Co-integration and Causality was built to conduct studies on causality relation between carbon intensity and coal consumption leading to providing important basis for the transition to a low carbon economy. The EG two-step method was performed to study the relation between carbon intensity and coal consumption of China during 1990-2015 and the co-integration and Granger test was constructed to build up the co-integration and error correction models for analysis of the interaction between carbon intensity and coal consumption. The results showed that in long term there is a stable co-integration relation and a positive correlation between carbon intensity and coal consumption; whereas fluctuations exist in short term and there is a one-way Granger causality of carbon intensity with respect to coal consumption.

Self-supervised learning is a method that learns general representation from unlabeled data. Masked image modeling (MIM), one of the generative self-supervised learning methods, has drawn attention showing state-of-the-art performance on various downstream tasks, though showing poor linear separability resulting from the token-level approach. In this paper, we propose a contrastive learning-based multi-view masked autoencoder for MIM, exploiting an image-level approach by learning common features from two different augmented views. We strengthen MIM by learning long-range global patterns from contrastive loss. Our framework adopts simple encoder-decoder architecture, learning rich and general representation by following a simple process: 1) two different views are generated from an input image with random masking and by contrastive loss, we can learn semantic distance of the representations generated by an encoder. By applying a high mask ratio, 80%, it works as strong augmentation and alleviates the representation collapse problem. 2) With reconstruction loss, decoder learns to reconstruct an original image from the masked image. We assess our framework by several experiments on benchmark datasets of image classification, object detection, and semantic segmentation. We achieve 84.3% fine-tuning accuracy on ImageNet-1K classification and 76.7% in linear probing, exceeding previous studies and show promising results on other downstream tasks. Experimental results demonstrate that our work can learn rich and general image representation by applying contrastive loss to masked image modeling.

Continual learning, a crucial facet of machine learning, involves the perpetual acquisition of valuable insights from incoming data, sans the necessity for full dataset access. Esteemed as a fundamental goal in artificial intelligence, continual learning grapples with an enduring challenge—catastrophic forgetting. A proficient model must exhibit adaptability for new data assimilation and robustness to retain existing knowledge. Class-incremental learning (CIL) aids the gradual integration of knowledge from newly introduced classes, forming a universal classifier. However, directly training the model with fresh class instances triggers a problem—forgetting distinguishing features of prior classes, causing a performance decline. Addressing such issues in machine learning, this survey aims to delineate significant challenges, outcomes, and recent advancements, including our contributions to CIL techniques, especially in image classification.

There is only a very short reaction time for people to find the best way out of a building in a fire outbreak. Software applications can be used to assist the rapid evacuation of victims; however, this is an arduous task, which requires an understanding of advanced technologies. Since well-known pathway algorithms (such as, Dijkstra, Bellman-Ford and A*) can lead to serious problems over performance, we decided to make use of deep reinforcement learning techniques. A wide range of strategies including a random initialization of replay buffer and transfer learning were assessed in three projects involving schools of different sizes. The results showed the proposal was viable and that in most cases the performance of transfer learning was superior. In addition, the study raised challenges that had to be faced in the future.

Sparse reward long horizon task is a major challenge for deep reinforcement learning algorithm. One of the key barriers is data-inefficiency. Even in the simulation environment, it usually takes weeks to training the agent. In this study, a data-efficiency training framework is proposed, where a curriculum learning is design for the agent in the simulation scenario. Different distributions of the initial state are set for the agent to get more informative reward during the whole training process. A fine-tuning of the parameters in the output layer of the neural network for value function is conduct to bridge the gap between sim-to-real. An experiment of UAV maneuver control is conducted in the proposed training framework to verify the method more efficient. We demonstrate that data-efficiency is different for the same data in different training stages.

This article discusses the comparison between digital and traditional face-to-face coaching within the scope of shadow education institutions. While analyzing the differences and similarities between the two educational models, both their advantages and disadvantages are thoroughly discussed. In this context, interviews were conducted with students and teachers who receive education in both face-to-face and digital coaching, and the positive and negative aspects of both institutions, suitable and unsuitable courses, the future situation, and the effects on students' academic achievements were revealed. According to the results obtained from the research, it is noteworthy that students who do not receive education in digital coaching have prejudices against digitalization. Additionally, no significant difference was found between the academic achievements of students receiving education in digital coaching and those receiving education in face-to-face coaching.

Despite the importance of few-shot learning, the lack of labeled training data in the real world, makes it extremely challenging for existing machine learning methods as this limited data set does not represent the data variance well. In this research, we suggest employing a generative approach using variational autoencoders (VAEs), which can be used specifically to optimize few-shot learning tasks by generating new samples with more intra-class variations. The purpose of our research is to increase the size of the training data set using various methods to improve the accuracy and robustness of the few-shot face recognition. Specifically, we employ the VAE generator to increase the size of the training data set, including the basic and the novel sets while utilizing transfer learning as the backend. Based on extensive experimental research, we analyze various data augmentation methods to observe how each method affects the accuracy of face recognition. We conclude that the face generation method we proposed can effectively improve the recognition accuracy rate to 96.47% using both the base and the novel sets.

Green and sustainable electrocatalytic conversion of nitrogen-containing compounds to ammonia are currently in high demand in order to replace the eco-unfriendly Haber-Bosch process. Model catalysts for the nitrate reduction reaction were obtained by electrodeposition of metal Co, Fe and bimetallic Fe/Co nanoparticles from aqueous solutions onto a graphite substrate. The samples were characterized by the following methods: SEM, XRD, XPS, UV-vis spectroscopy, cyclic (and linear) voltammetry, chronoamperometry and electrochemical impedance spectroscopy. Besides, the determination of electrochemically active surface was also performed for all electrocatalysts. The best electrocatalyst was a sample containing Fe-nanoparticles on the layer of Co-nanoparticles, which showed the Faradaic efficiency 58.2% (E=-0.785 V vs. RHE) at the ammonia yield rate of 14.6 μmol h-1 cm-2. An opinion was expressed on elucidation of the mechanism of coordinated electrocatalytic action of a bimetallic electrocatalyst. This work can serve primarily as a starting point for future investigations on electrocatalytic conversion reactions to ammonia using model catalysts of the proposed type.

Membrane gas absorption technology has been considered as a promising approach to mitigate CO2 emissions from power plants. The aim of this study is to evaluate the environmental impacts of CO2 absorption and desorption processes by hollow fiber membrane contactors using life cycle assessment methodology. On the basis of ReCipe 2016 Midpoint and ReCipe 2016 Endpoint methods, the research results show that, membrane gas absorption system exhibits the lowest environmental impacts across the majority assessed categories in comparison with chemical absorption and membrane gas separation systems. The CO2 capture process via membrane gas absorption has the most significant impact on METP category, with heat consumption as the primary contributing factor accounting for 55%, followed by electricity consumption accounting for 43.1%. According to the sensitivity analysis, heating by natural gas shows a better performance than other heat supply sources in improving overall environmental impacts. In addition, the increasing utilization of renewable energy in electricity supply reduces the global warming potential, fossil resource consumption and ozone formation.

A co-curing resin system consisting of 9368 epoxy resin for prepreg and 6808 epoxy resin for resin transfer molding (RTM) was developed. A corresponding preparation method of novel polymer composite bolted T-joint with internal skeleton and external skin was proposed based on prepreg-RTM co-curing process, and novel T-joints were fabricated. A series of conventional configuration T-joints based on RTM process and T-joints made of 2A12 aluminum alloy were prepared simultaneously. Bending performances were studied on these T-joints experimentally. The results indicate that 9368 epoxy resin and 6808 epoxy resin exhibit good compatibility in rheological and thermophysical properties. The novel T-joints prepared with prepreg-RTM co-curing process show no obvious fiber local winding or resin-rich regions inside, and the interface quality between internal skeleton and external skin is excellent. The main failure modes of the novel T-joint under bending load include separation of skin and skeleton and fracture along the thickness on the base panel; the skeleton carries the main bending load, but there is still load transfer between external skin and internal skeleton through their interface. The internal damages of the novel T-joint are highly consistent with surface damages observed visually, facilitating the detection and timely discovery of damages. The initial stiffness, damage initiation load, and ultimate load of the novel T-joint are 1.65 times, 5.89 times, and 3.45 times that of conventional T-joint, respectively. When considering the influence of density, the relative initial stiffness and relative ultimate load of the novel T-joint are 1.44 times and 2.07 times that of aluminum alloy T-joint, respectively.

The characteristics of excess aerobic granular sludge, related to its structure and chemical composition, limit the efficiency of anaerobic digestion. For this reason, pre-treatment methods and compositions with other organic substrates are used. The aim of the research was to determine the effects of co-digestion of pre-hydrodynamically cavitated aerobic granular sludge with waste fats on the efficiency of methane fermentation under mesophilic and thermophilic conditions. The addition of waste fats improved the C/N ratio and increased its value to 19. The greatest effects were observed in thermophilic anaerobic digestion at 55°C, where a 15% waste fat content in volatile soilds was ensured. This resulted in the production of 1278.2±40.2 mL/gVS biogas and 889.4±29.7 mL/gVS CH4. The CH4 content of the biogas was 69.6±1.3%. The increase in biogas and CH4 yield compared to pure AGS anaerobic digestion was 34.4% and 40.1%, respectively. An increase in the proportion of waste fats in the substrate had no significant effect on the efficiency of methane fermentation.

To accurately predict the matching relationships between the various components and the engine performance in the whole aero-engine environment, this study introduces a two-dimensional throughflow simulation method for the whole aero-engine. This method is based on individual throughflow solvers for the turbo-machinery and the combustor. It establishes a throughflow simulation model for the whole engine by integrating with the compressor-turbine co-operating equations and boundary conditions. The turbo-machinery throughflow solver employs a circumferentially averaged form of the time-dependent Navier-Stokes equations (N-S) as the governing equation. The combustor solver uses the Reynolds Average Navier-Stokes (RANS) method to solve flow and chemical reaction processes by constructing turbulence, combustion, and radiation models. The accuracy of the component solver is validated using Pratt & Whitney’s three-stage axial compressor (P&W3S1) and General Electric’s high-pressure turbine (GE-EEE HPT), and the predicted results are consistent with the experimental data. Finally, the developed throughflow method is applied to simulate the throttling characteristics of the WZ-X turboshaft engine. The results predicted by the throughflow program are consistent with the GasTurb calculations, including the trends of shaft power delivered, specific fuel consumption (SFC), inlet airflow, and total pressure ratio of the compressor. The developed method to perform throughflow simulation of the whole aero-engine eliminates the dependence on a general component map. It can quickly obtain the meridian flow field parameters and overall engine characteristics, which is expected to guide the design and modification of the engine in the future.

Contemporary diagnostic practice in psychiatry relies on the tick-box approach of diagnostic manuals. In light of the obvious limitations of diagnostic systems, narrative formulations, whether in the form of biological, psychodynamic or behavioural cognitive ones, have been proposed. Their approach, which can be caricatured as one of drawing arrows, enriches our clinical tools and practice but in their explicitly theoretical orientation, as well as expanding the field of discovery, they also constrain it. In two previous papers I summarized the resulting limitations, with specific reference to the patient’s experience and proposed further enriching clinical perspicacity by attending to images and parataxis. The specific intent of attending to the patient’s experiences through a process that explicitly eschews narrativization, but allows clinical phenomena to “hang loose”, is to facilitate a process of clinical discovery and healing through practices of co-production between patient and clinician. In the present paper, I expand the range of phenomena that may assist in this co-production through adding attention to dreams to that of images and parataxis. In particular, I turn to the work of the Italian novelist Guido Morselli. Morselli agrues that the self is created through bundling experiences together through acts of “will” in order to establish our personal narrative. In doing so, however, the “will” forbids access to phenomena that do not fit the dominant narrative. Experience is therefore impoverished. Dream consciousness is a type of consciousness not harnessed by the constraints of narrativity. It allows a slipping of the narrative vigilance which bundles phenomena together to create a coherent narrative and thus offers a richer menu of potential experiences, including “bizarre” images that can serve to challenge our usual self-narrative and offer a new perspective on ourselves.

This study investigates the effect of Re addition on the microstructure evolution and oxidation resistance of Co-Cr-Ta-Ti-C alloys. The alloys tested in this study consist of a Co-rich matrix with mixed HCP and FCC phases, together with TaTiC2 and Ti-based carbides. The addition of Re promotes the precipitation and growth of a CoCr-rich σ phase. Differential thermal analysis (DTA) showed that the melting temperatures did not change drastically up to 10 at% Re addition, whereas 15 at% Re addition significantly increased the melting temperature of the alloys. Oxidation tests in air at 1200°C showed that excessive Re content could lead to deterioration in oxidation resistance, mainly due to the accelerated formation of volatile Re oxides.

In recent years, frequent occurrences of low temperature disasters due to global climate change have significantly impacted the normal growth of crops. Research has indicated that inorganic selenium (which is more toxic) can not only enhance the effects of low temperature stress but also increase selenium content on plants; however, there is a scarcity of studies investigating the impacts of organic selenium and nano-selenium on crops. In order to investigate whether organic selenium and nano-selenium can enhance low-temperature tolerance and increase selenium content in pak choi (Brassica chinensis var. pekinensis. cv. ‘Suzhouqing’), different concentrations (0, 5, 10, 20 mg L-1) of exogenous selenium were applied in this experiment to assess their effects on plant growth, nutritional quality and antioxidant properties. RNA-Seq technology was used to sequence the transcriptome of the leaves. Based on the transcriptome data, a Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct a network that associates with physiological traits related to stress resistance. Two highly correlated gene co-expression modules were identified, and within them, nine hub genes associated with endocytosis, antioxidant stress, absorption, transport, and metabolism of selenium were discovered. The results indicated that the beneficial effects on yield and total selenium content under low temperature were attributed to (1) protection of photosynthetic pigments for enhancing photosynthetic capacity by the up-regulation of LHca2, LHcb1, LHca1, LHcb4 in KEGG pathway: photosynthesis-antenna proteins; (2) activation of antioxidant system for efficient ROS homeostasis such as SOD, POD and CAT by the genes such as Superoxide dismutase, Monodehydroascorbate and (3) selenium absorption by endocytosis, seleninum transportation by ABC transporter gene family and selenium metabolism related genes such as Cysteine synthase, Glutaredoxin. These findings provide a foundation for further investigation into the molecular mechanisms underlying the effects of organic selenium and nano-selenium on cyanochloride production at low temperatures.

The inaugural AIDS Program in Brazil was established in São Paulo in 1983, with the Instituto Adolfo Lutz appointed for laboratory assistance. Subsequently, research on HIV infections and HIV/HTLV (HIV/HTLV-1 and HIV/HTLV-2) co-infections were conducted. This narrative review focuses on studies from the Immunology Department (1983–2017) that significantly influenced AIDS diagnosis or provided epidemiological data such as prevalence rates, sex, age, and risk factors. Ten studies, encompassing over 8,000 individuals, are discussed. During 1983–1985, nearly all AIDS cases were attributed to homosexual/bisexual men aged 31 years old. Subsequently, heterosexual men and women emerged as risk groups owing to intravenous drug use (IDU) and/or unprotected sexual intercourse with AIDS patients or multiple partners per year. From 1985 onwards, vertical transmission led to child infections. HIV/HTLV co-infection rates decreased over time, initially associated with male IDU, and in the 2010s, with females, IDU, and individuals aged >40 years. Trends in HIV and HIV/HTLV co-infections among younger men and women (<30 years of age) were observed from 2015–2017. The changing characteristics and risk groups for HIV and HIV/HTLV co-infections over the years underscore the necessity for ongoing public policies to prevent retrovirus transmission, particularly among adolescents and young adults.

This paper explores the challenges of providing services of general interest in rural areas of Germany and proposes co-created mobility software solutions to address the specific needs of these regions. The research is part of the Smarte.Land.Regionen project, which aims to improve digital public services at the district level. Focusing on rural mobility, the paper introduces the concept of ride-sharing benches as a potential solution. Through workshops, surveys and market research, the study identifies barriers to the adoption of ride-sharing benches and investigates factors contributing to their success. The proposed software solution emphasizes user-centered development, geographic location of benches and prioritized matching of passengers. Findings highlight safety concerns, lack of reliability, and the importance of getting people who are theoretically interested in solutions to actively participate in them. The paper emphasizes the importance of collaborative development with county stakeholders. Future research should focus on sustaining and scaling digital solutions, measuring their impact on rural mobility, and ensuring their transferability to other regions. The ultimate goal is to contribute to inclusive and sustainable rural development by improving access to digital public services and promoting the adoption of tailored mobility solutions.

The genus Vigna is penta-tropical having more than 200 species with many desirable economically important traits. The aim of study was to validate the in-silico polymorphism of whole genome sequence developed mungbean specific SSR markers and their transferability among the different Vigna species. The present study utilized a set of 200 SSR markers developed from whole genome sequence of mungbean and validated using a diversity panel of 25 accessions which belongs to 13 Vigna species. Out of 200 SSR markers, 130 markers (65%) were polymorphic across the various Vigna species and the number of alleles amplified varied between 7 to24. The SSR markers showed more than 90 percent transferability across the different Vigna species accessions. Based on allelic data, the 25 Vigna accessions grouped into three clusters based upon the unweighted pair group method with arithmetic mean (UPGMA) phylogenetic tree. The principal co-ordinates analysis (PCA) biplot graph and UPGMA based neighbor joining clustering diagram showed similar pattern of Vigna accessions distribution. The population structure assessment has grouped the cultivated and wild species accessions into two sub-population. The estimated marker parameters such as polymorphic information content (0.09 – 0.84), marker index (0.091 – 3.342) and effective multiplex ratio (1.0 – 4.0) suggested their adequacy in several genetic studies such as parental selection, hybrid testing, genetic mapping and marker aided breeding programmes for genetic enhancement of species belonging to the Vigna genus.

In the end, this paper analyzed the economy from the investment in plant construction, raw material collection, product production and product sales. The resulting products include pyrolytic carbon, pyrolytic oil, pyrolysis gas. The results showed that when the treatment scale was 1 ton h−1, the pyrolysis plants with three loading modes will always be in a state of loss during the 20-year running time. When the processing scale was 9 ton h−1, the pyrolysis plant can be profitable in the first year. In the sensitivity analysis, the influence of three pyrolysis products on the total return on investment was explored. The results showed that the price change of pyrolysis oil has the greatest influence on the total return on investment (Total ROI) under the three loading methods, The price change of pyrolysis gas has minimal impact on the Total ROI. This study provides basic data and basis for the commercialization of coal-biomass co-pyrolysis technology and investment in plant construction.

The Density Wave Theory, which aims to explain the formation of spiral arms in disc galaxies, has been subject to discussion. According to this theory, at the co-rotation circle, the rotational velocity of the density waves or spiral arms matches that of the galactic matter. Consequently, this alignment should impede the formation of new stars within this co-rotation circle, leading to the presence of dark or dim gaps within the spiral arms. Additionally, the theory predicts the occurrence of the front side trail effect in the inner region of the co-rotation circle and the rear side trail effect in the outer region. However, despite these expectations, no such spiral patterns have been observed, prompting the need for revision or reconsideration of the Density Wave Theory.

The requirement for integration of power plants due to the cyclical rise in electrical energy consumption is due to fluctuating load demand with the current grid systems. This integration necessitates effective allocating loads to the power plants for a minimum grid-tied transmission line cost while meeting network constraints. In this paper, we formulate an optimisation problem of minimising the total operational cost of all committed plants transmitted to the grid while meeting network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed Particle Swarm Optimization (PSO) method resolve the optimisation problem using piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction with connected load of 850MW, 1263MW and 2630MW power demand are contrasted with previous PSO application cost values, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), (73.86%, 89.10) respectively, and significant environmental-benefit. The proposed co-optimisation approach can enhance a significant self-consumption ratio compared to the baseline method.

The growth of two pairs of co-cultures (Escherichia coli/Geotrichum candidum, and Staphylococcus aureus/Geotrichum candidum) with a starter culture of lactic acid bacteria was studied in milk at temperatures related to artisanal cheesemaking ripening. For an inoculum of approximately 106 CFU/mL, LAB not only induced an early stationary phase of E. coli (two strains BR and PS2) and S. aureus (strains 2064 and 14733) but affected their death phase. G. candidum was found to be the subject of interactions with LAB within a given temperature range only partially. To develop a tertiary model for the growth curves of the populations, a one-step approach was used, combining two types of primary models (Huang and Gimenez and Dalgaard) with secondary square root models for growth rate and lag time. Furthermore, the reparametrized Gompertz-inspired function with the Bigelow secondary model was used to describe the death phase of the E. coli and S. aureus strains. The prediction ability of the growth of the H-GD tertiary model for co-cultures was cross-validated within the strains and data sets in milk and milk medium with 1 % NaCl.

Abstract: An attractive application of crude glycerol is in the generation of biomethane by means of anaerobic co-digestion, involving the decomposition of the organic matter in two or more substrates by bacteria and archaea, in the absence of oxygen. Thus, the objective of this work was to evaluate the potential of neural networks and fuzzy logic to predict the production of biomethane from the anaerobic co-digestion of glycerol and/or sugarcane molasses. Firstly, a reactor model was implemented using Scilab, with Monod kinetics involving two substrates and an intermediate (M2SI model), to generate a database for subsequent fitting and evaluation of neural and fuzzy models. The neural network package of Matlab was used. Fuzzy modeling was applied using the Takagi-Sugeno approach available in the ANFIS package of Matlab. The biomethane production results simulated by M2SI were used in neural network modeling, firstly employing a “generic” network applicable to all 8 scenarios. A very good fit was obtained (R²>0.99). Excellent performance was also observed for specific artificial neural networks (one for each condition). The parameters of the M2SI model for the 8 different conditions were also mapped using a neural network, as a function of the organic material composition. A fit with R²>0.99 was obtained using 25 neurons. In the case of the fuzzy logic, RMSE of 18.88 mL of methane was obtained with 216 rules, which was a value lower than 0.5% of the order of magnitude of the accumulated methane. It could be concluded from the results that fuzzy logic and artificial neural networks offer excellent ability to predict methane production, as well as to parameterize the M2SI kinetic model (using neural networks).

Using the onset of the COVID 19 pandemic, this chapter examines the cryptocurrencies as safe haven investment for stocks of our time varying realization as to the economic chock centralized by the growing pandemic. Using daily data of COVID 19 measures and daily prices for 4 cryptocurrencies and 4 stocks assets for the whole year 2020, we apply both the VAR-DCC-GARCH and Wavelet Coherency models. New evidence of our chapter find that the Bitcoin and Etherum are highly correlated in the short and long horizon with the selected stocks. However for the case of the Litecoin and the XRP are correlated negatively with the stocks in the whole COVID19 period. We find evidence that, Bitcoin is strong safe haven asset for all the selected stocks during the COVID19 era, while the Litecoin is weak safe haven investment for all the stocks and the XRP is with lowest potential of safe haven investment for all the studied stocks. Within the study we are providing a diversification of hedging for the investors and policy makers suggesting that the cryptocurrencies acted as safe haven investment similar to the precious metals during historic crisis and as fiat money for any economic shocks might occurred. The abstract should summarize the contents of the paper in short terms, i.e. 150-250 words.

An essential metric for determining the efficacy of tuberculosis (TB) control programs is the evaluation of TB treatment outcomes. Assessing treatment outcomes is fundamental to facilitating the End TB Strategy set target. Clinic records from 457 patients with DR-TB were examined for data collection while 101 patients were followed up prospectively. Data were analyzed using Stata version 17.0. The odds ratio and 95% confidence interval were calculated to check the association between variables. P ≤ 0.05 was con-sidered statistically significant. Of the 427 participants, 65.8% had successful treatment whilst 34.2% had unsuccessful TB treatment. A total of 61.2% and 39% of the HIV-positive and HIV-negative participants had a successful TB treatment whilst 66% and 34% of both HIV-negative and positive participants had unsuccessful TB treatment. From 101 patients that were followed up, smokers had longer treatment outcomes compared to non-smokers. In the study with HIV/TB co-infection, men predominated. HIV and tuberculosis co-infection made therapy difficult with unfavorable effects on TB management. The treatment success rate (65.8%) is lower than the WHO threshold standard with a high proportion of patients lost to follow-up. The co-infection of tuberculosis and HIV resulted in undesirable treatment outcomes. Strengthening TB surveillance and control is recommended.

Determining genetic and non-genetic sources of variation in a breed is vital for the formulation of strategies for its conservation and improvement. The present study was aimed at estimating the (co)variance components and genetic parameters of Mecheri sheep by fitting six different animal models in the restricted maximum likelihood method, with a preliminary investigation on the performance of animals for non-genetic sources of variation. A total of 2616 lambs were studied, and varying levels of significance were found for the effect of period, season, parity of dam and birth type on different body weight traits. Direct heritability estimates derived from the best animal model for body weight at birth, 3 months, 6 months, 9 months and 12 months were 0.21, 0.24, 0.10, 0.15 and 0.09, respectively, and maternal heritabilities of the corresponding traits were 0.12, 0.05, 0.04, 0.04 and 0.04, respectively. The genetic correlations between body weight traits were all positive and moderate to strong except for birth weight with the other body weight traits. The significance of non-genetic factors studied in this work demanded a correction to improve the accuracy of the direct selection of lambs for body weight traits. The estimated genetic parameters identified the weaning weight as a selection criterion for the improvement in body weight of Mecheri lambs at different ages. Inbred individuals accounted for approximately 13% of the total population in the Mecheri sheep population studied. There were 877 founders in the population, and the actual effective size of the population was 128.48. The population's mean generation interval was 3.26. The mean inbreeding values ranged from 0.005 to 0.010 across generations. The population's average relatedness ranged from 0.001 to 0.014 across generations. Individual inbreeding was found to be 0.45 per cent for the entire population and 3.4 per cent for the inbred population.

This study presents three forms of interdisciplinary expertise in the healthcare design context to approach a particular multifaceted problem around the current healthcare for older adult patients with chronic low-back pain (LBP). Using an interdisciplinary co-design framework, first, our design approach performs the role of an initiator to define the problem by exploring the current context of healthcare. Second, it facilitates the experiences of experts and patients to reach the roots of the problem by functioning as a mediator. Third, our approach fulfills the primary role of healthcare design in producing new meanings considering the principles of patient-centeredness. These roles significantly contributed to the design of healthcare innovations. Our framework transformed the distributed disciplinary knowledge developed while tackling the multifaceted problem into new forms of expertise for collaboration in healthcare innovation.

The article is aimed at a brief comparison and analysis of the results of queries to IEEE Xplore and the leading abstract databases Scopus and Web of Science to identify research trends. Some errors in the Author Keywords in Web of Science have been revealed. Therefore, a more detailed analysis was conducted by comparing different types of key terms for IEEE Xplore and Scopus platforms only. I used IEEE Access journal metadata as indexed on both platforms. The sample match for IEEE Xplore and Scopus was achieved by comparing DOI. The IEEE Xplore metadata contains more types of key terms, which provides an advantage in analyzing research trends. Using NSPEC Controlled Terms from expert-compiled vocabulary provides a more stable data, which gives an advantage when considering the change of terms over time. Apriori, an algorithm for finding association rules, has been used to compare co-occurrence of terms for a more detailed description of sample subjects on both platforms. VOSviewer was used to analyze trends in scientific research based on IEEE Xplore data. The 2011-2021 ten-year period was divided into two sub-intervals for comparing the occurrence of Author Keywords, IEEE Terms, and NSPEC Controlled Terms. Using the IEEE conference proceedings bibliometric data, I illustrated the importance of context in estimating the rate of growth of publishing activity on a topic of interest.

Spatial planning holds a key role in preventing or mitigating the impacts of climate change on both cities and rural areas, taking a forward-thinking and holistic approach to urban and regional development. As such, spatial planning deals with challenges occurring at different scales and across sectors. The international literature points out the need for horizontal and vertical cooperation to tackle climate change impacts. While there is abundant knowledge regarding the challenges related to climate change at different spatial levels, procedural integration into planning frameworks and practice is currently under-researched. This paper presents a novel theoretical framework that integrates various steps towards a holistic, integrative and adaptive climate proofing process. An iterative process was used for conceptual development, based on literature review followed by external feedback meetings and two workshops with the core team of planning experts responsible for exchange across federal states. By specifically addressing the challenges relating to cross-regional and cross-sectoral planning, this novel framework attempts to (i) facilitate a hierarchy of measures, (ii) maximise co-benefits for various adaptation purposes and climate change mitigation and (iii) foster the long-term institutionalisation of integrative processes across sectors, planning areas and policy levels.

Most smallscale farmers still use the traditional way of agricultural crop farming, and relay mostly use of chemical fertilizers (CF). Recently CF have become expensive and could in some way have a negative impact on soil quality after long term application. However, co-application of biogas slurry (BGS) with CF could help reduce farming costs while improving dry matter yields, grain yields, primary macronutrient uptake of (Nitrogen) N, (Phosphorus) P, (Potasium) K, (Calcium) Ca and (Magnesium) Mg and soil concentration of pH, (organic carbon) OC, N, P, K, Ca and Mg after crop harvest. The study was a field experiment conducted in 2016-2017 and 2017-2018 growing seasons. The field experiment was arranged in a randomized complete block design with four replicates. The treatments were based on percentages of recommended N rates of 120 kg N ha-1 for maize production. The BGS/CF treatments were (i) 0/0, (ii) 0/120, (iii) 24/96, (iv) 48/72, (v) 72/48, (vi) 96/24, (vii) 120/0 kg N ha-1. The spreading of the combination of BGS/CF treatments was performed by hand and incorporated into the top soil (0-10 cm) in each experimental plots. BGS/CF (48/72) treatment resulted into higher dry matter yield in 2016-2017, which was higher than all other treatment combinations, while in the 2017-2018 season, treatment of (0/120) resulted into higher dry matter than all other treatment combinations. The 48/72 and 0/120 treatments resulted into similar grain yield in 2016-2017 season which were higher than all other treatments. Treatments of 48/72, 72/48 and 120/0 had higher N, P, K, Ca and Mg uptake than 0/0, 0/120, 24/96 and 96/24 treatments in both seasons. Soil pH, total N, K, and Mg were high from the treatment of (120/0) than all other treatments in 2016-2017 while in the 2017-2018 season, treatment of (48/72) had higher OC, P and K after maize harvest. The findings of his study show that co-application of BGS/CF at 48/72 and 72/48 have maize yield benefits compared to the two resources, BGS/CF (120/0) and BGS/CF (0/120), applied separately in soil especially in the arid and semi-arid regions.

Abstract. Background: This paper investigates the propagation of behaviorally transmitted traits with negative effect on host fitness. Methods: We analyze equilibrium between genetically transmitted and behaviorally transmitted competing propagators and consider whether a behavioral propagator is linked to reproduction (e.g. vertical culture transmission), or not. We employ combined genetic and behavior-induced fitness components for hosts, while behavioral propagators have replication factors to distinguish from what’s good for the host (fitness). Results: A trait which spreads faster than its marginal host fitness contribution reduces population will establish itself. The often transient nature of laterally transmitted traits may be a defense against accumulation of deleterious traits. Laterally transmitted traits with high spreading rate often do not equalize with genetic traits, spreading outside natural selection of the hosts. Vertical transmission reduces replication rate and allows group selection against deleterious behaviorally transmitted traits. Competing mutually exclusive propagators contribute to inequality and altruism, but compete through adverse fitness since exclusivity assumes low conversion. Conclusion: Behaviorally transmitted traits, in some cases a tremendous advantage, may also be a significant problem in the development of societies.

Early education is critical for improving energy efficiency. The purpose of this study is to explore the feasibility of Interactive Cross-Border Classes to increase awareness of energy efficiency among middle school students. We designed and tested an Interactive Cross-Border class between Chilean and Peruvian 8th-grade classes. The classes were synchronously connected and all students answered open-ended questions on an online platform. Some of the questions were designed to check conceptual understanding while others asked for suggestions of how to develop their economies while keeping CO₂ air concentration at acceptable levels. In real-time, the teacher reviewed the students’ written answers and the concept maps that were automatically generated based on their responses. Students peer-reviewed their classmates’ suggestions. This is part of an Asia-Pacific Economic Cooperation (APEC) STEM Education project on Energy Efficiency using APEC databases. We found high levels of student engagement, where students discussed not only the cross-cutting nature of energy, but also its relation to socioeconomic development and CO₂ emissions, and the need to work together to improve energy efficiency. In conclusion, Interactive Cross-Border classes are a feasible educational alternative, with potential as a scalable public policy strategy for improving awareness of energy efficiency among the population.

Transporter associated with antigen processing 1 (TAP1) gene codes for a transporter protein, which is responsible for tumor antigen presentation in the MHC I or HLA complex. A defect in the gene results in an inadequate tumor tracking. TAP1 may also influence multi drug resistance, which is an extreme threat in providing treatment by drugs which are chemotherapeutic. The gene of TAP1 was analyzed bioinformatically. It gave us prognostic data as a confirmation of whether it should be used as a biomarker. The expression level and pattern analysis were conducted using ONCOMINE, GENT2 and GEPIA2 online platforms. Samples with different clinical outcomes were investigated for expression and promoter methylation analysis was done in cancer vs normal tissues using UALCAN. The copy number alteration and mutation frequency and expression in different cancer studies were analyzed using cBioPortal. The PrognoScan and KM plotter survival analysis of significant data (p-value<0.05) was representing graphically. Pathway and Gene ontology analysis of gene correlated to TAP1 gene was presented using bar charts. After arranging the data in a single panel and correlating expression to prognosis, understanding mutational and alterations and comparing pathways, TAP1 may be a potential novel target to evade a threat against chemotherapy and the study gives new aspects to consider for immunotherapy in human breast, lung, liver and ovarian cancer.

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-seq is one of the most commonly used techniques to perform transcriptional experiments. Computational analysis on such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-seq data-sets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsig and identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50\% of the genes identified as influential have some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes have crucial roles or participate in processes associated with response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

This study aimed to assess the ecotoxicological effects of the interaction of fullerene (C₆₀) and benzo[a]pyrene (B[a]P) on the marine mussel, Mytilus galloprovincialis. The uptake of nC₆₀, B[a]P and mixtures of nC₆₀ and B[a]P into tissues was confirmed by GC-MS, LC-HRMS and ICP-MS. Biomarkers of DNA damage as well as proteomics analysis were applied to unravel the toxic effect of B[a]P and C₆₀. Antagonistic responses were observed at the genotoxic and proteomic level. Differentially expressed proteins (DEPs) were only identified in the B[a]P single exposure and the B[a]P mixture exposure groups containing 1 mg/L of C₆₀, the majority of which were down-regulated (~52%). No DEPs were identified at any of the concentrations of nC₆₀ (p < 0.05, 1% FDR). Using DEPs identified at a threshold of (p < 0.05; B[a]P and B[a]P mixture with nC₆₀), gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis indicated that these proteins were enriched with a broad spectrum of biological processes and pathways, including those broadly associated with protein processing, cellular processes and environmental information processing. Among those significantly enriched pathways, the ribosome was consistently the top enriched term irrespective of treatment or concentration and plays an important role as the site of biological protein synthesis and translation. Our results demonstrate the complex multi-modal response to environmental stressors in M. galloprovincialis.

Contemporary literature dealing with the governance and exploitation of common-pool natural resources was initiated by Elinor Ostrom in 1990, and has been growing fast ever since. On the contrary, within the same research stream, the study of the presence and economic role of common resources in entrepreneurial organizations is, to date, under-researched. This work endeavors to fill some gaps in this research perspective by: (i) spelling out a new-institutionalist framework for the analysis of the accumulation and governance of common capital resources within organizational boundaries; (ii) considering co-operative enterprises as the organizational form that, on the basis of historical record, and of behavioral and institutional characteristics, demonstrated to be most compatible with a substantial role for common and non-divided asset-ownership and with its governance thereof; (iii) evidencing and explaining the strong connection between cooperative longevity and the presence of non-divided asset ownership. The economic forces influencing the optimal level of self-financed common capital resources in co-operatives are enquired. Conclusions to the paper evidence the main reasons why the new approach can better explain than preceding ones the economic sustainability and longevity of cooperative enterprises.

Classifiers face a myriad of challenges in today’s data-driven world, ranging from overfitting and high computational costs to low accuracy, imbalanced training datasets, and the notorious black box effect. Furthermore, many traditional classifiers struggle with the robust handling of noisy and missing feature values. In response to these hurdles, we present classification methods that leverage the power of feature partitioning learning and outranking measures. Our classification algorithms offer an innovative approach, eliminating the need for prior domain knowledge by automatically discerning feature intervals directly from the data. These intervals capture essential patterns and characteristics within the dataset, empowering our classifiers with newfound adaptability and insight. In addition, we employ outranking measures to mitigate the influence of noise and uncertainty in the data. Through pairwise comparisons of alternatives on each feature, we enhance the robustness and reliability of our classification outcomes. The developed classifiers are empirically evaluated on several data sets from UCI repository and are compared with well-known classifiers including k Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), Naive Bayesian (NB) and Nearest Centroid (NC). The experiments result demonstrate that the classifiers based on feature interval learning and outranking approaches are robust to imbalanced data and to irrelevant features and achieve comparably and even better performances than the well-known classifiers in some cases. Moreover, our proposed classifiers produce more explainable models whilst preserving high predictive performance levels.

Globalization, knowledge economy, information technology advancements, and the rise of learning societies contribute to the concept of a learning city. The study examines the economic indicators of Yazd city, Iran, in the context of learning city development. A descriptive-analytical approach with a survey method and cluster sampling was employed. Data analysis involved Kolmogorov-Smirnov normality test, single-sample T-test, simple linear regression, and Kendall's W test. The normality test confirmed data normality. The T-test indicated that experts perceived the current state of most variables in Yazd as undesirable. Regression analysis revealed a significant positive association between the economic index and independent variables. Kendall's W test prioritized areas based on economic indicators, with the top 7 and bottom 5 areas identified for focused interventions. The study emphasizes the need to prioritize areas within Yazd based on their economic indicators. This prioritization informs strategies for enhancing social and legal aspects of Yazd's transformation into a learning city. Further research should explore effective methods to cultivate a full-fledged learning environment in the city.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

Online learning is gaining massive popularity with time. The e-learning platforms operate differently from traditional educational institutions and hence need different strategy for course recommendations. This survey aims to cover the major emerging research areas in e-learning recommender systems. Our study covers different areas of research including graph-based methodologies, ITS, query optimization, content-based, and collaborative filtering, big data, and association rules mining. This survey aimed to explore all major emerging directions of recommender systems in education. This study analyzed existing literature in all of the areas mentioned before and performed objective-based analysis. A brief performance analysis was also done for the researches where the values were comparable. Limitations of existing researches were also identified and studied to shed some light on future directions.

Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for traditional deep learning problems: resulting in faster training and improved performance. Despite the increasing interest in GNNs and their use cases, there is little research on their transferability. This research demonstrates that transfer learning is effective with GNNs, and describes how source tasks and the choice of GNN impact the ability to learn generalisable knowledge. We perform experiments using real-world and synthetic data within the contexts of node classification and graph classification. To this end, we also provide a general methodology for transfer learning experimentation and present a novel algorithm for generating synthetic graph classification tasks. We compare the performance of GCN, GraphSAGE and GIN across both the synthetic and real-world datasets. Our results demonstrate empirically that GNNs with inductive operations yield statistically significantly improved transfer. Further we show that similarity in community structure between source and target tasks support statistically significant improvements in transfer over and above the use of only the node attributes.

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

During the COVID-19 outbreak, most university courses have been offered on online platforms. A sudden shift from face-to-face classroom learning to online formats could influence the learning effectiveness of classes. This study aims to investigate differences in the learning effectiveness of online and face-to-face lecture courses. It also explores factors that impact the effectiveness of online instruction. These factors include interactions among learners, interactions between learners and the instructor, the quality of online platforms, learners’ ability to use devices and follow instructions, and learners’ situational challenges. The study participants were 261 university students at King Mongkut’s University of Technology Thonburi in Bangkok, Thailand. All participants were enrolled in at least one lecture course, such as history, humans and the environment, the environment and development, or general philosophy, during the 2019 academic year. A questionnaire was distributed to participants after they completed these courses in May 2020. Paired simple t-test analyses were used to compare the effectiveness of online and face-to-face classes, and a multiple regression analysis was used to identify factors that impact the learning effectiveness of online classes. The results show that online classes are less effective than face-to-face courses. The multiple regression analysis also revealed that the effectiveness of online learning was significantly impacted by learners’ ability to interact with classmates during class, their ability to interact with instructors after the class, the quality of online platforms, and disturbances or distractions in learners’ environments.

We study how abstract representations emerge in a Deep Belief Network (DBN) trained on benchmark datasets. Our analysis targets the principles of learning in the early stages of information processing, starting from the “primordial soup" of the under-sampling regime. As the data is processed by deeper and deeper layers, features are detected and removed, transferring more and more “context-invariant” information to deeper layers. We show that the representation approaches an universal model – the Hierarchical Feature Model (HFM) – determined by the principle of maximal relevance. Relevance quantifies the uncertainty on the model of the data, thus suggesting that “meaning" – i.e. syntactic information – is that part of the data which is not yet captured by a model. Our analysis shows that shallow layers are well described by pairwise Ising models, which provide a representation of the data in terms of generic, low order features. We also show that plasticity increases with depth, in a similar way as it does in the brain. These findings suggest that DBNs are capable of extracting a hierarchy of features from the data which is consistent with the principle of maximal relevance.

Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial Intelligence can aid in assessing the quality of the fruit using images. This paper presents a general machine-learning model for assessing fruit quality using deep image features. The model leverages the learning capabilities of the recent successful networks for image classification called Vision Transformers (ViT). The ViT model is built and trained with a combination of various fruit datasets and learned to distinguish between good and rotten fruit images. The general model demonstrated impressive results in accurately identifying the quality of various fruits such as Apples (with a 99.50% accuracy), Cucumbers (99%), Grapes (100%), Kakis (99.50%), Oranges (99.50%), Papayas (98%), Peaches (98%), Tomatoes (99.50%), and Watermelons (98%). However, it showed slightly lower performance in identifying Guavas (97%), Lemons (97%), Limes (97.50%), mangoes (97.50%), Pears (97%), and Pomegranates (97%).

Modelling both long and short-term user interests from historical data is crucial for accurate recommendations. However, unifying these metrics across multiple application domains can be challenging, and existing approaches often rely on complex, intertwined models which can be difficult to interpret. To address this issue, we propose a lightweight, plug-and-play interest enhancement module that fuses interest vectors from two independent models. After analyzing the dataset, we identify deviations in the recommendation performance of long and short-term interest models. To compensate for these differences, we use feature enhancement and loss correction during training. In the fusion process, we explicitly split long-term interest features with longer duration into multiple local features. We then use a shared attention mechanism to fuse multiple local features with short-term interest features to obtain interaction features. To correct for bias between models, we introduce a comparison learning task that monitors the similarity between local features, short-term features, and interaction features. This adaptively reduces the distance between similar features. Our proposed module combines and compares multiple independent long-term and short-term interest models on multiple domain datasets. As a result, it not only accelerates the convergence of the models but also achieves outstanding performance in challenging recommendation scenarios.

Over the past decade, research in the field of Deep Learning has brought about novel improvements in image generation and feature learning; one such example being a Generative Adversarial Network. However, these improvements have been coupled with an increasing demand on mathematical literacy and previous knowledge in the field. Therefore, in this literature review, I seek to introduce Generative Adversarial Networks (GANs) to a broader audience by explaining their background and intuition at a more foundational level. I begin by discussing the mathematical background of this architecture, specifically topics in linear algebra and probability theory. I then proceed to introduce GANs in a more theoretical framework, along with some of the literature on GANs, including their architectural improvements and image-generation capabilities. Finally, I cover state-of-the-art image generation through style-based methods, as well as their implications on society.

This study evaluated the using of machine vision in combination with deep learning to identify weeds in real-time for wheat production system. PMAS-Arid Agriculture university research farm were selected for collection of images (6000 total images) of weeds and wheat crops under different weather condition. During growing season, the database was constructed to identify the weeds. For this study two framework were used TensorFlow and PyTorch under CNNs and Deep learning. Deep learning perfromed better with in PyTourch value as compared to another model in Tensorflow. comparing with other networks such as YOLOv4, we concluded that our network reaches a better result between speed and accuracy. Specifically, the maximum precision of weed and wheat plants were 0.89 and 0.91 respectively with 9.43 ms and 12.38 ms inference time per image (640 × 640) NVIDIA RTX2070 GPU.

Tremendous progress has been witnessed in artificial intelligence within the domain of neural network backed deep learning systems and its applications. As we approach the post Moore’s Law era, the limit of semiconductor fabrication technology along with a rapid increase in data generation rates have lead to an impending growing challenge of tackling newer and more modern machine learning problems. In parallel, quantum computing has exhibited rapid development in recent years. Due to the potential of a quantum speedup, quantum based learning applications have become an area of significant interest, in hopes that we can leverage quantum systems to solve classical problems. In this work, we propose a quantum deep learning architecture; we demonstrate our quantum neural network architecture on tasks ranging from binary and multi-class classification to generative modelling. Powered by a modified quantum differentiation function along with a hybrid quantum-classic design, our architecture encodes the data with a reduced number of qubits and generates a quantum circuit, loading it onto a quantum platform where the model learns the optimal states iteratively. We conduct intensive experiments on both the local computing environment and IBM-Q quantum platform. The evaluation results demonstrate that our architecture is able to outperform Tensorflow-Quantum by up to 12.51% and 11.71% for a comparable classic deep neural network on the task of classification trained with the same network settings. Furthermore, our GAN architecture runs the discriminator and the generator purely on quantum hardware and utilizes the swap test on qubits to calculate the values of loss functions. In comparing our quantum GAN, we note our architecture is able to achieve similar performance with 98.5% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms other quantum based GANs in the literature for up to 125.0% in terms of similarity between generated distributions and original data sets.

The conventional machine learning (ML) algorithms are continuously advancing and evolving at a fast-paced by introducing the novel learning algorithms. ML models are continually improving using hybridization and ensemble techniques to empower computation, functionality, robustness, and accuracy aspects of modeling. Currently, numerous hybrid and ensemble ML models have been introduced. However, they have not been surveyed in a comprehensive manner. This paper presents the state of the art of novel ML models and their performance and application domains through a novel taxonomy.

This paper presents a new method : HIVEC to learn latent vector representations of graphs in a manner that captures the semantic dependencies of sub-structures. The representations can then be used in machine learning algorithms for tasks such as graph classification, clustering etcetera. The method proposed is unsupervised and uses the information of co-occurrence of sub-structures. It introduces a notion of hierarchical embeddings that allows us to avoid repetitive learning of sub-structures for every new graph. As an alternative to deep learning methods, the edit distance similarity between sub-structures is also used to learn vector representations. We compare the performance of these methods against previous work.

Undergraduate learning is a multifaceted concept that many researchers have investigated to promote student engagement, academic performance, etc. Exploring factors promoting learning among undergraduates in FYW programs, which are required for undergraduates might potentially contribute to sustainable, cross-disciplinary undergraduate education. This study used the 10-year longitudinal grade and survey data at San José State University (SJSU) and the data was collected from Academic Years (AYs) 2015 to 2020, and five student surveys constituted the sample size of 18,101 students. In 2016, SJSU changed the remedial writing course to college writing courses. Since then, students need to complete an online module course before placing themselves into a one-semester or two-semester writing course. The previous study showed that students with higher writing comfort and confidence tend to place themselves into a semester-long class. Regression analysis was applied to examine the relationships among factors promoting learning among those students. University admitted students from various diversity and backgrounds, the results of this study suggested that what students chose and persisted in were more important in advancing their academic success rather than prior learning experience or psychological factors before the class.

The COVID-19 pandemic has led to the growth of hybrid and online learning environments and the trend to introduce more technology into the classroom. One such change would be the use of smart synchronous hybrid learning environments (SSHLE), which are settings with both in-person and online students concurrently, and in which technology plays a key role in sensing, analyzing, and reacting throughout the teaching and learning process. These changing environments and the incorporation of new technologies can place a greater orchestration load on participants and a reduction in teacher agency. In this context, the aim of this paper is to analyse the orchestration load and teacher agency in different SSHLEs. The NASA-TLX model was used to measure the orchestration load in several scenarios. Questionnaires and interviews were used to measure teacher agency. The results obtained indicate that the orchestration load of the teacher tends to be high (between 60 and 70 points out of 100 of the NASA-TLX workload), especially when they lack experience in synchronous hybrid learning environments, and the orchestration load of the students tends to have average values (between 50 and 60) in the SSHLEs analysed. Meanwhile, the teacher agency does not appear to be altered but shows potential for improvement.

A common challenge in real-world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset. Domain Generalization approaches reach their limits when domain shifts become too large, making them occasionally unsuitable as well. In many (technical) domains, however, it is only the defect/ worn/ reject classes that are insufficiently represented, while the non-defect class is often available from the beginning. The proposed classification approach addresses such conditions and is based on a CNN encoder. Following a contrastive learning approach, it is trained with a modified triplet loss function using two datasets: Besides the non-defective target domain class (= 1st dataset), a state-of-the-art labeled source domain dataset that contains highly related classes (e.g., a related manufacturing error or wear defect) but originates from a (highly) different domain (e.g., different product, material, or appearance) (= 2nd dataset) is utilized. The approach learns the classification features from the source domain dataset while at the same time learning the differences between the source and the target domain in a single training step, aiming to transfer the relevant features to the target domain. The classifier becomes sensitive to the classification features and – by architecture – robust against the highly domain-specific context. The approach is benchmarked in a technical and a non-technical domain and shows convincing classification results. In particular, it is shown that the domain generalization capabilities and classification results are improved by the proposed architecture, allowing for larger domain shifts between source and target domains.

Deep learning has achieved lots of successes in many fields, but when trainable sample are extremely limited, deep learning often under or overfitting to few samples. Meta-learning was proposed to solve difficulties in few-shot learning and fast adaptive areas. Meta-learner learns to remember some common knowledge by training on large scale tasks sampled from a certain data distribution to equip generalization when facing unseen new tasks. Due to the limitation of samples, most approaches only use shallow neural network to avoid overfitting and reduce the difficulty of training process, that causes the waste of many extra information when adapting to unseen tasks. Euclidean space-based gradient descent also make meta-learner's update inaccurate. These issues cause many meta-learning model hard to extract feature from samples and update network parameters. In this paper, we propose a novel method by using multi-stage joint training approach to post the bottleneck during adapting process. To accelerate adapt procedure, we also constraint network to Stiefel manifold, thus meta-learner could perform more stable gradient descent in limited steps. Experiment on mini-ImageNet shows that our method reaches better accuracy under 5-way 1-shot and 5-way 5-shot conditions.

We introduce a modelling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e. the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression.

The revolutionary idea of complementary intelligence involves a unique strategy in which artificial intelligence (AI) works with human cognitive abilities embodied by natural intelligence (NI), creating a harmonious partnership instead of engaging in a competitive relationship. This innovative concept can potentially revolutionize how we approach scientific research and discovery. This cutting-edge process capitalizes on pooling the vast information from various distinct and autonomous sources to generate forecasts that frequently outshine human perspectives by significant margins, leading to groundbreaking advancements in scientific development based on hybrid intelligence. Multilayer networks extend traditional network theory to adjacent layers linked through copula nodes. Artificial intelligence, with its retrieval capacity to collect and interpret big data, sharply increases the number of nodes and their linking interpretation. This process, amplified by multilayer networks, dramatically improves the scalability of the networked ecosystem. Multilayer networks can represent NI and AI in complementary hyperplanes, connecting them with copula nodes and edge properties. Thus, it is possible to measure the interaction of NI and AI, assessing their scalable value co-creation properties. This study examines the interaction of NI and AI with an innovative and multidisciplinary approach. A theoretical framework of these related concepts precedes practical insights for possible applications in future research.

Volunteering has many social, emotional and health benefits and could be an option for people with younger/early onset dementia to engage in stage/age-appropriate, personally meaningful activity within their local community. Audio data of people with young onset dementia discussing their volunteering experiences were taken from the Dementia Diaries and co-analysed. This study explored how people with dementia volunteer in the UK, the benefits and barriers, and how we can improve access to volunteering opportunities. The findings suggest that stigma is the overarching concept (or net) which influences people’s experiences of volunteering with dementia. Within the stigma net, three themes were developed that employed a lifespan approach to challenging said stigma: 1) Valuing the past, 2) Valuing the present and 3) Valuing the future, each with additional subthemes. This research contributes to the limited pool of literature that highlights the clear benefits of volunteering for people with young onset dementia, while also considering how dementia stigma can create practical and institutional barriers to accessing volunteering. Guidance is provided for how to deliver meaningful and accessible volunteering opportunities.

A comprehensive investigation was undertaken at Vytautas Magnus University Experimental Station, located at 54°52’50” N latitude and 23°49’41” E longitude on soil (Epieutric Endocalcaric Planosol – PLe-gln-w) since 1999, to understand the impacts of different agrotechnical measures on soil health and crop yield. Two primary factors were assessed. Factor A incorporated practices of straw removal versus straw chopping and spreading, while Factor B evaluated a spectrum of tillage techniques: conventional deep ploughing, shallow ploughing, ploughless tillage, single seedbed discing, and two no-tillage practices, one of which involved cover crops. Findings from this long-term study highlight the significant potential of specific farming systems in enhancing soil organic carbon. It has a positive effect on the release of CO2 emissions from the soil, thus promoting soil resilience and increasing plant productivity. These insights are paramount in devising sustainable agricultural strategies to counter the challenges of climate change on agroecosystems. This research showcases the profound effects of combining residue management and tillage practices, setting a novel standard for sustainable soil management of climatic uncertainties.

Eco-agricultural technology is crucial to alleviating agricultural resource scarcity and environ-mental pressures. However, financial constraint affects its successful promotion. Digital finance has a significant impact on farmers, but existing research lacks an exploration of the impact of digital finance on farmers' adoption of eco-agricultural technology. This study focuses on Rice–Crayfish Co-cultivation Technology as an example. It utilizes survey data from 1,063 households in China. An Endogenous Switching Probit model is employed to solve self-selection bias. The results reveal several key findings. First, the average treatment effect on the treated is 51.5%. This indicates that if farmers who use digital finance were to stop using it, the probability of adopting rice–crayfish co-cultivation technology would decrease by 51.5%. Therefore, digital finance is beneficial for farmers in adopting this technology. Second, heterogeneity analysis reveals that digital finance has a greater promoting effect on older farmers, those with lower education levels, and higher proportions of agricultural income. This suggests a greater reliance on digital financial services among vulnerable groups. Third, digital finance promotes farmers' adoption of rice–crayfish co-cultivation technology by alleviating financial constraints, expanding information channels, and increasing social capital accumulation. Overall, the findings offer valuable insights for for-mulating supportive policies.

Marine lipids are recognized for their health promoting features, mainly for being the primary sources of omega-3 fatty acids, and therefore critical for human nutrition in an age when the global supply for these nutrients is experiencing an unprecedent pressure due to an ever-increasing demand. The seafood industry originates a considerable yield of co-products worldwide that, while already explored for other purposes, remain mostly undervalued as sustainable sources of healthy lipids, often being explored for low value oil production. These co-products are especially appealing as lipid sources since, besides the well-known nutritional upside of marine animal fat, particularly rich in omega-3 polyunsaturated fatty acids (PUFAs), they have also interesting bioactive properties, which may garner them further interest, not only as food, but also for other high-end applications. Besides the added value that these co-products may represent as valuable lipid sources, there is also the obvious ecological upside of reducing seafood industry waste. In this sense, repurposing these bioresources will contribute to a more sustainable use of marine animal food, reducing the strain on already heavily depleted seafood stocks. Therefore, untapping the potential of marine animal co-products as valuable lipid sources aligns with both health and environmental goals by guaranteeing additional sources of healthy lipids and promoting more eco-conscious practices.

Functional dependence of the axial zero-field splitting parameter D with respect to a properly chosen geometrical parameter (Dstr) in metal complexes is termed the Magnetostructural D-Correlation. In mononuclear hexacoordinate Ni(II) complexes with the ground electronic term 3B1g (3A2g in the regular octahedron) it proceeds along two intercepting straight lines allowing to predict the sign and magnitude of the D-parameter by knowing the X-ray structure alone; Dstr is constructed from the metal-ligand bond lengths. In hexacoordinate Co(II) complexes it is applicable only in the segment of the compressed bipyramid where the ground electronic term 4B1g is orbitally non-degenerate so that the spin Hamiltonian formalism holds true. The D vs Dstr correlation is strongly non-linear and it is represented by a set of decreasing exponentials. In tetracoordinate Co(II) complexes, on the contrary, the angular distortion from the regular tetrahedron is crucial so that the appropriate structural parameter Dstr is constructed of bond angles. All of these empirical correlations originate in the electronic structure of metal complexes that can be modelled by the generalized crystal-field theory. As the barrier to spin reversal in the single-molecule magnets is proportional to the D-value, for a rational tuning and/or prediction of the single-molecule magnetic behavior knowledge/prediction of the D-parameter is beneficial. In this review, we present the statistical processing of an extensive set of structural and magnetic data of Co(II) and Ni(II) complexes, which were published over the past 15 years. Magnetostructural D-correlations defined for this data set are reviewed in detail.

The effective treatment of human immunodeficiency virus (HIV), also known as acquired immunodeficiency syndrome (AIDS), has transformed a highly lethal disease into a chronic and manageable condition. However, better methods need to be developed for eliminating the virus from reservoirs, enhancing patient access and adherence to therapy, and improving treatment in the long term to reduce adverse effects. From the perspective of drug discovery, one promising strategy is the development of anti-HIV prodrugs. This approach aims to enhance the efficacy and safety of treatment, promoting the development of more appropriate and convenient systems for patients. In this review, we discussed the use of the prodrug approach for HIV antiviral agents and emphasized nucleoside reverse transcriptase inhibitors. We comprehensively described various strategies that are used to enhance factors such as water solubility, bioavailability, pharmacokinetic parameters, permeability across biological membranes, chemical stability, drug delivery to specific sites/organs, and tolerability. These strategies might help researchers conduct better studies in this field. We also reported successful examples from the primary therapeutic classes while discussing the advantages and limitations. In this review, we highlighted the key trends in the application of the prodrug approach for treating HIV/AIDS..

A comparative study of the physicochemical properties of natural bentonite clays of Pogodayevо (Republic of Kazakhstan, mod.1) and Dash-Salakhli (Republic of Azerbaijan, mod.2) deposits and modification of the bentonite clay with polyhydroxocations of iron (III) and aluminium (III). It-wasestablished that the modification of natural bentonitesusing polyhydroxocations of iron (III) and aluminium(III) by the method of "co-precipitation" leads to a change in their chemical composition, structural and sorption properties. The results showed that hydroxy-aluminum cations ([Al3O4(OH)24(H2O)12]7+) and poly-hydroxyl-Fe or polyoxo-Fe were intercalated into clay layers, which led to an increase in the values of d001 and specific surface areas compared to those of the original bentonite, from 37 to 120 for the Pogodaevo bentonite and from 51 to 172 respectively for bentonite from the Dash-Salakhli deposit. It is shown that modified sorbents based on natural bentonite are finely porous objects with a predominance of pores of 1.5−6.0 nm in size. As a result, there is a significant increase in the specific surface area of sorbents. Modification of bentonite with polyhydroxocations of iron (III) and aluminium (III) by the "co-precipitation" method also leads to an increase in the sorption capacity of the sorbents obtained with respect to nickel (II) cations. Modified bentonites were used for the adsorption of Ni (II) ions from the model solution. Ni (II) was absorbed in a neutral pH solution. The study of equilibrium adsorption showed that the data are in good agreement with the Langmuir isotherm model. The maximum adsorption capacity of the Ni (II) monolayer obtained from the Langmuir equation was 25.0 mg/g (mod. 1_Al_5-c), 18.2 mg/g (mod. 2_Al_5-c) for Al-bentonite and 16.7 mg/g (mod. 1_Fe_5-c), 10.1 (mod. 2_Fe_5-c) for Fe-bentonite. The kinetics of adsorption is considered. The high content of Al-OH anion exchange centresin them determines the higher sorption activity of Al-modified bentonites.

Scrub typhus (ST) is one of the most neglected tropical diseases, a leading cause of acute undifferentiated febrile illness in areas of the ‘tsutsugamushi triangle’, diagnosed frequently in South Asian countries. The bacteria Orientia tsutsugamushi is the causative agent of the disease, which enters to human body through the bite of trombiculid mite (Chigger) of the genus Leptotrombidium deliense.The diagnosis of the disease becomes challenging as its early symptoms mimic other febrile illnesses like dengue, influenza, and corona viruses. Lack of rapid, reliable, and cost-effective diagnostic methods further complicates the identification process. Northeast India, a mountainous region with a predominantly rural tribal population, has witnessed a resurgence of scrub typhus cases in recent years. Various ecological factors, including rodent population, habitat characteristics, and climatic conditions, influence its prevalence. Entomological investigations have confirmed the abundance of vector mites, highlighting the importance of understanding their distribution and the probability of transmission of scrub typhus in the region. Proper diagnosis, awareness campaigns, and behavioral interventions are essential for controlling scrub typhus outbreaks and reducing its impact on public health in Northeast India. Further research and community-based studies are necessary to accurately assess the disease burden and implement effective prevention strategies.

This research elucidates the dynamic expression of expansin genes during the wheat grain (Triticum aestivum L.) development process using comprehensive meta-analysis and experimental validation. We leveraged RNA-seq data from multiple public databases, applying stringent criteria for selection, and identified 60,852 differentially expressed genes across developmental stages. From this pool, 28,558 DEGs were found to exhibit significant temporal regulation in at least two different datasets, and were enriched for processes integral to grain development such as carbohydrate metabolism and cell wall organization. Notably, 30% of the 241 known expansin genes showed differential expression during grain growth. Hierarchical clustering and expression level analysis revealed temporal regulation and distinct contributions of expansin subfamilies during the early stages of grain development. Further analysis using co-expression networks underscored the significance of expansin genes, revealing their substantial co-expression with genes involved in cell wall modification. Finally, qPCR validation and grain morphological analysis under field conditions indicated a significant negative correlation between expression of select expansin genes and grain size and weight. This study illuminates the potential role of expansin genes in wheat grain development and provides new avenues for targeted genetic improvements in wheat.

The ultrasonic vibration assisted pressing process can improve the fluidity, the uneven distribution of density and particle size of WC-Co powder. However, the microscopic mechanism of ultrasonic vibration on the powder remains unclear. In this paper, WC and Co particles are simulated by three-dimensional spherical models of two particle sizes with the aide of the secondary development of Python. At the same time, the forming process of powder at mesoscale is simulated in the virtue of the finite element analysis software ABAQUS. The influence of vibration amplitude on the fluidity, the filling density and the stress distribution of WC-Co powder is investigated when the ultrasonic vibration is applied to the conventional pressing process. From simulation results, the ultrasonic vibration amplitude has great influences on the density of the compact. With the increase of the ultrasonic amplitude, the compact density also increases gradually. At the initial stage of the compaction, the particles move violently under the action of ultrasonic vibration, the fluidity is obviously enhanced, the arch bridge effect between particles is destroyed, the fine particles quickly fill the pores between the large particles, which increase the density, and the residual stress in the billet decreases after the compaction. From the experimental results, the size distribution of the billet is more uniform, the elastic aftereffect is reduced, the dimensional instability is improved, and the density curves obtained by experiment and simulation are within a reasonable error range.

Surface defect detection is one of the most widely used research areas in the field of image processing and machine vision. Detection of surface defects is used in visual inspection systems and medical image analysis. In this manuscript, an innovative method for detecting surface defects based on energy changes in co-occurrence matrices is presented in several directions. The method presented in this manuscript includes two stages of learning and testing. In the learning phase, to extract texture features, the gray level co-occurrence matrix operator is applied on the healthy image of the desired level. Then the energy value of the output matrix is ​​calculated. In the following, changes in the amount of energy are considered as statistical characteristics that are a good representative of the image of a healthy surface. Finally, with its help, a suitable threshold for the health of the images is obtained. Then, in the test phase, with the help of the calculated quorum, the defective windows that have suffered from non-normality are distinguished from the healthy surface sections. In the results section, the efficiency of the mentioned method has been measured on medical images and stone and ceramic images, and its detection accuracy has been compared with some previous effective methods. The advantages of the presented method include high accuracy, low calculations and compatibility with all types of levels due to the use of the learning stage. The proposed approach can be used in medical applications to diagnose abnormalities such as diseases. All extracted features are statistical, so its detection speed is higher than deep neural networks.

Mutations within the hepatitis B virus (HBV) genome have been associated with rapid progres-sion to hepatocellular carcinoma (HCC); however, there is limited information regarding the prevalence and impact of these mutations in most of sub-Saharan Africa, including Botswana. We aimed to determine the prevalence of HBV mutations known to be associated with progression to HCC using a retrospective, cross-sectional analysis of 48 previously generated HBV sequences from adults with concomitant HBV/HIV initiating HIV antiretroviral therapy in Botswana. The sequences were aligned with reference sequences, and HCC-associated mutations were manually identified using BioEdit. Sixteen (33.3 %) of 48 participant samples had 20 HCC-associated mu-tations. Seven HCC mutations were present in the core region, 4 in the preCore region, 7 in the X region, and one mutation in the surface region, as well as deletions within the preSurface 1 region. Seven of the 16 participants (43.8%) had multiple HCC-associated mutations. There were also previously uncharacterized mutations at positions with known HCC-associated mutations. HCC-associated mutations were common in this cohort; hence, some participants may require close clinical monitoring as they might be more prone to rapid disease progression. Other functionally uncharacterized polymorphisms were also detected and require characterization in future studies.

A digital text abstract presents the essential information of an article, and we can find the trend and value of the research by analyzing it rigorously and digging up knowledge. Therefore, this study focuses on the abstracts of index journals in China and Taiwan from July 2010 to June 2020 (a total of 3,283 abstracts). Through the concepts of text mining and natural language processing (NLP), it constructs processes such as text retrieval, text segmentation and word cloud analysis, TF-IDF weight analysis, co-word analysis, network analysis, and trend analysis, and analyses a large amount of text data. The results show that the scope of research in China covers the fields of social sports and sports science, and research in Taiwan covers both natural and social sciences. The network diagram highlights the richness of sports-related research fields in the two regions, but research on sports philosophy is relatively rare. It is suggested that all disci-plines/departments should re-allocate the same resources, so as to show a balanced development trend and help expand a new chapter in the sports academic field.

A polymer inclusion membrane (PIM) composed of 50 wt% poly(vinylidenefluoride-co-hexafluoropropylene) (PVDF-HFP) as its base polymer, 40 wt% Aliquat® 336 as its extractant and 10 wt% dibutyl phthalate (DBP) as plasticizer provided efficient extraction of vanadium(V) from its sulfate solutions adjusted to pH 2.5. It was suggested that V(V) was extracted as VO2SO4− via an anion exchange mechanism. Quantitative back-extraction was achieved in a sulfuric acid solution (6 mol L-1) containing 1 v/v% of hydrogen peroxide. It was assumed that the back-extraction process involved the oxidation of VO2+ to VO(O2)+ by hydrogen peroxide. The newly developed PIM with the optimized composition mentioned above exhibited excellent selectivity for V(V) in the presence of metallic species present in digests of spent alumina hydrodesulfurization catalysts (i.e., Al(III), Co(II), Cu(II), Fe(III), Mn(II), and Ni(II)). The co-extraction of Mo(VI) with V(V) was eliminated by its selective extraction at pH 1.1. The optimized PIM was characterized by contact angle measurements, atomic-force microscopy (AFM), energy dispersive X-ray spectroscopy (EDS), thermogravimetric analysis (TGA)/derivatives thermogravimetric analysis (DTGA), and the stress-strain measurements.

Background: Single nucleotide polymorphisms (SNPs) in the SLC10A1 gene, coding for a functional receptor of hepatitis B virus (HBV), sodium taurocholate co-transporting polypeptide (NTCP), may influence the susceptibility, the outcome, and disease course of HBV infection in some populations. Aim: to determine the prevalence of SNPs of NTCP gene, rs2296651 and rs943277, and their relationship with chronic HBV infection in a group of Egyptian patients. Methods: 137 patients with HBV and 65 healthy controls were enrolled, and the patients were divided into two groups; group I chronic HBV infection (68 patients with normal ALT and minimal or no liver necroinflammation or fibrosis) and group II chronic hepatitis B (69 patients with elevated ALT and moderate or severe liver necroinflammation). They were subjected to full history taking, clinical examination, laboratory investigations, abdominal ultrasound, and liver stiffness measurement using both Echosens® Fibroscan and acoustic radiation force impulse (ARFI). Real time PCR TaqMan 5’ allelic discrimination assay was applied to detect the SNPs in NTCP gene, rs2296651 and rs943277. Results: On studying the rs2296651 variant, all controls and patients had genotype GG without any significant association with HBV infection or disease progression. However, the rs943277 variant in all controls and 98% of patients had genotype GA, except for two chronic HBV infection patients who had genotype AA, but no significant difference between patients and controls was found. The non-invasive methods for liver fibrosis assessment ARFI, AST/platelet's ratio (APRI), and fibrosis-4 score (FIB-4) could predict the stages of fibrosis in agreement with Fibroscan with AUCOR 0.8, 0.79, and 0.76, respectively. Conclusion: These findings may suggest that there is no relation between these SNPs of the NTCP gene and susceptibility or chronicity of HBV infection in the Egyptian population. We also suggest that the use of the non-invasive methods for liver fibrosis assessment, ARFI, FIB-4, and APRI may decrease the need for liver biopsies in prediction of significant hepatic fibrosis in chronic HBV patients.

Triple negative breast cancer (TNBC) is the most aggressive breast cancer accounting for around 15% of identified breast cancer cases. TNBC, by lacking estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), is unresponsive to current targeted therapies. Existing treatment relies on chemotherapeutic treatment but, despite an initial response to chemotherapy, the inception of resistance and relapse is unfortunately common. Dasatinib is an approved second-generation inhibitor of multiple tyrosine kinases and literature data strongly support its use in the management of TNBC. However, dasatinib binds to plasma proteins and undergoes extensive metabolism through oxidation and conjugation. To protect dasatinib from fast pharmacokinetic degradation and to prolong its activity, it was encapsulated on poly(styrene-co-maleic acid) (SMA) micelles. The obtained SMA-dasatinib nanoparticles (NPs) were evaluated for their physicochemical properties, in vitro antiproliferative activity in different TNBC cell lines, and in vivo anticancer activity in a syngeneic model of breast cancer. Obtained results showed that SMA-dasatinib is more potent against 4T1 TNBC tumor growth in vivo compared to free drug. This enhanced effect was ascribed to the encapsulation of the drug protecting it from a rapid metabolism. Our finding highlights the often-overlooked value of nanoformulations in protecting its cargo from degradation. Overall, results may provide an alternative therapeutic strategy for TNBC management.

Based on electromagnetic heat transfer and metal phase transformation co-simulations, we modeled an AISI 1045 specimen under high-frequency heat treatment. Hardening zone predictions were confirmed through cooling and metal phase transformation simulations after obtaining the results from electromagnetic heat transfer simulations. The cooling process was modeled by applying the cooling coefficient of the cooling water in the same way as the actual heat-treatment process. To obtain the current flowing through the coil during high-frequency induction heating, the voltage was measured and applied using the resistance–inductance–capacitance circuit calculation method. Experimental and simulated results of the heating temperature and curing depth of an AISI 1045 specimen with a carbon content of 0.45% were compared; the comparison indicated good agreement between the two. Using the simulation results, we established a method for obtaining the current flowing through the induction coil for predicting the extent and depth of the hardening zone during high-frequency induction heat treatment.

In December 2019, pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection emerged in Wuhan City, Hubei Province, China. Early in 2020, the World Health Organization (WHO) announced a new name for the 2019-nCoV-caused epidemic disease: coronavirus disease 2019 (COVID-19) and declared COVID-19 to be the sixth international public health emergency. Cellular co-infection is a critical determinant of both viral fitness and infection outcome and plays a crucial role in shaping the host immune response to infections. In this study, sixty-eight public next-generation sequencing libraries from SARS-CoV-2 infected patients were retrieved from the NCBI Sequence Read Archive database using SRA-Toolkit. Using an alignment-free method based on K-mer mapping and extension, SARS-CoV-2 was identified in all except three patients. Influenza A H7N9 (3/68), Human immunodeficiency virus 1 (1/68), rhabdovirus isolate (3/68), Human metapneumovirus (1/68), coronaviruses NL63 (1/68), Parvovirus (1/68), Simian virus 40 (1/68), and hepatitis virus (1/68) genome sequences were detected in SARS-CoV-2 infected patients.

Angiogenesis is a term that describes the formation of new blood and lymphatic vessels from a pre-existing vasculature. This allows tumour cells to acquire sustenance in the form of nutrients and oxygen and the ability to evacuate metabolic waste. As one of the hallmarks of cancer, angiogenesis has been studied extensively in animal and human models to enable better understanding of cancer biology and the development of new anti-cancer treatments. Angiogenesis plays a crucial role in the process of tumour genesis, because solid tumour need a blood supply if they are to grow beyond a few millimeters in size. On the other hand, there is growing evidence that some solid tumour exploit existing normal blood supply and do not require a new vessel formation to grow and to undergo metastasis. This review of the literature will present the current understanding of this intricate process and the latest advances in the use of angiogenesis-targeting therapies in the fight against cancer.

Enhancing the effectiveness of search and rescue operation at sea is always a duty of utmost importance of the coastal states. The search area for distressed objects can be determined by using Monte Carlo simulation, combined with the Median-Filter. Once the search area has been identified, the success of search and rescue operations depends on the sweeping ability of search and rescue vessel at the probability area of the distress object with the minimum time. This is the important element to the success of the search and rescue operation as it minimizes the risk and cost for Search and rescue team. In this article, the authors study and propose the use of Bacterial Foraging Optimization Algorithm (BFOA) to calculate the optimal search and co-ordination route for many search and rescue vessels in Vietnam sea. The simulation results show that it is quite consistent with reality and BFOA can be effectively applied to determine a quick search.

The dye effluent is usually difficult to be degraded by conventional wastewater treatment in leather industry. In order to develop efficient and cost-effective treatment methods, we evaluate the effect of white-rot fungus immobilization for dye decolorisation in this paper. The Phanerochaete chrysosporium BKM-F-1767 was used for immobilization. This research found that the white-rot fungus immobilization had an obviously decolorisation effect in dye wastewater treatment, and plant carriers such as sorghum stalk and corn cob were helpful to the growth of Phanerochaete chrysosporium in white-rot fungus immobilization. Due to the stability and recyclability, the white-rot fungus co-immobilization was considered as the most suitable treatment for decolorisation of dye effluent which enjoyed the advantages of both adsorption immobilization and entrapment immobilization. Furthermore, the dye decolorisation evaluation was carried out to find the most suitable carrier for co-immobilization, and it found that sorghum stalk - calcium-alginate gel spherical particle (SS-CGPB) has better decolorisation effect than corn cobs - calcium-alginate gel spherical particle (CC-CGPB), and the dye decolorisation rate was 86.77%. After 5 cycles, the dye decolorisation rate was 85.87% which indicated the SS-CGPB preserved functional integrity successfully. By further analyzing the biodegradation process with white-rot fungus immobilization, the intermediate products were observed and the degradation pathway of acid golden yellow dye molecular was proposed. The results showed that the C-N single bonds attached to the central benzene in the dye molecule were attacked and destroyed in white-rot fungus co-immobilization treatment, thus the structure of dye molecule could be successfully degraded into small molecules which would be more easily treated by conventional treatment methods. Therefore, the white rot fungus co-immobilization might be appropriate for pre-treatment as an important biotechnology for the advanced treatment of dye effluent.

Irish beef farms have experienced poor viability longitudinally, with officially acknowledged ‘crisis’ levels in 2013. In response, beef Producer Organisation (PO) legislation was introduced. Through the lens of international evidence in the literature about how POs function, this paper presents an analysis of Irish stakeholders’ views in the context of a public consultation process. While stakeholders indicate the need for individual POs to collaborate, little emphasis is placed on other factors necessary for success: collaboration with other chain actors; and market differentiation of products. Stakeholders identified primary threats as poor engagement on the part of both farmers and processors/purchasers.

Crotoxin (CTX), the predominant toxin in Crotalus durissus terrificus snake venom (CdtV), has anti-inflammatory and immunomodulatory effects. Despite its inhibitory action on neutrophil migration and phagocytosis, CTX does not directly affect the production of reactive oxygen species (ROS) by the neutrophils. In contrast, it enhances the generation of reactive oxygen and nitrogen intermediates by macrophages. Given the importance of macrophage-neutrophil interactions in innate antimicrobial defense, the aim of this study was to investigate the effect of CTX on neutrophil ROS production and killing activity, either through CTX-treated macrophage co-culture or conditioned medium of CTX-treated macrophages. The results showed an important modulatory action of CTX on the neutrophil function as well as neutrophil-macrophage interactions, as demonstrated by the increased production of hydrogen peroxide, hypochlorous acid, nitric oxide and TNF-α, along with the increased fungicidal activity of neutrophils.

Urban areas face daunting economic challenges that have increased in scope in recent years. At the same time, cities provide for opportunities for growth and value creation. The interplay of these challenges and opportunities represents the area of intervention for policymakers and researchers. However, the traditional approach to urban policy, deriving from the managerial and economic policy theories, shows difficulties mainly related to the capacity to counterbalance the interests and expectations of a multitude of stakeholders participating to the value co-creation process and to bring all different components to synergically contribute to the sustainability of the system. The Author proposes an innovative approach to urban public policy, by adopting the theoretical corpus of the Viable Systems Approach ("VSA"). Urban planning will be particularly analysed, as systemic components of urban areas, with the aim to investigate the decision-making processes for an integrated, efficient, effective and sustainable management of the territory as a multidimensional, multisectoral and multi-stakeholder entity. By recovering different settings, the VSA intends the co-creation of value as the capacity of the viable systems to increase its surviving chances in its own context. The value derives, first of all, from the strategic decisions aimed at finding both dyadic and context consonance. Through its strategic decisions, the Governing Body of the territory develops a specific system as the overall synthesis of all possible systems organised within a specific area, identifying pattern of development agreed by the several stakeholders of the territory. In the dynamics of value co-creation in systemic multi-subjective organizations, where the level of complexity is particularly significant, the research for consonance among different stakeholders is particular important in order to achieve the most value for the territory. In this respect, the VSA may constitute a valid tool, helping to find the necessary capacities to imagine evolutionary paths towards new competencies.

The present work investigates primarily the morphology evolution of the body-centered-cubic (BCC)/B2 phases in AlxNiCoFeCr high-entropy alloys (HEAs) with increasing Al content, which has been neglected so far. There exist two types of microscopic morphologies of BCC and B2 phases in this HEA series: one is the weave-like morphology induced by the spinodal decomposition, and the other is the microstructure of a spherical disordered BCC precipitation on the ordered B2 matrix that appears in HEAs with a much higher Al content. The shape of coherent precipitates is found to be closely related to the lattice misfit between BCC and B2 phases, which is sensitive to Al. The mechanical properties, including the compressive yielding strength and microhardness of the AlxNiCoFeCr HEAs, are also discussed in light of the concept of the valence electron concentration (VEC).

Generating executable code from natural language instructions using Large Language Models (LLMs) presents challenges such as semantic understanding and handling ambiguous input. This study focuses on the SantaCoder model and explores the impact of in-context learning on code generation using the MBPP and HumanEval datasets for evaluation. Our results demonstrate significant improvements in three key metrics (defined in the paper): correctness@k, similarity@k and pass@k. To address the problem of selecting optimal demonstrations to maximize correctness and pass rates, we investigate two methods: latent concept selection and random selection in this paper. These findings highlight the effectiveness of in-context learning and the critical role of demonstration selection in enhancing the accuracy, efficiency, and versatility of the SantaCoder model in code generation.

Dementia education and training for workforce development is becoming increasingly important in bridging knowledge gaps among health and social care practitioners in the UK and internationally. Dementia Education for Workforce Excellence (DEWE) was developed during the COVID-19 pandemic blending both synchronous and asynchronous instruction and delivered across three different contexts: care homes, home care, and nurse education within the UK and India. This study aimed to evaluate DEWE using mixed methods with online survey data analyzed descriptively and interview data analyzed thematically. Integration of survey and interview data aimed toward a comprehensive evaluation of this novel approach for dementia workforce development. Thirty-four social care practitioners and nurse educators completed the online survey demonstrating high-level learner satisfaction; learning gains; behavioral change, and motivation to share new knowledge. Four key themes developed from the analysis of interviews (n=9) around participants’ pursuit of new knowledge; delivery modes in DEWE; learning gains and impact of DEWE; and adaptations for future program implementation. Findings suggest DEWE is an innovative resource that promotes person- and relationship-centered dementia care across all stages of one’s dementia journey. Cultural adaptations are recommended for international delivery to ensure contextual alignment and maximum impact.

Ransomware attacks have rapidly proliferated, inflicting severe financial damages on businesses and individuals. Machine learning approaches to automate ransomware detection have shown promise but grapple with challenges like limited training data. This study introduces a novel deep learning model for few-shot ransomware classification. The model employs entropy features derived directly from malware binaries coupled with a twin neural network architecture utilizing transfer learning. Tests on over 1000 samples across 11 families demonstrate a weighted F1-score of 85.8\%, surpassing existing methods. The approach mitigates biases in limited training data and preserves intricacies lost in image-based features. It exhibits precise classification capabilities even with sparse samples of new ransomware variants. The research highlights the potential of entropy-driven deep learning to equip defenses against emerging zero-day ransomware strains.

Breast cancer is a common malignant tumour and studies have shown that early and accurate detection is crucial for patients. With the maturity of medical imaging and deep learning development, significant progress has been made in breast cancer classification, which greatly improves the accuracy and efficiency of classification. This review focuses on deep learning, migration learning, GAN, and lifelong learning to elaborate and summarise the important roles arising from breast cancer detection. This review also examines the dataset and labeling issues required for breast cancer classification. In conclusion, at the end of the article, we look at future directions for breast cancer classification research, including cross-migration learning, multimodal data fusion, model interpretability, and lifelong learning, and also explore how to provide personalized treatment plans for patients.

At the beginning of 2020, with the onset of the pandemic, the traditional learning environment for learners drastically changed globally. Since then, most students/teachers have started and practiced distance and virtual learning/teaching. Thus, a technological breakthrough in virtual learning has followed. In connection with this, many countries worldwide have commenced allocating additional financing and funds for educational institutions' technological improvement and development. The long-term stay in distance learning has revealed and highlighted new problems students face: their knowledge level has decreased, they lack socialization skills, and they face psychological and physical health problems. Due to this negative impact on students, a need to research and evaluate how much the EU countries allocated to solve the distance learning-caused problems and what programs or models they prepared has emerged and encouraged further studies. The research has found that many countries increased their allocations very minimally, e.g., 0.0.1%, but some increased their available budgets to 32%. Notably, most countries did not separate distance learning exit funding from distance learning preparation funding. Based on the problems the countries saw, only a few states identified withdrawal from distance learning as a problem. Considering this, we set ourselves the goal to evaluate exit models from distance learning and allocated funding amounts. The following objectives were planned to achieve the goal: · to evaluate the global practice of exit from distance learning; · to determine the scope of funding for pandemic management; · to evaluate the amounts of funding allocated to manage pandemic-caused consequences and the GDP ratio. Research methods: mathematical-statistical analysis, empirical analysis, and analysis of scientific literature.

Reinforcement Learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although solutions like Hindsight Experience Replay (HER) have been proposed to alleviate these issues, the direct utilization of RL in autonomous vehicles remains constrained due to the intricate fusion of information and the possibility of system failures during the learning process. In this paper, we present a novel RL-based autonomous driving system technology that combines Obstacle Dependent Gaussian (ODG) RL, Soft Actor-Critic (SAC), and meta-learning algorithms. Our approach addresses key issues in RL, including the overestimation phenomenon and sparse reward problems, by incorporating prior knowledge derived from the ODG algorithm. We evaluated our proposed algorithm on official F1 circuits, using high-fidelity racing simulations with complex dynamics. The results demonstrate exceptional performance, with our method achieving up to 89% faster learning speed compared to existing algorithms in these environments.

This review paper delves into using micro-credentials in higher education ecosystems as a digital enablers. Micro-credentials, which are digital credentials that attest to a learner’s mastery of a specific skill or knowledge area, are becoming more popular in higher education. The paper examines the successful implementation of micro-credential frameworks in higher education, using case studies to demonstrate the advantages of micro-credentials. The review emphasizes the agility and flexibility of microcredentials, which enable learners to acquire new skills quickly and respond to changes in the job market. In addition, the paper discusses the digital nature of micro-credentials and how they allow institutionsto provide targeted, skills-based training that isrelevant to employers. It also explores how micro-credentials are delivered through online platforms, making them convenient and easily accessible for learners. The review underscores the significance of digital infrastructure, connectivity, and public utility for promoting micro-credentials. The paper argues that micro-credentials function as a digital enabler for higher edu- cation ecosystems, allowing learners to acquire targeted training and enabling institutions to expand their offerings and reach more students. The paper concludes by highlighting the potential for micro-credentials to help bridge the skills gap and equip learners with the skills necessary to succeed in today’s digital economy.

Tuberculosis (TB) disease still remain a major global threat due to the growing number of drug-resistant species and global warming. Despite the fact that there are new molecular diagnostic approaches, however, majority of developing countries and remote clinics depends on conventional approaches such as Tuberculin test, microscopic examinations and radiographic imaging (Chest X-ray). These techniques are hindered by several challenges which can lead to miss-diagnosis especially when interpreting large number of sample cases. Thus, in order to reduce workload and prevent miss-diagnosis, scientists incorporated computer-aided technology for detection of medical images known as Computer aided Detection (CADe) or Diagnosis (CADx). The use of AI-powered techniques has shown to improve accuracy, sensitivity, specificity. In this review, we discussed about the epidemiology, pathology, diagnosis and treatment of tuberculosis. The review also provides background information on Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Transfer Learning (TL) and their applications in detection of tuberculosis from both microscopic slide images and X-ray images. The review also proposed an IoT/AI powered system which allows transfer of results obtained from DL models with end users through internet networks. The concept of futuristic diagnosis, limitations of current techniques and open research issues are also discussed.

The use of Information Communication Technologies (ICT) in education brings up new possibilities of promoting the learning and health experiences. In this sense, education contexts of 21st century must consider these two areas of knowledge, especially their integration. This article presents learning practices developed with mobile devices and games, in order to improve learning and well-being in children and adolescents undergoing cancer treatment in non-formal educational setting. The methodology is based on qualitative case studies with content-based data analysis, involving informal interviews and observation methods. The study considers data from 5 patients who participated in the research between 2015 and 2019. The results demonstrate a positive influence of the practices with mobile technologies and games in terms of learning and in the well-being feeling of patients during the treatment.

Recent advancements in artificial intelligence (AI) and deep learning have revolutionized the field of protein engineering, particularly in the area of de novo protein design. This review article explores the impact of AI-driven approaches on protein design, with a specific focus on the role of structure prediction neural networks, such as AlphaFold and RoseTTAFold. The article discusses the paradigm shift brought about by these networks, which have enabled the design of proteins with unique structures and functions that are not found in nature. The review covers various aspects of AI-driven protein design, including the use of protein language models to harness evolutionary information, the development of de novo protein design workflows utilizing deep learning, and the application of generative models like RFdiffusion and RoseTTAFold All-Atom. The article also highlights the successes and applications of AI-driven protein design across diverse domains, such as enzyme engineering, antibody design, and vaccine development. Additionally, the review identifies current challenges and future directions in the field, emphasizing the need to address limitations in modeling conformational dynamics and designing proteins for in vivo functionality. The article concludes by underscoring the potential of AI-driven protein design to transform various aspects of science and technology, while also acknowledging the importance of interdisciplinary collaboration and the development of robust pipelines for the validation and optimization of designed proteins. Overall, this comprehensive review serves as a valuable resource for researchers and practitioners interested in understanding the current state and future prospects of AI-driven protein design.

Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatmnet, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.

Distance Learning has become the new standard, especially after the pandemic and due to the technological advances, that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. Students interact mainly through LMSs, leaving their digital traces that can be leveraged to improve the educational process. This work aims to propose a model that can capture the students' behaviors based on the clickstream data associated with the discussion forum and additionally to suggest interpretable patterns that will support education administrators and tutors in the decision-making process. To achieve our goal, we use Social Network Analysis (SNA) as networks represent complex interactions in a meaningful and easily interpretable way. Moreover, simple or complex network metrics are becoming available to provide valuable insights into the students’ social interaction. This study concludes that by leveraging the imprint of these actions in an LMS and using metrics of SNA, differences can be spotted in the communicational patterns that go beyond simple participation recording. Although HITS and PageRank algorithms were created with completely different targeting, it is shown that they can also reveal methodological features in students’ communicational approach.

Artificial Intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade has been remarkable. Specifically, Machine Learning (ML) and Deep Learning (DL) techniques in medicine have been increasingly adopted thanks to the growing abundance of health-related data, improved suitability of such techniques for managing large data-sets, and more computational power. The Italian scientific community has been instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years.

Documenting cultural heritage by using artificial intelligence (AI) is crucial for preserving the memory of the past and a key point for future knowledge. However, modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. If we want to rely on AI for these tasks, it is essential to understand what lies behind these models. Among the ways to discover AI there are the senses and the intellect. We could consider AI as an intelligence. Intelligence has an essence, but we do not know whether it can be considered “something” or “someone”. Important issues in the analysis of AI concern the structure of symbols -operations with which the intellectual solution is carried out- and the search for strategic reference points, aspiring to create models with human-like intelligence. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we propose KERMIT as a unit of investigation for a possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning and human knowledge.

Using data-driven models to solve predictive maintenance problems has been prevalent for original equipment manufacturers (OEMs). However, such models fail to solve two tasks that OEMs are interested in: (1) Making the well-built failure prediction models working on existing scenarios (vehicles, working conditions) adaptive to target scenarios. (2) Finding out the failure causes, furthermore, determining whether a model generates failure predictions based on reasonable causes. This paper investigates a comprehensive architecture towards making the predictive maintenance system adaptive and interpretable by proposing (1) an ensemble model dealing with time-series data consisting of a long short-term memory (LSTM) neural network and Gaussian threshold to achieve failure prediction one week in advance and (2) an online transfer learning algorithm and a meta learning algorithm, which render existing models adaptive to new vehicles with limited data volumes. (3) Furthermore, the Local Interpretable Model-agnostic Explanations (LIME) interpretation tool and super-feature methods are applied to interpret individual and general failure causes. Vehicle data from Isuzu Motors, Ltd., are adopted to validate our method, which include time-series data and histogram data. The proposed ensemble model yields predictions with 100% accuracy for our test data on engine stalling problem and is more rapidly adaptive to new vehicles with smaller error following application of either online transfer learning or the meta learning method. The interpretation methods help elucidate the global and individual failure causes, confirming the model credibility.

We discuss the implementation of a suite of virtual diagnostics at the FACET-II facility currently under commissioning at SLAC National Accelerator Laboratory. The diagnostics will be used for prediction of the longitudinal phase space along the linac, spectral reconstruction of the bunch profile and non-destructive inference of transverse beam quality (emittance) using edge radiation at the injector dogleg and bunch compressor locations. These measurements will be folded in to adaptive feedbacks and ML-based reinforcement learning controls to improve the stability and optimize the performance of the machine for different experimental configurations. In this paper we describe each of these diagnostics with expected measurement results based on simulation data and discuss progress towards implementation in regular operations.

The importance of energy systems and its role in economics and politics is not hidden for anyone. This issue is not only important for the advanced industrialized countries, which are major energy consumers, but is also important for oil-rich countries. In addition to the nature of these fuels which contains polluting substances, the issue of their ending up has aggravated the growing concern. Biofuels can be used in different fields for energy production like electricity production, power production or for transportation. Various scenarios have been written about the estimated biofuels from different sources in the future energy system. The availability of biofuels for the electricity market, heating and liquid fuels is very important. Accordingly, the need for handling, modelling, decision making and future forecasting for biofuels can be one of the main challenges for scientists. Recently, machine learning and deep learning techniques have been popular in modeling, optimizing and handling the biodiesel production, consumption and its environmental impacts. The main aim of this study is to evaluate the ML and DL techniques developed for handling biofuels production, consumption and environmental impacts, both for modeling and optimization purposes. This will help for sustainable biofuel production for the future generations.

While the use of crime data has been widely advocated in the literature, its availability is often limited to large urban cities and isolated databases tend not to allow for spatial comparisons. This paper presents an efficient machine learning framework capable of predicting spatial crime occurrences, without using past crime as a predictor, and at a relatively high resolution: the U.S. Census Block Group level. The proposed framework is based on an in-depth multidisciplinary literature review allowing the selection of 188 best-fit crime predictors from socio-economic, demographic, spatial, and environmental data. Such data are published periodically for the entire United States. The selection of the appropriate predictive model was made through a comparative study of different machine learning families of algorithms, including generalized linear models, deep learning, and ensemble learning. The gradient boosting model was found to yield the most accurate predictions for violent crimes, property crimes, motor vehicle thefts, vandalism, and the total count of crimes. Extensive experiments on real-world datasets of crimes reported in 11 U.S. cities demonstrated that the proposed framework achieves an accuracy of 73 and 77% when predicting property crimes and violent crimes, respectively.