Innovation in Science and Technology

Hacking Is an Unauthorized Access in Electronic Devices: Skills Technologies Are Necessary to Enhance Defense Strategies

2026-07-03T07:44:50+00:00

Hacking is the technique of finding the weaknesses in the network system that exploits to gain unauthorized access to personal or business data that are in the networks, and the hacker is responsible for the legal consequences of his/her actions. The hacker has basic knowledge, desire, motivation, deep patience, planning workability, and financial supports. Usually an unethical hacker (black hat hacker) is a malicious guy who tries to steal, leak, and destroy confidential and valuable data and other sensitive information of the computer systems without permission of the user. S/he can usually be organized into two types of attacks: mass attacks and targeted attacks. On the other hand, an ethical hacker (white hat hacker) tries to strengthen the security mechanisms of the organization by exploring the weaknesses of it. This study tries to discuss the aspects of unethical hacking, types of hackers, and their behaviors.

Design of BIM and IoT-Based Tunnel Construction Monitoring Data Fusion and Safety Early Warning System

2026-07-03T07:53:56+00:00

The tunnel construction environment is complex, and traditional monitoring methods suffer from poor timeliness and insufficient data utilization, making it difficult to support real-time safety management and control during tunnel construction. This paper designs a construction monitoring and early warning system that integrates Building Information Modeling (BIM) and Internet of Things (IoT) technologies, adopting a “cloud-edge-device” three-tier distributed architecture. At the device layer, a multi-source heterogeneous sensor network is deployed; at the edge layer, edge computing gateways are installed for local data preprocessing and real-time analysis; at the platform layer, a data fusion analysis engine and a BIM visualization engine are integrated. For data fusion, the belief Hellinger distance is introduced to improve the Dempster-Shafer (D-S) evidence theory, effectively resolving conflicts among highly contradictory evidence sources. For safety early warning, a “4+4+N” hierarchical index system is established, and a progressively deepened three-level early warning mechanism is constructed, encompassing single-index threshold judgment, multi-parameter fusion evaluation, and Long Short-Term Memory (LSTM) time-series prediction. Finally, the system is validated through field deployment at the Yingeling extra-long tunnel in Hainan Province. The results that the proposed data fusion method achieves an accuracy of 92.5%, a 20.2% improvement over traditional D-S evidence theory. The early warning system attains an accuracy of 92.5% with a false alarm rate of 8.3%, a missed alarm rate of 3.1%, and an average response time of 2.8 seconds providing reliable technical support for tunnel construction safety management.

A Survey of Classic Machine Learning Algorithms: Principles and Applications

2026-07-03T08:13:48+00:00

Classical machine learning algorithms constitute the fundamental cornerstone of modern data science and intelligent system development. While deep learning has achieved transformative breakthroughs across numerous fields in recent years, classical methods remain indispensable in practical scenarios characterized by limited training data, stringent interpretability requirements, or constrained computational resources. Nevertheless, existing studies generally lack a systematic, unified, and beginner-friendly comprehensive survey that integrates theoretical elaboration, multi-dimensional comparative analysis, and actionable algorithm selection guidance.

To fill this research gap, this paper presents a thorough investigation of ten representative classical machine learning algorithms: Linear Regression, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, Adaptive Boosting, K-Means, and Principal Component Analysis. Each algorithm is explicated within a consistent structural framework, covering its mathematical formulation, core working mechanism, distinct advantages, inherent limitations, and typical application scenarios. Furthermore, a horizontal comparative analysis is conducted across seven critical dimensions, and a practical algorithm selection framework with targeted recommendations for typical industrial scenarios is proposed.

This work constructs a complete and logically coherent knowledge system of classical machine learning, serving as an accessible and pragmatic reference for novice learners and engineering practitioners. It also provides insights into future integration trends between classical algorithms and emerging technologies including large language models, explainable artificial intelligence, edge intelligence, automated machine learning, and federated learning.

Research on the Transformation Mechanism of Enterprise Informatization Technical Achievements from the Perspective of Industry-University-Research-User Integration

2026-07-10T06:25:04+00:00

China has sufficient scientific research reserves in informatization, yet the market-oriented transformation efficiency of relevant technologies remains relatively low. The traditional Triple Helix Theory cannot adapt to the virtualization and scenario-dependent characteristics of informatization technical achievements. Based on the revised Quadruple Helix Theory, this paper selects 1,872 sets of balanced panel data of 312 listed specialized, sophisticated, unique and innovative digital enterprises from 2019 to 2024, and adopts the two-way fixed-effects chained mediation model and fsQCA configuration analysis method to explore the mechanism of four-dimensional industry-university-research-user coupling on the transformation of informatization achievements. The research concludes that the coupling degree of industry-university-research-user integration significantly improves achievement transformation performance, with end users exerting the optimal enabling effect. Digital information asymmetry and scenario adaptation barriers play a chained mediating role, whose indirect effect accounts for 61.29%. The digital business environment and digital intellectual property protection positively strengthen the enabling effect of coupling, and the effect presents heterogeneity in enterprise property rights, achievement types and regional conditions. There are four collaborative configurations for efficient achievement transformation, and insufficient user participation and inadequate cross-subject data trust are the core inducements of failed transformation. This paper improves the theoretical system of digital collaboration, constructs a closed-loop achievement transformation mechanism, and provides empirical evidence and management references for quality improvement of digital industry-university-research collaboration.

Advances in Geodetic Monitoring of Subsurface Gas Reservoirs: From Surface Deformation to Reservoir Characterization

2026-08-03T08:58:21+00:00

The safe and efficient management of subsurface gas reservoirs, including natural gas fields and underground gas storage (UGS) facilities, is paramount for global energy security and environmental stewardship. While traditional monitoring relies on invasive methods like well-logging and seismic surveys, these techniques can be costly and spatially limited. Over the past two decades, geodetic methods, particularly Interferometric Synthetic Aperture Radar (InSAR) and the Global Navigation Satellite System (GNSS), have emerged as powerful, non-invasive tools for monitoring reservoir behavior. By measuring minute surface deformations—subsidence and uplift—caused by pressure changes within the reservoir, geodesy provides critical insights into reservoir dynamics. This paper reviews the advances in the application of geodetic techniques for monitoring subsurface gas reservoirs. It discusses the evolution from simple deformation detection to sophisticated quantitative analysis through poroelastic modeling, data assimilation, and the synergistic use of multiple geodetic and geological datasets. These advancements now enable the characterization of reservoir geometry, the inference of pressure distributions, the assessment of caprock integrity, and the optimization of injection and withdrawal strategies, solidifying geodesy’s role as an indispensable component of modern reservoir management.

Spectral Semantic Analytics of Local Spaces with Neural Network Models

2026-08-03T09:04:27+00:00

This paper explores the development and application of spectral-semantic analytics methods for studying local spaces using neural network models. The approach is based on the integration of spectral data analysis (including that obtained using spectrometers and thermal imagers) with semantic models that enable the interpretation of spectral characteristics as carriers of semantic structures. The study examines methods for transforming spectral modality into linguistic and semantic modalities: this makes it possible to describe the physical properties of local spaces not only quantitatively (through spectral parameters) but also qualitatively—in the form of semantic profiles and semantic patterns. Particular attention is paid to the construction of spectral-semantic dictionaries and corresponding neural network architectures capable of identifying and formalizing the relationships between the spectral signatures of objects and their semantic load in a given context. Various types of spectrograms and spectral representations (including multi-band and hyperspectral data) are used to analyze local spaces, as well as a neural network metamodel that enables the generation of specialized spectral-semantic models for specific subject areas. Attention mechanisms are integrated into the architecture of the models, ensuring the selection of the most informative spectral ranges and spatial zones that are significant for the interpretation of meanings.

Multi-Point Geometric Curvature Inspection Framework with Unified-Datum Decoupling, Region-Aware Adaptive NSGA-II Layout and Closed-Loop Mold Compensation for Large-Size Laminated Automotive Windshield Glass in Mass Production

2026-08-04T06:55:24+00:00

Large-size PVB-laminated curved windshield glass for intelligent new-energy vehicles integrates electrochromic dimming, defrost circuits and DTV antennas. Its global free-form curvature contour directly governs three vehicle-level functions: body-trim assembly clearance uniformity, wiper contact-pressure distribution and long-term PUR-adhesive interfacial bonding durability. Conventional single-point or uniform-grid inspection methodology ignores hot-bending nonlinear local deformation in critical sensitive zones. Aggregated mass-production defect data of 126,000 workpieces (Tier-1 supplier NDA #ICS-2024-OMEGA, SHA-256 hash 4f8a...bc91) indicate that legacy inspection induces an 11.80% assembly-reject rate, an 8.35% after-sales wiper leakage complaint rate, and a process-capability index Cpk of 0.17 (incapable of process). To address low repeatability, undetected local over-tolerance and open-loop forming control, this work proposes a five-component systematic inspection optimization framework. (i) Geometric error decoupling. A physically rigorous decomposition E_total(x,y,z) = V_forming(x,y,z) + R(θ_x, θ_y, θ_z,t)·P(x,y,z) + N(0, σ_sensor²) based on rigid-body coordinate transform theory is established, with independent variance contributions quantified as 70.8%, 20.8% and 8.4% via Monte Carlo (n=10,000) and GUM-style uncertainty propagation (combined expanded uncertainty U = 0.016 mm at k=2, ISO/IEC Guide 98-3:2008). (ii) Region-aware adaptive NSGA-II. The proposed RA-NSGA-II algorithm maintains three independent regional sub-populations (wiper, dashboard, four-corner) each with self-tuned crossover-mutation operators that adapt to local subpopulation fitness landscape; this representation escapes the premature-convergence defect of classical NSGA-II [Srinivas-Patil 1994] caused by single-global-fitness averaging across irregular large-area free-form surfaces. The optimized 18-sensor layout achieves critical-zone coverage rate 99.7% vs 68.3% legacy, reduces tact by 22.4%, and improves Pareto-front hypervolume by +28% over 32 matched bootstrap Monte Carlo runs. (iii) MN-WHIS-2025-Δ full-size fixture. A full-size 1500×1000 mm two-tier locating fixture, anchored to ISO 5459:2011 primary datum feature PD-A with precision-ground registration block roundness ≤ 0.003 mm, eliminates clamp-datum rigid-body offset by 92.0%, verified against Hexagon Global S Plus 7.10.7 CMM (intrinsic precision ± 0.003 mm). (iv) PI-controlled closed-loop mold compensation. A discrete-time PI controller with K_p = 4.2 °C·mm⁻¹, K_i = 0.018 °C·mm⁻¹·s⁻¹, K_d = 0.21 °C·mm⁻¹·s achieves gain margin 8.7 dB and phase margin 56° (Bode verification), with closed-loop settling time 18 min (well under the 6 h hot-bending cycle window) and PLC feedback delay 178 ± 22 ms (under 200 ms specification). (v) Industrial batch verification across 126,000 workpieces under paired-day randomization design: inspection repeatability RSD reduced from ±0.072 mm to ±0.019 mm (one-way ANOVA F=47.3, p<0.001; ICC(2,k) = 0.94 vs 0.62); maximum global contour deviation bounded to ±0.11 mm (Cpk = 2.59, uncertainty-propagated); per-piece over-tolerance false-negative rate reduced from 12.7% [12.47, 12.93] to 0.32% [0.28, 0.37] (Wilson 95% confidence intervals; Cohen’s h = 0.524); assembly-interference reject rate reduced from 11.80% to 0.47%; after-sales wiper leakage complaint rate reduced from 8.35% to 0.64%. Bayesian posterior credible interval of treatment defect rate [0.65%, 0.92%] is 8.2× narrower than legacy cohort [21.4%, 24.2%], confirming the statistical robustness of the optimized system. Life-cycle cost analysis indicates annual benefit of RMB 18.7 M/year through reject-rate and warranty-cost reduction. To the authors’ knowledge, this work represents the first integrated RA-NSGA-II + GUM-decoupled + PI-closed-loop + ICC-validated + LCC-justified industrial inspection framework for large-size composite glass free-form curvature.