration on Service Design of Integrated Online and Offline for the Food Nearing Its Expiration Date from the Perspective of Opposing Food Waste
Authors: Wen Zhang
Published: April 2024

Combining online digital application systems with offline physical design is currently a relatively complete service design system. In view of the increasingly prominent problem of waste caused by the food nearing its expiration date at present, the application of a combination of online and offline design methods can provide users and businesses with a comfortable user experience and commercial value. This paper investigated the consumers' consumption view of the food nearing its expiration date, understood their attitude and consumption motivation, conducted user log and behavior insight research on convenience store consumers, explored service design opportunities and experience contacts, and reconstructed a systematic and rational service system that combines online and offline services, from food production, sales to recycling and reuse. Give full play to the advantages of offline entity and online transparency and openness, and saving from the source of food, and maximize the promotion of sales and recycling of the food nearing its expiration date. It provides consumers with a comfortable user experience and ensuring the maximization of the interests of the food industry in order to save food.
Precision Optimization Analysis of Online Measurement System for Laser Melting Pool Morphology in Additive Manufacturing Process
Authors: Sibo Liu, Aijun Wang, Xiaomei He Pengmou Ma,
Published: April 2024

Selective Laser Melting (SLM), as the most precise and highest quality processing technology in additive manufacturing, is widely used in fields such as aerospace. Online measurement of the laser melting pool, the basic unit of the SLM process, reflects the precision and process stability of SLM. The accuracy of the online measurement system for laser melting pool morphology, which is based on the principle of visual measurement, is crucial for accurate evaluation of the laser melting pool morphology. This paper firstly conducts research on visual calibration technology for the online measurement system of laser melting pool morphology, achieving precise positioning of the visual system and effectively calculating target-related feature information, which is then converted into corresponding digital information for further processing and analysis. Secondly, the paper completes the system error analysis and parameter optimization design, effectively improving the accuracy of the visual measurement system. Finally, theoptimized online measurement system is verified for accuracy and evaluated for precision, proving the feasibility and effectiveness of the methods presented in this paper.
A Study of University Students’ Innovation and Entrepreneurship Competencies under Internet Plus and China’s Dual Carbon Targets
Authors: Fei Yang Chan Li,
Published: April 2024

China’s policy on carbon peaking and carbon neutrality has sparked a new wave of emerging industries towards reaching the dual carbon targets. The growth of these industries meant that there is an urgent need to develop innovative entrepreneurs who are committed to realizing the dual carbon goals. Therefore, it is of great significance to analyze the innovation and entrepreneurship competencies of university students in achieving the goals. Under the background of internet plus, university students are critical in realizing the dual carbon goals as they possess specific characteristics compared to the comprehensive competencies of innovative entrepreneurs. With the help of the internet and through the combination of online and offline model, this paper examined university students in a specific area to determine the structure and influencing factors of their innovation and entrepreneurship competencies. The results indicate that in the era of internet plus, factors such as national policy, students’ place of origin, career direction, as well as innovation and entrepreneurship activities play a key role in the innovation and entrepreneurship competencies of university students towards reaching the dual carbon goals. More proper research needs to be conducted on university students with different personality traits even though university students in the era of internet plus can make use of innovation and entrepreneurship opportunities in achieving the dual carbon targets.
Research and Practice of Online Postgraduate Quality Management Systems in Local Institutions with the Context of Digital Network
Authors: Xihuang Lai
Published: April 2024

In the digital era, there is an urgent need to reconstruct the talent cultivation system with the support of computer technology, and it is crucial to create and build a quality control system for postgraduate education that meets the development of the times. The system is usually operated online using digital network technology. Most of the postgraduates admitted to the research group come from the same level of institutions or non-graduate training authorization units, and their scientific research ability is relatively weak, hence they need more guidance and help from the university and supervisors in the cultivation process. Relying on the university's "Intelligence +" academic characteristics, the group has explored and practiced digital transformation in terms of accurate publicity, quality improvement, and differentiation cultivation, which mainly includes the following two aspects. The first, take the online teaching management platform as the core, cooperate with the enrollment platform, the practice results platform, and the graduation work platform through the network, with the user experience of teachers and students as the test standard, mine data on the habits of teachers and students, realize the orderly operation of the data-driven postgraduate education process. The second is to improve the teaching management ability and innovation consciousness of teachers under the intelligence education conditions through data platform and online or underline staged training. At the same time, the Group will continue to pay attention to data security and privacy protection issues in the postgraduate QMS, and deeply explore the integration and application of big data technology and education management, so as to improve the management level of postgraduate education and adapt to the new situation and requirements of postgraduate education.
Research on Football Field Monitoring System Based on Internet of Things
Authors: Chongjiang Zhan
Published: April 2024

Football is one of the three major sports in the world, and it is an important part of a country's sports. At present, an important method of football tactics research is to use the Internet of Things data monitoring system to collect various data generated during the game and training, and then use computer software to obtain kinematic information. In order to improve the efficiency of physical energy allocation and technical and tactical level of athletes, and timely discover the problems of athletes and the implementation of coaches' tactics. Coaches urgently need a system that can collect various sports data in time. This paper proposes a football field data monitoring system based on the Internet of Things, which tests the performance of data monitoring through MATLAB. Finally, the validity and scientificity of the data monitoring system were initially verified.
Computational Analysis of Hydro-Mechanical Interaction in Sponge City Infrastructure
Authors: Dong Zhou
Published: April 2024

Sponge cities are urban areas that absorb and use rainfall like a sponge, thereby reducing flood risk and replenishing groundwater. However, the water-mechanical interactions in these systems are still poorly understood. In this study, the computer simulation fluid dynamics (CFD) method and Fluent software were used to evaluate the influence of soil leakage on pavement structure under the background of sponge city, focusing on two different soil layers. The fluid-structure coupling was analyzed by computer, and the pore water pressure changed significantly when the coupling effect was considered by computer software. When the penetration rate is 0.01 cm/s, the growth of plastic zone of soil is the smallest. Due to the upward seepage force caused by soil swelling and lateral water barrier, the strain index of road displacement is increased by 15%. The corresponding conclusion is drawn, which has practical significance.
Research on Fault Prediction and Health Management System of Power Electronic Devices Based on Computer Deep Learning Algorithm
Authors: Jiangping Nan
Published: April 2024

This paper proposes a power equipment maintenance management system based on network and deep learning algorithm. First of all, the actual power network equipment maintenance and maintenance management of the power company, so as to clarify the tasks to be completed by the system. ASPNET technology is used to realize the management of business form data and various transaction processes of each system, and the related technology of process engine is applied to the maintenance of each equipment. A load forecasting method of power system based on SVR is proposed. It makes the operation and maintenance work of this system more intelligent. Each function module of the whole system is designed in detail. The system basically meets the needs of power equipment maintenance and repair management.
Research on Building System of Audit Evidence Integration Model Driven by Computer Big Data
Authors: Feng Linlin
Published: April 2024

The database, data warehouse, model base and knowledge base are initially conceived based on the existing audit decision support system, t. Standardize the data in the two aspects of database and data storage, transform all kinds of structured data into a general DSS supported data structure, and summarize the information required in the audit process and the circulating business data according to the four aspects of audit purpose, audit risk, audit evidence and audit importance. This provides data support for constructing pattern library and knowledge base. In the pattern library, the paper designs and stores common patterns, which breaks the situation that CPA relies on computer to make decisions in independent audit. Through the research of knowledge expression and reasoning mechanism, the existing examples and existing knowledge are converted into knowledge format that can be recognized by machines for easy use. The paper makes an empirical study on the audit decision support system of purchasing and payment.
DRESCNN: Deep RESNET Convolutional Neural Network Based Classification of X-Ray Images for Detection of COVID-19
Authors: Vipul.V.Bag
Published: April 2024

In this research work a solution is proposed to address the challenges for detection of Coronavirus disease (COVID-19) Infection through an innovative healthcare framework. The proposed system leverages predictive analytics on patient information and facilitates consultation, eliminating the delay in diagnosis and treatment. The solution is modular, encompassing various health services, thereby consolidating major healthcare concerns under one unified platform. The images are the inputs for the models provided on this platform which are passed to the preprocessing algorithm followed by passing to pretrained models or an algorithm to analyze and predict the disease. Finally, classification of the images of X-ray for COVID-19 Infection detection using Deep Convolutional Neural Network Models like ResNet50, InceptionNet, MobileNet are used at a detailed level. After classifying these X-ray images with different CNN variants, a majority voting is applied for selecting more accurate class label. It is observed that ResNet50 is giving highest accuracy frequently. Hence Deep ResNet Convolutional Neural Network (DResCNN) is used. This proposed solution not only tackles the critical issue of delayed decision-making and treatment but also introduces cost-effective measures by minimizing the need for extensive hospital visits.
A Novel and Efficient Spatio-Temporal Co-location Pattern Mining Algorithm
Authors: Swati Meshram
Published: April 2024

Colocation pattern mining approaches aim at discovering neighboring relationships of distinct spatial features in geographic and temporal space.With big spatio-temporal dataset, there is large number of patterns often discovered. Then it is of importance to discover meaningful and patterns of interest which come as an aid in applications in use for humans and commercial use. To discover interesting patterns, we present in this article a co-location pattern mining algorithmic framework by considering neighbourhood, clustering, hashing, and mining methods. Neighbourhood relationship describes the closeness between the entities. The results of neighbourhood could be varied by varying the distance threshold. These objects exhibiting neighbouring entities are grouped using clustering technique. Clustering is a classical research approach that produces grouping of the entities. A clustering technique has been presented in the paper for spatio-temporal dimension that offers the advantage of faster grouping based on the neighbourhood relationship. Finally, a hash structure is utilized for faster access and retrieval of patterns. The proposed mining algorithm along with the distance and time threshold efficiently discovers the interesting spatio-temporal patterns and validates the patterns. The experiment conducted shows that the proposed co-location algorithm method yields effective and efficient outcome.
A Revised Model of Salinity Surface Reflection Coefficient at A range of Frequencies
Authors: Imadeldin Elsayed
Published: April 2024

The frequency of the incident signal wave can affect how salinity influences the seawater's reflection coefficient. The impact of salinity on the electromagnetic characteristics of seawater is generally more pronounced at multiple frequencies. This paper investigates a wide range of frequencies such as 100 MHz up to 1 THz under the 30 ppt salinity level, angle is 30-degree, distance traveled up to 100 meter, and sweater dielectric 81 to offer distinct attention that should respect each salinity value individually when designing the wireless coastal connection. Due to an undeniable disagreement among scholars regarding the relationship between salinity and frequency, the study provides a method to predict the reflection coefficient in different ranges of frequencies using derivation equations. Nevertheless, the distinct outcomes show under the 30 ppt salinity the reflection coefficient hit 0.015 dB at frequencies from 100 MHz up to 500 MHz and then growing up to reach 0.03 dB at 1 GHz, whereas under the same salinity level and from 100 GHz to 500 GHz achieved 0.2 up to 0.4 dB, and increase to hit 0.9 dB at 1 THz. The significance of the result would open the way to accommodate the salinity levels under the specific frequency and hence the accuracy of the link budget. Overall, the fluctuation in coastal link parameters such as frequency, power transmitted, and salinity level produces peculiar signal behavior that should be estimated in the coastline budget.
Detecting Three Different Diseases of Plants by Using CNN Model and Image Processing
Authors: Awad Bin Naeem
Published: April 2024

Agricultural output is critical to Pakistan's economy, and plant disease detection is critical for both the environment and human health. This work presents a CNN-based approach for detecting plant diseases, which is evaluated on sample photos to assess the temporal complexity and infected area. The model was given three disease cases: corn common rust, tomato bacterial spot, and potato early blight. Using the CNN algorithm, the CNN model attained an accuracy of 95.55% for tomato bacterial spot, 96.72% for Corn Common Rust, and 97.63% for Potato Early Blight. This method may aid in the diagnosis and treatment of plant diseases.
Performance analysis of Machine Learning Algorithms to classify Software Requirements
Authors: Sonali Idate
Published: April 2024

Building a software system requires a clear understanding of its purpose and its operational characteristics. Functional requirements establish the system's purpose, while non-functional requirements define its operational performance aspects. It is essential to identify and classify requirements accurately to develop reliable software. In this research paper, we aim to classify functional and non-functional software requirements using different Machine Learning algorithms and techniques. We utilized four popular classification models, including Logistic Regression, Support Vector Machines, Decision Tree, and Random Forest Multi-layer Perceptron Neural Network to classify the requirements. To enhance the accuracy of the classification, we also applied a technique based on cosine similarity to verify if the custom string provided as input is related to software requirements. The addition of cosine similarity improved the accuracy of classification and reduced the misclassification of non-requirements.
Recycling and Reuse of Basalt Fiber Reinforced Resin Materials with Artificial Intelligence Technology
Authors: Baijie Cai
Published: April 2024

Landfill and incineration of obsoleted Basalt Fiber Reinforced Resin (BFRR) not only waste resources, but also pollute environment. Therefore, this study investigates the stock, environmen-tal hazards, and recycling value of waste BFRR, analyze the current BFRR recycling technolo-gies, and summarizes the current challenges and future development of BFRR recycling. The results show that the amount of obsoleted BFRR is increasing, but traditional disposal methods are challenging for efficient resource utilization while also causing environmental pollution. Emerging disposal methods also have certain drawbacks, such as the most severe damage to re-cycled Basalt Fiber (BF) reaching 80%, low recycling rates, and some methods needing help to recover resin. Regenerated BF can be used for material reinforcement, 3D printing material prep-aration, and component production, while resin recycling materials can be used in the chemical industry.Digital image processing technology and artificial intelligence (AI) are effectively uti-lized to monitor the durability of BFRR material and identify its recycling trace. Integration of AI-generated big data enhances the efficiency of BFRR material recycling, contributing to envi-ronmental protection. Overall, the investigation and advancement of BFRR recycling are still in their nascent stages. Moving forward, it is imperative to delve deeper into exploring more effi-cient and intelligent environmentally sustainable recycling methodologies aimed at minimizing recycling expenses. This endeavor will facilitate the repurposing of waste materials, consequently mitigating the environmental footprint of waste.
Web Service Implementation “AI Yoga Trainer Using Human Pose Estimation” for Web Discovery
Authors: Milind Gayakwad
Published: April 2024

This research work is to create a application as web service to make it available on web for web service discovery. The most significant yoga stance is well-known on a global scale and supports the health advantages advocated by the ancient sages. OpenPose estimates that computer vision technology can help yoga practitioners achieve the greatest form and alignment. The integration of the OpenCV, Python, and MediaPipe frameworks is the main emphasis of this research project in order to develop an OpenPose estimation system for yoga poses. A camera is used to capture the yoga practitioner's motions, and a deep learning model is used to predict the important body parts. The algorithm then looks at these essential aspects to determine if the practitioner is doing the pose correctly or whether any adjustments are needed. The device gives the practitioner immediate feedback, allowing them to modify their alignment and posture as necessary. The OpenPose estimate system can help with the practice of yoga by offering detailed visualizations of the essential body parts throughout each posture in addition to providing real-time feedback Practitioners may more clearly understand the right form and alignment and make the required adjustments by utilizing this visualization. Additionally, the OpenPose estimation system enables the tracking of development over time. Practitioners who want to practice alone at home or may not have access to an instructor can also benefit from it.  This paper offers a MediaPipe, OpenCV, and Python-based OpenPose estimation system for yoga positions. For optimal form and alignment, the tool may offer real-time feedback and visual representations of key body parts throughout each position. It is useful tool for yoga teachers and students, enhancing yoga's safety and efficacy while supporting the practice.
Investigations of Regenerative Braking and Vibration Energy Conservation for Efficient Charging of Lithium-Ion Battery and Supercapacitor Hybrid System in Electric Vehicle Applications
Authors: Ravikant Nanwatkar
Published: April 2024

This study aims to determine new methods of making electric vehicles more energy efficient by focusing on regenerative braking and vibration energy conservation in a hybrid energy storage system integrating lithium-ion batteries and supercapacitors. The first part of the research identifies the regenerative braking system, evaluates its effectiveness, and proposes enhancements for capturing and storing braking energy. Additionally, an investigation is made into whether it would be possible to conserve vibrating energies through creative integration with energetic harvesting technologies. The purpose is to capture and convert vibrational power produced when a car moves into an electrical charge using piezoelectric device for hybrid power storage. More energy efficiency is achieved through this method while also trying to overcome the shortcomings of regenerative braking in some driving situations. This experiments involve Modeling, Simulation and development and testing of prototype systems under different driving and vibrating conditions as well as real-world performance evaluation. It will help to determine how effective the hybrid system that has been suggested enhances energy recovery and storage efficiency in electric vehicles.
Real-Time Traffic Sign Detection and Recognition System using Computer Vision and Machine Learning
Authors: Rahul Patil
Published: April 2024

The use of computer vision technology for our system has taken a huge leap and has revolutionized the way in which road safety is managed and maintained. This part of the paper represents the key components and functionalities of such a system which helps in enhancing the road safety and also in traffic management. The proposed system is meant to contain computer vision algorithms and some machine learning techniques. These will help in detecting and recognizing the traffic signs in real time video streams. With the help of deep learning models like convolutional neural networks, the proposed system can precisely identify various traffic signs present on roads. The major components of the proposed system include a camera interface which will capture real time video frames from the road, image processing modules for color space conversions, binary thresholding, contour detection and a classification module for the purpose of sign recognition. Deep learning frameworks like TensorFlow and OpenCV libraries are used for image processing and model training In the proposed system. The real time nature of this system allows the spontaneous detection and recognition of traffic signs enabling us to change the road conditions. Hence, the system contributes to prevent accidents and provide timely warnings to drivers. In addition, this system offers potential applications in traffic management and for autonomous driving system. With the help of accurate information, the system can contribute towards more efficient traffic flow and reduce congestion on roads. Overall, the system has very good impacts in road safety and efficiency. It shows advancement in road safety and traffic management.
Comparative Analysis of Human Pose Estimation Methods with Fall Detection and Smart Alarming System
Authors: Rahul Patil,
Published: April 2024

The population of old age peoples are increasing day by day and it will grow up by 31% of today’s population till 2031. In many cases the old age people are prone to fall. So, old age peoples can help in various aspect by using advanced technology. We already know that fall discovery is very much critical part of senior care as fall pose a threat to their health. To this challenge, this discussion and study proposes a fall detection model comprising three crucial phases: Pose Estimation, Fall Detection, and Smart Alerting System. Using computer vision model similar as OpenPose, PoseNet, MoveNet and YOLOv7. By the help of this models, we directly track the act of senior individualities in real time and identifies fall incidents grounded by abnormal changes in posture. When fall detect, the WhatsApp Alert message with fall detected frame and phone call will be received by Caregiver or their family members to take urgent actions. In case of fall, one can prevent from severe injuries which could be caused due to delay in mediation. These proposed models will enhance the safety of old age population.
Explainable Artificial Intelligence into Cyber-Physical System Architecture of Smart Cities: Technologies, Challenges, and Opportunities
Authors: Isha Batra
Published: April 2024

The demand for smart cities stems from the increasing urbanization, limitations on resources, and the aspiration to establish metropolitan areas that are more habitable, adaptable, and interconnected. The integration of Cyber physical Systems (CPS) into smart cities has proven advantageous across diverse areas, such as healthcare, transportation, and manufacturing but as the intricacy of CPS Architecture escalates, the cognitive processes involved in decision-making get progressively convoluted, hence posing a challenge in comprehending and elucidating the system's conclusions in Smart Cities. To enhance the comprehensibility of judgements, it is imperative to combine the CPS architecture with XAI. To visualize the benefits of this integration, the authors carried out a comprehensive analysis utilizing the Scopus database. Diverse search queries were formulated and executed to acquire pertinent articles. Following the search, a total of 24 papers pertaining to the integration of Explainable Artificial Intelligence (XAI) and Cyber-Physical Systems (CPS) were chosen for analysis. A comprehensive evaluation of these publications and a meta-analysis of the results received from the Scopus database allowed for a deep grasp of the topic. The importance of XAI and CPS in Smart Cities rests in their ability to enhance decision-making, promote transparency, and increase efficiency. This study examines the factors that come before and the outcomes that result from combining XAI and CPS, encompassing the technologies involved, the difficulties faced, and the potential advantages. Furthermore, this study highlights the urgent necessity to incorporate XAI with CPS, as evidenced by the research findings published in the Scopus database.
Design of circular slots loaded MIMO antenna with DGS at 28 GHz for 5G wireless services
Authors: Ravi Kumar Goyal
Published: April 2024

In this paper, a MIMO microstrip patch antenna with four radiating elements is designed and analyzed at 28 GHz. This paper addresses the problem of mutual coupling without adding a decoupling network. The decoupling method increases the complexity and volume of MIMO antennas. The CST Microwave simulator is used to simulate the MIMO antenna. The simulation and measurement results are compared to analyze the antenna's performance. The parameters of the MIMO patch antenna, including return loss, VSWR, gain, beam width, and radiation pattern, are evaluated at a center frequency of 28 GHz. The overall dimensions of the MIMO antenna are 60 × 42 × 1.6 mm3.An H-shaped slot is inserted in the middle of the ground plane to minimize mutual coupling among the radiating elements. The isolation amplitude between radiating elements is observed greater than 35 dB at 28 GHz frequency. A wide impedance bandwidth of 4 GHz (26.5 to 30.5) is achieved, by etching four small arcs into the patch’s corners. In this work, two circular slots are inserted on the radiating element to widen the bandwidth further by adjusting the surface current on each radiating patch. At the center frequency of 28 GHz, the Envelope Correlation Coefficient (ECC) is observed to be 0.0001, with a diversity gain of 10 dB. The antenna has a maximum radiation efficiency of 75% and a peak gain of 7.62 dB. This antenna can be used for 5G wireless services.
IoT Based Smart Kitchen Using Fuzzy Intelligence and Signal Processing
Authors: Narendran S P
Published: April 2024

Kitchen management remains a challenging task, despite many technological advancements. As a result, it's clearly time for us to get the kitchen work done with ease. The ingenious Kitchen uses the Internet of Things for design and features a mobile application as a user interface. Smart Kitchen includes a grocery management system, a fuzzy system for adaptive cooking and signal processing for identifying the cooker whistle. In the grocery management system, an ultra-sonic sensor measures the inventory level and using a Wi-Fi module, data updates the value of a particular key in the cloud. Users can see the amount of item left, be notified of shortage, and get the shopping list using the mobile application.
Presenting a Systematic Framework of Factors Influencing the Policy of University Governance Transformation
Authors: Seyed Mehdi Alvani Saeed Mirnejati, Hosein Aslipour,
Published: April 2024

Objective: This article uses the ultra-compound method to provide systematic framework of effective factors.
Method: Meta-composition method is choosing 470 related articles and research in 2000-2024 from Web of Science citation index. This research is qualitative and interpretive paradigm aiming to discover the characteristics of  development policy governance. Also, in terms of research time, it is cross-sectional.
Results: The characteristics of this new approach to transition of governance can be seen in four categories; The role of the state, university, society and the market. Each of these four groups also has parts below.
Conclusion: The issues of "power and policy developments, policy processes improvement" and "Information exchange,trust and diverse", the researchers have had more and more different research on issues of "qualitative and quantitative development" and "raising of the student's generation" and have not been studied in different dimensions. On this basis and regarding the importance of policy issues, implementation of policies, future research can pay more attention to the recent two dimensions. The findings of this study can be useful for applying new approaches to improve governance transformation in different policy fields.
DOI: https://doi.org/10.52783/pst.290
BTTAB – Broad-Based Testing of Energy-Efficient Demonstration Buildings with Thermally Activated Building Components
Authors: Lampersberger Paul , Becke Walter , Mayr Martin , Wolfesberger David
Published: April 2024

The expansion of renewable energy generation as well as extensive possibilities for energy storage are central cornerstones of the energy transition. Thermal building component activation can be a key aspect here, as it can be used multifunctionally as a heating and cooling delivery system and as a storage system for fluctuating renewables. These applications not only offer enormous potential for the integration of renewable energies and waste heat, but they also promise high economic attractiveness and impress with their simplicity in implementation, operation and use.
In order to investigate these properties of Thermally Activated Building Structures (TABS) on real buildings, in the study BTTAB [1] 18 demonstration buildings spread over Austria have been selected for monitoring and surveys. It is intended to provide information about the performance of the storage and energy flexibility potential on the one hand, and to generate essential findings with regard to user comfort, user satisfaction, economic aspects in construction and operation as well as functionality on the other.
This paper highlights the various applications of the thermal building component activation technology and provides already derived results relevant for the planning, design and operation of future buildings with TABS.
Schrödinger’s mammoth – ecological assembly in the age of humans
Authors: Lyons Kate
Published: April 2024

Ecologists have long been interested in how species assemble into communities. In particular, they are interested in how species traits, environmental factors, and biotic interactions affect species distributions, and membership and persistence in ecological communities. Determining whether assembly rules exist and what they are is particularly important in the face of ongoing climate change. However, despite decades of study, no clear consensus has emerged. In part because modern studies are limited by the short time scales over which they are able to collect data and by the fact that humans are incredibly successful ecosystem engineers who have affected almost every part of the planet. In contrast, paleontologists have been interested in the interplay between species traits and the environment, and how these relationships change over time in response to global forcing factors such as climate change. Increased knowledge of taphonomic processes has led to an understanding that fossil assemblages preserve reliable information about ecological communities and species interactions. By comparing the structure of these fossil assemblages with modern assemblages, we can begin to identify aspects of community structure that are similar across many taxonomic groups and across long time scales. We can also determine whether and how these patterns have changed with the increasing dominance of humans on the globe. Using a macroecological lens, I examine mammalian community structure over long and short time scales including metrics such as co-occurrence structure, body size distributions, and functional traits. I evaluate how these patterns change over time and with changes in global climate. Finally, I examine how some traits associated with extinction risk have changed over time and the consequences for ecological communities. I find that in paleoecological communities, there are consistent patterns over time in terms of co-occurrence structure, body size distributions, and extinction risk, but that many of these patterns change as human impacts increase including the role that functional traits play in mediating co-occurrence structure. These changes suggest that humans are fundamentally altering ecological communities and resetting ecological assembly rules. Paleontology has a key role to play in identifying the disruptions to assembly rules by humans and what that means for predicting how species are likely to respond to future climate change, habitat fragmentation and the loss of biotic interactions because of extinctions.
El contexto ritual de la molienda de cacao en San Nicolás, Ixmiquilpan, Hidalgo, México
Authors: Velasco Omar
Published: April 2024

San Nicolás is one of the old neighborhoods of Ixmiquilpan (Hidalgo, Mexico) that shares its traditional cuisine with the entire Mezquital Valley but stands out by its taste for champurrado (chocolate atole) and charape (pulque made from seeds and local fruits) on the days of its patronal feast. These two beverages are prepared in the context of an intense ritual that shows the integration between some elements of Mesoamerican religion and Catholicism. This article focuses on observing and analyzing cocoa grinding since some aspects have great relevance within the various food and ritual practices among the Otomi people of the Ixmiquilpan Valley. Cocoa grinding refers to an updating of traditional cuisine—to grinding as a ritual, to a bloodless sacrifice, to social reproduction, to the integration of the mechanism of reciprocity and donation, and to the closeness between cuisine and rituality around devotion to patron saints.
Cocinas, milpa y recolección: resistencias culinarias locales en la costa totonaca de Veracruz, México
Authors: Méndez Martínez Erika María
Published: April 2024

Local cuisines can be considered spaces of resistance for knowledges and culinary practices in the presence of the implementation of various tourism and heritage projects. Some of these resistances are the transmission of knowledges, rituals around cooking, and the sale of harvested and collected products. In consideration of the above, this article aims to show through ethnographic analysis the resistance carried out by Totonaca women with the sale of products harvested and collected in milpas and the mountain, which are acquired and used in daily and ritual foods, or as basic ingredients in the preparations of restaurants on the Totonaca coast of Veracruz. The fieldwork was conducted in different periods (between 2019 and 2022), privileging ethnographic recording, participant observation, and semi-structured interviews. Market tours were also carried out, as well as reviews of printed recipe books of Totonaca cuisine. The conclusion evidenced that resistances—such as the sale of harvested and collected edible plants—have made possible the construction of the so-called Totonaca cuisine, a cuisine that, although it has incorporated new and varied ingredients throughout its history, continues to have an intimate relationship with the milpa system and harvesting.