Modeling the impact of extreme summer drought on conventional and renewable generation capacity: methods and a case study on the Eastern U.S. power system

The United States has witnessed a growing prevalence of droughts in recent years, posing significant challenges to water supplies and power generation. The resulting impacts on power systems, including reduced capacity and the potential for power outages, underscore the need for accurate assessment methods to ensure the reliable operation of the nation's energy infrastructure. A critical step is to evaluate the usable capacity of a regional power system's generation fleet, which is a complex undertaking and requires precise modeling of the effects of hydrological and meteorological conditions on diverse generating technologies. This paper proposes a systematic, analytical approach for assessing the impacts of extreme summer drought events on the available capacity of hydro, thermal, and renewable energy generators. More specifically, the systematic framework provides plant-level capacity derating models for hydroelectric, once-through cooling thermoelectric, recirculating cooling thermoelectric, combustion turbine, solar PV, and wind turbine systems. Application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system in the PJM and SERC regions yields insightful results. By examining the daily usable capacity of 6,055 at-risk generators throughout the study region, we find that in the event of the recurrence of the 2007 southeastern summer drought in the near future, the usable capacity of all at-risk power plants may experience a substantial decrease compared to a typical summer, falling within the range of 71% to 81%. The sensitivity analysis reveals that the usable capacity would experience a more pronounced decline under more severe drought conditions. The findings of this study offer valuable insights, enabling stakeholders to enhance the resilience of power systems against the potential effects of extreme drought in the future.Comment: 15 pages, 16 figure

Stimulus Pulse-Based Distributed Control for the Locomotion of a UBot Modular Robot

A distributed control algorithm based on a stimulus pulse signal is proposed in this paper for the locomotion of a Modular Self-reconfigurable Robot (MSRR). This approach can adapt effectively to the dynamic changes in the MSRR's topological configuration: the functional role of the configuration can be recognized through local topology detection, dynamic ID address allocation and local topology matching, such that the features of the entire configuration can be identified and thereby the corresponding stimulus signals can be chosen to control the whole system for coordinated locomotion. This approach has advantages over centralized control in terms of flexibility and robustness, and communication efficiency is not limited by the module number, which can realize coordinated locomotion control conveniently (especially for configurations made up of massive modules and characterized by a chain style or a quadruped style)

Spatiotemporal Variations of Ecosystem Service Indicators and the Driving Factors Under Climate Change in the Qinghai–Tibet Highway Corridor

In recent decades, the influence of climate change and human activities on the ecosystem services (ES) in the Qinghai–Tibet Plateau (QTP) has been extensively investigated. However, few studies focus on linear traffic corridor area, which is heavily affected by human activities. Taking the Golmud–Lhasa national highway corridor as a case, this study investigated the land-use and land-cover change (LUCC) and spatiotemporal variations of ES indicators using ecosystem indices of fractional vegetation cover (FVC), leaf area index (LAI), evapotranspiration (ET), and net primary productivity (NPP) from 2000 to 2020. The results indicated that LUCC was faster in the last decade, mostly characterized by the conversion from grassland to unused land. In buffer within 3000 m, the proportions of productive areas represented the increased trends with distance. In terms of ES variations, the improved areas outweighed the degraded areas in terms of FVC, LAI, and NPP from 2000 to 2020, mostly positioned in the Qinghai Province. In addition, FVC, LAI, and NPP peaked at approximately 6000 m over time. With regard to influencing factors, precipitation (20.54%) and temperature (14.19%) both positively influenced the spatiotemporal variation of FVC. Nearly 60% of the area exhibited an increased NPP over time, especially in the Qinghai Province, which could be attributed to the temperature increase over the last two decades. In addition, the distance effects of climatic factors on ES indicators exhibited that the coincident effects almost showed an opposite trend, while the reverse effects showed a similar trend. The findings of this study could provide a reference for the ecological recovery of traffic corridors in alpine fragile areas

The effectiveness of shockwave therapy on patellar tendinopathy, Achilles tendinopathy, and plantar fasciitis: a systematic review and meta-analysis

BackgroundTendinopathy is a growing global concern affecting many people, like athletes, workers, and the elderly. Despite its commonality among the sporting population, there is no practical clinical guideline for patellar tendinopathy (PT). Furthermore, there is conflicting evidence between clinical guidelines on shockwave therapy’s application and clinical utility for Achilles tendinopathy (AT) and plantar fasciitis (PF). Thus, our aim of this study is to evaluate the evidence for shockwave therapy; to provide a Grading of Recommendation, Assessment, Development and Evaluation (GRADE) level of the evidence and effectiveness of shockwave therapy for patellar tendinopathy, Achilles tendinopathy, and Plantar fasciitis.MethodMedical Literature Analysis and Retrieval System Online (Medline), Embase, The Cumulative Index to Nursing and Allied Health Literature (CINAHL), Physiotherapy Evidence Database (PEDro) and China National Knowledge Infrastructure database (CNKI) were searched to find relevant studies published before December 14th, 2022.ResultsOur study showed that for PT in the short term, extracorporeal shockwave therapy (ESWT) or ESWT + eccentric exercise (EE) has a negligible effect on pain and function compared to a placebo or placebo + EE. On the contrary, ESWT significantly affects pain compared to conservative treatment (CT). For AT, ESWT has a small inconclusive effect on pain and function in the short term compared to EE. On the other hand, a placebo outperformed ESWT in improving function for AT but not pain outcomes. PF showed that ESWT significantly affects short- and long-term pain and function. When ESWT was compared to other interventions such as low laser therapy (LLLT), corticosteroid injection (CSI), or CT, there was a small inconclusive effect on pain and function in the short term.ConclusionThere is low-moderate evidence that ESWT has a negligible effect on pain and function for PT and AT. However, high-quality evidence suggests ESWT has a large effect on pain and function for PF.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023396835, identifier CRD42023396835

Crop Diversity for Yield Increase

Traditional farming practices suggest that cultivation of a mixture of crop species in the same field through temporal and spatial management may be advantageous in boosting yields and preventing disease, but evidence from large-scale field testing is limited. Increasing crop diversity through intercropping addresses the problem of increasing land utilization and crop productivity. In collaboration with farmers and extension personnel, we tested intercropping of tobacco, maize, sugarcane, potato, wheat and broad bean – either by relay cropping or by mixing crop species based on differences in their heights, and practiced these patterns on 15,302 hectares in ten counties in Yunnan Province, China. The results of observation plots within these areas showed that some combinations increased crop yields for the same season between 33.2 and 84.7% and reached a land equivalent ratio (LER) of between 1.31 and 1.84. This approach can be easily applied in developing countries, which is crucial in face of dwindling arable land and increasing food demand

Development and validation of ferroptosis-related lncRNAs signature for hepatocellular carcinoma

Background Hepatocellular carcinoma (HCC) with high heterogeneity is one of the most frequent malignant tumors throughout the world. However, there is no research to establish a ferroptosis-related lncRNAs (FRlncRNAs) signature for the patients with HCC. Therefore, this study was designed to establish a novel FRlncRNAs signature to predict the survival of patients with HCC. Method The expression profiles of lncRNAs were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. FRlncRNAs co-expressed with ferroptosis-related genes were utilized to establish a signature. Cox regression was used to construct a novel three FRlncRNAs signature in the TCGA cohort, which was verified in the GEO validation cohort. Results Three differently expressed FRlncRNAs significantly associated with prognosis of HCC were identified, which composed a novel FRlncRNAs signature. According to the FRlncRNAs signature, the patients with HCC could be divided into low- and high-risk groups. Patients with HCC in the high-risk group displayed shorter overall survival (OS) contrasted with those in the low-risk group (P  1, P  1, P < 0.05). Meanwhile, it was also a useful tool in predicting survival among each stratum of gender, age, grade, stage, and etiology,etc. This signature was connected with immune cell infiltration (i.e., Macrophage, Myeloid dendritic cell, and Neutrophil cell, etc.) and immune checkpoint blockade targets (PD-1, CTLA-4, and TIM-3). Conclusion The three FRlncRNAs might be potential therapeutic targets for patients, and their signature could be utilized for prognostic prediction in HCC

Proses Produksi Dan Pengemasan Yoghurtdicv. Cita Nasional Salatiga

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Data-driven study of major disruption prediction and plasma instabilities across multiple tokamaks

The use of nuclear fusion energy via magnetic-confinement tokamaks is one of a few encouraging paths toward future sustainable energy. Along the way, scientists need to learn to avoid plasma disruptions: these sudden and unexpected plasma terminations still represent one of the key challenges for tokamak devices. Forecasting plasma instabilities and disruptions using first-principle models has been demonstrated to be extremely difficult, due to the complexity of the problem and the high non-linearity of the system. To date, disruption and plasma instabilities prediction has been studied through two main approaches: data-driven versus physics-driven (or model-based). On the one hand, recent statistical and machine learning (ML) approaches based on experimental data have shown attractive results for disruption prediction, even in real-time environments. Different tokamak devices have different operational spaces, spatiotemporal scales for physics events, and plasma diagnostics. Therefore, most of these data-driven approaches were developed and optimized specifically for one device and did not show promising cross-device predictive ability. In addition, the complexity of these data-driven models limits their physics interpretability. Recent Deep-Learning (DL) based disruption prediction studies demonstrate the potential for acquiring a general representation of experimental data that can be used in cross-machine applications. On the other hand, model-based studies seek to identify event chains that can lead to disruptions through early event detection, which can help operators to avoid plasma instabilities disruptions. However, the extrapolation ability of physics-based models to new devices, especially to new physics regimes is still unclear. This thesis demonstrates the application of data-driven methods on plasma insta-bilities and disruption prediction via four major contributions. First, through explo-rative data analysis of thousands of shots on C-Mod, DIII-D and EAST tokamaks, the advantage of sequence-based disruption prediction model was shown. Based on this finding, a new Hybrid Deep-Learning (HDL) general disruption predictor was developed using C-Mod, DIII-D and EAST databases and it achieves state-of-the-art performance on three machines with only limited hyperparameter tuning. Dedicated cross-machine disruption prediction studies using this HDL model demonstrated that a significantly boosted accuracy on the target machine was achieved by training on 20 disruptive shots, thousands of non-disruptive shots from the target machine com-bined with hundreds of disruptive shots from other devices. In addition, by comparing the predictive performance of each individual numerical experiment, the disruptive shots from multiple devices were found to contain device-independent knowledge that can be used to inform predictions for disruptions occurring in a new device while non-disruptive shots were found to be machine-specific. Second, the cross-regime disruption prediction on multiple tokamaks using HDL model demonstrated data-driven disruption predictors trained on abundant Low Performance (LP) discharges work poorly on the High Performance (HP) regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Moreover, the cross machine experiments suggested matching operational parameters among tokamaks strongly improves cross-machine accuracy. Given these conclusions, a scenario adaptive strategy that works for all data-driven models was proposed for next generation tokamaks, such as ITER and SPARC, and highlight the importance of developing baseline scenario discharges of future tokamaks on existing machines to collect more relevant disruptive data. Third, the powerful HDL model was upgraded to an integrated ML model that can predict major disruption as well as multiple unstable events in tokamak plasmas that can facilitate the physics interpretation of output from the black box data-driven models and enables disruption avoidance by responding to early unstable events of plasmas. Enhanced cross-machine ability and improved warning time was also observed using the integrated ML model. Finally, among all different plasma unstable events, the = 1 tearing mode (TM) is considered to be one of the most important disruption precursors and its predictive ability is strongly desirable for ITER and SPARC. In the final part of this thesis, an empirical boundary for the = 1 tearing mode (TM) is developed via data-driven methods and verified on thousands of DIII-D discharges. The fitted boundary is a linear function of plasma equilibrium parameters such as collisionality, poloidal beta, and the MHD risk factor (a combination of the normal-ized electron temperature profile width, q95 and elongation). The boundary indicates with a value related to the probability of having the TM onset and it achieves 88% of shot-by-shot accuracy in offline analysis of DIII-D data. Preliminary cross-machine analysis of TM onset prediction shows potential applicability of the empirical bound-ary to C-Mod and EAST data as well, but the relative importance of the individual parameters is different for different devices. This suggests the existence of different trigger mechanisms for the TMs, implying that the boundary could be generalized using data from different tokamaks representing different trigger mechanisms to im-prove its extrapolability. Finally, this new proximity metric to the = 1 TM onset has been incorporated into the real-time in DIII-D plasma control system (PCS) and results from real-time experiments will be discussed.Ph.D