This paper proposes a probabilistic framework for systemic seismic risk analysis of process plants, considering physical-chemical and domino consequences. This framework integrates probabilistic models of hazard, components damage, domino consequences, process network, time and cost to quantify losses, including component repair costs, loss of containment (LOC), and production disruption. The domino consequences model is proposed here to determine the cascading failures of equipment utilizing Probit functions. The process network model evaluates network failure through system reliability analysis. The framework incorporates such time-dependent phenomena as deterioration and discounting, through scenario sampling, with each realization simulating random earthquakes over a given timespan. Uncertainties are propagated through the sampling procedure. The framework is showcased using a comprehensive application to a real-world gas sweetening process plant. Key results include the failure probability curve of the plant and the exceedance probability curve of losses. Results demonstrate that domino effects reduce the median fragility of the plant by 72%, thereby significantly amplifying its vulnerability. They also indicate a sevenfold increase in the risk of the plant and 86% increase in the downtime thereof, which are all exacerbated by deterioration. Moreover, cascading failures redistribute repair costs and redefine the most vulnerable components by identifying new critical ones.
Let G be a graph of order n and size m. If G is a connected graph of order at least 5, we show that χ(G)≤⌈mn⌉. Moreover, we prove that if the girth of G is greater than 2k+1, then χ(G)≤⌈(k+2)mk+2⌉+1. It was proved that for every graph G with no isolated vertices χ(G)≤2H(G), where H(G)=∑uv∈E(G)2d(u)+d(v). We improve this result with a shorter proof by showing that col(G)≤2H(G), where col(G)=max{δ(H)+1:H⊆G}. In the present paper, it is shown that if G is a triangle-free graph of order at least 3 with no isolated vertex, then χ(G)≤⌈H(G)⌉.
The increasing demand for antibacterial and biocompatible materials in biomedical applications prompts the development of titanium-copper (Ti-Cu) alloys as promising candidates. These alloys attract wide attention due to their potent antibacterial activity against various bacteria. This study thoroughly reviews the antibacterial properties, biocompatibility, and corrosion performance of Ti-Cu alloys in different circumstances. The effects of copper content, processing routes, surface treatments, and other parameters on antibacterial efficacy, cytocompatibility, osseointegration, and corrosion resistance are analyzed. The investigation spans multiple length scales, examining factors from bulk alloy composition to microstructural features like intermetallic phases and their morphology, distribution, and surface characteristics including topography and wettability. Ultimately, the ongoing challenges associated with Ti-Cu biomedical alloys are critically evaluated, mapping out strategic research directions to fully harness their multifunctional capabilities.
Sign language is the primary means of communication for millions worldwide, yet most communication tools cater to spoken and written languages, creating challenges for the deaf and hard-of-hearing community. To help bridge this communication gap, we developed a sign language recognition model that advances the ability of sign language users to communicate more effectively with hearing individuals and their surroundings, potentially enhancing health services, public services, and equal opportunities for the deaf community. Our research focuses on recognizing Iranian Sign Language words using state-of-the-art deep learning techniques, specifically transformers. We compiled a dataset named ISLR101, containing 101 frequently used Iranian Sign Language words in academic environments such as universities. We employed a hybrid network that combines early fusion and late fusion transformer encoder-based architectures, optimized via a genetic algorithm. The model was trained using features extracted from sign videos, including key points of the hands and lips, as well as the distance and angle between hands. Additionally, we incorporated embedding word vectors in a multi-task learning framework to achieve smoother and more efficient training. The developed model achieved a test accuracy of 90.2%. We further evaluated its performance on sentence recognition tasks using a windowing technique, demonstrating its capability to handle more complex language constructs. Additionally, we introduced sign language learning software that provides real-time feedback to users based on our model. A survey was conducted to assess the effectiveness and efficiency of this learning software and the impact of real-time feedback. This research and the resulting software represent a significant step toward the practical implementation of sign language recognition models in real-world applications, offering substantial benefits to the deaf community.
The iron and steel industry is one of the most energy-intensive industrial sectors, accounting for approximately 8 % of global final energy use and nearly 10 % of annual greenhouse gas emissions. Within this value chain, direct reduction iron (DRI) production is among the most energy-demanding stages and a key focus for decarbonization efforts. In this study, the conventional syngas supply for DRI, typically produced via steam methane reforming (SMR), is replaced by a high-temperature solid oxide electrolyzer (SOE)-based system. The required electrical energy and partial thermal energy are provided by solar energy and biomethane, respectively, while waste heat recovery from other steelmaking units is implemented to enhance overall efficiency. The main objectives are to reduce energy consumption, emissions, and production costs while improving exergy efficiency and maintaining compatibility with existing industrial infrastructure. Comprehensive energy, exergy, environmental, economic, and exergo-economic analyses are performed. A multi-objective optimization using a genetic algorithm is then applied to determine optimal operating conditions regarding the four main objectives. The optimized results are benchmarked against conventional DRI routes and literature data. The findings show a 12 % reduction (from 3.06 to 2.72 MWh per ton of DRI) in specific energy consumption and annual energy consumption can be reduced by up to 680 GWh, while CO2 emissions decrease by approximately 1 MtCO2 per year (from 0.6 to 0.2 tonCO2 per ton of DRI). Additionally, syngas production efficiency improves by nearly 30 %, and natural gas consumption is reduced by about 50 %. These results demonstrate the potential of the proposed SOE-based configuration for advancing low-carbon, energy-efficient ironmaking.
