Computer Life (CPL) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of computer, network, communication science and technology, and its application in human social life across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Modern Approaches to LLaMA Fine-Tuning: Parameter-Efficient Methods for Targeted Domain
The emergence of Large Language Models (LLMs) has revolutionized natural language processing across numerous domains. However, adapting these models to specialized applications while maintaining computational efficiency remains a significant challenge. This study presents a comprehensive analysis of parameter-efficient fine-tuning (PEFT) methods for LLaMA models, focusing on their application to healthcare, government, ocean science, and financial services. We evaluate Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), LongLoRA, and full fine-tuning approaches across multiple dimensions including computational requirements, memory usage, and domain-specific performance. LoRA achieves a 9-fold improvement in training efficiency while maintaining comparable performance, with only 1.2M additional parameters for LLaMA 7B models [1]. Healthcare applications showed 13-35% AUROC improvements [2], while software engineering tasks achieved 34-56% solve rates [3]. Memory optimization reduced peak GPU usage from 64GB to 37GB, with potential cost savings of up to 190x compared to commercial alternatives. These findings provide crucial insights for practitioners seeking to deploy LLMs efficiently in specialized domains while maintaining high performance standards.
Copyright (c) 2026 Computer Life https://creativecommons.org/licenses/by-nc/4.0
Large Language Models for Cybersecurity Intelligence, Threat Hunting, and Decision Support
Large language models (LLMs) have emerged as transformative technologies in cybersecurity, offering unprecedented capabilities in threat detection, vulnerability analysis, and intelligent decision-making. This review examines the application of LLMs across critical cybersecurity domains, including cyber threat intelligence (CTI), threat hunting, vulnerability detection, malware analysis, and decision support systems. The integration of LLMs such as Generative Pre-trained Transformer 4 (GPT-4), Bidirectional Encoder Representations from Transformers (BERT), Large Language Model Meta AI (LLaMA), and domain-specific models like SecureFalcon has demonstrated remarkable potential in automating complex security tasks, enhancing analyst productivity, and enabling proactive defense mechanisms. However, the deployment of LLMs in cybersecurity contexts introduces unique challenges, including prompt injection vulnerabilities, data poisoning risks, hallucination concerns, and ethical considerations regarding adversarial use. This paper synthesizes recent research advances, evaluates current LLM architectures and their security applications, examines real-world implementation challenges, and identifies critical gaps requiring further investigation. Through comprehensive analysis of over sixty recent studies, we highlight how LLMs are reshaping cybersecurity practices while emphasizing the necessity for robust security frameworks, continuous model validation, and responsible deployment strategies to mitigate emerging risks associated with these powerful artificial intelligence (AI) systems.
Copyright (c) 2025 Computer Life https://creativecommons.org/licenses/by-nc/4.0
Dual DoF Self-adaptive Bionic Ankle’s Spring-Cable System Design and Dynamic Simulation
This study focuses on the design and dynamic simulation of a spring-cable-driven system for a dual-degree-of-freedom (2-DOF) adaptive bionic ankle joint. The research aims to overcome the limitations of conventional ankle joint designs in complex terrains, including insufficient stability caused by single-degree-of-freedom configurations and inadequate real-time terrain adaptability. To address these challenges, the study developed a biomimetic ankle model capable of replicating the sophisticated motion patterns of human ankle joints. The proposed design incorporates a lightweight cable-driven mechanism with remotely positioned actuators. Through systematic simulation and optimization using ADAMS dynamic simulation software, key parameters were determined: a spring stiffness coefficient of 900 N/m, ground contact angle of 20°, and motor rated speed of 250 rpm.
Intelligent Q&A application of rules and regulations based on large models and RAG technology
Employees face high learning costs and inefficient retrieval due to the increasing volume and complexity of company rules and regulations. This paper proposes an intelligent Q&A system using OCR, vectorization, Large Language Models (LLM), and Retrieval-Augmented Generation (RAG). We detail the knowledge base construction, prompt engineering, and Q&A module design. Example validation demonstrates the system’s accuracy, completeness, and logical output. Results show it significantly improves employee learning efficiency, offering an innovative solution for rules & regulations informatization.
Adaptive Reinforcement Learning with Cross-Modal Attention for Anomaly Detection
Traditional anomaly detection systems often struggle with evolving patterns and multimodal data environments, limiting their effectiveness in dynamic real-world scenarios. This paper presents a novel framework that integrates Adaptive Reinforcement Learning (ARL) with Cross-Modal Attention (CMA) mechanisms for enhanced anomaly detection capabilities. The proposed Adaptive Reinforcement Learning with Cross-Modal Attention (ARL-CMA) framework employs a hierarchical attention architecture combined with Deep Q-Network (DQN) processing to adaptively learn from multimodal sensory inputs including temporal, spatial, and categorical data streams. Our approach addresses the critical challenges of temporal dependency modeling, cross-modal feature alignment, and dynamic threshold adaptation in anomaly detection systems. The framework incorporates a two-level attention mechanism operating at word-level and sentence-level granularities that enables selective focus on discriminative features across different data modalities, while the reinforcement learning component utilizes convolutional neural architectures for continuous adaptation of detection strategies based on environmental feedback. Experimental evaluations demonstrate significant improvements in detection accuracy, with ROC curve analysis showing superior performance compared to existing state-of-the-art methods including binary classifiers, dictionary-based approaches, and autoencoder techniques. The ARL-CMA framework achieves substantially higher true positive rates across all false positive rate ranges, establishing its effectiveness for practical anomaly detection applications in complex, multimodal environments.
LLM-Enhanced Financial Appraisal of Mechanical Carbon Capture and Storage Systems through Automated Technical-Economic Analysis
The escalating deployment of mechanical carbon capture and storage (CCS) technologies demands sophisticated financial appraisal methodologies that can accurately assess the complex technical-economic relationships inherent in these capital-intensive systems. This research presents a novel Large Language Model (LLM) enhanced framework for automated financial analysis of mechanical CCS projects, addressing critical gaps in traditional valuation approaches through advanced natural language processing, automated data synthesis, and intelligent financial modeling capabilities. The proposed system integrates state-of-the-art LLM architectures with domain-specific financial engineering principles to provide comprehensive technical-economic analysis that encompasses capital expenditure optimization, operational cost modeling, revenue stream quantification, and risk assessment across diverse CCS technologies including Direct Air Capture (DAC), point-source capture, and geological storage systems. Through extensive evaluation across 156 CCS projects representing $47.2 billion in total capital investment, our LLM framework demonstrates remarkable improvements in financial analysis accuracy by 34.7%, analysis time reduction of 78%, and risk assessment precision enhancement of 42.3% compared to traditional financial modeling approaches. The system successfully processes complex technical documentation including engineering specifications, environmental impact assessments, regulatory compliance reports, and market analysis data to generate detailed financial projections with confidence intervals and sensitivity analyses. Real-time market data integration enables dynamic updating of financial models based on evolving carbon credit prices, technology costs, and regulatory frameworks, with model recalibration completed within 2.7 hours compared to weeks required for manual analysis updates. The framework incorporates advanced uncertainty quantification through Monte Carlo simulation enhanced with LLM-generated scenario analysis, providing probabilistic financial projections that account for technology performance variations, market volatility, and regulatory changes. Automated report generation capabilities produce investment-grade financial documentation that satisfies due diligence requirements for institutional investors while providing interactive dashboards for real-time project monitoring and performance tracking. Validation against actual CCS project outcomes demonstrates superior predictive accuracy with mean absolute percentage errors below 8.3% for capital cost estimation and 11.7% for operational expense forecasting across 24-month prediction horizons.
Multi-Agent Reinforcement Learning for Coordinated Smart Grid and Building Energy Management Across Urban Communities
The increasing complexity of urban energy systems necessitates sophisticated coordination mechanisms between smart grids and building energy management systems to achieve optimal energy efficiency and sustainability goals. This research presents a novel Multi-Agent Reinforcement Learning (MARL) framework designed to facilitate coordinated energy management across interconnected urban communities. The proposed system employs distributed intelligent agents that operate autonomously while maintaining collaborative decision-making capabilities for energy distribution, consumption optimization, and demand response coordination. Through extensive simulation studies conducted across three metropolitan areas involving 1,247 residential and commercial buildings over a 12-month period, our findings demonstrate significant improvements in energy efficiency, with average reductions of 23.4% in peak demand loads and 18.7% in overall energy consumption costs. The MARL approach exhibits superior performance compared to traditional centralized control systems, particularly in handling dynamic load balancing and renewable energy integration challenges. These results contribute substantially to the advancement of sustainable urban energy infrastructure and provide practical insights for large-scale smart city implementations.
Frequency-Stabilized Laser Utilizing Acetylene Molecular Absorption Lines as Frequency References
Frequency-stabilized lasers, as core components in precision optical systems and communication networks, demonstrate direct correlations between their frequency stability and critical performance metrics such as optical communication capacity and spectral measurement resolution. While conventional ultra-stable optical cavity solutions achieve exceptional stability, their practical implementation faces inherent limitations due to complex temperature control requirements and vibration isolation demands, hindering cost-effective miniaturization for industrial applications. Acetylene molecules, leveraging their intrinsic absorption spectral lines in communication bands, offer novel opportunities for developing compact frequency-stabilized lasers. This paper presents a new stabilization system based on acetylene molecular absorption characteristics. Through optimized optical path design and improved feedback control architecture, effective laser frequency locking in communication bands has been achieved, providing a cost-efficient stabilization solution for applications in fiber-optic communications and quantum sensing technologies.
Analysis of vehicle and pedestrian recognition algorithm based on improved YOLO
This paper expounds a vehicle pedestrian recognition algorithm based on improved YOLO. It analyzes the shortcomings of current algorithms and proposes improved schemes such as Feature Pyramid Network (FPN) and loss function adjustment. It extracts multi-layer image features through convolutional layers, performs feature fusion and multi-scale detection, and introduces Focal Loss instead of the original classification loss on the basis of the existing loss function to improve the current detection performance and solve the problem of inaccurate recognition of small targets.
Surface defect detection algorithm for special-shaped electronic components based on improved RT-DETR
The detection of surface defects of special-shaped electronic components is a key link to improve the level of insertion technology of special-shaped plug-in machines. Traditional manual detection is susceptible to human subjectivity, the efficiency of template matching algorithm is low, and in the case of insufficient sample data, the existing deep learning technology has problems such as low accuracy and insufficient real-time performance in defect detection. In order to improve the accuracy and real-time performance of special-shaped component detection, this paper improves the object detection model RT-DETR and proposes a real-time multi-dimensional feature adaptive network (RT-MDAFNet): Firstly, the Adaptive Fusion Pyramid Network (AFPN) is designed at the feature fusion layer of the model, and the adaptability and feature extraction ability of the model to multi-scale targets are improved through the dynamic channel attention mechanism and selective feature fusion mechanism. Then, the adaptive channel-spatial aggregation network module (SASE-RepNet) was designed to improve the detection accuracy and efficiency in complex backgrounds by combining multi-level feature aggregation, channel adaptive weight allocation and spatial selectivity enhancement mechanism. In the absence of existing datasets, a dataset of special-shaped electronic components was constructed, and the RT-MDAFNet model was compared with 8 baseline models, including DETR, Faster R-CNN, YOLO series, etc.
Detection of irregular-shaped electronic components
Research on Real-Time Detection Method for River Floating Objects Using UAVs Based on Lightweight YOLOv8s Model
To address the challenge of identifying small floating objects in river debris detection, this study proposes an enhanced model that integrates YOLOv8s with Large Separable Kernel Attention (LSKA) and Asymmetric Kernel Convolution (AKConv). The optimized architecture reduces the parameter count of YOLOv8s from 11.1M to 8.3M while improving mAP50 from 68.0% to 77.9% on the "Water Surface Floating Object Dataset" developed by the China Institute of Water Resources and Hydropower Research. Experimental results demonstrate that the proposed method significantly enhances detection accuracy, strengthens occlusion recognition capability, and improves network adaptability to complex river environments. This advancement effectively upgrades the real-time monitoring performance of UAVs in riverine applications, providing an efficient solution for water resource management.
Multi-Objective Optimization of Aluminum Plate Passive Shielding Structure for EV-WPT Systems Based on Grey Wolf Optimization Algorithm
With the rapid development of electric vehicle wireless power transfer (EV-WPT) technology, human electromagnetic safety issues caused by leakage magnetic fields have become a critical challenge restricting its industrialization. This study focuses on optimizing the aluminum plate passive shielding structure of EV-WPT systems to balance electromagnetic shielding performance and energy transmission efficiency. An electromagnetic simulation model of the EV-WPT system (with a 11kW power capacity and dual LCC compensation circuit) was established, and the principle of metal shielding based on the eddy current effect was analyzed. The Grey Wolf Optimization (GWO) algorithm was employed for multi-objective optimization, targeting minimization of magnetic induction intensity (leakage magnetic field) and maximization of transmission efficiency. Results show that the optimized aluminum plate structure effectively reduces leakage magnetic field while maintaining high transmission efficiency. This research provides a scientific basis for enhancing electromagnetic safety and promoting the safe application of EV-WPT technology.
Theoretical Mechanism and Implementation Path of Intelligent Technology Enabling Intelligent Transformation of Libraries
This study employs research surveys, expert interviews, and literature analysis to explore the fundamental concepts, driving mechanisms, internal principles, challenges, and implementation pathways of digital intelligence technology in library intelligent transformation. Through both exogenous and endogenous drivers, digital intelligence technology is integrated into library construction, operations, services, governance, and resource systems, empowering libraries to achieve digitalization, efficiency enhancement, smart transformation, and low-carbon transition. Challenges in implementing intelligent transformation through digital intelligence technology include high renovation costs, fragmented smart modules, outdated conceptual understanding, talent shortages, and lack of standardized frameworks. Implementation strategies encompass: adhering to the "reader-first" principle in intelligent transformation, rationally advancing operational mechanism reforms, strategically selecting intelligent transformation models based on institutional conditions, guiding transformation with national standards, and enhancing governance capabilities through interdisciplinary talent development.
Learning Causal and Sequential Patterns in Student Knowledge Tracing via Transformer-Augmented Bayesian Networks
Student knowledge tracing requires sophisticated modeling approaches that can capture both the causal relationships between learning concepts and the sequential patterns of knowledge acquisition over time. Traditional knowledge tracing methods struggle to simultaneously model causal dependencies and temporal learning dynamics while maintaining interpretability for educational practitioners and maintaining computational efficiency for real-time applications. The challenge lies in developing frameworks that can learn complex causal structures from educational data while effectively capturing the sequential nature of learning processes and providing actionable insights for personalized education. This study proposes a novel Transformer-Augmented Bayesian Network (TABN) framework that integrates transformer architectures with probabilistic graphical models to enable comprehensive modeling of causal and sequential patterns in student knowledge tracing. The framework employs transformer networks to capture long-range sequential dependencies in learning trajectories while utilizing Bayesian networks to model causal relationships between knowledge concepts. The integrated approach enables joint learning of causal structures and sequential patterns through end-to-end optimization while maintaining probabilistic interpretability essential for educational applications. Experimental evaluation using large-scale educational datasets demonstrates that the proposed framework achieves 41% improvement in knowledge tracing accuracy compared to traditional methods. The TABN approach results in 36% better prediction of learning outcomes and 44% improvement in causal relationship discovery between knowledge concepts. The framework successfully combines the sequential modeling capabilities of transformers with the causal reasoning advantages of Bayesian networks, resulting in 32% better interpretability scores and 28% improvement in educational decision support compared to existing knowledge tracing approaches.
A Hierarchical Deep Reinforcement Learning Strategy for Self-Adaptive CPU Scheduling in Multi-tenant Database Systems
Multi-tenant database systems face significant challenges in CPU resource allocation due to competing performance requirements across diverse tenant workloads with varying priorities, Service Level Agreements (SLAs), and resource consumption patterns. Traditional static scheduling approaches fail to adapt to dynamic tenant demands and changing system conditions, leading to SLA violations, resource wastage, and degraded overall system performance. This study proposes a Hierarchical Deep Reinforcement Learning (HDRL) strategy for self-adaptive CPU scheduling in multi-tenant database environments. The framework employs a two-tier architecture where a global scheduler manages tenant prioritization and resource allocation policies, while local schedulers optimize CPU utilization within individual tenant contexts. Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) algorithms enable dynamic adaptation to evolving tenant workloads and system resource availability. Experimental evaluation using multi-tenant workload benchmarks demonstrates that the proposed strategy achieves 41% improvement in SLA compliance rates while reducing average query response time by 35% across all tenant categories. The hierarchical approach successfully balances resource fairness with performance optimization, resulting in 27% better resource utilization efficiency compared to conventional scheduling methods.
A Path Planning Algorithm for Unmanned Overhead Cranes Integrating Improved A* and D* Lite with Kinematic Optimization
Aiming at the problems of excessive redundant nodes, unsmooth paths, and poor adaptability to dynamic environments in the path planning of unmanned cranes in coil warehouses, this paper proposes a hybrid path planning algorithm (AD* Lite) integrating improved A* and D* Lite, along with a quintic polynomial kinematic optimization method. Firstly, a collision detection mechanism is designed to prevent paths from passing through obstacle vertices, and a redundant node deletion strategy is introduced to enhance path smoothness. Secondly, a multi-scenario adaptive hybrid algorithm is constructed by combining the static planning advantages of the A* algorithm and the dynamic re-planning capability of the D* Lite algorithm. Furthermore, quintic polynomials are used to optimize the path, integrating kinematic constraints such as speed, acceleration, and turning radius. Experiments show that under the satisfaction of kinematic constraints, the improved fusion algorithm improves various indicators such as path length, search time, number of turns, and number of searched grids by approximately 40%.
WiFi Channel Modeling Method in Industrial Wireless Network Scenarios
As an important support for the intelligent manufacturing system, the industrial Internet is the cornerstone of the comprehensive interconnection of people, machines and things. Compared with other networking technologies, Wi-Fi has advantages such as high rate, low cost and a large number of compatible devices. It is an ideal choice for networking in industrial environments. However, its networking quality mainly depends on signal coverage and the provided throughput. With the rise of mobile edge computing that relies on high-throughput data transmission such as video streams, new challenges have been posed to the networking quality of industrial Wi-Fi with dynamic terminals. In response to the above problems, this paper proposes a channel modeling method for wifi networks in industrial scenarios.
A Unified Framework for Anomaly Detection and Root Cause Analysis in Microservice Systems
Modern software applications increasingly rely on microservice architectures for scalability, flexibility, and rapid deployment. However, this architectural paradigm introduces new complexities in monitoring system behavior, identifying anomalies, and determining their root causes across distributed services. Existing solutions often address anomaly detection and root cause analysis (RCA) in isolation, leading to fragmented insights and delayed resolution. This paper proposes a unified framework that integrates real-time anomaly detection with automated RCA using machine learning and graph-based dependency modeling. The framework continuously monitors telemetry data—including metrics, logs, and traces—and applies an ensemble of statistical and deep learning models for multivariate anomaly detection. Detected anomalies are then contextualized through a service dependency graph and analyzed using causal inference techniques to identify the most probable root causes. We evaluate the framework on both synthetic benchmarks and real-world microservice deployments. Experimental results show that it achieves high precision and recall in anomaly detection while significantly reducing RCA latency compared to baseline methods. By combining anomaly detection and RCA in a cohesive pipeline, the proposed framework enhances system observability and reduces mean time to recovery (MTTR), thus improving operational resilience in complex microservice environments.
Image Super-Resolution Reconstruction Method Based on Lightweight CNN-Transformer
Existing Transformer-based image super-resolution reconstruction methods suffer from excessive parameters and high training costs. To address these issues, we propose a lightweight CNN-Transformer-based image super-resolution reconstruction method. A CNN-Transformer module is designed using weight sharing, and a channel attention module is used to fully fuse image information, which improves the reconstruction of local and global features of the image. Meanwhile, depth-wise separable convolutions are used and the covariance matrix of cross-channel self-attention is calculated, which effectively reduces the number of parameters in the Transformer and lowers the computational cost. Then, a High-Frequency Residual Block (HFRB) is introduced to further focus on the texture and detail information in the high-frequency range. Finally, the choice of activation function required for Transformer to generate self-attention is discussed. Analysis shows that the GELU activation function can better promote feature aggregation and improve network performance. Experiments show that the method in this paper can effectively reconstruct more texture and details of the image while maintaining lightweight.
The Design of Medical Waste Recycling Network Based on Location and Path Collaborative Optimization
This thesis designs and optimizes a medical waste recycling network for enhanced efficiency and reduced environmental/social impacts. Addressing China's surge in waste volume, it introduces a dual-layer optimization model optimizing both collection point selection and transportation routing. The model incorporates innovative "loading reliability" and "travel time reliability" constraints to handle uncertainty. A hybrid algorithm (Simulated Annealing + Improved Harmony Search Genetic Algorithm) solves the problem, optimizing facility location and time-constrained vehicle routes. Case study in Shanghai demonstrated the model's effectiveness. Joint optimization outperformed single optimizations, reducing total costs and showing coordination importance. Improved reliability was found to reduce overload and delay risks, optimizing system performance. The research provides theoretical support and practical solutions for robust and economical medical waste management.
HP-YOLO11n: A lightweight model for surface defect detection of liquor bottle caps based on improved YOLO11n
In production, defect detection is crucial for ensuring product quality and consumer satisfaction. To address the issues of surface defect detection in liquor bottle caps and the large number of algorithm parameters, this study improves YOLO11n and proposes a more lightweight and higher-precision HP-YOLO11n algorithm. Firstly, We employ the improved HGNetv2 backbone network as our model backbone, which makes the model more lightweight while ensuring the accuracy of model detection. Secondly, added a P2 detection layer to YOLO11n, incorporating high-resolution feature maps and rich detailed information to enhance the model's overall recognition performance. Finally, we remove the P5 layer used for detecting large targets, which reduces the number of parameters and computational load while maintaining accuracy. The experimental results show that the HP-YOLO11n algorithm achieves a mean average precision mAP@0.5 of 87.06%, which is 1.52 percentage points higher than the original YOLOv11n algorithm, while reducing the number of parameters by 44.57%, making it more accurate and lightweight.
Research on the Location Selection and Path Optimization of Pharmaceutical Distribution Centers Considering Supply Chain Resilience
This study explores the location-routing optimization for pharmaceutical cold chain distribution, addressing the challenges of supply stability and efficiency. A dual-objective model minimizing cost and time is proposed, incorporating supply chain resilience. An improved NSGA-II algorithm based on multiverse theory enhances solution quality and convergence. Using real data from South China, the study analyzes distribution center resilience and reveals trade-offs via Hyper Volume analysis. The findings support practical decision-making and enhance supply chain reliability.
Numerical Simulation and Analysis of the Vibration Characteristics of the String Vibration Screen Mesh
With the deepening of oil and gas drilling, the increase of ultrafine particles and the complexity of waste water-based drilling mud have intensified the difficulty of solid-liquid separation. This paper addresses problems such as screen mud cake and clogging of fine mesh screens during the operation of drilling fluid vibrating screens. A novel string vibration-based detachment process is proposed, utilizing a single excitation source to induce string vibration in the filter screen. This promotes the detachment of filter cake from the separation membrane, reduces membrane fouling, and facilitates the regeneration of the filtration medium. The physical method improves the dehydration efficiency of waste mud and the recovery of the liquid phase. A string vibration model of the filter screen is established to study the amplitude, acceleration, and stress-strain characteristics under different vibration frequencies. Results show that the amplitude fluctuations of surface particles on the filter screen follow a parabolic trend with frequency variation; amplitude fluctuations near the edges of the screen are greater than at the center, with the most pronounced fluctuations occurring at 40 Hz. Particles near the excitation source exhibit strong periodic oscillations. Acceleration varies significantly with frequency, showing intense fluctuations between 40 Hz and 50 Hz. Surface particle oscillations are periodic, with rapid velocity fluctuations near the excitation source. When the frequency exceeds 40 Hz, acceleration sharply increases, then drastically decreases after 60 Hz. Compared with traditional vibrating screens, the string vibration process produces heterogeneous particle oscillations on the filter surface with higher acceleration magnitudes and faster variation rates, thereby facilitating more effective detachment of materials.
On the relationship between study duration and academic performance in students
The focus of this paper is to address an educational research question – the correlation between a student’s practice duration and performance in problems. This intends to offer rule-of-thumb advice for students in formulating a particular study plan. Throughout the paper, we investigate such a topic by using data from roboTutor tablets, which provide granular, real-time insights into students' learning patterns. These tablets enable the collection of data on various study parameters such as study duration, accuracy, and time of day, offering a comprehensive view of the factors affecting academic performance. In addition to the basic analysis, we design a few naïve approaches, such as basic 2-dimensional scatter plot and aggregation, to capture any potential patterns in the data. By comparing the results from each of these approaches, we develop an educated inference about the correlation between study time and performance. The findings suggest that while the correlation exists under certain fixed effects, the relationship is not straightforward and is influenced by various external and internal factors such as time of day, subject type, and the individual characteristics of students. This research aims to provide valuable insights into the significance of a structured and scientifically informed study routine, emphasizing flexibility in planning.
Vegetable Sales and Pricing Correlations: Insights from a RANSAC Regression Analysis
The shelf life of general vegetable products in daily life is relatively short. As the sales time increases, the quality of vegetable products deteriorates. Most varieties cannot even be sold overnight. How to replenish and price the goods reasonably and timely is difficult. Extremely important and difficult. This paper develops a reasonable replenishment and pricing strategy by establishing a mathematical model and using planning methods. Using the collected sales volume and cost-plus pricing of vegetable commodities, RANSAC regression was performed to obtain the regression equation of the total sales volume of each vegetable category on the cost-plus pricing. Finally, the law of total sales volume and cost-plus pricing of each vegetable category was analyzed, and a set of equations with insignificant negative correlations was summarized.
Research on Fault Diagnosis of Special Equipment Based on CAN Bus Expert System
This paper addresses the issues of complex faults, incomplete detection equipment, and insufficient technical capabilities of maintenance personnel in CAN bus special equipment. By integrating several mainstream fault diagnosis methods, a fault diagnosis device for special equipment is designed based on an expert system. The device mainly acquires data parameters of special equipment and diagnoses the corresponding fault causes and elimination methods through logical judgment of the expert system, effectively improving the efficiency and accuracy of fault diagnosis and elimination. In light of the latest technological developments, corresponding improvement and development directions are proposed.
Open-World Class Incremental Learning with Adaptive Threshold
Existing class incremental learning (CIL) settings are mainly based on the closed-world assumption. Its training and testing sets only contain in-distribution (ID) samples. However, CIL methods are typically applied in open-world scenarios where out-of-distribution (OOD) samples are widely present, leading to unpredictable behavior of intelligent agents. The OOD categories may also change as the incremental tasks progress. In this paper, we focus on handling a realistic and challenging new setting called Open-World Class Incremental Learning (OWCIL). Specifically, OWCIL includes the accumulated OOD categories during testing to simulate the open-world continual learning scenario. Moreover, it does not provide any OOD samples for model training. Existing methods integrate techniques for CIL and OOD detection to address scenarios similar to the OWCIL. However, they either fail to handle evolving OOD categories or require OOD samples during training, limiting their performance under OWCIL. We propose an adaptive threshold (AT) method to handle the accumulated OOD categories. Additionally, a discriminative optimization method is introduced to determine the threshold without OOD samples. The effectiveness of our proposed method has been validated through extensive experiments on multiple benchmark datasets under OWCIL. Detailed analysis shows that both AT and discriminative optimization can clearly boost performance.