Photovoltaic (PV) systems can operate off the maximum power point (MPP) for various reasons. Understanding when off- MPP behavior occurs is essential to the maintenance and operation (O&M) of PV systems. To detect off-MPP data, a reference power is usually needed, which can be obtained by system modeling that generally relies on physical model parameters. Traditional methods commonly obtain these parameters based on the initial condition of the PV system such as from the module datasheet. However, these parameters often do not reflect the current condition of the on-site PV system, which is likely to suffer from degradation and faults after years of operation with degraded parameters. Thus, we propose an off-MPP analysis algorithm based on the PV-Pro method, which can extract the model parameters (like series and shunt resistance) at the current operating condition only using the routine production data. In this way, the system power, current, and voltage can be accurately modeled. The off-MPP points are detected by comparing the measured power with the one modeled by PV-Pro. Points with large disagreement in power are further analyzed by deconvolving it into the error of the current and voltage at MPP, which allows tracing the error source of the off- MPP and provides valuable information for the O&M of PV systems. This off-MPP analysis is demonstrated on a 271kW PV field system, where it is shown that most of the off- MPP points are caused by the reduced DC current.

To enable health monitoring and fault diagnosis of PV modules using current-voltage characteristics (I–V curves), it is generally necessary to correct the I–V curves measured under different environmental conditions to the standard condition. The most common correction methods are those from IEC 60891: 2021 standard. However, these methods can introduce significant errors when dealing with degraded PV modules due to the inability to account for changes in resistance. To address this, we propose an improved I–V curve procedure, denoted Pdynamic, which considers different types of degradation by dynamically deriving the correction coefficients from the measured I–V curves. To evaluate the performance, we simulate I–V curves across a wide range of irradiance and temperature for the healthy and degraded module, where the degradation involves increased series resistance, decreased shunt resistance, or both. The results reveal that Pdynamic can produce corrected I–V curves closer to the reference ones than Procedures 1, 2, and 4 of the IEC 60891:2021 standard. Moreover, Pdynamic exhibits resilience to both seasonal fluctuations and varying levels of degradation. These results highlight Pdynamic as a promising and robust I–V curve correction method, particularly for degraded PV modules. A Python-based open-source tool for this procedure is also available at https://github.com/DuraMAT/IVcorrection.

Physics-based circuit parameters like series and shunt resistance are essential to provide insights into the degradation status of photovoltaic (PV) arrays. However, calculating these parameters typically requires a full current–voltage characteristic (I-V curve), the acquisition of which involves specific measurement devices and costly methods. Thus, I-V curves of the PV system level are often not available. This paper proposes a methodology (PVPRO) to estimate these I-V curve parameters using only operation (string-level DC voltage and current) and weather data (irradiance and temperature). PVPRO first performs multi-stage data pre-processing to remove noisy data. Next, the time-series DC data are used to fit an equivalent circuit single-diode model (SDM) to estimate the circuit parameters by minimizing the differences between the measured and estimated values. In this way, the time evolutions of the SDM parameters are obtained. We evaluate PVPRO on synthetic datasets and find an excellent estimation of both SDM and the key I-V parameters (e.g., open-circuit voltage, short-circuit current, maximum power, etc.) with an average relative error of 0.55%. The performance, especially the extracted degradation rate of parameters, is robust to various measurement noises and the presence of faults. In addition, PVPRO is applied to a 271 kW PV field system. The relative error between the real and estimated operation voltage and current is less than 1%, suggesting that degradation trends are well captured. PVPRO represents a promising open-source tool to extract the time-series degradation trends of key PV parameters from routine operation data.

Bone mass is determined by the balance between bone formation, carried out by mesenchymal stem cell-derived osteoblasts, and bone resorption, carried out by monocyte-derived osteoclasts. Here we investigated the potential roles of p38 MAPKs, which are activated by growth factors and cytokines including RANKL and BMPs, in osteoclastogenesis and bone resorption by ablating p38α MAPK in LysM+monocytes. p38α deficiency promoted monocyte proliferation but regulated monocyte osteoclastic differentiation in a cell-density dependent manner, with proliferating p38α^-/- cultures showing increased differentiation. While young mutant mice showed minor increase in bone mass, 6-month-old mutant mice developed osteoporosis, associated with an increase in osteoclastogenesis and bone resorption and an increase in the pool of monocytes. Moreover, monocyte-specific p38α ablation resulted in a decrease in bone formation and the number of bone marrow mesenchymal stem/stromal cells, likely due to decreased expression of PDGF-AA and BMP2. The expression of PDGF-AA and BMP2 was positively regulated by the p38 MAPK-Creb axis in osteoclasts, with the promoters of PDGF-AA and BMP2 having Creb binding sites. These findings uncovered the molecular mechanisms by which p38α MAPK regulates osteoclastogenesis and coordinates osteoclastogenesis and osteoblastogenesis.

Bone marrow-derived mesenchymal stromal cells (BM-MSCs) are capable of differentiating into osteoblasts, chondrocytes, and adipocytes. Skewed differentiation of BM-MSCs contributes to the pathogenesis of osteoporosis. Yet how BM-MSC lineage commitment is regulated remains unclear. We show that ablation of p38α in Prx1+ BM-MSCs produced osteoporotic phenotypes, growth plate defects, and increased bone marrow fat, secondary to biased BM-MSC differentiation from osteoblast/chondrocyte to adipocyte and increased osteoclastogenesis and bone resorption. p38α regulates BM-MSC osteogenic commitment through TAK1-NF-κB signaling and osteoclastogenesis through osteoprotegerin (OPG) production by BM-MSCs. Estrogen activates p38α to maintain OPG expression in BM-MSCs to preserve the bone. Ablation of p38α in BM-MSCs positive for Dermo1, a later BM-MSC marker, only affected osteogenic differentiation. Thus, p38α mitogen-activated protein kinase (MAPK) in Prx1+ BM-MSCs acts to preserve the bone by promoting osteogenic lineage commitment and sustaining OPG production. This study thus unravels previously unidentified roles for p38α MAPK in skeletal development and bone remodeling.

Nutrients are absorbed solely by the intestinal villi. Aging of this organ causes malabsorption and associated illnesses, yet its aging mechanisms remain unclear. Here, we show that aging-caused intestinal villus structural and functional decline is regulated by mTORC1, a sensor of nutrients and growth factors, which is highly activated in intestinal stem and progenitor cells in geriatric mice. These aging phenotypes are recapitulated in intestinal stem cell-specific Tsc1 knockout mice. Mechanistically, mTORC1 activation increases protein synthesis of MKK6 and augments activation of the p38 MAPK-p53 pathway, leading to decreases in the number and activity of intestinal stem cells as well as villus size and density. Targeting p38 MAPK or p53 prevents or rescues ISC and villus aging and nutrient absorption defects. These findings reveal that mTORC1 drives aging by augmenting a prominent stress response pathway in gut stem cells and identify p38 MAPK as an anti-aging target downstream of mTORC1.