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The emission characteristics of regulated emissions and unregulated emissions were studied under the full loads and the cold/hot transient cycle (WHTC) on a six-cylinder, multi-point injection, and spark ignition methanol engine. The results show that the emissions of THC and CO for the methanol engine is at a high level without three-way catalyst (TWC) and the emission of NOX could be controlled at a lower level than 400 mg/kW·h with exhaust gas recirculation (EGR). The emissions of THC, CO and NOX are low with TWC, whose weighted results under WHTC could be kept at a lower level than the limits in China VI Standard. It is a technical difficulty to control the emissions of methanol (CH3OH) and formaldehyde (HCHO) for methanol engines, especially under the cold WHTC, there has a high risk for the emission of CH3OH exceeding the limit of standard.

Machine Learning (ML) has become one of the important tools for High Energy Physics analysis. As the size of the dataset increases at the Large Hadron Collider (LHC), and at the same time the search spaces become bigger and bigger in order to exploit the physics potentials, more and more computing resources are required for processing these ML tasks. In addition, complex advanced ML workflows are developed in which one task may depend on the results of previous tasks. How to make use of vast distributed CPUs/GPUs in WLCG for these big complex ML tasks has become a popular research area. In this paper, we present our efforts enabling the execution of distributed ML workflows on the Production and Distributed Analysis (PanDA) system and intelligent Data Delivery Service (iDDS). First, we describe how PanDA and iDDS deal with large-scale ML workflows, including the implementation to process workloads on diverse and geographically distributed computing resources. Next, we report real-world use cases, such as HyperParameter Optimization, Monte Carlo Toy confidence limits calculation, and Active Learning. Finally, we conclude with future plans.

The Vera C. Rubin Observatory will produce an unprecedented astronomical data set for studies of the deep and dynamic universe. Its Legacy Survey of Space and Time (LSST) will image the entire southern sky every three to four days and produce tens of petabytes of raw image data and associated calibration data over the course of the experiment’s run. More than 20 terabytes of data must be stored every night, and annual campaigns to reprocess the entire dataset since the beginning of the survey will be conducted over ten years. The Production and Distributed Analysis (PanDA) system was evaluated by the Rubin Observatory Data Management team and selected to serve the Observatory’s needs due to its demonstrated scalability and flexibility over the years, for its Directed Acyclic Graph (DAG) support, its support for multi-site processing, and its highly scalable complex workflows via the intelligent Data Delivery Service (iDDS). PanDA is also being evaluated for prompt processing where data must be processed within 60 seconds after image capture. This paper will briefly describe the Rubin Data Management system and its Data Facilities (DFs). Finally, it will describe in depth the work performed in order to integrate the PanDA system with the Rubin Observatory to be able to run the Rubin Science Pipelines using PanDA.

Monitoring services play a crucial role in the day-to-day operation of distributed computing systems. The ATLAS Experiment at LHC uses the Production and Distributed Analysis workload management system (PanDA WMS), which allows a million computational jobs to run daily at over 170 computing centers of the WLCG and opportunistic resources, utilizing 600k cores simultaneously on average. The BigPanDA monitor is an essential part of the monitoring infrastructure for the ATLAS Experiment that provides a wide range of views, from top-level summaries to a single computational job and its logs. Over the past few years of the PanDA WMS advancement in the ATLAS Experiment, several new components were developed, such as Harvester, iDDS, Data Carousel, and Global Shares. Due to its modular architecture, the BigPanDA monitor naturally grew into a platform where the relevant data from all PanDA WMS components and accompanying services are accumulated and displayed in the form of interactive charts and tables. Moreover the system has been adopted by other experiments beyond HEP. In this paper we describe the evolution of the BigPanDA monitor system, the development of new modules, and the integration process into other experiments.

In recent years, advanced and complex analysis workflows have gained increasing importance in the ATLAS experiment at CERN, one of the large scientific experiments at LHC. Support for such workflows has allowed users to exploit remote computing resources and service providers distributed worldwide, overcoming limitations on local resources and services. The spectrum of computing options keeps increasing across the Worldwide LHC Computing Grid (WLCG), volunteer computing, high-performance computing, commercial clouds, and emerging service levels like Platform-as-a-Service (PaaS), Container-as-a-Service (CaaS) and Function-as-a-Service (FaaS), each one providing new advantages and constraints. Users can significantly benefit from these providers, but at the same time, it is cumbersome to deal with multiple providers, even in a single analysis workflow with fine-grained requirements coming from their applications’ nature and characteristics. In this paper, we will first highlight issues in geographically-distributed heterogeneous computing, such as the insulation of users from the complexities of dealing with remote providers, smart workload routing, complex resource provisioning, seamless execution of advanced workflows, workflow description, pseudointeractive analysis, and integration of PaaS, CaaS, and FaaS providers. We will also outline solutions developed in ATLAS with the Production and Distributed Analysis (PanDA) system and future challenges for LHC Run4.

Oxidative stress is recognized as a prominent factor contributing to follicular atresia and ovarian aging, which leads to decreased laying performance in hens. Reducing oxidative stress can improve ovarian function and prolong the laying period in poultry. This study investigates the impact of Pterostilbene (PTS), a natural antioxidant, on ovarian oxidative stress in low-laying chickens. Thirty-six Hy-Line White laying chickens were evenly divided into four groups and fed diets containing varying doses of PTS for 15 consecutive days. The results showed that dietary supplementation with PTS significantly increased the laying rate, with the most effective group exhibiting a remarkable 42.7% increase. Furthermore, PTS significantly enhanced the antioxidant capacity of aging laying hens, as evidenced by increased levels of glutathione, glutathione peroxidase, superoxide dismutase, catalase, and total antioxidant capacity in the ovaries, livers, and serum. Subsequent experiments revealed decreased expressions of Bax, Caspase-3, and γ-H2AX, along with an increased expression of BCL-2 in the ovaries and livers of laying hens. PTS supplementation also positively affects fat metabolism by reducing abdominal fat accumulation and promoting fat transfer from the liver to the ovary. To elucidate the mechanism underlying the effects of PTS on ovarian function, a series of in vitro experiments were conducted. These in vitro experiments revealed that PTS pretreatment restored the antioxidant capacity of D-galactose-induced small white follicles by upregulating SIRT1/Nrf2 expression. This protective effect was inhibited by EX-527, a specific inhibitor of SIRT1. These findings suggest that the natural antioxidant PTS has the potential to regulate cell apoptosis and fat metabolism in laying chickens by ameliorating oxidative stress, thereby enhancing laying performance. [ABSTRACT FROM AUTHOR]

Acute lung injury (ALI) caused by sepsis is clinically a syndrome, which is featured by damage to the alveolar epithelium and endothelium. In this study, we employed mice models of cecal ligation and puncture (CLP) and primary mice pulmonary microvascular endothelial cells (MPVECs) in vitro to investigate the effect of miR-128-3p in ALI caused by sepsis.

Aging is not only a key internal cause of age-related diseases in humans but also poses a threat to the productivity of farm animals with longer breeding cycles, such as laying chickens. Various measures were taken to prolong the laying period by reducing oxidative stress to improve poultry ovarian functions. Within the mitochondria, SIRT3, a member of the Sirtuin family, plays an important role in post-translational modifications and the regulation of protein activities involved in energy metabolism and oxidative response. This study aimed to investigate the alleviating effect of a bioactive lignan Honokiol (HKL) on oxidative stress in aging chicken ovaries in order to retard decline in egg production. The results showed that HKL treatment restored the abnormal balance between cell proliferation and apoptosis, and it enhanced the antioxidant capacity of the H2O2-induced small white follicles (SWFs) by activating the SIRT3/AMPK pathway. Moreover, HKL significantly increased total egg production, the number of yellow follicles, and the mRNA expression of yolk synthesis and deposition-related genes, serum estrogen, and antioxidant levels. These findings suggest that HKL holds promise in enhancing the egg productivity of aging laying chickens by promoting yolk deposition and reducing ovarian oxidative stress. [ABSTRACT FROM AUTHOR]