Search Results (30)

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

The 2012 Emilia earthquake highlighted the vulnerability of fortified architecture. Based on the observed seismic behaviors, this research proposes a GIS geodatabase, designed with a proactive approach, for the prediction and prevention—at a territorial scale—of the most frequent damage mechanisms of the investigated typology. The designed geo-database allows for the identification of possible correlations between constructive features and the occurrence of damage, through statistical and geo-referenced analysis. Moreover, the designed geodatabase, by enabling the comparison of the damage level data with the seismic action of the site, through INGV (National Institute of Geophysics and Volcanology) shakemaps, allowed the definition of experimental fragility curves, for three of the most common damage mechanisms. By applying these functions to castles in the province of Parma, it was possible to define future seismic risk scenarios for the mechanisms considered, thanks to the use of the seismic hazard map. Therefore, the described methodology could be functional to identify the most urgent and high-priority interventions in order to optimize the management of economic resources. The final aim is to promote the application of the concept of minimum intervention, and more in general to preserve the architectural heritage, avoiding emergency interventions and aiming instead to apply planned conservation strategies. Full article

This study addresses the urgent need for transitioning to clean energy systems to achieve net-zero emissions and mitigate climate change. It introduces an algebraic modeling framework inspired by the nuclear fission six-factor formula to optimize the construction rates of clean power plants, with a focus on Small Modular Reactors (SMRs). The framework integrates four key factors affecting SMR deployment: Public Acceptance (PA), Supply Chain Readiness (SC), Human Resource (HR) Availability, and Land Availability (LA), including their associated sub-factors. The proposed algebraic formula optimizes projections from the existing Dynamic Integrated Climate-Economy (DICE) model. By capturing socio-economic and environmental constraints, the model enhances the accuracy of clean energy transition scenarios. In the case of Ontario’s pathway to achieving net-zero emissions, the results indicate that incorporating the algebraic formula reduces the SMR construction rate projected by the DICE model from 5.2 to 3.7 units per year by 2050 and from 2.7 to 1.9 units per year by 2100. This reduction highlights the need for accelerated readiness in key deployment factors to avoid delays in reaching net zero targets, reinforcing the importance of strategic investments in PA, SC, HR, and LA. Validation against historical nuclear deployment data from the U.S., Japan, and Canada confirms the model’s ability to reflect real-world trends, with PA and SC emerging as the most influential factors. In addition to informing SMR planning, this approach offers a structured tool for prioritizing policy actions and can be adapted to other clean technologies, enhancing strategic decision making in support of net-zero goals. Full article

Ensuring the safety of autonomous vehicles (AVs) in complex and high-risk traffic scenarios remains a critical unresolved challenge. Current AV planning methods exhibit limitations in generating robust driving trajectories that effectively avoid collisions, highlighting the urgent need for improved planning strategies to address these issues. This paper introduces a novel iterative optimization framework that incorporates safety-critical trajectory generation to enhance AV planning. The use of the HighD dataset, which is collected using the wide-area remote sensing capabilities of unmanned aerial vehicles (UAVs), is fundamental to the framework. Remote sensing enables large-scale real-time observation of traffic conditions, providing precise data on vehicle dynamics, road structures, and surrounding environments. To generate safety-critical trajectories, the decoder within the conditional variational auto-encoder (CVAE) is innovatively designed through a data-mechanism integration method, ensuring that these trajectories strictly adhere to vehicle kinematic constraints. Furthermore, two parallel CVAEs (Dual-CVAE) are trained collaboratively by a shared objective function to implicitly model the multi-vehicle interactions. Inspired by the concept of “learning to collide”, adversarial optimization is integrated into the Dual-CVAE (Adv. Dual-CVAE), facilitating efficient generation from normal to safety-critical trajectories. Building upon this, these generated trajectories are then incorporated into an iterative optimization framework, significantly enhancing the AV’s planning ability to avoid collisions. This framework decomposes the optimization process into stages, initially addressing normal trajectories and progressively tackling more safety-critical and collision trajectories. Finally, comparative case studies of enhancing AV planning are conducted and the simulation results demonstrate that the proposed method can efficiently enhance AV planning by generating safety-critical trajectories. Full article

With the expansion of the Internet of Things (IoT) and artificial intelligence (AI) technologies, multilingual scenarios are gradually increasing, and applications based on multilingual resources are also on the rise. In this process, apart from the need for the construction of multilingual resources, privacy protection issues like data privacy leakage are increasingly highlighted. Comparable corpus is important in multilingual language information processing in IoT. However, the multilingual comparable corpus concerning privacy preserving is rare, so there is an urgent need to construct a multilingual corpus resource. This paper proposes a method for constructing a privacy-preserving multilingual comparable corpus, taking Chinese–Uighur–Tibetan IoT based news as an example, and mapping the different language texts to a unified language vector space to avoid sensitive information, then calculates the similarity between different language texts and serves as a comparability index to construct comparable relations. Through the decision-making mechanism of minimizing the impossibility, it can identify a comparable corpus pair of multilingual texts based on chapter size to realize the construction of a privacy-preserving Chinese–Uighur–Tibetan comparable corpus (CUTCC). Evaluation experiments demonstrate the effectiveness of our proposed provable method, which outperforms in accuracy rate by 77%, recall rate by 34% and F value by 47.17%. The CUTCC provides valuable privacy-preserving data resources support and language service for multilingual situations in IoT. Full article

The recycling and proper management of PET bottles is needed to avoid plastic pollution, as well as to achieve a plastic circular economy. In this study, we examined the recycling system of PET bottles and their material flows by life cycle, from production to recycling and disposal under different scenarios with reduced consumption for PET bottles. In Korea, PET bottle recycling has been managed by the extended producer responsibility system since 2003. As of 2021, annual usage of PET bottles per capita in Korea was estimated to be approximately 6.5 kg. The recycling rate of PET bottles after collection was determined to be 79% in 2021. However, recycled PET (or r-PET) from mechanical recycling is mostly used in low-grade PET applications such as short fibers, bands, and sheets. More than 112 thousand tons of r-PET chips would be needed to meet the 30% recycled content requirement in PET bottles in 2030 by regulation. Thus, there is an urgent need for high-quality secondary raw materials (i.e., food-grade) by adopting advanced sorting and recycling technologies for PET bottles with recycled content. PET bottle-to-bottle recycling practices can ensure the production of high-quality recycled materials and move Korea toward a plastic circular economy. Full article

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme. Full article

The Alligator Gar (Atractosteus spatula) is a very dangerous species that has an extremely destructive impact on the ecological environment in aquatic areas. As a kind of ornamental fish, it is spreading rapidly all over the world. In order to avoid or delay the spread of Alligator Gar in China, it is urgent to further monitor and clarify its distribution range within the country. In this study, a maximum entropy model (MaxEnt) and geographical information system (ArcGIS) were used to identify suitable regions under climate change scenarios in China, and the significance of environmental factors that shape this species’ distribution were evaluated. According to the geographical distribution data of Alligator Gar and selected environmental variables, the potential habitat area of Chinese Alligator Gar under four climate scenarios in the present (2000s) and future (2050s and 2070s) was predicted. (1) The main environmental variables affecting the potential geographical distribution of Alligator Gar are temperature factor variables (the mean temperature of the warmest quarter and the mean temperature of the coldest quarter), precipitation factor variables (precipitation in the driest month and in the driest quarter), and altitude. (2) Under the current climate conditions, the suitable breeding areas of Alligator Gar are mainly concentrated in the Guangxi Zhuang Autonomous Region, Guangdong Province, and Hainan Province. Under the four future climate scenarios, the distribution area of the total suitable area of Alligator Gar will gradually decrease, and the suitable area of Alligator Gar will spread to high latitudes. Full article

Shortages of water resources and labor make it urgent to improve irrigation efficiency and automation. To respond to this need, this study demonstrates the development of an adaptive border irrigation system. The inflow is adjusted based on the functional relationship between the advance time deviation and the optimal adjustment inflow rate, thereby avoiding the real-time calculation of infiltration parameters required by traditional real-time control irrigation systems. During the irrigation process, the inflow rate is automatically adjusted based only on the advance time deviation of the observation points. The proposed system greatly simplifies the calculation and reduces the requirements for field computing equipment compared with traditional real-time control irrigation systems. Field validation experiments show that the proposed system provides high-quality irrigation by improving the application efficiency, distribution uniformity, and comprehensive irrigation performance by 11.3%, 10.7%, and 11.0%, respectively. A sensitivity analysis indicates that the proposed system maintains a satisfactory irrigation performance for all scenarios of variations in natural parameters, flow rates, and border length. Due to its satisfactory irrigation performance, robustness, facile operation, and economical merit compared with traditional real-time control irrigation systems, the proposed system has the potential to be widely applied. Full article

As one of the key technologies restricting the development of intelligent ships, autonomous collision avoidance has attracted the attention of many scholars all over the world. Existing research on collision-avoidance behavior focuses more on collision risk assessment and local path-planning methods for studies on the human-like sequential logic of the whole collision-avoidance process, as well as the decision-making process of various stages. Further in-depth thinking is needed urgently. Based on this, a construction method of a human-like sequential decision chain for the autonomous collision avoidance of unmanned ships is proposed through the construction of a collision-avoidance rule base and strategy set, efficient data access based on the Knowledge Graph concept, global collision risk assessment considering sequential decision process, and the construction of a complete collision-avoidance logic process to simulate the decision-making process of humans in complex multi-ship encounters in open waters. For multi-ship encounter scenarios, considering the sequential decision-making process of collision avoidance, a method was proposed to divide the collision risk of the target ship into direct collision risk and potential collision risk. The validity and reliability of the constructed sequential decision chain are verified by simulation experimental results. The results show that the method is effective for collision avoidance (especially multi-ship collision avoidance) in open waters and can provide a theoretical basis and technical support with good interpretability for the decision-making process of an unmanned ship’s autonomous collision avoidance. Full article

Highways are one of the most suitable scenarios for automated driving technology. For conditionally automated driving, drivers are required to take over the vehicle when the system reaches its boundary. Therefore, it is necessary to evaluate the driver’s takeover performance and take-over safety differences under typical segments of highways. The experiment was conducted in a driving simulator. Three typical highway segments were constructed: a long straight segment, a merging segment and a diverging segment. Under each segment, a 2 × 2 factorial design was adopted, including two traffic densities (high density and low density) and two kinds of time budget (5 s and 7 s). The results showed that time budget and traffic density affected drivers’ take-over performance and safety. As the time budget decreased, the driver’s reaction time decreased and the braking amplitude increased. As traffic density increased, the lateral deviation rate increased. The maximum steering angle and steering wheel reversal rate in general tended to increase with scenario urgency. Meanwhile, drivers paid more attention to the longitudinal control on the long straight segment, which was reflected in the maximum braking amplitude and directional reversal rate. However, drivers paid more attention to the lateral control on the diverging segment, which was reflected in the maximum lateral deviation rate and the minimum steering wheel reversal rate. The study will contribute to the safety assessment of take-over behavior in highway avoidance scenarios and provide a theoretical basis for the design of a human–machine interaction system. Full article

Infective endocarditis (IE) is still a life-threatening disease with frequent lethal outcomes despite the profound changes in its clinical, microbiological, imaging, and therapeutic profiles. Nowadays, the scenario for IE has changed since rheumatic fever has declined, but on the other hand, multiple aspects, such as elderly populations, cardiovascular device implantation procedures, and better use of multiple imaging modalities and multidisciplinary care, have increased, leading to escalations in diagnosis. Since the ESC and AHA Guidelines have been released, specific aspects of diagnostic and therapeutic management have been clarified to provide better and faster diagnosis and prognosis. Surgical treatment is required in approximately half of patients with IE in order to avoid progressive heart failure, irreversible structural damage in the case of uncontrolled infection, and the prevention of embolism. The timing of surgery has been one of the main aspects discussed, identifying cases in which surgery needs to be performed on an emergency (within 24 h) or urgent (within 7 days) basis, irrespective of the duration of antibiotic treatment, or cases where surgery can be postponed to allow a brief period of antibiotic treatment under careful clinical and echocardiographic observation. Mainly, guidelines put emphasis on the importance of an endocarditis team in the handling of systemic complications and how they affect the timing of surgery and perioperative management. Neurological complications, acute renal failure, splenic or musculoskeletal manifestations, or infections determined by multiresistant microorganisms or fungi can affect long-term prognosis and survival. Not to be outdone, anatomical and surgical factors, such as the presence of native or prosthetic valve endocarditis, a repair strategy when feasible, anatomical extension and disruption in the case of an annular abscess (mitral valve annulus, aortic mitral curtain, aortic root, and annulus), and the choice of prosthesis and conduits, can be equally crucial. It can be hard for surgeons to maneuver between correct pre-operative planning and facing unexpected obstacles during intraoperative management. The aim of this review is to provide an overview and analysis of a broad spectrum of specific surgical scenarios and how their challenging management can be essential to ensure better outcomes and prognoses. Full article

Acute kidney injury (AKI) and sudden exacerbation of chronic kidney disease (CKD) frequently necessitate urgent kidney replacement therapy (UKRT). Peritoneal dialysis (PD) is recognized as a viable modality for managing such patients. Urgent-start peritoneal dialysis (USPD) may be associated with an increased number of complications and is rarely utilized. This review examines recent literature investigating the clinical outcomes of USPD in CKD and AKI. Relevant research was identified through searches of the MEDLINE (PubMed), Scopus, Web of Science, and Google Scholar databases using MeSH terms and relevant keywords. Included studies focused on the emergency use of peritoneal dialysis in CKD or AKI and reported treatment outcomes. While no official recommendations exist for catheter implantation in USPD, the impact of the technique itself on outcomes was found to be less significant compared with the post-implantation factors. USPD represents a safe and effective treatment modality for AKI, although complications such as catheter malfunctions, leakage, and peritonitis were observed. Furthermore, USPD demonstrated efficacy in managing CKD, although it was associated with a higher incidence of complications compared to conventional-start peritoneal dialysis. Despite its cost-effectiveness, PD requires greater technical expertise from medical professionals. Close supervision and pre-planning for catheter insertion are essential for CKD patients. Whenever feasible, an urgent start should be avoided. Nevertheless, in emergency scenarios, USPD does remain a safe and efficient approach. Full article

In many universities, students’ physical ability data are collected and stored in the cloud through various sensing devices to save computational and storage costs. Therefore, how to effectively access data while ensuring data security has become an urgent issue. Key-policy attribute-based encryption (KP-ABE) not only enables secure one-to-many communication and fine-grained access control but also adapts to data sharing in static scenarios, making it more suitable for the cloud sharing of physical ability data. In this paper, we construct an online/offline multi-authority key-policy attribute-based encryption with a cryptographic reverse firewall for physical ability data. This scheme uses multi-authority to avoid the single point of failure crisis of a single authority, and is combined with a cryptographic reverse firewall to resist backdoor attacks. In addition, the scheme uses outsourcing decryption to save users’ computing costs, and utilizes offline/online technology to move a large amount of computing offline, reducing the online burden. Finally, the experiment shows the feasibility of the scheme. Full article

Cancer is the leading cause of death, acting as a global burden, severely impacting the patients’ quality of life and affecting the world economy despite the expansion of cumulative advances in oncology. The current conventional therapies for cancer which involve long treatment duration and systemic exposure of drugs leads to premature degradation of drugs, a massive amount of pain, side effects, as well as the recurrence of the condition. There is also an urgent demand for personalized and precision-based medicine, especially after the recent pandemic, to avoid future delays in diagnosis or treatments for cancer patients as they are very essential in reducing the global mortality rate. Recently, microneedles which consist of a patch with tiny, micron-sized needles attached to it have been quite a sensation as an emerging technology for transdermal application to diagnose or treat various illnesses. The application of microneedles in cancer therapies is also being extensively studied as they offer a myriad of benefits, especially since microneedle patches offer a better treatment approach through self administration, painless treatment, and being an economically and environmentally friendly approach in comparison with other conventional methods. The painless gains from microneedles significantly improves the survival rate of cancer patients. The emergence of versatile and innovative transdermal drug delivery systems presents a prime breakthrough opportunity for safer and more effective therapies, which could meet the demands of cancer diagnosis and treatment through different application scenarios. This review highlights the types of microneedles, fabrication methods and materials, along with the recent advances and opportunities. In addition, this review also addresses the challenges and limitations of microneedles in cancer therapy with solutions through current studies and future works to facilitate the clinical translation of microneedles in cancer therapies. Full article