The Lancang-Mekong Sub-region (LMSR) is experiencing escalating climate change impacts, including intensified hydrological variability, ecosystem degradation, and threats to food and water security. Given the varying economic development levels and inadequate climate proofing infrastructure, the countries in the LMSR are confronted with the dual challenges of economic growth and environmental protection. This review synthesizes and evaluates existing research on climate governance in the LMSR, with a focus on institutional frameworks, cooperative initiatives, and persistent governance gaps. The LMSR has established a multi-level climate governance framework within environmental governance. Addressing climate change and its adverse effects has become a crucial area of cooperation for the region in implementing the Sustainable Development Goals (SDGs). While climate change cooperation in the LMSR faces challenges, including the lack of binding agreements, fragmented adaptation strategies, and asymmetrical power dynamics, these challenges remain significant. Key stressors, such as hydropower development, biodiversity loss, and transboundary water disputes, are often addressed in isolation rather than through integrated governance approaches.
This paper addresses the iterative learning control (ILC) problem for discrete-time linear systems with unknown system matrices and arbitrary initial state shifts in each iteration. A data-driven learning control algorithm is proposed, which leverages the linearity of the system by collecting an output error set after running the controller for a certain number of iterations and then constructing an ideal controller from this dataset to achieve error-free tracking. This approach relaxes the conventional convergence conditions typically required in ILC. It is shown that the proposed algorithm can guarantee complete tracking at all time points except the initial one within a finite number of iterations. The effectiveness of the algorithm is demonstrated through three simulation examples.
RATIONALE:Isomerism is common in new psychoactive substances (NPSs). Due to insignificant differences in their structure and properties, NPS isomers are hard to separate and identify, posing challenges for forensic laboratories to accurately identify them. Delta-8-tetrahydrocannabinol (Δ8-THC), a double-bond isomer of delta-9-tetrahydrocannabinol (Δ9-THC), is synthesized from cannabidiol (CBD). METHODS:To explore the MS fragmentation pattern of Δ8-THC and Δ9-THC, we systematically investigated them by gas chromatography-quadrupole time-of-flight mass spectrometry (GC-Q-TOF/MS) and liquid chromatography coupled to high-resolution quadrupole Orbitrap mass spectrometry (LC-Q-Orbitrap/MS). RESULTS:For GC-Q-TOF/MS analysis, significant differences in the ion abundance of the two characteristic ions at m/z 314 and 299 between these two isomers (Δ8-THC and Δ9-THC) stem from the varied stability of the free radicals they generate after losing an electron. Moreover, the ratio of the ion abundance of the fragment ions at m/z 314 and 299 can be utilized to recognize these two isomers. For LC-Q-Orbitrap/MS analysis, the fragmentation patterns of Δ8-THC were similar to those of Δ9-THC. Additional investigations by computational study revealed that Δ8-THC cationic radical can undergo a stepwise process like a retro Diels-Alder reaction to release an isoprene molecule directly but Δ9-THC via a double-hydrogen atoms transfer, shifting the location of carbon-carbon double bond, which indicates the different peaks at m/z 246 of Δ8-THC and 231 of Δ9-THC in EI-MS fragmentation processes. CONCLUSIONS:Distinguishing accurately between Δ8-THC and Δ9-THC assists drug laboratories with in-depth analysis of drug sources, providing exact intelligence and scientific support to law enforcement.
In many countries, bare footprints collected from crime scenes can be used as evidence for forensic identification, which involves the linear measurement of quantitative characteristics. Compared with several other methods of footprint measurement, the Reel method has been proven to be reliable and has been used in many countries. The Chinese method for bare footprint measurement is the official method issued by Ministry of Public Security of China, but its reliability has not been tested. This study focuses on the reliability test of the Chinese method for bare footprint measurement, which can serve as a reference for the verification in the process of footprint identification. Sixty-four volunteers were randomly selected to make dust and inked footprints, and five metrics of each footprint were measured and re-measured by three raters to test the reliability. These metrics consisted of four main ones from the Chinese method, with one from the Reel method for comparison. Based on intraclass correlation coefficients, the standard error of measurement and 95% Bland-Altman limits of agreement, test-retest reliability, and rater reliability within and among three raters were analysed. The Chinese method finally demonstrated a high degree of reliability. Although the Reel method achieved a marginally higher reliability score, its advantage was slight. Furthermore, the Chinese method for bare footprint measurement is not only reliable but also simple to operate, making it a viable supplement to the Reel method in contexts outside of China. In the process of footprint identification in China, when footprints are re-measured or measured by different appraisers for verification, the reliability values given in this paper could be used as a reference.
Intelligent virtual autopsy faces a profound semantic misalignment driven by scarce multimodal data and insufficient fine-grained cognitive mapping, leaving models vulnerable to complex post-mortem noise and catastrophic 'shortcut learning'. To bridge this misalignment, we curate ForVA, a standardized multimodal virtual autopsy dataset of 1.2 million image-text pairs across 9 categories of death causes, and propose GCM-CLIP,a semantics-enhanced contrastive learning framework with an adaptive semantic decoupling module acting as a high-precision "semantic filter". Mechanistic analysis shows GCM-CLIP sharpens semantic discrimination, reduces intra-/inter-class pathological feature overlap (from 0.830/0.709 to 0.566/0.452), delivers a 25\% relative gain in zero-shot classification accuracy and 6-8\% improvements in cross-modal retrieval. Clinically, it empowers junior practitioners to achieve senior-level diagnostic precision and functions as an unbiased "second reader" to capture lesions overlooked due to cognitive anchoring. This work provides a reproducible paradigm for foundation models in high-stakes, data-scarce fields, offering transformative implications for forensic objectivity and judicial justice globally.
