Modeling Techniques Research Articles

On the Replicability of the Thermodynamic Modeling of Spectroscopic Titration Data in the Nickel(II) En System

ABSTRACTCharacterizing complicated solution phase systems in situ requires advanced modeling techniques to capture the intricate balances between the many chemical species. Due to the error inherent in any scientific measurement, a spectrophotometric titration experiment with nickel(II) and ethylenediamine (en) was repeated six times using an autotitrator to test the replicability of the data and the consistency of the resulting thermodynamic model. All six datasets could be modeled very tightly (R2 > 99.9999%) with the following eight complexes: [Ni]2+, [Ni2en]4+, [Nien]2+, [Ni2en3]4+, [Nien2]2+, [Ni2en5]4+, [Nien3]2+, and [Nien6]2+. The logK values for the stepwise associative reactions agree with existing literature values for the majority species ([Nienn = 1–3]2+) and matched expectations for the minority species; 95% confidence intervals for each logK value were determined via bootstrapping, which quantifies the variability in the binding constant value that is supported by a given dataset. The repeated experiments, which could not be successfully concatenated together, demonstrate that replication is crucial to capturing all the variability in the logK values. Conversely, bootstrapped confidence intervals across multiple experiments can be readily combined to generate an appropriate range for an experimentally determined binding constant.

Cheminformatics-aided discovery of potential allosteric site modulators of ubiquitin-specific protease 7

Ubiquitin-specific peptidase 7 (USP7) is a deubiquitinating enzyme that mediates the stability and activity of numerous proteins. At basal expression levels, USP7 stabilizes p53 protein, even in the presence of excess MDM2. However, its overexpression leads to the deubiquitination of MDM2 at a rate faster than p53, leading to p53 degradation and pro-tumorigenic roles. Consequently, it is an attractive target for anticancer drug discovery via the modulation of its allosteric site from which the protein is activated. In this study, molecular modeling techniques and cheminformatics approaches were employed to unravel the potential of eighty compounds to serve as its allosteric site modulators. The compounds were initially subjected to virtual screening. Subsequently, the binding free energies of the top four compounds with the highest binding affinities were calculated, and their drug-likeness, and pharmacokinetic and toxicity profiles were evaluated. Ultimately, the complexes of the protein and hit compounds were subjected to a 100 nanoseconds (ns) molecular dynamics simulation. The results of the study revealed eight compounds from the compound library with docking scores ranging from − 7.491 to -11.43 kcal/mol, compared to P217564, which exhibited a docking score of -5.671 kcal/mol. The top four compounds with the highest affinities possessed drug-like properties, and good pharmacokinetic and toxicity profiles, and their predicted inhibitory potentials showed they will be effective at minimal concentration. Also, molecular dynamics simulation confirmed the stability of the protein-ligand complexes. Conclusively, the compounds identified in this study are worthy of further evaluation for the development of allosteric site modulators of USP7.

Interval-Valued Random Matrices

This paper introduces a novel approach that combines symbolic data analysis with matrix theory through the concept of interval-valued random matrices. This framework is designed to address the complexities of real-world data, offering enhanced statistical modeling techniques particularly suited for large and complex datasets where traditional methods may be inadequate. We develop both frequentist and Bayesian methods for the statistical inference of interval-valued random matrices, providing a comprehensive analytical framework. We conduct extensive simulations to compare the performance of these methods, demonstrating that Bayesian estimators outperform maximum likelihood estimators under the Frobenius norm loss function. The practical utility of our approach is further illustrated through an application to climatology and temperature data, highlighting the advantages of interval-valued random matrices in real-world scenarios.

Exploring Trends in Transportation Management: A Review of Scientific Papers on Dimension AI Database from 2020-2024

This qualitative study explores contemporary trends and advancements in sustainable transportation management practices, focusing on articles published between 2020 and 2024 and sourced from sustainability journals. A comprehensive search strategy using the Dimension AI database with the keywords "Transportation" and "Management". Articles were first screened by their title, abstract, and keywords, from which 63 manuscripts were chosen for thematic analysis. The key findings were the importance of sustainable traffic management systems, including congestion measures, infrastructure resilience, and environmental impact assessment. Another strand that emerged was data-driven approaches for smart city traffic management, which included novel techniques for effective decision-making. The study has especially highlighted the requirements of strategies that would set up the innovation in modeling techniques and resilience frameworks for transportation management. This study offers significant insight into modern trends in sustainable transportation management, which should be of immense value to policymakers, urban planners, and transportation professionals in seeking ways to overcome the problems associated with modern urban mobility while ensuring environmental sustainability.

Non-Conventional Data for Farming-Related Air Pollution: Contributions to Modelling and Risk Assessment in the Lombardy Region, Italy

Air pollution is one of the most critical global health concerns today. While emissions from industrial activities and combustion processes are the primary threats to air quality, intensive farming activities also contribute significantly, especially through ammonia emissions that promote the formation of secondary pollutants, such as particulate matter. Advancements in air quality research have been achieved by enhancements in emissions characterisation, modelling techniques, and sensor technology, expanding the availability of air pollution data beyond traditional ground sensor observations, which are often lacking in rural agricultural areas. Accordingly, this paper demonstrates the advantages of integrating traditional and non-conventional data to investigate farming-related air pollution through a case study in the Lombardy Region, Northern Italy. The study incorporates an array of data sources, including ground sensors and atmospheric composition model estimates. The concurrent utilisation of these diverse datasets is explored through machine learning modelling, focusing on assessing the influence of agricultural activities on particulate matter distribution patterns. Finally, the reliability of non-conventional air pollution data for health risk assessment applications is also investigated. The paper critically discusses the main findings based on empirical results, highlighting the significance of integrating multiple data sources to complement traditional air quality monitoring while outlining the main limitations in terms of the accuracy and usability of such non-conventional data.

Models and applications of stochastic programming with decision‐dependent uncertainty in power systems: A review

AbstractStochastic programming is a competitive tool in power system uncertainty management. Traditionally, stochastic programming assumes uncertainties to be exogenous and independent of decisions. However, there are situations where statistical features of uncertain parameters are not constant but dependent on decisions, classifying such uncertainties as decision‐dependent uncertainty (DDU). This is particularly the case with future power systems highly penetrated by multi‐source uncertainties, where planning or operation decisions might exert unneglectable impacts on uncertainty features. This paper reviews the stochastic programming with DDU, especially those applied in the field of power systems. Mathematical properties of diversified types of DDU in stochastic programming are introduced, and a comprehensive review on sources and applications of DDU in power systems is presented. Then, focusing on a specific type of DDU, that is, decision‐dependent probability distributions, a taxonomy of available modelling techniques and solution approaches for stochastic programming with this type of DDU and different structural features are presented and discussed. Eventually, the outlook of two‐stage stochastic programming with DDU for future power system uncertainty management is explored, including both exploring the applications and developing efficient modelling and solution tools.

Advanced Modeling of Hydrogen Turbines Using Generalized Conformable Calculus

This article addresses critical challenges in the transition to clean energy sources by highlighting the importance of advanced mathematical modeling and computational techniques in turbine design and operation. Specifically, we extend and generalize the work of Camporeale to advance the modeling of hydrogen turbine systems. By utilizing conformable calculus, we develop dynamic equations that analyze key aspects of turbine performance, including temperature variations in turbine blades, angular velocities of rotating shafts, and mass--energy balances within the plenum and combustion chamber. Furthermore, we incorporate Kirchhoff's equation in its generalized conformable integral form, enhancing the precision of energy balance calculations and improving the representation of heat transfer processes in the combustion chamber. This methodology introduces novel perspectives in hydrogen turbine research, contributing to the advancement of sustainable and efficient technologies. Our comprehensive approach aims to provide more accurate and efficient predictions of turbine behavior, thereby impacting the design and optimization of hydrogen-based clean energy systems.

Unveiling Weak Signals of Emergence in Underwater Sensing Research Trends

Detecting emerging research trends is crucial as it allows for the proactive identification and monitoring of novel and influential topics in the scientific community. Monitoring research trends aids researchers, institutions, and policymakers in allocating resources, fostering innovation, and staying competitive in rapidly changing scientific landscapes. The growing significance of underwater sensing technologies in various domains has propelled research endeavours aimed at understanding the characteristics of academic discourse in this field. In this work, we comprehensively analyzed the academic research topics related to underwater sensing technologies using advanced computational methodologies. Leveraging natural language processing, topic modelling, and weak signal detection techniques, and focusing on underwater sensing as the case technology, we dissect a large corpus of scholarly articles published between 2007 and 2021 to unveil underlying thematic patterns and emergent trends within this domain while shedding light on signals of emerging technologies. Among the eighty extracted topics, six research topics were identified and recognized as emerging weak signals, and validated by experts. Notably, deep learning for underwater imaging was the only topic that transitioned from being weak to a strong signal in the final period.

Antecedents and consequences of distributed leadership in Indian higher education

PurposeThe concept of distributed leadership (DL) has been widely advocated within higher education (HE). Yet, there have been few empirical investigations and little theory development outside Western contexts to date. This study presents a unique conceptualisation of DL and tests it empirically in India.Design/methodology/approachThis study tests a moderated-mediation model by exploring the antecedents and consequences of DL in HE. Standardised questionnaires were drawn from literature and completed by a sample of 269 respondents from six top-ranked (elite) Indian higher education institutions (HEIs). Structural equation modelling (SEM) and multi-group analysis techniques were used to analyse the data.FindingsResults demonstrated that empowering power structure (EPS) is positively related to DL, whilst participation in decision-making (PDM) strengthened this relationship. Further, it is also noted that DL mediates the relationship between EPS and behavioural outcomes of employee voice and silence.Practical implicationsThe findings suggest that a DL approach can be effective at enhancing employee voice and reducing employee silence within HEIs in (and perhaps beyond) India. The research also suggests that where institutions implement EPS alongside opportunities for PDM, this can help foster and sustain DL.Originality/valueBy exploring antecedents (EPS and PDM) and consequences (voice and silence), this paper presents a novel approach to studying DL. The focus on Indian HE offers a more nuanced empirical understanding of DL in a non-Western context.

Enhancing Selenium Removal Using Pre-Treated Natural Clay: Experimental Investigation and Predictive Modeling

Abstract A heat treatment methodology was adopted as a pretreatment strategy, altering the porous structure of the clay to minimize leaching for selenium adsorption in an aqueous system. Rigorous experiments were carried out in batch mode to determine optimal parameters across various variables, including contact time, adsorbent dosages, selenium concentrations, pH, temperature, and stirring speed during selenium removal using natural clay. Investigating several kinetic and isotherm models revealed the best fitting for the pseudo-second-order and the Langmuir isotherm. Endothermic and spontaneous characteristics of the adsorption process were shown during thermodynamic analysis. In this study, a predictive model for the efficiency of selenium separation was established using Response Surface Methodology (RSM). Additionally, an Artificial Neural Network (ANN), a data-driven model, was employed for comparative analysis. The predictive model exhibited a high degree of agreement with experimental data, demonstrated by a low relative error of <0.10, a high regression coefficient of >0.97), and a substantial Willmott-d index of >0.95. Moreover, the efficacy of pre-activated clay in selenium removal was assessed, revealing the superior performance of ANN models over RSM models in forecasting the efficiency of the adsorption process. This research significantly advances an effective and sustainable material for selenium removal, providing valuable insights into predictive modeling techniques applicable to similar contexts to boost scale-up confidence during industrial implementation in affected regions.

Review of Cell Level Battery (Calendar and Cycling) Aging Models: Electric Vehicles

Electrochemical battery cells have been a focus of attention due to their numerous advantages in distinct applications recently, such as electric vehicles. A limiting factor for adaptation by the industry is related to the aging of batteries over time. Characteristics of battery aging vary depending on many factors such as battery type, electrochemical reactions, and operation conditions. Aging could be considered in two sections according to its type: calendar and cycling. We examine the stress factors affecting these two types of aging in detail under subheadings and review the battery aging literature with a comprehensive approach. This article presents a review of empirical and semi-empirical modeling techniques and aging studies, focusing on the trends observed between different studies and highlighting the limitations and challenges of the various models.

Squirrel Cage Induction Motors Accurate Modelling for Digital Twin Applications

The ongoing industrial revolution emphasizes the importance of precise machinery monitoring. Among these machines, induction motors (IMs) stand out due to their large numbers, which imply a significant part of industrial energy consumption. To achieve accurate in-service IM monitoring, robust modelling is required, with a particular emphasis on in situ constraints. In this study, we create a precise digital model for squirrel cage induction motors (SCIMs) that can be used in Industry 4.0 digital twin applications. To achieve this, we survey the existing literature, describe the main modelling techniques, identify the best models in terms of ease of implementation, and ensure the accuracy of our digital representation. We develop four methods, namely finite element analysis (FEA), thermal modelling, circuit-based models, and quantum-based fuzzy logic control, as a crucial first step in implementing digital twin (DT) technology for IMs. The quantum fuzzy logic is based on the transition from classical equations to the quantum equation determining the speed of the motor in the quantum world by passing through the Schrödinger equation. We propose the DT level of integration architecture for IMs based on the industry 4.0 reference architecture model. Finally, the main tools used to successfully implement DT for IMs are revealed.

Probing the magnetic fields of starspots with transit mapping

Starspots, regions of strong magnetic fields, serve as indicators of stellar activity and the dynamo mechanism at play in the interior of stars. The magnetic fields of main-sequence stars play a crucial role in driving stellar activity. An effective approach to better understanding stellar magnetic fields and activity lies in the detailed characterisation of starspot properties. We propose a new method for estimating the magnetic fields of starspots that employs modelling techniques of planetary transit mapping, which provides estimates of the size, intensity, and location of spots on the stellar photosphere. A starspot's maximum magnetic field was calculated using the linear relationship with the spot flux deficit, $ spot $ (the spot's brightness times its area) and the well-characterised relation for sunspots determined in this work, $B_ spot (G). Applying this relationship to previously mapped spots on the photospheres of 14 FGK and M stars yields spot maximum magnetic fields ranging from 2700 G to 4600 G, with an overall average of $3900 400$ G. We looked for correlations between starspot magnetic fields and stellar properties.\ We did not find any correlation between a spot's mean extreme magnetic field and effective temperature, nor the differential shear. However, a weak anti-correlation is seen between the spots' magnetic field and stellar age as well as between the magnetic field and the rotation period. When compared with previous results of small-scale magnetic field measurements, the B values obtained here are basically constant and near the saturation limit found for rapid rotators. This implies that it is not the intensity of the magnetic field of starspots that decreases with age but rather the filling factor. This result offers a unique window into the magnetic dynamo of stars.

Numerical Analysis and Experimental Validation of Trimaran and Pentamaran Resistance at Various Separation Distances

Trimaran and pentamaran are multihull types with an odd number of hulls, namely three and five hulls, generally consisting of one center hull and two or four side hulls smaller than the center hull, which can reduce ship resistance. The trimaran and pentamaran have a more complex phenomenon than the monohull, because of the interaction between the main hull and the side hull, which causes interference caused by changes in flow velocity, pressure changes, and wave interactions generated by each hull. The objective of this study is to analyze the resistance of trimaran and pentamaran NPL-4b models with transom-symmetrical hull and separation distances, specifically S/L ratios of 0.2, 0.3, and 0.4. Since a more limited base of experience exists for multihull ships, experimental or numerical modeling techniques are essential for designers. Numeric investigations were conducted using Numeca software, and experiments were performed in a towing tank. Both methods follow ITTC procedures. Furthermore, the numerical analysis with CFD simulation modifies the Navier-Stokes equation using the turbulence model k-ꞷ SST to generate the RANS equation so that unsteady fluid flow problems can be calculated. It can be implemented in predicting resistance in Froude numbers (Fr) 0.2 to 0.6 at the systematic series of trimaran and pentamaran hull shapes at the model scale. The simulation results were compared to experimental data to validate the resistance of the ships. Overall, good resistance was produced by the trimaran and pentamaran in the C configuration at an S/L ratio of 0.4 with a total resistance coefficient CT of 6.60 x 10-3 and 7.37 x 10-3, respectively. The two results are in good agreement, both methods have a discrepancy of 3.70 %. Fluctuations influence the resistance in the wetted surface area (WSA), wave interactions between hulls, ship velocity, and wave propagation in the aft hull.

MODIS Evapotranspiration Forecasting Using ARIMA and ANN Approach at a Water-Stressed Irrigation Scheme in South Africa

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme.

The border in common: The Janus-faced place attachment of cross-border regions

For several decades, cross-border areas have been the setting for the successive deployment of various cross-border cooperation and integration mechanisms. These mechanisms aim primarily to facilitate mobility and interaction between actors through cross-border cooperation projects designed to create institutional cross-border areas. The current article takes an approach centred on attachment to places with the aim of determining whether cross-border areas make sense for their inhabitants. The analyses are based on a cross-sectional survey of 3,200 inhabitants of three institutional cross-border areas: Greater Geneva, the Basque Eurocity and the Eurometropolis of Lille Kortrijk Tournai. The methodology introduces into the field of border studies the technique of structural equation models for the measurement of place attachment using different explanatory variables. Several original results are found. First, place attachment is composed of both the sense of belonging to a country and the residential location of the members ego’s (the respondent) personal network. Second, two notable forms of attachment have an ambivalent relationship within the institutional cross-border areas: attachment to the border region of residence and attachment to the neighbouring border region. Specifically, the estimated attachment to the neighbouring border region favours the declared attachment to the institutional cross-border area. Subsequently, the estimated attachment to either border region also helps explain the choice of location for regular activities. Finally, the reported attachment to the institutional cross-border area is dependent on the individual’s social position. These results open up new perspectives on European integration policies that could rely more strongly on attachment to strengthen social cohesion in institutional cross-border areas.

How do Carbon Emissions Spillovers Reshape Metal Market Dynamics? Time– Frequency Insights on Precious and Non-precious Metals

Global disruptions, such as health crises and geopolitical tensions, significantly impact both climate and commodity dynamics. This study, well-grounded on environmental finance theory, input–output modelling, socio-transition philosophy and behavioural finance perspectives, explores the evolving interactions between carbon emissions (CE) and metal markets during COVID-19 pandemic and Russia–Ukraine war (RUW). The study uses time-varying parameter vector autoregressive model (TVP-VAR) technique to evaluate time and frequency varying connectedness between CE and metal markets from 3 January 2020 to 28 June 2024. During the COVID-19 pandemic, initial connectedness among selected markets peaked at 85%, averaging 46%, highlighting a significant CE–metal nexus that necessitates strategic responses. In the RUW period, connectedness averaged 47.82%. CE influence metal markets primarily in the short term. Wavelet coherence analysis reveals that palladium and platinum are highly sensitive to CE over the long term, while gold and silver may serve as effective diversifiers and hedges against carbon-related risks in metal investments. The study is relevant for investors in the metal sector with environmental considerations. JEL Classifications: G110, Q430, L720

Analysing the Factors Impacting the Mindfulness and Wellbeing of Faculty Members of Arts and Science Self-Financing Colleges in Kerala

Teaching in the modern academic environment is more complex, which makes it necessary to examine the challenges Kerala’s self-finance institutions arts and science institutes’ staff members are experiencing. This study investigates workplace stress among academic staff in self-financing arts and science institutes in Kerala. It analyses stress management techniques, including wellbeing initiatives and emotional intelligence training, and identifies the main stressors based on knowledge management (KM). The goal of the study is to improve faculty members’ performance and wellbeing in these institutions by providing information regarding relevant stress-reduction techniques that are specific to KM. The factors causing workplace stress have been measured as (a) demographic factors, (b) institutional factors and (c) psychological factors. Also, the stress management activities for the faculty members were considered a wellbeing programme, emotional intelligence, training and development. Initially, data collection was performed and 156 faculty members worked in arts and science self-financing colleges of the Kerala district. For a survey, in accordance with the proposed hypothesis, a systematic questionnaire has been created and sent to the faculty members who operate in Kerala. Moreover, statistical tests were performed on the gathered data using structural equation modelling (SEM) techniques. This investigation has revealed the characteristics that affect the faculty and staff working for an organisation in terms of workplace stress and excellent fit. The findings reveal that demographic demographics, institutional issues and stress management strategies influence faculty members in Kerala’s arts and self-finance colleges.

Gas Tunnel Engineering of Prolyl Hydroxylase Reprograms Hypoxia Signaling in Cells

AbstractCells have evolved intricate mechanisms for recognizing and responding to changes in oxygen (O2) concentrations. Here, we have reprogrammed cellular hypoxia (low O2) signaling via gas tunnel engineering of prolyl hydroxylase 2 (PHD2), a non‐heme iron dependent O2 sensor. Using computational modeling and protein engineering techniques, we identify a gas tunnel and critical residues therein that limit the flow of O2 to PHD2’s catalytic core. We show that systematic modification of these residues can open the constriction topology of PHD2’s gas tunnel. Using kinetic stopped‐flow measurements with NO as a surrogate diatomic gas, we demonstrate up to 3.5‐fold enhancement in its association rate to the iron center of tunnel‐engineered mutants. Our most effectively designed mutant displays 9‐fold enhanced catalytic efficiency (kcat/KM=830±40 M−1 s−1) in hydroxylating a peptide mimic of hypoxia inducible transcription factor HIF‐1α, as compared to WT PHD2 (kcat/KM=90±9 M−1 s−1). Furthermore, transfection of plasmids that express designed PHD2 mutants in HEK‐293T mammalian cells reveal significant reduction of HIF‐1α and downstream hypoxia response transcripts under hypoxic conditions of 1 % O2. Overall, these studies highlight activation of PHD2 as a new pathway to reprogram hypoxia responses and HIF signaling in cells.

Gas Tunnel Engineering of Prolyl Hydroxylase Reprograms Hypoxia Signaling in Cells.

Cells have evolved intricate mechanisms for recognizing and responding to changes in oxygen (O2) concentrations. Here, we have reprogrammed cellular hypoxia (low O2) signaling via gas tunnel engineering of prolyl hydroxylase 2 (PHD2), a non-heme iron dependent O2 sensor. Using computational modeling and protein engineering techniques, we identify a gas tunnel and critical residues therein that limit the flow of O2 to PHD2's catalytic core. We show that systematic modification of these residues can open the constriction topology of PHD2's gas tunnel. Using kinetic stopped-flow measurements with NO as a surrogate diatomic gas, we demonstrate up to 3.5-fold enhancement in its association rate to the iron center of tunnel-engineered mutants. Our most effectively designed mutant displays 9-fold enhanced catalytic efficiency (kcat/KM=830±40 M-1 s-1) in hydroxylating a peptide mimic of hypoxia inducible transcription factor HIF-1α, as compared to WT PHD2 (kcat/KM=90±9 M-1 s-1). Furthermore, transfection of plasmids that express designed PHD2 mutants in HEK-293T mammalian cells reveal significant reduction of HIF-1α and downstream hypoxia response transcripts under hypoxic conditions of 1 % O2. Overall, these studies highlight activation of PHD2 as a new pathway to reprogram hypoxia responses and HIF signaling in cells.