Single Hybrid Research Articles

Review of Machine Learning Methods for River Flood Routing

River flood routing computes changes in the shape of a flood wave over time as it travels downstream along a river. Conventional flood routing models, especially hydrodynamic models, require a high quality and quantity of input data, such as measured hydrologic time series, geometric data, hydraulic structures, and hydrological parameters. Unlike physically based models, machine learning algorithms, which are data-driven models, do not require much knowledge about underlying physical processes and can identify complex nonlinearity between inputs and outputs. Due to their higher performance, lower complexity, and low computation cost, researchers introduced novel machine learning methods as a single application or hybrid application to achieve more accurate and efficient flood routing. This paper reviews the recent application of machine learning methods in river flood routing.

Tailoring the Effect of NiCo‐Metal‐Organic Frameworks by Doping Manganese as a Highly Active Redox Electrode for Electrochemical Energy Storage Technologies: Supercapattery Devices

Metal–organic frameworks with complex geometry have garnered significant attention in technological fields due to their diverse properties. Multimetallic metal‐organic frameworks (MOFs) have potential to demonstrate superior conductivity and efficiency compared to pristines, but, they have not been explored to research on large scale. Herein, significant effect of binary (NiCo‐) and ternary (NiCoMn‐) MOFs is explored with a novel approach using an organic ligand hexamethylene tetra‐amine (HMT). The as‐synthesized MOFs with their distinctive structures are thoroughly examined to analyze their structural, morphological, and electrochemical properties. In a standard half‐cell setup, the electrochemical behaviors of both samples are investigated, as both electrodes exhibit specific capacities of 349 and 905 C g−1, at 3 mV s−1, individually. Due to promising electrochemical responses of NiCoMnMOF, a supercapattery device is constructed as NiCoMnMOF//AC. This hybridization leads to the remarkable specific energy (78 Wh kg−1) and specific power (3500 W kg−1) with stability rate of ≈96% and Coulombic efficiency of 99% after undergoing 5000 galvanostatic charge–discharge cycles, making it highly suitable for modern applications. To gain better insight of supercapattery device, a semiempirical approach is employed, using Dunn's modulation. Thus, the synergy of NiCoMnMOF//AC in the single hybrid device exhibiting battery‐like characteristics holds tremendous potential for driving innovations in energy storage technology.

Analogous Design of a Microlayered Silicon Oxide-Based Electrode to the General Electrode Structure for Thin-Film Lithium-Ion Batteries.

Development of miniaturized thin-film lithium-ion batteries (TF-LIBs) using vacuum deposition techniques is crucial for low-scale applications, but addressing low energy density remains a challenge. In this work, structures analogous to SiOx-based thin-film electrodes are designed with close resemblance to traditional LIB slurry formulations including active material, conductive agent, and binder. The thin-film is produced using mid-frequency sputtering with a single hybrid target consisting of SiOx nanoparticles, carbon nanotubes, and polytetrafluoroethylene. The thin-film SiOx/PPFC (plasma-polymerized fluorocarbon) involves a combination of SiOx and conductive carbon within the PPFC matrix. This results in enhanced electronic conductivity and superior elasticity and hardness in comparison to a conventional pure SiOx-based thin-film. The electrochemical performance of the half-cell consisting of thin-film SiOx/PPFC demonstrates remarkable cycling stability, with a capacity retention of 74.8% up to the 1000th cycle at 0.5 C. In addition, a full cell using the LiNi0.6Co0.2Mn0.2O2 thin-film as the cathode material exhibits an exceptional initial capacity of ≈120 mAh g-1 at 0.1 C and cycle performance, marked by a capacity retention of 90.8% from the first cycle to the 500th cycle at a 1 C rate. This work will be a stepping stone for the AM/CB/B composite electrodes in TF-LIBs.

Single inductor multi-PZTs SECE and electromagnetic voltage multiplier rectifier hybrid interface circuit

This paper proposes a single inductor multi-piezoelectric transducers (PZTs) SECE and electromagnetic voltage multiplier rectifier hybrid interface circuit (MP-SECE-VMR), which harvest energy from multi-input PZTs and electromagnetic generator (EMG). The MP-SECE-VMR adopts a voltage multiplier rectifier circuit, utilizing the diode-connected MOSFET configuration which used as diodes to effectively reduce voltage losses during rectification. Experimental tests confirm that this circuit efficiently extracts energy from PZTs despite differences in their phase relationships. The low input voltage from the EMG is effectively elevated through the voltage multiplier rectifier, enabling simultaneous extraction with the PZTs. The proposed MP-SECE-VMR circuit extracts 1.2 mW of power from two PZTs with open-circuit amplitudes of 9 V and 11 V respectively, as well as from an EMG input with an open-circuit amplitude of 3 V and a frequency of 5 Hz. Furthermore, the proposed circuit effectively enhances output power while expanding the working load range compared to single-source energy extraction.

An Empirical Analysis of a Set of Hybrid Heuristics for the Solution of the Resource Leveling Problem

Consideration is given to the heuristic solution of the resource leveling problem (RLP) in project scheduling with limited resources. The objective is to minimize the changes in the level of resource usage from period to period over the planning horizon of the project while keeping the project duration fixed. First, we present two novel greedy schedule algorithms for the RLP solution. The performance of the proposed algorithms is investigated as low-level hybrids in the context of three famous population-based heuristics, namely differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). Then, we additionally present two new high-level hybridization schemes (HS), referred to herein as parallel and serial HS, respectively, which combine DE, GA, and PSO in a single hybrid solution algorithm. Detailed experimentation over known complex datasets measures the efficiency of the new hybrids. Statistical analysis employed rank the hybrids according to their solution efficiency. Moreover, comparisons between the developed best hybrid and commercial project management software show a substantial higher performance for the former over real-world construction projects.

Forced convection analysis of Williamson-based magnetized hybrid nanofluid flow through a porous medium: Nonsimilar modeling

The purpose of this work is to analyze the convective horizontal flow of Williamson-based magnetized hybrid nanofluid. The flow of Williamson fluid with heat transfer is stimulated in a porous media over a stretching surface. Two different type of nanoparticles such as, molybdenum disulfide ( Mo S 2 ) and copper (Cu) with engine oil (Eo) as base fluid are considered in this work. The convective flow of Williamson fluid is inspected based on Khanefer model. The governing system is transformed into nondimensionalized partial differential equations (PDE’s) by utilizing nonsimilar transformation. The transformed system is analytically approximated using local nonsimilarity (LNS) and perturbation approach which approximate the nondimensional PDE’s by nondimensional ordinary differential equations (ODEs). The LNS and perturbation ODEs are simulated utilizing finite difference-based algorithm bvp4c. The outcomes of simulation for heat transfer and convective flow over stretching surface incorporating the evolvement with local Nusselt number (Nu) for distinct values of prandtl number are compared with previous published numerical results. The appropriate range of governing nondimensionalized parameters is evaluated to detect the variance of physical quantities namely, velocity ( f ′ ( η ) ) of the fluid, temperature ( θ ′ ( η ) ) of the fluid, co-efficient of local Nusselt number (Nu) and co-efficient of local skin friction (Cf). The percentage difference between single nano-fluid and hybrid nano-fluids is made which are given in tabular form. To the best of author knowledge, nonosimilar study of hybrid-based magnetized Williamson nanofluid is not yet investigated. This work is anticipated to offer crucial information for the implementation of innovative heat transfer devices in the future and serve as an invaluable resource for researchers looking at the flow of nanofluids under various assumptions.

Hybrid machine learning method for classification and recommendation of vector-borne disease

<p>Vector-borne diseases (VBD) are a class of infectious illnesses that are transmitted to humans and animals through the bites of arthropod vectors, such as mosquitoes, ticks, and fleas. These diseases are caused by a variety of pathogens, including bacteria, viruses, and parasites, and are a significant global public health concern. Vector-borne diseases are prevalent in many parts of the world, particularly in tropical and subtropical regions, where the vectors thrive. This research has contributed by constructing a hybrid machine learning based prediction model, which helps to discover patients who are infected by vector-borne disease at an earlier stage and also helps with the categorization and diagnosis of severe vector-borne disease. The model that has been proposed is made up of units: data conversion, data preprocessing, normalization, extraction of feature, splitting of dataset, and classification and prediction unit. The fact that the suggested prediction model is capable of identifying vector-borne disease in its early phases as well as categorizing the kind of disease using the medical report of a sufferer is one of the innovative aspects of the model. The 7 distinct conventional machine learning and single hybrid machine learning (HML) are applied for classification and Recurrent Neural Network (RNN) based reinforcement learning are utilized for recommendation. In order to evaluate the effectiveness of the system that’s been proposed, a number of tests were carried out. A dataset consisting of 1539 different cases of a disease transmitted by vectors has been collected. The 11 common vector-borne diseases namely malaria, dengue, Japanese encephalitis, kala-azar and chikungunya were taken for experimental evaluation. The performance accuracy of the proposed prediction model has been measured at 98.76%, which assists the healthcare team in making decisions on a timely basis and ultimately helps to save the patient’s lives. The final phase system provides the recommendation for those classifiers resulting in four different classes such as normal, mild, moderate and severe respectively. The recommendation is also demonstrating future direction for cure of vector borne disease.</p>

Effect of retarders on the properties of ultra-high strength alkali-activated concrete

Alkali-activated slag-fly ash-silica fume cement (AA-SFSC) is suitable for the production of clinkerless alkali-activated binder-based ultra-high strength concrete (AAB-UHSC). However, using a relatively low water-to-binder ratio and a high alkali dosage to prepare AAB-UHSC leads to rapid setting and a loss of flowability. Thus, the setting time of the AA-SFSC needs to be controlled. In our study, the effect of various retarders (single retarder borax and hybrid retarder a combination of zinc nitrate hexahydrate and sodium gluconate) on the AA-SFSC was investigated. The setting characteristics, reaction kinetics, mechanical performance, pore structure alteration, mineralogical and molecular transformations, and microstructure of the AA-SFSC were systematically examined. The results showed that both borax and a combination of zinc nitrate hexahydrate and sodium gluconate had a remarkable retarding effect on AA-SFSC with a low water-to-binder ratio and high alkali dosage. However, borax had a detrimental effect on the compressive strength, and the effect of adding zinc nitrate hexahydrate and sodium gluconate as the hybrid retarders on the compressive strength depended on the dosage; and a dosage of 1% zinc nitrate hexahydrate and 2% sodium gluconate was recommended for prolonging the setting time of AA-SFSC without compromising the mechanical performance.

Predicting Temperature and Humidity in Roadway with Water Trickling Using Principal Component Analysis-Long Short-Term Memory-Genetic Algorithm Method

The heat dissipated from high geo-temperature underground surrounding rocks is the main heat source of working faces, while thermal water upwelling and trickling into the roadway will notably deteriorate the mine’s climate and thermal comfort. Predicting airflow temperature and relative humidity (RH) is conductive to intelligent control of air conditioning cooling and ventilation regulation. To accommodate this issue, an intelligent technique was proposed, integrating a genetic algorithm (GA) and long short-term memory (LSTM) based on rock temperature, inlet air temperature, water temperature, water flow rate, RH, and ventilation time. A total of 21 input features including over 200 pieces of data were collected from an independently developed modeling roadway to construct a dataset. Principal component analysis (PCA) was conducted to reduce features, and GA was used to tune the LSTM and PCA-LSTM architectures for best performance. The following research results were yielded. The proposed PCA-LSTM-GA model is more reliable and efficient than the single LSTM model or hybrid LSTM-GA model in predicting the air temperature Tfout and humidity RHout at the end of the water trickling roadway. The importance scores (ISs) indicate that Tfout is mainly influenced by the surrounding rock temperature (IS 0.661) and the inlet air temperature (IS 0.264). While RHout is primarily influenced by the rock temperature in the water trickling section (IS 0.577), the inlet air temperature (IS 0.187), and the trickling water temperature and flow rate (total IS 0.136), and it has an evident time effect. In addition, we developed relevant equipment and provided engineering practice methods to use the machine learning model. The proposed model, which can predict the mine microclimate, serves to facilitate coal and geothermal resource co-mining as well as thermal hazard control.

Assembly and annotation of 2 high-quality columbid reference genomes from sequencing of a Columba livia × Columba guinea F1 hybrid.

Pigeons and doves (family Columbidae) are one of the most diverse extant avian lineages, and many species have served as key models for evolutionary genomics, developmental biology, physiology, and behavioral studies. Building genomic resources for columbids is essential to further many of these studies. Here, we present high-quality genome assemblies and annotations for 2 columbid species, Columba livia and Columba guinea. We simultaneously assembled C. livia and C. guinea genomes from long-read sequencing of a single F1 hybrid individual. The new C. livia genome assembly (Cliv_3) shows improved completeness and contiguity relative to Cliv_2.1, with an annotation incorporating long-read IsoSeq data for more accurate gene models. Intensive selective breeding of C. livia has given rise to hundreds of breeds with diverse morphological and behavioral characteristics, and Cliv_3 offers improved tools for mapping the genomic architecture of interesting traits. The C. guinea genome assembly is the first for this species and is a new resource for avian comparative genomics. Together, these assemblies and annotations provide improved resources for functional studies of columbids and avian comparative genomics in general.

Hybrid Polymer Salogels for Reversible Entrapment of Salt-Hydrate-Based Thermal Energy Storage Materials.

One of the challenges preventing wide use of inorganic salt hydrate phase change materials (PCMs) is their low viscosity above their melting point, leading to leakage, phase segregation, and separation from heat exchanger surfaces in thermal management applications. The development of a broad strategy for using polymers that provide tunable, temperature-reversible shape stabilization of a variety of salt hydrates by using the lowest possible polymer concentrations is hindered by differences in solubility and gelation behavior of polymers with change in the type of ion. This work addressed the challenge of creating robust, temperature-responsive shape-stabilizing polymer gels (i.e., salogels) using a low cost PCM, calcium chloride hexahydrate (CaCl2·6H2O, CCH). Due to the extremely high (9 M) concentration of chloride ions and the tendency to salting-out polymer chains, the previous strategy of using single-polymer salogels was not successful. Thus, this work introduced a strategy of using two polymers, poly(vinyl alcohol) and ultrahigh molecular weight polyacrylamide (PVA and PAAm, respectively), along with borax as a cross-linker to achieve temperature-reversible, shape-stable salogels. This system resulted in robust salogels whose gel-to-sol transition temperature (Tgel) was tunable within an application-relevant range of gelation temperature (30-80 °C). This behavior was enabled by a synergistic combination of dynamic covalent cross-links between PVA units and entanglements of PAAm chains which were combined into a single hybrid network. The hybrid salogels had <5 wt % polymer content, maintaining ∼95% of the heat of fusion of the pure PCM. Importantly, the noncovalent nature of gelation supported thermo-reversibility of gelation, shape stability, and retention of thermal properties over 50 melting/crystallization cycles.

Functional relationships between yield of maize (Zea mays L.) and its components

Summary A field experiment was carried out in the years 2017–2019 on the fields of the Experimental Station in Chrząstowo, belonging to the Research Centre for Cultivar Testing in Słupia Wielka. It was carried out for 3 years in the same split-plot design with 2 experimental factors in 3 field replicates. The following factors were studied: A – 1st order factor – maize variety: A1 – ES Bombastic (FAO 230-240) – single cross hybrid (SC), A2 – ES Abakus (FAO 230-240) – three-way cross hybrid (TC, stay-green), A3 – ES Metronom (FAO 240) – single cross hybrid (SC, stay-green + roots power). B – 2nd order factor – type of nitrogen fertilizer: B1 – control (without N application), B2 – ammonium nitrate, B3 – urea, B4 – ammonium nitrate + N-Lock, B5 – urea + N-Lock, B6 – Super N-46, B7 – UltraGran stabilo. In this study, we investigated whether there was a functional relationship between maize grain yield and ear number, TSW (thousand-seed weight), and seed number per ear. Additionally, we investigated whether there was a functional relationship between maize grain yield and ear number, TSW, and seed number per ear for each type of fertilization in a given study year, as well as for each type of fertilization regardless of year.

An experimental and numerical study on the hybrid effect of basalt fiber and polypropylene fiber on the impact toughness of fiber reinforced concrete

As the civil aviation industry advances, higher requirements are put forward for the performance of airport cement pavement. The frequent takeoffs and landing of aircraft will cause continuous and large impact load on airport pavement, and combined with long-term natural factors, there will be various structural diseases. These diseases not only affect the service life of pavement, navigation safety. To enhance the impact resistance and durability of concrete, the incorporation of fiber has proven an effective approach, and basalt fibers (BF) and polypropylene fibers (PF) are selected as reinforcement materials in this study. Firstly, the effects of single BF and hybrid BF and PF on the mechanical properties of concrete were explored through compressive and flexural tests considering fiber volume contents (0.1%, 0.15, 0.2%) and lengths (6 mm, 9 mm, 12 mm). Then, the drop hammer impact test method, recommended by ACI committee 544, was refined to evaluate the impact resistance toughness of concrete. Finally, the numerical simulation of the impact test was established to explain the concrete impact damage mechanism. The results show that under less than 0.15% volume content, the single BF can effectively enhance the compressive strength, flexural strength, and impact resistance of concrete. Compared with the single BF, the combination of 12 mm BF and 6 mm PF yields favorable results, encompassing improved mechanical properties and impact resistance, thus exhibiting a positive hybrid effect. The increase in PF length (9 mm) leads to a reduction in compressive strength but an improvement in flexural strength and impact resistance. The simulation of the impact test is constructed and the accuracy is confirmed by comparing the impact times and failure patterns of the specimens. The tensile damage of plain concrete is much greater than the compressive damage, and the cracks will spread rapidly from the upper surface to the lower surface. Compared with plain concrete, the crack width of hybrid fiber reinforced concrete is larger, but the damage degree is more uniform. Fiber can absorb a large amount of impact energy, reduce the emergence of micro-cracks, and improve the impact toughness and toughness coefficient of concrete.

Book Review: Advanced Technologies for Solid, Liquid, and Gas Waste Treatment

There is an urgent need to develop effective ways to efficiently recycle the world's limited resources in the solid, liquid and gaseous phases to achieve sustainability. This could be achieved in accordance with allowable effluent standards, economic considerations and long service life through either single units or hybrid (integrated) units. In this regard, this book is a valuable resource for both academic and industrial researchers, bringing together the latest developments in compost, advanced oxidation, reactive membrane, adsorptive, electrocoagulative, and pyrolytic processes for the treatment of solid wastes, wastewater, and toxic gases. The strengths and weaknesses of the technologies are compared throughout the 15 chapters with extensive tabulated data from the literature and well-illustrated figures. Recommendations for the future and challenges are provided at the end of each section.

Load analysis of an off-grid hybrid power system using micro-hydro and diesel power technology in the remote areas of Bangladesh

Hybrid power technology plays an important role in the field of energy systems. This technology can be used with or without a grid by merging renewable and non-renewable sources. This study analyses electricity generation through an off-grid hybrid technology, which is composed of micro-hydro power and diesel generator power in a remote area of Bangladesh. In this investigation, some of the crucial cases will be examined, which are the optimum turbine placement with the aid of fluid flow simulation using the flow parameters, electric power production in a single power unit and hybrid power system unit, and cost analysis of the hybrid power system unit in order to inspect the feasibility of the system.

A risk-adjusted cumulative sum analysis of the progression from a novice to an expert surgeon at a single institution

Background/objectiveLaparoscopic surgery for rectal cancer is challenging for novice surgeons because it requires a sharp dissection in a narrow pelvis with visual limitations. Therefore, this study aimed to analyze the learning curve and clinical outcomes of laparoscopic surgery for rectal cancer performed by a novice surgeon en route to becoming an expert. MethodsIn total, 119 patients who underwent laparoscopic surgery for rectal cancer performed by a single surgeon between June 2010 and December 2019 were analyzed. A single hybrid model based on the operative time, open conversion, complications, and resection margin involvement was generated to assess the success of laparoscopic surgery. Furthermore, the learning curve was evaluated using the risk-adjusted cumulative sum (RA-CUSUM) method. ResultsThe learning period was categorized into three phases according to the RA-CUSUM method (phase 1, 1st–33rd cases; phase 2, 34th–84th cases; and phase 3, 85th–119th cases). Tumor size (p = 0.004), distal resection margin (p = 0.003), and the number of harvested lymph nodes (p < 0.001) significantly increased with the learning period. The time to tolerable soft diet became shorter according to the learning period (p = 0.017). Advanced T stage (p = 0.024) and adjuvant chemotherapy (p = 0.012) were more common in phase 3. ConclusionsThis study suggested that the initial technical competence of laparoscopic surgery for rectal cancer was acquired in the 33rd case. Technical mastery was achieved in the 84th case. After mastering the technique, the surgeon tended to challenge more advanced cases, however, the complication rates did not increase.

ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data

ObjectiveBio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. ApproachIn this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main resultsWe evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). SignificanceWith limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.

Estimating Parameters of Ballistic Target by Single Hybrid Radar Based on Vortex and Wideband Electromagnetic Waves

Estimating Parameters of Ballistic Target by Single Hybrid Radar Based on Vortex and Wideband Electromagnetic Waves

Preface

IC3DDose 2022 - 12th International Conference on 3D and Advanced Dosimetry The 12th IC3DDose conference on advanced radiation dosimetry was held in person in Quebec City, Quebec, Canada from June 20 to 23, 2022. The meeting was scheduled back-to-back with the Canadian Organization of Medical Physicists’ (COMP) annual scientific meeting, offering unique opportunities for scientific knowledge sharing and extensive networking. It was a refreshing restart for the IC3DDose community after the cancelled meeting in 2020 and a reduced scope virtual meeting in 2021.A total of 71 participants from 11 countries met for 3.5 days in a mix of refresher/review sessions and proffered presentations covering state-of-the-art dosimetry encompassing all major dosimetry techniques used for standard external beam and brachytherapy. Presenters also covered advanced applications addressing the challenges of radiation measurement in small field, MR-Linac, microbeam and FLASH dosimetry. A total of 14 invited presentations and 25 contributed talks were given the at meeting including papers in a Rising Star Competition and in a single hybrid session with virtual participants from Australia, China and Japan. The associated papers presented in these proceedings add to an already extensive collection of 118 review and 574 proffered papers in 9 previous volumes of the Journal of Physics Conference Series from the past conferences.A feature for this year’s meeting was that all contributed speakers on a given day were asked to mount their presentation slides on poster boards for a dedicated closing discussion session entitled “Meet the speakers of the day”. This allowed attendees to engaged in deeper discussions, beyond the question periods at the end of their talks. The daily sessions reinforced the IC3DDose philosophy to foster strong mingling of all participants, from trainees to more experienced attendees. A keynote address and the final morning of sessions were open to COMP registrants also. This made the topics of the IC3DDose meeting available to a much wider audience.The meeting was a great success and the attendees developed new expertise and new networks through the many interactions over the meeting’s four days. Colleagues from Aarhus Denmark announced at the end of the meeting that they will now undertake to organize a 13th meeting in 2024.The local organizing committee sincerely thanks the sponsors for our meeting: Elekta and Varian as bronze sponsor and MedScint in the start-up category. We further acknowledge support from COMP, l’ Association Québécoise des Physicien(ne)s Médicaux Cliniques, l’ Université Laval and CRCHU de Quebec-Université Laval.Louis Archambault, Luc Beaulieu and L. John Schreiner(for the organizing and scientific committee)List of International Scientific Committee, Local Organizing Committee, Detailed Program of the 12th International Conference on 3D and Advanced Dosimetry (IC3Ddose) June 2022, Château Frontenac, Quebec City, Contents, About, A Warm Thank You to our Sponsors, at a Glance, Day 1 – June, Day 2 – Day 3 – June 22nd, Day 4 –, a Warm Thank You to our Sponsors are available in this pdf.

Research on the complete vehicle control strategy of the composite accumulator hydraulic hybrid power system

The compound accumulator is an energy storage device consisting of a large accumulator and a small accumulator. Compared with the traditional single accumulator hydraulic hybrid vehicle, it has the characteristics and advantages of fast braking response for the small accumulator and more energy recovery for the large accumulator, combining braking characteristics and energy recovery rate. This paper proposes the control strategy for parallel hydraulic hybrid vehicle based on compound accumulator, which can determine the working timing for large and small accumulators according to the specific driving conditions, thereby improving the braking performance and fuel economy for the vehicle. Under NEDC conditions, this paper uses dynamic programming algorithm to find the optimal control sequence for torque distribution and energy management of the hydraulic hybrid vehicle with composite accumulator, obtains the control rules and logic thresholds of the logic threshold control strategy, and obtains the switching rule of the working timing of the composite accumulator. The simulation of specific cycle conditions is carried out to verify the rationality of vehicle control strategy and to explore the energy-saving mechanism of parallel hydraulic hybrid power system based on composite accumulator. The NEDC urban cycle test was carried out, and the comparison between the test and simulation results showed that the hydraulic hybrid operation mode, large and small accumulator pressure, and engine fuel consumption during the test were basically consistent with the simulation results and verified the control effect of the vehicle control strategy and the energy-saving mechanism of the parallel hydraulic hybrid system based on the composite accumulator.