A Cost-Effective Cyber-Defense Strategy: Attack-Induced Region Minimization and Cybersecurity Margin Maximization

Recent years have witnessed increasing cyber-attack reports, e.g., the false data injection (FDI) cyber-attacks, which result in massive damage to power systems. This paper proposes a cost-effective two-stage cyber-defense strategy, which minimizes the FDI attack-induced region in the system planning stage, followed by the cybersecurity margin maximization in the system operation stage. First, this paper proposes a shaping cyber-defense strategy that achieves a balance between shaping the FDI attack-induced region and minimizing the cyber-defense meters. The proposed shaping cyber-defense strategy is formulated as a one-leader-multi-follower bi-level problem, which is converted into a single-level mixed-integer linear programming (MILP) problem with closed-form lower bounds of the big-M. Then, via optimal dispatch of operation points, this paper proposes a dispatching cyber-defense strategy, which achieves a trade-off between maximizing the cybersecurity margin and minimizing the additional operation cost. This leads to a balance between the safest-but-expensive operation point (i.e., Euclidean Chebyshev center) and the cheapest-but-dangerous operation point. Simulation results on a modified IEEE 14 bus system verify the effectiveness and cost-effectiveness of the proposed shape-and-dispatch cyber-defense strategy

Two-Stage Submodular Optimization of Dynamic Thermal Rating for Risk Mitigation Considering Placement and Operation Schedule

Cascading failure causes a major risk to society currently. To effectively mitigate the risk, dynamic thermal rating (DTR) technique can be applied as a cost-effective strategy to exploit potential transmission capability. From the perspectives of service life and Braess paradox, it is important and challenging to jointly optimize the DTR placement and operation schedule for changing system state, which is a two-stage combinatorial problem with only discrete variables, suffering from no approximation guarantee and dimension curse only based on traditional models. Thus, the present work proposes a novel two-stage submodular optimization (TSSO) of DTR for risk mitigation considering placement and operation schedule. Specifically, it optimizes DTR placement with proper redundancy in first stage, and then determines the corresponding DTR operation for each system state in second stage. Under the condition of the Markov and submodular features in sub-function of risk mitigation, the submodularity of total objective function of TSSO can be proven for the first time. Based on this, a state-of-the-art efficient solving algorithm is developed that can provide a better approximation guarantee than previous studies by coordinating the separate curvature and error form. The performance of the proposed algorithms is verified by case results

Multi-channel mode converters based on in-line fiber modal interferometer

A modal interferometer was proposed to realize multi-channel mode conversion in two mode fiber. The near-filed pattern confirmed the LP01 mode was converted into LP11 mode at the destructive wavelengths. The mode conversion was realized at 20-channels in the C+L wavelength band with conversion efficiency up to 99.5% and insertion loss lower than 0.6 dB

Transition in metabolic health phenotypes across general adiposity categories and association with the risk of depression: a prospective analysis

Background The association between obesity and depression may partly depend on the contextual metabolic health. The effect of change in metabolic health status over time on subsequent depression risk remains unclear. We aimed to assess the prospective association between metabolic health and its change over time and the risk of depression across body mass index (BMI) categories. Methods Based on a nationally representative cohort, we included participants enrolled at the wave 2 (2004–2005) of the English Longitudinal Study of Ageing and with follow-up for depression at wave 8 (2016–2017). Participants were cross-classified by BMI categories and metabolic health (defined by the absence of hypertension, diabetes, and hypercholesterolemia) at baseline or its change over time (during waves 3–6). Logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the risk of depression at follow-up stratified by BMI category and metabolic health status with adjustment for potential confounders. Results The risk of depression was increased for participants with metabolically healthy obesity compared with healthy nonobese participants, and the risk was highest for those with metabolically unhealthy obesity (OR 1.62, 95% CI 1.18–2.20). Particularly hypertension and diabetes contribute most to the increased risk. The majority of metabolically healthy participants converted to unhealthy metabolic phenotype (50.1% of those with obesity over 8 years), which was associated with an increased risk of depression. Participants who maintained metabolically healthy obesity were still at higher risk (1.99, 1.33–2.72), with the highest risk observed for those with stable unhealthy metabolic phenotypes. Conclusions Obesity remains a risk factor for depression, independent of whether other metabolic risk factors are present or whether participants convert to unhealthy metabolic phenotypes over time. Long-term maintenance of metabolic health and healthy body weight may be beneficial for the population mental well-being

First description of the female of Cyrtodactylus dianxiensis Liu & Rao, 2021, with extended diagnosis of this species (Squamata, Gekkonidae)

Cyrtodactylus dianxiensis Liu & Rao, 2021 was originally described based on only two adult male specimens from Tongbiguan Nature Reserve, Dehong Autonomous Prefecture, western Yunnan, China. So far, no information on the females of this species is available. During comprehensive herpetofaunal investigations in 2022, one female specimen of C. dianxiensis was collected from Tongbiguan Nature Reserve. The female specimen agrees well with the original description of C. dianxiensis, and also shows some slight differences in coloration. This study reported the female specimen of this species for the first time, and provided a description and photos of the female specimen; meanwhile, we extended the diagnosis of this species

Vulnerability of vegetation activities to drought in Central Asia

Central Asia (CA) is a continental region that is sensitive to water conditions. Hence, drought has one of the primary effects on the vegetation activities in CA and could vary with climate change. However, it is still unclear how the drought vulnerability of vegetation differs among vegetation types and varies with drought scales in CA. Therefore, this paper studied the drought vulnerability of vegetation in CA from 1982–2015. Droughts were detected by using the standardized precipitation evapotranspiration index (SPEI), and the vegetation activities were represented by the Normalized Difference Vegetation Index (NDVI). Only the areas with no change in vegetation types were analyzed, in order to avoid interference with changes in land use. Results showed that both the duration and intensity of droughts were higher in the central, southwestern, and northeastern CA. The growing season (April–October) NDVI decreased by −0.0095 ± 0.0065 per decade in response to drying trends of 0.21 ± 0.22 unit aridity index per decade in these drought-concentrated regions. Forests and savannas/woody savannas were more vulnerable to drought from July–September, and their vulnerabilities were higher to droughts with longer time scales. Shrublands and grasslands were more vulnerable to drought from April–May and May–September, respectively, and the vulnerabilities during these months were higher for the droughts at 6–12 months scales. Twelve months was the optimal (most vulnerable) drought scale for the shrublands and grasslands and the secondary drought scale for the savannas/woody savannas. Further analysis of the vulnerability of vegetation to 12 months drought found that it generally increased with the increase of the drought magnitude (duration or intensity) to some peak values and then decreased. However, the vulnerability of forests and savannas/woody savannas increased with the drought intensity. Results would help for the drought risk assessment of vegetation in CA

Impacts of Climate Change on Wildfires in Central Asia

This study analyzed fire weather and fire regimes in Central Asia from 2001–2015 and projected the impacts of climate change on fire weather in the 2030s (2021–2050) and 2080s (2071–2099), which would be helpful for improving wildfire management and adapting to future climate change in the region. The study area included five countries: Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan. The study area could be divided into four subregions based on vegetation type: shrub (R1), grassland (R2), mountain forest (R3), and rare vegetation area (R4). We used the modified Nesterov index (MNI) to indicate the fire weather of the region. The fire season for each vegetation zone was determined with the daily MNI and burned areas. We used the HadGEM2-ES global climate model with four scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) to project the future weather and fire weather of Central Asia. The results showed that the fire season for shrub areas (R1) was from 1 April to 30 November, for grassland (R2) was from 1 March to 30 November, and for mountain forest (R3) was from 1 April to 30 October. The daily burned areas of R1 and R2 mainly occurred in the period from June–August, while that of R3 mainly occurred in the April–June and August–October periods. Compared with the baseline (1971–2000), the mean daily maximum temperature and precipitation, in the fire seasons of study area, will increase by 14%–23% and 7%–15% in the 2030s, and 21%–37% and 11%–21% in the 2080s, respectively. The mean MNI will increase by 33%–68% in the 2030s and 63%–146% in the 2080s. The potential burned areas of will increase by 2%–8% in the 2030s and 3%–13% in the 2080s. Wildfire management needs to improve to adapt to increasing fire danger in the future