Organisms in the wild have cryptic life stages that are sensitive to changing environmental conditions and can be difficult to survey. In this study, I used mark-recapture methods to repeatedly survey Anaea aidea (Nymphalidae) caterpillars in nature, then modeled caterpillar demography as a hidden Markov process to assess if temporal variability in temperature and density influence the survival and growth of A. aidea over time. Individual encounter histories result from the joint likelihood of being alive and observed in a particular stage, and I included hidden states by separating demography and observations into parallel and independent processes. I constructed a demographic matrix containing the probabilities of all possible fates for each stage, including hidden states, e.g., eggs and pupae. I observed both dead and live caterpillars with high probability. Peak caterpillar abundance attracted multiple predators, and survival of fifth instars declined as per capita predation rate increased through spring. A time lag between predator and prey abundance was likely the cause of improved fifth instar survival estimated at high density. Growth rates showed an increase with temperature, but the most likely model did not include temperature. This work illustrates how state-space models can include unobservable stages and hidden state processes to evaluate how environmental factors influence vital rates of cryptic life stages in the wild.

To predict the remaining useful life (RUL) of proton exchange membrane fuel cell (PEMFC) in advance, a prediction method based on the voltage recovery model and Bayesian optimization of a multi-kernel relevance vector machine (MK-RVM) is proposed in this paper. First, the empirical mode decomposition (EMD) method was used to preprocess the data, and then the MK-RVM was used to train the model. Then, the Bayesian optimization algorithm was used to optimize the weight coefficient of the kernel function to complete the parameter update of the prediction model, and the voltage recovery model was added to the prediction model to realize the rapid and accurate prediction of the RUL of PEMFC. Finally, the method proposed in this paper was applied to the open data set of PEMFC provided by FCLAB, and the prediction accuracy of the RUL of PEMFC was obtained by 95.35%, which showed that the method had good gen-eralization ability and verified the accuracy of the prediction for the RUL of PEMFC.

Bayesian techniques for engineering problems, that rely on Gaussian process (GP) regression, are known for their ability to quantify epistemic and aleatory uncertainties and for being data efficient. The mathematical elegance of applying these methods usually comes at a high computational cost when compared to deterministic and empirical Bayesian methods. Furthermore, using these methods becomes practically infeasible in scenarios characterized by a large number of inputs and thousands of training data. The focus of this work is on enhancing Gaussian Process-based metamodeling and model calibration tasks, when the size of the training datasets is significantly large. To achieve this goal, we employ a stochastic variational inference algorithm that enables rapid statistical learning of the calibration parameters and hyperparameter tuning, while retaining the rigor of Bayesian inference. The numerical performance of the algorithm is demonstrated on multiple metamodeling and model calibration problems with thousands of training data.

Aim: To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run. Methods: A field-based protocol of continuous fatigue repeated hourly induced physical (~45 minutes) and cognitive (~10 minutes) fatigue on one healthy participant. Physical load was a 3.8 km, 200 m vertical gain, trail run with acceleration and electrocardiogram (ECG) data collected using a single sensor. Cognitive load was a Multi Attribute Test Battery (MATB) and separate assessment battery including the Finger Tap Test (FTT), Stroop, Trail Making A and B, Spatial Memory, Paced Visual Serial Addition Test (PVSAT), and a vertical jump. A fatigue prediction model was implemented using a Convolutional Neural Network (CNN). Results: When the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R2 0.71) and Jump Height (R2 0.78) were the most sensitive while the other tests were less sensitive (R2 values Stroop 0.49, Trail Making A 0.29, Trail Making B 0.05, PVSAT 0.03, spatial memory 0.003). Best prediction results were achieved with a rolling average of 200 predictions (102.4 s), during set activity types, mean absolute error for ‘walk up’ (MAE200 12.5%) and range of absolute error for ‘run down‘ (RAE200 16.7%). Conclusion: We were able to measure cognitive and physical fatigue using a single wearable sensor during a practical field protocol including contextual factors in conjunction with a neural network model. This research has practical application to fatigue research in the field.

In order to solve the problem of fault diagnosis for CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and ensemble learning model is proposed. First, various monitoring signals including vibration signal, noise signal and current signal are collected. Then, the monitoring signals are preprocessed and the time-domain, frequency-domain and time-frequency domain feature indexes are extracted to construct a multi-dimensional mixed domain feature set. Finally, the feature set is putting into the constructed DoubleEnsemble-LightGBM model to realize the fault diagnosis of the feed system. The experimental results show that the model can achieve good diagnosis results under different working conditions for both the public data set and the feed system test bench data set, and the average overall accuracy is 91.07% and 98.06% respectively. Compared with XGBoost and other advanced ensemble learning models, the method has better accuracy. It provides technical support for the stable operation and intelligent of CNC machine tools..

In daily travel and activities, pathfinding is a significant process. They are often used in transportation routes calculation. They have now evolved to be able to solve most situations of the pathfinding and its related problems. This review describes previous and recent studies on the pathfinding algorithms. It reviews the development of pathfinding algorithms in a classification base on their usage. The aim is to summarize the application of the pathfinding algorithms for the readers interested in the subject that can be used as a supplement.

Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries by applying circular economy strategies that extend the life cycle of their components, like remanufacturing and repurposing, or by using efficient recycling to reintegrate the gained materials in producing new battery systems. This paper aims to develop a multi-method self-configuring simulation model for investigating disassembly scenarios while considering the battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a battery of Mercedes-Benz and the automated disassembly station of the project DeMoBat at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios, the component-oriented and the accessibility-oriented disassembly, and compare them using the simulation model to determine several indicators, such as the frequency of tool change, the number and distribution of robot routes, the tool utilization, and the disassembly time.

It is important for food recognition to separate each ingredient within a food image at the pixel level. In this paper, we propose a new approach to segment ingredients by utilizing a CNN-based Single-Ingredient Classification Model. In detail, we firstly introduce a standardized biological-based hierarchical ingredient structure and construct a single-ingredient image dataset based on this structure. Then, we build a single-ingredient classification model based on a novel convolutional neural network (CNN) architecture that utilizes an attention mechanism. Afterwards, we propose a new framework for segmentation using the above single-ingredient classification model as the backbone. In this framework, two methods are involved in segmenting ingredients in the food images. We introduce five evaluation metrics (IoU, Dice, Purity, Entirety, Loss of GTs) to assess the performance of ingredient segmentation in terms of ingredient classification. Extensive experiments demonstrate the effectiveness of the proposed method, achieving an maximal mIoU of 0.65, mDice of 0.77, mPurity of 0.83, mEntirety of 0.80, and mLoGTs of 0.06 on the FoodSeg103 dataset. The results confirm that our CNN-based architecture achieves higher segmentation performance compared to ResNet18 and EfficientNet-B0 when used as the backbone for ingredient segmentation. We believe that our ingredient segmentation approach lays the foundation for subsequent ingredient recognition.

In real-life scenario, there are many mathematical tools to handle the incomplete and imprecise data. One of them is the fuzzy approach. This article aims to contribute to the literature of fuzzy multi-objective dynamic programming issues involving the fuzzy objective functions. The piecewise quadratic fuzzy numbers characterize these fuzzy parameters. Some basic notions in the problem under the α-pareto optimal solution concept is redefined and analyzed to study the stability of the problem. Furthermore, a technique, named as enhancing decomposition approach, is presented for achieving a subset for the parametric space that contains the sameα-pareto optimal solution. For a better understanding and comprehension of the suggested concept, a numerical example is provided.

Rapid developments in the field of underwater photogrammetry have given scientists1the ability to produce accurate 3-dimensional (3D) models which are now increasingly used in the representation and study of local areas of interest. This paper addresses the lack of systematic analysis of 3D reconstruction and navigation fusion strategies, as well as associated error evaluation of models produced at larger scales in GPS-denied environments using a monocular camera (often in deep-sea scenarios). Based on our prior work on automatic scale estimation of Structure from Motion (SfM)-based 3D models using laser scalers, an automatic scale accuracy framework is presented. The confidence level for each of the scale error estimates is independently assessed through the propagation of the uncertainties associated with image features and laser spot detections using a Monte Carlo simulation. The number of iterations used in the simulation was validated through the analysis of the final estimate behaviour. To facilitate the detection and uncertainty estimation of even greatly attenuated laser beams, an automatic laser spot detection method, mitigating the effects of scene texture, was developed, with the main novelty of estimating the uncertainties based on the recovered characteristic shapes of laser spots with radially decreasing1 intensities. The effects of four different reconstruction strategies resulting from the combinations of Incremental/GlobalSfM, and thea priori/a posterioriuse of navigation data were analyzed using two distinct survey scenarios captured during the SUBSAINTES 2017 cruise (doi: 10.17600/17001000). The study demonstrates that surveys with multiple overlaps of non-sequential images result in a nearly identical solution regardless of the strategy (SfM or navigation fusion), while surveys with weakly connected sequentially acquired images are prone to produce broad-scale deformation (doming effect) when navigation is not included in the optimization. Thus the scenarios with complex survey patterns substantially benefit from using multi-objective BA navigation fusion. In all cases, the errors in the models are inferior to 5%, with errors often being around 1%. The effects of combining data from multiple surveys were also evaluated. The introduction of additional vectors in the optimization of multi-survey problems successfully accounted for offset changes present in the underwater USBL-based navigation data and thus minimize the effect of contradicting navigation priors. Our results also illustrate the importance of collecting a multitude of evaluation data at different locations and moments during the survey.

Immersion in salt-lake solution was adopted to periodically test the concrete chloride-ion diffusion coefficient. The regression analysis was also completed. Also investigated was the time-dependent law of concrete chloride-ion diffusion coefficient with time. The influence of chloride-ion concentration in solution, water-to-cement ratio, and corrosion time on the largeness and accumulation rate of the concrete chloride-ion diffusion coefficient was also analyzed. Test results show that the concrete chloride-ion diffusion coefficient gradually decreased with increasing time and increased with increasing chloride-ion concentration in a salt lake . Taking into account the influence of factors such as water–binder ratio, chloride-ion concentration, and time-varying characteristics, a multi-factor calculation model for the concrete chloride-ion diffusion coefficient was established. Combining the prediction results and the measured data reported in this paper, the effectiveness and applicability of the established concrete chloride-ion diffusion coefficient calculation model were compared and verified, and the durability design and service life of a concrete structure under cool chlorine were compared. The results of analysis provide important boundary conditions.

Neural-objective and image optimization approaches for drilling fluid rheology automation are crucial for drilling engineering optimization. A myriad of intelligent computational models are employed to predict and monitor the parameters of mud rheology and filter cake permeability posture using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and a model optimization tool. 498 datasets of synthetic-based mud (SBM) flat rheology from a drilling mud laboratory at Nazarbayev University were imported into a user-friendly MATLAB application using image processing and nftool. Apart from the Google TensorFlow Sequential API DNN architecture used, a Levenberg-Marquardt training algorithm with input sigmoid hidden neurons 10, 12, and 18 coupled with a linear output layer was also used to predict the SBM flow index. OBM and SBM filter cakes were processed for void spaces; however, a model study was extrapolated to maximize filtration volume Vf. The study’s findings show that the ANN-FF model employed for rheological property monitoring and prediction had higher steady and exponential levels of accuracy and a correlation coefficient of 0.96-0.99. More so, SBM and OBM image processing presented an area of void spaces of 1790M2 and 1739M2, respectively. The porosity and permeability postures of both SBM and OBM resulted in a significant void space capable of maximizing the flow index. In addition, the single-objective modelling based on a genetic algorithm did validate the experimental rheological data for flow index or filtration volume Vf optimization; the study resulted in the finding that the constrained physics-informed objective function hindered maximizing oil recovery and instead predicted possible formation damage. It is empirical to note that automating flow predictions with neural, objective, and image functions has presented an alternative novel method for non-programmers using MATLAB and Google Colabs that is capable of enhancing mud rheological parameters, drilling efficiency, and hydrocarbon recovery.

In this paper we deal with the resolution of a fuzzy multiobjective programming problem using the level sets optimization. We compare it to other optimization strategies studied until now and we propose an algorithm to identify possible Pareto efficient optimal solutions.

The proliferation of deep learning in natural language processing (NLP) has led to the development and release of innovative technologies capable of understanding and generating human language with remarkable proficiency. Atinuke, a Transformer-based neural network, optimises performance across various language tasks by utilising a unique configuration. The architecture interweaves layers for processing sequential data with attention mechanisms to draw meaningful affinities between inputs and outputs. Due to the configuration of its topology and hyperparameter tuning, it can emulate human-like language by extracting features and learning complex mappings. Atinuke is modular, extensible, and integrates seamlessly with existing machine learning pipelines. Advanced matrix operations like softmax, embeddings, and multi-head attention enable nuanced handling of textual, acoustic, and visual signals. By unifying modern deep learning techniques with software design principles and mathematical theory, the system achieves state-of-the-art results on natural language tasks whilst remaining interpretable and robust.

Multi-Faith Spaces (MFS) are a relatively recent invention that quickly gained in significance. On the one hand, they offer a convenient solution for satisfying needs of people with diverse beliefs in the institutional context of hospitals, schools, airports, etc. On the other hand, as Andrew Crompton pointed out, they are politically significant because the multi-faith paradigm “is replacing Christianity as the face of public religion in Europe” (2012, p. 493). Due to their ideological entanglement, MFS are often used as the means to promote either a more privatised version of religion, or a certain denominational preference. Two distinct designs are used to achieve these means: negative in the case of the former, and positive in the latter. Neither is without problems, and neither adequately fulfils its primary purpose of serving diverse groups of believers. Both, however, seem to follow the biases and main problems of secularism. In this paper, I analyse recent developments of MFS to detail their main problems and answer the question, whether the MFS, and the underlying Multi-Faith Paradigm, can be classified as a continuation of secularism.

In recent years, how to combine intelligent prospecting algorithms such as random forest with a large number of geological and mineral data for quantitative prediction of exploration geochemistry has become an important topic of concern to quantitatively improve the accuracy of target delineation. The ore-forming geological conditions in the central Kunlun area of Xinjiang are great and have good prospecting prospects. However, due to the exhaustion of shallow deposits and the lag of geological prospecting work in the past ten years, there has been no expected breakthrough in the search for large and super-large metal deposits for many years. There has been a serious shortage of reserve resources. The use of new theories, new methods and new technologies for mineral resources investigation and evaluation has become an urgent need in the current prospecting work. In view of this, based on the existing spatial database of geological and mineral resources in the central Kunlun of Xinjiang, combined with the geological characteristics, genesis and metallogenic regularity of the area, this paper carried out a series of studies on gold polymetallic minerals with the help of geographic information system and data science programming software platform. The researchers integrated geological and regional geochemical data, and constructed a random forest metallogenic discriminant model based on two different sampling methods (integrated random undersampling and selection of training samples) to predict the mineralization of gold polymetallic minerals in the central Kunlun area of Xinjiang and delineate the metallogenic target area. The quantitative prediction of gold polymetallic mineral resources in the central Kunlun area of Xinjiang by two random forest models is compared and discussed: the known ore spots, fault structures and geochemical information are extracted, and the known gold polymetallic ore spots and geochemical data are used to form a training set and a prediction set to construct a machine learning random forest model. The results of prediction evaluation and metallogenic prospect division show that for different sampling methods, the performance evaluation parameters of the training process show that the prediction accuracy of the selected training samples is higher, and the selected training samples are more reliable because they can fully learn the complex information of the original data. In the metallogenic prospect prediction and metallogenic potential division, the random forest model of selecting training samples has more reference value and further exploration research significance in the production problem considering the actual exploration cost because of its small area of high potential prediction area and high proportion of ore bearing per unit area. At the same time, this study innovatively improves the prediction accuracy, reduces the exploration risk, and expands the prospecting idea of machine learning algorithm in mathematical geology in the central Kunlun area of Xinjiang. The delineated metallogenic potential area has positive guiding significance for the actual gold polymetallic prospecting work in this area.

Object recognition is an essential element of machine intelligence tasks. However, one model cannot practically be trained to identify all the possible objects it encounters. An ensemble of models may be needed to cater to a broader range of objects. Building a mathematical understanding of the relationship between various objects that share comparable outlined features is envisaged as an effective method of improving the model ensemble through a pre-processing stage, where these objects' features are grouped under a broader classification umbrella. This paper proposes a mechanism to train an ensemble of recognition models coupled with a model selection scheme to scale-up object recognition in a multi-model system. An algorithmic relationship between the learnt parameters of a trained classification model and the features of input images is presented in the paper for the system to learn the model selection scheme. The multiple models are built with a CNN structure, whereas the image features are extracted using a CNN/VGG16 architecture. Based on the models' excitation weights, a neural network model selection algorithm, which links a new object with the models and decides how close the features of the object are to the trained models for selecting a particular model for object recognition is developed and tested on a five-model neural network platform. The experiment results show the proposed model selection scheme is highly effective and accurate in selecting an appropriate model for a network of multiple models.

This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via systematic survey and synthesis of related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on specific performance and users expectations as well as its technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a 3-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks.

Multi-channel and multi-parameter near-infrared spectrometers (NIRS) have gradually become a new research direction and hot spot due to their ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks on the evaluation of reliability, sensitivity, stability and response consistency of the NIRS instrument. In this study, a set of test methods (the ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analysis crosstalk and verifying the reliability of NIRS were conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency in the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for the NIRS instrument reliability testing.

The paper proposed a modification of the GeoSER(D) model previously developed by us by detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents, this made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of 3 types of the strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. The paper showed that SARS COV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the consequence, not the initiator of the epidemic. Fluctuations in the dynamics of various indicators of the spread of SARS COV 2 associated with the difference in the daily schedule on weekdays and weekends. It has been shown that people's daily schedules strongly influence the spread of SARS COV 2. For the more contagious "rapid" strains of SARS COV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS COV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious – "fast" strain of the SARS COV 2 virus (omicron) spreads faster in public transport. For less contagious – "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.

This study presents a research plan that utilizes data obtained from wearable devices to identify human activities and gain insights into human behavior. We developed a model capable of classifying activities similar to human behavior and evaluated the effectiveness and generalization capabilities of this model. The data underwent initial preprocessing, including standardization and normalization. Additionally, recognizing the inherent similarities between human activity behaviors, we introduced a multi-layer classifier model. The first layer is a random forest model based on stepwise regression, which may encounter reduced accuracy for similar activities. The second layer employs a Support Vector Machine (SVM) model based on Kernel Fisher Discriminant Analysis (KFDA). KFDA is used to reduce the dimensionality of data points with potential confusion, followed by SVM for classification. The model was experimentally evaluated and applied to four benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results demonstrate that our approach achieved recognition accuracies of 99.71%, 98.71%, 99.12%, and 97.6% on these datasets, indicating excellent recognition performance. Furthermore, to assess the model's generalization ability, we performed K-fold cross-validation on the random forest model and utilized ROC curves for the SVM classifier. The results indicate that our multi-layer classifier model exhibits robust generalization capabilities.

With the growth of air traffic demand in busy airspace, there is an urgent need for airspace sectorization to increase air traffic throughput and ease the pressure on controllers. The purpose of this paper is to develop a method framework that can perform airspace sectorization automatically, reasonably, which can be used as an advisory tool for controllers as an automatic system, especially for eliminating irregular sector shapes generated by simulated annealing algorithm (SAA) based on region growth method. Two graph cutting method, dynamic Monte Carlo method by changing location of flexible vertices (MC-CLFV) and Monte Carlo method by radius changing (MC-RC) were developed to eliminating irregular sector shapes generated by SAA in post-processing. The experimental results show that the proposed method framework of AS can automatically and reasonably generate sector design schemes that meet the design criteria. Our methodology framework and software can provide assistant design and analysis tools for airspace planners to design airspace, improve the reliability and efficiency of airspace design, and reduce the burden of airspace planners. In addition, this lays the foundation for reconstructing airspace with more intelligent method.

Although there are several access control systems in the literature for flexible policy management in multi-authority and multi-domain environments, achieving interoperability & scalability, without relying on strong trust assumptions, is still an open challenge. We present HMBAC, a distributed fine-grained access control model for shared and dynamic multi-authority and multi-domain environments, along with Janus, a practical system for HMBAC policy enforcement. The proposed HMBAC model supports: (a) dynamic trust management between different authorities; (b) flexible access control policy enforcement, defined at domain and cross-domain level; (c) a global source of truth for all entities, supported by an immutable, audit-friendly mechanism. Janus implements the HMBAC model and relies on the effective fusion of two core components. First, a Hierarchical Multi-Blockchain architecture that acts as a single access point that cannot be bypassed by users or authorities. Second, a Multi-Authority Attribute Based Encryption protocol that supports flexible shared multi-owner encryption, where attribute keys from different authorities are combined to decrypt data distributedly stored in different authorities. Our approach was implemented using Hyperledger Fabric as the underlying blockchain, with the system components placed in Kubernetes Docker container pods. We experimentally validated the effectiveness and efficiency of Janus, while fully reproducible artifacts of both our implementation and our measurements are provided.

Non-alcoholic steatohepatitis (NASH) is a complex disease resulting from chronic liver injury associated with obesity, type 2 diabetes, and inflammation. Recently, multi-target drugs have been proposed to treat complex diseases including NASH; however, their manufacturing pro-cesses remain time- and cost-intensive and inefficient. Therefore, to overcome these limitations, we developed UniStac, a novel enzyme-mediated conjugation platform for multi-specific drug development. UniStac demonstrated high conjugation yields, optimal thermal stabilities, and robust biological activities. Through such platform, we designed a tetra-specific compound, C-192, targeting Glucagon-like peptide 1 (GLP-1), Glucagon (GCG), Fibroblast growth factor 21 (FGF21), and Interleukin-1 receptor antagonist (IL-1RA) simultaneously for the treatment of NASH. The biological activity and treatment efficacy of C-192 were confirmed both in vitro and in vivo using a methionine–choline-deficient (MCD) diet-induced mouse model. C-192 exhibited profound therapeutic efficacies compared to conventional drugs, including liraglutide and dulaglutide. C-192 significantly improved alanine transaminase levels, triglyceride accumula-tion, and the non-alcoholic fatty liver disease activity score. Our results collectively provide clin-ical feasibility of UniStac for developing multi-specific drugs, and C-192 as an innovative thera-peutic opportunity for severe NASH, as it exerts synergistic effects across multiple targets.

Low-frequency electron paramagnetic resonance (EPR) is used to extract the EPR parameter A-mid and support the approximate X-band value of g-mid for Ba(Co_yZn_1/3-yTa_2/3)O₃. Although cobalt hyperfine structure for the [+/−1/2> state is often unresolved at X-band or S-band, it is resolved in measurements on this compound. This allows for detailed analysis of the molecular orbital for the [+/−1/2> state, which is often the ground state. Moreover, this work shows that the EPR parameters for Co substituted into Zn compounds gives important insight into the properties of zinc binding sites.

Shale gas reservoirs (SGR) are important replacements for conventional energy resources and have been widely exploited by hydraulic fracturing technologies. On the one hand, due to the inherent ultra-low permeability and porosity, there is stress sensitivity in the reservoirs generally. On the other hand, hydraulic fractures and the stimulated reservoir volume (SRV) generated by the massive hydraulic fracturing operation have contrast properties with the original reservoirs. These two phenomena bring huge challenges in SGR transient pressure analysis. Although some works in the literatures have been done on the transient pressure analysis of multi-fractured horizontal wells in SGR, unfortunately, none of them has taken the stress sensitivity and spatially varying permeability of SRV zone into consideration simultaneously. To fill this gap, this paper first idealizes the SGR to be four linear composite regions. What’s more, SRV zone is further divided into sub-sections on the basis of non-uniform distribution of proppant within SRV zone which easily yields spatially varying permeability away from the main hydraulic fracture. The stress sensitivity is characterized by the varying permeability depended on the pore pressure. By means of perturbation transformation and Laplace transformation, an analytical multi-linear flow model (MLFM) is obtained and validated by the comparison with the previous model. On the basis of our model, the flow regimes are identified and the sensitivity analysis of critical parameters are conducted to further understand the transient pressure behaviors. The research results provided by this work are of significance for well test interpretation and production performance analysis of SGR.

We used the objective analysis method in junction with the successive correction method to assimilate MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) data into Chimère model in order to improve the modeling of fine particulate matter (PM2.5) concentrations and AOD field over Europe. A data assimilation module was developed to adjust the daily initial total column aerosol concentrations based on a forecast-analysis cycling scheme. The model is then evaluated during one-month winter period to examine how such data assimilation technique pushes the model results closer to surface observations. This comparison showed that the mean biases of both surface PM2.5 concentrations and AOD field could be reduced from -34 to -15% and from -45 to -27%. The assimilation however leads to false alarms because of the difficulty to distribute AOD550 over different particles sizes. The impact of the influence radius is found to be small and depends on the density of satellite data. This work, although preliminary, is important in terms of near-real time air quality forecasting using Chimère model and can be further developed to improve modeled PM2.5 and ozone concentrations.

Background: Recently some photon models of a Wigner's friend experiment have led investigators to suggest objective reality does not exist, and to publish non-academic articles with such claims. The public is not equipped to evaluate the severe limitations of these experiments. The separation of Wigner from the experiment and use of only reversible coherent processes for the friend allow operations that are not possible in ordinary reality according to the latest quantum research. Methods: We suggest directly testing the implied claim that objective reality, including incoherent objects with irreversible non-destructive memory, can be held in superposition. We suspect it will fail, but provide for a graduated approach that may discover something about the conditions for superposition collapse. To this end we design a thought experiment to model the objective world, investigating under what conditions experimenters in the same world (ensemble member) will be able to record a result and find it does not appear to change. An observer has a viewing apparatus and a memory apparatus. A second uncorrelated viewer of the same recorded result is employed to obtain objectivity. By hypothesis the uncorrelated second viewer obtains the same view of the measurement record as the first observer. There are not two measurements. This is not an investigation of hidden variables. Results: To model the objective world, incoherent and irreversible processes must be included. To test for superposition, coherence has to be established. These seem to present a contradiction. Conclusions: The thought experiment has suggested new places to look other than size for the origin of objective reality from the quantum world, casts doubt on the Many-Worlds interpretation, and provides a method of testing it.

Wireless Sensor Networks (WSN) is the network of the resource-constrained network which forms the foundation of the Internet of Things (IoT). The Routing Protocol for Low-power and Lossy Networks (RPL) is responsible for generating and managing data routing paths. Nodes implementing RPL uses the mechanics of Objective Function (OF) to select the preferred next-hop node – parent node, and optimal routing path to the destination node. If routing decisions are not efficiently made, this results in increased collision domain, leading to packet losses and packet retransmission which impairs the network operational lifetime. In this study, we present the Contiki Operating System (OS), a state-of-the-art OS for IoTs, ContikiRPL; Contiki variant of RPL. We investigated the performance of RPL with respect to its two OFs; Objective Function Zero (OF0) and the Minimum Rank with Hysteresis Objective Function (MRHOF). The performance of these OFs was evaluated on the following metrics; Packet Delivery Ratio (PDR), Power consumption, and network latency. The result shows that MRHOF outperformed OF0 on all metrics with an overall average PDR of 91.5%, a latency of 44ms, and power consumption of 1.72mW across all nodes. This results in optimal network performance with improved network operational lifetime.

The semi-enclosed estuary is very susceptible to changes in the physical and environmental characteristics of the inflow from the land and the coastal sea weather and so the continuous and comprehensive monitoring is necessary for managing the estuary. Nevertheless, the standard procedure or schematic framework has not been proposed appropriately to determine how many instruments are necessary and where they need to be deployed to detect critical changes. Therefore, the present work proposes a systematical strategy for the deployments of the monitoring array by using the combination of the graphical optimization with the objective mapping technique. In order to reflect the spatiotemporal characteristics of the bay, the representative variables and eigenvectors are determined by the Empirical Orthogonal Function (EOF), and the cosine angle among them are calculated for a design index of optimization. At the recommended locations, the sampled representative variables are interpolated to reconstruct their spatiotemporal distribution and compared with the distributions of the true values. Analysis confirms that the selected locations, even with a small number of points, can be used for on-site monitoring. Also, the present framework suggests how to determine installable regions for real-time monitoring stations, which reflect the global and local characteristics of the semi-enclosed estuary.

Background In SILIUS (NCT01214343), combination of sorafenib and hepatic arterial infusion chemotherapy did not significantly improve overall survival in patients with advanced hepatocellular carcinoma (HCC) compared with sorafenib alone. In this study, we explored the relationship between objective response by mRECIST and overall survival (OS) in the sorafenib group, in the combination group and in all patients in the SILIUS trial. Methods Association between objective response and OS in patients treated with sorafenib (n=103), combination (n=102) and all patients (n=205) were analyzed. The median OS of responders was compared with that of non-responders. Landmark analyses were performed according to objective response at several fixed time points, as sensitivity analyses, and the effect on OS was evaluated by Cox regression analysis with objective response as a time-dependent covariate, with other prognostic factors was performed. Results In the sorafenib group, OS of responders (n = 18) was significantly better than that of non-responders (n = 78) (p < 0.0001), where median OS was 27.2 (95% CI, 16.0–not reached) months for responders and 8.9 (95% CI, 6.5–12.6) months for non-responders. HRs from landmark analyses at 4, 6, and 8 months were 0.45 (p=0.0330), 0.37 (p=0.0053), and 0.36 (p=0.0083), respectively. Objective response was an independent predictor of OS based on unstratified Cox regression analyses. In the all patients and the combination group, similar results were obtained. Conclusion In the SILIUS trial, objective response was an independent prognostic factor for OS in patients with HCC.

The city-port involves a decisive reality for the economic development of the territories and nations, capable of significantly influencing the conditions of well-being and quality of life, and of making the Circular City Model operational, preserving and enhancing seas and marine resources in a sustainable way, through the construction of appropriate production and consumption models, with attention to relations with the urban and territorial system. The Circular Economy paradigm identifies the ideal context in the city-port to rethink traditional development models and make ports driver areas for the regeneration of the city and metropolitan territories, in compliance with the EU Directive 2014/89 which considers maritime spatial planning as a tool for public authorities and stakeholders to achieve an integrated approach, promoting the development of maritime and coastal economies and the sustainable use of resources. The paper, starting from these assumptions, presents an adaptive decision-making process for the strategies development of the Naples (Italy) commercial port, aimed at re-establishing a sustainable city-port relationship and making operative Circular Economy principles.

Deep neural networks (DNNs) have gained prominence in addressing regression problems, offering versatile architectural designs that cater to various applications. In the field of earthquake engineering, seismic response prediction is a critical area of study. Simplified models such as single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) systems have traditionally provided valuable insights into structural behavior, known for their computational efficiency facilitating faster simulations. However, these models have notable limitations in capturing the nuanced nonlinear behavior of structures and the spatial variability of ground motions. This study focuses on leveraging ambient vibration (AV) measurements of buildings, combined with earthquake (EQ) time-history data, to create a predictive model using a neural network (NN) in image format. The primary objective is to predict a specific building's earthquake response accurately. The training dataset consists of 1,197 MDOF 2D shear models, generating a total of 32,319 training samples. To evaluate the performance of the proposed model, termed MLPER (Machine Learning based Prediction of building structures' Earthquake Response), several metrics are employed. These include mean absolute percentage error (MAPE) and mean deviation angle (MDA) for comparisons in the time domain. Additionally, we assess magnitude-squared coherence values and phase differences (Δφ) for comparisons in the frequency domain. This study underscores the potential of MLPER as a reliable tool for predicting building earthquake response, addressing the limitations of simplified models. By integrating AV measurements and EQ time-history data into a neural network framework, MLPER offers a promising avenue for enhancing our understanding of structural behavior during seismic events, ultimately contributing to improved earthquake resilience in building design and engineering.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

In this paper, we propose a method to find all efficient solutions of an interval multiple objective linear programming (IMOLP) problem with interval coefficients in the objective functions, the constraint matrix and the right-hand side vector. The set of all efficient solutions of an IMOLP problem can completely supply information to the decision makers in finding their most preferred solutions but, up to now, not all solutions of an IMOLP problem can be found by known methods. A numerical example is given to illustrate the working of the proposed method.

In this paper, we propose a method for the generation of simulation test scenarios for autonomous driving. Based on the requirements of standard regulatory test scenarios, we can generate virtual simulation scenarios and functional scenario libraries for autonomous driving, which can be used for simulation verification of different ADAS functions. Firstly, the design operation domain (ODD) of the functional scenario is selected and the weight values of the ODD elements taken are calculated; then a combination test algorithm based on parameter weights is improved to generate autonomous driving virtual simulation test cases for the ODD elements, which can effectively reduce the number of generated test cases compared with the traditional combination test algorithm; then the traffic participant elements under each test case are Then, the values of the subelements under the traffic participant element in each test case are sampled and clustered to obtain hazard specific scenarios. Finally, the specific scenarios are applied to the automatic emergency braking (AEB) system on the model-in-the-loop (MIL) testbed to verify the effectiveness of this scenario generation method.

The relationship between the street environment and the health, education, mobility, and criminal behaviors of its citizens has long been investigated by economists, sociologists and urban planners. Home buyers were found to pay a premium for better street appearance. Prior studies considering streetscapes mainly focus on objective measures such as the number of nearby trees, the tree canopy area, or the view index of physical features such as greenery, sky or building. However, subjective perceptions may have complex or subtle relationships to physical features, individual physical features or simply summing them up do not capture people’s comprehensive perception. In contrast, this study proposed a new approach for the urban-scale application to quantify both subjectively and objectively measured streetscape scores for six important perception qualities, namely Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity. Built on prior quantitative studies in urban design quality and emerging applications in deep learning and open source street view imagery for urban perceptions, we integrated existing frameworks to (1) effectively collect and evaluate both subjectively and objectively- measured perceptions; (2) investigate the coherence and divergence in ML-predicted subjective scores and formula-derived objective scores; and (3) compare their effects in affecting house prices taking Shanghai as a case study using a large-scale dataset on home transactions. The results implied: first, the percentage increase in sales price attributable to street scores is significant for both subjective and objective measurements. In general subjective scores explained more variance over structural attributes and objective scores in hedonic price model. Particularly, objective Greenness, subjective Safety and Imageability scores positively affected house prices. Second, for Greenness and Imageability scores, the subjective and objective measures exhibited opposite signs in affecting house prices, which implied that there might be mechanisms related to the psychological, social-demographical characteristics of street users that have not been fully incorporated by objective measures that taking view indices or recombination of them. In addition, certain objective measure might outperform subjective counterpart when the connotation of the perception is self-evident and not complicated, for example the Greenness. For those concepts were not familiar to the average person, subjective framework exhibits better performance. This is the first study comprehensively expanding hedonic price method with both subjectively and objectively measured streetscape qualities. It suggested that city authorities could levy a street environment tax to compensate the public budget invested in street environment where developers secured benefits from a price premium. This study enriches our understanding of the economic values of the subjective and objective measures street qualities. It sheds light on promising future study areas where the coherence and divergence of the two measurements should be further stressed.

Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents' dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.

Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based architecture to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data is extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimizes the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimizing computational performance compared to other models.

One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a multi-DBN weighted voting classification algorithm is proposed on the basis of the Deep Belief Network (DBN) classifier and combined with the idea of voting method, which is implemented on the nodes of the WSN monitoring system by means of "upper training, lower transplantation" appraoch. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of 84.63% across four different types of moving targets. The experiment reveals that the multi-DBN weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single DBN classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater time overhead to run.

Unmanned Aerial Vehicles (UAVs) image object detection has great application value in military and civilian fields. However, the objects in the captured images from UAVs have problems of large scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.

Rapid assessment of maize yields in smallholder farming system is important to understand its spatial and temporal variability and for timely agronomic decision-support. Imageries acquired with unmanned air vehicles (UAV) offer opportunity to assess agronomic variables at field scale, however, it is not clear if this can be translated into reliable yield assessment on smallholder farms where field conditions, maize genotypes, and management practices vary within short distances. In this study, we assessed the predictability of maize grain yield using UAV-derived vegetation indices (VI), with(out) biophysical variables, in smallholder farms. High-resolution images were acquired with UAV-borne multispectral sensor at 4 and 8 weeks after sowing (WAS) on 31 farmers’ managed fields (FMFs) and 12 nearby Nutrient Omission Trials (NOT), all distributed across 5 locations within the core maize region of Nigeria. The NOTs included non-fertilized and fertilized plots (with and without micronutrients), sown with open-pollinated or hybrid maize genotypes. Acquired multispectral images were post-processed into several three (s) vegetation indices (VIs), normalized difference vegetation index (NDVI), normalized difference red-edge (NDRE), green-normalized difference vegetation index (GNDVI). Biophysical variables, plant height (Ht) and percent canopy cover (CC), were measured with the georeferenced plot locations recorded. In the NOTs, the nutrient status, not genotype, influenced the grain yield variability and outcome. The maximum grain yield observed in NOTs was 9.3 tha^-1, compared to 5.4 tha^-1 in FMF. Without accounting for between- and within-field variations, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r<0.02, P>0.1), but significant correlations were observed at 8WAS (r≤0.3; p<0.001). Ht was positively correlated with grain yield at 4WAS (r=0.5, R²=0.25, p<0.001), and more strongly at 8WAS (r=0.7, R²=0.55, p<0.001), while relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMF (separately) through linear mixed-effects modeling, predictability of grain yield from UAV-derived VIs was generally (R²≤0.24), however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R² ≥0.62, RMSEP≤0.35) in NOTs but not in FMF. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), than in actual farmer-managed fields where various confounding agronomic factors can amplify noise-signal within the vegetation canopy.

In this work, we show that a late fusion approach to multi-modality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of Computer Vision (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a CNN and optimised MLP and the Leap Motion model is implemented by an evolutionary optimised deep MLP topology search. Next, the two best networks are fused for synchronised processing which results in a better overall result (94.44%) since complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multi-modality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via BSL, all three models outperform standard random weight distribution when classifying ASL, and the best model overall for ASL classification was the transfer learning multi-modality approach which scored 82.55% accuracy.

Urgent environmental challenges and emerging additive manufacturing (AM) technologies push research towards more performant and new materials. In the field of metallurgy, high entropy alloys (HEAs) have recently represented a topic of intense research because of their promising properties, such as high temperature strength and stability. Moreover, this class of multi-principal element alloys (MPEAs) have opened up researcher community to unexplored compositional spaces, making prosper literature of high-throughput methodologies and tools for rapidly screening large number of alloys. However, none of the methods has been aimed to design new MPEAs for AM process known as selective laser melting (SLM) so far. Here we conducted nanoindentation testing on single scan tracks of elemental powder blends and pre-alloyed powders after ball milling of AlTiCuNb and AlTiVNb. Results show that nanoindentation can represent an effective technique to gain information about phase evolution during laser scanning, contributing to accelerate the development of new MPEAs.

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.

Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and propose a Transformer-based Time and Team Reinforcement Learning scheme (T3OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on decentralized partially observed Markov decision processes (Dec-POMDP) to instantiate this problem. Second, by introducing and modifying the transformer-based observation sequence, QMIX is redefined to adapt to the complicated road structure, restricted moving spaces and constrained observations, so as to control vehicles to pursue the target combining the vehicle’s observations. Third, a multi-intersection urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed T3OMVP scheme achieves significant improvements relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is available at https://github.com/pipihaiziguai/T3OMVP.

Abstract: Thermal desalination is yet a reliable technology in the treatment of brackish water and seawater; however, its demanding high energy requirements have lagged it compared to other non-thermal technologies such as reverse osmosis. This review provides an outline of the development and trends of the three most commercially used thermal or phase change technologies worldwide: Multi Effect Distillation (MED), Multi Stage Flash (MSF), and Vapor Compression Distillation (VCD). First, state of water stress suffered by regions with little fresh water availability and existing desalination technologies that could become an alternative solution are shown. The most recent studies published for each commercial thermal technology are presented, focusing on optimizing the desalination process, improving efficiencies, and reducing energy demands. Then, an overview of the use of renewable energy and its potential for integration into both commercial and non-commercial desalination systems is shown. Finally, research trends and their orientation towards hybridization of technologies and use of renewable energies as a relevant alternative to the current problems of brackish water desalination are discussed. This reflective and updated review will help researchers to have a detailed state of the art of the subject and to have a starting point for their research, since current advances and trends on thermal desalination are shown.

Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset enhances the water information and reduces the data dimension. In particular, the Cloud-Free label provided in the database effectively mitigates the issue of scarcity of labeled samples Since a single form or uniform kernel size cannot handle the variety in size and shape of water bodies, we proposed the gated multi-filter inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize the attention mechanism by introducing the Gated Channel Transform (GCT) skip connection and integrating GCT into GMF- Inception to further improve model performance. Extensive experiments on three benchmarks, including WIPI, Chengdu and GF2020 datasets demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 of 0.9191, 0.7410, and 0.8421, respectively, which average F1 on three datasets is 0.0045 higher than the U-Net model and reduces 62% average GFLOPs. New WIPI dataset, code and trained models have been released on GitHub

Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

The hybrid bond graph has been studied in depth for scalar bond graphs, but how does this translate to the multi-bond graph? Here, the controlled junction – used to model structural switching such as contact – is extended to the multi-bond case. This is a simple process, assuming that all bonds switch simultaneously (which makes physical sense). A controlled 0-junction is applied to multi-bond graph of a car, which can lose contact with the ground in cornering. Dynamic causality features, but this can be accommodated using an equational submodel in 20-Sim (in a manner similar to that used with scalar bond graphs). The junction is proposed for subsequent work to develop a validated multi-body dynamics car model in cornering.

The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.

In this paper, a new approach to the sliding-mode control of single-phase inverters under linear and non-linear loads is introduced. The main idea behind this approach is to utilize a non-linear, flexible and multi-slope function in controller structure. This non-linear function makes the controller possible to control the inverter by a non-linear multi-slope sliding surface. In general, this sliding surface has two parts with different slopes in each part and the flexibility of the sliding surface makes the multi-slope sliding-mode controller (MSSMC) possible to reduce the total harmonic distortion, to improve the tracking accuracy, and to prevent overshoots leading to undesirable transient-states in output voltage which are occurred when the load current sharply rises. In order to improve the tracking accuracy and to reduce the steady-state error, an integral term of the multi-slope function is also added to the sliding surface. The improved performance of the proposed controller is confirmed by simulations and finally, the results of the proposed approach are compared with a conventional SMC and a SRFPI controller.

The paper presents the results of the research concerning an evaluation of information 1 technology of gene expression profiles processing stability with the use of gene expression profiles 2 with different levels of noise component. The information technology is presented as a structural 3 block-chart, which contains all stages of the studied data processing. The hybrid model of objective 4 clustering based on SOTA algorithm and the technology of gene regulatory networks reconstruction 5 have been studied to evaluate the stability to the level of the noise component. The results of the 6 simulation have shown that the hybrid model of objective clustering has high level of stability 7 to noise component and vice versa, the technology of gene regulatory networks reconstruction is 8 very sensitivity to level of noise component. The obtained results indicate the importance of gene 9 expression profiles preprocessing at early stage of gene regulatory network reconstruction in order to 10 remove background noise and non-informative genes in terms of used criteria

Fragmentation processes of multiply charged ions of C10H8 isomers produced in fast proton collision (velocity between 1.41 and 2.4 a.u.) is discussed in view of their fundamental molecular dynamics. Especially so in the mechanisms to produce a variety of carbon clusters from such ions. This aspect is assessed with the help of a multihit analysis of daughter ions in coincidence with elimination of H+ and CHn+ (n = 0 to 3). The elimination of H+/C+ was found to be very different from CH3+ loss. The loss of CH3+ was found to be accompanied with the formation of heavy ions like C9H5+, C9H5++ and C7H3+ by a cascade of momentum correlated dissociations. The most probable structures of large fragment ions in the CH3+ loss cascade are predicted with the help of computed electronic energy and the multihit ToF correlations of second and third hit. In addition, we report observation of super-dehydrogenation of naphthalene and azulene targets, with evidence of complete dehydrogenation in a single collision.

In gravitational theory and astrophysical dynamics, singular initial value problems (IVPs) are frequently encountered. Finding the solutions to this class of IVPs can be challenging due to their complex nature. This study strives to circumvent the complexity by proposing a numerical method for solving such problems. The approach proposed in the current research seeks solutions to the IVP by partitioning the domain [0,L] of the problem into two intervals and solving the problem on each domain. The study seeks a closed-form solution to the IVP in the interval containing the singular point. A linearization technique and piecewise partitioning of the domain not containing the singularity are applied to the nonlinear IVP. The resulting linearized differential equation is solved using the Chebyshev spectral collocation method. Some examples are presented to illustrate the efficiency of the proposed method. Numerical analysis of the solution and residual errors are shown to ascertain convergence and accuracy. The results suggest that the technique gives accurate convergent solutions using a few collocation points.

Dracunculiasis (also known as Guinea worm disease) is caused by Dracunculus medinensis parasite and it spreads by drinking water containing Larvae of Guinea worm. The lack of safe water facilities, preventions and treatments resulted in highly dangerous consequences in its endemic regions. The economy of the affected regions totally falls down due to less production which is the result of agricultural field worker’s bad health. In this study, a multi epitope vaccine was designed against Dracunculus medinensis by using immune-informatics. The vaccine was designed by using T-Cell and B-Cell epitopes derived from Dracunculus medinensis proteins (Lactamase-B domain-containing protein, G-Domain containing protein and Ferrochelatase) in addition to Adjuvants and Linkers. The tertiary structure, physiochemical properties and immunogenic elements of vaccine were achieved. The validation of tertiary structure was accessed, and quality was achieved. In addition, the world coverage of parasite’s CTL and HTL epitopes is 95.61%. The stability of the chimeric vaccine was achieved through disulfide engineering. The molecular docking with Toll Like Receptor 4 (TLR-4) of vaccine showed its binding efficiency followed by Molecular Dynamic Simulation. The immune simulation suggested the mediated cell immunity and repeated antigen clearance. At the end, the optimized codon was used in in silico cloning to ensure vaccine’s higher exposure in bacterium E. coli strain K12. With further assessments, it is believed that the proposed multi epitope vaccine has strong immunogen to control Dracunculus medinensis which may result in better social and economic conditions of endemic regions.

Introduction Tuberculosis is common in Pakistan. Due to various factors including socioeconomic factors, compliance is poor to anti-tuberculosis drugs, leading to resistance. We aim to determine the prevalence of Multidrug resistance (MDR) tuberculosis in Pakistani population.Methods A prospective observational study was conducted from April 1, 2019, to December 31, 2019, in the Pulmonology department of a tertiary care hospital in Pakistan. Culture and sensitivity were assessed using a sputum sample or, in cases of an absent sputum sample, from Broncho alveolar lavage.ResultsApproximately 71.3% percent patients who had tuberculosis were found to be resistant to Isoniazid and around 48.6% did not respond to Rifampin. Multi-drug resistant was found in 29.4% participants.ConclusionMulti-drug resistance tuberculosis is very prevalent in Pakistan, which may increase burden on health care system and may lead to various complications of tuberculosis.

Phenotypic studies require large datasets for accurate inference and prediction. Collecting plant data in a farm can be very labor intensive and costly. This paper presents the design, architecture (hardware and software) and deployment of a distributed modular agricultural multi-robot system for row crop field data collection. The proposed system has been deployed in a soybean research farm at Iowa State University.

In the framework of maritime surveillance, vessel detection techniques based on spaceborne Synthetic Aperture Radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. The paper deal with this topic presenting a novel approach exploiting a cascade application of a pre-screening, and discrimination phase. The latter leverage the SAR spectrum by means of sub-looks analysis. For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images. For a future syngergic exploitation of more SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM and COSMO-SkyMed images, is comprehensive of multiple images gathered over the same area with short time lag (below 15 minutes). Finally, the diversified processing chains and the results for each mission product and scenarios are discussed. Being the first dataset of SLC (Single Look Complex) SAR multi-frequency data, the present work intends to encourage additional research in this promising field of research.

The term “conservation,” as it relates to biodiversity in a Western context, has a storied past and as conservation science and societal values have evolved, consensus over its precise meaning has remained elusive. The broad scope of contemporary definitions hampers effective communication during a period of environmental crisis and is troublesome for any derivative concept which aims to empirically quantify the efforts of the conservation sector. This presents an avoidable hindrance to the systematic planning of the conservation field. To help remedy this situation, we provide an outcome separation framework that is based on the expected degree of separation between an action’s proximate outcome, from its intended, ultimate outcome for a habitat and/or species. Framing a definition of conservation through this lens of outcome separation allows for conservation-related actions to be clearly categorized into one of three discrete tiers: primary, secondary, and tertiary. A distillation of this tiered framework also provides a new definition of biodiversity conservation that is more rigorous and adaptable to future conceptual evolutions of the field.

Residual dizziness after a liberating maneuver is often referred to by patients as a more disabling set of symptoms than the positional vertigo itself. This situation seems to involve more than half of subjects with labyrintholithiasis. The authors examine the hypothesis according to which residual dizziness involves subjects with labyrintholithiasis on the basis of otoconia.

This research focuses on the customer orientation of insurance brokers, whose activity is regulated by the Law of 26/2006 of July 17 on the mediation of private insurances and reinsurances. The goal is to ascertain whether the intermediation inherent in the insurance broker’s activity, which implies a customer-oriented service, entails a positive behaviour that transcends the immediate environment, reaching society. This study presents a comparative analysis between the insurance brokerage society, characterised by providing a personalised customer service, and banks’ advisory services on insurance. To this end, we study the evolution of the total volume of business and new production, compared for a portfolio of insurance products. The results presented in this research suggest that the customer values the advisory service provided by the broker. However, for a particular business segment in standardised insurance products and products related to banking assets, customers are more likely to resort to the bank’s services. In addition, the results indicate that the commission percentages applied by the entities operating in the banking insurance channel exceed those perceived by the insurance broker. With all this, intermediation in the development of the insurer’s activity can entail an ethical and social behaviour that involves customer-orientation and, possibly, social service, which does not always benefit the insurer.

Deep convolutional neural network (CNN) has shown their great advantages in the single image deraining task. However, most existing CNN-based single image deraining methods still suffer from residual rain streaks and details lost. In this paper, we propose a deep neural network including the Multi-scale feature extraction module and the channel attention module, which are embed in the feature extraction sub-network and the rain removal sub-network respectively. In the feature extraction sub-network, the Multi-scale feature extraction module is constructed by a Multi-layer Laplacian pyramid, and is then integrated multi-scale feature maps by a feature fusion module. In the rain removal sub-network, the channel attention module, which assigns different weights to the different channels, is introduced for preserving image details. Experimental results on visually and quantitatively comparison demonstrate that the proposed method performs favorably against other state-of-the-art approaches

Defect detection in ferromagnetic substrates is often hampered by non-magnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of non-magnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multi-frequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3 % for an extended lift-off range of up to 10 mm.

Porous ceramics can be realized by different methods and are used for manifold applications, like cross-flow-membranes or wall-flow-filters, porous burners, solar receivers, structural design elements or catalytic supports. Within this paper three different alternative process routes are presented, which can be used to manufacture porous ceramic components with different properties or even graded porosity. The first process route bases on additive manufacturing (AM) of macro porous ceramic components, the second on AM of a polymeric template, which is used to manufacture porous ceramic components via replica technique. Finally, the third process route bases on an AM technology, which allows the manufacturing of multi-material or multi-property ceramic components, like components with dense and porous volumes in one complex shaped component.

In this article, we introduce some multi-messenger studies of blazars. Main contents contain the following parts, 1. the subclasses of blazars and the observational characteristics, 2. emission variabilities from the multi-messenger observations, 3. the physical origin for the multi-messenger studies. Based on mid-IR observations collected from the $WISE$ database, we collect two sources (BZQ J1700+6830 and BZQ J1842+6809) to analyze the mid-IR variabilities. \\ (1) For BZQ J1700+6830, the period is $P\approx$ 42 hr at the four bands ($W1$, $W2$, $W3$, $W4$); for BZQ J1842+6809, the period is $P\approx$ 123 hr at the two bands ($W1$, $W2$). \\ (2) Using the periodicities, we calculate that the central black hole mass ($BH$) should be $M = (5.44\sim33.0)\times10^8M_{\odot}$ for BZQ J1700+6830 and $M = (1.13\sim6.88)\times10^9M_{\odot}$ for BZQ J1842+6809.\\ (3) For the two sources, during the flaring stage, we find anti-clockwise variation trends based on the time-based relations built by the spectral indices ($\alpha$) and flux densities ($F$).\\ (4) We calculate the Doppler factor ($\delta$) when the sources are in the dimming or brightening stages, and find that with the frequency increasing, the Doppler factor become stronger.

The paper considers new effects of the nanoscale state of matter, which open up prospects for the creation of electronic devices using new physical principles. The contact of chemically homogeneous different sizes hydrated nanoparticles of yttrium-stabilized zirconium oxide (ZrO2 – x %mol Y2O3, x=0, 3, 4, 8; YSZ) with particle sizes of 7.5 nm and 7,5 nm; 7.5 nm and 9 nm; 7.5 nm and 11 nm; 7.5 nm and 14 nm in the form of compacts obtained using high hydrostatic pressure (HP-compacts of 300MPa) was studied at direct and alternating current. A unique size effect of the nonlinear (semiconductor) dependence of the electrical properties (in the region U <2.5 V, I ≤ 2.7 mA) of the contact of different-sized YSZ nanoparticles of the same chemical composition is revealed, which indicates the possibility of creating semiconductor structures of a new type based on chemically homogeneous nanostructured systems. The electronic structure of the near-surface regions of nanoparticles of a special type of oxide materials and the possibility, on this basis, to obtain specifically rectifying properties of the contacts were studied theoretically. Models of surface states of the Tamm type are constructed, but considering the Coulomb long-range action. The discovered variance and its dependence on the curvature of the surface of nanoparticles made it possible to study the conditions for the formation of a contact potential difference in cases of nanoparticles of the same radius (synergistic effect), different radii (doped and undoped variants), as well as to discover the possibility of describing a group of powder particles from material within the Anderson model. The established effect makes it possible to solve the problem of diffusion instability of semiconductor heterojunctions and opens up prospects for creating electronics devices with a fundamentally new level of properties for use in various fields of the national economy and breakthrough critical technologies.

Fluorescence microscopy provides an unparalleled tool for imaging biological samples. However, producing high-quality volumetric images quickly and without excessive complexity remains a challenge. Here, we demonstrate a simple multi-camera structured illumination microscope (SIM) capable of simultaneously imaging multiple focal planes, allowing for the capture of 3D fluorescent images without any axial movement of the sample. This simple setup allows for the acquisition of many different 3D imaging modes, including 3D time lapses, high-axial-resolution 3D images, and large 3D mosaics.

Our work uses Iterative Boltzmann Inversion (IBI) to study the coarse-grained interaction between 20 amino acids and the representative carbon nanotube CNT55L3. IBI is a multi-scale simulation method that has attracted the attention of many researchers in recent years. It can effectively modify the coarse-grained model derived from the Potential of Mean Force (PMF). IBI is based on the distribution result obtained by All-Atom molecular dynamics simulation, that is, the target distribution function, the PMF potential energy is extracted, and then the initial potential energy extracted by the PMF is used to perform simulation iterations using IBI. Our research results have gone through more than 100 iterations, and finally, the distribution obtained by coarse-grained molecular simulation (CGMD) can effectively overlap with the results of all-atom molecular dynamics simulation (AAMD). In addition, our work lays the foundation for the study of force fields for the simulation of the coarse-graining of super-large proteins and other important nanoparticles.

Multi-morbidity and polypharmacy are common in older people and pose a challenge for health and social care systems especially in context of global population ageing. They are complex and interrelated concepts in the care of older people that require early detection and patient centred decision making that are underpinned by the principles of multidisciplinary led comprehensive geriatric assessment (CGA). Personalised care plans need to remain responsive and adaptable to the needs of a patient, enabling an individual to maintain their independence.

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a 5-year period between 2003-2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS operations) over two regions: I) inside radar effective coverage and II) immediately outside radar coverage. The outcomes of the evaluation indicate a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

Burrs form due to plastic deformation of materials during machining processes, such as milling and drilling. Deburring can be very difficult when the burrs are not easily accessible for removal. In this study, abrasive flow machining (AFM) was adopted for deburring the edges of milling specimens. Based on the experimental observations on AL6061 specimens, the deburring performance was characterized in terms of flow speed, the local curvature of the streamline near the burr edge, and shear stress. A new objective function that can predict the extent of deburring is proposed based on these characteristics and validated through experiments. Based on this new objective function, a prediction of deburring performance on the burr edge of the intersecting holes with offset was performed. The results between the predicted and experimental observation results were in reasonable agreement.

Bioelectrocatalysis provides the intrinsic catalytic-functions of redox enzymes to non-specific electrode reactions and is the most important and basic concept for biosensors. This review starts by describing fundamental characteristics of bioelectrocatalytic reactions in mediated and direct electron transfer types from a theoretical viewpoint and summarizes amperometric biosensors based on multi-enzymatic cascades and for multi-analyte detection. The review also introduces prospective aspects of two new concepts of biosensors: mass-transfer-controlled (pseudo)steady-state amperometry at microelectrodes with enhanced enzymatic activity without calibration curves and potentiometric coulometry at enzyme/mediator-immobilized biosensors for absolute determination.

This article proposes the use of a multi-scale and multi-sensor approach to collect and modelling 3D data concerning wide and complex areas in order to obtain a variety of metric information in the same 3D archive, based on a single coordinate system. The employment of these 3D georeferenced products is multifaceted and the fusion or integration among different sensors data, scales and resolutions is promising and could be useful for the generation of a model that could be defined as hybrid. The correct geometry, accuracy, radiometry and weight of the data models are hereby evaluated comparing integrated processes and results from Terrestrial Laser Scanner (TLS), Mobile Mapping System (MMS), Unmanned Aerial Vehicle (UAV), terrestrial photogrammetry, using Total Station (TS) and Global Navigation Satellite System (GNSS) as topographic survey. The entire analysis underlines the potentiality of the integration and fusion of different solutions and is a crucial part of the “Torino 1911” project whose main purpose is mapping and virtually reconstructing the 1911 Great Exhibition settled in the Valentino Park in Turin (Italy).

Does cell clustering influence intrinsic and acquired multi-drug resistance (MDR) differently? To address this question, we studied cultured monolayers (representing individual cells) and cultured spheroids (representing clusters) formed by drug-naïve (intrinsic MDR) and drug-exposed (acquired MDR) lines of ovarian cancer A2780 cells by cytometry of reaction rate constant (CRRC). MDR efflux was characterized by accurate and robust “cell number vs. MDR efflux rate constant (kMDR)” histograms. Both drug-naïve and drug-exposed monolayer cells presented unimodal histograms; the histogram of drug-exposed cells was shifted towards higher kMDR value suggesting greater MDR activity. Spheroids of drug-naïve cells presented a bimodal histogram indicating the presence of two subpopulations with different MDR activity. In contrast, spheroids of drug-exposed cells presented a unimodal histogram qualitatively similar to that of the monolayers of drug-exposed cells but with a moderate shift towards greater MDR activity. The observed greater effect of cell clustering on intrinsic than on acquired MDR can help guide the development of new therapeutic strategies targeting clusters of circulating tumor cells.

In this paper, we consider a new class of polynomials which is called the multi-poly-Euler polynomials. Then, we investigate their some properties and relations. We provide that the type 2 degenerate multi-poly-Euler polynomials equals a linear combination of the degenerate Euler polynomials of higher order and the degenerate Stirling numbers of the first kind. Moreover, we provide an addition formula and a derivative formula. Furthermore, in a special case, we acquire a correlation between the type 2 degenerate multi-poly-Euler polynomials and degenerate Whitney numbers.

Inspired by the definition of degenerate multi-poly-Genocchi polynomials given by using the degenerate multi-polyexponential functions. In this paper, we consider a class of new generating function for the degenerate multi-poly-Bernoulli polynomials, called the type 2 degenerate multi-poly-Bernoulli polynomials by means of the degenerate multiple polyexponential functions. Then, we investigate their some properties and relations. We show that the type 2 degenerate multi-poly-Bernoulli polynomials equals a linear combination of the weighted degenerate Bernoulli polynomials and Stirling numbers of the first kind. Moreover, we provide an addition formula and a derivative formula. Furthermore, in a special case, we acquire a correlation between the type 2 degenerate multi-poly-Bernoulli numbers and degenerate Whitney numbers.

A multi-decadal change analysis of the irrigation ponds in Taoyuan, Taiwan was conducted by using multi-source data including digitized ancient maps, declassified single-band CORONA satellite images, and multispectral SPOT images. Supervised LULC classifications were conducted using four textural features derived from the single-band CORONA images and spectral features derived from SPOT images. Post-classification analysis revealed that the number of irrigation ponds in the study area decreased during the post-World War II farmland consolidation period (1945 – 1965) and the subsequent industrialization period (1970 – 2000). However, efforts on restoration of irrigation ponds in recent years have resulted in gradual increases in the number (9%) and total area (12%) of irrigation ponds in the study area.

Jujube (Ziziphus jujuba Mill.) stands as a pivotal fruit tree with significant economic, ecological, and social value. Recent years have witnessed remarkable strides in multi-omics-based biological re-search on jujube. This review began by summarizing advancements in jujube genomics. Subse-quently, we provided a comprehensive overview of the integrated application of genomics, tran-scriptomics, and metabolomics to explore pivotal genes governing jujube domestication traits, quality attributes (including sugar synthesis, terpenoids, and flavonoids), and responses to abiotic stress, and discussed the transcriptional regulatory mechanisms underlying these traits. Further-more, challenges in multi-omics research on jujube biological traits were outlined, and we pro-posed the integration of resources such as pan-genomics and sRNAome to unearth key molecules and regulatory networks influencing diverse biological traits. Incorporating these molecules into practical breeding strategies, including gene editing, transgenic approaches, and progressive breeding, holds the potential for achieving molecular-design breeding and efficient genetic en-hancement of jujube.

IP Multimedia Subsystem (IMS) supports high-speed transmission of multi-media like data, audio, and video between mobiles and other wireless devices. IMS is the most suitable for multimedia communication across multiple networks using the Internet of Things (IoT) and cloud computing. IoT-IMS can efficiently manage the huge amount of data generated by smart devices for audio-video during live streaming applications. During these services, the main problem is that an intruder can launch a Session Initialization Protocol (SIP) messages flooding attack on IMS that does not have the functionality to prevent itself from these types of attacks. In this paper, an intrusion detecting and preventing system (IDPS) is proposed that effectively detects the register flooding and spoofing attacks using SIP messages. We proposed a watermark embedding algorithm on the user side and an extraction algorithm on receiving side. A test-bed is set up for IMS where we have considered four different scenarios to evaluate the IDPS approach as compared to counterparts. Results illustrate the dominance of our scheme in terms of response time, register flooding attack detection ratios, CPU load utilization, fault detection ratio, and memory utilization. The fault detection ratio averagely 98.18

Background: Medulloblastoma (MB) comprises four broad molecular subgroups - wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 respectively with subgroup-specific developmental origins, unique genetic profiles, distinct clinico-demographic characteristics, and diverse clinical outcomes. WNT-MB has the best outcomes (5-year survival >90%) with contemporary multi-modality treatment prompting attempts at treatment de-intensification to reduce late toxicity. This is a retrospective audit of clinical outcomes in WNT-MB patients treated at a tertiary-care comprehensive cancer centre. Methods: Patients with molecularly confirmed WNT-MB treated with maximal safe resection followed by post-operative standard-of-care risk-stratified adjuvant radio(chemo)therapy were identified retrospectively via electronic search of the neuro-oncology database. Data regarding clinico-demographic characteristics, histo-molecular features, treatment details, patterns of failure, and survival outcomes was retrieved from electronic medical records and/or hospital case files. Time-to-event outcomes were analyzed using Kaplan-Meier methods and compared with the log-rank test. Results: Between 2004 to 2020, 74 patients with WNT-MB were registered in the neuro-oncology unit of the institute. Seven patients treated on a prospective clinical trial of therapy de-intensification were excluded leaving 67 patients that constitute the present study cohort. Median age at presentation was 12 years (inter-quartile range 9-18 years) with a male preponderance (2:1). Six patients (1 post-operative mortality and 5 without adequate details of treatment or outcomes) were excluded from the survival analysis which was restricted to 61 patients. At a median follow-up of 72 months, Kaplan-Meier estimates of 5-year progression-free survival and overall survival were 87.7% and 91.2% respectively. Traditional high-risk features such as large residual tumor (≥1.5cm2) and leptomeningeal metastases (M+) did not significantly impact upon survival in this molecularly-characterized WNT-MB cohort. Conclusions: This retrospective clinical audit confirms excellent survival in WNT-MB patients treated with contemporary multi-modality therapy. Lack of prognostic impact of conventional high-risk features suggests the need for refined risk-stratification and potential de-intensification of therapy.

In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior, based on variational (approximate Bayesian) inference. Expanding upon Husserl’s model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and to thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory; particularly, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitate the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals.

The fidelity of the decadal experiment in Coupled Model Intercomparison Project Phase-5 (CMIP5) has been examined, over different climate variables for different temporal and spatial scales, in many previous studies. However, most of the studies were for the temperature and temperature-based climate indices. A quite limited study was conducted on precipitation of decadal experiment and no attention was paid to a catchment level. This study evaluates the performances of eight GCMs (MIROC4h, EC-EARTH, MRI-CGCM3, MPI-ESM-MR, MPI-ESM-LR, MIROC5, CMCC-CM, and CanCM4) for the monthly hindcast precipitation of decadal experiment over the Brisbane River catchment in Queensland, Australia. First, the GCMs datasets were spatially interpolated onto a spatial resolution of 0.050×0.050 (5 km× 5 km) matching with the grids of observed data and then were cut for the catchment. Next, model outputs are evaluated for temporal skills, dry and wet periods, and total precipitation (over time and space) based on the observed values. Skill test results reveal that model performances varied over the initialization years and showed comparatively higher scores from the initialization year 1990 and onward. Models with finer spatial resolutions show comparatively better performances as opposed to the models of coarse spatial resolutions where MIROC4h outperformed followed by EC-EARTH and MRI-CGCM3. Comparing the skills, models are divided into three categories (Category-I: MIROC4h, EC-EARTH, and MRI-CGCM3; Category-II: MPI-ESM-LR and MPI-ESM-MR; and Category-III: MIROC5, CanCM4, and CMCC-CM). Three multimodel ensembles’ mean (MMEMs) are formed using the arithmetic mean of Category-I (MMEM1), Category-I and II (MMEM2), and all eight models (MMEM3). The performances of MMEMs are also assessed using the same skill tests and MMEM2 performed best which suggests evaluating the models before the formation of MMEM.

In this review, we will cover a general overview of the current panorama on lncRNAs with an actual or potential role as biological markers in cancer. We will discuss examples of multi-omics approaches that integrates information on somatic aberrations, gene expression and epigenomics, with the scope of providing a more comprehensive view of the functional impact of lncRNA profiles and how these paradigm can be exploited for the discovery and selection of lncRNAs with a functional role and their use as variables informing on progression and prognostic and help guiding the selection of therapeutic strategies for cancers. Finally, we propose a perspective for future evolution of the study of lncRNAs and the discovery of their functional and harnessing their potential to assist in clinical management of malignancies.

Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics, without requiring a high voltage forming process. The effect of current compliance on the Y2O3 RRAM devices was investigated and revealed that as the set current compliance values increased, the resistance values gradually decreased. Consequently, intermediate resistance values were obtained, which are suitable for multi-level cell (MLC) switching. The fabricated Y2O3 RRAM devices were capable of functioning as an MLC with a capacity of 2 bits in one cell, utilizing three low resistance states and one common high resistance state. The potential of the Y2O3 RRAM for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y2O3 RRAM demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This suggests that the proposed RRAM has the potential to be utilized as a memory component in practical neuromorphic systems.

In this work, we propose a self-supervised multi-agent system that meets the online learning of clustering tasks for video behavior recognition spatio-temporal tasks. Encoding visual behavioral actions as discrete temporal sequence(DTS). Real-time clustering recognition task in a multi-agent system for continuous model building, training, and correction. Finally, we implemented a fully decentralized multi-agent system and completed its feasibility verification in a surveillance video application scenario on vehicle path clustering.

Along with the popularity of the Internet, people are exposed to more and more ways of micro-videos, and a huge amount of micro-video data has emerged. micro-videos have gradually become the Internet content preferred by the public, and a large number of micro-video apps have also emerged, such as Tiktok and Kwai. Intelligent classification and mining of micro-videos can greatly enhance user experience, improve business operation efficiency and enhance user experience. Through deep intelligent analysis and mining of micro-videos, important information in micro-videos can be extracted to provide an important basis for beautifying videos, content appreciation, video recommendation, content search, etc. In the past, content understanding for short videos often used human work annotation, but in recent years, with the great success of deep convolutional neural networks in image recognition, short video content understanding based on this method has gradually developed. Nowadays, most recognition algorithms extract the feature representation of each frame independently and then fuse them. However, while extracting the feature representation, some low-level semantic features are lost, which makes the algorithm unable to accurately distinguish the category of the video. At present, the algorithm of micro-video recognition based on deep learning has surpassed the iDT algorithm, making these traditional methods fade out of people’s view. In this paper according to the micro-video classification task, a new network model is proposed to concatenate features of each modality into the overall features of various modalities through the network, and then fuse the various modal features with the attention mechanism to obtain the whole micro-video features, which will be used for classification. In order to verify the effectiveness of the algorithm proposed in this paper, experiments are conducted in the public dataset, and it is shown the effectiveness of our model.

This paper addresses a wildlife monitoring problem using a team of UAVs for efficient monitoring of wildlife. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to efficient wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work.

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria – hypothesis, data types, strategies, study design and study focus – to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw a particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

The increasing number of robots around us will soon create a demand for connecting these robots in order to achieve goal-driven teamwork in heterogeneous multi-robot systems. In this paper, we focus on robot teamwork specifically in dynamic environments. While the conceptual modeling of multi-agent teamwork has been studied extensively during the last two decades, related engineering concerns have not received the same degree of attention. Therefore, this paper makes two contributions. The analysis part discusses general design challenges that apply to robot teamwork in dynamic application domains. The constructive part presents a review of existing engineering approaches for challenges that arise with dynamically changing runtime conditions. An exhaustive survey of robot teamwork aspects would be beyond the scope of this paper. Instead, we aim at creating awareness for the manifold dimensions of the design space and highlight state-of-the-art technical solutions for dynamically adaptive teamwork, thus pointing at open research questions that need to be tackled in future work.

From ancient time, electric grid system developed as one way direction in which users get the electricity from generators to far end. However, it is not the consumer centric as its one way process and consumer have no way to communicate to the server. Thus, with the development of digital revolution, the grid converted to smart grid and meter converted to smart meter. In smart grid, the protocol follows the bidirectional way of communication with support of consumers in the system. Recently in 2016, Jo et al. proposed the scheme for smart grid system using privacy preserving model and claimed to be efficient and secure. However, in this paper we have analyzed the scheme of Jo et al. and proved that the scheme is vulnerable to Replay attack and afterwards shows the change in protocol to withstand against this attack.

Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.

The concept of transit-oriented development (TOD) has been widely recognized in recent years for its role in reducing car traffic, improving public transportation, and enhancing traffic sustainability. This paper conducts empirical research on a developed rail transit network, using Shanghai as a case study. In addition to traditional TOD features, other factors based on urban rail transit are introduced, including multi-level modeling (MLM), which is used to analyze the possible factors influencing rail patronage. To avoid the bias of research results led by the correlation between independent variables, factors are divided into two levels. The first level includes three groups of variables: the built environment, station characteristics, and socioeconomic and demographic characteristics. The second level includes a set of variables which are regional characteristics. Results show that the most significant impact on train patronage is station location in the business district area. Other factors that have a positive effect on promoting rail transit travel include the number of service facilities around the station, degree of employment around the station, economic level, intensity of residential development, if the station is a transfer station, the operating period of the station, and the size of the large transportation hub around the station.

We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative machine learning algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of proximity and allowable backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model that doesn't include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF approach therefore may prove to be a powerful `spatially aware' alternative to other discriminative classifiers.

Aiming at the problems that are poor generalization performance, low retrieval accuracy and large time consumption of existing content-based image retrieval system, the hierarchical image retrieval method based on multi feature fusion is proposed in this paper. The retrieval accuracy rates on Corel5K, UKbeach and Holidays are 68.23(Top 1), 3.73(N-S) and 88.20(mAp), respectively. The experimental results show that the method proposed in this paper can effectively improve the deficiency of single feature retrieval and save time significantly in the premise of a small amount of loss of accuracy.

The variations of visibility, PM mass concentration and mixing layer height (MLH) at four major urban-industry regions (Shenyang, Anshan, Benxi and Fushun) in multi-cities of central Liaoning over northeast China were evaluated from 2009-2012 to characterize the dynamics effect on air pollution. The annual mean visibilities were about 13.7±7.8km, 13.5±6.5km, 12.8±6.1km and 11.5±6.8km in Shenyang, Anshan, Benxi and Fushun, respectively. The pollution load (PM×MLH) shown a weaker vertical diffusion in Anshan with a higher PM concentration in the near-surface. High concentrations of fine mode particles may be partially attributed to the biomass burning emissions from September in Liaoning Province and surrounding regions in Northeast China as well as the coal burning during the heating period with lower MLH in winter. The increasing wind speed has a similar change as the increasing of mixing layer height to make the effect on the aerosol vertical diffusion. The visibility on the non haze-fog days was about 2.5-3.0 times higher than that on hazy and fog days. The fine particle concentrations of PM2.5 and PM1.0 on the haze and fog days were ~1.8-1.9 times and ~1.5 times higher than that on no hazy-fog days. The MLH during fog pollution showed more declining trend than haze pollution compared with non haze-fog days. The results of this study could provide the useful information to better recognize the effects of vertical pollutants diffusion on air quality in the multi-cities of central Liaoning over Northeast China.

Multi-microgrid has many new characteristics, such as bi-directional power flows, flexible operation modes and variable fault currents with different control strategy of inverter interfaced distributed generations (IIDGs). All these featuring aspects pose challenges to multi-microgrid protection. In this paper, current and voltage characteristics of different feeders are analyzed when fault occurs in different positions of multi-microgrid. Based on the voltage and current distribution characteristics of the line parameters, a new protection scheme for the internal fault of multi-microgrid is proposed, which takes the change of phase difference and amplitude of measured bus admittance as the criterion. This scheme with high sensitivity and reliability, has a simple principle and is easy to be adjusted. PSCAD/EMTDC is used in simulation analysis, and simulation results have verified the correctness and effectiveness of the protection scheme.

Based on the definition of double fuzzy relations, two types of new rough set models are constructed, which are multi-granulation double fuzzy rough sets called optimistic and pessimistic multi-granulation double fuzzy rough sets, and some of their properties were studied. In addition, we study the relationship between optimistic and pessimistic multi-granulation double fuzzy rough sets and double fuzzy rough sets.

In recent years, an interest in multimedia services has grown expeditiously where the main part is comprised of video. Firms as well as subscribers require higher resolutions, framerates and sampling precision which results to higher amount of data need for processing, storing and transmitting. Therefore, a big challenge arises for researchers to develop new compression standards which should reduce the huge amount of data and keep quality at the same level. This paper examines compression performance of the latest and currently most used video codecs, namely H.266/VVC, AV1, H265/HEVC and H.264/AVC. The test set consists of seven sequences with various content at 8K, Ultra HD and Full HD resolution and encoded to bitrates from 1 to 15 Mbps for Full HD and Ultra HD resolutions and from 5 to 50 Mbps for 8K resolution, respectively. Codec performance was measured using PSNR, SSIM and VMAF objective quality metrics. In terms of the Bjøntegaard-Delta (BD) model, the results showed that H.266/VVC outperforms all other codecs, namely H.264/AVC,H.265/HEVC and AV1, respectively. Averaged bitrate savings were approximately 78\% for H.266/VVC, 63\% for AV1 and 53\% for H.265/HEVC relative to H.264/AVC, 59\% for H.266/VVC, 22\% for AV1 compared to H.264/AVC and 46\% for H.266/VVC relative to AV1, all for 8K resolution. The results also showed that the performance varies depending on resolution – with higher resolution, the efficiency of newly developed codecs such as H.266/VVC and AV1 is greater, which confirmed the fact that the H.266/VVC and AV1 codecs have been primarily developed for videos at high resolutions as 8K and/or UHD.