Final Hybrid Research Articles

Hybrid Supports for Oligonucleotide Synthesis: Controlled Pore Glass Derivatives with Branched Amine-Ended Polyether or Polyimine.

Controlled pore glass (CPG), differing in pore size and subsequent specific surface, was chemically modified by: (1) increasing surface susceptibility for amine functionalization via reaction with oxirane-type (active) and alkyl/aryl-type (inactive towards amine compounds) silane pro-adhesive compounds, and (2) immobilization of trimethylolpropane tris[poly(propylene glycol), amine terminated] ether, comb-like 8-arm octa[poly(ethylene glycol) amine] with each branch amine terminated, and a poly(propylene imine) amine-terminated second-generation dendrimer. The increase in surface density of amine functions-monitored by UV-Vis technique adopted for quantitative measurements of Ruhemann's purple intensity-improved final loading capacity, characterized by dimethoxytrityl cation absorption. Obtained materials proved their applicability in automatic oligonucleotide (ON) synthesis, especially when silanized 2000 Å CPG modified with 8-arm octa[poly(ethylene glycol) amine], with deduced empirical formula CPG-silane-(NH)5.7PEG-(NH2)2.3, was used for long-chain (150 nucleotides) ONs synthesis. This can be regarded as a good CPG support for this purpose. Moreover, hybrid supports with different porosity allowed the synthesis of shorter ONs with satisfactory yield and purity, monitored by RP-HPLC and MALDI-TOF. On the molecular level, two competitive mechanisms seem to influence the utility of the final hybrid support: spatial availability of active sites and the propensity of the functionalizer to bond with the CPG surface.

Hybrid Dendrimer Network based on Silsesquioxane and Glycidyl Methacrylate for Enhanced Adsorption of Iodine and Dyes in Environmental Remediation.

A novel hybrid network was synthesized in two steps: the first step involved the attachment of glycidyl methacrylate (GMA) to octa(aminophenyl) silsesquioxane (OAPS) through a ring-opening reaction, forming a hybrid dendrimer structure, and the second step involved the cross-linking of hybrid dendrimer using an azobisisobutyronitrile initiator to create the final hybrid network of OAPS-GMA. The synthesized hybrid material was comprehensively characterized using fourier transform infrared Spectroscopy (FTIR), nuclear magnetic resonance ((1H, 13C, and 29Si NMR) spectroscopy, thermogravimetric Analysis (TGA), and scanning electron microscopy (SEM). The BET surface area was found to be 25.44 m2/g, and significant 2.341 cm3/g of total pore volume was observed. The TGA analysis shows that the material is highly stable up to 450 °C. The synthesized network demonstrated remarkable adsorption capacities for iodine and dyes. It exhibited an iodine adsorption capacity of 3.4 g/g from vapors and 874 mg/g from solution. Additionally, it showed significant adsorption capacities for Rhodamine B and Congo red, with values of 762 mg/g and 517 mg/g, respectively. This study not only provides a novel method for preparing GMA-functionalized silsesquioxane-based porous hybrid polymers but also contributes to advancing solutions for environmental pollution issues.

High-Performance Broadband Photodetectors Combining Perovskite and Organic Bulk Heterojunction Bifunctional Layers

Perovskite can be used to prepare high-performance photodetectors due to its excellent optical properties. However, the detection range of perovskite photodetectors is mostly limited to the visible light range, restricting their further development and application. In recent years, combining perovskite with organic bulk heterojunctions to prepare photodetectors with broadband detection capability has proven to be an effective strategy. Through this approach, the response spectrum of the photodetector can be flexibly regulated, and organic compounds can improve the perovskite film quality by passivating defects and inhibit the penetration of water molecules in the air, thereby improving the device performance and stability. In this work, we propose and demonstrate the feasibility of combining MAPbI3 perovskite with PTB7-Th:COTIC-4F to prepare high-performance photodetectors with wide spectral response characteristics. With the assistance of an organic bulk heterojunction, the defects of perovskite crystals are effectively passivated, and the detection spectrum of the device is successfully extended to about 1100 nm. As a result, the responsivity achieved is 0.58 A/W, 1.19 A/W, and 1.41 A/W under laser illumination of 532 nm, 808 nm, and 980 nm, with the power density of 5 μW/cm2 at the bias voltage of −0.5 V, respectively, which is one of the best performances among vertical device structures of this type. Moreover, the stability of the final hybrid film has been greatly improved. This work provides a new approach to the preparation of high-performance and broadband perovskite photodetectors.

Low Silver/Copper Exchange in a Copper-Phosphate Enzyme Nanoflower Hybrid Extremely Enhanced Antimicrobial Efficacy against Multidrug Resistant Bacteria.

Infections caused by bacteria that are resistant to many drugs are a major threat to public health in many countries around the world. Here we demonstrate the creation of heterogeneous catalytic nanomaterials with outstanding antimicrobial properties against several superbugs. We have shown that replacing a small amount of copper in a generated copper-phosphate-enzyme nanoflower hybrid with silver drastically increases the antimicrobial capacity of the nanomaterial. In this sense, it has been confirmed that the exchange generated silver phosphate nanoparticles on the Cu nanoflowers, with control of the nanoparticle diameter size. The Fenton catalytic activity of the Ag-containing nanobiohybrids was affected, showing better performance with lower amounts of silver in the final hybrid. This effect was confirmed by their antimicrobial efficacy against Escherichia coli, where the Ag4Cu32@CALB hybrid displayed a log reduction of 3.9, an efficiency more than 5000 times higher than that obtained with copper nanoflowers (Cu36@CALB). The hybrid also showed excellent efficacy against other bacteria such as Klebsiella pneumoniae, Pseudomonas aeruginosa, and Mycobacterium smegmatis with log reductions of 7.6, 4.3, and 1.8, respectively.

Modeling and forecasting carbon dioxide emission in Pakistan using a hybrid combination of regression and time series models

Carbon dioxide (CO2) emissions continue to rise globally despite efforts to combat climate change. Energy industry emissions are a pressing global issue, causing devastating impacts. Hence, it is vital to accurately and efficiently forecast CO2 emissions. Thus, this study comprehensively analyzes forecasting CO2 emissions by comparing various hybrid combinations of regression and time series methods to explore the CO2 emissions in Pakistan. First, divide the yearly time series of CO2 emissions into the long-run curve trend series and the residual subseries. The long-run curve trend subseries is modeled using parametric and nonparametric regression methods, while various standard time series models are used to forecast the residual subseries. However, the forecasts of each subseries will be combined to obtain the final forecast of CO2 emissions. This work used four different accuracy mean errors, a statistical test, and a graphical analysis as performance measures to evaluate the proposed hybrid forecasting technique. The findings confirmed that the proposed hybrid combination forecasting technique is highly accurate and efficient in forecasting CO2 emissions. Likewise, according to the proposed final optimal hybrid combination forecasting model, Pakistan's per capita CO2 emissions will be 1.130215 metric tons in 2030. Pakistan's escalating emission trend signals that creative solutions must be implemented to curb it. Thus, the government must price carbon footprints, regulate electricity from zero-carbon sources, reduce population, encourage afforestation in densely populated areas, adopt clean technology, and fund research.

An explainable stacking-based approach for accelerating the prediction of antidiabetic peptides

Diabetes is a chronic disease that is characterized by high blood sugar levels and can have several harmful outcomes. Hyperglycemia, which is defined by persistently elevated blood sugar, is one of the primary concerns. People can improve their overall well-being and get optimal health outcomes by prioritizing diabetes control. Although the use of experimental approaches in diabetes treatment is cost-effective, it necessitates the development of many strategies for evaluating the efficacy of therapies. Researchers can quickly create new strategies for managing diabetes and get vital insights by enabling virtual screening with computational tools and procedures. In this study, we suggest a predictor named STADIP (STacking-based predictor for AntiDiabetic Peptides), a new method to predict antidiabetic peptides (ADPs) utilizing a stacked-based ensemble approach. It uses 12 different feature encodings and seven machine-learning techniques to construct 84 baseline models. The impacts of various baseline models on ADP prediction were then thoroughly examined. A two-step feature selection method, eXtreme Gradient Boosting with Sequential Forward Selection (XGB-SFS), was employed to determine the optimal number, out of 84 PFs to enhance predictive performance. Subsequently, utilizing the meta-predictor approach, 45 selected PFs were integrated into an XGB classifier to formulate the final hybrid model. The proposed method demonstrated superior predictive capabilities compared to constituent baseline models, as evidenced by evaluations on both cross-validation and independent tests. During extensive independent testing, STADIP achieved promising performance with accuracy and mathew's correlation coefficient of 0.954 and 0.877, respectively. It is anticipated that it will be useful tool in helping the scientific community to identify new antidiabetic proteins.

Enhancing Urban Traffic Management Through Hybrid Convolutional and Graph Neural Network Integration

Traffic congestion has made city planning and citizen well-being difficult due to fast city growth and the increasing number of vehicles. Traditional traffic management fails to solve urban transportation's ever-changing issues. Traffic prediction and control systems are vital for enhancing Traffic Flow (TF) and minimizing congestion. Smart cities need advanced prediction models to regulate urban TF as traffic management becomes more complex. This paper introduces a hybrid Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) model for better real-time traffic management. The hybrid model combines CNNs' spatial feature extraction with GNNs' structural and relational data processing to analyze and predict traffic conditions. Traffic camera images are pre-processed to extract spatial characteristics. Traffic network graph construction is used for structural research. The model accurately captures traffic topology and space. The proposed method sequentially processes spatial data with CNNs and integrates them with GNNs. The final hybrid model is trained on one year of traffic data from diverse circumstances and events. The hybrid model is compared to CNN, GNN, and traditional Traffic Prediction Models (TPM) like ARIMA and SVM utilizing MAE, RMSE, and MAPE. The hybrid GNN+CNN model outperforms benchmark models with lower MAE, RMSE, and MAPE across several prediction intervals.

MAPLE deposition of hybrid PLGA-Fe3O4- Cypress-PEDOT: PSS coatings

We report on the Matrix Assisted Pulsed Laser Evaporation, laser technology for depositing biocompatible, antimicrobial, hydrophilic, and biodegradable complex hybrid polymeric system loaded with essential cypress-oil and magnetite nanoparticles as resorbable implants, capable of targeting possible hyperthermia applications, an anticancer moderate field heating therapy. Magnetite nanoparticles based on iron oxide (Fe3O4) coated with Cypress essential oil (denoted: Fe3O4- Cypress) and embedded in PLGA (poly(lactic-co-glycolic acid) (denoted: PLGA-Fe3O4- Cypress-) and PLGA - poly(3,4-ethylene dioxythiophene) doped with poly(styrene sulfonate) anions) (PEDOT: PSS) mixture (denoted: PLGA-Fe3O4- Cypress- PEDOT: PSS) were used as MAPLE targets. The controlled drug delivery of the active Cypress oil, an antimicrobial therapeutic agent from Fe3O4- Cypress nanoparticles could be possible by applying an external radio frequency (RF) magnetic field. The Fe3O4-Cypress-based powders as well as the final hybrid coatings have been characterized in terms of stoichiometry, morphology, magnetic, antimicrobial properties, biocompatibility, and response to external physical stimuli. FTIR analyses confirmed the quasi-stoichiometric laser transfer of organic compounds while the XRD evidenced the semicrystalline structure of deposited thin films. SEM and AFM images evidence that conductive polymer addition led to the films' relief flattening and a decrease in the coatings' thickness and roughness by changing the polymeric packaging. The samples containing conductive polymer exhibited 3 times higher current and corrosion rate values. All coatings are hydrophilic and revealed enhanced cellular viability when cultured with osteoblast-like MG-63 cells. The composite structures exhibited significant antimicrobial activity against Gram-positive (Staphylococcus aureus), and Gram-negative (Escherichia coli) bacteria, as well as to the opportunistic yeast Candida albicans.

Exploring lifelong overactive bladder: Transitions, evidence, and clinical implications; A modified Delphi process.

Overactive bladder (OAB) is a prevalent urological condition characterized by urinary urgency, with or without urgency urinary incontinence, accompanied by increased daytime frequency and nocturia. However, the current definition of OAB lacks a specified time frame, hindering our understanding of the temporal aspects and transitions that occur within the OAB spectrum. A modified Delphi study was conducted in three rounds, involving a panel of international experts in functional urology, urogynaecology, geriatrics, transitional medicine, and pediatric urology. The study took place between February 2023 and June 2023 and employed two sequential rounds of online surveys, followed by a final hybrid group discussion session in June 2023. The Delphi process resulted in a consensus definition of lifelong OAB as a persistent and continuous condition that may manifest differently from birth and evolve over time, with varying levels of clinical perception. The course of its progression is influenced by transition periods and modifying factors, mainly anatomical, hormonal, and psychosocial/stressors. Three main transition periods were identified: achievement of daytime continence, adulthood to elderly, and transition to frail elderly. The panel also considered the therapeutic and diagnostic implications of lifelong OAB, as well as future research prospects in terms of importance and feasibility. Future longitudinal research is needed to develop this concept and further identify transitions and temporal dynamics.

Statistical-physical method for simulating the transport of microplastic-antibiotic compound pollutants in typical bay area

Microplastics and antibiotics are emerging pollutants in the environment and have received widespread attention globally. In coastal areas, microplastic and antibiotic pollution is ubiquitous and often overlapping. Microplastic-antibiotic compound pollutants that are formed through adsorption have thus become a major concern. However, modeling knowledge of microplastic transport in coastal areas is still limited, and research on the impact of compound pollutants caused by Polythene (PE)-antibiotics in such settings is in early stages. In this study, using a lattice Boltzmann method (LBM) and temporal Markov method (TMM) under a statistical-physical framework, we simulated pollutant transport and PE-antibiotic compound pollutants in coastal areas. First, a series of models are proposed, including an LBM wave-current coupling model, an LBM antibiotic transport model, an LBM particle-tracking model, a TMM microplastic transport model and the final LBM-TMM hybrid compound pollutant model. Then, the suitability and applicability of the models was validated using experimental data and numerical simulations. Finally, the models were applied to a study area, Laizhou Bay (China). The simulation results demonstrate that adsorption will reduce the concentration of antibiotics in the water environment. Within 44 days, the adsorbed antibiotic carried by PE particles migrate further, and the width of the pollution zone escalates from 234.2 m to 689.0 m.

Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis.

A critical clinical indicator for basal cell carcinoma (BCC) is the presence of telangiectasia (narrow, arborizing blood vessels) within the skin lesions. Many skin cancer imaging processes today exploit deep learning (DL) models for diagnosis, segmentation of features, and feature analysis. To extend automated diagnosis, recent computational intelligence research has also explored the field of Topological Data Analysis (TDA), a branch of mathematics that uses topology to extract meaningful information from highly complex data. This study combines TDA and DL with ensemble learning to create a hybrid TDA-DL BCC diagnostic model. Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as skin lesions, and DL features are generated by fine-tuning a pre-trained EfficientNet-B5 model. The final hybrid TDA-DL model achieves state-of-the-art accuracy of 97.4% and an AUC of 0.995 on a holdout test of 395 skin lesions for BCC diagnosis. This study demonstrates that telangiectasia features improve BCC diagnosis, and TDA techniques hold the potential to improve DL performance.

A triazolium-based fluorophore intercalated in layered double hydroxides: from simple syntheses to bright solid-state luminescence.

This study presents the intercalation into Layered Double Hydroxides (LDHs) of two sulfonated organic molecules featuring the mesoionic triazolium scaffold. These sulfonated fluorophores exhibited excellent solubility in aqueous basic solutions, facilitating their compatibility with the synthesis of LDHs through coprecipitation methods. We applied the size-matching interlayer space (SMIS) approach by substituting a portion of a mono- or dianionic surfactant used in LDH preparation by the sulfonated fluorophore, we aimed to match the size of the luminescent interleaved guest effectively. Our investigation focused on two anion spacers: the classic monoanionic dodecyl sulfate (DS) and the dianionic phenylene dipropionate (PPA). Our results indicated that the latter spacer allowed a more efficient insertion of the fluorescent guest. Thermal resistance analysis underscored the robustness of the final hybrid materials, suggesting a promising design strategy for luminescent materials when applied in diverse applications.

Stance-Based Fake News Identification on Social Media with Hybrid CNN and RNN-LSTM Models

Today, fake news can be readily generated and disseminated via social media platforms. Misinformation and hoaxes propagated via online social media or traditional news media are commonly referred to as fake news. Stance-based fake news is based on the opinions of an audience rather than providing correct facts. This study presents a hybrid model focusing on the CNN model and RNN-LSTM model to identify fake news. A balanced dataset of 216k news items called ‘SherLock-FakeNewsNet’ is explored throughout the study resulting in the proposed hybrid model. The NLTK toolkit is utilized to perform some noise reduction. After that, the Keras tokenizer and pre-processor are used for the tokenization and pre-processing steps. Next, the text data is fitted and represented using pre-trained GloVe word embeddings. CNN model is applied with Conv1D layers and MaxPooling layers to extract higher-level features of the text. Following this, to detect the longer context of the given text and to capture interdependencies among word sequences LSTM units are employed in the RNN model. Dropout layers are carefully chosen to reduce overfitting in the final hybrid model. The proposed hybrid model achieves the highest accuracy rate of 92% by outperforming most of the conventional models today with an average Precision of 91%, Recall of 91%, and F1-Score of 91% using an Adam optimizer and a Binary Cross-Entropy loss function. Based on the five experiments performed in the study, we have presented the results by comparing our proposed hybrid model with five related datasets.

Fluorescent and ratiometric humidity sensor based on cyano-substituted oligo(p-phenylene)/silica gel hybrid materials

We have synthesized a kind of cyano-substituted oligo(p-phenylene) hybrid materials by Knoevenagel condensation reaction, which can be used as ratiometric relative humidity (RH) sensor. When the RH increased from 5 % to 90 %, the maximum emission peak of C1-3/SG gradually redshifted from 530 nm to 575 nm with the fluorescence changed from green to orange. This phenomenon can be attributed to the water vapor induced aggregation of dyes on the matrix. The non-covalent force between the dyes and the silica gel could be adjusted by altering the structures of the dyes, and the RH sensing property of the final hybrid materials could be further regulated. R2-3/SG hybrid fabricated by R2 with large steric isooctyl groups possessed indiscernible RH sensing properties, while the R1-3/SG without hydrogen bond interactions between dyes and substrate was more susceptible to water vapor. The surface properties and specific surface area of the substrate were critical for RH sensing. RH sensing properties were not presented for the substrates without hydroxyl groups or low specific surface area such as PET or PP fibers. Moreover, we have prepared a kind of C1 loaded paper strips, which exhibited potentially ability in anticounterfeiting application.

Evaluation of the nephroprotective properties of silver nanoparticles formulated by Pistacia atlantica aqueous extract under ultrasonic condition in streptozotocin-induced diabetic nephropathy in mice

Green formulated silver nanoparticles offer a great promise in biomedicine. Administration of streptozotocin causes severe hepatotoxicity and nephrotoxicity by increasing the blood glucose. The development of nephroprotective drugs through eco-friendly production routes is a major challenge for current pharmacology and nanotechnology. The creation of cutting-edge technology for the synthesis of effective nephroprotective agents is crucial on a global scale. So in the current study, we report herein the bio-inspired proficient preparation of silver NPs over Pistacia atlantica (P.a.) aqueous extract as natural reducing/capping and stabilizing agent (P.a./Ag NPs) under ultrasonic irradiation. The final hybrid nanocomposite was thereafter characterized by a range advanced analytical methods like, FE-SEM, ICP-OES, EDX, TEM, and elemental mapping. Diabetes was induced by administration of 60 mg/kg of streptozotocin (STZ) intraperitoneally in 100 mature male mice and they were randomly divided into 5 groups. The negative control group received normal saline and treatment groups received glibenclamide with dose 0.5 mg/kg and 10 and 40 μg/kg of P.a./Ag nanocomposite through gavage for 50 days. On the last day, serum levels of samples blood glucose, urea and creatinine were measured. After tissue processing, 5 μm sections of the kidneys were prepared and they were stained by periodic acid Schiff (PAS) and used for stereological analysis. The kidney weight, kidney volume (Volume of cortex, medulla, glomerulus, proximal and distal tubules, collecting ducts, loop of Henle, interstitial tissues, and vessels) and kidney structures length (length of proximal and distal tubules, collecting ducts, loop of Henle, and vessels) decreased significantly (p ≤ 0.05) after treatment with high dose of P.a./Ag NPs nanocomposite (p > 0.05). The increased levels of blood glucose and urea were decreased (p ≤ 0.05) significantly in P.a./Ag nanocomposite-treated groups as compared to the untreated diabetic. This study suggested using from P.a./Ag nanocomposite as an antidiabetic and nephroprotective drug in the developing countries.

Hybrid deep modeling of a CHO-K1 fed-batch process: combining first-principles with deep neural networks.

Introduction: Hybrid modeling combining First-Principles with machine learning is becoming a pivotal methodology for Biopharma 4.0 enactment. Chinese Hamster Ovary (CHO) cells, being the workhorse for industrial glycoproteins production, have been the object of several hybrid modeling studies. Most previous studies pursued a shallow hybrid modeling approach based on three-layered Feedforward Neural Networks (FFNNs) combined with macroscopic material balance equations. Only recently, the hybrid modeling field is incorporating deep learning into its framework with significant gains in descriptive and predictive power. Methods: This study compares, for the first time, deep and shallow hybrid modeling in a CHO process development context. Data of 24 fed-batch cultivations of a CHO-K1 cell line expressing a target glycoprotein, comprising 30 measured state variables over time, were used to compare both methodologies. Hybrid models with varying FFNN depths (3-5 layers) were systematically compared using two training methodologies. The classical training is based on the Levenberg-Marquardt algorithm, indirect sensitivity equations and cross-validation. The deep learning is based on the Adaptive Moment Estimation Method (ADAM), stochastic regularization and semidirect sensitivity equations. Results and conclusion: The results point to a systematic generalization improvement of deep hybrid models over shallow hybrid models. Overall, the training and testing errors decreased by 14.0% and 23.6% respectively when applying the deep methodology. The Central Processing Unit (CPU) time for training the deep hybrid model increased by 31.6% mainly due to the higher FFNN complexity. The final deep hybrid model is shown to predict the dynamics of the 30 state variables within the error bounds in every test experiment. Notably, the deep hybrid model could predict the metabolic shifts in key metabolites (e.g., lactate, ammonium, glutamine and glutamate) in the test experiments. We expect deep hybrid modeling to accelerate the deployment of high-fidelity digital twins in the biopharma sector in the near future.

Engineering the Morphostructural Properties and Drug Loading Degree of Organic-Inorganic Fluorouracil-MgAl LDH Nanohybrids by Rational Control of Hydrothermal Treatment.

Layered double hydroxides (LDHs) or hydrotalcite-like compounds have attracted great attention for the delivery of anticancer drugs due to their 2D structure, exhibiting a high surface-to-volume ratio and a high chemical versatility. The drug is protected between the layers from which it is slowly released, thus increasing the therapeutic effect and minimizing the side effects associated to nonspecific targeting. This work aimed to design LDHs with Mg and Al (molar ratio of 2/1) in brucite-like layers, which retained fluorouracil (5-FU; 5-FU/Al = 1, molar ratio) in the interlayer gallery as the layers grow during the co-precipitation step of the synthesis. To rationally control the physicochemical properties, particularly the size of the crystallites, the aging step following the co-precipitation was performed under carefully controlled conditions by changing the time and temperature (i.e., 25 °C for 16 h, 100 °C for 16 h, and 120 °C for 24 h). The results revealed the achievement of the control of the size of the crystals, which are gathered in three different agglomeration systems, from tight to loose, as well as the loading degree of the drug in the final organic-inorganic hybrid nanomaterials. The role played by the factors and parameters affecting the drug-controlled release was highlighted by assessing the release behavior of 5-FU by changing the pH, solid mass/volume ratio, and ionic strength. The results showed a pH-dependent behavior but not necessarily in a direct proportionality. After a certain limit, the mass of the solid diminishes the rate of release, whereas the ionic strength is essential for the payload discharge.

Improved tests for stock return predictability

– Predictive regression methods are widely used to examine the predictability of (excess) stock returns by lagged financial variables characterized by unknown degrees of persistence and endogeneity. We develop a new hybrid test for predictability in these circumstances based on simple regression t-statistics. Where the predictor is endogenous, the optimal, but infeasible, test for predictability is based on the t-statistic on the lagged predictor in the basic predictive regression augmented with the current period innovation driving the predictor. We propose a feasible version of this augmented test, designed for the case where the predictor is an endogenous near-unit root process, using a GLS-based estimate of the innovation used in the infeasible test regression. The limiting null distribution of this statistic depends on both the endogeneity correlation parameter and the local-to-unity parameter characterizing the predictor. A method for obtaining asymptotic critical values is discussed and response surfaces are provided. We compare the asymptotic power properties of the feasible augmented test with those of a (non augmented) t-test recently considered in Harvey et al. and show that the augmented test is more powerful in the strongly persistent predictor case. We then propose using a weighted combination of the augmented statistic and the t-statistic of Harvey et al., where the weights are obtained using the p-values from a unit root test on the predictor. We find this can further improve asymptotic power in cases where the predictor has persistence at or close to that of a unit root process. Our final hybrid testing procedure then embeds the weighted statistic within a switching-based procedure which makes use of a standard predictive regression t-test, compared with standard normal critical values, when there is evidence for the predictor being weakly persistent. Monte Carlo simulations suggest that overall our new hybrid test displays superior finite sample performance to comparable extant tests.

Assessment of a Second Life City Vehicle Refurbished to Include Hybrid Powertrain Technology

Due to increased powertrain efficiency, electrified propulsion has seen significant diffusion in the automotive sector in recent years. Despite the possible reduction in tailpipe CO2 emissions, the advancements in the technology are not sufficient to tackle the challenge of global greenhouse emissions. An additional action could be the use of second life vehicles to drastically reduce the emissions associated with vehicle manufacturing and recycling/disposal. Urban vehicles are the most suitable to be electrified due to the large start-and-stop cycling and the possibility of using regenerative braking. Therefore, this work considered the hypothesis of hybridizing a small size passenger car with parallel and Series technology. The powertrain is designed for an old vehicle suitable for second life use after refurbishment. A numerical model of the propulsion components was built and applied after previous validation in homologation conditions. Several urban cycles representative of European cities were considered. The final hybrid model is compared with two baselines: non-hybrid and pure electric version already lunched in the market by the manufacturer. The findings indicate that used HEV cars could be a viable option for cutting CO2 emissions from city vehicles without reducing their range. In comparison to non-hybrid vehicles, the series can typically reduce CO2 emissions by 41%, compared to the P2’s 32%.

Hybrid modelling to improve operational wave forecasts by combining process-based and machine learning models

Operational wave forecasting plays an important role in ensuring safe navigation and in the prediction of tidal windows for harbour approach channels. The underlying nearshore process-based wave models need to be accurate for a wide range of different conditions, from more common mild wave conditions to the occasional high energy (storm) conditions. In this work, an innovative hybrid modelling approach is proposed to improve the accuracy of operational wave forecasts. An operational wave model is combined with a machine learning model which is trained using wave measurements within the wave model domain. This hybrid modelling approach is applied to the Dutch North Sea, covering four major harbour approach channels.The final hybrid operational wave model results in a significant average error decrease compared to just the process-based model, amounting to 21.7% for the wave energy density and 25.3% for the wave direction. The error reduction for the spectral wave parameters is even larger, with a 33.3% smaller error in spectral wave height and a 38.8% smaller error in spectral wave period. As this approach is generically applicable to spectral wave models, it contains the potential for significant improvements in operational modelling.