The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

The focus of recent research shifts towards complex ‘whole organism’ responses (i.e., in multiple functional traits) in adaptation to or to defend against stressors under complex environmental conditions. This increasing complexity is challenging to analyse and demands sophisticated tools to drive meaningful conclusions from those data. Trait-based regression models, multivariate analyses, like principal component analyses, and plasticity indices can be used to tackle challenges with those complex investigations. But those methods have substantial limitations, like the need for high sample size, multi-dimensionality of results or the need for trait coordination in high-dimensional space, or the calculation on the population level, which might buffer or cover the de facto occurring individual effects. To improve and simplify studies on ‘whole organism’ responses, analyses, and their interpretation, we developed the Index for Adaptive Responses. This straightforward framework can unite all traits of an organism in one number. A newly developed transformation method, included in this framework, comprises a normalisation and standardisation to a baseline or control without changing the data or variance structure of the original data. We assessed the performance and accuracy of the framework with an application in an extensive predator-prey case study, with simulations and application examples using literature data. We show that the Index for Adaptive Responses respects adaptations as well as maladaptations and outperforms established approaches. The framework is robust against outliers and non-gaussian distribution. We further show that the qualitative prediction of the adaptiveness of included traits is highly accurate, even under challenging conditions, e.g., low replicate numbers. Functions and algorithms of the framework are provided with an R package but can easily be translated into other programming languages. The Index for Adaptive Responses will simplify future research on complex adaptive responses and improves our understanding of these responses’ ecological as well as evolutionary implications.

This study focuses on the formulation and analysis of a numerical method to compute singularly perturbed functional differential equations (SP-FDEs) involving large mixed shifts. SP-FDEs are a class of differential equations with both shifts and small perturbation parameter. Such equations arise in various scientific fields to model phenomena with memory effects and perturbed dynamics. In this particular study, the considered SP-FDEs involve large negative shift (delay) and large positive shift (advance). Dealing with large mixed shifts in SP-FDEs gives rise to challenges in terms of stability analysis, numerical methods, and convergence of a solution. In this paper, the influence of the large mixed shifts is treated by choosing a uniform mesh discretized in such a way that the term with the shifts lie on the nodal points. Then, using the Numerov’s finite difference method, the continuous problem is transformed into a finite difference scheme and the influence of the perturbation parameter is handled by introducing a fitting factor in the difference scheme. Stability estimate and convergence analysis of the developed scheme are investigated and proved. Numerical experiments are carried out to demonstrate the validity and applicability of the developed scheme. It is shown that the numerical scheme obtained in this work is parameter-robust of second order convergence rate.

Clinical agreement between two quantitative measurements on a group of subjects is generally assessed with the help of the Bland-Altman (B-A) limits. The interpretation regarding agreement is based on whether B-A limits are within the pre-specified clinical tolerance. Thus, clinical tolerance limits are necessary for this method. We argue in this communication that such limits of clinical tolerance can be directly used for assessing agreement and plead that this nonparametric approach is simple and robust to the distribution pattern and outliers. Such direct use of clinical tolerance limits has more flexibility, and it is more effective in assessing the extent of agreement.

Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. This results from the fact that the number of measured parameters is significantly lower than the number of components’ performance parameters needed to describe the real engine. In these conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, loss factors etc. can appear out of the physical range. The current methods of engine model identification have developed considerably to provide stable, precise and physically adequate solutions. Presented in this work is an estimation method of engine components’ parameters based on multi-criteria identification which provides stable estimations of parameters and their confidence intervals with the known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularised identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Forming objective functions and scalar convolutions synthesis of these functions is used to estimate gas-path components’ parameters. A comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, Gas Path Analysis and also adapting dynamic models for the needs of the engine control system.

Aim: To investigate the effects of intensive physical exercise on retinal microvascular regulation in healthy volunteers by the adaptive optics retinal camera measurement. Methods: We included healthy volunteers (11 men and 14 women) aged 20.6± 0.9. Heart rate (HR) and systolic and diastolic blood pressure (SBP, DBP) were recorded before and after a submaximal physical exertion of continuously riding a training ergometer. The superior temporal retinal artery measurements were captured using the adaptive optics (AO) camera rtx1TM (Imagine Eyes, Orsay, France) without pupil dilatation. We compared measures of vessel diameter (VD), lumen diameter (LD), two walls (Wall 1, 2), wall-to-lumen ratio (WLR), and wall cross-sectional analysis (WCSA) before and immediately after the cessation of exercise. Results: Cardiovascular parameters: After exercise, SBP, DBP and HR changed significantly from 130.2±13.2 to 159.7±15.6 mm Hg, 81.2±6.3 to 77.1±8.2 mm Hg and 80.8±16.1 to 175.0±6.2 bpm, respectively (p < 0.002). Retinal microcirculation analysis: Before the exercise, the mean LD was 96.0±6.8, VD 118.5±8.3, WLR 0.234±0.02, WCSA 3802.8±577.6. The mean LD and Wall 1 did not change significantly after exercise but slightly decreased. The VD, Wall 2, WLR and WCSA decreased significantly (p

Anatomy education plays a critical role in medical practice, and the level of anatomical knowledge among students and physicians significantly impacts patient care. This article presents a pilot project conducted by the University of Milan aimed at exploring the effectiveness of the Area9's Rhapsode platform, an intelligent tutoring system that uses artificial intelligence to personalize learning and collect data on mastery acquisition. The study focused on liver anatomy and employed a flipped classroom approach, incorporating adaptive learning modules and an interactive in-class session. The project's execution, outcomes and preliminary pedagogical considerations are discussed. The results demonstrated improved learning quality, positive repurposing of study time, enhanced metacognitive awareness among students, with most of students demonstrating conscious mastery of the materials and a clear understanding of their level of competence. This approach, by providing valuable insights into the potential of artificial intelligence-based adaptive learning systems in anatomy education, could address the challenges posed by limited teaching hours, shortage of anatomist and the need for individualized instruction, with recommendations for future research and the expansion of adaptive learning modules to other anatomical districts.

Cervical cancer remains a major public health problem. Despite the screening programs, cervical cancer is the second most commonly diagnosed cancer. Most of the cases occur in less developed countries, and it is the third leading cause of cancer death among females. In 2000–2007, the five-year relative survival rate for European women diagnosed with cervical cancer was 62%.[1] Standard treatment for locally advanced cervical cancer reveals external beam radiotherapy (EBRT) with concurrent chemotherapy followed by image-guided adaptive brachytherapy (IGABT) boost [2, 3]. In the last years, the Gynaecological (GYN) working group composed of Group Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) has developed new recommendations for the treatment of cervical cancer based on IGABT. The treatment plan includes 3-dimensional (3D) imaging based on magnetic resonance imaging (MRI) or computed tomography (CT), adaptive to the target volume and organ at risk [4–6]. The brachytherapy insertion is made under anaesthesia. At the time of the application insertion, the ultrasound is used to guide the dilatation of the uterine canal and assess the depth of the uterine cavity. In this paper, we review the data available in international literature about the use of ultrasound (US) in brachytherapy treatment of cervical cancer.

In industry 4.0 era, video applications such as surveillance visual systems, video conferencing, or video broadcasting have been playing a vital role. In these applications, for manipulating and tracking objects in decoded video, the quality of decoded video should be consistent because it largely affects to the performance of the machine analysis. To cope with this problem, we propose a novel perceptual video coding (PVC) solution in which a full reference quality metric named Video Multimethod Assessment Fusion (VMAF) is employed together with a deep convolutional neural network (CNN) to obtain the consistent quality while still achieving the high compression performance. First of all, to achieve the consistent quality requirement, we propose a CNN model with an expected VMAF as input to adaptively adjust the quantization parameters (QP) for each coding block. Afterwards, to increase the compression performance, a Lagrange coefficient of Rate-Distortion optimization (RDO) mechanism is adaptively computed under Rate-QP and Quality-QP models. Experimental results show that the proposed PVC has achieved two targets simultaneously: the quality of video sequence is kept consistently with an expected quality level and the bit rate saving of the proposed method is higher than traditional video coding standards and relevant benchmark, notably with around 10% bitrate saving in average.

Studies evaluating candidate tick-derived proteins as anti-tick vaccines in natural hosts have been limited due to high costs. To overcome this problem, animal models are used in immunization tests. The aim of this article was to review the use of rabbits as an experimental model for the evaluation of tick-derived proteins as vaccines. A total of 57 tick proteins have been tested for their immunogenic potential using rabbit as model for vaccination. The most commonly used rabbit breeds were New Zealand (73.8%), Japanese white (19%), Californians (4.8%) and Flemish lop-eared rabbit (2.4%). Anti-tick vaccines efficacy resulted in up to 99.9%. Haemaphysalis longicornis (17.9%) and Ornithodoros moubata (12.8%) were the most common tick model in vaccination trials. Experiments in rabbits have revealed that some proteins (CoAQP, OeAQP, OeAQP1, Bm86, GST-Hl, 64TRP, serpins and voraxin) can induce immune responses against various tick species. In addition, in some cases it was possible to determine that the vaccine efficacy in rabbits was similar to experiments performed in natural hosts (e.g. Bm86, IrFER2, RmFER2, serpins and serine protease inhibitor). In conclusion, results have shown that prior to performing anti-tick vaccination trials using natural hosts, rabbits can be used as suitable experimental models for these studies

3D object detection is essential for an accurate and reliable autonomous driving system. Currently, the methods used by the state-of-the-art two-stage detectors are not flexible enough and their fea-ture extraction capabilities are very limited to cope effectively with the disorder and irregularity of point clouds. In this paper, we combine the advantages of both PV-RCNN and PAConv (Position Adaptive Convolution) to create a completely new network, FANet, in order to overcome the ir-regularity and disorder of point clouds. The convolution in our network builds convolutional ker-nels from a basic weight matrix, whose combined coefficients are learned adaptively by LearnNet from relative points. This network allows for flexible modeling of complex spatial variations and geometric structures in the 3D point cloud, enabling better extraction of point cloud features and producing high-quality 3D proposal boxes. Compared to other methods, FANet is superior in terms of 3D object detection accuracy. Extensive experiments on the KITTI dataset have shown a signif-icant improvement in our approach.

Fires are complex multi-physics problems that span wide spatial scale ranges. Capturing this complexity in computationally affordable numerical simulations for process studies and “outer-loop” techniques (e.g., optimization and uncertainty quantification) is a fundamental challenge in reacting flow research. Further complications arise for propagating fires where a priori knowledge of the fire spread rate and direction is typically not available. In such cases, static mesh refinement at all possible fire locations is a computationally inefficient approach to bridging the wide range of spatial scales relevant to fire behavior. In the present study, we address this challenge by incorporating adaptive mesh refinement (AMR) in fireFoam, an OpenFOAM solver for simulations of complex fire phenomena involving pyrolyzing solid surfaces. The AMR functionality in the extended solver, called fireDyMFoam, is load balanced, models gas, solid, and liquid phases, and allows us to dynamically track regions of interest, thus avoiding inefficient over-resolution of areas far from a propagating flame. We demonstrate the AMR capability and computational efficiency for fire spread on vertical panels, showing that the AMR solver reproduces results obtained using much larger statically refined meshes, but at a substantially reduced computational cost. We then leverage the computational efficiency of the AMR solver to demonstrate an optimization framework for fire suppression based on the open-source Dakota toolkit.

In this research work, an adaptive scheme for the coordinated protection of AC Microgrids using directional overcurrent (DOCR) relays is presented. Protection of AC MGs is a complex and challenging issue due to the dynamic nature of the network including, a) its capability to reconfigure the modes of operation ranging from grid-connected to the islanded mode, c) bidirectional-power flow capability, and c) integration of intermittent renewable energy resources with real-time variations in the resource availability. Consequently, the fault current contributions may largely vary depending upon the incident conditions on the network. Conventional protection schemes, generally designed for radial networks, and unidirectional power flow from the source end to the load may either mal-operate or exhibit very poor performance, if not adapted according to the dynamic conditions of the network. To address this issue, a communication-based adaptive protection scheme capable to adapt its settings according to the generation resource availability and network configuration is presented in this work. The proposed scheme consists of an intelligent central protection unit (ICPU) capable to update the settings and communicate it to the individual relays based on the pre-calculated offline settings. The directional overcurrent relays employed in the scheme use two-stage settings, i.e. definite time and inverse definite minimum time characteristics for the effective coordination among the downstream and upstream relays. An adaptive algorithm for ICPU operation is presented and a case study is implemented for a modified IEEE 9-bus system using DigSilent Power factory. The results for various scenarios including, a) grid-connected mode of operation, b) islanded mode of operation, and c) variable distributed generation mode are obtained and compared to the static scheme, which validates the effectiveness of the proposed scheme.

Weight gain affects about 10-20% of patients after bariatric surgery. It is a phenomenon that is difficult to understand and to intervene due to its complexity and etiological heterogeneity. In the present study, we investigated, from a network analysis perspective, the associations between weight regain, psychological, sociodemographic factors and physical activity in patients undergoing bariatric surgery. The sample consisted of 124 patients, of both sexes, aged 39 ± 9.1 years, who had undergone surgical intervention for more than 18 months. After voluntary consent, respondents answered questionnaires and instruments directly on the Google Forms platform. The results indicated that weight gain was negatively associated with the items of depression, anxiety and stress, binge eating and with the dimensions of the personality questionnaire (negative affectivity -0.182; detachment -0.078; antagonism -0.107; disinterest - 0.198 and psychoticism -0.158). The centrality indicators revealed that the characteristics of disinterest and negative affectivity and most of the items on the depression, anxiety and stress scale had a greater expected influence (values ​​from 1,043 to 1,502), indicating that these are the most sensitive variables to intervention and who need more attention from health professionals.

Progressive technologies and practices are shifting the possibilities of building design and improving work efficiency. Constantly changing site conditions require different procedures and designs that take into account these changing conditions, whether it is a design solution, a change in environmental conditions, or just sustainability factors. Adaptive building design offers opportunities to cope with changing factors to achieve the highest possible level of building quality. This case study deals with the topic of adaptive formwork design for building renovation, taking into account sustainability. Aim of the article is an investigation and demonstration of the building information modelling (BIM) environment used for the adaptive design of formwork elements for the building renovation in the context of sustainability. The object of the case study is a building in the center of Kosice, Slovakia. BIM environment allows prompt and correct adaptation of the formwork design to changing conditions of lighting, ventilation, heating and temperature during the design of the building.

Reinforcement learning (RL) is an extensively applied control method for the purpose of designing intelligent control systems to achieve high accuracy as well as better performance. In the present article, the PID controller is considered as the main control strategy for brushless DC (BLDC) motor speed control. For better performance, the fuzzy Q-learning (FQL) method as a reinforcement learning approach is proposed to adjust the PID coefficients. A comparison with the adaptive PID (APID) controller is also performed for the superiority of the proposed method, and the findings demonstrate the reduction of the error of the proposed method and elimination of the overshoot for controlling the motor speed. MATLAB/SIMULINK has been used for modeling, simulation, and control design of the BLDC motor.

Although Mast cells are known as key drivers of type I allergic reactions, there is increasing evidence for their critical role in host defense. MCs do not only play an important role in initiating innate immune responses, but also influence the onset, kinetic and amplitude of the adaptive arm of immunity, or fine-tune the mode of the adaptive reaction. Intriguingly, MCs have been shown to affect T cell activation by direct interaction or indirectly by modifying properties of antigen-presenting cells, and can even modulate lymph node-borne adaptive responses remotely from the periphery. In this review, we provide a summary of recent findings that explain how MCs act as a link between the innate and the adaptive immunity, all the way from sensing inflammatory insult to orchestrating the final outcome of the immune response.

Recently, various relations and criteria have been presented to establish a proper relationship between control systems and control Global Positioning System (GPS)-intelligent buoy system. Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. This study aims at developing a novel controller based on adaptive fuzzy neural network to optimally track the dynamically positioned vehicle on water with unavailable velocities and unidentified control parameters. In order to model the network with the proposed technique, uncertainties and the unwanted disturbances are studied in the neural network. The presented study aims at developing a neural controlling which applies the vectorial back-stepping technique to the surface ships, which have been dynamically positioned with undetermined disturbances and ambivalences. Moreover, the objective function is to minimize the output error for the neural network (NN) based on closed-loop system. The most important feature of the proposed model for the positioning buoys is its independence from comparative knowledge or information on the dynamics and the unwanted disturbances of ships. The numerical and obtained consequences demonstrate that the controller system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.

Early childhood is considered to be a period of rapid development, with the acquisition of abilities predicting future positive school competences. Motor, cognitive and social difficulties related to cancer therapies heavily impact the development of children with cancer. This study focused on two main aims: to assess the developmental pathways of preschool children with acute lymphoblastic leukaemia one year post-treatment and to compare these abilities both with those of a control group of healthy peers and with Italian norms. Forty-four children and their families, recruited through the Haematology-Oncologic Clinic of the Department of Child and Woman Health (University of Padua), agreed to participate to this study. The children’s mean age was 4.52 years (SD = 0.94, range = 2.5-6 years), equally distributed by gender, all diagnosed with Acute Lymphoblastic Leukaemia. Matched healthy peers were recruited through paediatricians’ ambulatories. Each family was interviewed adopting the Vineland Adaptive Behaviour Scales. Paired sample Wilcoxon tests revealed that children were reported to have significantly more developmental difficulties than their healthy peers. When compared with Italian norms they scored particularly low in verbal competence, social and coping skills. No significant association were found between treatment variables and developmental abilities. These findings suggest that the creation of specialized interventions both for parents and children may fill the possible delays in children’s development probably due to stress, lack of adequate stimulation or difficult adaptation.

Early childhood is considered to be a period of rapid development, with the acquisition of abilities predicting future positive school competences. Motor, cognitive and social difficulties related to cancer therapies heavily impact the development of children with cancer. This study focused on two main aims: to assess the developmental pathways in preschool children with leukaemia one year post-treatment; and to compare these abilities with those of a control group of healthy peers. Forty-eight children and their families, recruited through the Haematology-Oncologic Clinic of the Department of Child and Woman Health (University of Padua), agreed to participate in this study. The children’s mean age was 4.36 years (SD = 1.07, range = 1.91–6 years), equally distributed by gender, most of whom were diagnosed with Acute Lymphoblastic Leukaemia (N = 44). Matched healthy peers were recruited through paediatricians’ ambulatories. Each family was interviewed adopting the Vineland Adaptive Behaviour Scales. Paired sample t-tests revealed that children, especially aged 42–72 months, were reported to have significantly more developmental difficulties than their healthy peers, particularly in verbal competence, social and coping skills and gross motor abilities. These findings suggest that the creation of specialized interventions for both parents and children may fill the possible delays in children’s development due to toxic therapies and their associated hospitalisation.

LncRNA are involved in multiple regulatory pathways, its versatile mode of action has disclosed a new layer in gene regulation. They are reportedly modulated during plant development, with specific tissue functions and in response to stresses. In this study, we analyzed LncRNA from leave samples collected from the legume Copaifera langsdorffii (copaiba) from two divergent ecosystems: Cerrado (CER) and Atlantic Rain Forest (ARF). We identified 8020 novel lncRNAs, from which 2893 transcripts were regulated above 2-fold and 566 above 5-folds in either condition. This putative lncRNA set was compared with seven Fabaceae genomes, of which 1747 and 1879 transcripts (from ARF and CER, respectively) aligned to at least two genomes. Further, 2194 copaiba lncRNAs were successfully mapped to at least one of six Fabaceae transcriptomes. The secondary structures of the lncRNAs that were conserved and differentially expressed between the populations were predicted using in silico methods. Our results indicate the potential involvement of lncRNAs in the adaptation of C. langsdorffii to two different biomes.

The robust fusion estimation problem of a multi-sensor system with communication constraints, random packet drops, d-step state delays, and deterministic control inputs is investigated. The state augmentation method is used to transform the time delay system into a non-time delay system, and then a robust fusion estimation algorithm based on sensitivity penalty is proposed and the pseudo-cross-covariance matrix is updated. An event-triggered robust fusion estimator with state delay and deterministic control inputs is considered to make the model closer to reality while effectively reducing network congestion and lowering communication costs. Conditions are given to ensure that the estimation errors of the robust fusion estimator are uniform boundedness. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

As an emerging network paradigm, Space-Air-Ground integrated networks (SAGIN) has attracted the attentions from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortages of computing and storage resources in mobile devices greatly affect the quality of experiences for intelligent applications. Hence, we devise to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MEC). To facilitate efficient processing, we need to solve the optimal task offloading decisions. Different from the existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capability for an edge computing node, the uncertainty of the transmission latency caused by the heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we firstly describe a task offloading decision problem in new challenge environments. But, we cannot use the standard robust optimization and stochastic optimization methods to obtain the optimal result under the uncertain network environments. In this paper, we propose the condition value at risk-aware distributionally robust optimization algorithm, named as CVAR-DRO, to solve the task offloading decision problem. The proposed CVAR-DRO method combines the distributionally robust optimization and the condition value at risk model for solving the optimal result. And then, We have evaluated our approach in simulation SAGIN environments with the confidence interval, the number of mobile task-offloading and the various parameters. We compare our proposed CVAR-DRO algorithm with the state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the brute algorithm. The experimental results show that CVAR-DRO can get a sub-optimal mobile task-offloading decision. Overall, CVAR-DRO is more robust than others to the new challenges mentioned above in SAGIN.

Near-neutral pH and low redox potentials (EH) are considered favorable conditions for radionuclides immobilization in deep repository systems in clay geological formations. Cigeo is a future Industrial Centre for Geological Disposal for Nuclear Waste (NW) for high-level and intermediate level long-lived radioactive waste, to be built in France, at 500 m depth within the Callovo-Oxfordian clay formation. Deep knowledge of the mechanisms and kinetics of corrosion occurring at the external surface of tubings made of API 5L X65 carbon steel (CS-X65) in contact with COx pore water, is essential for a reversible NW management of the Cigeo site. By using robust handmade electrodes and electrochemical and gravimetric techniques, this work aimed to determine the corrosion phenomenology and kinetics of CS-X65 in contact with COx pore water flowing continuously through a Multi-Parameter Probe (MPP) device, placed at 500 m depth, for more than 180 days. It also aimed at demonstrating the reliability and the robustness of the measurement methodology for the corrosion evaluation of the metallic liner of the future nuclear waste disposal site. Two iron oxidants were encountered, oxygen, who was depleted, and proton, who was accompanied by hydrogen sulfide. Corrosion mechanisms and kinetics were well established for two CS-X65, electrochemically perturbed and not. The corrosion thickness loss rates stand between 0.016 and 0.032 mm/y.

Building fires are preventable incidents that have proven to be both deadly and costly. Addressing their root causes will lead to safer neighbourhoods for families and businesses to live and operate in. Multiple studies have established the effect of residents’ socioeconomic compositions on an area’s building fire rates; however, the existing model based on the classical stepwise regression procedure has several limitations. This paper aims to construct a more accurate predictive model of building fire rates based on a set of explanatory socioeconomic variables. In building the socioeconomic model, a backward elimination by Robust Final Predictor Error (RFPE) criterion is proposed to enhance the forecasting capability of the model. The proposed method has been implemented on the census data and the fire incident data of the South East Queensland region in Australia. A cross-validation was then conducted to assess the model’s accuracy. In addition, comparative analyses of other elimination criteria, such as p-value, adjusted R-squared, Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC) and predicted residual error sum of squared (PRESS), were conducted. The cross-validation analyses demonstrate that the proposed criterion is a more accurate predictive model based on a couple of goodness-of-fit measures. All in all, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modelling of building fires.

We discuss the generation of various reproduction ratios or numbers to monitor the outbreak of Covid-19 or other epidemics and examine the effects of intervention/relaxation measures. A detailed SEIR algorithm is described for their computation, with applications given to the current Covid-19 outbreak in several countries in America (Argentina, Brazil, Mexico, US) and Europe (France, Italy, Spain and UK). The corresponding matlab script, complete and ready to use, is provided for free downloading.

In the context of video background-foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method can process per video frame from a small set of measurements, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-l₁ minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background-foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods.

This paper considers a two-way relay network, where two source nodes exchange messages through several relays in the presence of an eavesdropper, and the channel state information (CSI) of the eavesdropper is imperfectly known. The amplify-and-forward relay protocol is used and the relay beamforming weights are designed. The model is built up to minimize the total relay transmit power while guaranteeing the quality of service at users and preventing the eavesdropper from decoding the signals. Due to the imperfect CSI, a semi-infinite programming problem is obtained. An algorithm is proposed to solve the problem, and the iterative points are updated through the linesearch technique, where the feasibility are preserved during iterations. The optimality property is analyzed. The obtained subproblems are quadratic constrained quadratic programming problems, either with less than $4$ constraints or with only one variable, which are solved optimally. Simulation results demonstrate the importance of the proposed model, and imply that the proposed algorithm is efficient and converges very fast, where more than 85% of the problems are solved optimally.

Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

For many engineering applications it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in finite plasticity or fluid mechanics. In many approaches length scale parameter have been introduced that are related to a specific micro structure. An alternative approach is possible, if a thermodynamically consistent framework is used for material modelling as shown in the present contribution. However, even if sophisticated and thermodynamically consistent material models can be established there are still not yet standard experiments to determine higher order material constants. In order to contribute to this ongoing discussion system identification based on the method of self-adaptive filtering is proposed in this paper. To evaluate the effectiveness of this approach it has been applied to second-order gradient materials considering longitudinal vibrations. Based on thermodynamically consistent models that have been solved numerically it has been possible to prove that system identification based on self-adaptive filtering can be used effectively for both narrow-band and broadband signals in the field of second-order gradient materials. It has also been found that the differences identified for simple materials and gradient materials allow for condition monitoring and detection of gradient effects in the material behavior.

This article focuses on artificial intelligence in educational technology, starting with an introduction to educational technology, an interdisciplinary field of study that covers the design, development, utilization, and evaluation of technology and digital tools in educational Settings. A detailed description of its definition in an academic context - a multidisciplinary field of computer science and cognitive science that deals with the development of computational systems that exhibit intelligent behaviour, describing its areas of coverage and scope of application. It then introduces the benefits of AI in education technology, specifically addressing personalized learning, adaptive learning systems, automated scoring and feedback, virtual tutors and chatbots, data analytics, as well as content recommendations and natural language processing, accessibility and inclusion. Then it introduces the main concepts of AI personalized learning: AI personalized learning uses the power of artificial intelligence to meet the unique needs and preferences of individual learners, as well as its key principles and main characteristics. Adaptive learning systems harness the power of artificial intelligence and data analytics to tailor the learning experience to each student's needs and abilities, as well as its core principles and strengths. It also introduces the main operating principles of automated grading and feedback: it uses artificial intelligence and computer algorithms to evaluate and evaluate student assignments, tests and exams without the direct involvement of human graders, and its associated benefits. Secondly, the main concepts of virtual tutor are introduced: Virtual tutor is a computer-based system that uses artificial intelligence and machine learning algorithms to provide students with personalized and interactive educational support and its key characteristics and main advantages, and then the nature and benefits of chatbots and help in the field of education. The final conclusion summarizes the benefits and future challenges of integrating AI into educational technology.

Since 2019, notable global viral outbreaks have occurred necessitating further research and healthcare system investigations. Following the COVID—19 pandemic, an unexpected duality has occurred of SARS–CoV–2 and monkeypox virus (MPXV) infections. Monkeypox virus is of the Orthopoxviridae genus, belonging to the family Poxviridae. Zoonotic transmission (animal to human transmission) may occur. The Orthopoxviridae genus includes other Orthopoxviruses (OPXV) present in animal host reservoirs that include cowpox viruses (CPXV), vaccinia virus (VACV) and variola virus (VARV), with the latter being causal agent of smallpox and excessive mortality. The aim in this review is to present facts about MPXV specific pathogenesis, epidemiology, and immunology alongside historical perspectives. Monkeypox virus was rarely reported outside Africa before April 2000. Early research since 1796 contributed towards eradication of VARV leading to immunisation strategies. The World Health Organisation (WHO) announcement that VARV had been eradicated was confirmed in 1980. On the 23rd of July 2022, the WHO announced MPXV as a health emergency. Therefore, concern due to propagation of MPXV causing MPOX disease requires clarity. Infected hosts display symptoms like extensive cellular initiated rashes and lesions. Infection with MPXV makes it difficult to differentiate from other diseases or skin conditions. Anti–viral therapeutic drugs were typically prescribed for smallpox and MPOX disease; however, the molecular and immunological mechanisms with cellular changes remain of interest. Furthermore, no official authorised treatment exists for MPOX disease. Some humans across the globe may be considered at risk. Historically, presenting symptoms of MPOX resemble other viral diseases. Symptoms include rashes or lesions like Streptococcus, but also human herpes viruses (HHV) including Varicella zoster (VZV).

Due to the complex underground environment, pumping machines are prone to produce numerous failures. The indicator diagrams of faults are similar in a certain degree, which produces indistinguishable samples. As the samples increases, manual diagnosis becomes difficult, which decreases the accuracy of fault diagnosis. For accurately and quickly judging the fault type, we propose an improved adaptive activation function and apply it to five types of neural networks. The adaptive activation function improves the negative semi-axis slope of the ReLU activation function by combining the gated channel conversion unit to improve the performance of the deep learning model. The proposed adaptive activation function is compared with the traditional activation function through the fault diagnosis data set and the public data set. The results show that the activation function has better nonlinearity, can improve the generalization performance of deep learning model, the accuracy of fault diagnosis. In addition, the proposed adaptive activation function can also be well embedded in other neural networks.

CD4+ T cells orchestrate and regulate immunity within jawed vertebrates, yet our understanding of their evolution, development, and cellular physiology has only begun to be unearthed in the past few decades. Discoveries of genetic diseases that ablate this cellular population have provided insight into their critical functions while transcriptomics, proteomics, and highresolution microscopy have recently revealed new insights into CD4+ T cell anatomy and physiology. This article compiles historical, microscopic, and multi omics data which can be used as a reference atlas to dissect cellular physiology within these influential cells and further understand pathologies like HIV infection that inflict human CD4+ T cells.

The development of anti-pain technologies in the complex treatment of pain syndromes is one of the most urgent tasks of modern medicine. We have undertaken a placebo-controlled experimental study of the therapeutic potential of low-intensity laser radiation when applied to acupuncture points, which are directly related to the autonomic nervous system. The adaptation effect of puncture photobiomodulation on the induction of stress-mediated autonomic reactions, oxidative metabolism and microcirculation in animals during the acute phase of pain stress was revealed. The data obtained are of interest for use in the complex rehabilitation of patients with pain syndrome.

In recent years, gamification has captured the attention of researchers and educators, particularly in science education, where students often express negative emotions. Gamification methods aim to motivate learners to participate in learning by incorporating intrinsic and extrinsic motivational factors. However, the effectiveness of gamification has yielded varying outcomes, prompting researchers to explore adaptive gamification as an alternative approach. Nevertheless, there needs to be more research on adaptive gamification approaches, particularly concerning motivation, which is the primary objective of gamification. In this study, we developed and tested an adaptive gamification environment based on specific motivational and psychological frameworks. This environment incorporated adaptive criteria, learning strategies, gaming elements, and all crucial aspects of science education for six classes of 3rd-grade students in primary school. We employed a quantitative approach to gain insights into the motivational impact on students and their perception of the adaptive gamification application. We aimed to understand how each game element experienced by students influenced their motivation. The findings of our study revealed encouraging results in terms of increased motivation and engagement among students, as well as the influence of different game elements when connected with an individual's profile based on a multidimensional adaptive framework.

Routing a person through a traffic network presents a tension between selecting a fixed route that is easy to navigate and selecting an aggressively adaptive route that minimizes the expected travel time. We propose to create non-aggressive adaptive routes in the middle-ground seeking the best of both these extremes. Specifically, these routes still adapt to changing traffic conditions, however we limit the number of adjustments made in the route. This improves the user experience, by providing a continuum of options between saving travel time and minimizing navigation. We design strategies to model single and multiple route adjustments, and investigate enumerative techniques to solve these models. To alleviate the intractability with handling real-life traffic data, we develop efficient algorithms with easily computable lower and upper bounds. We finally present computational experiments highlighting the benefits of limited adaptability in terms of reducing the expected travel time.

Wireless cellular communication technology has developed into a very resourceful commodity worldwide. Today, people of all races can hardly live without means of voice and data cellular communication technology. Imprecise propagation loss estimation leads to high power waste, high co-channel interference and poor service quality in cellular communication system networks. This paper proposes a realistic adaptive fine-tuning method for distinctive propagation loss estimation over a microcellular communication radio links based on signal power measurements from Long Term Evolution radio broadband networks, taking non-line of sight (NLOS) and line of sight (LOS) environments into consideration. The methodology is verified by measurements taken in non-line of sight and line of sight signal propagation scenarios. The results showed that the estimated propagation losses using the proposed realistic adaptive tuning models were more accurate than the existing Cost -231 modelling estimation approach

Wireless cellular communication technology has developed into a very resourceful commodity worldwide. Today, people of all races can hardly live without means of voice and data cellular communication technology. Imprecise propagation loss estimation leads to high power waste, high co-channel interference and poor service quality in cellular communication system networks. This paper proposes a realistic adaptive fine-tuning method for distinctive propagation loss estimation over a microcellular communication radio links based on signal power measurements from Long Term Evolution radio broadband networks, taking non-line of sight (NLOS) and line of sight (LOS) environments into consideration. The methodology is verified by measurements taken in non-line of sight and line of sight signal propagation scenarios. The results showed that the estimated propagation losses using the proposed realistic adaptive tuning models were more accurate than the existing Cost -231 modelling estimation approach.

The runtime environment is an important concern for self-adaptive systems (SASs). Although researchers have proposed many approaches for developing SASs that address the issue of uncertain runtime environments, the understanding of these environments varies depending on the objectives, perspectives, and assumptions of the research. Thus, the current understanding of the environment in SAS development is ambiguous and abstract. To make this understanding more concrete, we describe the landscape in this area through a systematic literature review (SLR). We examined 128 primary studies and 14 unique environment models. We investigated concepts of the environment depicted in the primary studies and the proposed environment models based on their ability to aid in understanding. This illustrates the characteristics of the SAS environment, the associated emerging environmental uncertainties, and what is expressed in the existing environment models. This paper makes explicit the implicit understanding about the environment made by the SAS research community and organizes and visualizes them.

Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system (CNS) characterized by varying degrees of demyelination of uncertain etiology, and is associated with specific environmental and genetic factors. Upon recognition of CNS antigens, the immune cells initiate an inflammatory process which leads to destruction and deterioration of the neurons. Innate immune cells such as macrophages, dendritic cells and natural killer cells are known to play critical roles in the pathogenesis of MS. Also, the activation of peripheral CD4+ T cells by CNS antigens leads to their extravasation into the CNS causing damages that exacerbates the disease. This could be accompanied by dysregulation of T regulatory cells and other cell types functions. Experimental autoimmune encephalomyelitis (EAE) is a mouse model used to study the pathophysiology of MS disease. In this review, we highlight the roles of innate and adaptive immune players in the pathogenesis of MS and EAE.

Standard evolutionary theory, supported by mathematical modelling of outbred, dispersed populations predicts that ageing is not an adaptation. We recently argued that in clonal, viscous populations, programmed organismal death could promote fitness through social benefits and has, in some organisms (e.g. Caenorhabditis elegans), evolved to shorten lifespan. Here we review previous adaptive death theory, including consumer sacrifice, biomass sacrifice, and defensive sacrifice types of altruistic adaptive death. In addition we discuss possible adaptive death in semelparous fish, coevolution of reproductive and adaptive death, and adaptive reproductive senescence in C. elegans. We also describe findings from recent tests for the existence of adaptive death in C. elegans using computer modelling. Such models have provided new insights into how trade-offs between fitness at the individual and colony levels mean that senescent changes can be selected traits. Exploring further the relationship between adaptive death and social interactions, we consider examples where adaptive death results more from action of kin than from self-destructive mechanisms and, to describe this, introduce the term adaptive killing of kin.

We applied a framework to assess climate change vulnerability of 52 major vegetation types in the western United States to provide spatially-explicit input to adaptive management decisions. The framework addressed climate exposure and ecosystem resilience; the latter derived from analyses of ecosystem sensitivity and adaptive capacity. Measures of climate change exposure used observed climate change (1981-2014) and then climate projections for the mid-21st century (2040-2069 RCP 4.5). Measures of resilience included (under ecosystem sensitivity) landscape intactness, invasive species, fire regime alteration, and forest insect & disease risk, and (under adaptive capacity), measures for topo-climate variability, diversity with functional species groups, and vulnerability of any keystone species. Outputs are generated per 100km² hexagonal area for each type. As of 2014, moderate climate change vulnerability was indicated for >50% of the area of 50 of 52 types. By the mid-21^st century, all but 19 types face high or very high vulnerability with >50% of the area scoring in these categories. Measures for resilience explain most components of vulnerability as of 2014, with most targeted vegetation scoring low in adaptive capacity measures and variably for specific sensitivity measures. Elevated climate exposure explains increases in vulnerability between the current and mid-century time periods.

Using the sustainable livelihoods analytical framework, adaptability of cattle raising to multiple stressors (e.g. climate change and market conditions) in the dry tropics of Chiapas, Mexico was evaluated. Three case studies located in the Frailesca region of Chiapas were analyzed: (I) peasant cattle raising in a rural village in the Frailesca Valley; (II) peasant cattle raising in a rural village in a natural protected area in the Frailesca Highlands; and (III) holistic cattle raising by farmers with private land ownership in the Frailesca Valley. Adaptability was evaluated using an index on a scale of one to a hundred; average values were: case I = 20.9 ± 1.4; case II = 32.1 ± 1.8; and case III = 63.6 ± 3.5. In order to increase farms adaptability and reduce the vulnerability of cattle raising families, there is a need to modify public policy to take into account the conditions of the most vulnerable farmers (cases I and II). Given the economic, environmental, and social context of Mexico´s dry tropics, establishing ecological or organic cattle raising and silvopastoral systems may reduce the vulnerability of farm families and increase their level of adaptability of their farms to multiple stressors.

When a rotary machine is running, from which the acquired vibro-acoustic signals enable to reveal its operation status and health condition. The study proposed a DSP-based adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm with an online real-time nature for signal interpretation and machine condition monitoring. Theoretical derivation and numerical implementation of computation schemes are briefly introduced. An online real-time monitoring system based on the AV2KF_OT algorithm, which was implemented through both a digital signal processor and a user interface coded by using LabVIEW, was developed. Two experimental tasks were applied to justify the proposed technique, including (i) the detection of startup on the fluid-induced whirl performed through a journal-bearing rotor rig, and (ii) the separation of close orders from the measured signals of a multifunction transmission-element ball-bearing bench.

Neurodevelopmental disorders are characterized by an amalgam of genetic and epigenetic faults implicating neuronal protein-related loss-of-function and gain-of-function respectively, hence their inclusion into the category of complex diseases. Neurodevelopmental disorders are regarded as autism spectrum disorder due to this reason; that the risk factors and phenotype constitute a spectrum of faults and protein overactivity. One example of an epigenetic factor leading to the onset of neurodevelopmental delays constitutes a set of immune response-based dysregulations, as well as the excessive growth of the adaptive immune memory of the fetus or the baby following significant infectious disease during pregnancy, as well as in babies aged 0-2, given the existence of profound links between developmental immunity and developmental neurology. Given that genetic information very likely represents a form of energy, the intake of excessive pathogen-related information may transiently overload the adaptive immune memory similarly to the manner functional pathogens do whilst inducing significant illness, and if events as such occur during important stages of neuro-immunological development, then there could often be irreversible effects upon the rate of development in neurological networks that normally have a major importance in the modulation of the related cognitive and behavioral phenotype. The full extent of evidence may only be obtained via a thorough study of the applications of physical laws into biology and medicine, given the foundational role of the physical matter in the development and maintenance of life. A common misconception is that neurodevelopmental delays are characterized solely by an accumulation of protein loss-of-function incidences in the central nervous system area, rather than a disruption of neuronal region developmental rates. For example, an overexpression of neuronal proteins related to cognition factors that is accompanied by an underexpression of proteins related to social behavior is phenotypically manifested in people with sharper rational thinking and poorer communication skills, with an impact on the abilities of decision-making as well, and this tends to be regarded as a classical example of the clinical manifestation of neurodevelopmental delays. More severe forms of autism spectrum disorder tend to implicate a more generalistic impact upon the developmental rates of key human behaviors, including those related with the development and maintenance of social and intimate relationships. Likewise, more severe implications upon regular developmental rates tend to bring a major impact upon the healthy function of related functions. As a result, lower functioning forms of neurodevelopmental delays bring fresh concerns with regards to a possible uncanny increase of incidences of deviated sexuality during years of adulthood, and in the worst cases, such deviations may lead to the planning and development of criminal activity, particularly in individuals who are or become prone to breaking the moral code that established the civilized society. What holds the concerns valid is the exponential increase in the number of lower functioning autism cases throughout the world and the major signs that such an increase will continue with a full force. One major solution to this potential future problem would be a much higher investment into awareness of the exact genetic and environmental factors that directly or indirectly result in the onset of lower-functioning autism and of the current research efforts to develop the matching support for people found in situations as such. The most important aim of future research with regards to such a scenario would be a separation of disease and disability with choices made to step into more severe forms of disease and into areas that are outside of the established moral circle.

Neuromuscular symptoms in long COVID patients are common. Since adequate diagnostics are still missing, investigating muscle function might be beneficial. The holding capacity (maximal isometric Adaptive Force; AFisomax) was previously suggested to be especially vulnerable for impairments. This longitudinal, non-clinical study aimed to investigate the AF in long COVID patients in recovery process. AF parameters of elbow/hip flexors were assessed in 17 patients at three timepoints (pre: long COVID state, post: immediately after first treatment, end: recovery) by an objectified manual muscle test. The tester applied an increasing force on the limb of the patient, who had to resist isometrically for as long as possible. The intensity of 13 common symptoms were queried. At pre, patients started to lengthen their muscles at ~50% of the maximal AF (AFmax), which was then reached during eccentric motion, indicating unstable adaptation. At post and end, AFisomax increased significantly to ~99% and 100% of AFmax, respectively, reflecting stable adaptation. AFmax was statistically similar for all three timepoints. Symptoms intensity decreased significantly from pre to end. In conclusion, maximal holding capacity seems to be impaired in long COVID patients and increases with substantial health improvement. AFisomax might be a suitable sensitive functional parameter to assess long COVID patients and to support therapy process.

In the last years, advances in materials engineering based on adaptive electronics have found a new paradigm to optimize drawbacks in signal processing. A two-layer MnO/ZnO:Zn heterostructure envisioned for frequency adaptive electronic signal processing is synthesized by sputtering where the use of internal states allows reconfigurability to obtain new operating modes at different frequency input signals. An X-Ray Diffraction (XRD) is performed on each layer, MnO:Mn had a cubic and ZnO:Zn a hexagonal structure with preferential growth in [111] and [002] directions respectively. It is determined that the coupling in this heterojunction is compatible with a mismatch less 1 % due texture in each layer which implies a low number of defects. An electrical characterisation with an oscilloscope and signal generator was carried out to obtain the time-response signals and current-voltage (I-V) curves where no degradation is detected when changing from a range of frequencies (100 Hz – 1 MHz). An equivalent circuit is proposed to explain the effects in the interface. Finally, the MnO/ZnO:Zn heterojunction delivers states that are stable, repeatable, and reproducible and it demonstrates how the interaction of the materials can be used in adaptive device applications applying frequencies and internal states as to create new and innovative design schematics, reducing the numbers of components/connections in a system for future sustainable electronics.

Improper design of friction pendulum bearings can lead to poor seismic reduction performance and may result in the failure of local vulnerable components. And the parameter design of friction pendulum bearings mainly relies on experience and verification calculations at present. This paper proposes an adaptive genetic algorithm considering the overall evolution state of the population, adjusting crossover and mutation probabilities adaptively based on individual fitness and population diversity. Compared to traditional algorithms, it exhibits better global search capabilities and convergence efficiency. Combining the improved genetic algorithm with finite element models, a parameter optimization method is proposed. The parameters of friction pendulum bearings are optimized, providing reference for the design of seismic isolation devices.

Climate change is unpredictable and can change rapidly or over time; anthropogenic stressors work synergistically to strengthen their impact on vulnerable ecosystems and the fisheries sector. This study documented the past occurrence of extreme climate change impacts, such as supertyphoon Pablo (ST: Bopha), which severely affected Baganga, Davao Oriental, about ten years ago. A mixed-methods data collection was used, combining semi-structured interviews with small-scale fishers (N=120) in four fishing villages and focus group discussions (N=4, participated by about 4 focus groups) on assessing the impacts and their adaptation after climatic events occurred. Our findings indicate that the four selected fishing villages were exposed to common factors of climate change events that destroyed many lives and their livelihood. Including a decrease in catch per trip and the fishing operations disruption (98%). However, due to the impact of the supertyphoon, small-scale fishers in Baganga developed psychological distress (emotional 44% and physical 24%) due to the extreme event or disaster and economic losses (32%). In order to survive, their adaptation strategies relied on government and non-government-provided provisions and projects, e.g., planting trees, constructing a sea wall with wave breakers, cash for work, and tourism as an alternative livelihood. Despite this, our study revealed that the community had limited knowledge on the impacts of climate change, lack clear management goals especially for fishers, and the widespread use of illegal fishing gears. The local government must strongly implement rules, policies, and adaptation measures that will contribute to better and more resilient communities. This can help them to withstand future shocks and provide sustainable fisheries resources that will benefit all the fishers. The study will be useful to policymakers, disaster management group and organization that will help assist affected communities highly affected by climatic events.

The present paper provides a new definition for dual spline curves in a geometric intuitive way based on adaptive curve refinement techniques. The dual spline is an implementation of the interpolatory subdivision scheme for curve modeling, which is comprised of polynomial segments of different degrees. Specially, the dual spline curves are mainly aims to solve the difficult geometry defeaturing problems in the existing computer-aided technology. Dual spline curves maintain various desirable properties of conventional curve modeling methods, such as local adaptive subdivision, high interpolation accuracy and convergence, continuous and discontinuous boundary approximation. By adding fictitious and intrinsic nodes inside or at the vertices of interpolation elements, the dual spline is flexible and convenient for approximating a set of ordered points or discrete segments. Combined with the Lagrange interpolation polynomial and meshless method, the proposed approach is capable of approximating the non-smooth boundary for geometry defeaturing. Experimental results are given to verify the validity, robustness, and accuracy of the proposed method.

The work focuses on the possibility of application of tunable magnetic devices in electrical power systems with adaptive features. The idea underlying this type of operation, which depends on interaction with magnetic fluxes in a quassi-linear range of magnetic core characteristics, is a new approach to the design of magnetic elements. The suitable examples of adaptive electrical power systems can be devices for improving the quality of electricity. When used in compensators of reactive power or both reactive and distortion power, tunable magnetic devices offer much wider possibilities for the compensation, compared to “standard“ solutions, involving compensators based on fixed inductors. Nevertheless, the application of the proposed device in electrical systems such as is only an example of its possible implementations in the area of electrical power. In this work the following issues are covered: exemplary solution of an adaptive ‘passive’ compensator, rules for the operation of tunable magnetic device, and results investigation of the laboratory model of an electrical system based on such device.

Deep convolutional neural networks (CNN) are favored methods widely used in medical image processing due to their assured shown performance. Recently, the emergence of new lung diseases and the possibility of early detection of their symptoms has attracted many researchers to classify diseases by training deep CNNs on lung CT images. The trained networks are expected to distinguish between lung indications in different diseases, especially at the early stages of them. With the hope of achieving this purpose, we proposed an efficient deep CNN called AFEX-Net with adaptive feature extraction layers that successfully extract distinguishing features and classify chest CT images. The efficiency of the proposed network has two aspects: it is a lightweight network with low number of parameters and fast training and it has adaptive pooling layers and adaptive activation functions to increase its level of compatibility to the input data. The proposed network has been evaluated on a dataset with more than 10K chest CT slices, while an efficient pre-processing method is developed to remove any bias from the images. Additionally, we evaluated the performance of the proposed model on the public COVID-CTset dataset to prove the generalisability of our model. The obtained results confirm the competence of the proposed network in confronting medical images, where prompt and accurate learning is required.

The impacts of climate change are already felt across the globe, and (SIDS) are at the forefront. Small Islands Developing States (SIDS) are extremely vulnerable to climate change and adaptation is crucial, however they often lack funding or the fiscal capacity to make the necessary invest-ments and require support from climate finance instruments. The Green Climate Fund (GCF) was designed with the objective of achieving a “paradigm shift” towards low-carbon and climate resilient country-driven development pathway. Despite the amounts invested, assessing the impacts of climate finance on adaptation and adaptive capacity, particularly at the institutional level remains a challenge. Researchers identified two key components for more efficient adapta-tion policies at the national level: the degree of adaptation mainstreaming and institutional adaptive capacity. In SIDS, institutional capacity at the national level is seen as a key component to achieve the objectives of climate change strategies, and is supported by several programmes, including the Green Climate Fund Readiness Preparatory Support Programmes. However, to date few studies have analysed the linkages between climate finance, adaptation mainstreaming and adaptive institutional capacity. Through the review of the Readiness Grants and semi-structured interviews in three Caribbean SIDS, this research assess how climate finance may promote in-stitutional change through the mainstreaming of adaptation policies at the national level and contribute to more institutional adaptive capacity. It shows that the grants had a positive impact, which can be limited to by the strength of the institutions in place. These results demonstrate that access to climate finance can create a window of opportunity for countries to accelerate institu-tional change and allow to make recommendations on how to maximise the impacts adaptation funds. More in-depth studies would be needed to examine the complementary influence of the different climate finance flows (multilateral or bilateral) and their interplay with national institu-tional mechanisms.

Recently, two pig-to-human kidney transplants and a pig-to-human heart transplant were completed. The kidney trials involved a patient who was deceased and a patient who was brain dead. They seemed to indicate that pig kidneys can be at least somewhat functional in humans. However, patients still have to be under severe immunosuppression - and the first patient to receive a porcine heart passed away after two months. It is difficult to know exactly which proteins we need to overexpress or underexpress/knockout in a porcine organ to negate the human recipient’s immunological response to it. And testing different porcine organ genetic modifications in baboons can cost around $500,000 per transplant. But there might be a way to decrease immunogenicity where we don’t have to worry so much about modifying the animal’s organs genetically. First, however, we would have to prevent complement factor-mediated lysis of the porcine vascular endothelial cells, which we have made much progress on with triple knockout animals. Then, we could modify the porcine organ so that the cells of said organ secrete a small molecule or peptide that acts as a chemorepellent for the host immune cells. The host immune cells can be modified via bone marrow transplant or vector delivery to express the chemorepulsion receptor.

The compelling effects of climate change on forests may have been underestimated in the past few decades in practical forestry. Although the first attempts to draw attention to this complex problem appeared almost half a century ago, the debate has been conceptual rather than experimental and applicative. At first glance, the con-cerns were mainly related to sustainable forest management (SFM) issues, which obviously needed attention. Over time, the effects of climate change have been mainly considered in the context of the SFM; they started from various and somewhat different scales and goals. Over time, more research and awareness of the im-portance of SFM under the pressure of climate change have led to the development of a clearer field that can be defined as ‘adaptive forest management’ - to climate change. One of the characteristics of this discipline is to be featured by the absence of univocal methods and / or objectives to be pursued but to identify, verify, and adapt methods to the various climatic and forest types and conditions found in the field. Therefore, this work shows some phases of forest planning and management concepts and criteria over time and recalls some innovative and / or adaptive methods related to the approach to forest planning and management under climate change

In the past two decades, metaheuristic optimization algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the filed of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. Existing research, however, fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time.

With the introduction of HTTP/3, whose transport is no longer the traditional TCP protocol but the novel QUIC protocol, research for solutions to the unfairness of Adaptive Streaming over HTTP (HAS) has become more challenging. That is, because of different transport layers, the HTTP/3 may not be available for some networks and the clients have to use HTTP/2 for their HAS applications instead. Therefore, the scenario that HAS over HTTP/3 (HAS/3) compete against HTTP/2 (HAS/2) must be considered seriously. However, there have been a shortage of investigations on the performance and the origin of the unfairness in such a cross-protocol scenario in order to produce proper solutions. Therefore, this paper provides a performance evaluation and root-cause analysis of the cross-protocol unfairness between HAS/3 and HAS/2. It is concluded that, due to differences in the congestion control mechanisms of QUIC and TCP, HAS/3 clients obtain larger congestion windows, thus requesting higher video bitrates than HAS/2. As the problem lies in the transport layer, existing client-side ABR-based solutions for the unfairness from the application layer may perform suboptimally for the cross-protocol case.

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.

Statistical modeling lies at the heart of product design and development throughout numerous engineering disciplines, especially since processing large amounts of data has become increasingly ubiquitous. While mathematical statistics provide elegant guidance pertaining to the question of whether or not some particular underlying modeling assumptions are justified and appropriate, when pursuing a more comprehensive assessment of product design and development other considerations often increase in significance. Therefore, we will examine and analyze the tedious interactions and implications of statistical modeling choices and product liability exposure. To the best of our knowledge, this paper is the first to draw attention to and explore some often overlooked or oversimplified dangers and pitfalls that enter the equation when product design heavily relies on statistical modeling. In particular, through a diligent analysis of both statistical and legal aspects we will explore how statistically optimal procedures may yield far from optimal outcomes in terms of product liability when applied to actual real life problems and why suboptimal nonparametric or robust approaches may constitute better alternatives.

Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber-attacks, threats, risks, damages, or unauthorized access. Artificial Intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real-world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI-based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this paper, we provide a comprehensive view on "Cybersecurity Intelligence and Robustness", emphasizing multi-aspects AI-based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero-day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI-based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society.

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.

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.

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.

While providing safe water for the rural poor is considered a basic human right, there are numerous issues associated with existing technologies with shortcomings. Performance issues have continued to fail to meet the needs of impoverished families due to issues including high cost, difficulties with performance, and continuing needs for maintenance. These issues have severely interfered the safe water accessibility. Key aspects of the Guelph Water Filter (GWF) system can avoid/minimize many of these issues. The GWF as described herein enables delivery of low cost, long-term performance at 3Log removal of E.coli and can deliver 1 to 3 L of treated water per hour. The GWF is simple to operate, has an ability to provide sufficient water for a family, maintains longevity of performance, is easy to maintain and has protection against breakage during the cleaning process, is repairable at village level, and operates using a sizable reservoir of water to supply raw water, meaning the technology does not need to be refilled frequently. Hence, the capability of the novel GWF technology is shown to bypass many of the troublesome features of alternative low-tech water treatment technologies. The potential for the GWF to function for 2 days continuously avoids the need for young girls to fetch raw water frequently during a day, thereby enabling them to attend school. Hence, the GWF enhances the potential to result in ‘safe water and full schools’, providing the opportunity for girls to receive education and capture socio-economic benefits for the community.

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.

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.