Politehnica University of Timișoara

Recent global events, including the Coronavirus Disease (COVID-19) pandemic and the intensification of geopolitical tensions, have reshaped energy market structures and renewed interest in the role of geopolitical risk in economic dynamics. In this context, natural gas has emerged as a pivotal component of the energy transition, while simultaneously becoming increasingly exposed to geopolitical disruptions. This paper investigates this context using a time–frequency framework based on wavelet techniques, employing monthly observations from January 1999 to July 2025. We analyze how these interactions evolve across different horizons and under changing global conditions. The results reveal a structurally asymmetric transmission mechanism. While the United States (US) gas market appears relatively insulated from geopolitical shocks in the short run, the European Union (EU) market exhibits a strong and persistent sensitivity, particularly at medium-term frequencies. Moreover, the analysis highlights a transatlantic price transmission pattern, with US gas prices systematically leading European prices, suggesting a hierarchical structure of global gas markets. At longer horizons, we uncover a feedback mechanism whereby energy prices themselves help shape geopolitical risk. These findings are consistent with the emergence of a more fragmented and geopolitically driven energy system, in which the interaction between energy markets and geopolitical risk is both time-varying and mutually reinforcing. From a policy perspective, the results underscore the relevance of the energy trilemma and highlight the need to incorporate geopolitical risk into energy and macroeconomic policy frameworks.

Image dehazing, a crucial task in low-level vision, supports numerous practical applications, such as autonomous driving, remote sensing, and surveillance. This paper proposes IHDCP, a novel Inverted Haze Density Correction Prior for efficient single image dehazing. It is observed that the medium transmission can be effectively modeled from the inverted haze density map using correction functions with various gamma coefficients. Based on this observation, a pixel-wise gamma correction coefficient is introduced to formulate the transmission as a function of the inverted haze density map. To estimate the transmission, IHDCP is first incorporated into the classic atmospheric scattering model (ASM), leading to a transcendental equation that is subsequently simplified to a quadratic form with a single unknown parameter using the Taylor expansion. Then, boundary constraints are designed to estimate this model parameter, and the gamma correction coefficient map is derived via the Vieta theorem. Finally, the haze-free result is recovered through ASM inversion. Experimental results on diverse synthetic and real-world datasets verify that our algorithm not only provides visually appealing dehazing performance with high computational efficiency, but also outperforms several state-of-the-art dehazing approaches in both subjective and objective evaluations. Moreover, our IHDCP generalizes well to various types of degraded scenes. Our code is available at https://github.com/TaoLi-TL/IHDCP.

Natural and human-made disasters threaten cities increasingly, thus requiring a combination of disaster risk reduction and sustainable development strategies. Although vulnerability assessment methods and urban sustainability policies have improved significantly, these two fields remain separate, leading to fragmented policies that may work against resilience objectives. This paper provides an overview by conducting a systematic review of 87 peer-reviewed studies published between 2000 and 2024 that describe and analyze the intersection of disaster prevention policies and sustainability practices in urban planning. Thematic analysis was employed, and five major themes were revealed: policy implementation frameworks, climate adaptation strategies, preparedness mechanisms, vulnerability assessment approaches, and sustainability evaluation systems. The findings reveal a critical disconnect: on the one hand, vulnerability assessments highlight the structural–technical aspect and, at the same time, ignore the sustainability indicators (resource efficiency, social equity, and ecosystem services), while on the other hand, sustainability frameworks deliberately shut out disaster risk awareness from the core evaluation criteria. This methodological separation produces policy conflicts where disaster interventions may compromise environmental goals, and sustainability initiatives may increase hazard vulnerability. This review concludes that resilient cities require assessment methodologies synthesizing disaster risk and sustainability dimensions. A novel conceptual integration framework is suggested that combines hazard-exposure-vulnerability analysis with environmental–social–economic sustainability pillars, thus laying the groundwork for future operational tools. This joint viewpoint accepts that hazards mainly affect development that is not sustainability-oriented, while sustainable systems through adaptive design and equitable resource distribution inherently lower the vulnerability.

This review addresses the escalating global water crisis driven by water pollution, especially by heavy metal ions, a consequence of rapid industrialization and population growth. Due to their high toxicity, solubility, and persistence, heavy metals pose a severe threat to human health and ecosystems through bioaccumulation. The analysis highlights a strategic shift in wastewater management from simple elimination of the toxics metal ions to the recovery of metal ions with economic value. Given the increasing complexity of industrial effluents, the scientific community is intensifying its focus on evaluating the technical and financial feasibility of various treatment technologies. Significant research is being conducted to address these environmental issues, and innovative technologies are being developed to enhance the quality of water contaminated by metal ions. On the other hand, to prevent pollution, plans containing several barriers must be established, including management, economic, and technical ones. Ultimately, the reuse of treated wastewater is the only viable long-term solution for securing global drinking water supplies. A new analysis focused on the transition from traditional, inefficient, and costly wastewater treatment to advanced, resource recovery-oriented systems is essential. The current perspective shows a clear need to advance beyond synthetic laboratory studies to real-world applications while addressing operational barriers to support a circular economy based on simple disposal of the toxic metal ions to the recovery of metals with economic value (e.g., copper, gold, silver, rare metals). Also, although the field has been explored, a new review is imperative because current technologies that show high efficiency (up to 99%) in the removal of toxic metal ions (adsorption, membrane filtration, electrochemical processes) face major challenges, such as the formation of large volumes of toxic sludge, membrane fouling, and high operating costs.

In this paper the Cramer-Rao Lower Bounds (CRLBs) for unbiased estimators of the parameters of complexvalued noisy damped sinusoid are comprehensively analyzed assuming that enough samples are acquired in an interval duration equal to the signal time constant. At first accurate, but simple, expressions for the CRLBs are derived. Then, even simpler expressions are obtained assuming that the observation duration is either enough smaller or enough greater than the signal time constant. Leveraging on the derived expression, constraints on the number of analyzed samples that allow the optimization of the tradeoff between estimation accuracy, estimation delay, and processing effort are derived. They can be useful to optimize measurement procedures aimed at estimating unknown signal parameters. The accuracies of the derived results are verified through computer calculations and the application of a state-of-the-art almost unbiased and statistically efficient algorithm.