Time-domain spectroscopy (TDS) in the Terahertz (THz) frequency range is gaining in importance in nondestructive testing of dielectric materials. One application is the layer thickness measurement of a coating layer. To determine the thickness from the measurement data, the refractive index of the coating layer must be known in the surveyed frequency range. For perpendicular incidence of the radiation, methods exist to extract the refractive index from the measurement data itself without prior knowledge. This paper extends these methods for non-perpendicular incidence, where the polarization of the radiation becomes important. Furthermore, modifications considering effects of surface roughness of the coating are introduced. The new methods are verified using measurement data of a sample of Inconel steel coated with yttria-stabilized zirconia (YSZ) and with COMSOL simulations of the measurement setup. To validate the thickness measurements, scanning electron microscopy (SEM) images of the layer structure are used. The results show good agreement with an average error of 1% for the simulation data and under 4% for the experimental data compared to reference measurements.

Software is playing the most important role in recent vehicle innovation, and consequently the amount of software has been rapidly growing last decades. Safety-critical nature of ships, one sort of vehicles, makes Software Quality Assurance (SQA) has gotten to be a fundamental prerequisite. Just-In-Time Software Defect Prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. The first case study of SDP in maritime domain reported feasible prediction performance. However, we still consider that the prediction model has still rooms for improvement since the parameters of the model are not optimized yet. Harmony Search (HS) is a widely used music-inspired meta-heuristic optimization algorithm. In this article, we demonstrated that JIT-SDP can produce the better performance of prediction by applying HS-based parameter optimization with balanced fitness value. Using two real-world datasets from the maritime software project, we obtained an optimized model that meets the performance criterion beyond baseline of previous case study throughout various defect to non-defect class imbalance ratio of datasets. Experiments with open source software also showed better recall for all datasets despite we considered balance as performance index. HS-based parameter optimized JIT-SDP can be applied to the maritime domain software with high class imbalance ratio. Finally, we expect that our research can be extended to improve performance of JIT-SDP not only in maritime domain software but also in open source software.

In WPA3 secure connection is executed in two sequential stages. Firstly, in authentication and association stage a pairwise master key (PMK) is generated. Secondly, in post-association stage a pairwise transient key (PTK) is generated from PMK using the traditional 4-way handshake protocol. To reduce the heavy computation of the first stage PMK caching can be used. If client and AP are previously authenticated and has PMK cache, client can skip the first heavy stage and reuse the cached PMK to directly execute the 4-way handshake. But PMK caching is a very primitive technology to manage shared key between client and AP and there are many limitations; AP has to manage stateful cache for multiple clients, cache lifetime is limited, etc. Paired token (PT) \cite{LZ} is a new secondary credential scheme that provides stateless pre-shared key (PSK) in client-server environment. Server issues paired token (public token and secret token) to authenticated client where public token has the role of signed identity and secret token is a kind of shared secret. Once client is equipped with PT, it can be used for many symmetric key based cryptographic applications such as authentication, authorization, key establishment, etc. In this paper we apply the PT approach to WPA3 and try to replace the PMK caching with the one-time authenticated key establishment using PT. At the end of the authentication and association stage AP securely issues PT to client. Then in reassociation stage client and AP can compute the same one-time authenticated PMK from PT in stateless way and compute PTK using the traditional 4-way handshake protocol. Using this kind of stateless reassociation technology AP can provide high performance service to huge number of clients.

AI is leveraging all aspects of life. Medical services are not untouched. Especially in the field of medical image processing and diagnosis. Big IT and Biotechnology companies are investing millions of dollars in medical and AI research. The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform like Web Application when ported to Smartphone for Real-time inference, which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goals of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Applications. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.

The magnetic field of the earth plays an important role in the ecosystem, and the magnetic field of celestial bodies is also important in the formation of cosmic large-scale structures, but the origin and evolution of the celestial magnetic field is still an unresolved mystery. Many hypotheses to explain the origin have been proposed, but there are some insurmountable difficulties for each one. At present, the theory widely accepted in scientific society is the dynamo model, it says that, the movement of magnetofluid inside celestial bodies, which can overcome the Ohmic dissipative effect and generate persistent weak electric current and macroscopic magnetic field. However, this model needs an initial seed magnetic field, and the true values of many physical parameters inside the celestial body are difficult to obtain, and there is no stable solution to the large range of fluid motion. These are all difficulties for the dynamo model. Furthermore, it is difficult for the dynamo to explain the correlation between the dipole magnetic field and angular momentum of a celestial body. In this paper, by calculating the interaction between spin of particles and gravity of celestial body according to Clifford algebra, we find that a rotational celestial body provides a field $\Omega^\alpha$ for spins, which is similar to the magnetic field of a dipole, and the spins of charged particles within the celestial body are arranged along the flux line of $\Omega^\alpha$, then a macroscopic magnetic field is induced. The calculation shows that the strength of $\Omega^\alpha$ is proportional to the angular momentum of the celestial body, which explains the correlation between the magnetic intensity and angular momentum. The results of this paper suggest that further study of the effects of internal variables such as density, velocity, pressure and temperature of a celestial body on $\Omega^\alpha$ may provide some new insights into the origin and evolution of celestial magnetic field.

A relationship between the functional Schr\"odinger representation and the precanonical quantization of a nonlinear scalar field theory is extended to arbitrary curved space-times. The canonical functional derivative Schr\"odinger equation is derived from the manifestly covariant precanonical Schr\"odinger equation in a singular limiting case when the ultraviolet parameter $\varkappa$ introduced by precanonical quantization is identified with the invariant delta-function at equal spatial points. In the same limiting case, the Schr\"odinger wave functional is expressed as the trace of the multidimensional product integral of Clifford-algebra-valued precanonical wave functions restricted to a certain field configuration. Thus the standard QFT in curved space-time in functional Schr\"odinger representation emerges from the precanonical formulation of quantum fields as a singular limiting case.

The article presents formal proof that the Special Theory of Relativity is wrong, that is, the interpretation of the mathematics on which STR is based, proposed by Einstein is incorrect. The article shows that there are infinitely many kinematics in which one-way speed of light is always equal to c. The kinematics of Special Theory of Relativity (STR) is only one of those infinitely many kinematics. It presents that mathematics on which STR kinematics is based can be interpreted differently and this leads to other conclusions on the properties of this kinematics. In this article, the whole class of linear transformations of time and coordinate was derived. Transformations were derived on the assumption that conclusions from Michelson-Morley’s and Kennedy-Thorndikea’s experiments are met for the observer from each inertial frame of reference, i.e. that the mean velocity of light in the vacuum flowing along the way back and forth is constant. It was also assumed that there is at least one inertial frame of reference, in which the velocity of light in a vacuum in each direction has the same value c, and the space is isotropic for observers from this distinguished inertial frame of reference (universal frame of reference). Derived transformations allow for building many different kinematics according to Michelson-Morley’s and Kennedy-Thorndikea’s experiments. The class of transformations derived in the study is a generalization of transformations derived in the paper [10], which consists in enabling non-zero values of parameter e(v). The idea of such a generalization derives from the person, who gave me this extended transformations class for analysis and publication.

The properties of the dark matter, dark energy, graviton and photon are discussed in terms of the new three-dimensional quantized space model. Three new particles (bastons) with the electric charges (EC) are proposed as the dark matters. It is proposed that the EC, LC and CC charges are aligned along the time axes but not along the space axes. The photon is confined on its corresponding three-dimensional quantized space. However, the graviton can be evaporated into other three-dimensional quantized spaces. The rest mass of the electron neutrino (n_e) of 3.494·10⁻³ eV/c² is obtained from the experimental vacuum energy density in terms of quantum field theory (QFT). The rest mass and force range of the massive g(0,0,0) graviton with the Planck size are m_g = 3.1872·10⁻³¹ eV/c² and x_r = 3.0955·10²³ m = 10.0 Mpc, respectively, based on the experimental rest mass and rms charge radius of the proton. The possible diameter (10 Mpc) of the largest galaxy cluster is remarkably consistent with the gravitational force range (10 Mpc). Then, the diameter of the largest dark matter distribution related to the largest galaxy cluster is 9.2865·10²³ m = 30 Mpc equal to the force range of the massive g(0) graviton with the rest mass of 1.0624·10⁻³¹ eV/c². Because of the huge number (N) of the evaporated gravitons, the very small Coulomb’s constant of about 10⁻⁴⁸k and large gravitation constant of 10⁶G_N are expected for the charged dark matters. Therefore, F_c(mm) > F_g(dd) > F_g(mm) > F_g(dm) > F_c(dd) > F_c(dm) = 0 for the proton-like particle. The proposed weak gravitational force between the dark matters and normal matters explains the observed dark matter distributions of the bullet cluster, Abell 1689 cluster and Abell 520 cluster. The transition from the galaxy without the dark matters to the galaxy with the dark matters are explained. Also, the accelerated space expansion is caused by the new space quanta created by the evaporated gravitons into the x1x2x3 space and repulsive electromagnetic force between dark matters corresponding to the dark energy. The decreasing coupling constant of the strong force, neutron lifetime anomaly and the pressure distribution inside the proton are explained by the unobservable proton and hadronization. And the rest mass of 1.4 TeV/c² is assigned to the Le particle with the EC charge of −2e. The proposed rest mass (26.12 eV/c²) of the B1 dark matter is indirectly confirmed from the supernova 1987A data. Also, the gravitation constant has been changing with the time because of the graviton evaporation.

Natural time is a new time domain introduced in 2001. The analysis of time series associated with a complex system in natural time may provide useful information and may reveal properties that are usually hidden when studying the system in conventional time. In this new time domain, an entropy has been defined and complexity measures based on this entropy as well as its value under time-reversal have been introduced and found applications in various complex systems. Here, we review these applications in the electric signals that precede rupture, e.g., earthquakes, in the analysis of electrocardiograms, as well as in global atmospheric phenomena like the El Nino/La Nina Southern Oscillation.

The aim of this work is to propose an angular positioning solution for the round cutting insert of an indexable insert torus milling cutter, dedicated to the multi-axis milling process under HSM conditions. The solution was based on the development of a wear model using a nonlinear estimation method with an eigenfunction, the development of a tool life model taking into account parameter of the cutter blade working angle and the method of reference marking and insert mounting, as well as the measurement methodology. The own function adopted to approximate the wear of the tool over its useful life is adequate and provides a prediction accuracy of 90%. The developed procedure for angular positioning and measurement enables the entire cutting edge of the cutting blade to be fully used, so that the total lifespan of the tool as an indexable insert tool is significantly extended. In addition, the measured largest defect values between the worn cutting edge and the nominal outline of the insert indicate the location of “notching” type wear.

Recently, the destructive impact of wildfires has proliferated; for instance, the August Complex wildfire in 2020 burned around 4% of California's landmass. This has caused increased economic damage and risk to human life. Additionally, climate change is anticipated to increase the severity of wildfires, making it imperative for accurate and efficient detection of wildfires. Machine Learning approaches allow for the automatic detection of wildfires, simultaneously prioritizing accuracy and efficiency, with minimal human intervention, thus decreasing the likelihood of increased economic damage and increasing firefighting responses. Convolutional Neural Networks (CNNs), while showing promise, are often limited by their inability to learn and capture deep spatial dependencies in satellite imagery tasks. In this paper, we propose FlameViT, a novel wildfire detection architecture based on Vision Transformers (ViT). Satellite images are more efficient to obtain and can cover wide areas prone to wildfires. We obtain a dataset of 40K+ satellite images from Canada's Open Government Portal, allowing FlameViT to be optimized to detect wildfires in satellite imagery. FlameViT uses Patch Extractor and Patch Embedding layers, followed by multiple Transformer Encoder layers with Multi-Head Self-Attention and feed-forward neural networks. FlameViT is hyperparameter-tuned and achieves a validation accuracy of 95%, outperforming various baselines in the wildfire detection task. FlameViT shows the power of Vision Transformers for wildfire detection tasks, and in conjunction with the use of satellite imagery, can provide an efficient and accurate way of detecting wildfires.

CAR-T cell therapy has emerged as a groundbreaking approach in cancer treatment, leveraging the immune system to target and eliminate cancer cells. While currently focused on hematologic malignancies, the future holds promise for expanding CAR-T cell therapy to diverse cancers, enhancing its efficacy, and improving safety profiles. Recent advances in tumor immunology have propelled CAR-T cell therapy as a game-changer in treating diseases such as relapsed/refractory B-cell acute lymphocytic leukemia (B-ALL), non-Hodgkin lymphoma (NHL), and multiple myeloma (MM). Six CAR-T cell products have received FDA approval for treating R/R B cell malignancies. However, challenges persist, including complications like cytokine release syndrome (CRS), coagulopathy, and cytopenias. Strategies to mitigate these complications involve targeting distinct antigens with dual- or multi-targeted CAR-T cells and enhancing immunogenicity. Additionally, γδ CAR-T cells show promise in recognizing antigen- negative leukemia cells in an MHC-independent manner while reducing the risk of inducing graft-versus-host disease (GVHD). As CAR-T cell therapy continues to evolve through innovation and discovery, it holds the potential to revolutionize cancer treatment, offering new hope to patients and reshaping the landscape of oncology with its precision and efficacy.

Understanding and predicting damage progression within thin plain-woven CFRP components is an essential issue for scheduling maintenance intervals and structural inspections, and typically require extensive test series. However, uncertainties always remain and result in conservative design and unnecessary downtimes due to the repeated inspections of a healthy structure. SHM can be used to reduce these uncertainties, as it allows to evaluate the condition of a mechanical structure during operation. Numerous SHM methods are developed , that achieve different levels of damage identification but are often investigated at small coupons with non-realistic structural damages and loading conditions on both, structure and sensor. The present research investigates the electrical impedance tomography (EIT) featuring an elastoresistive thin-film sensor to evaluate damage that propagates from a circular hole in a large plate under cyclic tensile-tensile multi-block loading. Applied fatigue loads were increased multiple times up to a total number of two million cycles. During the loading damage initiated at the hole and continuously propagated into the plate. Repeated EIT evaluations of the applied sensor and validating evaluations by means of digital image correlation clearly revealed the robustness of the hardware and the potential of the SHM method for damage evaluation of plain-woven CFRP components.

AbstractIntroduction: Increasing knowledge of obstetric danger signs (ODS) and encouraging birth preparedness and complication readiness (BPCR) practices are strategies to increase skilled maternal health service utilization in low-income countries. One of the methods to increase mothers' knowledge about ODS and promote BPCR practice is through health education intervention (HEI). However, the effect of context-specific community-based health education led by women's groups on these outcomes has not been comprehensively studied, and the existing evidence is inconclusive. Thus, we aimed to evaluate the effect of a context-specific community-based HEI led by women's groups on mothers' knowledge regarding ODS and BPCR practices in southern Ethiopia.Methods: An open-label, two-arm parallel group cluster-randomized controlled trial was conducted from January to August 2023 on pregnant women from 24 clusters (kebeles) (12 interventions and 12 controls) in the northern zone of the Sidama region. The Open Data Kit smartphone application was utilized to collect data. The intention-to-treat analysis was used to compare outcomes between groups. We fitted multi-level mixed-effects modified Poisson regression with robust standard error to account for between and within cluster effects. Results: One thousand and seventy pregnant women (540 in the intervention and 530 in the control clusters) responded to this study, making the overall response rate 95.02%. Excessive vaginal bleeding (94.3% in the interventional group vs. 88.7% in the control group) was the commonest ODS mentioned during childbirth. Overall, 68.7% of women in the intervention group and 36.2% of mothers in the control group had good knowledge of ODS (P-value < 0.001). Saving money and materials (97.1% in the interventional group vs. 92.7% in the control group) was the most frequently practiced BPCR plan. Overall, 64.3% of women in the intervention group and 38.9% of mothers in the control group practiced BPCR (P-value < 0.001). HEI significantly increased overall knowledge of ODS (adjusted risk ratio [ARR]: 1.71; 99% CI: 1.14-2.57) and improved overall BPCR practice (ARR: 1.55; 99% CI: 1.02-2.39). Conclusions: A community-based HEI led by women's groups improved mothers' knowledge regarding ODS and BPCR practices in a rural setting in southern Ethiopia. Interventions designed to increase women's knowledge of ODS and improve BPCR practice must implement context-specific, community-based HEI that aligns with World Health Organization recommendations. Trial registration: NCT05865873

Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on the Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of ER and HCP measurements using data from multiple NDE technologies.

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understand the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an on-line adaptive manner in pre-crisis segments. The on-line sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint to the importance of the U.S. housing bubble as trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states and could work as a comparative impact rating of specific economic events.

: Seasonal influenza (a.k.a flu) is responsible for considerable morbidity and mortality across the globe. The three recognized pathogens that cause epidemics during the winter season are influenza A, B and C. The influenza virus is particularly dangerous due to its mutability. Vaccines are an effective tool in preventing seasonal influenza, and their formulas are updated yearly according to WHO recommendations. However, in order to facilitate decision-making in the planning of the intervention, policymakers need information on the projected costs and quantities related to introducing the influenza vaccine, in order to help governments obtain an optimal allocation of the vaccine each year. In this paper, an approach based on a Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA) model is introduced to optimize the allocation of influenza vaccination. A bi-objective model is formulated to control the infection volume, and reduce the unit cost of the vaccination campaign. An SIR (Susceptible–Infected–Recovered) model is employed for representing a potential epidemic. The model constraints are based on the epidemiological model, time management, and vaccine quantity. A two-phase optimization process is proposed: guardian control followed by contingent controls. The proposed approach is an evolutionary metaheuristic multi-objective optimization algorithm with a local search procedure based on a hash table. Moreover, in order to optimize the scheduling of a set of policies over a predetermined time to form a complete campaign, an extended CENSGA is introduced with a variable-length chromosome (VLC) along with mutation and crossover operations. To validate the applicability of the proposed CENSGA, it is compared with the classical Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results are analyzed using graphical and statistical comparisons in terms of cardinality, convergence, distribution and spread quality metrics, illustrating that the proposed CENSGA is effective and useful for determining the optimal vaccination allocation campaigns.

Manufacturing systems need to meet I4.0 guidelines to deal with uncertainty in scenarios of turbulent demand for products. The engineering concepts to define the service’s resources to manufacture the products will be more flexible, ensuring the possibility of re-planning in operation. These can follow the engineering paradigm based on capabilities. The virtualization of industry components and assets achieves the RAMI 4.0 guidelines and (I4.0C), which describes the Asset Administration Shell (AAS). However, AAS are passive components that provide information about I4.0 assets. The proposal of specific paradigms is exposed for managing these components, as is the case of multi-agent systems (MAS) that attribute intelligence to objects. The implementation of resource coalitions with evolutionary architectures (EAS) applies cooperation and capabilities’ association. Therefore, this work focuses on designing a method for modeling the asset administration shell (AAS) as virtual elements orchestrating intelligent agents (MAS) that attribute cooperation and negotiation through contracts to coalitions based on the engineering capabilities concept. The systematic method suggested in this work is partitioned for the composition of objects, AAS elements, and activities that guarantee the relationship between entities. Finally, Production Flow Schema (PFS) refinements are applied to generate the final Petri net models (PN) and validate them with Snoopy simulations. The results achieved demonstrate the validation of the procedure, eliminating interlocking and enabling liveliness to integrate elements behavior.

The complexity of urban spatial configuration, which affects human-well being and landscape functioning, needs acquisition and 3d visualisation data to inform decision-making process better. One of the main challenges in sustainability research is to conceive spatial models which are capable of adapting to changes in scale and recalibrating the related indicators depending on the degree of detail and data availability. In this perspective, the inclusion of the third dimension into Urban Ecosystem Services (UES) assessment studies highlights the details of urban structure-function relationships, improves modelling and visualisation of data and impacts, aiding decision-makers to localise, assess and manage urban development strategies. The main goal of the proposed framework concerns mapping, evaluating and planning of the UES within a 3d-virtual environment to improve the visualisation of the spatial relationships among the services allocation and the urban fabric density.

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

This study examines the potential of combining decision support approaches to identify optimal bundles of ecosystem services. A forested landscape, Zona de Intervenção Florestal of Paiva and Entre-Douro and Sousa (Portugal), is used to test and demonstrate this potential. The landscape extends over 14,000 ha, representing 1,976 stands. The property is fragmented into 376 holdings. The overall analysis was performed in three steps. First, we selected six alternative solutions (A to F) in a Pareto frontier generated by a multiple criteria method within a decision support system (SADfLOR) for subsequent analysis. Next, an aspatial strategic multi-criteria decision analysis (MCDA) analysis was performed with the Criterium DecisionPlus (CDP) component of another decision support system (EMDS) to assess the aggregate performance of solutions A to F for the entire forested landscape with respect to their utility for delivery of ecosystem services. For the CDP analysis, SADfLOR data inputs were grouped into two sets of primary criteria: Wood Harvested and Other Ecosystem Services. Finally, a spatial logic-based assessment of solutions A to F for individual stands of the study area was performed with the NetWeaver component of EMDS. The NetWeaver model was structurally and computationally equivalent to the CDP model, but the key NetWeaver metric is a measure of the strength of evidence that solutions for specific land stands were optimal for the unit. Solutions D and B performed best in the aspatial strategic MCDA analysis, and a composite of the maps generated by NetWeaver demonstrated the spatial basis for the performance of solutions D and B in individual land stands. We conclude with a discussion of how the combination of decision support approaches encapsulated in the two systems could be further automated.

Prediabetes is a significant risk factor for the development of Type 2 diabetes, cardiovascular diseases, chronic kidney disease, and other complications. Early diagnosis of prediabetes, coupled with education on lifestyle changes that support blood glucose management, are crucial for the prevention or delay of type 2 diabetes and related complications. This study aimed to examine the effects of individualized nutrition therapy (INT) combined with continuous glucose monitoring (CGM) on blood glucose control in individuals with prediabetes who are overweight or obesese. Participants (mean age ± SD: 55 ± 6 years; BMI: 31.1 ± 4.1 kg/m²) were randomly assigned to either the treatment group (n = 15) or the control group (n = 15). Both groups received INT and CGM, but the control group was blinded to the CGM data until the end of the study. Participants were followed for 30 days and visited the lab every 10 days for CGM replacement, study measurements, and dietary consultations. The treatment group showed a significant increase in the percent of time in the target blood glucose range (p=0.02) and a significant decrease in mean blood glucose concentration (p&lt;0.05), glucose management indicator (p=0.02), percent coefficient of variation for blood glucose (p=0.01), and the percent time spent in the high or very high blood glucose ranges (p= 0.04). These changes were not statistically significant for the control group. We conclude that adding CGM feedback to INT resulted in better management of blood glucose levels in overweight or obese individuals with prediabetes.

This study addresses the gap in methodological guidelines for Neuroergonomic attention assessment in safety-critical tasks, focusing on validating EEG indices including the Engagement Index (EI) and Beta/Alpha ratio alongside subjective ratings. A novel task-embedded reaction time paradigm was developed to evaluate the sensitivity of these metrics to dynamic attentional demands in a more naturalistic multitasking context. By manipulating attention levels through varying secondary tasks in the NASA MATB-II task while maintaining a consistent primary reaction-time task, the study successfully demonstrated the effectiveness of the paradigm. Results indicate that both Beta/Alpha ratio and EI are sensitive to changes in attentional demands, with Beta/Alpha being more responsive to dynamic variations in attention, and EI reflecting more the overall effort required to sustain performance, especially in conditions where maintaining attention is challenging. The potential for predicting the attention lapses through integration of performance metrics, EEG measures, and subjective assessments was demonstrated, providing a more nuanced understanding of dynamic fluctuations of attention in multitasking scenarios, mimicking those in real-world safety-critical tasks. These findings provide a foundation for advancing methods to accurately monitor attention fluctuations and mitigate risks in critical scenarios, such as train-driving or automated vehicle operation, where maintaining high attention level is crucial.

The term Air Quality (AQ) pertains to the standard of air, which has a direct impact on the health and well-being of individuals. It is crucial to maintain high Air Quality (AQ), for better health and productivity. Though most of people spend more time indoors nowadays, it is not to be overlooked that the pollutant concentration in indoor air is directly linked to the outdoor air quality. Due to the changes in occupancy patterns, outdated maintenance of ventilation systems, and structural flaws in buildings, the indoor air is significantly polluted; pollutants emitted outdoors enter indoor air through open windows, ventilation systems and infiltration. In this paper, we have proposed a new technique, DTMCPM (discrete-time Markov chain (DTMC) model for the analysis and forecasting of AQ, using the power method). Specialized sensors and equipment to measure different pollutants, such as particulate matter, ozone, nitrogen dioxide, sulfur dioxide, benzene and toluene, are used to collect data by the Institute of Pakistan Air Quality Monitor in various cities; the data is converted to US EPA standard for all pollutant species. This data is used to calculate the transition matrix, steady-state values and mean return rate for the analysis of IAQ. The calculated and actual return rates have been compared and the proposed model is found to have a low average prediction error of 2.356%.

Human Pose Estimation (HPE) is a critical technology in computer vision with diverse applications ranging from healthcare to sports analysis. This project presents a method for detecting the 2D stance of multiple persons in an image using a nonparametric representation known as Part Affinity Fields (PAFs). By leveraging the first 10 layers of the VGG-19 convolutional neural network and training on the COCO dataset, our model effectively identifies and associates key points of the human body.The architecture employs a two-branch system that jointly learns part locations and their associations through sequential prediction. This enables the model to maintain real-time performance while achieving high accuracy, regardless of the number of persons in the image. To enhance accessibility, we developed a mobile application using Flutter and TensorFlow Lite, allowing real-time pose estimation via a mobile device’s front camera. The app provides immediate feedback on physical exercises and yoga poses, making it an invaluable tool for fitness enthusiasts and healthcare professionals. Visual outputs such as heatmaps and PAFs confirm the model’s capability to accurately localize and connect key points. Despite potential challenges such as data quality and hyperparameter tuning, the results indicate that our approach is both reliable and practical for real-world deployment. This project not only advances the state-of-the-art in HPE but also opens up possibilities for future enhancements, including integrating 3D pose estimation and applying the technology in augmented and virtual reality applications.

Protein 53 (P53) shows different functions either to support or to contrast malignant growth. P53 history tells that discoveries of molecular features have contributed to the emergence of P53 functions. Conversely, the shift of paradigms demonstrates that P53 functions coexist even if are in conflict with one another. Since 1979, five paradigms have been assembled then deconstructed by taking into account P53 functions of viral protein, oncogene, tumor suppressor gene, transcription factor, apoptotic factor. By summarizing the past five disassembled P53 paradigms, in this investigation the current paradigm involving P53 in the modulation of a composite bio-morphological entity namely, tumor immune microenvironment (TIME) is analyzed. The aims of this investigation were to provide an overview of solid and validated knowledge occurring in all P53 paradigms and to determine P53 abilities to modify TIME through manipulation of viruses. As a result, definitive P53 evidence concerns its genetic localization, amino acids composition, its isoforms and subcellular expression in organelles. P53 “mutome” assembles all mutations affecting this complex protein. Lastly, both categories of tumor promoting and non-oncogenic viruses related to P53 were scrutinized. In conclusion, paradigms teach that P53 is undruggable because its drugs inevitably target all P53 functions.

Digital photogrammetry has attracted widespread attention in the field of geotechnical and geological survey due to its low-cost, ease of use and contactless mode. In this work, with the purpose of studying the progressive block surficial detachments of a landslide we developed a monitoring system based on fixed multi-view time-lapse cameras. Thanks to a new-developed photogrammetric algorithm based on the comparison of photo sequences through a structural similarity metric and the computation of the disparity map of two convergent views we can quickly detect the occurrence of collapse events, determine their location and calculate the collapse volume. With the field data obtained at the Perarolo landslide site (Belluno Province, Italy), we have preliminary tested the effectiveness of the algorithm and its accuracy in the volume calculation. The method of quickly and automatically obtaining collapse information proposed in this paper can extend the potentials of landslide monitoring system based on videos or photo sequence and it will be of great significance for further research on the link between the frequency of collapse events and the driving factors.

This paper discusses the Quantum Temporal Resonator (QTR), a theoretical device leveraging quantum entanglement and metamaterials with negative refraction indices to achieve controlled temporal displacement. By establishing a network of entangled particles within a Bose-Einstein Condensate (BEC) and utilizing a resonant cavity made of advanced metamaterials, the QTR creates localized distortions in space-time. The frequency and amplitude of temporal harmonics within this cavity are finely tuned to allow for precise temporal displacement of matter and information. Numerical simulations demonstrate the coupling between the quantum wave function and electromagnetic fields, validating the theoretical model. The results show distinctive patterns in the wave function and perturbations in the electric field, supporting the feasibility of achieving controlled temporal displacement. This study explores the theoretical framework, mathematical modeling, experimental setup, and potential applications of the QTR, providing a comprehensive analysis of its feasibility and implications.

The paper introduces a control hybrid model that is designed on the basis of OOSEM (Object-Oriented Systems Engineering Method), MDA (Model-Driven Architecture) concepts, RealTime UML/SysML (Unified Modeling Language/Systems Modeling Language), and an algorithm based on UKF (Unscented Kalman Filter). This hybrid model enables the implementation of control elements for autonomous underwater vehicles (AUVs) and can be adapted to reuse for most standard AUV platforms. To obtain this goal, the dynamic model of AUV is integrated with the following specializations of OOSEM/MDA, in which the analysis model is clarified via the use-case model definition and then combines with HA (hybrid automata) to precisely define the requirements for control. Next, the designed model is specialized via real-time UML/SysML to get the core control blocks, which describe the behaviors and structures of control parts in detail. This design model is then transformed into the model of implementation with the assistance of round-trip engineering to conveniently realize the controller of AUVs. Based on this new model, an AUV controller for low-cost turtle-shaped AUVs is implemented that performs tracking for a planar trajectory with accepted feasibility.

Teachers’ time resources are becoming increasingly scarce and time poverty will have a direct impact on the long-term sustainability of their professional development. However, the current state of teachers’ time poverty and its potential harmful consequences are unclear. Therefore, this research conducts two cross-sectional studies to investigate the current situation and group differences in time poverty among Chinese teachers (Sample 1), as well as the influence of teachers’ time poverty on job burnout (Sample 2). The results indicate that: (1) there is a high level of time poverty among teachers; (2) there is no significant difference in gender, teaching years, or teaching section, and there is a significant difference in teaching responsibilities; (3) time poverty could positively and significantly predict job burnout, and job burnout could positively and significantly predict mental health; (4) mental health factors (including depression, anxiety and stress) play the mediating role in the relationship between teachers’ time poverty and job burnout. To create a sustainable development environment for teachers, it is suggested that teachers improve time management skills and establish a self-regulation mechanism; schools should clarify the boundaries of teachers’ work and respect their right to rest; and society should provide positive and adequate spiritual support.

Droplet-based microfluidics has revolutionized numerous fields such as biomedical research, pharmaceuticals, drug discovery, food engineering, flow chemistry, and cosmetics. This paper presents a comprehensive study focusing on the detection and characterization of droplets with volumes in the nanoliter range. Leveraging the precise control of minute liquid volumes, we introduced a novel spectroscopic On-Chip microsensor equipped with integrated microfluidic channels for droplet generation, characterization, and sensing, simultaneously. The microsensor, designed with Interdigitated-Ring-Shaped Electrodes (IRSE) and seamlessly integrated with microfluidic channels, offers enhanced capacitance and impedance signal amplitudes, reproducibility, and reliability in droplet analysis. We were able to make analyses of droplets length in the range 1.0-6.0 mm, velocity 0.66-2.51 mm/s, droplet volume 1.07nL-113.46nL. Experimental results demonstrated that the microsensor's has a great performance in terms of droplet size, velocity, and length, with a significant signal amplitude of capacitance and impedance, and real-time detection capabilities, thereby highlighting its potential for facilitating microcapsule reactions and enabling on-site real-time detection for chemical and biosensor analyses on-chip.

Noninvasive brain stimulation technique can be used for modulation of motor behaviors such as reaction time (RT). The main aim of this study was to investigate whether RT during a sequential visual isometric pinch task (SVIPT) can be affected by anodal transcranial direct current stimulation (a-tDCS) over three different stimulation sites such as dorsolateral prefrontal cortex (DLPFC), primary motor cortex (M1) or posterior parietal cortex (PPC). We also aimed to assess if stimulation of these cortical sites affect the transfer of learning during SVIPT. A total of 48 right-handed healthy participants were randomly assigned to one of the four a-tDCS groups: 1) left M1, 2) left DLPFC, 3) left PPC and 4) sham. A-tDCS was applied concurrent with SVIPT in which the participants were precisely controlled their forces to reach seven different target forces from 10 to 40% of maximum voluntary contraction (MVC) presenting on a computer screen with right dominant hand. Four test blocks were randomly performed at baseline and 15 min after the intervention including sequence and random blocks with either hand. Our results showed significant differences between a-tDCS groups on ratio RT in the sequence blocks of both the right trained (P = .041) and left untrained hands (P = .023) for target forces of 15% and 30 % of MVC, respectively. In sequence blocks, M1 showed significant elongation in ratio RT for the target force of 15% and 30 % MVC compared to PPC and Sham groups in the right hand. In sequence blocks with left hand, this negative effect were found between M1-DLPFC and M1-Sham groups. In random blocks, no significant differences were found among four groups at each target forces for the left hand while there were significant differences for 15% (P =. 04) and 40% MVC (P =. 035) in the right hand. Our findings suggest that three different stimulation sites (DLPFC, M1 or PPC) were differentially affected by a single session of a-tDCS at some target forces during SVIPT. The effects of a-tDCS on ratio RT were transferred in the left untrained hand for sequence blocks but not random blocks. Further research is needed to understand fundamental aspects of these main areas of the brain on temporal processing in a precision control task such as SVIPT.

In an epoch characterized by the swift pace of digitalization and urbanization, the essence of community well-being hinges on the efficacy of urban management. As cities burgeon and transform, the need for astute strategies to navigate the complexities of urban life becomes in-creasingly paramount. This study employs time series analysis to scrutinize citizen interactions with the coordinate-based problem mapping platform in the Municipality of Patras in Greece. The research explores the temporal dynamics of reported urban issues, with a specific focus on identifying recurring patterns through the lens of seasonality. The analysis, employing the sea-sonal decomposition technique, dissects time series data to expose trends in reported issues and areas of the city that might be obscured in raw big data. It accentuates a distinct seasonal pattern, with concentrations peaking during the summer months. The study extends its approach to forecasting, providing insights into the anticipated evolution of urban issues over time. Projec-tions for the coming years show a consistent upward trend in both overall city issues and those reported in specific areas, with distinct seasonal variations. This comprehensive exploration of time series analysis and seasonality provides valuable insights for city stakeholders, enabling informed decision-making and predictions regarding future urban challenges.

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics plays a crucial role in proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This research underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. The paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates ETL/ELT processes, AI techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city's coordinate-based problem mapping platform. By employing an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios. This approach enables users to visualize, analyze, and predict the response of the urban system at the neighborhood level. Consequently, antecedent and predictive patterns, trends, and correlations are visualized at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

Candidiasis is one of the global public health problems. In fluconazole resistant Candida spp. (FRCS), mutations in ergosterol biosynthesis gene 11 (ERG 11) have been commonly found. This study aimed to determine the profile of FRCS isolates and to detect mutations in part of ERG 11 gene in fluconazole resistant Candida albicans (FRCA) isolates. Study was conducted on 150 isolates of FRCS obtained from patients admitted in a tertiary care hospital, South India from September 2019 to December 2021. Candida spp. was identified by MALDI-TOF MS and fluconazole resistance was detected by VITEK 2 system. Following which a part of the ERG 11 gene of the FRCA isolates were amplified, purified, sequenced and a phylogenetic tree was constructed. Out of the 150 FRCS, the most common FRCS was C. auris followed by C. glabrata, C. krusei, C. parapsilosis, C. albicans, C. tropicalis and C. guilliermondii. Sequencing of amplicons detected a single missense mutation and 44 silent mutations. We opined that fluconazole resistance in FRCA isolates may be due to mecha-nisms other than the studied one in our region. Phylogenetic analysis of amplified sequences of 12 FRCA isolates observed three clusters and all 12 sequences showed relatedness to Malaysian 2019 isolates.

Remediation of IT issues encoded into domain-specific or user-defined alerts occurring in cloud environments and customer ecosystems in a vast majority of cases suffers from accurate recommendations which could be timely supplied for recovery of performance degradations. That, of course, is hard to realize through furnishing those abnormality definitions with an appropriate expert knowledge which varies from one environment to another. At the same time, in a large proportion of support cases, the reported problems under Global Support Services (GSS) or Site Reliability Engineering (SRE) treatment ultimately go down to the product teams making them waste costly development hours on investigating self-monitoring metrics of our solutions. Therefore, the mean-time-to-resolution (MTTR) rates of problems/alerts are significantly impacted from lack of a systematic approach towards adopting AI Ops. That would imply building, maintaining, and continuously improving/annotating a data store of insights that ML models are trained and generalized across the whole customer base and corporate cloud services. Our ongoing study is in line with such a vision and validates an approach that learns the alert resolution patterns in such a global setting and explains them using interpretable AI methodologies. The knowledge store of causative rules is then applied in predicting potential sources of the application degradation reflected in an active alert instance. In this communication, we share our experiences with a prototype solution and up to date analysis demonstrating how root conditions are discovered with a high accuracy for a specific type of problem. It is validated against the historical data of resolutions performed by heavy manual development efforts. We also offer a Dempster-Shafer theory-based rule verification framework for experts as a what-if analysis tool to test their hypotheses about underlying environment.

Recently, smart mobility intelligent traffic services have become a critical task in Intelligent Transportation Systems (ITS). This involves not only the use of advanced sensors and controllers but also the ability to respond to real-time traffic situations at intersections, alleviate congestion, and generate policies to prevent traffic jams. DRL (Deep Reinforcement Learning) provides a natural framework for processing tasks. In DRL, each intersection can control itself and coordinate with neighbors to achieve optimal network-wide policies. However, comparing approaches remains a challenging task due to the existence of numerous possible configurations. This research performs a critical comparison of various traffic controllers found in the literature. It demonstrates that using a nonlinear approximator for coordination mechanisms and enhancing observability at each intersection are key performance drivers.

In this paper, new types of M-fractional wave solutions of mathematical physics model named as truncated M-fractional (1+1)-dimensional non-linear simplified Modified Camassa-Holm model are achieved by applying the modified simplest equation (MSE), Sardar sub-equation and generalized Kudryashov techniques. The gained solutions containing dark, bright, periodic and mixed wave solitons. Effect of fractional order derivative is also discussed. Achieved wave solitons are verified by Mathematica tool. Few of the gained wave solitons are also described through 2-dimensional, 3-dimensional and contour graphs through Mathematica tool. The gained solutions are helpful for the further development of concerned model. Finally, these techniques are simple, fruitful and effective to deal with non-linear FPDEs.

Dietary restrictions or reductions such as fasting for weight loss, are often difficult to adhere to in the long term due to increased appetite and food cravings. Recently, gastrointestinal delivery of bitter hops extract has been shown to be effective at reducing appetite in men. Our aim was to determine the effect of bitter hops on appetite and cravings in women, using a day-long, water-only fast. Thirty adult women were recruited and required to fast for 24 h from 1800 h to 1800 h on three occasions and given an ad libitum meal to break each fast. Treatments of either a placebo or one of two doses (high dose; HD: 250 mg or low dose; LD: 125 mg) of a bitter hops-based appetite suppressant (Amarasate®) were given twice per day at 16 and 20 h into the fast. Treatment with both the HD and LD treatment groups exhibited a significant (p < 0.05) reduction in appetite and cravings for food. Two participants reported loose stools and one reported heart-burn while on the HD treatment, and one participant reported loose stools while on the LD treatment. These data suggest that appetite suppressant co-therapy may be useful in reducing hunger during fasting in women and shows that gastrointestinal delivery of bitter compounds may also be an effective method of reducing cravings for food.

Background:As cyber-physical systems (CPS) continue to grow, it is progressively crucial to address the challenges of data protection using artificial intelligence (AI). Objective:The goal of this research is to provide an up-to-date overview of the security and privacy issues of CPS that incorporate AI techniques. Method:To achieve this, the author conducted a systematic literature review, focusing on 35 relevant articles. Results:The data collected from these studies was then categorized into three main areas: 1) different security and privacy issues, 2) application areas and vulnerabilities, and 3) the AI techniques used to address security and privacy concerns. The literature review highlights that intrusion detection and cyberattacks are the most commonly studied areas in CPS, while Machine Learnning (ML)-based attacks and vessel trajectory are less explored. The review identifies various CPS applications such as water treatment, energy, healthcare, and transportation that address security and privacy concerns. However, a relatively small proportion of studies focused on the manufacturing domain. The review also notes that while supervised machine learning algorithms under the classification category are commonly used to address data protection issues, there are comparatively fewer studies that have implemented automation processes using robots and deep learning. Limitations:The articles related to blockchain-based research were not included in this review to focus solely on AI techniques. Conclusion:The results of this analysis indicate that there is a significant need for innovative AI/ML techniques to protect intelligent systems and networks from ML-based security threats.

The unstable international economic situation is reflected in the supply chain stress, lack or increased cost of some raw materials, fuel or semi-finished products is forcing organizations to perform new optimization initiatives in the utilization of their equipment and assets pointed to obtain the maximum value from them, while maintaining and even improving the quality of their products. The achievement of these objectives involves the reduction or minimization of equipment downtime to maintain the advantage over their competitors and ensure the organization's competitiveness. The intelligent maintenance system (IMS) provides adequate support for decision-making related to equipment maintenance, since poor maintenance results in unplanned stoppages, with the consequent additional cost and increased customer dissatisfaction, and an over-maintenance can result in an additional labor cost, time and the replacement of parts that are in good conditions. The utilization of new tools and technologies introduced by Industry 4.0 offers multiple opportunities for enhancement through communication and computerized data processing, aiming to improve the maintainability of a hydrogen compressor using neural networks based on attention mechanisms combined with linear regression.

This work communicates analysis of aluminum monoxide, AlO, laser-plasma emission records using line strength data and the ExoMol astrophysical database. A nonlinear fitting program computes comparisons of measured and simulated diatomic molecular spectra. Predicted cyanide spectra of the AlO, ${\rm B}\ ^2\,\Sigma^+ \longrightarrow {\rm X} \ ^2\,\Sigma^+$, $\Delta {\rm v} = 0, \pm 1, \pm 2, + 3$ sequences and progressions compare nicely with 1 nanometer resolution experimental results. The analysis discusses experiment data captured during laser ablation of Al$_2$O$_3$ with 266-nm, 6-mJ pulses. The accuracy of the AlO line strength data is better than one picometer. This work presents as well comparison of the $^{27}$Al$^{16}$O line strength and of ExoMol data for spectral resolutions of 0.1nm and 0.07nm. Accurate AlO databases show a volley of applications in laboratory and astrophysical plasma diagnosis.

We exploit the properties of complex time to obtain an analytical relationship based on considerations of causality between the two conserved quantities of a system’s Hamiltonian and its Entropy Production. In natural units, when complexified, the one is simply the Wick-rotated complex-conjugate of the other. A Hilbert transform relation is constructed in the formalism of Quantitative Geometrical Thermodynamics which enables system irreversibility to be handled analytically within a framework that unifies both the microscopic and macroscopic scales, and which also unifies the treatment of both reversibility and irreversibility as complementary parts of a single physical description. In particular the thermodynamics of two unitary entities are considered: the alpha particle which is absolutely stable (that is, trivially reversible with zero entropy production), and a black hole whose absolute disequilibrium (unconditional irreversibility) is characterized by a non-zero entropy production for which we show an alternate derivation confirming our previous one (Universe 7, 2021, 325). The thermodynamics of a canonical decaying harmonic oscillator are also considered. In this treatment the complexification of time also enables a meaningful physical interpretation of both “imaginary time” and “imaginary energy”.

In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.

The meridian line is a basic instrument for positional astronomy, it was used to study the motion of Sun, Moon, planets and the position of stars by measuring position and time of their passage through the meridian plane. The accuracy of such positions was dependent on precise theories of the atmospheric refraction (Cassini, 1655 and Laplace, 1825) and by the use of reference marks present originally on the meridian line, and now cancelled by the centuries. From October 27, 2018 the new pinhole of the meridian line in the Basilica of S. Maria degli Angeli in Rome (1702) is a circle 25 mm wide and 6.11 mm thick and its position is fixed, in order to perform a series of observations of astrometric quality, the IGEA campaign. The comparison of the observed positions of the meridian passages of the Sun, Southern and Northern limbs, with the ephemerides of Calsky.org and Stellarium 0.20.2 for the Sun are examined for the dates of the ingresses into the zodiacal signs, when the ecliptic longitude is exactly 0°/180° (Aries and Libra, spring and fall equinox), 30°/150° (Taurus, Virgo), 60°/120° (Gemini, Leo), 90° (Cancer), 330°/210° (Pisces, Scorpio), 300°/240° (Aquarius/Sagittarius), 270° (Capricorn). The former geometrical calibration of the marks present on the line, with a total station, is compared with another calibration done with a metal and laser meter. The first star on the floor of the Basilica representing the position of the Sun on August 20, 1702 when the pope Clement XI visited the meridian line, financed by him, has been calibrated with the solar image. The present pinhole is 4.4±0.1 mm South with respect to the original one of 1702.

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real-time. Based on Weight Agnostic Neural Networks (WANNs) methodology, we propose an automated searching neural net architectures strategy that can perform various tasks such as identify zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

One of the key components of application performance monitoring (APM) software is 2 AI/ML empowered data analytics for predictions, anomaly detection, event correlations and root 3 cause analysis. Time series metrics, logs and traces are three pillars of observability and the valuable 4 source of information for IT operations. Accurate, scalable and robust time series forecasting and 5 anomaly detection are desirable capabilities of the analytics. Approaches based on neural networks 6 (NN) and deep learning gain increasing popularity due to their flexibility and ability to tackle complex 7 non-linear problems. However, some of the disadvantages of NN-based models for distributed cloud 8 applications mitigate expectations and require specific approaches. We demonstrate how NN-models 9 pretrained on a global time series database can be applied to customer specific data using transfer 10 learning. In general, NN-models adequately operate only on stationary time series. Application 11 to non-stationary time series requires multilayer data processing including hypothesis testing for 12 data categorization, category specific transformations into stationary data, forecasting and backward 13 transformations. We present the mathematical background of this approach and discuss experimental 14 results from the productized implementation in Wavefront by VMware (an APM software) while 15 monitoring real customer cloud environments.

The evaluation of geometric defects is necessary in order to maintain the integrity of structures over time. These assessments are designed to detect damages of structures and ideally help inspectors to estimate the remaining life of structures. Current methodologies for monitoring structural systems, while providing useful information about the current state of a structure, are limited in the monitoring of defects over time and in linking them to predictive simulation. This paper presents a new approach to the predictive modeling of geometric defects. A combination of segmented from point clouds are parametrized using the convex hull algorithm to extract features from detected defects, and a stochastic dynamic model is then adapted to these features to model the evolution of the hull over time. Describing a defect in terms of its parameterized hull enables consistent temporal tracking for predictive purposes, while implicitly reducing data dimensionality and complexity as well. In this study, 2D point clouds analogous to information derived from point clouds were first generated over simulated life-cycles. The evolutions of point cloud hull parameterizations were modeled as stochastic dynamical processes via autoregressive integrated moving average (ARIMA) and vectorized autoregression (VAR) and compared against ground truth. The results indicate that this convex hull approach provides consistent and accurate representations of defect evolution across a range of defect topologies and is reasonably robust to noisy measurements, however assumptions regarding the underlying dynamical process play a significant the role in predictive accuracy. The results were then validated on experimental data from fatigue testing with high accuracy. Longer term, the results of this work will support finite element model updating for predictive analysis of structural capacity.

This work communicates laser-plasma experiments in a gaseous mixture of hydrogen and nitrogen. Time-resolved spectroscopy measures the first four Balmer series hydrogen lines together with selected neutral and ionized nitrogen lines. Optical breakdown plasma is generated in a 1:1 hydrogen:nitrogen mixture at ambient temperature and 0.27-atm pressure. Time-resolved spectroscopy records emitted radiation with spatial resolution along the slit height for the H_α, H_β, H_γ, and H_δ lines. For 13 selected time delays from 0.25 µs to 3.25 µs and 0.025 µ gate-widths, micro-plasma diagnostics is evaluated. Of interest are the peak-separation and width of H_δ and width of H_γ for electron densities in the range of 0.1 to 1.0×10¹⁷ cm^-3, and comparisons with H_β and H_α diagnostics. Integral inversions interrogate spatial distributions of the plasma expansion. Applications include laboratory and stellar astrophysics plasma diagnosis.

Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and rare survey has been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human error and to simplify management, self-management CPS (SCPS) is a wise choice. And to achieve dependable self-management, systematic solution is necessary to verify the design and to guarantee the safety of self-adaptation decision, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focus on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means; then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaption, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaption, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.

A new type of ordinary differential equation is introduced and discussed, namely, the time-dependent order ordinary differential equations. These equations can be solved via fractional calculus and are mapped into Volterra integral equations of second kind with singular integrable kernel. The solutions of the time-dependent order differential equations smoothly deforms solutions of the classical integer order ordinary differential equations into one-another, and can generate or remove singularities. An interesting symmetry of the solution in relation to the Riemann zeta function and Harmonic numbers was also proved.

Direct Behavior Rating (DBR) as a behavioral progress monitoring tool can be designed as longitudinal assessment with only short intervals between measurement points. The reliability of these instruments has been evaluated mostly in observational studies with small samples based on generalizability theory. However, for standardized use in the pedagogical field, a larger and broader sample is required in order to assess measurement invariance between different participant groups and over time. Therefore, we constructed a DBR with multiple items to measure the occurrence of specific externalizing and internalizing student classroom behaviors on a Likert scale (1 = never to 7 = always). In a pilot study, two trained raters observed 16 primary school students and rated the student behavior over all items with a satisfactory reliability. In the main study, 108 regular primary school students, 97 regular secondary school students and 14 students in a clinical setting were rated daily over one week (five measurement points). IRT analyses confirmed the instrument’s technical adequacy, and latent growth models demonstrated the instrument’s stability over time. Further development of the instrument and study designs to implement DBRs are discussed.

The nature of contemporary Spatial Data Infrastructures lies in the provision of geospatial information in an on-demand fashion. Though recent applications identified the need to react to real-time information in a time-critical way. In particular, research efforts in the field of geospatial Internet of Things have identified substantial gaps in this context, ranging from a lack of standardization for event-based architectures to the meaningful handling of real-time information as ''events''. This manuscript presents work in the field of Event-driven Spatial Data Infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of Spatial Data Infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches - developed in different research projects - to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall Event-driven Spatial Data Infrastructure.

J/ψ, a charmonium bound state made of a charm and an anti-charm quark, has been discovered in the 1970s and confirmed the quark model. Because the mass of charms quarks is significantly above the Quantum Chromodynamics (QCD) scale ΛQCD, charmonia are considered as excellent probes to test perturbative Quantum Chromodynamics (pQCD) calculations. Over the past decades, they have been studied extensively at different high energy colliders. However, their production mechanisms, which involves multiple scales, are still not very well understood. Recently, in high multiplicity p+p collisions at RHIC and at the LHC, a significant enhancement of J/ψ production yield has been observed, which suggests a strong contribution from the Multi-Parton Interaction (MPI). This is different from the traditional pQCD picture where charm quark pairs are produced from a single hard scattering between partons in p+p collisions. In this work, we will report the J/ψ normalized production yield as a function of normalized charged particle multiplicity over a board range of rapidity and event multiplicity in the J/ψ→μ+μ− channel with PHENIX Run 15 p+p data at \sqrt{s}=200 GeV. The results are compared with PYTHIA 8 simulations with the MPI option on and off. Finally, the outlooks of J/ψ in p+Au and Au+p collisions along with Color Glass Condensate (CGC) predictions and of multiplicity dependent ψ(2S)/J/ψ ratio in p+p data will be briefly discussed.

The design and usage of the addressed combined fiber-optic sensors (ACFOS) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated. The main development trend of the combined fiber-optic sensors (CFOS), consisting of the fiber Bragg grating (FBG) and the Fabry-Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The addressed fiber Bragg structures (AFBS) usage instead of the FBG in the CFOS leads not only to significant cheapening of the sensor system due to microwave photonics interrogating methods, but also to increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of the greenhouse gases (СО2, NO2, CH4, OX) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of Karunen-Loeff transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, it allows determining the gas concentration at the AFBS address frequencies. The estimations have shown that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of −60…+300 °C with an accuracy of 0.1–0.01 °C, and the gas concentration in the range of 10…90% with the accuracy of 0.1–0.5%.

Footprints are powerful indicators for evaluating the impact of the bioeconomy of a country on environmental goods, domestically and abroad. In this study, we apply a hybrid approach combining a Multi-Regional Input-Output model and land use modelling to compute the agricultural land footprint (aLF). Furthermore, we added information on land-use change to the analysis and allocated land conversion to specific commodities. The German case study shows that the aLF abroad is larger by a factor of 2.5 to 3 than the aLF in Germany. In 2005 and 2010, conversion of natural and semi-natural land-cover types abroad allocated to Germany due to import increases was 2.5 times higher than the global average. Import increases to Germany slowed down in 2015 and 2020, reducing land conversion attributed to the German bioeconomy to the global average. The case study shows that the applied land footprint provides clear and meaningful information for policymakers and other stakeholders. The presented methodological approach can be applied to other countries and regions covered in the underlying database EXIOBASE. It can be adapted, also for an assessment of other ecosystem functions, such as water or soil fertility.

Electric moped scooter sharing services have recently experienced strong growth rates, particularly in Europe. Due to their compactness, environmental-friendliness and convenience, shared e-mopeds are suitable modes of transport in urban mobility to help reduce the environmental impact. However, its traffic-related, economic and environmental effects are merely represented in academic research. We used passenger car traffic data in Berlin generated by the multi-agent transport simulation framework MATSim to develop a python-based simulation, resembling an e-moped sharing system. Based on the results, a total cost of ownership and a life cycle assessment for fleet sizes of 2,500, 10,000 and 50,000 vehicles were conducted. The results indicate that a substantial part of all passenger car trips in Berlin can be substituted. The larger the fleet, the more and longer trips are replaced. Simultaneously, the efficiency in terms of fleet utilization decreases. The scenario with 10,000 e-mopeds offers the lowest total distance-based costs for sharing operators, whereas a fleet consisting of 2,500 vehicles exhibits the lowest environmental emissions per kilometer driven over the expected lifespan of a shared e-moped. Based on the renewable energy potential for 2050 forecasted by the German Federal Environment Agency, a significant overall decline in environmental impacts can be achieved.

Exercise-based cardiac rehabilitation requires patients to perform a set of certain prescribed exercises a specific number of times. Local muscular endurance (LME) exercises are an important part of the rehabilitation program. Automatic exercise recognition and repetition counting, from wearable sensor data is an important technology to enable patients to perform exercises independently in remote settings, e.g. their own home. In this paper we first report on a comparison of traditional approaches to exercise recognition and repetition counting, corresponding to supervised machine learning and peak detection from inertial sensing signals respectively, with more recent machine learning approaches, specifically Convolutional Neural Networks (CNNs). We investigated two different types of CNN: one using the AlexNet architecture, the other using time-series array. We found that the performance of CNN based approaches were better than the traditional approaches. For exercise recognition task, we found that the AlexNet based single CNN model outperformed other methods with an overall 97.18% F1-score measure. For exercise repetition counting , again the AlexNet architecture based single CNN model outperformed other methods by correctly counting repetitions in 90% of the performed exercise sets within an error of ±1. To the best of our knowledge, our approach of using a single CNN method for both recognition and repetition counting is novel. In addition to reporting our findings, we also make the dataset we created, the INSIGHT-LME dataset, publicly available to encourage further research.

This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled colour correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields better edge and contrast enhancement results without halo effect, colour degradation and is faster and more adaptive than all other algorithms from the literature.

The concept of neutrosophic number is a significant mathematical tool to deal with real scientific problems because it can tackle indeterminate and incomplete information which exists generally in real problems. In this article, we use neutrosophic numbers (a + bI), where a and bI denote determinate component and indeterminate component respectively. We explore the situations in which the input information is needed to express in terms of neutrosophic numbers. We define score functions and accuracy functions for ranking neutrosophic numbers. We then define a cosine function to determine unknown criteria weights. We define neutrosophic number harmonic mean operators and proved their basic properties. Then, we develop two novel MCGDM strategies using the proposed aggregation operators. We solve a numerical example to demonstrate the feasibility and effectiveness of the proposed two strategies. Sensitivity analysis with variation of “I” on neutrosophic numbers is performed to demonstrate how the preference ranking order of alternatives is sensitive to the change of “I”. The efficiency of the developed strategies is ascertained by comparing the obtained results from the proposed strategies with the existing strategies in the literature.

As artificial intelligence (AI) makes significant headway in various arenas, the field of Speech-Language Pathology is at the precipice of experiencing a transformative shift towards automation. This study introduces QuickPic AAC, an AI-driven application designed to generate topic-specific displays from photographs in a "just-in-time" manner. Using QuickPic AAC, this study aimed to (a) determine which of two AI algorithms (NLG-AAC and GPT-3.5) results in greater specificity of vocabulary (i.e., percentage of vocabulary kept/deleted by clinician relative to vocabulary generated by QuickPic AAC; percentage of vocabulary modified); and to (b) evaluate perceived usability of QuickPic AAC among practicing speech-language pathologists. Results revealed that the GPT-3.5 algorithm consistently resulted in greater specificity of vocabulary and that speech-language pathologists expressed high user satisfaction for the QuickPic AAC application. These results support continued study of the implementation of QuickPic AAC in clinical practice and demonstrate the possibility of utilizing topic-specific displays as just-in-time supports.

This paper focuses on enhancing the object detection and decision-making capabilities of Autonomous Emergency Medical Response Systems (AEMRS). Using advanced sensors, Convolutional Neural Networks (CNN), and reinforcement learning, we propose a model that processes environmental data to identify obstacles and make optimal navigation decisions. The integration of these technologies aims to minimize response time and improve the efficiency of emergency medical services. The perception system utilizes radar, LiDAR, and camera inputs to create a comprehensive understanding of the environment. These data are processed through a series of CNN layers to detect and classify objects such as vehicles, pedestrians, and road signs. On the decision-making front, a Q-learning algorithm is employed to enable the ambulance to learn from its interactions with the environment, continuously improving its route planning and collision avoidance strategies. By combining these advanced AI techniques, the proposed AEMRS can significantly enhance the speed and reliability of emergency responses, ultimately saving more lives. This paper presents the design, implementation, and simulation results of the proposed system, demonstrating its potential to revolutionize emergency medical services.

The reliability of concrete cable-stayed bridge structure is time variant. This is due to strength deterioration and time-variant mechanical and environmental loadings. Demands for keeping concrete cable-stayed bridge structure in functional and reliable states require maintenance interventions. Limited financial resources require optimal maintenance strategies. These strategies have to consider the effects of uncertainties not only on the structural reliability deterioration but also on the maintenance interventions. By analyzing variation of concrete cable-stayed concrete cable-stayed bridge liabilities and benefit expectation through different maintenance programs, on the condition of satisfying the concrete cable-stayed bridge performance, with the maximum expected benefit as the target to discuss repair and reinforcement strategies for the concrete cable-stayed bridges in service. The method takes into account the present performance and future expected benefit, optimizing the performance of the concrete cable-stayed bridge and the expected benefit through determination policies of concrete cable-stayed concrete cable-stayed bridge maintenance program. The proposed method also includes an advanced cost model which takes into account the interaction between costs of maintenance interventions and their effect on reliability. Examples are presented demonstrating the application of the proposed method to a stock of existing deteriorating concrete cable-stayed bridges under various maintenance scenarios.

Carbon fiber reinforced polymer (CFRP) bending structure is widely used in the aviation field. The occurrence and propagation of delamination damage will reduce the safety of in-service bending structures. As a detection tool with high damage sensitivity, Lamb wave can effectively identify delamination damage. In this study, the signal difference coefficient (SDC) was introduced to quantify delamination damage and evaluate the sensitivity of A0 and S0 mode lamb waves to delamination damage. The simulation results show that compared with the S0 mode Lamb wave, the A0 mode Lamb wave exhibits higher delamination damage sensitivity. The delamination damage can be quantified based on the strong correlation between the SDC and the delamination damage size. The control effect of the linear array PZT phase time-delay method on Lamb wave mode was investigated by simulation. The phase time-delay method realizes the generation of a single-mode Lamb wave, which can separately excite the A0 mode and S0 mode Lamb wave to identify delamination damage of different sizes. The A0 mode Lamb wave was excited by the developed one-dimensional linear comb transducer (LCT), which was used to conduct the detection experiment on the CFRP bending plate with delamination damage sizes of Φ6.0 mm, Φ10.0 mm, and Φ15.0 mm. The experimental results verify the correctness of the simulation. According to the Hermite interpolation results of the finite-element simulation data, the relationship between the delamination damage size and the SDC was fitted by the Gaussian function, which can accurately quantify the delamination damage. The absolute error of the delamination damage quantification with fitting expression is not larger than 0.8 mm, and the percentage error is not more than 8%.

Cooling fans are one of the critical components of air-forced (AF) dry-type transformers for regulating internal temperatures. Therefore, effective malfunction detection is crucial to maintain the transformer temperature within an acceptable range and prevent overheating. Regular maintenance occurs periodically and issues with cooling fans may arise between maintenance periods, leading to prolonged operation under malfunctioning conditions and potential failures. In addition, utilities typically have online information about whether a fan works or not without providing information about cooling fan malfunctioning circumstances. To address these challenges, this study proposes learning-based online monitoring techniques to detect malfunctions in AF transformer cooling fans. Random forests (RFs) and convolutional neural networks (CNNs) are developed to classify the audio signals from cooling fans into normal and malfunctioning classes. Unlike RFs, which require separate feature extraction, CNNs are trained based on spectrogram images derived from audio signals. Thus, various time-frequency techniques are utilized for feature extraction in RFs. Besides, multiple data augmentation techniques are employed to enhance the dataset size and diversity. Algorithmic performance is optimized through hyperparameter tuning and classifier threshold adjustment. Simulations reveal that CNNs outperform RFs, whereas the latter provides superior interpretability of acoustic features compared to the former.

There are no investigations about the outcomes of idiopathic PVCs catheter ablation (CA) in athletes compared to sedentary population. We conducted a prospective single-centre observational study. The primary and secondary procedural outcomes were premature ventricular contractions (PVCs) post-ablation reduction in athletes vs. non-athletes group and in agonist vs. leisure-time athletes. The third was the evaluation of the resumption of physical activity and the improvement of symptoms in agonist and leisure-time athletes. From January 2020 to October 2022 we enrolled 79 patients with RVOT/LVOT/fascicular PVC presumed origin. The median percentage of decrease between the pre-procedure and post-procedure Holter monitoring in non-athletes group was 96 (IQR 68 – 98) and 98 in athletes group (IQR 92 – 99) (p=0,08). Considering the athletes, the median percentage of decrease in PVCs number was 98 (IQR 93-99) and 98 (IQR 87-99) respectively in leisure-time and agonistic athletes (p=0,42). Sixteen (70%) leisure time and 17 (90%) agonist athletes (p=0,24) have resumed physical activity 3 months after PVCs CA; among agonistic athletes, 59% have resumed competitive physical activity. Much of leisure-time (88%) and agonist (70%) athletes experienced an improvement in symptoms after ablation. PVC CA was effective and safe in both groups, reducing symptoms and allowing a quick and safe return to sports activities in athletes.

Spin-orbit coupling and nonparaxiality effects during propagation of vortex vector light beams in a cylindrical graded-index waveguide are investigated by solving the full three-component field Maxwell’s equations. Symmetry breaking effects for left- and right-handed circularly polarized vortex light beams propagating in a rotationally symmetric graded-index optical fiber are considered. The mode-group delay in a graded-index fiber due to spin-orbit interaction is demonstrated. It is shown that the relative delay times between vortex pulses of opposite circular polarizations of the order of 10 ps/km can be observed in graded-index fibers for high-order topological charges.

The decline in assisted reproductive technology (ART) success among older women, attributed to decreased oocyte quantity and quality, poses a significant challenge. Currently, no consensus ex-ists on the optimal ovarian stimulation protocol for older women undergoing IVF. This retrospec-tive registered cohort study aimed to compare cumulative live birth rates (CLBR), time to live birth (TTLB), and cost-effectiveness in women over 35 years using gonadotropin-releasing hor-mone agonist (GnRHa) or clomiphene-primed ovarian stimulation (CPOS). We performed pro-pensity score matching (PSM) on 2,871 IVF cycles in women over 35 who received either GnRHa or CPOS protocols, resulting in 375 cycles in each group to compare treatment outcomes. Addition-ally, a decision tree model was utilized to assess the cost-effectiveness of the two protocols. Fol-lowing PSM, both groups showed similar baseline characteristics. The CPOS protocol exhibited a higher cycle cancellation rate (13.07% vs 8.00%, p=0.032), yet maintained comparable fertilization and embryo quality. While CLBRs per initial cycle (41.07% vs. 45.33%, p=0.269) and delivery out-comes were similar between the groups after a 24-month follow-up, TTLB was longer in the CPOS group. Notably, the average cost per live birth in the CPOS group was 21.27% less than that in the GnRHa group (￥32,301.42 vs ￥39,174.22). In conclusion, for women over 35 undergoing IVF, the CPOS protocol demonstrated similar CLBR to GnRHa but with lower costs, suggesting its potential as a viable and cost-efficient ovarian stimulation option.

The Mediterranean diet (MED) is highly recommended. Medical nutrition therapy is the cornerstone of diabetes treatment. We assessed adherence to the MED, and resultant micronutrient intake and glycemic control in youth with type 1 diabetes. Twenty adolescents, median age 18 years (interquartile range: 15.5-21), median diabetes duration 9 years (7-14), using continuous glucose monitoring devices, received personalized diet regimes based on MED. At 6 months post-intervention, the caloric intake remained unchanged; however, the carbohydrate proportion was lower (p=0.058) and the intakes of some monounsaturated fats increased (p=0.049). Sodium intake exceeded the recommended daily allowance by 250% (p=0.653), before and after the intervention. For blood glucose, the percent time-in-range 70-180 mg/dL improved from 52% (38-60) to 63% (47-71) (p=0.047). The total insulin dose decreased marginally, from 0.76 u/kg (0.64-0.97) to 0.72u/kg (0.61-0.89) (p=0.067). BMI z-score and waist circumference did not change (p=0.316 and p=0.161, respectively). Diastolic blood pressure percentile decreased from 73% (68-88) to 69% (50-79) (p=0.028), and LDL cholesterol from 114mg/ dl (105-134) to 104mg/dl (96-124) (p=0.059). The Israeli Mediterranean diet screener score increased, from 8 (7-11) to 13 points (12-14) (p=<0.001). The MED-based intervention demonstrated feasibility and improvements in glycemic outcomes and in certain nutritional parameters.

Evaporating liquid sessile drop deposited on horizontal surface is an important object of applications (printing technologies, electronics, sensorics, medical diagnostics, hydrophobic coatings, etc.) and theoretical investigations (microfluidics, self-assembly of nanoparticle, crystallization of the solute, etc.). The arsenal of formulas for calculating the slow evaporation of an axisymmetric drop of capillary dimensions deposited on a flat solid surface is reviewed. Such characteristics as vapor density, evaporation flux density, total evaporation rate are considered. Exact solutions obtained in the framework of the Maxwellian model, in which the evaporation process of the drop is limited by vapor diffusion from the drop surface to the surrounding air, are presented. The summary covers both well-known results obtained during the last decades and new results published by us in the last few years, but practically unknown to the wide scientific community. The newest formulas, not yet published in refereed publications, concerning exact solutions for a number of specific contact angles are also presented. In addition, new approximate solutions are presented for the first time (total evaporation rate and mass loss per unit surface area per unit time in the whole range of contact angles ), which can be used in modeling without requiring significant computational resources.

Keywords: Baseband, GeoSurf Constellation, Interference, Linear Distortions, Passband, Time Delay, Rain attenuation, Synthetic Storm Technique, ultra–wideband.

This paper presents a limited scope dynamic probabilistic risk assessment (D-PRA) on the survivability of commercial of the shelf (COTS) drones tasked with surveilling areas with varying radiation levels after a nuclear accident. The D-PRA is based on a discrete-dynamic event tree (D-DET) approach, which couples with the OpenEPL error propagation framework to model sequences leading to Loss of Mission (LOM) scenarios due to component failures in the drone’s navigation system. Radiation effects are simulated by calculating the total ionizing dose (TID) against the permissible limit per component, and errors are propagated within the electronic hardware and software blocks to quantify navigation system availability per radiation zone. The proposed methods are integrated into the traditional event tree/fault tree approach and the most vulnerable components are radiation-hardened (RAD-HARD) to the extent specified by a predefined mission success criterion. The results demonstrate the usefulness of the proposed approach in performing trade studies for incorporating COTS components into RAD-HARD drone designs.

Coastal wave modeling and forecasting are essential in oceanography, sustainable marine energy, and ocean engineering. Precise forecasting of wave speed and direction are crucial for offshore operations, marine energy, risk management, environmental management, coastal and sustainable maritime management. The coastline of Brunei Darussalam can generate between 15 and 126 Giga Watt of wave energy. In this experimental research, we used two numerical approaches, the finite difference, and spectral element methods, to model and simulate wave speed and direction. We calculated the mean error between numerical and analytical solutions. We proposed a novel, promising, univariate time series forecasting model by combining the Long Short-Term (LSTM) with KerasTuner hyperparameters tuning and optimization techniques. This method helps us to improve the accuracy and efficiency of time-series forecasting.. The experimental data was computed from high-precision Acoustic Doppler Current Profiler (ADCP) sensor data. This research is part of the preliminary feasibility analysis of wave energy production in Brunei Darussalam and net zero commitment for a sustainable environment. Seven independent forecast experiments were performed for wave speed and direction in degree and radian units for 1, 3, 6, 8, 10, 12, and 24 hours. Mean squared error (MSE) was adopted as a metric for both training and testing. The experimental results reveal that the wave speed forecast has the lowest MSE compared to direction, regardless of the unit of measure, but has a longer duration. In addition, the direction forecast in the degree unit has the lowest errors compared to the unit of radians; the latter has a longer running time than the former. The model has delivered optimal results throughout the experiments with minor training and test errors. We conducted a thorough evaluations on two benchmark time series datasets, which include the study dataset and air quality index dataset, to validate the performance of the proposed model with other models. The proposed model outperforms cutting-edge forecasting models, such as the conventional LSTM, ARIMA, and Prophet. The model has the slightest forecast error compared to the existing literature’s result.

This article presents analysis of carbon Swan, C$_2$, laser-plasma emission records using line strength data, C$_2$-Swan-lsf, and the ExoMol astrophysical database. Line-strength data fitting of 0.25-nanometer spectral resolution ExoMol-computed spectra for a 6,000-Kelvin temperature C$_2$ Swan system, ${\rm d}\ ^3\,\Pi_{\rm g} \longrightarrow {\rm a} \ ^3\,\Pi_{\rm u}$, $\Delta {\rm v} = 0, \pm 1$, reveals a temperature of 5,640 Kelvin. The six per cent lower temperature is associated primarily with the accuracy of the transition wavelengths in the ExoMol vs. C$_2$-Swan-lsf data. The analysis of experiment data examines spectra that are recorded following laser-induced optical breakdown in carbon monoxide. The laser plasma data are recorded with 0.35-nm spectral resolution. The temperature inferences are elaborated when using non-linear fitting with both databases. The results show temperatures in excess of 6,000 Kelvin for the $\Delta {\rm v} = -1$ sequence, and for a time delay of 30\,$\upmu$s from laser plasma initiation. The accuracy of the C$_2$ Swan bands line strength data is of the order of 1 picometer. These line strength data are also utilized for analysis of laser-induced fluorescence experiments that employ a spectral resolution of 5.5 picometer, and a temperature of 2,716 Kelvin is inferred. Accurate C$_2$ databases show many applications in laboratory diagnosis and interpretation of astrophysical plasma records.

Deep Learning (DL) and Machine Learning (ML) are widely used in many fields, but rarely used in Frequency Estimation (FE) and Slope Estimation (SE) of signals. Frequency and slope estimation for Frequency-Modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sonar, and radar measurements. In this work, artificial neural network (ANN) and convolutional neural network (CNN) are used in frequency and slope estimation for FM signals under Additive White Gaussian Noise (AWGN) and Additive Symmetric alpha Stable Noise (SαSN). SαS distributions are impulsive noise disturbances found in many communication environments like marine systems; their distribution lacks a closed-form Probability Density Function (PDF), except for specific cases, and infinite second-order statistic, hence Geometric SNR (GSNR) is used in this work to determine the impulsiveness of noise in a mixture of Gaussian and SαS noise processes. ANN is a machine learning classifier, designed with few layers for reducing FE and SE complexity while getting higher accuracy as compared with classical techniques. CNN is a deep learning classifier, designed with many layers for FE and SE, and proved to be more accurate than ANN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful for FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption, which is important in many systems such as some Internet of Things (IoT) sensor applications. After training DCNN for frequency and slope estimation of LFM signals, the performance of DCNN (in terms of accuracy) can give acceptable results at very low signal-to-noise ratios where TFD fails, giving more than 20dB difference in the GSNR working range.

Demographic change is leading to the aging of German society. As long as the baby boom co-horts are still of working age, the working population will also age - and decline as soon as this baby boom generation gradually reaches retirement age. At the same time, there has been a trend towards increasing absenteeism (times of inability to work) in companies since the zero years, with the number of days of absence increasing with age. We present a novel stochastic forecast approach that combines population forecasting with forecasts of labor force participation trends, considering epidemiological aspects. For this, we combine a stochastic Monte Carlo-based cohort-component forecast of the population with projections of labor force participation rates and morbidity rates. This article examines the purely demographic effect on the economic costs associated with such absenteeism due to the inability to work. Under expected future employment patterns and constant morbidity patterns, absenteeism is expected by close to 5 percent by 2050 relative to 2020, associated with increasing economic costs of almost 3 percent. Our results illustrate how strongly the pronounced baby boom/ baby bust phenomenon determines demographic development in Germany in the midterm.

This paper presents an approach to the Timoshenko beam theory (TBT) using the finite difference method (FDM). The Timoshenko beam theory covers cases associated with small deflections based on shear deformation and rotary inertia considerations. The FDM is an approximate method for solving problems described with differential equations. It does not involve solving differential equations; equations are formulated with values at selected points of the structure. In addition, the boundary conditions and not the governing equations are applied at the beam’s ends. The model developed in this paper consisted of formulating differential equations with finite differences and introducing additional points at the beam’s ends and at positions of discontinuity (concentrated loads or moments, supports, hinges, springs, brutal change of stiffness, spring-mass system, etc.). The introduction of additional points allowed us to apply the governing equations at the beam’s ends. Moreover, grid points with variable spacing were considered, the grid being uniform within beam segments. First-order, second-order, and vibration analyses of structures were conducted with this model. Furthermore, tapered beams were analyzed (element stiffness matrix, second-order analysis, vibration analysis). Finally, a direct time integration method (DTIM) was presented; the FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. The results obtained in this paper showed good agreement with those of other studies, and the accuracy was increased through a grid refinement. Especially in the first-order analysis of uniform beams, the results were exact for uniformly distributed and concentrated loads regardless of the grid.

Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. A time series of 33 COSMO-SkyMed ascending images was also used for comparison. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. Taking into account the presence of east-west horizontal motion, our findings indicate that the accuracy of the decomposed vertical velocity can be improved by up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr. This confirmed that the displacement in Ho Chi Minh City area is mainly vertical downward. Together, these results reinforced the remarkable suitability of ESA's SAR Sentinel-1 for subsidence applications, even for non-European countries such as Vietnam and Southeast Asia.

With reference to our recently proposed Planck Scale White Hole Cosmology (PS-WHC) or Flat Space Cosmology (PS-FSC), we make an attempt to quantify galactic dark matter and flat rotation speeds in terms of galactic visible matter and cosmic mass. Considering recently observed dwarf galaxies having very little dark matter and assuming a time dependent reference mass unit of $M_X\cong \left(\mbox{3.0 to 4.0}\right)\times 10^{38}$ kg, we suggest an empirical relation for galactic dark matter $M_d$ via galactic visible mass $M_v$ as,$M_d \cong \frac{M_v^{3/2}}{M_X^{1/2}}$. This relation helps in fitting flat rotation speeds starting from 8 km/sec (for Segue 2) to 500 km/sec (for UGC12591). Modifying MOND's galactic flat rotation speed relation with Hubble mass $M_0\cong \left(\frac{c^3}{2GH_0}\right)$ of the universe, ratio of galactic flat rotation speed $V_G$ to speed of light $c$ can be shown to be approximately $\frac{V_G}{c} \cong 0.5 \left(\frac{M_v}{M_0}\right)^{1/4}$. Considering the sum of galactic dark matter and visible matter, ratio of galactic flat rotation speed to speed of light can be shown to be approximately $\frac{V_G}{c}\cong 0.25 \left(\frac{M_v+M_d}{M_0}\right)^{1/4}$. With further study, dark matter's nature, effect and distribution can be understood in terms of visible matter's extended gravity and extended theories of gravity can be understood with 'distance cosmic mass' rather than the empirical 'minimum acceleration'.

Conditional heteroskedastic financial time series are commonly modelled by (G)ARCH processes. ARCH(1) and GARCH were recently established in C[0,1] and L^2[0,1]. This article provides sufficient conditions for the existence of strictly stationary solutions, weak dependence and finite moments of (G)ARCH processes for any order in C[0,1] and L^p[0,1]. It deduces explicit asymptotic upper bounds of estimation errors for the shift term, the complete (G)ARCH operators and the projections of ARCH operators on finite-dimensional subspaces. The operator estimaton is based on Yule-Walker equations, and estimating the GARCH operators also involves a result estimating operators in invertible linear processes being valid beyond the scope of (G)ARCH. Moreover, our results regarding (G)ARCH can be transferred to functional AR(MA).

Satellite communication (Satcom) is an artificial geostationary satellite that facilitates a wide range of telecommunications. Considering its quality of service (QoS) and security is crucial in government/military applications. The most challenging situation for efficient Satcom is radio frequency interference (RFI) environment. Thus, it is necessary to ensure that transmissions are incorruptible or at least sense the quality of its spectrum. This paper presents a new method to recognize received signal characteristics using a hierarchical classification in a multi-layer perceptron neural network. We consider signal modulation and the type of RFI as the characteristics of a real-time video stream transmitted in the direct broadcast satellite. Four different modulation types are investigated in this study. Moreover, the combination of the communication signal with various kinds of interference and their effects on the classification method widely have been analyzed. Besides, two robust feature selection techniques have been developed to reduce the data-set dimensional, which leads to optimizing the classification process. The results show that the Genetic Algorithm (GA) slightly outperforms Principal Component Analysis (PCA) for feature selection. Furthermore, the robustness of the proposed techniques is assessed to detect unknown signals at different signal to noise ratios.

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

Signal-to-noise ratio (SNR) is an important index to evaluate radiation performance and image quality of optical imaging systems under low illumination background. Under the nighttime lighting condition, the illumination of remote sensing objects is low and varies greatly, usually ranging from several lux to tens of thousands of lux. Nighttime light remote sensing imaging requires high sensitivity and large dynamic range of detectors. Luojia 1-01 is the first professional nighttime light remote sensing satellite in the world. In this paper, we took the nighttime light remote sensing camera carried on the satellite as research object, proposed an in-orbit SNR test method based on time series images to overcome the problem of low spatial resolution. We first analyzed the process of luminous flux transmission between objects and satellite and established a radiative transfer model. By combining the parameters of large relative aperture optical system and high sensitivity CMOS device, we established SNR model and specially analyzed the effect of exposure time and quantization bits on SNR. Finally we used the proposed in-orbit test method to calculate SNR of lighting images acquired by satellite. And the measured result is in good agreement with the model predicted data. Under the condition of 10lx illumination, the SNR of typical objects can reach 27.02dB, which is much better than the requirement of 20dB for engineering application.

Search for periodic signals from blazars has become widely discussed topic in recent years. This is because periodic signals bear imprints of the processes occurring near the innermost regions of blazars which are mostly inaccessible to our direct view. Such signals provide insights into various aspect of blazar studies including disk-jet connection, magnetic field configuration and, more importantly, strong gravity near the supermassive black holes and release of gravitational waves from the binary supermassvie black hole systems. In this work, we report detection of a periodic signal in the radio light curves of the blazar J1043+2408 spanning ~10.5 years. We performed multiple methods of time series analysis, namely, epoch folding, Lomb-Scargle periodogram, and discrete auto-correlation function. All three methods consistently reveal a repeating signal with a periodicity of ~563 days. To robustly account for the red-noise processes usually dominant in the blazar variability and other possible artifacts, a large number of Monte Carlo simulations were performed. This allowed us to estimate a high significance (99.9% local and 99.4% global) against possible spurious detection. As possible explanations, we discuss a number of scenarios including binary supermassive black hole system, Lense-Thirring precession, and jet swing and precession.

Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community and therefore was defined by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Within the scope of the Satellite Application Facility for Land Surface Analysis (LSA SAF) of EUMETSAT, a near-real time (NRT) daily albedo product was developed in the last decade from observations provided by the SEVIRI instrument on board the geostationary satellites of the Meteosat Second Generation (MSG) series. In this study we present a new collection of albedo satellite products based on the same satellite data. The MSG Ten-day ALbedo (MTAL) product incorporates MSG observations over 31 days with a frequency of NRT production of 10 days. The MTAL collection is more dedicated to climate analysis studies compared to the daily albedo that was initially designed for the weather prediction community. For this reason, a homogeneous reprocessing of MTAL was done in 2018 to generate a Climate Data Record (CDR). The resulting product is called MTAL-R and has been made available to the community in addition to the NRT version of the MTAL product which has been available for several years. The retrieval algorithm behind the MTAL products comprises three distinct modules: one for atmospheric correction, one for daily inversion of a semi-empirical model of the bidirectional reflectance distribution function, and one for monthly composition that also determines surface albedo values. In this study the MTAL-R CDR is compared to ground surface measurements and concomitant albedo products collected by sensors on-board polar-orbiting satellites (SPOT-VGT and MODIS). We show that MTAL-R meets the quality requirements if MODIS or SPOT-VGT are considered as reference. This work leads to 14 years of production of geostationary land surface albedo products with a guaranteed continuity in the LSA SAF for the future years with the forthcoming third generation of European geostationary satellites.

Fogo volcano erupted in 2014-15 producing an extensive lava flow field in the summit caldera that destroyed two villages, Portela and Bangaeira. The eruption started with powerful explosive activity, lava fountains, and a substantial ash column accompanying the opening of an eruptive fissure. Lava flows spreading from the base of the eruptive fissure produced three arterial lava flows. By a week after the start of the eruption, a master lava tube had already developed within the eruptive fissure and along the arterial flow. In this paper, we analyze the emplacement processes on the basis of observations carried out directly on the lava flow field, remote sensing measurements carried out with a thermal camera, SO₂ fluxes, and satellite images, in order to unravel the key factors leading to the development of lava tubes. These were responsible for the rapid expansion of lava for the ~7.9 km length of the flow field, as well as the destruction of the Portela and Bangaeira villages. The key factors leading to the development of tubes were the low topography and the steady magma supply rate along the arterial lava flow. Comparing time-averaged effusion rates (TADR) obtained from satellite and Supply Rate (SR) derived from SO₂ flux data, we estimate the amount and timing of the lava flow field endogenous growth, with the aim of developing a tool that could be used for hazard assessment and risk mitigation at this and other volcanoes.

The safety of lithium-ion batteries is critical to the safety of battery electric vehicles (BEVs). The purpose of this work is to develop a method to predict battery thermal runaway in full electric vehicle crash simulation. The thermal-electrical-mechanical coupled finite element analysis is used to model an individual lithium-ion battery cell, a battery module, a battery pack, and a battery electric vehicle with 24 battery modules in a live circuit connection. The lithium-ion battery is modeled using a representative approach, with each battery internal component individually modeled to represent its geometric shape and realistic thermal, mechanical, and electrical properties. A resistance heating solver and Randles circuit model built with generalized voltage source are used to simulate the battery electrical behavior. The thermal simulation of the battery accounts for the heat capacity and thermal conductivity of various cell component materials, as well as heat conduction, radiation, and convection at their interfaces. The mechanical property of battery cell and battery module models is validated using spherical punch tests. The electrical property of the battery cell and battery module models is verified against CircuitLab simulation in an external short circuit test. The simulation result of battery module’s internal resistance agrees with the experimental data and the literature value. The multi-physics coupling phenomenon is demonstrated with a cylindrical compression simulation on the battery module. The multi-physics BEV model with 24 live battery modules is used to simulate the external short-circuit test and the side pole impact test. The simulations run time is less than 24 hours. The results demonstrated the feasibility of using representative battery model and multi-physics analysis to predict battery thermal runaway in full electric vehicle crash analysis.

Empirical Orthogonal Functions (EOF) analysis between the anomalies of Lake Titicaca water levels (WLs) and the Pacific Interdecadal Oscillation (IPO) reveals a correlation coefficient of 0.71 between the WLs and the principal component (PC1). These findings demonstrate the high accuracy of the IPO in representing the positive and negative phases of the water levels. By utilizing wavelet functions, we identified multi-decadal variability with periods ranging between 20 and 30 years. The statistical techniques employed indicate a relationship between IPO and ENSO; however, they operate on different timescales of climate variability. The IPO exhibits longer-term variability, typically spanning 20-30 years, while ENSO events occur on average every 2-7 years. Nevertheless, the IPO has the ability to modulate the strength and frequency of El Niño and La Niña events, and vice versa. During the positive phase of the IPO, there is a tendency for more frequent and intense El Niño events, accompanied by fewer La Niña events. This can be attributed to warmer sea surface temperatures in the central and eastern Pacific, which create favorable conditions for El Niño development. Conversely, during the negative phase of the IPO, there is a tendency for more frequent and intense La Niña events, with fewer occurrences of El Niño events, due to cooler sea surface temperatures in the central and eastern Pacific.

Both SARS-CoV-2 and SARS-CoV initially appeared in China and spread to other parts of the world. SARS-CoV-2 has generated a COVID-19 pandemic causing more than 6 million human deaths worldwide while the SARS outbreak quickly ended in six months with a global total of 774 reported deaths. One of the factors contributing to this stunning difference in the outcome between these two outbreaks is the inaccuracy of the RT-qPCR tests for SARS-CoV-2, which generated a large number of false-negative and false-positive test results that have misled patient management and public health policy-makers. This article presented Sanger sequencing evidence to show that the RT-PCR diagnostic protocol established in 2003 for SARS-CoV can in fact detect SARS-CoV-2 accurately due to the well-known nonspecific PCR amplification of DNAs with similar sequences. Using nested RT-PCR followed by Sanger sequencing to retest 50 patient samples collected in January, 2022 and sold as RT-qPCR positive reference confirmed 21 (42%) were false-positive. Although the other 29 positive isolates were categorized as Omicron variant by partial sequencing of the N gene, and the RBD and the NTD of the S gene, 9 (31%) showed focal to complete sequencing failure in the S gene segments due to multi-allelic SNPs. During the course of the study, an Omicron variant isolate containing a BA.1 NTD and a BA.2 RBD in its S gene was also detected. Routine partial S gene sequencing of all PCR-positive samples can timely discover multi-allelic SNPs and viral recombination in the circulating variants for investigation of their impacts on vaccine efficacies, therapeutics and diagnostics.

The most extensive urban growths in the next 30 years are expected to occur in developing countries. Lagos, Nigeria - Africa’s second most populous megacity- is a prime example. To achieve more sustainable and resilient cities, there is a need for modeling the urban growth patterns of major cities and analyzing their implications. In this study, the urban growth of Lagos state was modeled using the Multi-Layer Perceptron (MLP) neural network for the transition modeling and the Markov Chain analysis for the change prediction, achieving a model accuracy of 81.8%. An innovative visual validation of the model results using the ArcGIS was combined with kappa correlation statistics. The results show that by 2031, built-up areas will be the most spatially extensive LULC class in the study area with percentage coverage of 34.1% as opposed to 9% in 1986. The coverage of bare areas is also expected to increase by 53% between 2016 and 2031. Conversely, 24.9% and 68.3% loss of forestlands and wetlands respectively, are expected between 2016 and 2031. In view of the 11^th goal of SDGs which focuses on achieving sustainable cities and communities, the objectives of African Union’s Agenda 2063, and based on the urban growth trends observed, the study recommends a prioritization of vertical expansion as opposed to the current horizontal urban growth trends in the study area.

In this paper we describe and exploit a geometric framework for Gibbs probability densities and the associated concepts in statistical mechanics, which unifies several earlier works on the subject, including Souriau's symplectic model of statistical mechanics, its polysymplectic extension, Koszul model, and approaches developed in quantum information geometry. We emphasize the role of equivariance with respect to Lie group actions and the role of several concepts from geometric mechanics, such as momentum maps, Casimir functions, coadjoint orbits, and Lie-Poisson brackets with cocycles, as unifying structures appearing in various applications of this framework to information geometry and machine learning. For instance, we discuss the expression of the Fisher metric in presence of equivariance and we exploit the property of the entropy of the Souriau model as a Casimir function to apply a geometric model for energy preserving entropy production. We illustrate this framework with several examples including multivariate Gaussian probability densities, and the Bogoliubov-Kubo-Mori metric as a quantum version of the Fisher metric for quantum information on coadjoint orbits. We exploit this geometric setting and Lie group equivariance to present symplectic and multisymplectic variational Lie group integration schemes for some of the equations associated to Souriau symplectic and polysymplectic models, such as the Lie-Poisson equation with cocycle.

The exploring comprehensive review of cutting-edge techniques for securing cloud-stored data and managing sensitive information in the context of smart cities through the application of Ciphertext-Policy Attribute-Based Encryption (CP-ABE). It highlights the innovative integration of blockchain technology with CP-ABE, which introduces a decentralized and tamper-resistant key management system, thereby enhancing the overall security framework in cloud environments where data sharing is prevalent. Introducing an online/offline multi-authority CP-ABE scheme, characterized by hidden policies, offers significant advancements in protecting user attributes and access structures, ensuring that sensitive information remains confidential even during encryption and decryption processes. This dual approach not only fortifies security but also optimizes the efficiency of data-sharing mechanisms. Furthermore, the paper delves into implementing hidden sensitive policies and keyword search techniques within smart city infrastructures, which are designed to facilitate secure and efficient data retrieval. These techniques ensure that while data remains accessible to authorized users, privacy is rigorously maintained. Collectively, these approaches represent significant strides in bolstering the security and confidentiality of data in both cloud-based and smart city applications, addressing the growing demand for robust and efficient data management solutions in increasingly interconnected environments.

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

Clustering is a data mining task used for the data extraction from the data bases or files. Clustering is used to find unknown groups present in the data sources like files or data bases. This paper focuses on clustering algorithms performance dependency on the parallel clustering platforms and the clustering algorithms along with their clustering criteria. The problems with the present Traditional clustering algorithms were throughput and data source size changes (scalability). So they can’t address the big data. So for handling the huge volumes of data, parallel clustering algorithms along with clustering criteria were used. For processing the big Data Parallel clustering algorithms are of two types based on computing platforms used. They were the horizontal scaling platforms and vertical scaling platforms.

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

The safe and reliable operation of autonomous vehicles in complex and dynamic environments is heavily dependent upon exteroceptive and proprioceptive sensors. A LiDAR system generates accurate 3D point clouds, which are crucial for the detection, classification, and tracking of multiple targets. LiDAR data, however, presents significant challenges due to its density, noise, and varying sampling rates. In this study, various clustering and MTT techniques for LiDAR point clouds will be identified and classified within the context of autonomous driving to assess the state-of-the-art methods’ key challenges and their performances. We have categorized clustering and MTT methods used in AV applications, identified research gaps and challenges and analyzed existing algorithms and their challenges in detail. Researchers and practitioners in the field of autonomous driving will find this review to be a valuable resource.

Monitoring crown closure evolution using multi-temporal Light Detection and Ranging (LiDAR) surveys is a method that we expect to be increasingly adopted given the availability of LiDAR sensors and the accumulating survey archives. However, little attention was devoted to comparing crown closure estimates from independent surveys. Although survey parameters cannot be modified after the data collection, we speculate that the error associated to crown closure estimates comparison can be reduced by selecting optimal post-survey parameters. In this study, we compared crown closure estimates of three airborne LiDAR surveys from 2018 (40 pt/m²) used as a reference, and two lower-density surveys from 2016 (4.5 pt/m²) and 2018 (2 pt/m²). We studied the effect of the height threshold used to separate canopy points and the grid resolution, using skewness and variance of lagged difference of crown closure. Crown closure estimates using low height thresholds were more different across surveys, resulting in higher root mean squared error (RMSE), bias and more different variograms. Results show that optimal height threshold was 3 m and grid resolution was 25 m, although there was room for decision (RMSE of 7% and 5%, and bias of 4% and 0% for 2016 and 2018 low-density surveys).

The production of lithium-ion battery cells is characterized by a high degree of complexit due to numerous cause-effect relationships between process characteristics. Knowledge about the multi-stage production is spread among several experts, rendering tasks such as failure analysis challenging. In this paper, a method is presented, which includes expert knowledge acquisition in production ramp-up by combining Failure Mode and Effects Analysis (FMEA) with a Bayesian Network. We show the effectiveness of this holistic method by building up a large scale, cross-process Bayesian Failure Network in lithium-ion battery production. Using this model, we are able to conduct root cause analyses as well as analyses of failure propagation. The former support operators in identifying root causes once a cell possesses a specific failure by calculating most-probable explanations matched to the individual battery cell data. The latter enable us to analyze propagation of failures and deviations in the production chain and thus provide support for placement of quality gates, leading to a significant reduction in scrap rate. Moreover, it gives an insight into which process steps are key drivers for which final product characteristics.

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

This paper describes a Long Short-Term Memory (LSTM) neural network model used to simulate the dynamics of the OC3 reference design of a Floating Offshore Wind Turbine Spar unit. It crafts an advanced neural network with an encoder-decoder architecture capable of predicting the Spar’s motion and fairlead tensions time series. These predictions are based on wind and wave excitations across various operational and extreme conditions. The LSTM network, trained on an extensive dataset from over 300 fully coupled simulation scenarios using OpenFAST, ensures a robust framework that captures the complex dynamics of floating platform under diverse environmental scenarios. This framework’s effectiveness is further verified by thoroughly evaluating the model’s performance, leveraging comparative statistics and accuracy assessments to highlight its reliability. This methodology contributes to substantial reductions in computational time. This research aims to provide critical insights that facilitate the optimization of offshore wind turbines, marking a step forward in the quest for more efficient and dependable renewable energy solutions.