Real-time Flow Research Articles

Real-time lava flow forecasting during the 2022 Mauna Loa eruption response

On November 27, 2022, Mauna Loa (Hawai‘i) erupted for the first time in ∼38 years, initially producing lava flows that covered the floor of its summit caldera, Moku‘āweoweo. Over the first 12 h following the summit eruption, four main fissures opened on Mauna Loa’s Northeast Rift Zone, with “fissure 3” quickly becoming the dominant source of lava flows. For the next 12 days, fissure 3 produced a 19-km-long lava flow to the north, crossing the Mauna Loa Weather Observatory access road and coming within 2.8 km of inundating the Daniel K. Inouye Highway (Saddle Road). Within 40 min of fissure 3 opening, inundation modeling efforts had begun. For the duration of the eruption, the computational fluid dynamics model Lava2d was run in real time, using flow front locations and other field observations to make sequential forecast improvements, eventually producing a set of models which accurately predicted the routing and arrival times of lava from fissure 3. These models were used to inform timing estimates of possible future inundation of the Saddle Road. As the eruption progressed, almost 4000 Lava2d models were made using high-performance computing resources, providing critical information on uncertainty in multi-week forecasts. To our knowledge, this was the first ever real-time physics-based ensemble lava flow modeling and forecasting effort. Here, we present a chronology of these real-time efforts, focusing on the successes and limitations of this approach.

Multi-scale influences on Escherichia coli concentrations in shellfish: from catchment to estuary.

Sustainability of bivalve shellfish farming relies on clean coastal waters, however, high levels of faecal indicator organisms (FIOs, e.g. Escherichia coli) in shellfish results in temporary closure of shellfish harvesting beds to protect human health, but with economic consequences for the shellfish industry. Active Management Systems which can predict FIO contamination may help reduce shellfishery closures. This study evaluated predictors of E. coli concentrations in two shellfish species, the blue mussel (Mytilus edulis) and the Pacific oyster (Crassostrea gigas), at different spatial and temporal scales, within 12 estuaries in England and Wales. We aimed to: (i) identify consistent catchment-scale or within-estuary predictors of elevated E. coli levels in shellfish, (ii) evaluate whether high river flows associated with rainfall events were a significant predictor of shellfish E. coli concentrations, and the time lag between these events and E. coli accumulation, and (iii) whether operation of Combined Sewer Overflows (CSO) is associated with higher E. coli concentrations in shellfish. A cross-catchment analysis gave a good predictive model for contamination management (R2 = 0.514), with positive relationships between E. coli concentrations and river flow (p=0.001), turbidity (p=0.002) and nitrate (p=0.042). No effect was observed for catchment area, the number of point source discharges, or agricultural land use type. 64% of all shellfish beds showed a significant relationship between E. coli and river flow, with typical lag-times of 1-3 days. Detailed analysis of the Conwy estuary indicated that E. coli counts were consistently higher when the CSO had been active the previous week. In conclusion, we demonstrate that real-time river flow and water quality data may be used to predict potential risk of E. coli contamination in shellfish at the catchment level, however, further refinement (coupling to fine-scale hydrodynamic models) is needed to make accurate predictions for individual shellfish beds within estuaries.

Enhancing origin–destination flow prediction via bi-directional spatio-temporal inference and interconnected feature evolution

Origin–destination (OD) flow prediction is crucial for predicting inter-station passenger flows in intelligent transport systems. However, previous OD prediction methods have ignored the delay of OD flows and failed to focus on the supplementary effect of the arrival OD flow (Out-OD) flow data on OD flows. We innovatively propose an OD flow prediction method based on Bidirectional Attention and Interconnected Feature Evolution (BiST-IF) to address these challenges. Firstly, we propose a correction method based on period delay probability and real-time flow features for OD flow information. Furthermore, for the flow information of different periods, we design a bi-directional attention module to achieve the preliminary prediction by deconstructing and analyzing the temporal and flow features of OD flow and fusing the temporal and spatial features. After that, we design the mutual information module of Out-OD and OD flow, combining it with the new gating algorithm to enhance the periodic features. Finally, the prediction results from the periodic pattern and the temporary fluctuation of OD flow are fused to obtain the OD flow prediction results. Extensive experiments on two real-world datasets show that the prediction performance of BiST-IF significantly outperforms other state-of-the-art models. Specifically, BiST-IF improves the MAE and RMSE by an average of 7.55% and 7.31% on the HZMetro dataset, and 5.77% and 2.33% on the NYC-TOD2018 dataset, respectively, compared to the best baseline approach.

Dual-Wavelength Confocal Laser Speckle Contrast Imaging Using a Deep Learning Approach

This study developed a novel dual-wavelength confocal laser speckle imaging platform. The system includes both visible and near-infrared lasers and two imaging modes: confocal and wide-field laser speckle contrast imaging. The experimental results confirm that the proposed system can be used to measure not only blood flow but also blood oxygen saturation. Additionally, we proposed a blood flow perfusion imaging method called BlingNet (a blood flow imaging CNN) based on the laser speckle contrast imaging technique and deep learning approach. Compared to the traditional nonlinear fitting method, this method has superior accuracy and robustness with higher imaging speed, making real-time blood flow imaging possible.

Analysis of the role of PANoptosis in intervertebral disk degeneration via integrated bioinformatics analysis and experimental validation

Intervertebral disc degeneration (IVDD) is an age-related orthopedic degenerative disease characterized by recurrent episodes of lower back pain, and death of nucleus pulposus cells (NPCs) has been identified as a key factor in the pathophysiological process of IVDD episodes. Recent studies have shown that “ PANapoptosis ”, a newly characterized form of cell death, has emerged as an important factor contributing to the development of several diseases. However, studies on the specific mechanisms of its role in the development of IVDD are lacking. The aim of this study was to explore the characterization of PANoptosis in IVDD and to identify potential biomarkers and therapeutic targets as well as therapeutic agents. We constructed a PANoptosis gene set, based on the GEO database, and used weighted gene co-expression network analysis (WGCNA) and differential expression analysis to identify PANoptosis genes associated with the pathophysiological process of IVDD episodes by Gene Set Enrichment Analysis (GSEA), immune infiltration, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore the underlying biological mechanisms of PANoptosis and its role in IVDD. Comprehensive bioinformatics analysis showed that seven key genes (APAF1, MEFV, NLRP3, TNF, GSDMD, AIM2, and IRF1) of PANoptosis have good diagnostic value. In addition, we predicted potential therapeutic agents, among which Andrographolide (AG) had the highest correlation and binding affinity to the target. Finally, we performed Western blotting and quantitative real-time polymerase chain reaction (qRT-PCR) assays, molecular docking, and cell flow to validate the expression of PANoptosis-related genes and the therapeutic effect of AG. We further divided SD rats into sham-operated, IVDD model, and Andrographolide-treated groups, administered AG at 50 mg/kg via gavage for one month, and observed significant therapeutic effects through HE staining. This study identifies key PANoptosis genes and demonstrates the potential of AG as a therapeutic agent for IVDD.

Abstract 4134426: Targeting the TRIM29-PERK signaling axis for treating viral myocarditis

Introduction: Viral myocarditis is an inflammatory disease of the myocardium that significantly contributes to sudden death in children and young adults. The COVID-19 pandemic underscores the critical need to understand the pathogenesis and develop effective treatment strategies for viral myocarditis. Methods: To investigate the role of the novel E3 ligase TRIM29 in viral myocarditis, we used wild-type and TRIM29 knockout mice infected with coxsackievirus B3 (CVB3) and encephalomyocarditis virus (EMCV), to induce the myocarditis. Additionally, wild-type mice infected with CVB3 were treated with the protein kinase RNA-like endoplasmic reticulum kinase (PERK) inhibitor GSK2656157 or a DMSO control to evaluate potential therapeutic interventions. Mice survival and heart function were monitored using transthoracic echocardiography, and hearts were harvested for histological and immunohistochemical analysis. Techniques including real-time PCR, western blotting, co-immunoprecipitation, enzyme-linked immunoassay, flow cytometry, over-expression, and knockdown were employed to elucidate the pathogenic mechanisms by which TRIM29 regulates the endoplasmic reticulum stress response after viral infection. Results: We found that TRIM29 was highly induced by cardiotropic viruses, including CVB3 and EMCV, and promoted PERK-mediated endoplasmic reticulum (ER) stress, apoptosis, and reactive oxygen species (ROS) responses, which restricted viral replication in cardiomyocytes in vitro . TRIM29 deficiency protected mice from viral myocarditis by enhancing cardiac antiviral functions and reducing PERK-mediated inflammation and immunosuppressive cells in vivo . Mechanistically, TRIM29 interacted with PERK to catalyze the SUMOylation of PERK, maintaining its stability and thereby promoting PERK-mediated signaling pathways. Furthermore, we demonstrated that the PERK inhibitor GSK2656157 mitigated viral myocarditis by disrupting the TRIM29-PERK interaction, which improved cardiac function, enhanced cardiac antiviral responses, and reduced inflammation and immunosuppressive cells in vivo . Conclusions: Our findings provide the first evidence that the TRIM29-PERK signaling axis can be targeted for the treatment of viral myocarditis. This suggests that targeting the TRIM29-PERK axis could effectively mitigate viral myocarditis and associated cardiovascular diseases.

Research on passenger flow control at metro transfer stations based on real-time flow calculation of streamlines

PurposeThe volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.Design/methodology/approachThe synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.FindingsUtilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.Originality/valueThe construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.

Vehicle flow indication and identification using FBG sensors

Abstract This paper proposes a vehicle flow monitoring system utilizing fiber Bragg grating (FBG) sensor technology. The system comprises a prototype road model along with a car with varying loads. The FBG sensor is embedded in the road surface for sensing the strain induced by the vehicle movement. The laboratory experiments were conducted using an embankment model equipped with FBG sensor to identify vehicle movement under static and movable loads. Under static condition, different loads were applied on the car and the corresponding wavelength shifts have been measured using FBG-interrogator. Under dynamic condition, the vehicle was moved with certain speed and the corresponding wavelength shifts were recorded over time. The sensitivity of FBG for vehicle load sensing has been obtained as 0.3 pm/N. The proposed vehicle flow sensor system offers good accuracy and sensitivity and has potential applications for real-time vehicle flow monitoring. The proposed sensor system can be used to analyze road surface irregularities, crucial for assessing heavy-vehicle fatigue.

Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning

The acceleration of urbanization has led to increasingly severe traffic congestion, creating an urgent need for effective traffic signal control strategies to improve road efficiency. This paper proposes an adaptive traffic signal control method based on offline reinforcement learning (Offline RL) to address the limitations of traditional fixed-time signal control methods. By monitoring key parameters such as real-time traffic flow and queue length, the proposed method dynamically adjusts signal phases and durations in response to rapidly changing traffic conditions. At the core of this research is the design of a model named SD3-Light, which leverages advanced offline reinforcement learning to predict the optimal signal phase sequences and their durations based on real-time intersection state features. Additionally, this paper constructs a comprehensive offline dataset, which enables the model to be trained without relying on real-time traffic data, thereby reducing costs and improving the model’s generalization ability. Experiments conducted on real-world traffic datasets demonstrate the effectiveness of the proposed method in reducing the average travel time. Comparisons with several existing methods highlight the clear advantages of our approach in enhancing traffic management efficiency.

SRAGE attenuates myocardial ischemia-reperfusion injury by recruiting Tregs through chemokine CCL4

Abstract Background Immune response has recently been shown to play a crucial role in myocardial ischemia-reperfusion (MIR). Our previous studies have confirmed that soluble receptors for advanced glycation end-products (sRAGE) could reduce myocardial ischemia-reperfusion injury (MIRI) by increasing the number of regulatory T cells (Tregs). However, the precise mechanism is still incompletely understood. Purpose The present study aimed to determine which kind of chemokines play a major role in recruiting Tregs during MIRI. The potential mechanism of sRAGE on the chemokine production was further explored. Methods We modeled mice with cardiac-specific overexpression of sRAGE by crossing the floxed sRAGE mouse model with the cardiomyocyte-specific Cre transgenic mouse. Experimental models were established utilizing left anterior descending coronary artery ligation in mice and oxygen-glucose deprivation/reoxygenation (OGD/R) in vitro. Transcriptomics, quantitative real-time polymerase chain reaction, immunohistochemistry, flow cytometry, and western blotting were used to determine the chemokines and related pathways involved in the recruitment of Tregs during MIRI. Results Using transcriptomics, quantitative real-time polymerase chain reaction, and western blotting approach, we identify the chemokine CCL4 plays a crucial role during regulatory T cell recruitment in the heart after MIRI. In vivo studies show that depletion of CCL4 reduces Tregs infiltration into the ischemic myocardium, subsequently deteriorating cardiac injury post-MIR. Mechanistically, sRAGE promotes cardiomyocytes to express and secrete CCL4 via the JNK/AP-1 pathway in vitro. Selective inhibition of the JNK/AP-1 pathway could decrease CCL4 production in sRAGE overexpression cardiomyocytes. Conclusions sRAGE induces Tregs recruitment and alleviates MIRI via the JNK/AP-1/CCL4 pathway, preserving myocardial function and impeding inflammation and apoptosis, providing new perspectives for immunotherapeutic targets in MIRI treatment.

AI-Driven Neural Networks for Real-Time Passenger Flow Optimization in High-Speed Rail Networks

AI-Driven Neural Networks for Real-Time Passenger Flow Optimization in High-Speed Rail Networks

Performance Analysis of Firewall and Virtual Private Network (VPN) Usage in Video Conferencing Applications

Rapid developments in information technologies have made these technologies indispensable elements of our lives with application areas such as e-government, e-commerce, e-health, e-learning. Particularly the global Covid-19 pandemic period has led to forced improvements in video conferencing applications, which enable users in different locations at the same time to communicate via video and audio over internet. Developments in technology, which cause the rapid increase of applications served over internet, also cause a significant increase in the number of devices connected to internet and the data traffic flowing over internet. As a result, the security needs of applications used over internet, such as video conferencing applications, are increasing in proportion to the increasing security threat risks. The use of firewall and VPN (Virtual Private Network) are the most basic security solutions for applications used over internet. A firewall is a device, which is positioned between a corporate network and the internet cloud, filtering incoming and outgoing traffic to and from the network according to defined rules. VPN, on the other hand, provides a secure point-to-point connection to a corporate network through the internet cloud. In this study, the effects of firewall and VPN usage in video conferencing applications were analyzed in terms of application performance. In video conferencing applications; since there is a real-time, bidirectional and large-scale data flow between the participants, delay and packet loss determine the performance of these applications. Analyzing how these applications perform when used with firewalls and VPN will guide further improvements in network protocols, components and related applications. In the study in which the simulation method was used, the data obtained from the simulation of different scenarios created with the OPNET tool were analyzed comparatively.

A Review on Secure Outsourcing and Privacy-Preserving Traffic Monitoring in Fog-Enabled VANETs

New possibilities for fog-based vehicle monitoring have emerged with the expansion of fog computing, but present privacy concerns provide a significant barrier that limits the extent to which vehicles can participate. Because of its potential to improve road network security and vehicle productivity, the field of Vehicle-based Ad hoc Networks (VANETs) is gaining prominence. The security problems with VANETs, such as data confidentiality and message access control are still need better solutions to provide better Quality of Service (QoS). The effectiveness of VANET networks is diminished due to their instability problem. Vehicles continually add requests to the Road Side Unit (RSU) queue whenever they want specific information. For ever-evolving networks like VANETs, better routing is a continual process. Fundamental problems arise in large-scale systems when centralized procedures are used to assign jobs to the nodes along a route. The present centralized system for computing and safety has many needs, including the protection of data storage, user authentication, access control, system availability across multiple network connections, and the provision of a real-time data flow overview. Distributed problem solving and work sharing between multiple agents can improve the system's scalability. This paper provides a brief survey on the secured outsourcing and privacy preservation-based traffic monitoring model with routing and task scheduling models in Fog-enabled VANETS. This survey presents the limitations of the traditional models that help the researchers design new solutions for secure outsourcing in VANETs.

Simulation of Launch Pad Atomization for Military Weapon System

This study aims to optimize three critical parameters (inclination angle, hook lifting, and sparking) for a missile launching system by combining precise mathematical methods, IoT concepts, and advanced sensor technologies. The system uses an Inertial Navigation System (INS) in conjunction with gyro and proximity sensors to ensure accurate target interception by carefully determining the optimal launch angle. The Internet of Things concept is integrated effortlessly, with a clever coordinating WiFi networking, weight estimation, and sensor data processing for real-time cloud data flow. Security protocols protect the integrity of the data, and a reliable power supply system guarantees reliable operation. The project incorporates both Hot Start and Cold Start functionalities, enabling dependable launches and prompt adaptations to dynamic changes. Flexible igniting mechanisms such as Chemical, Electrical, and Electro-Chemical systems are available, and reliable and secure missile launches are prioritized with safety features like Spark Damping Inner Tube Coating and Moisture-Resistant igniting. All things considered, this project prioritizes security, dependability, and adaptability while addressing critical elements required for a sophisticated and efficient missile launching system.

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

MODELS FOR REAL-TIME TRAFFIC FLOW MANAGEABILITY AND DECISION-MAKING IN INTELLIGENT TRANSPORTATION SYSTEMS

This article explores models in Intelligent Transportation Systems for real-time traffic flow manageability, focusing on decision-making processes. It covers forecasting, planning, implementing, and controlling strategies to manage traffic flow and ease congestion. Traffic flow prediction models, like dynamic route guidance and traffic flow prediction, utilize historical data and real-time inputs for proactive decision-making. Traffic flow planning models, such as dynamic route guidance index and route efficiency factor, aid in route selection and signal timing optimization. In order to streamline the boundless complexity, the authors assume that it is effective to delineate the managerial capacity paradigm of intelligent transportation systems into the two separate scenarios of “stable and known situation” and “unstable and with large uncertainty situation”. The article proposes a hypothesis to improve the decision-making process in traffic flow. The distinction between these two situations is essential for the smooth running of the business and requires a thorough understanding of the traffic flow in real time, making decisions in intelligent transport systems in order to direct the traffic. The article focuses on data-driven decisions for smoother traffic flow.

An efficient approach of meshless node placement in three-dimensional subsurface flow modeling

Meshless methods are valuable tools for real-time subsurface flow modeling and are particularly beneficial for adaptively adjusting node positions when incorporating supplementary measurements. However, an algorithm is required for automatically generating high-quality meshless nodes to adapt to irregularly shaped aquifers and arbitrarily distributed wells. This paper introduces a three-dimensional adaptive meshless node placement technique based on advancing front nodes and proposes a sigmoid exclusion circle to ensure adaptability to specific positions of interest. It is crucial for groundwater simulations to have specified computational points at wellbore locations. The generated meshless nodes were used as computational points in the generalized finite difference method. The node quality was assessed by comparing the numerical solutions to the analytical solution, achieving errors of the order of 10–12. The application scenarios of irregularly shaped aquifers demonstrated the high efficiency of the three-dimensional node placement in generating up to 10,000 nodes in <5 s. The effectiveness of the algorithm was verified using three-dimensional irregular computational domains. The algorithm could be easily adapted to two-dimensional problems, and an irregular boundary example is presented. The validation and application cases highlighted the versatility of the proposed method and established its potential for real-world hydrogeological applications.

Loss of mitochondrial Ca2+ response and CaMKII/ERK activation by LRRK2R1441G mutation correlate with impaired depolarization-induced mitophagy

BackgroundStress-induced activation of ERK/Drp1 serves as a checkpoint in the segregation of damaged mitochondria for autophagic clearance (mitophagy). Elevated cytosolic calcium (Ca2+) activates ERK, which is pivotal to mitophagy initiation. This process is altered in Parkinson’s disease (PD) with mutations in leucine-rich repeat kinase 2 (LRRK2), potentially contributing to mitochondrial dysfunction. Pathogenic LRRK2 mutation is linked to dysregulated cellular Ca2+ signaling but the mechanism involved remains unclear.MethodsMitochondrial damages lead to membrane depolarization. To investigate how LRRK2 mutation impairs cellular response to mitochondrial damages, mitochondrial depolarization was induced by artificial uncoupler (FCCP) in wild-type (WT) and LRRK2R1441G mutant knockin (KI) mouse embryonic fibroblasts (MEFs). The resultant cytosolic Ca2+ flux was assessed using live-cell Ca2+ imaging. The role of mitochondria in FCCP-induced cytosolic Ca2+ surge was confirmed by co-treatment with the mitochondrial sodium-calcium exchanger (NCLX) inhibitor. Cellular mitochondrial quality and function were evaluated by Seahorse™ real-time cell metabolic analysis, flow cytometry, and confocal imaging. Mitochondrial morphology was visualized using transmission electron microscopy (TEM). Activation (phosphorylation) of stress response pathways were assessed by immunoblotting.ResultsAcute mitochondrial depolarization induced by FCCP resulted in an immediate cytosolic Ca2+ surge in WT MEFs, mediated predominantly via mitochondrial NCLX. However, such cytosolic Ca2+ response was abolished in LRRK2 KI MEFs. This loss of response in KI was associated with impaired activation of Ca2+/calmodulin-dependent kinase II (CaMKII) and MEK, the two upstream kinases of ERK. Treatment of LRRK2 inhibitor did not rescue this phenotype indicating that it was not caused by mutant LRRK2 kinase hyperactivity. KI MEFs exhibited swollen mitochondria with distorted cristae, depolarized mitochondrial membrane potential, and reduced mitochondrial Ca2+ store and mitochondrial calcium uniporter (MCU) expression. These mutant cells also exhibited lower cellular ATP: ADP ratio albeit higher basal respiration than WT, indicating compensation for mitochondrial dysfunction. These defects may hinder cellular stress response and signals to Drp1-mediated mitophagy, as evident by impaired mitochondrial clearance in the mutant.ConclusionsPathogenic LRRK2R1441G mutation abolished mitochondrial depolarization-induced Ca2+ response and impaired the basal mitochondrial clearance. Inherent defects from LRRK2 mutation have weakened the cellular ability to scavenge damaged mitochondria, which may further aggravate mitochondrial dysfunction and neurodegeneration in PD.

CURRENT TRENDS IN PHOTOVOLTAIC THERMAL (PVT) SYSTEMS: A REVIEW OF TECHNOLOGIES AND SUSTAINABLE ENERGY SOLUTIONS

This systematic review explores advancements and challenges in photovoltaic thermal (PVT) systems, focusing on efficiency improvements, cooling mechanisms, material innovations, Internet of Things (IoT) integration, and cost and environmental considerations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 45 articles were analyzed to provide a comprehensive understanding of current trends and future directions in PVT technology. Findings reveal that cooling mechanisms, particularly liquid-based and nanofluid-based systems, are essential for maintaining high PVT efficiency under diverse environmental conditions. Material advancements, including phase change materials (PCMs) and nanotechnology, have enhanced thermal management and energy storage capabilities, yet their high costs and environmental impacts remain significant barriers to broader adoption. IoT and smart grid integration have transformed PVT system functionality by enabling real-time monitoring, predictive maintenance, and energy flow adjustments within connected energy networks. However, persistent challenges—including high initial investment costs, environmental concerns related to materials, and the need for adaptable designs—highlight areas for future research. Advancements in adaptive materials, sustainable cooling solutions, and digital automation are essential for developing cost-effective, resilient PVT systems that contribute to global renewable energy goals. This review provides a foundation for future research to address the identified challenges and maximize the potential of PVT systems in sustainable energy production.

The N-terminal fragment of histone deacetylase 4 (1-669aa) promotes chondrocyte apoptosis via the p53-dependent endoplasmic reticulum stress pathway.

Exogenous administration of the histone deacetylation 4 (HDAC4) protein can effectively delay osteoarthritis (OA) progression. However, HDAC4 is unstable and easily degrades into N-terminal (HDAC4-NT) and C-terminal fragments, and the HDAC4-NT can exert biological effects, but little is known about its role in chondrocytes and cartilage. Thus, the roles of HDAC4-NT fragments (1-289aa, 1-326aa and 1-669aa) in chondrocytes and cartilage were evaluated via real-time cell analysis (RTCA), safranin O staining, Sirius Red staining and nanoindentation. Molecular mechanisms were profiled via whole-transcriptome sequencing (RNA-seq) and verified invitro and invivo by a live cell real-time monitoring system, flow cytometry, western blotting and immunohistochemistry. The results showed that 1-669aa induced chondrocyte death and cartilage injury significantly, and the differentially expressed genes (DEGs) were enriched mainly in the apoptotic term and p53 signalling pathway. The validation experiments showed that 1-669aa induced chondrocyte apoptosis via the endoplasmic reticulum stress (ERS) pathway, and up-regulated p53 expression was essential for this process. Thus, we concluded that the HDAC4-NT fragment 1-669aa induces chondrocyte apoptosis via the p53-dependent ERS pathway, suggesting that in addition to overexpressing HDAC4, preventing it from degradation may be a new strategy for the treatment of OA.