Mass transport perspective on an accelerated exclusion process: Analysis of augmented current and unit-velocity phases

In an accelerated exclusion process (AEP), each particle can "hop" to its adjacent site if empty as well as "kick" the frontmost particle when joining a cluster of size $\ell \leq \ell_\text{max}$. With various choices of the interaction range, $\ell_\text{max}$, we find that the steady state of AEP can be found in a homogeneous phase with augmented currents (AC) or a segregated phase with holes moving at unit velocity (UV). Here we present a detailed study on the emergence of the novel phases, from two perspectives: the AEP and a mass transport process (MTP). In the latter picture, the system in the UV phase is composed of a condensate in coexistence with a fluid, while the transition from AC to UV can be regarded as condensation. Using Monte Carlo simulations, exact results for special cases, and analytic methods in a mean field approach (within the MTP), we focus on steady state currents and cluster sizes. Excellent agreement between data and theory is found, providing an insightful picture for understanding this model system.Comment: 13 pages, 8 figure

A comparative study of marginal loss pricing algorithms in electricity markets

Due to the development of new technologies, change of generation mix and appearance of newly formed energy supply hubs, there is a large year-on-year change in the marginal loss factors in power systems. Since any change of marginal loss factors could have significant impacts on payment of loads and profitability of generators, it is necessary to carry out a comparative study on the loss factor-based locational marginal pricing methods. Considering that a systematic comparison of various locational marginal pricing methods has not been reported in existing publications, this work presents a comparative study of the loss factor-based locational marginal pricing methods that are widely adopted in electricity markets. Advantages and disadvantages of each locational marginal pricing method are explored in detail, and could serve as references in selecting appropriate locational marginal pricing methods in practice. The selected five locational marginal pricing models are tested in two standard power systems, that is, the IEEE 5-bus and 39-bus systems. Then, through numerical experiments and detailed analysis, key findings about the reference point dependency of loss factors, accuracy of loss estimation, load payment, generation income, and market settlement surplus are summarised and elaborated. It is found that marginal loss factors-based locational marginal pricing methods tend to produce a higher market settlement surplus and can lead to a lower generation income than other locational marginal pricing methods

Performance Evaluation of Semi-supervised Learning Frameworks for Multi-Class Weed Detection

Effective weed control plays a crucial role in optimizing crop yield and enhancing agricultural product quality. However, the reliance on herbicide application not only poses a critical threat to the environment but also promotes the emergence of resistant weeds. Fortunately, recent advances in precision weed management enabled by ML and DL provide a sustainable alternative. Despite great progress, existing algorithms are mainly developed based on supervised learning approaches, which typically demand large-scale datasets with manual-labeled annotations, which is time-consuming and labor-intensive. As such, label-efficient learning methods, especially semi-supervised learning, have gained increased attention in the broader domain of computer vision and have demonstrated promising performance. These methods aim to utilize a small number of labeled data samples along with a great number of unlabeled samples to develop high-performing models comparable to the supervised learning counterpart trained on a large amount of labeled data samples. In this study, we assess the effectiveness of a semi-supervised learning framework for multi-class weed detection, employing two well-known object detection frameworks, namely FCOS and Faster-RCNN. Specifically, we evaluate a generalized student-teacher framework with an improved pseudo-label generation module to produce reliable pseudo-labels for the unlabeled data. To enhance generalization, an ensemble student network is employed to facilitate the training process. Experimental results show that the proposed approach is able to achieve approximately 76\% and 96\% detection accuracy as the supervised methods with only 10\% of labeled data in CottenWeedDet3 and CottonWeedDet12, respectively. We offer access to the source code, contributing a valuable resource for ongoing semi-supervised learning research in weed detection and beyond.Comment: 11 pages, 7 figure

Spot electricity market design for a power system characterized by high penetration of renewable energy generation

The continuous growth of renewable generation in power systems brings serious challenges to electricity markets due to their characteristics different from conventional generation technologies. These challenges come from two dimensions, including short-term (energy and ancillary service markets) and long-term (long-term bilateral and capacity markets) aspects. Under this background, the design of energy and ancillary service markets is studied for power systems with a high penetration level of variable renewable generation. In the proposed spot market mechanism, energy and frequency regulation service (FRS) bids are jointly cleared, where renewable generators are motivated to proactively manage the intermittency and uncertainty of their power outputs. The proposed market mechanism can also ensure the adequacy of FRS capacity for compensating variability of renewables. Besides, in order to ensure the execution of spot market clearing outcomes, this paper established a penalty scheme for mitigating the real-time fluctuations of renewable generation outputs in the spot market. Differences between real-time generation outputs and market clearing outcomes are managed within a certain limit by imposing the designed penalty prices on deviations. Finally, the feasibility and efficiency of the developed market mechanism and algorithms are manifested in the case studies

A Decentralized Distribution Market Mechanism Considering Renewable Generation Units With Zero Marginal Costs

A key feature of electricity generation in a distribution network is manifested by renewable generation with zero marginal cost. Existing market mechanisms are likely to fail in supporting such decentralized transactions while providing a reasonable price signal to compensate for the investment cost of renewable generators. Given this background, this paper first describes an average pricing market (APM) mechanism for pricing zero marginal cost renewable generation outputs in the distribution network. Then, a decentralized formulation of the APM mechanism is derived using the alternating direction method of multipliers (ADMM). Convergence of the decentralized mechanism can be guaranteed under some mild conditions for parameter setting. Finally, case studies are carried out to demonstrate the presented market mechanism. Simulation results show that the problem of always bidding a zero price by renewable generators in some existing markets can be avoided. The presented method also provides a solution for organizing decentralized electricity transactions in the distribution network and can converge to similar results with those obtained by the centralized one, with a relative error less than 5%

Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges

The past decade has witnessed the rapid development of ML and DL methodologies in agricultural systems, showcased by great successes in variety of agricultural applications. However, these conventional ML/DL models have certain limitations: They heavily rely on large, costly-to-acquire labeled datasets for training, require specialized expertise for development and maintenance, and are mostly tailored for specific tasks, thus lacking generalizability. Recently, foundation models have demonstrated remarkable successes in language and vision tasks across various domains. These models are trained on a vast amount of data from multiple domains and modalities. Once trained, they can accomplish versatile tasks with just minor fine-tuning and minimal task-specific labeled data. Despite their proven effectiveness and huge potential, there has been little exploration of applying FMs to agriculture fields. Therefore, this study aims to explore the potential of FMs in the field of smart agriculture. In particular, we present conceptual tools and technical background to facilitate the understanding of the problem space and uncover new research directions in this field. To this end, we first review recent FMs in the general computer science domain and categorize them into four categories: language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs. Subsequently, we outline the process of developing agriculture FMs and discuss their potential applications in smart agriculture. We also discuss the unique challenges associated with developing AFMs, including model training, validation, and deployment. Through this study, we contribute to the advancement of AI in agriculture by introducing AFMs as a promising paradigm that can significantly mitigate the reliance on extensive labeled datasets and enhance the efficiency, effectiveness, and generalization of agricultural AI systems.Comment: 16 pages, 2 figure

Stimulus Intervals Modulate the Balance of Brain Activity in the Human Primary Somatosensory Cortex: An ERP Study

Neuronal excitation and inhibition occur in the brain at the same time, and brain activation reflects changes in the sum of excitation and inhibition. This principle has been well-established in lower-level sensory systems, including vision and touch, based on animal studies. However, it is unclear how the somatosensory system processes the balance between excitation and inhibition. In the present ERP study, we modified the traditional spatial attention paradigm by adding double stimuli presentations at short intervals (i.e., 10, 30, and 100 ms). Seventeen subjects participated in the experiment. Five types of stimulation were used in the experiment: a single stimulus (one raised pin for 40 ms), standard stimulus (eight pins for 40 ms), and double stimuli presented at intervals of 10, 30, and 100 ms. The subjects were asked to attend to a particular finger and detect whether the standard stimulus was presented to that finger. The results showed a clear attention-related ERP component in the single stimulus condition, but the suppression components associated with the three interval conditions seemed to be dominant in somatosensory areas. In particular, we found the strongest suppression effect in the ISI-30 condition (interval of 30 ms) and that the suppression and enhancement effects seemed to be counterbalanced in both the ISI-10 and ISI-100 conditions (intervals of 10 and 100 ms, respectively). This type of processing may allow humans to easily discriminate between multiple stimuli on the same body part

Effect of beraprost sodium on renal function and p38MAPK signaling pathway in rats with diabetic nephropathy

Purpose: To investigate the effect of beraprost sodium (BPS) on renal function and P38MAPK pathway in diabetic nephropathy (DN) rats.Methods: Sprague Dawley (SD) rats (n = 30) were randomly divided into three groups, viz, normal control (NC), diabetic nephropathy (DN) and beraprost sodium (BPS). Creatinine (Cr), blood urea nitrogen (BUN) and fasting blood glucose (FBG), were determined by Hitachi 7020 automatic biochemical analyzer, while low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG) and total cholesterol (TC) were measured by Olympus 400 automatic biochemical analyzer. Western blot analysis was performed to examine protein expression. Interleukin-6 (IL-6), hs-CRP, and TNF-α levels were evaluated using enzyme linked immunosorbent assay (ELISA).Results: After 8 weeks of treatment, renal function indices (urine output, KW/BW, UAlb/24 h, Cr and BUN), blood lipid indices (FBG, LDL-C, TG and TC) and inflammatory factors levels (IL-6, hs-CRP and TNF-α) in DN group were higher than NC group (p < 0.05). In BPS group, renal function and blood lipid indices and inflammatory factor levels decreased when compared to DN group (p < 0.05). Furthermore, BPS inhibited the protein expression of p-P38MAPK, TGF-β1 and COX-2.Conclusion: Beraprost sodium improves renal function in DN rats by inhibiting P38MAPK signalingpathway

Key index framework for quantitative sustainability assessment of energy infrastructures in a smart city: An example of Western Sydney

Human society is experiencing a rapidly changing environment in which energy shortages and an ongoing climate crisis have been identified as two of the major challenges to the sustainable development of human civilization. In the face of these challenges, the concept of a smart city is proposed which aims at achieving sustainable development, increasing the quality of life, and improving the cost-effectiveness of existing and new energy infrastructures. To this end, this study proposes a general framework with a three-tier story chart for guiding the establishment of sustainability assessment models and the selection of their indicators. In addition, a quantitative analysis method is developed for assessing the sustainability of energy infrastructures in a smart city, which indicates how the long-term sustainability of the energy infrastructure can be achieved. The proposed method incorporates extensive environmental, economic, and social indicators, which go beyond conventional facility-level criteria and seamlessly relate to the broader community that benefits from the renewable energy transition (including energy construction, operations, and energy services). The proposed methodologies can be implemented through collecting the corresponding history data of the indicators and following the analysis procedures presented in this study. The proposed methodology can serve as a supporting tool for decision-making on new infrastructure investment and policymaking toward sustainable development. Case studies in Western Sydney were carried out to demonstrate the feasibility and efficiency of the proposed methodologies