Gongcheng Kexue Yu Jishu/Advanced Engineering Science

With the surge in digital imagery across law, healthcare, and media, ensuring authenticity and tamper-proof evidence has become a critical concern. The current survey offers an in-depth overview of how blockchain technology can strengthen digital image forensics (DIF), which exploit weaknesses involving unauthentic changes, metadata tampering, and ineffective chain of custody. The paper starts by a brief description of the issues of the traditional DIF systems, especially in distributed systems and IoT-based systems. It then categories the blockchain-integrated frameworks like LEChain, FIF-IoT, BB-RICP, and BlockShield by focusing on their architectures, encryption mechanisms, models of consensus, and performance results. These systems demonstrate the ability to store safely, cryptographically verify, query at low latency and manage evidence that is in real time. One of the assessments compared was a comparative analysis, showing such measures as throughput, tamper detection, and energy efficiency, wherein blockchain has application in increasing forensic integrity and traceability. These applications reach into law enforcement, surveillance, and legal trials, with blockchain enhancing the admissibility and provenance of evidence. In spite of these developments, the paper cites remaining issues on scalability, privacy, interoperability, and incorporation of AI. And the future directions include quantum-resistant cryptography, AI-driven decision model, cross-chain compatibility, and mobile-supported forensic solutions to design secure and legally compliant forensic infrastructures.

Future 6G physical layers need to work together to improve peak power, spectrum confinement, reliability, energy, and latency when channels are not stationary and service slices are not the same.  Traditional approaches for reducing PAPR and shaping spectra, such as PTS/SLM, μ-law companding, and fixed orthogonal precoding, work to some extent but are not very reliable across different types of waveforms (UFMC, OTFS) and power-domain multiplexing (NOMA).  To suggest a single, AI-driven signal-shaping architecture that combines a hybrid orthogonal precoding bank (DFT/DST/SRC/ZC) with a probabilistic deep compander (PDC) and selective/clustered companding (SCC).  A learning controller, such as Deep Reinforcement Learning (PPO) or a Cultural-History Optimization Algorithm (CHOA), changes the precoding choice, companding profile, cluster aggressiveness, NOMA split, besides delay–Doppler grid (for OTFS) to minimize a multi-objective loss over PAPR, out-of-band (OOB) emissions, EVM/BER, energy per bit, and latency.  During exploration, safety shields make sure that power-amplifier headroom, spectrum masks, and latency budgets are followed.  Synthetic but reasonable results show that improvements are consistent across waveforms.  When CCDF is 10⁻³, PAPR goes down from 11.8 dB (OFDM) besides 10.6 dB (UFMC) to 6.9 dB (Proposed-UFMC) and 6.5 dB (Proposed-OTFS).  The power outside of the band (OOB) goes up to −44.5 dBm.  For 16-QAM, BER at 10 dB drops to 9.8×10⁻³ (Proposed-UFMC) and 7.5×10⁻³ (Proposed-OTFS).  The framework has good energy-latency trade-offs: 0.62 mJ/bit at 1.3 ms (URLLC slice) and 0.45 mJ/bit at 3.8 ms (Green slice).  Ablations show how each module works: taking off AI controller lowers PAPR to 8.3 dB and OOB to −39.1 dBm.  These results indicate that suggested stack is a feasible 6G-level solution that integrates waveform diversity adaptive, safety-conscious optimization.

The fast growth in the scale of large-scale data systems in sectors like cybersecurity, finance, healthcare, and industrial surveillance has enhanced the necessity of powerful anomaly detecting methods that can operate without indicated data. This paper explores the use of unsupervised machine learning to detect anomalies in high-dimensional and large-scale unhomogeneous data. Four exemplary algorithms, namely: Isolation Forest, One-Class Support Vector Machine (OC-SVM), Local Outlier Factor (LOF) and Autoencoder neural networks were evaluated systematically on several large-scale datasets with network traffic, transactional and sensor records. The experimental findings proved that deep learning-based Autoencoders had a top overall detection performance with an average precision of 93.6, recall of 90.8, F1-score of 92.2, and AUC of 0.96, which showed they were most effective in identifying non-linear patterns involving complex physiological behavior. The Isolation Forest was also particularly well performing obtaining an F1-score of 89.8% with much lower training time (71 seconds on 500k records) and much lower detection latency (1.8 ms per instance), which was similar to real time applications. Conversely, LOF displayed lower scalability and performance breakdown in a high-dimensional environment as well as OC-SVM. The proposed framework is competitive or better with other related work of recent interest and, therefore, can be compared with other comparable studies. On the whole, this study can contribute to practical understanding of the choice of unsupervised techniques of anomaly detection regarding the scale of a system, the nature of collected data, and the limitations of its operation.

Drilling super duplex stainless steel (SDSS 2507) is challenging because of its high strength and poor thermal conductivity, which lead to excessive heat generation during machining. In conventional flood cooling, the generated heat is not sufficiently dissipated, and prolonged exposure to cutting fluids can pose health risks to operators. Cryogenic cooling, particularly using liquid nitrogen (LN₂), offers an effective alternative for machining hard-to-cut materials. This study evaluates the drilling performance of a multilayer coated tool (AlCrN + TiN) on SDSS 2507 under dry and cryogenic LN2 environments. Experiments were carried out at different cutting speeds (40, 50, and 60 m/min) and feed rates (0.04, 0.06, and 0.08 mm/rev), while maintaining a constant drilling depth of 20 mm. The influence of cooling conditions on thrust force, cutting temperature, surface roughness, micro-structural characteristics, and tool wear was systematically examined. The results indicate that cryogenic drilling significantly improves machining performance compared to dry drilling. Reductions of up to 40% in thrust force, 52% in cutting temperature, and 52% in surface roughness were achieved under cryogenic conditions. Micro-structural analysis confirmed improved surface integrity, and tool wear was notably lower when LN2 was used, demonstrating the effectiveness of cryogenic cooling for drilling SDSS 2507.