This paper presents a comprehensive and adaptive framework for orchid conservation programs illustrated with data from published and unpublished case studies. There is a specific focus on West Australian terrestrial orchids, but many of the approaches have universal relevance. Aspects of the framework include (1) setting appropriate objectives, (2) establishing effective collaborations between scientists, volunteers and regulators to fill knowledge and funding gaps, (3) use of survey and demographics data to determine extinction risks and management requirements for species, (4) effective habitat management to overcome threats such as grazing, (5) finding potential new habitats by modelling climate and site data, (6) investigating the effectiveness of pollinators and (7) using seed baiting to detect mycorrhizal fungi. The relative cost and effectiveness of different methods used to propagate orchids for translocation are compared. Methods known to be successful, in order of complexity, include placement of seed in situ, vegetative propagation, symbiotic germination in non-sterile organic matter, symbiotic germination in sterile culture, asymbiotic sterile germination and clonal division in tissue culture. These form a continuum of complexity, cost, time required, faculties needed, as well as the capacity to maintain genetic diversity and produce seedlings preadapted to survive in situ. They all start with seed collection and lead to seed storage, living collections used as tuber banks and seed orchards, as well as translocation for conservation. They could also lead to commercial availability and sustainable ecotourism, both of which are needed to reduce pressure on wild plants. Overall, there has been a strong preference to use relatively complex, expensive and time-consuming methods for orchid conservation, despite evidence that simpler approaches have also been successful. These simpler methods, which include in situ seed placement and non-sterile germination on inorganic substrates, should be trialled in combination with more complex orchid propagation methods as part of an adaptive management framework. It is essential that orchid conservation projects harness the unique biological features of orchids, such as abundant seed production and mycorrhizal fungi which are far more widespread than their hosts. This is necessary to increase the efficiency and coverage of recovery actions for the largest and most threatened plant family.

HTTP/2 video streaming has caught a lot of attentions in the development of multimedia technologies over the last few years. In HTTP/2, the server push mechanism allows the server to deliver more video segments to the client within a single request in order to deal with the requests explosion problem. As a result, recent research efforts have been focusing on utilizing such a feature to enhance the streaming experience while reducing the request-related overhead. However, current works only optimize the performance of a single client, without necessary concerns of possible influences on other clients in the same network. When multiple streaming clients compete for a shared bandwidth in HTTP/1.1, they are likely to suffer from unfairness, which is defined as the inequality in their bitrate selections. For HTTP/1.1, existing works have proven that the network-assisted solutions are effective in solving the unfairness problem. However, the feasibility of utilizing such an approach for the HTTP/2 server push has not been investigated. Therefore, in this paper, a novel proxy-based framework is proposed to overcome the unfairness problem in adaptive streaming over HTTP/2 with the server push. Experimental results confirm the outperformance of the proposed framework in ensuring the fairness, assisting the clients to avoid rebuffering events and lower bitrate degradation amplitude, while maintaining the mechanism of the server push feature.

Recently, the medical image processing is extensively used in several areas. In earlier detection and treatment of these diseases is very helpful to find out the abnormality issues in that image. Here there are number of methods available for segmentation to detect the lung nodule of computer tomography (CT) image. The main result of this paper, the earlier detection of lung nodules using Pre-processing techniques of top-hat transform, median and adaptive bilateral filter was compared both filtering methods and proved the adaptive bilateral filter is suitable method for CT images. The proposed segmentation technique uses novel strip method and the image is split into number of strips 3, 4, 5 and 6. A marker- watershed method based on PSO and Fuzzy C-mean Clustering method was proposed method. Firstly, the input image was reduced noise reduction and smoothing and the filter image is using strips method and then the image is segmented by marker watershed method. Secondly, the enhanced PSO technique was used to locate the better accurate value of the clustering centers of Fuzzy C-mean Clustering. Final stage, with the accurate value of centers and the enhanced target function and the small region of the segmented object was clustered by Fuzzy C-mean. In segmentation algorithm presented in this paper gives 95% of accuracy rate to detect lung nodules when strip count is 5.

This paper proposes a crack recognition method based on high-resolution line array cameras and adaptive lifting algorithm. By defining the crack rate, this algorithm calculates the ratio of the crack area to the area of the entire collected image to characterize the damage extent of the current section. The algorithm first uses image preprocessing to reduce the image noise, then uses histogram equalization to enhance the feature of the crack region, divides the whole image into multiple sub-blocks, and extracts region features in the sub-block. At the same time, this algorithm defines related feature descriptors, and constructs weak classifiers according to each feature descriptor, and converts the weak classifiers into strong classifiers by using an adaptive lifting algorithm. Finally, this algorithm realizes the division of the crack regions. Experimental results show that the proposed algorithm can meet the actual needs and is better than other classical algorithms.

Molenaar’s manifesto on psychology as idiographic science brought the N = 1 times series perspective firmly to the attention of developmental scientists. The rich intraindividual variation in complex developmental processes requires the study of these processes at the level of the individual. Yet, the idiographic approach is all but easy in practical research. One major limitation is the collection of short interval times series of high quality data on developmental processes. In this paper we present a novel measurement approach to this problem. We developed an online practice and monitoring system which is now used by thousands of Dutch primary school children on a daily or weekly basis, providing a new window on cognitive development. We will introduce the origin of this new instrument, called Math Garden, explain its setup, and present and discuss ways to analyze children’s individual developmental pathways.

This study presents a French translation and validation of the Durand Adaptive Psychopathic Traits Questionnaire (DAPTQ), an instrument for assessing adaptive traits known to correlate with the psychopathic personality. Bilingual (French and English) individuals from France and Canada (N = 141, 52% in France, M_age = 29.73, SD = 9.09) completed both versions of the DAPTQ (French and English), alongside measurements of perceived stress, trait anxiety, authentic leadership and creativity. Correlation between the DAPTQ total and subscales across versions showed strong associations (r = .84 to .96). The DAPTQ – French version also demonstrated good internal consistency (α = .87), convergent validity, and concurrent validity. These findings support the cross-cultural equivalence of the DAPTQ and therefore its effectiveness as a valid assessment method of adaptive traits.

The concept of smart morphing blades, which can control themselves to reduce or eliminate the need for active control systems, is a highly attractive solution in blade technology. In this paper an innovative passive control system based on Shape Memory Alloys (SMAs) is proposed. On the basis of previous thermal and shape characterization of a single morphing blade for a heavy-duty automotive cooling axial fan, this study deals with the numerical analysis of the aerodynamic loads acting on the fan. By coupling CFD and FEM approaches it is possible to analyze the actual blade shape resulting from both the aerodynamic and centrifugal loads. The numerical results indicate that the polymeric blade structure ensures proper resistance and enables shape variation due to the action of the SMA strips.

The severe acquired respiratory coronavirus–2 (SARS–CoV-2) infection has initiated both acute and chronic COVID–19 disease between 2020 and 2023, currently evolving with other homologous prior coronavirus strains of the Nidoviridae order, which encompasses other prevalent alpha/ beta coronaviruses, but also the Middle East Respiratory Syndrome (MERS-CoV) and SARS-CoV-1, with recent SARS–CoV–2 variants, increasing demands for effective immunogens and therapeutic approaches that will reduce global disease burden and further infection from SARS–CoV-2 affected individuals that may experience post acute sequelae (PASC) or “Long COVID”. Following a worldwide programme of prophylactic vaccination, there is still a dilemma in the efforts to find prophylactic and early therapeutic approaches that would treat novel SARS-CoV-2 variants and prevent future epidemics or pandemics within host human and animal populations, where zoonotic or cross species transfer naturally occurs. Concerns about viral immune escape intersect at a specific point; a gained evolutionary ability of several viruses to co–infect and compete against previous scientific advances since 1796 that remain undetected or asymptomatic during the early stages of infection progressing to symptomatic and severe disease via the double methylation of the 5' end of eukaryotic DNA or RNA-based viral genomes, the 7-MeGpppA2’-O-Me cap, and its double methylation capping process is performed by the activated viral 2’ - O - Methyltransferase (MTase) enzyme, a complex of two viral non-structural proteins (NSPs) joined together through an activation process (NSP10/16) and by N7-Methyltransferase (N7-MTase/NSP14), respectively. Moreover, it was discovered that polymorphic viruses translate NSP1, which prevents the activation of various Pattern Recognition Receptors (PRRs), and consequently, detection of Pathogen-Associated Molecular Patterns (PAMPs) and Damage-Associated Molecular Patterns (DAMPs) alike. NSP1 also silences important interferon-encoding genes (INGs) and interferon-stimulated genes (ISGs), is signalled in a paracrine manner to neighbouring cells, and that induces the apoptosis of host cells, inducing an effect of “trace erase” effect and making the viral infection as immunologically “invisible” as possible during the initial, key stages of viral replication and distribution, all such mechanisms occurring independently of the viruses in cause. Another important viral NSP is NSP14, as it plays two functional roles that are independent of each other; to produce new viral genetic material for the purpose of maintaining the validity of the viral genome as well, and not just transfer a methyl group to the 5’ end of the viral genome. Other viral NSPs share a role with NSP1, 10, 14 and 16 in directly suppressing the activation of PRRs and ISGs, and all such viral proteins help the virus in its process of self-camouflaging against first- and second-line immunity, thereby often severely impacting the quality of the produced adaptive immune responses. The outcome of all such phenomena is the sharp decrease in the host Type I and Type III interferons' (IFNs) rate of synthesis by the host cells, that would usually occur and affect homeostatic cellular pathways, resulting in further viral replication and induced apoptosis. Nonetheless, effects of microbial immune evasion during the development of other viral or carcinogenic pathologies are not widely known. In short, polymorphic viruses developed a proportionate evolutionary response against developed adaptive immune responses, by currently relying on gaps mostly situated in the natural immune system in their process of molecular self-camouflaging. Scientists developed numerous approaches of early treatment that generally showed good success rates and fewer risks of adverse events, and the still early present stages of COVID-19 research should also be taken into consideration whilst filtering for the most appropriate solutions. For example, the administration of recombinant human interferons I and III into the nasal mucosa cellular layer, as key mediators of anti–viral activity, can simulate intracellular infection and stimulate cellular activity in a timely manner, training the innate and adaptive immune system cells to develop and appropriately stimulate an adequate immune response through B and T cells. Another example could involve the treatment of natural and adaptive lymphocytes with a low dose of IFNs I and possibly III, prior to their insertion into the host lymphatic system, possibly alongside additional recruitment of plasmacytoid dendritic cells (pDCs) as further interferon “factories”, all with the purpose of early infection management. It might be that focusing on directly offering the immune system the information about the genetics and protein structure of the pathogen, rather than training its first-line mechanisms to develop faster, excessively increases its specificity, making it reach a level that brings the virus the opportunity to evolve and escape previously-developed host immune mechanisms. It is until the scientific community realises this potentially crucial aspect that large proportions of the world population will probably continue to face serious epidemics and pandemics of respiratory diseases over the coming several decades, evidenced with dengue fever and more recently, monkeypox and possibly avian flu. Of note, it has been indicated that IFN I and / or III display significant immunising, early therapeutic and clinical disease onset-attenuating effects for many other microbial evoked diseases, as well as for a number of oncological diseases.

The increasing complexity of IP network infrastructures necessitates efficient and adaptive configuration management solutions. This paper presents an AI-driven framework that integrates machine learning and reinforcement learning techniques to enhance the management of configurations for IP network devices. The proposed approach leverages a comprehensive dataset comprising configuration parameters and performance metrics to train predictive models. Experimental results demonstrate that the framework achieves an accuracy of 88% in predicting optimal configurations, significantly outperforming traditional methods. Additionally, the framework exhibits an average response time of 150 milliseconds for applying configuration changes, underscoring its efficiency. A reinforcement learning agent is implemented to adapt to dynamic network conditions, yielding improved decision- making over time. The user interface designed for the framework facilitates real-time monitoring and visualization of network configurations. The findings suggest that the AI- driven framework not only streamlines configuration management processes but also empowers network administrators to proactively address network challenges. Future work will focus on scalability, integration with emerging technologies, and user feedback mechanisms to further enhance the framework's effectiveness.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

The objective of this study, which is divided into two parts, is twofold: to address long standing challenges in the sensing of atmospheric turbulence-induced wavefront aberrations in strong scintillation conditions via comparative analysis of several basic scintillation resistant wavefront sensing (SR-WFS) architectures and iterative phase retrieval (IPR) techniques (Part I, this paper), and to develop a framework for the potential integration of SR-WFS techniques into practical closed-loop non-astronomical atmospheric adaptive optics (AO) systems (Part II). In this paper we consider basic SR-WFS mathematical models and phase retrieval algorithms, tradeoffs in sensor design and phase retrieval technique implementation, and methodologies for WFS parameter optimization and performance assessment. The analysis is based on wave-optics numerical simulations imitating realistic turbulence-induced phase aberrations and intensity scintillations, and optical field propagation inside SR-WFSs. Several potential issues important for practical implementation of SR-WFS and IPR techniques such as requirements for phase retrieval computational grid resolution, tolerance with respect to optical element misalignments, and the impact of camera noise and input light non-monochromaticity are also considered. Results demonstrate that major wavefront sensing requirements desirable for AO operation in strong intensity scintillations can potentially be achieved by transitioning to novel SR-WFS architectures based on iterative phase retrieval techniques.

Ever since the discovery of dendritic cells by Ralph Steinman and Zanvil Cohn in 1973, it is increasingly evident that dendritic cells are integral for adaptive immune responses, and there is an undeniable focus on them for vaccines development. Fungal infections, often thought in-nocuous, are becoming significant threats due to increased immunocompromised or im-mune-suppressed population and climate change. Further, the recent COVID-19 pandemic unraveled the wrath of fungal infections and devastating outcomes. Invasive fungal infections cause significant case fatality rates ranging from 20% to 90%. Regrettably, no licensed fungal vaccines exist, and there is an urgent need for preventive and therapeutic purposes. In this re-view, we discuss the ontogeny, subsets, tissue distribution, and functions of lung dendritic cells. In the latter part, we summarize and discuss the studies on the DC-based vaccines against pulmonary fungal infections. Finally, we highlight some emerging potential avenues that can be incorporated for DC-based vaccines against fungal infections.

This qualitative study explores the dynamics of supplier relationships among Italian restaurant managers, aiming to uncover the strategic practices and challenges in supplier relationship management (SRM) within the culinary industry. Through semi-structured interviews with a diverse sample of restaurant managers across Italy, the study examines how trust, communication, adaptive sourcing strategies, and innovation influence supplier interactions and operational outcomes. Findings reveal that trust-building is foundational to establishing reliable supplier partnerships, crucial for ensuring consistent delivery of high-quality ingredients essential for culinary excellence. Effective communication channels and negotiation techniques are pivotal in clarifying expectations, resolving conflicts, and fostering collaborative relationships with suppliers. Adaptive sourcing strategies, including diversification of supplier bases and local sourcing initiatives, enable managers to navigate market dynamics and meet consumer demands effectively. Innovation emerges as a catalyst for differentiation and sustainability in supplier relationships, with managers engaging in collaborative product development and embracing sustainability initiatives to align with consumer preferences and corporate values. Despite facing challenges such as pricing pressures, supply chain logistics complexities, and regulatory compliance, proactive management strategies and operational agility enable managers to mitigate risks and capitalize on opportunities in supplier management. Theoretical implications enrich understanding of SRM dynamics in the culinary sector, while practical implications suggest strategies for optimizing supplier relationships, enhancing operational efficiency, and sustaining competitive advantage. Future research could explore technological advancements, consumer trends, and longitudinal impacts of SRM practices on business performance within the restaurant industry.

The integration of robotic devices into healthcare settings for rehabilitation purposes represents a rapidly advancing field with significant benefits for both therapists and patients. However, standalone robot systems face limitations such as inefficient processing of patient data and logistical challenges for patients, such as travel time to rehabilitation centers. To address these shortcomings, this paper proposes the integration of cyber-physical systems into rehabilitation robotics. Cyber-physical systems leverage network connectivity and real-time data processing to enhance the efficiency and convenience of robot-based rehabilitation. By seamlessly integrating sensors, actuators, and computational algorithms, cyber-physical rehabilitation systems empower therapists to more effectively treat patients by providing actionable insights derived from comprehensive data analysis. This enables therapists to tailor treatment plans precisely to each patient’s unique needs, ultimately leading to improved outcomes. Additionally, cyber-physical rehabilitation systems facilitate remote monitoring and intervention, allowing therapists to oversee patient progress and make timely adjustments to treatment protocols without the need for in-person visits. This not only saves patients valuable time and resources but also ensures continuity of care, particularly for individuals residing in remote or underserved areas. Furthermore, cyber-physical rehabilitation systems enable collaborative decision-making by facilitating communication and information sharing among healthcare professionals, further enhancing the quality and efficacy of rehabilitation services. In summary, integrating cyber-physical systems into rehabilitation robotics represents a significant advancement in leveraging technology to optimize patient care and outcomes, promising to revolutionize rehabilitation therapy.

The increasing complexity of vehicular traffic and the prevalence of accidents, particularly rear-end collisions, necessitate enhancements in automotive safety technologies. Adilights, an innovative automotive lighting system, addresses this need by introducing a dynamic visual communication method between vehicles. This system features a novel design that employs variable-intensity brake lights configured in an upside-down "U" shape to indicate braking severity. The intensity and pattern of the lights change in response to the pressure applied to the brake pedal, providing clear and immediate feedback to following drivers. This design not only improves the perceptibility of braking signals, particularly in dense traffic and poor visibility conditions but also aims to reduce the frequency of rear-end collisions significantly. The paper details the development and testing of Adilights, discusses its integration with current vehicular systems, and evaluates its effectiveness through simulated scenarios. The introduction of Adilights marks a step forward in the use of adaptive lighting systems for enhanced vehicular communication and road safety.\cite{Lee2007RearEnd}

COVID-19, caused by the SARS-CoV-2 virus, has spread around the world and killed around 6.9 million people. Rapid and accurate diagnosis is essential for preventing and controlling the disease, reducing transmission and consequently saving lives. RT-PCR is the gold standard test used to detect the disease. However, the test is expensive and the result is time-consuming, which makes mass testing difficult, especially in countries with limited resources. In addition, the test has high analytical specificity and low diagnostic sensitivity, which leads to false-negative results. Several studies in the literature report the presence of hematological and biochemical alterations in infected patients and use these alterations with machine learning algorithms to help diagnose the disease. Therefore, this article presents the results obtained by different neural network architectures based on Adaptive Resonance Theory (ART) for the diagnosis of COVID-19. The study was conducted in two distinct stages: the first consisted of selecting the best ART network among several, using three open-access datasets and comparing the results with the literature. In the second stage, the chosen model was tested on a dataset containing patients from various hospitals in four countries. In addition, the model was subjected to external validation, including data from a country not present during the training and adjustment of the model, in order to validate the robustness and generalization capacity of the model. The results obtained by the ART networks in this study are promising, outperforming not only classical models, but also the deep learning models often used in the literature. Validation on data from different countries strengthens the model’s reliability and effectiveness.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

In this paper, an adaptive proportional and integral (PI) controller for speed control of electric drives is developed. The adaptive PI controller is designed based on model reference adaptive identification and the Lyapunov function so that the system stability can be proved. To identify the inertia, the viscous friction torque coefficient, and the load torque simultaneously and smoothly, sinusoidal speed reference is usually employed. A modified motion equation considering the low-pass filter for feedback speed and the Coulomb friction torque is introduced and the relationship between the identified parameters and their actual values are clarified. A high-precision digital simulation model is built based on MATLAB/Simulink. The effectiveness of the proposed method is verified by simulations under various conditions.

In this paper, we present findings from a qualitative interview study, which highlights the difficulties and challenges with quantifying carbon emissions, and discusses how to move productively through these challenges by drawing insights from studies of deep uncertainty. Our research study focuses on the digital sector and was governed by the research question: how do practitioners researching, working or immersed in the broad area of sustainable digitisation (researchers, industry, NGOs, and policy representatives) understand and engage with quantifying carbon? Our findings show how stakeholders struggled to measure carbon emissions across complex systems, the lack of standardisation to assist with this, and how these challenges led stakeholders to call for more data to address this uncertainty. We argue that calls for more data to address this uncertainty obscure the fact that there will always be uncertainty, and that we must learn to govern from within it.

The implementation of neural networks (NN) on edge devices enables local processing of wireless data but faces challenges such as high computational complexity and memory requirements when deep neural networks (DNN) are used. Shallow neural networks customized for specific problems are more efficient, requiring fewer resources, and resulting in a lower latency solution. An additional benefit of the smaller network size is that it is suitable for real-time processing on edge devices. The main concern with shallow neural networks is their accuracy performance compared to DNNs. In this paper, we demonstrate that a customized adaptive activation function (AAF) can meet the accuracy of a DNN. We designed an efficient FPGA implementation for a customized segmented spline curve neural network (SSCNN) structure to replace the traditional fixed activation function with an AAF. We compared our SSCNN with different neural network structures such as real-valued time delay neural network (RVTDNN), augmented real-valued time delay neural network (ARVTDNN), and deep neural networks with different parameters. Our proposed SSCNN implementation uses 40% fewer hardware resources and no Block RAMS compared to the DNN with similar accuracy. We experimentally validate this computationally efficient and memory-saving FPGA implementation of SSCNN for digital predistortion of RF power amplifiers using the AMD/Xilinx RFSoC ZCU111. The implemented solution uses less than 3% of the available resources. The solution also enables an increase of the clock frequency to 221.12 MHz, allowing the transmission of wide bandwidth signals.

In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate that MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability.

Climate change impacts threaten sustainable development efforts. The magnitude of the impacts, however, varies with socio-ecological characteristics of locations. This is the reason there is con-sensus on the necessity for climate change adaptive capacity building that is country driven, based on and responsive to local needs. However, information on context specific capacity building needs in developing countries is not readily available. The objective of this study was to establish location specific awareness, training, educational, research and technology capacity building needs for climate change adaptation among small-holder farmers in Uganda. Structured interviews were undertaken with 465 households from five agro-ecological zones selected based on the level of vulnerability of agricultural systems to the main climate variation and change hazards. Results reveal substantial capacity building needs in all the zones. Majority of the farmers needed capacity building for interventions on soil water conservation practices for adapting to drought and un-predictable rainfall. For all zones, education, research, and technology were perceived as key needs. However, the needs varied among zones. These results demonstrate the importance of context specificity in adaptation efforts. The study provides agro-ecological and social system specific in-formation for climate change adaptation planning and policy interventions for effective capacity building

In this paper, the filtering method of biomedical image denoising is described comprehensively. Firstly, it introduces the biomedical image denoising, describes the relationship between biomedical image denoising and medical care, introduces the filtering methods, the filtering methods of biomedical image denoising, the challenges encountered by the current filtering methods, and other application fields of filtering methods. Firstly, the background of biomedical image denoising is introduced. Biomedical image denoising is a challenge. Different imaging modes have different noise characteristics, and noise levels can vary greatly depending on the specific application. Secondly, it describes that biomedical image denoising plays an important role in medical care, and the biomedical image directly affects the patient's diagnosis, treatment plan and the overall quality of medical care service. Then the filtering method is introduced in detail, describing the core concepts and related features of linear filtering, nonlinear filtering and frequency domain filtering, and then focusing on the adaptive filtering method, describing the characteristics, conditions of use, common algorithms and advantages of adaptive filtering method. Then the filter methods of biomedical image denoising are introduced, and the core concepts of Gaussian filter, median filter, total variation denoising and Wiener filter are introduced respectively. Then, the challenges encountered by filtering methods are described, such as the accurate selection of filters, the balance between noise reduction and image detail preservation are introduced. Finally, the application of filtering method in other fields is mentioned, such as audio processing, speech recognition and so on. In summary, this paper comprehensively expounds the denoising and filtering methods of biomedical images, the filtering methods of medical image denoising, the relationship between medical image denoising and medical care, and the challenges encountered by filtering methods.

In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using the real-time data from a hydropower turbine testing system. This led to a semi-physical modeling in which the first principles and data-driven modeling are integrated to produce dynamic models for the DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Run-time validation techniques are required because complex Self-adaptive systems must consistently offer acceptable behavior for important services. The run-time testing can offer further confidence that a Self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for Self-adaptive systems. To ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the SPEA2 algorithm facilitates both the execution and adaptation of run-time testing operations.

A strong correlation between karyotype diversity and species richness in mammals was first reported over forty years ago: in mammalian phylogenetic clades, standard deviation of karyotype diversity (KD) closely corresponds to the standard deviation of species richness (SR). These initial studies, however, did not control for phylogenetic signal, raising the possibility that the correlation was due to phylogenetic relatedness among species in a clade. Accordingly, karyotype diversity trivially reflects species diversity simply as a passive consequence of adaptive radiations. A more recent study in mammals has controlled for phylogenetic signal and established the correlation as phylogenetically independent, suggesting that species diversity cannot, in itself, explain the observed corresponding karyotype diversity. The correlation is therefore remarkable because the mechanisms contributing to karyotype diversity are evolutionarily independent of the mechanisms contributing to species diversity. Recently, it was shown in salamanders that the two processes generating genetic diversity and species diversity are indeed independent and operate in parallel, suggesting a potential non-adaptive and non-causal relationship between the two fundamental variables involved in adaptive radiations. The following will examine the relationship between KD and SR in the context of Motoo Kimura’s theory of non-adaptive radiation.

The goal of this paper is the performance evaluation of a deep learning approach when deployed in fifth-generation (5G) millimeter wave (mmWave) multicellular networks. To this end, the optimum beamforming configuration is defined by two neural networks (NNs) that are properly trained, according to mean square error (MSE) minimization. The first network has as input the requested spectral efficiency (SE) per active sector, while the second network the corresponding energy efficiency (EE). Hence, channel and power variations can now be taken into consideration during adaptive beamforming. The performance of the proposed approach is evaluated with the help of a developed system level simulator via extensive Monte Carlo simulations. According to the presented results, machine learning (ML)-adaptive beamforming can significantly improve EE compared to the standard non-ML framework. Although this improvement comes at the cost of increased blocking probability (BP) and radiating elements (REs) for high data rate services, the corresponding increase ratios are significantly reduced compared to the EE improvement ratio. In particular, considering 21.6 Mbps per active user and ML adaptive beamforming, then EE can reach up to 5.3 Mbps/W, which is significantly improved compared to the non-ML case (0.9 Mbps/W). In this context, BP does not exceed 2.6%, which is slightly worse compared to 1.7% in the standard non-ML case. Moreover, approximately 20% additional REs are required, with respect to the non-ML framework.

Shape Memory Polymer (SMP) actuators are smart materials that adapt and switch shape in response to environmental conditions, allowing controlled and dynamic building systems. The integrated SMP strip enabled the adaptive facade shading system to operate independently and with flexibility under certain thermoresponsive conditions to regulate building performance. This study aims to improve adaptive façade performance in a hot-humid climate by determining optimal thermal activation factors for the shape memory polymer. This was achieved by comprehensively examining and analysing the impact of various SMP activation parameters on energy efficiency, thermal comfort, and visual comfort. In addition, dynamic IES-VE building energy simulation was used to evaluate the thermal performance of the adaptable facade. The results were validated using a multicriteria approach to compare with the existing glass façade of a high-rise apartment. The results showed that the coactivation technique, which combines solar radiation and air temperature triggers, proved to be the most efficient strategy to improve the performance of the adaptive façade. This strategy minimised heat gain, lowered indoor temperatures, reduced glare, and enhanced daylight comfort, resulting in notable energy savings through reduced cooling consumption. The study concluded that the utilisation of SMP actuators with fine-tuning activation parameters significantly optimised adaptive façade performance, promoting functionality, efficiency, and sustainability in buildings.

Background: With the increasing global incidence of diabetes mellitus (DM), diabetic retinopathy (DR) has become one of the leading causes of blindness in developed countries. DR leads to changes in retinal neurons and microcirculation. Rtx1TM (Imagine Eyes, France) is a microscope that allows histological visualisations of cones and retinal microcirculation throughout the DM duration. Objective: This study aimed to analyse the cones and retinal microvascular changes in 50 diabetic individuals and 18 healthy volunteers. The patients participated in the initial visit and two fol-low-up appointments: one and two years after the study, beginning with rtx1 image acquisition, visual acuity assessment, macular OCT scans and blood measurements. Results: The study revealed significant differences in the cone density, mosaic arrangement and vascular morphology between healthy and diabetic patients. The final measurements have shown decreased photoreceptor and microvascular parameters in the DR group compared to the control group. Furthermore, in the 2-year follow-up, both groups' rtx1-acquired morphological changes were statistically significant. Conclusions: Rtx-1 technology was successfully used as a non-invasive method of photoreceptors and retinal vasculature assessment over time in patients with diabetic retinopathy. The study has revealed a trend toward more vascular morphological changes occurring over time in diabetic patients.

Infrared detection plays an important role in the military, aerospace, and other fields, which has the advantages of all-weather, high stealth, and strong anti-interference. However, infrared dim small target detection suffers from complex backgrounds, low signal-to-noise ratio, blurred targets with small area percentages, and other challenges. In this paper, we proposed a multiscale YOLOv5-AFAM algorithm to realize high-accuracy and real-time detection. Aiming at the problem of target intra-class feature difference and inter-class feature similarity, the Adaptive Fusion Attention Module - AFAM was proposed to generate feature maps that are calculated to weigh the features in the network and make the network focus on small targets. This paper proposed a multiscale fusion structure to solve the problem of small and variable detection scales in infrared vehicle targets. In addition, the downsampling layer is improved by combining Maxpool and convolutional downsampling to reduce the number of model parameters and retain the texture information. For multiple scenarios, we constructed an infrared dim and small vehicle target detection dataset, ISVD. The multiscale YOLOv5-AFAM was conducted on the ISVD dataset, compared to YOLOv7, mAP@0.5 achieves a small improvement while the parameters are only 17.98% of it. By contrast with the YOLOv5s model, mAP@0.5 was improved by 4.3% with a 6.6% reduction in the parameters. Experiments results demonstrate that the multiscale YOLOv5-AFAM has a higher detection accuracy and detection speed on infrared dim and small vehicles.

The risks for complications of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection are higher in obese individuals. Obesity is a state of chronic low-grade inflammation, with high leptin levels due to leptin resistance, high basal levels of other pro-inflammatory cytokines such as TNF-alpha, MCP-I and IL-6, and low adiponectin levels, thus contributing to a state of defective innate immunity as well as impaired B and T cell responses. Obesity is a risk factor for metabolic syndrome, diabetes, cardiovascular disease and hypertension. It has been observed that pre-existence of these diseases confers a higher risk of severe SARS CoV2 infection as well as the need for intensive care; even below the age of 60 years if their body mass index (BMI) is greater than 30 kg/m2, and even more so if it is > 35 kg/m2. The metabolic factors contributing to the changes in altering the immune mechanisms in obese individuals and how this enhances the susceptibility to infection and development of serious SARS-CoV2 infection have been the subject of many debates. Future development of targeted therapy and guidelines will be benefited by greater understanding into these metabolic pathways.

The main problem in creating anti-coronavirus vaccines that target mainly proteins of the outer membrane of the virus remains the rapid variability of the RNA genome of the pathogen that encodes these proteins. In addition, the introduction of technologies that can provide affordable and fast production of flexible vaccine formulas that easily adapt to the emergence of new subtypes of SARS-CoV-2 is required. Universal oligonucleotide vaccine can take into account the dynamics of rapid changes in the virus genome, as well as be synthesized on automatic DNA synthesizers in large quantities in a short time. In this brief report, the effectiveness of four phosphorothioate constructs of the La-S-so type oligonucleotide vaccine will be evaluated for the first time on transgenic mice [B6.Cg-Tg (K18-ACE2)2]. In our primary trials, the oligonucleotide vaccine increased the survival rate of animals infected with SARS-CoV-2 and also reduced the destructive effects of the virus on the lung tissue of mice. The obtained results show the perspective of the development of vaccine constructs of the La-S-so type for the prevention of coronavirus infections, including those caused by SARS-СoV-2.

Forest vegetation dynamics (succession and dominance) is an ecological phenomenon that is still difficult to characterize and integrate into management practices. In this study, the understanding of forest dynamics is explored based on Grime’s theoretical triangular model of plant adaptive strategies using the example of Lithuania’s forest ecosystems. The idea behind this is the hypothesis that forest dynamics is linked to natural selection as an evolutionary process that exhibits differential species responses to competition, stress, and disturbance. The aim of this study is to explore the adaptive relationships in hemi-boreal forests. Grime’s and Pierce’s secondary CSR strategies, which describe various equilibria between competitiveness (C), stress tolerance (S), and ruderalism (R), were considered to reflect four establishment and development adaptive specialization characteristics of forest tree species. As a result of the study, four types of tree functional groups were identified: stress-resistant ruderals, competitive stress-sensitive ruderals, ruderal stress-sensitive competitors, and stress-resistant competitors. Based on this, we propose that reforestation move away from single species regeneration by implementing the maintenance of these four types of functional groups. In conclusion, forest management must consider the existence of the established equilibria between plant competitiveness, stress tolerance, and ruderalism. The formal concepts presented in this article can serve as a guide for future relevant research and development of appropriate methods for studying real forests. This study is unique in that no previous work has linked forest dynamics and natural selection in the context of Lithuania’s forest ecosystems.

Abstract: Obstructive sleep apnea (OSA) is well known to often improve with non-supine positioning as opposed to su¬pine positioning. Emerging research supports a role for sleep position management in patients with central sleep apnea (CSA) as well. We report a case of de novo Complex Sleep apnea Syndrome (CompSAS) in a 78-year-old female, who presented after a car accident due to unclear syncope. Diagnostic polysomnography (PSG) showed a moderate OSA. A CompSAS developed under Automatic positive airway pressure (APAP), while download data of 4 years showed a good adherence. No significant benefit was reported under Adaptive Servo Ventilation (ASV) and BiPAP-ST, while a reduction of CSA in non-supine position was noticed. Oxygen and sleep positional therapy (SPT) were considered resulting in a significant improvement of CSA and sleep quality. Further research on the prevalence of positional CSA is needed.

This article investigates the dynamics of socio-ecological systems (SESs) unsustainability. By adopting a theoretical standpoint grounded in systems’ theory, the analysis shows how SESs’ teleology (or final cause) is of the utmost relevance for understanding unsustainability and how it is pivotal for envisioning possible evolutionary trajectories towards sustainability. Building on the contributions of both system and social scientists, the study argues that SES’s teleology is determined by dominant social ontologies that require a dialectical lens to be properly dealt with. The article therefore proposes the adoption of the adaptive cycle heuristic complemented by an historical-geographical approach based on world-ecology theory as a means to dynamically model of SESs’ behaviour. Such a perspective allows for the direct comparison between the four stages of the panarchy cycle (reorganization, exploitation, conservation, and release) and the four stages theorized by the world-ecology dialectics (expansion, appropriation, capitalization, crisis). In conclusion, the article claims that both system and social scientists would benefit from including in their analysis concepts and definitions from the other field, since both provide valuable insights about SESs' processes of change and both are necessary to envision transition pathways towards sustainability.

Introduction: The aim of our study was to evaluate the efficacy and toxicity of a daily-adaptive MR-guided SBRT on 1.5 T MR-linac in patients affected by lymphnode oligometastases from PCa. Materials and Methods: The present study is a prospective observational study conducted in a single institution (protocol n°: MRI / LINAC n. 23748). Patients with oligometastatic lymphnodes from PCa treated with daily-adaptive MR-guided SBRT on 1.5T MR-linac were included in the study. Minimum required follow-up of 3 months after SBRT. Primary end-point was local progression-free survival (LPFS). Secondary end-points were: nodal progression-free survival (NPFS), and progression-free survival (PFS), and toxicity. Results: 118 lymphnode oligometastases from PCa were treated with daily-adaptive 1.5T MR-guided SBRT in 63 oligometastatic patients. 63.5% patients were oligoprogressive and 36.5% oligoprogressive. Two-year LPFS was 94.5%. Median NPFS was 22.3 months, and the 2-year NPFS was 46.5%. Having received hormone therapy before SBRT was correlated with lower NPFS at the multivariate analysis (1-y NPFS 87.1% versus 42.8%; p= 0.002 - HR 0.199, 95% CI 0.073-0.549). Furthermore, the oligorecurrent state during ADT was correlated with a lower NPFS than the oligoprogressive state. Median PFS was 10.3 months, the 2-year PFS was 32.4%. Patients treated with hormone therapy before SBRT had a significantly lower 1-year PFS the others (28% versus 70.4%; p= 0.01 - HR 0.259, 95% CI 0.117-0.574). No acute and late toxicities occurred during treatment. Conclusion: the present is the largest prospective study of 1.5T lymphnode SBRT on MR-linac in patients with PCa. Lymphnode SBRT by 1.5T MR-linac provides high local control rates with an excellent toxicity profile.

It is a truism that whole energy system models underpin the development of policies for energy system decarbonisation. But recent reviews have thrown doubt on the appropriateness of such models for addressing the multiple goals for future energy systems, in the face of emergent real-world complexity and the evolution of stakeholder’s priorities. Without an understanding of the changing priorities of policy makers and expectations of stakeholders for future systems, system objectives and constraints are likely to be ill-defined, and there is a risk that models may be inadvertently instrumentalised. Adopting a system architecture perspective, the authors have undertaken a three-year programme of research to explore strategies for decarbonising heat in the UK, with interaction with and elicitation of needs from stakeholders at its heart. This paper presents the procedure, methods, and results of an exercise in which experts from stakeholder organisations across the energy system were interviewed. Analysis of interview data reveals two broad approaches to heat decarbonisation which can be broadly defined as either adaptive or transformative. Specific insights gained from these interviews enabled our modelling teams to refocus their work for exploration with a wider circle of stakeholders. Results suggests that this iterative approach to formalising model-policy interaction could improve the transparency and legitimacy of modelling and enhance its impact on policy making.

Breaking up prolonged sitting by short bouts of light physical activities including standing and walking has been shown to be beneficial for people with type 2 diabetes (T2D). This paper presents the development of an android mobile app to deliver a just-in-time adaptive intervention (JITAI) to reduce sedentary time in people with T2D. A total of six design workshops were conducted with seven experts to identify design requirements, a behavioural framework, and required contextual adaptations for the development of a bespoke mobile app (iMOVE). Moreover, a focus group was conducted among people with T2D as potential end-users (N=10) to ascertain their perceptions of the app. Feedback from the focus group was used in subsequent iterations of the iMOVE app. Data were analysed using an inductive qualitative thematic analysis. Based on workshops, key features of iMOVE were developed, including simplicity (e.g., navigation, login), colours and font sizes, push notifications, messaging algorithms and a triggering system for breaking up sitting time and moving more. Based on the user testing results, a goal setting tab was added, font sizes were made larger, the brightness of colours was reduced, and a colour indicator was used to indicate device connectivity with an activity tracker. A user-centric app was developed to support people with T2D to transition from sedentary to active lifestyles.

The ACC feature when activated augments the engine performance in real-time. This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by considering all the effecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the vehicle-level vectors (VLV). We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived for each second. Thus three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. Performance comparison between predicted and constant ACCSSPs indicates that the predictive model outperforms constant ACCSSP.

Computer systems are involved in many critical human applications today, so that a small error can lead to serious and dangerous problems. These errors can be from an error in the incorrect design of the user interface to an error in the program code. The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable Software according to the goals and needs of the organization makes measuring the quality of software products. an important issue for most organizations and institutions, To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured by Adaptive Neural Fuzzy Inference System. In recent years, powerful systems called fuzzy inference systems on The basis of adaptive neural network (ANFIS) has been used in various sciences. Using the power of neural network training and the linguistic advantage of fuzzy systems, these types of systems have been able to realize the advantages of the two in terms of analyzing very powerful complex processes. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study. It was applied at different levels to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the adaptive neural-fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

The power system vulnerability leads to faults and the severity of the fault may lead to prolonged load-shedding. The power system needs to be configured in extreme failure scenarios for protecting the network from further contingencies and prolonged load-shedding. Distributed generation resources (DGs) can be useful to form intentional islands after faults to maintain the continuity of power supply to loads based on their weightage during faulty periods and to reduce overall load shedding duration. Power system is bound to collapses and secondary collapse in the formed island is possible. This research represents a novel method of impedance based path finding for intentional islanding, which adapts itself with the changes in the demands, DGs outputs or further severities during restoration period. In this adaptive islanding approach, network adjusts itself with the changes in either the load demands or renewable DGs outputs and rearranges the restoration plan by curtailing or adding some of the loads through controllable switches. Further a secondary collapse in the existing island is studied by injecting multiple faults at various positions of the network to validate the system resilience to cope with severities. A short-term load forecasting approach is used to predict changes in load demands and variations in DG outputs during the islanding scheme. During the restoration period, these variations are tracked and the islands are modified accordingly. In order to minimize the overall generation cost by using less fuel, an economical approach is used in the selection of controllable DGs. The proposed approach is formulated as a multi-objective, that incorporates several operational constraints and simulation is carried out using the modified IEEE 69-bus distribution system to assess the efficacy of the proposed model.

Novel coronavirus disease (COVID-19) has affected nearly 7 million individuals and claimed more than 0.4 million lives to date. There are several reports of gender differences related to infection and death due to COVID-19. This raises important questions such as “Whether there are differences based on gender in risk and severity of infection or mortality rate?” and “What are the biological explanation and mechanisms underlying these differences?” Emerging evidence has proposed sex-based immunological, genetic, and hormonal differences to explain this ambiguity. Besides biological differences, women have also faced social inequities and economic hardships due to this pandemic. Several recent studies have shown that independent of age males are at higher risk for severity and mortality in COVID-19 patients. Although susceptibility to SARS-CoV-2 was found to be similar across both genders in several disease cohorts, a disproportionate death ratio in men can be partly explained by the higher burden of pre-existing diseases and occupational exposures among men. From an immunological point of view, females can engage a more active immune response, which may protect them and counter infectious diseases as compared to men. This attribute of better immune responses towards pathogens is thought to be due to high estrogen levels in females. Here we review the current knowledge about sex differences in susceptibility, the severity of infection and mortality, host immune responses, and the role of sex hormones in COVID-19 disease.

This paper presents a comparison between a kite model with a constant-length tether and a model based on a system identification algorithm. The concept of system identification is applied to predict the uncertainties related to the variation of the wind speed and the shape deformation of the tethered membrane wing during flight. A pole-placement controller is used to ensure that the kite follows the planned flight path. Thus, we can determine the required locations of the closed loop poles, and then enforce them by changing the controller's gains in real-time. The capability of the system identification algorithm to recognize sudden changes in the dynamic model, and the ability of the controller to stabilize the system in the presence of such changes are confirmed. Furthermore, the system identification algorithm is applied to determine the parameters of a kite with variable-length tether used in a flight test of the 20 kW kite power system of TU Delft. Experimental data of this test were compared with the system identification results in real-time and significant changes were observed in the parameters of the dynamic model which heavily affect the resulting response.

In this paper, we present a finite element simulation of an adaptive piezoelectric fluid-membrane lens modeled in COMSOL Multiphysics. The simulation couples the piezoelectric effect with the fluid dynamics to model the interaction between piezoelectric forces and fluid forces. Also, the simulation is extended to model the thermal expansion of the fluid. Finally, we compare the simulation and experimental results of the adaptive lens refractive power at different actuation levels and temperatures.

With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improving data storage, transmission and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. By introducing the idea of compressive sensing in compressed reconstruction, the spectral adaptive grouping distributed compressive sensing algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The experimental results showed that comparing with orthogonal matching pursuit(OMP) and gradient projection reconstruction(GPSR), the proposed algorithm can significantly improve the visual effect of image reconstruction in the spatial domain. The PSNR in low sampling rate(sampling rate is lower than 0.2) increases by 13.72dB than OMP and 1.66dB than GPSR. In the spectral domain, the average normalized root mean square error、the mean absolute percentage error and the mean absolute error of the proposed algorithm is35.38%，31.83% and 33.33% lower than GPSR respectively.. Therefore, the proposed algorithm can achieve relatively high reconstructed efficiency.

In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UFK based MAP and based ML, the proposed algorithm provides better convergence and stability.

This paper presents the analysis of four error models that can appear in the field of adaptive systems, where adaptive laws for parameter estimation are represented as fractional order differential equations whose order can switch between an order within the range (0,1) and 1. These switched adaptive laws have been proposed in the past few years to improve the balance between control energy and system performance, which is often a challenge when using traditional integer-order or fractional-order adaptive laws in adaptive identification and control. Boundedness of the solutions is proved for all cases, together with the convergence to zero of the estimation/tracking error. Additionally, sufficient conditions for parameter convergence are presented, showing that the excitation condition required for parameter convergence in the vector case is also sufficient for parameter estimation in the matrix case.

Globally, 77\% of the elderly aged 65 and above suffer from multiple chronic ailments, according to recent research. However, several barriers within the healthcare system in the developing world hinder the adoption of home-based patient management. IoMT is beneficial for healthcare personnel, but its application raises security concerns, particularly in authentication. Several authentication techniques have been proposed which, however, lack a balance of security and usability. By analyzing the shortcomings of the existing authentication methods and the available sensors on smartphones, this paper proposes to explore the possibility of using a smartphone’s embedded sensors for elderly user identification process in IoMT. We proposed an adaptive authentication technique for the elderly patients using the MAPE-K$_{\text{HMT}}$ framework by developing an android application that takes as input, the user’s contextual information including device fingerprint, location, network, and user data to monitor the user and estimate the risk associated with a user using the Naïve Bayes algorithm. The risk score was then used as input to the rule-based module that assigned authenticators according to user age, health condition and available authenticators. The model demonstrated high accuracy in distinguishing between authorized and unauthorized access attempts and supported the usable security goal for the elderly users.

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels’ long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels’ correlations are remarkable and stable for images generated by a known physical principle, such as infrared images and medical CT images. And the corresponding non-extensive parameter q can be evaluated by using self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of Shannon entropy algorithm and the original Tsallis entropy algorithm, in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

This study investigates how organizations adapt their procurement practices in response to supply chain disruptions, aiming to enhance resilience and ensure operational continuity. Through qualitative research involving interviews with procurement professionals and industry experts, the study identifies key adaptive strategies employed by organizations, including supplier diversification, enhanced inventory management, digital transformation, supplier collaboration, and contingency planning. These strategies are explored in depth, highlighting their implementation challenges and the outcomes achieved. Despite facing obstacles such as financial constraints, technological barriers, and supplier resistance, organizations have demonstrated a proactive approach in leveraging these strategies to mitigate risks and improve supply chain resilience. The findings reveal that adaptive procurement strategies have led to significant outcomes, including strengthened supplier relationships, increased operational efficiency, better risk management, and competitive advantage. These outcomes underscore the strategic importance of agility, technological integration, collaborative partnerships, and effective leadership in responding to disruptions and maintaining competitiveness. The study also discusses implications for practice and policy, emphasizing the need for continued investment in technology, collaboration, and resilience-building initiatives across supply chains. Overall, this research contributes to the understanding of how organizations can adapt and innovate in the face of dynamic supply chain challenges, providing insights that can guide future research and inform strategic decision-making in procurement and supply chain management.

：Non-Euclidean data, such as social networks, and citation relationships between documents, has node information and structural information. Graph Convolutional Network(GCN) can automatically learn node features and association information between nodes. The core ideology of the graph convolutional network is to aggregate node information by using edge information, thereby generating a new node feature. In the process of updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node, the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the Adaptive Attention Mechanism(AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution(MHGC). Finally, we adopt cross-entropy(CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on AAM, MHGC and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). Experiments show that the classification accuracy has achieved outstanding performances on Cora, Citeseer and Pubmed datasets.

As most of the videos are destined for human perception, many techniques have been designed to improve video coding based on how the human visual system perceives video quality. In this paper, we propose the use of two perceptual coding techniques, namely contrast masking and texture masking, jointly operating under the High Efficiency Video Coding (HEVC) standard. These techniques aim to improve the subjective quality of the reconstructed video at the same bit rate. For contrast masking, we propose the use of a specific weighting matrix for each block size (from 4×4 up to 32×32). We achieve average Bjøntegaard Delta-Rate (BD-rate) gains of between 2.5% and 4.48%, depending on the perceptual metric and coding mode used. The texture masking scheme is based on the classification of each coding unit, using its mean directional variance features as input to a support vector machine model. According to this classification, the block’s energy, the type of coding unit, and its size, an over-quantization is computed to provide a QP offset (DQP) for this coding unit. By applying both techniques in the HEVC Reference Software, an overall average of 5.79% BD-rate gain is achieved.

Sirtuins (SIRTs) are a family of evolutionarily conserved enzymes, and the aim of this study was to disclose/re-annotate uncertain paralogs in gilthead sea bream, expanding from seven to ten the repertoire of Sirts in this Perciform fish. Such gene expansion revealed up to three copies of sirt3 (sirt3.1a/sirt3.1b/sirt3.2) and two of sirt5 (sirt5a/sirt5b). Phylogenetic and synteny attempts disclosed the duplication of SIRT3/Sirt3 branch as a process shaped by the 2R whole genome duplication (WGD) at the early vertebrate evolution, with still vestiges in all living fish, and some Sarcopterygii. In gilthead sea bream and other Percomorphaceae, this duplication was accompanied by short segmental duplications of sirt3.1 (clustered to the mammalian SIRT3 ortholog), making a conserved tandem-duplicated synteny block (psmd13-sirt3.1a/b-drd4-cdhr5-ctsd). Conversely, the expansion of the Sirt5 branch was shaped by the 3R WGD as a specific event of the teleost fish lineage. Intriguingly, gene expression profiling across tissues and developmental stages highlighted a conserved high expression of sirt3.1 and sirt5a in skeletal muscle, while the duplicated sirt3.2 and sirt5b displayed an overall muscle suppression of transcriptional activity, and an enhanced expression on immune-relevant tissues and gills, respectively. Altogether offers new insights about the function, regulation, and timing of Sirt expansion.

: This paper presents a comparison between direct integer orders or classical model reference adaptive control (IO-DMRAC) versus its fractional orders counterpart (FO-DMRAC) applied to the F-16 aircraft longitudinal model pitch rate control. A substantial improvement is achieved when using fractional order adaptive control, since, in addition to performance indices, such as Integral Square-Error criterion (ISE) and Integral Square-Input criterion (ISU) being lower, the oscillations of the system output (pitch rate) in the transient period, are eliminated. Moreover, it is the first attempt in literature of adaptive control that a FO-DMRAC is applied to the pitch rate control of an aircraft. A F-16 short-period model, whose relative degree is equal to 1 (n^*=1) is analyzed by mean of simulations with good results. A FO-DMRAC is designed, for a specific operating flight conditions and compared with its IO-DMRAC counterpart. Furthermore, the boundedness of all signals, including the internal ones ω(t) are assured.

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing the sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with the uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems with a focus on the cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in the offline and online process optimization approaches have been presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, and reduced energy consumption and carbon emissions are independently investigated for the offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, the paper explores the challenges and opportunities inherent in developing a robust cyber-physical optimization system

Efficient traffic systems control in large cities with complex traffic management of signalized intersections is a challenging task, particularly when dealing with high traffic volumes. The city of Zagreb faces this challenge, as all administrative and governmental institutions are located in the old city centre, and routes for escorted vehicles have a significant impact on the traffic network. This paper addresses the issue of the impact of unconditional priority for escorted vehicles on the energy efficiency of the urban traffic network in the city of Zagreb. The traffic network model is developed by using the PTV Vissim microsimulation software. The evaluation was conducted with node (delay, queue length, number of stops) and network evaluation parameters (CO2 emission, NOx emission, PM10 emission, and fuel consumption). The results show that unconditional priority has minimal impact on energy consumption and exhaust emission in the observed scenario. This is a significant result considering all actions that have to be done to manage the passage of the escorted vehicles through the traffic network.

In this paper, an adaptive technology and the interconnection and damping assignment passivity-based control method are combined to solve the stabilization problem for underactuated mechanical systems with uncertainties (including matched and unmatched). Uncertainties include unknown friction coefficients and unknown terms in kinetic energy and potential energy. A novel adaptive interconnection and damping assignment passivity-based control scheme is proposed and an adaptive stabilization controller is designed to make the closed-loop system locally stable. Verification is conducted on the ball and beam system, taking into account uncertainties of friction coefficients, kinetic energy, and potential energy. The locally asymptotic stability is demonstrated using the LaSalle’s invariance principle and approximate linearization. The effectiveness of the proposed control law is verified through numerical simulations.

During meat processing, lactic acid bacteria (LAB) have to competitively adapt to the hostile en-vironment produced by curing additives (CA). The objective of this study was to investigate the ability of Latilactobacillus curvatus CRL 705, a bioprotective strain of meat origin, to adapt to CA. A physiological and proteomic approach was performed. CRL 705 was grown in a chemically de-fined medium (CDM) containing specific concentrations of CA (NaCl, nitrite, sucrose and ascor-bic acid). The results showed minor differences in growth kinetics in the presence of CA. Glucose consumption, present in CDM, and production of lactic acid and bacteriocins were not signifi-cantly affected. Proteomic analyses indicated that most of the identified proteins (36 out of 39) mainly related to carbohydrate metabolism (18%), posttranslational modifications (15.6%), energy production and conversion (11.1%), translation (11.1%) and nucleotide metabolism (8.9%), were under expressed. In response to the studied CA, CRL 705 slowed down its general metabolism, achieving slight changes in physiological and proteomic parameters. The observed performance is another characteristic that extends the well-known competitive profile of CRL 705 as a meat starter- and -bioprotective culture. This is the first report dealing with the impact of CA on LAB proteomics.

Traditional end-effector robots for arm rehabilitation are usually attached a­t the hand, primarily focusing on coordinated multi-joint training. Therapy at an individual joint level of the arm for severely impaired stroke survivors is not always possible with existing end-effector robots. The Arm Rehabilitation Robot (AREBO)—an end-effector robot—was designed to provide both single and multi-joint assisted training while retaining the advantages of traditional end-effector robots, such as ease of use and cost-effectiveness (compared to exoskeletons). This work presents the design, optimization, and characterization of AREBO for training single-joint movements of the arm. The AREBO has three actuated and three unactuated degrees of freedom, allowing it to apply forces in any arbitrary direction at its endpoint and self-align to arbitrary orientations within its workspace. The AREBO’s link lengths were optimized to maximize its workspace and manipulability. The AREBO provides single-joint training in both unassisted and adaptive weight support modes using a human arm model to estimate the human arm's kinematics and dynamics without using additional sensors. The characterization of the robot’s controller and the algorithm for estimating the human arm parameters were performed using a two degrees of freedom mechatronic model of the human shoulder joint. The results demonstrate that: (a) the movements of the human arm can be estimated using a model of the human arm and robot’s kinematics, (b) the AREBO has similar transparency to that of existing arm therapy robots in the literature, and (c) the adaptive weight support mode control can adapt to different levels of impairment in the arm. This work demonstrates how an appropriately designed end-effector robot can be used for single-joint training, which can be easily extended to multi-joint training. Future work will focus on the evaluation of the system on patients with any neurological condition requiring arm training.

Multi-level direct networks fueled by the evolving technology of active optical cables and increasing pin-bandwidth achieve reduced diameters and cost, with high-radix switches. These networks, like Dragonfly, are becoming the preferred aspirants for extreme high-performance parallel machines such as exa-scale computing. In this paper, we introduce a Hyper Z-Tree topology, which deploys the Z-Tree, a variant of fat-tree, as a computing node of a Generalized Hypercube (GHC) configuration. The resulting configuration provides higher bisection bandwidth, lower latency for some applications and higher throughput. Furthermore, the levels of the fat tree offer several path diversities across the GHC dimensions, hence conceding a more fault tolerant architecture. To profit from these path diversities, we propose two adaptive routing algorithms, which are extensions to the routing algorithm suggested in HyperX topology. These two algorithms exhibit better latencies and throughput than the HyperX.

The adaptive identification method developed to evaluate the parameters of the Bouc-Wen hysteresis (BWH). The adaptive approach based on the use of adaptive observers. We synthesize adaptive identification algorithms using the second Lyapunov method. The boundedness of adaptive system processes shown in coordinate and parametric spaces. We prove exponential dissipativity of processes in an adaptive system. Estimating method proposed for signaling uncertainty in the system.

The paper analyses the national DK-model Hydrological Information and Prediction (HIP) system and HIP portal viewed as a ‘Digital Twin’ and how the introduction of real-time dynamic updating of the DK-model HIP simulations can give room for plug-in sub-models with real-time boundary conditions made available from a HIP portal. The possible feedback to a national real-time risk knowledge base during extreme events (flooding and drought) is also discussed. Under climate change conditions, Denmark is likely to experience more rain in winter, more evapotranspiration in summer, intensified cloudbursts, drought, and sea level rise. These challenges have been addressed as part of the Joint Governmental Digitalization Strategy 2016-2020 for better use and sharing of public data about the terrain, water, and climate to support climate adaptation, water management, and disaster risk reduction. This initiative included the development of a new web-based data portal (HIP portal) developed by the Danish Agency for Data Supply and Infrastructure (SDFI). GEUS delivered 5 terra-byte of hydrological model data to the portal with robust calibration methods and hybrid Machine Learning (ML) being key parts of the deliverables. The paper discusses the challenges and potentials of further developing the HIP Digital Twin with ‘plug-in Digital Twins’ for local river basins including feedback to the national level.

Abstract. This article proposes new fuzzy controller to regulation the voltage, damp the oscillations and limit the overshoot behavior in single-machine infinite-bus power system. Two different independent MISO fuzzy controllers are designed. First controller, is voltage stabilizer and it composes of two adaptive fuzzy modules and proportional gain AVR. The voltage stabilizer is designed to regulate the terminal voltage, damp the voltage oscillation and limit the voltage overshoot to the industrial maximum allowable limit. The input signals of this stabilizer is are taken from the terminal voltage and the imposed voltage disturbance that can be read from any smart relay. The fuzzy output is used as feedback signal to control the AVR in order to achieve stabilized voltage equal to the reference voltage. The second fuzzy controller is designed with two input signals; “mechanical angle” and its derivative, and one output which is used as feedback signal to adapt the damping torque in order to damp the mechanical oscillation fast and with maximum overshoot not exceeding 10%, and hence damp the power angle oscillation that is important in the PSS improvement. MATLAB-SIMULINK was used for modelling and the test cases simulation. The results show very good performance for the proposed fuzzy controllers.

Grounded on urban morphology studies, the research tries to overcome the analysis of the permanents elements of the city seeking for a transitional paradigm in urban morphology, aiming at grasping the dynamics in urban evolution and providing operative tools for urban regeneration design in an adaptive approach. A combination of four actions of urban analysis is here suggested to highlight urban dynamics: a. Sorting the transitional steps of urban morphologies (within rapid market processes), b. Underlining rules and Processes characterizing urban coding in transition, c. Mapping urban assemblages in the adaptive city and d. Reading and representing urban permutation phenomenon. The results of this multifaced and multidimensional set of analytical tools allow to outline a new design thinking paradigm moving towards a parametric approach to urban design of cities in transition broadening the extent of urban regeneration process and supporting urban policies in the framework community based approach.

Season plays a key role in the development of outdoor spaces for pedestrians in hot humid cities. This research studies the influence of seasonal variations on pedestrian thermal comfort on the pedestrian level by means of meteorology and field observations of selected footpaths in the major tourist area of Malacca. This experiment was carried out on selected clear calm days indicative of each season during the development of a research project, and hourly meteorological transects from 10:00 am to 6:00 pm and questioned 200 respondents on their thermal awareness, comfort, and preferences were conducted. Adaptation, thermal comfort vote, thermal preference, age, season and hour of the day were significant non-meteorological factors, apart from meteorological information. The findings of analyzes showed that the thermal experience and expectation existed and in different seasons people changed perceptions for the outside thermal environment. Almost 80% local tourist and 55 % international tourist was accepted Physiologically Equivalent Temperature (PET) range affected by the local climate and thermal adaptation. The subjective thermal sensation on physiological equivalent temperature generated an acceptable physiological equivalent temperature of 32.6°C to 36.8°C based on the seasonal variations for Malacca tourist zone in Malaysia. These findings shed light on the optimal design of outdoor spaces for increasing the utilization rate. The seasonal variation must be taken into account so that the outdoor landscape design provides more opportunities for different seasons to communicate with the atmosphere and so enhance thermal comfort and utilization.

Hungateiclostridium thermocellum ATCC 27405 is a promising bacterium with a robust ability to degrade lignocellulosic biomass complexes, including crystalline cellulose components, through a multienzyme cellulosomal system. In contrast, it exhibits poor growth on simple monosaccharides such as fructose and glucose. This phenomenon raises many important questions concerning its glycolytic pathways and sugar transport systems. Until now, the detailed mechanisms of H. thermocellum adaptation to growth on monosaccharides have been poorly explored. In this study, adaptive laboratory evolution was applied to train the bacterium on monosaccharides, and genome resequencing was used to detect the genes that had mutated during adaptation. RNA-seq data of the 1st-generation culture growing on either fructose or glucose revealed that several glycolytic genes in the EMP pathway were expressed at lower levels in these cells than in cellobiose-grown cells. After 8 generations of culture on fructose and glucose, the evolved H. thermocellum strains grew faster and yielded greater biomass than the nonadapted strains. Genomic screening also revealed several mutation events in the genomes of the evolved strains, especially in genes responsible for sugar transport and central carbon metabolism. Consequently, these genes could be applied as targets for further metabolic engineering to improve this bacterium for bioindustrial usage.

The manual muscle test (MMT) is a flexible diagnostic tool, which is used in many disciplines, applied in several ways. The main problem is the subjectivity of the test. The MMT in the version of a “break test” depends on the tester’s force rise and the patient’s ability to resist the applied force. As a first step, the investigation of the reproducibility of the testers’ force profiles is required for valid application. The study examined the force profiles of n=29 testers (n=9 experiences (Exp), n=8 little experienced (LitExp), n =12 beginners (Beg)). The testers performed 10 MMTs according to the test of hip flexors, but against a fixed leg to exclude the patient’s reaction. A handheld device recorded the temporal course of the applied force. The results show significant differences between Exp and Beg concerning the starting force (padj=0.029), the ratio of starting to maximum force (padj=0.005) and the normalized mean Euclidean distances between the 10 trials (padj=0.015). The slope is significantly higher in Exp vs. LitExp (p=0.006) and Beg (p=0.005). The results also indicate that experienced testers show inter-tester differences and partly even a low intra-tester reproducibility. That highlights the necessity of an objective MMT-assessment. Furthermore, an agreement on a standardized force profile is required – a suggestion is given.

Mast cells are long-lived, granular, myeloid-derived leukocytes that have significant protective and repair functions in tissues. Mast cells sense disruptions in the local microenvironment and are first responders to physical, chemical and biological insults. When activated, mast cells release growth factors, proteases, chemotactic proteins and cytokines thereby mobilizing and amplifying the innate and adaptive immune system. Mast cells are therefore significant regulators of homeostatic functions and may be essential in microenvironmental changes during pathogen invasion and disease. During infection by helminths, bacteria and viruses, mast cells release antimicrobial factors to facilitate pathogen expulsion and eradication. Mast cell-derived proteases and growth factors protect tissues from insect/snake bites and exposure to ultraviolet radiation. Finally, mast cells release mediators that promote wound healing in the inflammatory, proliferative and remodeling stages. Since mast cells have such a powerful repertoire of functions, targeting mast cells may be an effective new strategy for immunotherapy of disease and design of novel vaccine adjuvants. In this review, we will examine how certain strategies that specifically target and activate mast cells can be used to treat and resolve infections, augment vaccines and heal wounds. Although these strategies may be protective in certain circumstances, mast cells activation may be deleterious if not carefully controlled and any therapeutic strategy using mast cell activators must be carefully explored.

The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered the pedagogical strategies that adapt to the real individual skills of the students. An important innovation in this direction is the Adaptive Educational Systems (AES) that support automatic modeling study and adjust the teaching content on educational needs and students' skills. Effective utilization of these educational approaches can be enhanced with Artificial Intelligence (AI) technologies in order to the substantive content of the web acquires structure and the published information is perceived by the search engines. This study proposes a novel Adaptive Educational eLearning System (AEeLS) that has the capacity to gather and analyze data from learning repositories and to adapt these to the educational curriculum according to the student skills and experience. It is a novel hybrid machine learning system that combines a Semi-Supervised Classification method for ontology matching and a Recommendation Mechanism that uses a hybrid method from neighborhood-based collaborative and content-based filtering techniques, in order to provide a personalized educational environment for each student.

There is an increasing trend that Unmanned Combat Air Vehicles (UCAVs) are employed to complete different combat missions in modern wars. This paper investigates a UCAV routing problem, which simultaneously considers the decisions for the configuration of weapons carried by the UCAV subject to its capacity and the allocation of weapons to the targets subject to their destroying requirements. An integer linear programming model is developed to formulate the problem. An adaptive large neighborhood search (ALNS) heuristic is proposed to solve the problem, in which seven neighborhood structures are designed and employed. Randomly generated instances covering the small, medium and large sizes are used to test the proposed ALNS algorithm. CPLEX is also utilized to solve the small-size instances, whose results are compared with that obtained by the ALNS algorithm. And the extensive experimental results confirm the effectiveness and superiority of the proposed ALNS algorithm.

The distribution of Mexican Magnolia species´ occur under restricted climatic conditions. As many other tree species from the tropical montane cloud forests (TMCF), Magnolia species appear to be sensitive to drought. Through the use of dendrochronological techniques, this study aims to determine the climate influence on the vessel traits of M. vovidesii and M. schiedeana which are endangered tree species that are endemic to the Sierra Madre Oriental in eastern Mexico. Because most of the tree species in TMCFs are sensitive to climate fluctuations, it is necessary to investigate the differences in the climatic adaptability of the vessel architecture of these trees. This could allow us to further understand the potential peril of climate change on TMCFs. We compared vessel frequency, length and diameter in drought and non–drought years in two Mexican Magnolia species. We used tree–rings width and vessel traits to assess the drought effects on Magnolias’ diffuse–porous wood back to the year 1929. We obtained independent chronologies for M. vovidesii with a span of 75 years (1941–2016), while for M. schiedeana we obtained a span of 319 years (1697–2016). We found that temperature and precipitation are strongly associated with differences in tree–ring width (TRW) between drought and non–drought years. Our results showed anatomical differences in vessel trait response between these two Magnolia species to climatic variation. We suggest that our approach of combining dendroclimatic and anatomical techniques is a powerful tool to analyse anatomic wood plasticity to climatic variation in Magnolia species.

The segmentation and de-noising are basic operations, required in every signal processing and classification system. The classical segmentation and de-noising approaches are time-invariant. Consequently, it results in the post processing of an unnecessary information and causes an increase in the system processing activity and power consumption. In this context, an efficient event-driven segmentation and de-noising technique is proposed. It is founded on the principles of level crossing and activity selection. Therefore, it can adapt its sampling frequency, segmentation window length and position along with the filter order by analyzing the input signal local characteristics. As a result, the computational complexity and the power consumption of the proposed system is reduced compared to the counter ones. The suggested system performance is compared with the classical one. It is done for the case of a multi-channel Electroencephalogram (EEG) signals. Results show a noticeable compression gain with an effective adaptation of the de-noising filters order. It aptitudes a significant computational gain, transmission data rate reduction and power consumption reduction of the proposed technique, compared to the counter ones. It shows that the proposed solution is an attractive candidate to embed in the new generation EEG wearables.

In this paper, the set-membership affine projection (SM-AP) algorithm is utilized to censor non-informative data in big data applications. To this end, the probability distribution of the additive noise signal and the excess of mean-squared error (EMSE) in steady-state are employed in order to estimate the threshold parameter of the single threshold SM-AP (ST-SM-AP) algorithm aiming at attaining the desired update rate. Furthermore, by defining an acceptable range for the error signal, the double threshold SM-AP (DT-SM-AP) algorithm is proposed to detect very large errors due to the irrelevant data such as outliers. The DT-SM-AP algorithm can censor non-informative and irrelevant data in big data applications, and it can improve misalignment and convergence rate of the learning process with high computational efficiency. The simulation and numerical results corroborate the superiority of the proposed algorithms over traditional algorithms.

In addition to image filtering in the spatial and frequency domains, fractal characteristics induced algorithms offers considerable flexibility in the design and implementations of image processing solutions in areas such as image enhancement, image restoration, image data compression and spectrum of applications of practical interests. Facing up to a real-world problem of identifying workpiece surface defects, a generic adaptive fractal filtering algorithm is proposed, which shows advantages on the problems of target recognition, feature extraction and image denoising at multiple scales. First, we reveal the physical principles underlying between signal SNR and its representative fractal dimension indicative parameters, validating that the fractal dimension can be used to adaptively obtain the image features. Second, an adaptive fractal filtering algorithm (Abbreviated as AFFA) is proposed according to the identified correlation between the image fractal dimensions and the scales of objects, and it is verified by a benchmarking image processing case study. Third, by using the proposed fractal filtering algorithm, surface defects on a flange workpiece are identified. Compared to conventional image processing algorithms, the proposed algorithm shows superior computing simplicity and better performance Numerical analysis and engineering case studies show that the fractal dimension is eligible for deriving an adaptive filtering algorithm for diverse-scale object identification, and the proposed AFFA is feasible for general application in workpiece surface defect detection.

For condensed systems, the incorporation of quantum chemical solvent effects into molecular dynamics simulations has been a major concern. To this end, quantum mechanical/molecular mechanical (QM/MM) techniques are popular and powerful options to treat gigantic systems. However, they cannot be directly applied because of temporal and spatial discontinuity problems. To overcome these problems, in a previous study, we proposed a corrective QM/MM method, size-consistent multipartitioning (SCMP) QM/MM, and successfully demonstrated that, using SCMP, it is possible to perform stable molecular dynamics simulations by effectively taking into account solvent quantum chemical effects. The SCMP method is characterized by two original features: size-consistency of a QM region among all QM/MM partitioning and partitioning update. However, in our previous study, the performance was not fully elicited compared to the theoretical upper bound, and the optimal partitioning update protocol and parameters were not fully verified. To elicit the potential performance, in the present study, we simplified the theoretical framework and modified the partitioning protocol.

Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information based change detection methods have been proposed during past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. While the whole bi-temporal images are scanned pixel-by-pixel, the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by two land cover change cases with Landsat bi-temporal remote sensing images. In comparison to several widely used change detection methods, the proposed approach can achieve a land cover change inventory map with a competitive accuracy.

Within the field of guilt and shame, two competing perspectives have been advanced. The first, the social-adaptive perspective, proposes that guilt is an inherently adaptive emotion and shame is an inherently maladaptive emotion; thus, those interested in moral character development and psychopathology should work to increase an individual’s guilt-proneness and decrease an individual’s shame-proneness. The functionalist perspective, in contrast, argues that both guilt and shame can serve a person adaptively or maladaptively—depending on the situational appropriateness, duration, intensity, and so forth. This paper reviews the research conducted supporting both positions, critiques some issues with the most widely used guilt- and shame-proneness measure in the social-adaptive research (the TOSCA), and discusses the differences in results found when assessing guilt and shame at the state versus trait level. The conclusion drawn is that although there is broad support for the functionalist perspective across a wide variety of state and trait guilt/shame studies, the functionalist perspective does not yet have the wealth of data supporting it that has been generated by the social-adaptive perspective using the TOSCA. Thus, before a dominant perspective can be identified, researchers need to (1) do more research assessing how the social-adaptive perspective compares to the functionalist perspective at the state level, and (2) do more trait research within the functionalist perspective to compare functionalist guilt- and shame-proneness measures with the TOSCA.

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar the targets are detected by sonar equation (with constant threshold) which increase the detection error in shallow water. Purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and prior distribution is estimated by Maximum Likelihood algorithm. Finally, target was detected by combining the detection points in both domains using LMS adaptive filter. Results of this paper has showed that proposed method has improved true detection rate about 27% compare other the best detection method.

Enterprise Architecture (EA) development with an Agile approach is believed to be an alternative solution to various current EA development challenges. Agile Enterprise Architecture (EA) is an EA development that applies Agile principles and focuses on collaborative and incremental de-velopment, adaptation, and sharing information about business and IT modes to guide the ef-fective implementation of Agile projects. There have been many discussions of literature related to EA management, but literature review studies that specifically describe Adaptive or Agile EA management are still rare. The research method of this study follows two consecutive phases: bibliometric analysis and content based analysis. First, bibliometric analysis utilizing the VOSviewer software to identify the state of the field and global research patterns in Agile EA from 2012 to 2023, including author and institutional distributions, frequent categories and keywords, contributions from journals and authors, and highly cited works. Next, thematic analysis and meta-analysis to reveal various frameworks or models used in agile EA implementation and its dimensions. The extracted bibliometric and content data from 59 selected articles were processed using the VOSviewer tool for identifying and understanding the relationships between main concepts through network mapping. There are several Agile-based EA frameworks based on empirical research found in EA development, such as Adaptive Enterpise Architecture, Adaptive Integrated Digital Architecture Framework (AIDAF), Lean Enterprise Architecture Frameowrk (LEAF), Scrum for EA, and several other Agile EA frameworks developed that have been ob-served. Various agile methods have been adopted in developing the agile EA framework, in-cluding its application in various industrial sectors in several countries, the risks faced and future research opportunities to increase the effectiveness of agile EA implementation in Digital Transformation.

The article presents data on bio-morphological, reproductive and ecological-physiological features of endangered ornamental species Amberboa moschata (Asteraceae) in natural habitats of the Ararat valley of Armenia and cultivated under ex situ conditions in the Yerevan Botanical Garden. According to conducted exploration, the plants cultivated have satisfactory adaptive capacity, a complete development cycle, the ability to form mature seeds and self-renewal by seeds, and higher parameters of total moisture, transpiration intensity and photosynthesis compared to natural ones. The diploid cytotype of the species A. moschata has been found to be 2n=32. The karyotype is asymmetric, with chromosomes ranging in size from 0.77 to 1.91 µm. The average pollen fertility of A. moschata is high, 96.7–96.9% in both natural and cultivated samples. A scale of ornamental properties of A. moschata has been compiled, including 15 characteristics of the inflorescence, shoot, leaf, fruit and plant. The total duration of plant ornamental period under cultivation is about 98 days, the maximum ornamental effect is observed during the flowering period of 68–70 days. The studied species is recommended for creating living collections in botanical gardens and utilization in ornamental horticulture and landscaping as measures for its ex situ conservation. The article is illustrated with a map, original photographs and tables.

This paper reviews the pioneering work related to the generation of a sequence of prompts in Pretrained Language Models (PLMs) to automatically complete a complex writing task. The paper identifies the main categories of automated multi-step prompting and discusses their advantages and disadvantages. The paper also proposes an additional approach for automated multi-step prompting with iterative four main phases: in the first phase a sequence of prompts is geared to elicit the structure of the requested task. This is done by identifying the task’s main parts, the general contents of the different parts and their precedence relations. The structure of each main part is iteratively explored to find secondary and tertiary parts if they exist. A second phase is followed by a sequence of prompts that fill in the primary, secondary, and tertiary parts, and seek to fine-tune the result. The third phase reviews the generated text and criticizes it. Based on this criticism a revision is prepared. The fourth phase is fine-tuning the document, with emphasis on section length and text tone. Future research could deal with the system identification of points where the system should initiate interaction with the user for desired feedback and guidance.

Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite sector clustering to achieve robust whole sky image-based cloud classification, adaptive segmentation, and recognition under intricate illumination dynamics. A bespoke YOLOv8 architecture attains over 95% categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research, while offering a paradigm for cloud analytics transferable to various meteorological stations.

Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here we use the AMIS approach to fit a von Misses distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from a 3D meteorological data. The primary objective is to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

Unmanned aerial vehicle (UAV), or drone is recognized for its potential to improve efficiency and address last-mile delivery issues. As a result, there has been a lot of activity in recent years in the field of drone scheduling and routing. Unlike the vehicle routing problem, drone route design is difficult due to several operational characteristics, such as speed optimization, multitrip operation, and energy consumption estimation. On the one hand, drone energy consumption is a complex nonlinear function of both speed and payload in practice. On the other hand, the high operating speed of drones can significantly curtail the drone range, thereby limiting the efficiency of drone delivery systems. Most of the existing drone delivery models either assume constant drone speed or do not consider the effect of drone speed and parcel weight on energy consumption, leading to costly or energy-infeasible routes. This paper addresses the trade-off between speed and flight range in a multi-trip drone routing problem with variable flight speeds (DRP-VFS), in which a team of homogeneous drones is employed for delivery services. We propose a new model to particularly consider energy constraints using a nonlinear energy consumption model and treat drone speeds as decision variables so that various drone speeds can be adopted in applications. The DRP-VFS is initially formulated as mixed-integer linear programming (MILP) to minimize total energy consumption. To solve large-scale instances, we propose a three-phase adaptive large neighborhood search (ALNS) algorithm. The experimental results demonstrate that suboptimal solutions can be found effectively in practical scenarios using the proposed method. Furthermore, results indicate that operating drones at variable speeds leads to about 21% of energy savings compared to fixed speeds, boasting advantages in cost-savings and range extensions.

Obsessive–compulsive disorder (OCD) is believed to follow a waxing and waning course, often according to environmental stressors. During the COVID-19 pandemic, pre-existing OCD symptoms were reported to increase and to change from checking to washing behaviors, while new-onset symptoms were predominantly of the hoarding type. In the present study, we followed the evolution of OCD symptoms, anxiety, depression, and insight of illness in forty-six OCD patients throughout the pandemic. Clinical measures were collected at four different time points before and during the COVID-19 pandemic in Italy. Within-subject comparisons were used to compare clinical scale scores across time, and correlations were examined between patients' baseline characteristics and changes in clinical scores. We found that all clinical measures increased during the first Italian lockdown, with respect to the pre-pandemic values. Anxiety decreased during the temporary elimination of restriction provisions, whereas severity of OCD symptoms and insight returned to pre-pandemic values during the second mandatory lockdown. These results were observed only in two sub-groups of patients: those taking benzodiazepines and those with shorter illness duration. Our findings suggest the need of additional clinical attention to these specific sub-groups of OCD patients in case of particularly distressing circumstances, while pointing to a possible adaptive role of their OCD symptoms when the environment requires a higher care of hygiene and extraordinary supply of essential resources.

With end-to-end encryption for video streaming services becoming more popular, network administrators face new challenges in preserving network performance and user experience. Video ads may cause traffic congestion and poor Quality of Experience. Because of the natural variation in user interests and network situations, traditional algorithms for increasing QoE may face limitations. To solve this problem, we suggest a novel method that uses user facial emotion recognition to deduce QoE and study the effect of ads. We use open-access Face Emotion Recognition (FER) datasets and extract facial emotion information from actual observers to train machine learning models. Participants were requested to watch ad videos and provide feedback, which will be used for comparison, training, testing, and validation of our suggested technique. Our tests show that our approach beats the ITU-T P.1203 standard in terms of accuracy by 37.1%. Our method provides a hopeful answer to the problem of increasing user engagement and experience in video streaming services.

This study introduces the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), an innovative machine learning-based approach to personalize educational pathways. DFDLOF leverages advanced algorithms for in-depth analysis of learner data, enabling the dynamic customization of educational content and methodologies. Its application in diverse online platforms, including Khan Academy and Coursera, underscores its adaptability and scalability. DFDLOF enhances learner engagement and outcomes by providing tailored learning experiences and real-time feedback. Key insights reveal its strengths in personalized education, particularly in dynamically adapting learning paths and the importance of real-time feedback. Challenges identified include integration complexities and data privacy concerns. Future research directions emphasize the need for longitudinal impact studies and broader demographic applications. This study contributes significantly to the evolving domain of adaptive learning technologies, demonstrating the transformative potential of machine learning in personalizing education.

The Internet of Things (IoT) aims to connect all objects and facilitate data interchange, with data accuracy being paramount. While IoT has seen extensive adoption in conventional sectors, its application in the petroleum industry, especially with downhole equipment, poses challenges due to the extreme high-temperature and high-pressure conditions, making accurate measurements elusive. This Paper we aim to enhance the accuracy of downhole pressure measurements in slim-hole drilling parameter monitoring, mitigate the temperature influences on strain gauges, and surmount the intrinsic limitations of the traditional BP neural network, such as sluggish convergence speeds, high sensitivity to initial weights and biases, unstable learning rates, and a tendency to fall into local minima. This study introduces a temperature compensation model using a chaotic-initiated adaptive whale optimization algorithm (C-I-WOA) to optimize a BP neural network, which is henceforth referred to as the C-I-WOA-BP model. Initially, we harnessed chaotic mapping techniques to refine the initialization process of the whale optimization algorithm (WOA) population, thereby bolstering the heterogeneity of the algorithm. Subsequently, we integrated an adaptive weight recalibration mechanism, refined the encompassing methodology of the WOA, and bolstered its capabilities for expansive global scouting and meticulous local refinement in the solution arena. Subsequently, the WOA was used to optimize the weight coefficients of the BP neural network. Finally, we conducted a detailed comparative analysis by juxtaposing the performance metrics of the BP, C-WOA-BP, I-WOA-BP, and C-I-WOA-BP networks. The experimental results indicate that the C-I-WOA-BP model demonstrates faster convergence speed, superior global search capabilities, and enhanced local exploitation potential compared to the other models. Additionally, it provides a more stable and accurate reduction in temperature-induced errors in drilling pressure parameter measurements.

The hybrid method proposed in the study, ANFIS-GWO, combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO) for the diagnosis of liver disorders. ANFIS is a powerful tool that combines the advantages of neural networks and fuzzy logic to create a hybrid model capable of handling complex and uncertain data. GWO is a metaheuristic optimization algorithm inspired by the social behaviour of grey wolves. In the ANFIS-GWO method, the hyperparameters of ANFIS are optimized using GWO. This optimization process aims to fine-tune the ANFIS model based on the available dataset, which consists of 7 characteristic attributes and 354 samples related to liver diseases. By adopting the hyper-parameters, the ANFIS-GWO method enhances the overall performance and accuracy of the diagnostic system. To evaluate the effectiveness of the ANFIS-GWO intelligent medical system, the study employs classification accuracy, sensitivity, and specificity analysis. Classification accuracy measures the overall correctness of the system in predicting liver disease cases. Sensitivity refers to the system’s ability to correctly identify individuals with liver disorders, while specificity measures its ability to correctly identify those without liver disorders. Experimental results demonstrate that the performance of the ANFIS-GWO method surpasses that of traditional Fuzzy Inference Systems (FIS) and ANFIS models that do not undergo an optimization phase. This suggests that the integration of GWO optimization significantly improves the diagnostic accuracy of the ANFIS model for liver disease diagnosis.

This study compared the relative effect of two trace mineral supplementation strategies recom-mended in France for newborn beef calves. 600 calves were supplemented with 20 mg oral sele-nium (OTM group) at birth (D0) or by injection (ITM group) of a multi-mineral solution (60 mg of Zn, 10 mg of Mn, 15 mg of Cu, 5 mg of Se) on D0, D30 and D60. Mortality and the incidence rate of diseases, including diarrhea, omphalitis, pneumonia, as well as medicinal treatments, were recorded from D0 to D210. The incidence rate of omphalitis was significantly lower in the ITM group than in the OTM group (respectively 11% vs. 17%, P = 0.036). The cumulative inci-dence rate of all health troubles was lower in the ITM group than in the OTM group (P=0.007). Except for pneumonia, incidence of diarrhea (24% vs. 22%), use of oral (7% vs. 6%) or IV rehydra-tion therapy (4% vs. 2%) or use of antibiotics (43.3% vs. 38.0%) and mortality (3% vs 2%) were numerically higher in OTM group than in ITM group (n.s.). In this study, ITM supplementation is as efficient as oral supplementation regarding calves' health status. It reduces the risk of ompha-litis at the calf level effectively.

Motivation: It is essential for the diagnosis and treatment of renal cancers to segment kidney tumours precisely and effectively. In medical image segmentation tasks, deep learning models have demonstrated promising results, and the UNet model is widely employed in this field. However, optimising the UNet model for kidney tumour segmentation further can improve its efficacy and deployment feasibility. Related Works: Previous works have explored various techniques to improve the efficiency of deep learning models in medical image segmentation. Image partitioning methods divides the input image into smaller regions, enabling parallel processing and reducing memory requirements. Pruning techniques eliminates redundant or insignificant weights, neurons and connections, resulting in a more compact and efficient model architecture. However, the UNet model architecture and its complexity in the context of kidney tumor segmentation using UNet remains unexplored. Methodology: The proposed methodology consists of adaptive partitioning and weight pruning. Adaptive partitioning divides the UNet model into smaller sub-models, facilitating parallel processing and accelerating inference without compromising segmentation accuracy. Weight pruning techniques remove redundant or less significant weights from the UNet-P model, reducing complexity and improving inference time by eliminating unnecessary computations. The adaptive partitioning and weight pruning processes are seamlessly integrated within the UNet-P architecture, resulting in an optimized model for kidney tumor segmentation. Results: We performed experiments utilising a dataset of KiT 23 CT scan images containing images of kidney malignancies. Compared to the standard UNet model, the optimised UNet-PWP model with adaptive partitioning and weight pruning obtained significant efficiency gains. The adaptive partitioning permitted parallel computation of sub-models, which accelerated inference times. Weight pruning decreased the UNet-PWP model’s complexity without compromising segmentation accuracy, thereby enhancing efficiency. The results of our experiments demonstrated the efficacy of the proposed method with 98% accuracy, demonstrating its potential for deployment in health sector.

This paper studies the trajectory tracking control of an autonomous underwater vehicle (AUV) based on a stochastic uncertain nonlinear system. We investigate the time-varying gain adaptive control method looking for possible approaches to alleviate the heavy computational burden. Under nonlinear growth, conditions satisfy polynomial growth conditions. These two problems are resolved the fast response time and good path tracking, respectively. Novel adaptive algorithms are developed exploiting the dynamic properties of the AUV motion. By appropriately turning the original controller design problems into parameters choosing problems and then solving them in a functional time-varying observer technical theorem. In order to deal with the station error of system converges to arbitrarily small domains with stochastic uncertain disturbance. A coordinate transformation is proposed for all system states to meet boundedness. We show that the convergence of the AUV trajectory errors can be guaranteed by the proposed contraction constraints in the stability analysis, closed-loop stability is proved, and the system is asymptotically probabilistic in the global scope. They are taking advantage of the guaranteed stability. Extensive simulation studies on the AUV model demonstrate the effectiveness and robustness of the proposed approach. A real-time time-varying gain constructive control strategy is further developed for the Hardware-in-the-loop simulation; the controller design results are imported into the AUV actuator model to verify the effectiveness of the controller design.

This essay review explores the concept of climate-smart forestry as an approach to mitigating and adapting to climate change. It highlights the need for a holistic approach to forestry that considers regional differences and synergies between ecosystem services. The essay discusses various management strategies at different scales, including species selection, mixed species cultivation, and the promotion of the bioeconomy. It also examines the trade-offs and synergies between different forest uses and the effects of forest management on ecosystem services. Furthermore, the review addresses major abiotic and biotic damage risks to forests, such as wildfires, windstorms, and bark beetle attacks, and proposes risk management strategies. Lastly, the review delves into the management of peatland forests, discussing the role of drainage, the impact of ditch network maintenance, and the challenges of peat ash fertilization. Overall, the review provides valuable insights into climate-smart forestry and its potential to contribute to climate change mitigation and adaptation.

The use of artificial intelligence (AI) applications in education has the potential to revolutionize the learning experience for international students, who face unique challenges when studying in a foreign country. This paper explores various examples of AI applications in education and their potential impact on international students. AI applications such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research are examined for their potential to improve learning efficiency and provide customized education support. It also explores the significant risks and limitations associated with AI technologies, such as privacy, cultural differences, language proficiency, and ethical implications. To maximize the potential benefits of AI applications in higher education, it is crucial to implement appropriate safeguards and regulations. This paper provides a starting point for research on the potential impact of artificial intelligence on international students’ educational experiences and how AI may be integrated into educational administration and learning processes.

With the development of deep learning technology, various structures and research methods for super-resolution restoration of natural images and document images have been introduced. In particular, a number of recent studies have been conducted and developed in image restoration using generative adversarial network. Super-resolution restoration is ill-posed problem because of some complex restraints such as a lot of high-resolution images being restored for the same low-resolution image and also difficulty in restoring noises like edges, light smudging, and blurring. In this study, we utilized the spatially adaptive de-normalization (SPADE) structure for document image restoration to solve previous problems such as edge unclearness, hardness to catch features of texts, and the image color transition. Consequently, it can be confirmed that the edge of the character and the ambiguous stroke are restored more clearly when contrasting with the other previously suggested methods. Also, the proposed method’s PSNR and SSIM scores are geting 8% and 15% higher, respectively, compared to the previous methods.

The development of the automotive industry leads to the improvement of the designs of automatic transmissions and drives and to the development of methods for monitoring and diagnosing their condition. The trend of improving transmissions comes down mainly to the endless improvement of existing designs of CVTs and variators, which inevitably leads to the complication of designs and methods for their control and diagnostics.There is a need to create a fundamentally new simplified gearbox based on the scientific achievements of mechanics. However, the created structures remained inoperable due to the lack of theoretical justification.The adaptive gear variator developed by the author, which has CVT functions, is a mechanism with two degrees of freedom and with an additional constraint of a fundamentally new type, called a force - speed constraint. The force - speed constraint imposes a force restriction on the movement of links, while maintaining the number of degrees of freedom in the kinematics. Monitoring the state of the developed gearbox in the form of a non-switchable gear variator is greatly simplified, since the largest number of faults occur in the control system, and there is no control system in the gear variator. It seems possible to apply the digital twin method to diagnose the developed simplified CVT.The proposed article is devoted to a theoretical description of a fundamentally new adaptive gearbox, created on the basis of the latest achievements in mechanics, with the prospect of developing a simplified monitoring and diagnostic system for it

In a previous paper, the authors dealt with the current showstoppers for morphing systems and with the reasons that have inhibited their commercial applicability. In this work, the authors ex-press a critical vision of the current status of the proposed architectures and the needs that should be accomplished to make them viable for installation onboard of commercial aircraft. The distinc-tion is essential because military and civil issues and necessities are very different, and both the solutions and difficulties to be overcome are widely diverse. Yet, still remaining in the civil seg-ment, there can be other differences, depending on the size of the aircraft, from large jets to com-muters or general aviation, in turn classifiable in tourism, acrobatic, ultralight and so on, each with their own peculiarities. Therefore, the paper wants to try to trace a common technology de-nominator, if possible, and envisage a future perspective of actual applications.

This work proposes a systematic topology optimization approach to simultaneously design the morphing functionality and actuation in three-dimensional wing structures. The actuation is assumed to be a linear strain-based expansion in the actuation material and a three-phase material model is employed to represent structural and actuating materials, and void. To ensure both structural stiffness with respect to aerodynamic loading and morphing capabilities, the optimization problem is formulated to minimize structural compliance while morphing functionality is enforced by constraining a morphing error between actual and target wing shape. Moreover, a feature mapping approach is utilized to constrain and simplify actuator geometries. A trailing edge wing section is designed to validate the proposed optimization approach. Numerical results demonstrate that three-dimensional optimized wing sections utilize a more advanced structural layout to enhance structural performance while keeping morphing functionality than two-dimensional wing ribs. The work presents the first step towards systematic design of three-dimensional morphing wing sections.

Immune dysfunction has long been reported by medical professionals regarding astronauts suffering from opportunistic infections both during their time in space and a short time period afterwards once back on Earth. Various species of prokaryotes on board these space missions or cultured in a microgravity analogue exhibit increased virulence, enhanced formation of biofilms, and in some cases develop specific resistance for specific antibiotics. This poses a substantial health hazard to the astronauts confined in constant proximity to any present bacterial pathogens on long space missions with a finite number of resources including antibiotics. Furthermore, some bacteria cultured in microgravity develop phenotypes not seen in Earth gravity conditions, providing novel insights into bacterial evolution and research.

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R² equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.