Explore the vast range of titles available.

The unanticipated spike in international food prices in 2007-08 hit many developing countries hard. International prices for food and other agricultural products increased by more than 100 percent between early 2007 and mid-2008. Prices for food cereals more than doubled; and those for rice doubled in the space of just a few months. The food price increases were particularly hard on the poor and near-poor in developing countries, many of whom spend a large share of their income on food and have limited means to cope with price shocks. An estimated 1.29 billion people in 2008 lived on less than $1.25 a day, equivalent to 22.4 percent of the developing world population. In addition, the Food and Agriculture Organization estimated that 923 million people were undernourished in 2007. Simulation models suggested that poverty rose by 100-200 million people and the undernourished increased by 63 million in 2008. The World Bank organized rapidly for short-term support in the crisis, launching a fast-track program of loans and grants, the Global Food Crisis Response Program (GFRP). The GFRP mainly targeted low-income countries, and provided detailed policy advice to governments and its own staff on how to respond to the crisis. The Bank also scaled up lending for agriculture and social protection to support the building of medium-term resilience to future food price shocks. The International Finance Corporation responded by sharply increasing access to liquidity for agribusinesses and agricultural traders in the short and medium term, as well as new programs to improve incentives for agricultural market participants. This evaluation assesses the effectiveness of the World Bank Group response in addressing the short-term impacts of the food price crisis and in enhancing the resilience of countries to future shocks.

The reduction of emissions from shipping is necessary to combat climate change. One viable option is to change the fuels utilized. In this study, we investigate the environmental and economic performance of marine diesel oil (MDO), liquified natural gas (LNG), liquified biogas (LBG), and a mixture of LNG and LBG. We study a real case of a roll-on/roll-off passenger ship (RoPax) in Finland. Life cycle thinking is applied to assess the environmental impact, covering emissions from well to propeller (raw material extraction, fuel production, transportation, storage, and combustion), while the economic implications are estimated through future fuel prices and carbon pricing from 2023 to 2050. The carbon pricing covers different carbon tax schemes, namely stated policies scenario (STEPS), sustainable development scenarios (SDS), and net-zero emissions (NZE). STEPS reflects the existing measures and policies under development; SDS pursues to meet the goal of Paris Agreement, while NZE aims to reach net zero. Adopting LNG would improve carbon dioxide emissions, but the overall climate change impact was not significantly lower than MDO. It is also found that the biggest environmental improvement can be obtained by switching to LBG, although future availability can be an issue. The economic assessment shows that LBG has the highest fuel price uncertainties, although its carbon cost will be the lowest. Alternatively, using LNG & LBG mixture can serve as a transition path to contain climate change while dealing with its price uncertainty and availability.

Underwater optical imaging plays a crucial role in maritime safety, enabling reliable navigation, efficient search and rescue operations, precise target recognition, and robust military reconnaissance. However, conventional underwater imaging methods often suffer from severe backscattering noise, limited detection range, and reduced image clarity—challenges that are exacerbated in turbid waters. To address these issues, Underwater Laser Range-Gated Imaging has emerged as a promising solution. By selectively capturing photons within a controlled temporal gate, this technique effectively suppresses backscattering noise-enhancing image clarity, contrast, and detection range. Nevertheless, residual noise within the imaging slice can still degrade image quality, particularly in challenging underwater conditions. In this study, we propose an enhanced U-Net neural network designed to mitigate noise interference in underwater laser range-gated images, improving target recognition performance. Built upon the U-Net architecture with added residual connections, our network combines a VGG16-based perceptual loss with Mean Squared Error (MSE) as the loss function, effectively capturing high-level semantic features while preserving critical target details during reconstruction. Trained on a semi-synthetic grayscale dataset containing synthetically degraded images paired with their reference counterparts, the proposed approach demonstrates improved performance compared to several existing underwater image restoration methods in our experimental evaluations. Through comprehensive qualitative and quantitative evaluations, underwater target detection experiments, and real-world oceanic validations, our method demonstrates significant potential for advancing maritime safety and related applications.

The coal mine accidents seriously affect the safety and efficiency of mining for coal mining enterprises. The reliable emergency rescue (ER) processes are explored to minimize the loss of accidents. This paper introduces the stochastic Petri net (SPN) and Markov chain (MC) models based on the system structure flow to analyze the ER processes of coal mine accidents. In addition, a triangle fuzzy strategy is presented to optimize the SPN model. The “9·28” major water inrush accident in Shanxi Fenxi Zhengsheng Coal Company of China is adopted to evaluate the time performance and accident data of the ER process. The MC model-based steady-state probabilities of the system under various states are used to calculate the average delay time of this system. The triangular fuzzy strategy is used to analyze the change value of the total time in the ER system at the unit transition speed when the firing rate of each transition is changed, which finds the most time-consuming key activities in the ER process. The results show that SPN and MC can reflect the dynamic behaviors of ER process, which provides a reference for the rescue operations of other coal mine accidents. The triangular fuzzy strategy can quickly find out the key activities affecting the ER time, which greatly decreases the calculations generated by analyzing the total time of the system changed at the unit transition speed.

The Centralized Traffic Control (CTC) system plays an important role in ensuring safe and efficient rail transportation operations. It is mainly responsible for the implementation and adjustment of the train operation schedule through the automatic control of the station signalling equipment. The major task of the CTC system is to achieve a high rail transportation operation efficiency under the precondition of safety. For this purpose, it is necessary to select appropriate safety control schemes for the CTC system. In this paper, a formal approach is proposed to quantitatively evaluate the operation efficiencies of the CTC system with respect to different safety control schemes. The proposed approach adopts stochastic coloured Petri nets as the means of description for the system model, and evaluates the operation efficiency of the CTC system based on the data collected during the simulation of the system model. To exemplify the proposed approach, the safety control scheme of prohibiting a passenger train from passing a freight train through adjacent rail tracks between two adjacent stations is studied. The results of the case study show the feasibility of the proposed approach.

In this paper, we study the extent to which net neutrality, defined as price non-discrimination, is welfare improving in comparison to non-net-neutrality. We consider a two-sided congested internet service provider (ISP) that acts as a monopoly platform. The congestion is basically caused by the overuse of the fixed ISP’s bandwidth by content providers. Unlike end-users, we allow content providers to be heterogeneous in their sensitivity to congestion. The analysis reveals that the ISP monopolist, by departing from the net neutrality regime, price-favors the most congestion sensitive providers. We argue that these providers play a crucial role in creating traffic and generating profit for the ISP platform. In our paper, whether net neutrality improves or harms social welfare depends on a critical threshold of the platform equilibrium congestion level. This threshold is an indicator or a proxy that indicates for a planner whether or not net neutrality rules should be repealed. When the platform congestion level lies below the threshold, we show that non-net-neutrality makes the society better-off. Exceeding the threshold, two effects are identified: profit-increase effect and consumers’ surplus-reduction effect. If the latter outweighs the former, net neutrality increases social welfare when compared to non-net-neutrality.

Precise real-time obstacle recognition is both vital to vehicle automation and extremely resource intensive. Current deep-learning based recognition techniques generally reach high recognition accuracy, but require extensive processing power. This study proposes a region of interest extraction method based on the maximum difference method and morphology, and a target recognition solution created with a deep convolutional neural network. In the proposed solution, the central processing unit and graphics processing unit work collaboratively. Compared with traditional deep learning solutions, the proposed solution decreases the complexity of algorithm, and improves both calculation efficiency and recognition accuracy. Overall it achieves a good balance between accuracy and computation.

In this paper we study the impact of uncertainty about future innovations in quality and costs on consumers' technology adoption decisions. We model the uncertainty in the technology's quality and costs as a Markov process and consider three models of the adoption decision. The first model assumes that consumers do a simple net present value (NPV) analysis that compares the NPV of adopting to that of not adopting, without considering the possibility of waiting. The second model is a stochastic dynamic program that considers the possibility of waiting and views the adoption decision as a one-time event, i.e., the consumer will only make a single purchase, the only question is when. The third model allows repeat purchases so the consumer may \"upgrade\" by purchasing new versions of the technology whenever it suits her. We study structural properties of these models, e.g., the following: What changes in qualities and costs will make the consumer better off? What changes will encourage adoption? We will see that the simple NPV and single-purchase model have many intuitive properties: with the right notion of improvements and reasonable assumptions about the technology changes, we find that improvements in the technology make the consumer better off and encourage adoption. Here improvements are defined using a partial order on quality and cost pairs. The results are more complicated in the repeat-purchase model. Under the same conditions on technology changes, technology improvements will make the consumer better off. However, except for special cases of transitions, these improvements may make the consumer better off and discourage adoption.

One of the important tasks that air traffic management services are faced with today is the task of maximising airport capacity. This can be achieved at the tactical level through proper organisation of air traffic around an airport. In recent years, many methods and algorithms for scheduling aircraft landings have been developed; they take into account various optimisation goals. The aim of this paper was to create a method that would allow one to evaluate landing aircraft sequences resulting from these control algorithms, especially in the presence of random disturbances. This method involves modelling the landing aircraft sequence by using Petri nets. The model and the computer tool that have been developed make it possible to take into account different kinds of disturbances and examine the effectiveness of various control strategies under these conditions. This paper presents two experiments that test disturbances with different characteristics and of different intensities. It has been shown that small but more frequent disturbances lead to the worsening of evaluation scores for a given sequence to a lesser extent than rare but larger disturbances. This is particularly important for control algorithms in which the focus is on high aircraft density. If the type of particular disturbances is properly assessed, then it will be possible to assist the decision-maker (air traffic controller) by providing him/her with quantitative evaluations of possible solutions.