Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to identify tissue-specific gene regulatory networks. eQTL data (P<5e-8) for 16,259 genes were identified and their summary association statistics were obtained from the eQTLGene database. Next, eQTLs that display both cis and trans regulatory effects were selected and the association between their corresponding genes were examined using Mendelian randomization. A total of 169 genes that exerted trans-regulatory impacts on 692 genes were identified. 90% of the genes (N=749) aggregated into a gene regulatory network significantly enriched in hemo-immune processes. The robustness of finding was confirmed through simulation. The identified network displayed the scale-free topology. This provided the reason to examine the association of the network’s hub genes with the phenome. The outcome of analyses revealed GSDMB and ORMDL3 impact several disorders of immune origin and ALDH2 overexpression contributes to obesity. This study reports trans-eQTLs can be used to identify tissue-specific gene regulatory networks and describes a workflow to achieve this purpose. The network identified in this study showed scale-free topology indicating the hub genes of a GRN could be targeted to prevent disease outcomes.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

This paper proposes an optimal trading algorithm based on a novel application of the conventional Control Engineering (CE). We consider a fundamental CE concept, namely, the feedback control and apply it to the Algorithmic Trading (AT). The concrete feedback control strategy is designed in a form of the celebrated Proportional-Integral-Derivative (PID) model. The highly fluctuating nature of the modern financial markets has led to the adoption of a model-free realisation of the generic PID framework. The control theoretical methodology we propose is combined with the advanced statistics for the historical market data. We obtain a specific log-normal probability distribution function (pdf) associated with the specific quantities associated with the available stock data. The empirical log-normal pdf mentioned above makes it possible the necessary PID gains optimization. For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in the financial engineering, namely, the "stock market energy" concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python based prototype software. We finally apply the corresponding prototype software to a dataset from the Binance BTC / USDT stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT.

Machine Learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, the project provides insights into the model workings to help determine the reliability of the machine learning algorithm predictions. The results are not intuitive. It was found that the models were focusing significantly on less relevant features and at times, unsound ranking of the features to make the predictions. The paper therefore argues that it is important for research in applied machine learning to provide insights into explainability of the models in addition to other performance metrics like accuracy. This is particularly important for applications in critical domains such as healthcare. A future direction is to investigate methods to quantify the effectiveness of the machine learning models in terms of the insights from their explainability.

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional manual inspection methods are time-consuming, labor-intensive, and often lack the spatial resolution required for detailed analysis. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary objective was to develop a methodology for precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This was achieved by integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. UAV imagery was ac-quired bi-weekly at altitudes of 40m and 70m, capturing the entire growth cycle of a corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. Local spatial analysis and image processing techniques were em-ployed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. For enhanced accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area esti-mates by eliminating interference from soil and shadows. The proposed methodology enables precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time, providing valuable insights for precision agriculture practices. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop mon-itoring at the individual plant level.

This study evaluates the agricultural carbon emission efficiency of Shandong Province from 2011 to 2020 using the DEA-SBM model. The nexus between endogenous and exogenous variables is estimated using the Tobit model. The findings reveal an overall increasing trend in agricultural carbon emissions, with significant variations in efficiency values among different cities, leading to a severe polarization. Spatial evaluation shows a high distribution trend in the central region and low in the eastern and western regions of Shandong Province. The empirical tests conducted for Shandong Province and its three regions indicate that urbanization plays a major role in sup-porting the growth of agricultural carbon emission efficiency, while the education level of the la-bor force has a suppressive impact. Economic development and crop cultivation structure, how-ever, have no significant influence. The impact of these variables varies across the eastern, central, and western regions. The proposed countermeasures include improving planting structure and reducing brain drain in the eastern region, strengthening agricultural and rural inputs and in-creasing the added value of agricultural products in the central region, and intensifying the link-age between urbanization and industrial layout in the western region to reduce agricultural car-bon emissions efficiency in Shandong Province.

Data creation is often the only way for researchers to produce basic geospatial information for the pursuit of more complex tasks and procedures such as those that lead to the production of new data for studies concerning river basins, slope morphodynamics, applied geomorphology and geology, urban and territorial planning, detailed studies, for example, in architecture and civil engineering, among others. This exercise results from a reflection where specific data processing tasks executed in Google Sketchup (Pro version, 2018) can be used in a context of interoperability with Geographical Information Systems (GIS) software. The focus is based on the production of contour lines and Digital Elevation Models (DEM) using an innovative sequence of tasks and procedures in both environments (GS and GIS). It starts in Google Sketchup (GS) graphic interface, with the selection of a satellite image referring to the study area—which can be anywhere on Earth's surface; subsequent processing steps lead to the production of elevation data at the selected scale and equidistance. This new data must be exported to GIS software in vector formats such as Autodesk Design Web format—DWG or Autodesk Drawing Exchange format—DXF. In this essay the option for the use of GIS Open Source Software (gvSIG and QGIS) was made. Correcting the original SHP by removing “data noise” that resulted from DXF file conversion permits the author to create new clean vector data in SHP format and, at a later stage, generate DEM data. This means that new elevation data becomes available, using simple but intuitive and interoperable procedures and techniques which confgures a costless work flow.

A series of novel isobutylchalcones (A1-A20) were prepared, evaluated for their cytotoxic activity and characterized by FTIR, ¹H NMR, ¹³C NMR, and elemental analysis data. The logic behind the design is to synthesize and compare chalcones containing electron releasing lipophilic isobutyl substituent on aromatic ring A and the B ring with aromatic ring containing a range of electron releasing and electron withdrawing groups as well as heteroaromatic rings for their cytotoxic activity. The compounds were tested against HT-29 (colon cancer), MCF-7 (breast cancer) and DU-145 (prostate cancer) cell lines using methotrexate (IC₅₀ 12 ± 1 (HT-29), 9 ±1 (MCF-7) 5 ± 1 (DU-145)) as reference standard. Compound A6 having 2,4-difluorphenyl moiety was most potent of the series against all the three cell lines and notably A6 was mainly effective against DU-145 cell lines with an IC₅₀ value of 18 µg/mL. The critical structural features required for the activity against all the cell lines were identified through pharmacophore model using PHASE^TM which has recognised a 5 point AHHRR model and is consistent with the cytotoxic activity of the tested compounds.

This paper focuses on a joint model to analyse longitudinal proportion and survival data. After transforming the longitudinal proportional data by a logit function, we adopt a partially linear mixed-effect model for it, in which nonlinear function of time is fitted via using B-splines technique and a centered Dirichlet Process Mixture Model (CDPMM) is specified for a general distribution of random effects. The survival data is assumed to a Cox proportional hazard model, the sharing random effects joint model is developed for the two types of data. Combining the Gibbs sampler and the Metropolis-Hastings algorithm, we propose a Bayesian Lasso (BLasso) method to simultaneously estimate unknown parameters and select important covariates. Simulation studies are conducted to investigate the finite sample performance of the proposed methods. An example from the MA.5 research experiment is used to illustrate the proposed methodologies.

Background: Traditional omic analysis relies on p-value and fold change as selection criteria. There is an ongoing debate on their effectiveness in delivering systemic and robust interpretation, due to their dependence on assumptions of conformity with various parametric distributions.Here, we propose a threshold-free selection method based on robust, non-parametric statistics, ensuring independence from the statistical distribution properties and broad applicability. Such methods could adapt to different initial data distributions, contrary to statistical techniques based on fixed thresholds. Methods: Our work extends the Rank Products methodology with a neutral selection method of high information-extraction capacity. We introduce the calculation of the RP distribution’s entropy to isolate the features of interest by their contribution to the distribution’s information content. The aim is a methodology performing threshold-free identification of the differentially expressed features, which are highly informative about the phenomenon under scrutiny. Conclusions: Applying the proposed method on microarray (transcriptomic and DNA methylation) and RNAseq count data of varying sizes and noise presence, we observe robust convergence for the different parameterisations to stable cutoff points. Functional analysis through BioInfoMiner and EnrichR was used to evaluate the information potency of the resulting feature lists. Overall, the derived functional terms provide a systemic description highly compatible with the results of traditional statistical hypothesis testing techniques. The methodology behaves consistently across different data types. The feature lists are compact and information-rich, indicating phenotypic aspects specific to the tissue and biological phenomenon i nvestigated. Selection by information content measures efficiently addresses problems, emerging from arbitrary thresholding, thus facilitating the full automation of the analysis.

The value of an NBA basketball player varies at each crucial point in time, depending on the player’s career and performance. This study constructs a player’s value assessment model. The model comprises two parts. First, from an objective perspective, entropy is employed to measure each player’s achievement in five categories—rebounds, assists, steals, blocked shots, and scores. The total entropy assessment value is calculated and then combined with the players’ scores to develop a player's value matrix to assess the relative value model among players of the same type.

Measles is a highly contagious airborne disease that is endemic in many developing countries with low levels of vaccination coverage. In this paper, we propose a deterministic compartmental mathematical model for measles disease dynamics. We establish the global stability conditions for the disease-free and endemic equilibria using the Lyapunov function stability method. \textcolor{red}{Using arbitrary parameters it is obtained that the proposed model exhibits forward bifurcation}. Numerical integration using MATLAB software was used to simulate the model solution of the forward problem. We calibrate the proposed model to estimate the parameters with a 95\% confidence interval (CI) using real data from Ethiopia by formulating an inverse problem. We notice that the model fits well with the real data of Ethiopia. The estimated basic reproduction number ($R_0$) is found to be $R_0=1.3973$ which proves that the disease is endemic. It is also obtained from the local sensitivity analysis that reducing the transmission rate and increasing the vaccination rate could effectively minimize the $R_0$.

Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe "what" has to be done instead of "how". Configuration models enable to use a declarative representation of business processes, deciding the most appropriate work-flow in each case. Unfortunately, data dependencies among the activities and how they can affect the correct execution of the process, has been overlooked in the declarative specifications and configurable systems found in the literature. In order to find the best process configuration for optimizing the execution time of processes according to data dependencies, we propose the use of Constraint Programming paradigm with the aim of obtaining an adaptable imperative model in function of the data dependencies of the activities described declarative.

Fast Field-Cycling (FFC) Nuclear Magnetic Resonance (NMR) relaxometry is a powerful non-destructive magnetic resonance technique that enables, among other things, the investigation of slow molecular dynamics at low magnetic field intensities. FFC-NMR relaxometry measurements provide insight into molecular motion across various timescales within a single experiment. This study focuses on a \textit{model-free} approach, representing the NMRD profile $R_1$ as a linear combination of Lorentzian functions, thereby addressing the challenges of fitting data within an ill-conditioned linear least-squares framework. Tackling this problem, we present a comprehensive review and experimental validation of three regularization approaches to implement the model-free approach to analysing NMRD profiles. These include: (1) \nameUpe, utilizing locally adapted $L_2$ regularization; (2) \nameL, based on $L_1$ penalties; and (3) a hybrid approach combining locally adapted $L_2$ and global $L_1$ penalties. Each method's regularization parameters are determined automatically, according to the Balancing and Uniform Penalty principles. % Our contributions include the implementation and experimental validation of the \nameUpe{} and \nameUp{} algorithms, and the development of a "dispersion analysis" technique to assess the existence range of the estimated parameters. The objective of this work is to delineate the variance in fit quality and correlation time distribution yielded by each algorithm, thus broadening the set of software tools for the analysis of sample structures in FFC-NMR studies. The findings underline the efficacy and applicability of these algorithms in the analysis of NMRD profiles from samples representing different potential scenarios. 1

Studies have shown that in many practical applications, data interpolation by splines leads to better approximation and higher computational efficiency as compared to data interpolation by a single polynomial. Data interpolation by splines can be significantly improved if knots are allowed to be free rather than at a priori fixed locations such as data points. In practical applications, the smallest possible curvature is often desired. Therefore, optimal splines are determined by minimizing a derivative of continuously differentiable functions comprising the spline of the required order. The problem of obtaining an optimal spline is tantamount to minimizing derivatives of a nonlinear differentiable function over a Banach space on a compact set. While the problem of data interpolation by quadratic splines has been accomplished analytically, interpolation by splines of higher orders or in higher dimensions is challenging. In this paper, to overcome difficulties associated with the complexity of the interpolation problem, the interval over which data points are defined, is discretized and continuous derivatives are substituted by their discrete counterparts. It is shown that as the mesh of the discretization approaches zero, a resulting near-optimal spline approaches an optimal spline. Splines with the desired accuracy can be obtained by choosing an appropriate mesh of the discretization. By using cubic splines as an example, numerical results demonstrate that the linear programming (LP) formulation, resulting from the discretization of the interpolation problem, can be solved by linear solvers with high computational efficiency and resulting splines provide a good approximation to the optimal splines.

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

Background: This study retrospective evaluates patients with stage IV melanoma treated with Nivolumab and Ipilimumab combination therapy from two regional oncology centers from Romania between the years of 2019 up to the end of 2022. Methods: The data were analyzed in SAS for Windows, V9.4. The survival curves were estimated using the Kaplan-Meier method, and survival distributions were compared with log-rank test. The effects of the main clinical and pathological variables on OS and PFS were investigated with Cox regression. Results: Kaplan-Meier curve of OS in all evaluable patients enrolled in the study resulted in a median OS of 346 days (95% CI: 150-NA) and a median PFS of 211 days (95% CI: 113-430). 45.3% of the patients experienced adverse events during the Nivolumab + Ipilimumab treatment with some of them having multiple organ systems involved. Discussion: The OS values were lower than that reported in approval clinical trials, but the results show a marked improvement when comparing to results obtained by chemotherapy regimens previously used in these scenarios. Conclusion: This study provides real-world insights into the survival data and safety profiles of combination therapy with anti-PD-1 antibodies and anti-CTLA-4 antibodies.

The worldwide interconnected objects, called Internet of Things (IoTs), have been increasingly growing in the last several years. Different social media platforms and devices are continuously generating data about individuals and facilitate the technological and the social convergence of their Internet-based data and services with globalized users. These social and device-related IoTs create rooms for data breaches as such platforms provide ability to collect private and sensitive data. We assert that data breaches are fundamentally failures of access control - most users are too busy or technically ill-equipped to understand access control policy expressions and decisions. We argue that this is symptomatic of globalised societies structured by the conditions of algorithmic modernity; an era in which our data is increasingly interdependent on, and enmeshed with, ever more complex systems and processes that are vulnerable to attack. Ethically managing data breaches is now too complex for current access control systems, such as Role-Based Access Control (RBAC) and Context-Aware Access Control (CAAC). These systems do not provide an explicit mechanism to engage in decision making processes, about who should have access to what data and when, that are involved in data breaches. We argue that a policy ontology will contribute towards the development of Ethical CAAC better suited to attributing accountability for data breaches in the context of algorithmic modernity. We interrogate our proposed Ethical CAAC as a theoretical construct with implications for future policy ontology models and data breach countermeasures. An experimental study on the performance of the proposed framework is carried out with respect to a more generic CAAC framework.

Dyadic data contains co-occurrences of objects, which is often modeled by finite mixture model which in turn is learned by expectation maximization (EM) algorithm. Objects in traditional dyadic data are identified by names, causing the drawback which is that it is impossible to extract implicit valuable knowledge under objects. In this research, I propose the so-called attributed dyadic data (ADD) in which each object has an informative attribute and each co-occurrence of two objects is associated with a value. ADD is flexible and covers most of structures / forms of dyadic data. Conditional mixture model (CMM), which is a variant of finite mixture model, is applied into learning ADD. Moreover, a significant feature of CMM is that any co-occurrence of two objects is based on some conditional variable. As a result, CMM can predict or estimate co-occurrent values based on regression model, which extends applications of ADD and CMM.

This study analyzes the effect of economic growth and income distribution on health inequality using data from the Organization for Economic Cooperation and Development (OECD). A panel analysis was conducted by combining 21 years of data (from 2000 to 2020) from 38 OECD countries. Life expectancy and avoidable mortality were set as the dependent variables; gross domestic product and poverty gap were set as the independent variables; body mass index, consumption, smoking rate, healthcare personnel, number of beds in health service facilities, national medical expenses, and unemployment were set as control variables. First, economic growth and poverty gap significantly affect life expectancy and avoidable mortality. It has been confirmed that to resolve health inequality, growth during growth and distribution must be prioritized. Second, it has been confirmed that as the poverty gap increases, life expectancy increases and the avoidable mortality rate decreases. A mechanism different from the existence of a medical security system targeting the lowest class or an increase in the welfare of society worked. In conclusion, as a policy proposal to alleviate health inequality, this study suggests that policy intervention is necessary to block or alleviate the negative impact of income inequality in health care policies.

Although other methods exist to store and manage data in modern information technology, the standard solution are file systems. Therefore keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone are lacking several important capabilities for FAIR data management: The two most striking are insufficient visualization of data and inadequate possibilities for searching and getting an overview. Research data management systems (RDMS) can fill this gap, but many do not support the simultaneous use of the file system and the RDMS. This simultaneous use can have many benefits, but keeping data in the RDMS in synchrony with the file structure is challenging. Here, we present concepts that allow to keep file structures and semantic data models (in RDMS) synchronous. Furthermore, we propose a specification in yaml-format that allows for a structured and extensible declaration and implementation of a mapping between the file system and data models used in semantic research data management. Implementing these concepts will facilitate the re-use of specifications for multiple use cases. Furthermore, the specification can serve as a machine-readable and, at the same time, human-readable documentation of specific file system structures. We demonstrate our work using the Open Source RDMS CaosDB.

While considering the sensitivity over the parameter optimization, it is essential to determine which parameters have the most significant implications on model performance. This study focuses on the baseflow recession constant as one of the independent basin parameters to forecast low flows, perform hydrograph analysis, and calibrate rainfall-runoff models for significant improvement. Prior studies examined that the optimization of data adjustment parameters can improve the hydrological model performance and determine the minimum acceptable data length for data scarce regions using the Xinanjaing model. However, it is essential to pay special attention to the sensitivity of the recession constant, which can also impact the model performance during the data scarcity. Therefore, this study extends the research to comprehend the recession constant sensitivity over data adjustment parameters in the shorter datasets leading to more reliable parameter estimation. In terms of that, this study explores how recession constant affects hydrological parameter estimation on annual scale while keeping data adjustment parameters constant in continuous hydrological modeling, employing the Xinanjiang (XAJ) model as a case study. This study considered two approaches of recession constant (cg); (i) assessing the relationship between cg and the data adjustment parameter (Cep), for the 28-year datasets, (ii) investigating the significant impacts of the sensitivity of cg over Cep in shorter datasets which can affect the estimation of the acceptable minimum data length in the data scarce basins. The study underscores the importance of the recession constant sensitivity for reliable continuous hydrological model predictions, especially in data-scared areas. The study’s outcomes enhance the understanding of the importance of parameter sensitivity and its relationship in conceptual hydrological modeling during the data limitations.

The development and generalization of Digital Volume Correlation (DVC) on X-ray computed tomography data highlight the issue of long term storage. The present paper proposes a new model-free method for pruning the DVC data. The size of the remaining sampled data can be user-defined, depending on the needs concerning storage space. The data pruning procedure is deeply linked to hyper-reduction techniques. The DVC data of a resin-bonded sand tested in uniaxial compression is used as an illustrating example. The relevance of the pruned data is tested afterwards for model calibration. A new Finite Element Model Updating (FEMU) technique coupled with an hybrid hyper-reduction method is used to successfully calibrate a constitutive model of the resin bonded sand with the pruned data only.

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data–driven grey–box identification approach provided the physically meaningful nonlinear continuous–time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte–Carlo analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model–based and data–driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

The main contribution of this paper is to develop a graphical tool to evaluate the suitability of a candidate parametric model to fit a data set. The practical motivation is to check the adequacy of the so called SAINT model proposed in Jarner and Kryger (2011). This model has been implemented in practice by an important European pension fund concerned with setting annuity reserves for all current or former employees of Denmark. So, one particular relevant question is whether this mortality model is actually fitting old-age. Our graphical test can be described as follows. First we transform the data by means of the parametric model which is being evaluated. Should the model be correct, the transformed data will be in accordance with an Exponential distribution with rate 1. Then we construct a family of local linear hazard estimates based on the data on the transformed scale. Finally we use the statistical tool SiZer to assess the goodness-of-fit of the Exponential distribution to the data on the transformed scale. If the model is correct the SiZer map should not reveal any significant feature in the family of kernel estimates. Our method allow us to establish a diagnostic regarding the validity of the SAINT model when describing mortality patterns in four different countries.

Rollover prevention of partially-filled tank trucks is an industry teaser, with the core challenge being real-time perception and observation of the liquid state inside the tank. In order realize re-liable observation of sloshing liquid, this article firstly proposes a sloshing modeling method based on multi-degree-of-freedom pendulum model, and derives the double mass trammel pendulum model (DMTP, 2DOF) accordingly, which accurately reflects the sloshing dynamics under more pervasive working conditions. Secondly, a free surface fluctuation sensor is designed based on magnetostriction, capable of measuring the inclination and height of the liquid level inside tanks filled with hazardous chemical. Finally, the unscented Kalman filter (UKF) is utilized to synthesize the information of the two, establishing a credible real-time observation of the sloshing liquid. Verified through vehicle-fluid coupled co-simulation, under condition of con-secutive double lane change, observation error of the proposed method is only 25.9% that of the open-loop calculation, providing a secure guarantee for the observation of the state variables of the single pendulum model (SP) used for most kinds of anti-rollover controller.

Diets that restrict the amount of carbohydrates positively affect markers related to metabolic syndrome (MS). However, completely carbohydrate-free diets have been studied in lesser extent, being their effect on biochemical and morphometric parameters in a rat model of MS the main objective of this study. MS was induced by a high-glucose diet for 20 weeks in male Wistar rats with a subsequent exposition to an essentially carbohydrate-free diet (CFD) for 20 weeks. Daily measurements of food consumption and monthly measurements of body weight were made. At week 40, glucose, insulin, lipid profile, ketone bodies, C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), urea, creatinine, liver and muscle glycogen, and serum, hepatic, renal and pancreatic malondialdehyde (MDA) were assessed. Exposition to CFD showed decrease in caloric intake and body weight. Likewise, the group showed normalization in liver and serum MDA parameters, insulin, lipid profile, ALT, liver glycogen, and creatinine. The CFD decreased glucose concentration and the inflammatory state, also, increased the concentration of ketone bodies and urea. The change towards CFD reverses the metabolic alterations generated with MS except for glycemia and uremia, probably due to the high rate of gluconeogenesis generated by the high protein content in the diet.

Conducted within the scope of geochemical exploration in Eastern Thailand, this study aims to detect geochemical anomalies and potential mineral deposits. The objective was to interpret intricate spatial dispersion patterns and concentration levels of deposit pathfinder elements, specifically arsenic (As), copper (Cu), and zinc (Zn), using a comprehensive array of stream sediment geochemistry data. Methodologies involved integrating multifractal properties and traditional statistics, facilitated by the GeoDAS and ArcGIS platforms as instrumental analytical tools. In total, 5,376 stream sediment samples were collected and evaluated, leading to the development of an in-depth geochemical map. The results indicated distinct geological units marked by substantially elevated average values of the aforementioned elements. Identification of geochemical anomalies was achieved through the spatial distribution method and the subsequent application of the Spectrum-Area (S-A) multifractal model. An intriguing link was found between high As concentrations and gold deposits in the area, suggesting As as a viable pathfinder element for gold mineralization. The anomaly maps, generated from the stream sediment data, spotlighted potential zones of interest, offering valuable guidance for future mineral exploration and geological inquiries. Nonetheless, it is vital to recognize that the increased values noted in these maps may be influenced by regional geological factors, emphasizing the necessity for a diverse set of analytical methods for accurate interpretation. The study's significance lies in its pioneering use of the S-A multifractal model in geochemical data analysis. This innovative approach has deepened our comprehension of geochemical dispersion patterns and improved the precision of mineral exploration.

Purpose: Pancreatic cancer (PAAD) is one of the most lethal malignancies. Immunotherapy is largely ineffective in PAAD. T-cell exhaustion contributes to the resistance for immunotherapies. We investigated the prognostic potential of T cell exhaustion-related genes (TEXGs). Methods: Single-cell RNA (scRNA) sequencing dataset from TISCH, and bulk sequencing datasets from TCGA and GTEx were applied to screen differently expressed TEXGs. Kaplan-Meier survival, LASSO regression, and Univariate/Multivariate Cox regression analysis were performed to construct TEXGs risk model. This model was used to predict the prognosis, tumor immune microenvironment, and immunotherapy response. PAAD cohorts from ICGC and GSE71729 datasets were used to validate this risk model. Pan-cancer expression of SPOCK2 was performed in TISCH database. Results: A six-gene (SPOCK2, MT1X, LIPH, RARRES3, EMP1, and MEG3) risk model was constructed. Low-risk PAAD patients had longer survival time in both training dataset (TCGA-PAAD, n=178) and validation datasets (ICGC-PACA-CA, ICGC-PAAD-US, and GSE71729, n=412). Multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic variable for PAAD. High-risk patients correlated with cancer-related KEGG pathway enrichment, higher mutation frequency of KRAS and TP53 genes, and immunosuppressive status. Immunohistochemistry staining confirmed the changes of TEXGs in clinical samples. Moreover, pan-cancer scRNA sequencing datasets from TISCH analysis indicated that SPOCK2 might be a novel marker of exhausted CD8+ T cells. Conclusion: In conclusion, we established and validated a T cell exhaustion-related prognostic signature for PAAD patients. Moreover, our study suggests that SPOCK2 may be a novel marker of exhausted CD8+ T cells.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

A coupled stratospheric chemistry-meteorology model was developed by combining the Canadian operational weather prediction model Global Environmental Multiscale (GEM) with a comprehensive stratospheric photochemistry model from the Belgian Assimilation System for Chemical ObsErvations (BASCOE). The coupled model was called GEM-BACH for GEM-Belgian Atmospheric CHemistry. The coupling was made across a chemical interface that preserves time splitting while being modular, allowing GEM to run with or without chemistry. An evaluation of the coupling was performed by comparing the coupled model, refreshed by meteorological analyses every 6 hours, against the standard offline chemical transport model (CTM) approach. Results show that the dynamical meteorological consistency between meteorological analysis times far outweighs the error created by the jump resulting from the meteorological analysis increments at regular time intervals, irrespective whether a 3D-Var or 4D-Var meteorological analysis is used. GEM-BACH forecast refreshed by meteorological analyses every 6 hours were compared against independent measurements of temperature, long-lived species, ozone and water vapor. The comparison showed a relatively good agreement throughout the stratosphere except for an upper-level warm temperature bias and an ozone deficit of nearly 15%. Arguments in favor of using the same horizontal resolution for chemistry, meteorology, and meteorological analysis increments are also presented. In particular, the coupled model simulation during an ozone hole event gives better ozone concentrations than a 4D-Var chemical assimilation at a lower resolution.

The Stirling engine is a simple type of external-combustion engine with an external combustion engine based on cyclic compression and expansion of gas at different temperature levels. It has high efficiency; low vibration levels, simple structure and can run on any combustible fuels. It has been the object of numerous studies. This paper presents an analysis of a Stirling engine model GENOA 03 for electric power generation, of 3 KW of nominal power with pressurized air as working fluid, currently under development. To improve its performance and ensure a good operational reliability, it is necessary to carry out a modelling of the engine in all its operating range. This requires complex numerical models that simulate the behaviour of any element of the engine in a cycle. Two typologies of thermodynamic models are developed in this work: isothermal and adiabatic. The main benefits and shortcomings of each model are mentioned. The geometry and conditions of the engine have been adapted through the Matlab ® tool, in order to obtain the operative conditions of the cooler that you want to replace, as well as an approximation to the expected behaviour.

In Korea, the underground silo structure for low- and intermediate-level radioactive waste disposal facilities has been constructed and operated since 2014. Large-scale earthquakes occurred in 2016 and 2017, respectively, in Gyeongju and Pohang areas near the underground silo structures, and interest in the stability of the underground silo increased significantly. In this paper, one-dimensional free field analyses have been carried out before the three-dimensional silo dynamic analyses subjected to earthquake loadings. As an additional study, a new form of finite element equilibrium equation is derived in terms of relative motions, which is essentially the same equation as that expressed in terms of total motions where the base shear force is applied for earthquake load. The accuracy of conventional finite element solutions is evaluated by directly comparing them with closed-form solutions by frequency domain analysis such as SHAKE91.

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, we derive the parametric form by representing the unresolved high wavenumber Fourier modes as functionals of the resolved variables’ trajectory. The reduced models are nonlinear autoregression (NAR) time series models, with coefficients estimated from data by least squares. The NAR models can accurately reproduce the energy spectrum, the invariant densities, and the autocorrelations. Taking advantage of the simplicity of the NAR models, we investigate maximal and optimal space-time reduction. Reduction in space dimension is unlimited, and NAR models with two Fourier modes can perform well. The NAR model’s stability limits time reduction, with a maximal time step smaller than that of the K-mode Galerkin system. We report a potential criterion for optimal space-time reduction: the NAR models achieve minimal relative error in the energy spectrum at the time step where the K-mode Galerkin system’s mean CFL number agrees with the full model’s.

Identifying or authenticating a computer user are necessary steps to keep systems secure on the network and to prevent fraudulent users from accessing accounts. Keystroke dynamics authentication can be used as an additional authentication method. Keystroke dynamics involves in-depth analysis of how you type on the keyboard, analysis of how long a key is pressed or the time between two consecutive keys. This field has seen a continuous growth in scientific research. In the last five years alone, about 10,000 scientific researches in this field have been published. One of the main problems facing researchers is the small number of public data sets that include how users type on the keyboard. This paper aims to provide researchers with a data set that includes how to type free text on the keyboard by 80 users. The data were collected in a single session via a web platform. The dataset contains 410,633 key-events collected in a total time interval of almost 24 hours. In similar research, most datasets are with texts written by users in English. The language in which the users wrote for this research is Romanian. This paper also provides an extensive analysis of the data set collected and presents relevant information for the analysis of the data set in future research.

The article proposes a method for determining the p-value of correlations in compositional data, i.e., those data that arise as a result of dividing the original values by their sum. Data organized in this way are typical for many fields of knowledge, but there is still no consensus on methods for interpreting correlations in such data. In a space closed by normalizing quantity, correlation coefficients behave differently than under normal conditions: their probabilities of occurrence do not coincide with those inherent in the standard scale of estimates. In the tens of the new millennium, almost all newly emerging methods for estimating correlation in compositional data began to require mandatory log-transformation of the variable values. In the method proposed here there are no log-transformations. We return to the early stages of attempting to solve the problem and rely on negative shifts in correlations in the multinomial distribution. In modeling the data, we use a hybrid method that combines the hypergeometric distribution with the distribution of any other law. During our work on the calculation method, we found that the number of degrees of freedom in compositional data measures discretely only when all the normalizing sums are equal and that it decreases when the sums are not equal, becoming a continuously varying quantity. Estimation of the number of degrees of freedom and the strength of its influence on the magnitude of the shift in the distribution of correlation coefficients is the basis of the proposed method.

Despite their obvious differences, biological systems at different scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are usually obscured by the parcelled terminology employed by various scientific sub-disciplines. To explore these commonalities, this papers a comparative study of diverse applications of the maximum entropy principle, ranging from amino acids up to societies. By presenting these studies under a common language, this paper establishes a unified view over seemingly highly heterogeneous biological scenarios.

The particulars of stimulus-response experiments done on dynamic biosystems clearly limit what one can learn and validate about their structural interconnectivity (topology), even when collected kinetic output data are perfect (noise-free). As always, available access ports and other data limitations rule. For linear systems, exponential modes, visible and hidden, play an important role in understanding data limitations, embodied in what we call dynamical signatures in the data. We show here how to circumscribe and analyze modal response data in compartmentalizing model structures – so that modal analysis can be used constructively in systems biology model building – for nonlinear (NL) as well as linear biosystems. We do this by developing and exploiting the modal basis for dynamical signatures in hypothetical (perfect) input-output data associated with a structural model – one that includes inputs and outputs explicitly – and for NL as well as linear biosystems. The methodology establishes model dimensionality (size, complexity) from particular data sets; helps select among multiple candidate models (model distinguishability); helps in designing new experiments to extract “hidden” structure; and helps to simplify (reduce) models to their essentials. For NL biosystems, results are not as comprehensive, similarly informative about their dominant dynamical properties, and unified with linear models on invariant 2-dimensional manifolds in phase space. Some automation of these highly technical aspects of biomodeling is also introduced.

Using the Super-SBM method, this study calculates the carbon emission efficiency (CEE) of 30 provinces in mainland China from 2011 to 2019. Then, using the systematic GMM model, spatial Durbin model, and mediating effect model, it examines the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on CEE. It was found that (1) the DE significantly promoted regional CEE, but had a negative effect on CEE in provinces with high economic correlation; (2) mechanism studies showed that the DE improved CEE by reducing energy intensity, promoting industrial upgrading and green technology innovation; (3) regional heterogeneity analysis found that the DE significantly improved CEE in eastern provinces, but not in central and western provinces. DE improves CEE in provinces with high level of economic development, but decreases CEE in provinces with low level of economic development.This paper provides some empirical and theoretical references for the development of DE to improve CEE.

This research looks into the evolving dynamics of ransomware, shifting from conventional encryption-based attacks to sophisticated data exfiltration strategies. Employing the Bidirectional Encoder Representations from Transformers (BERT) model, the study analyzes network traffic patterns to detect ransomware activities, offering new insights into their covert operations. The findings emphasize the need for advanced AI tools in cybersecurity, highlighting the significance of adapting and innovating defense strategies to counter the changing landscape of ransomware threats. The study contributes to a deeper understanding of ransomware evolution and underscores the importance of integrating AI in cybersecurity practices.

We investigate the impact of incoming nuclear data on the predictions of the neutron star (NS) stripping model for short gamma-ray bursts. The main area to which we pay attention is the NS crust. We show that the uncertain properties of the NS equation of state can significantly influence the stripping time tstr, the main dynamical parameter of the model. Based on the known time delay (tstr≈1.7s) between the peak of the gravitational wave signal GW170817 and the detection of gamma photons from GRB170817A, we obtain new restrictions on the nuclear matter parameters, in particular, the symmetry energy slope parameter: L&lt;114.5MeV. In addition, we study the process of nucleosynthesis in the outer and inner crusts of a low-mass NS. We show that the nucleosynthesis is strongly influenced by both the incoming nuclear data and the equation of state of the NS matter.

The main aim of this paper is to explore the issue of big data and to propose a conceptual framework for big data, based on the temporal dimension. The Temporal Big Data Maturity Model (TBDMM) is a means for assessing organization’s readiness to fully profit from big data analysis. It allows the measurement of the current state of the organization’s big data assets and analytical tools, and to plan their future development. The framework explicitly incorporates a time dimension, providing a complete means for assessing also the readiness to process temporal data and/or knowledge that can be found in modern sources, such as big data ones. Temporality in the proposed framework extends and enhances the already existing maturity models for big data. This research paper is based on a critical analysis of literature, as well as creative thinking, and on the case-study approach involving multiple cases. The literature-based research has shown that the existing maturity models for big data do not treat the temporal dimension as the basic one. At the same time, dynamic analytics is crucial for a sustainable competitive advantage. This conceptual framework was well received among practitioners, to whom it has been presented during interviews. The participants in the consultations often expressed their need of temporal big data analytics, and hence the temporal approach of the maturity model was widely welcomed.

Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.

The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

The growing adoption of distributed energy production technologies, and the potential of energy underutilization when it is produced by non-connected groups, has raised interest in ‘sharing economy’ concepts in the electricity sector. We suggest that mechanisms, such as peer-to-peer (P2P) energy trading, will allow users to exchange their surplus energy for mutual benefits, stimulate the adoption of renewable energy, encourage communities to ‘democratically’ control their own energy supplies for local development, and improved energy efficiency, and create many other benefits This approach is receiving increasing attention across the world, particularly in Germany, the Netherlands and Australia. Nevertheless, the actual development and implementation of these platforms is slow and mostly limited to trial activities. This study investigates the challenges and barriers facing P2P energy trading developments based on previous academic and industry studies. We provide a comprehensive multidimensional barrier analysis through a PESTLE approach to assess the barriers from a variety of perspectives, including the political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) aspects. This approach clarifies the many intersecting problem fields of P2P trading in renewable energy, and the paper identifies a list of such barriers, and discusses the prospects for addressing these issues. We also elaborate on the importance of incentive-based P2P market design.

Degradation of environment quality is currently the prime cause of the recent occurrence of natural disasters; it also contributes in the increase of the area that is prone to natural disasters. This research is aimed to map the potential of areas around Pesanggrahan river in DKI Jakarta by segmenting the Digital Elevation Model derived from LIDAR data. The objective of this segmentation is to find the watershed lines of the DEM image. Data processing in this research is using LIDAR data which take the ground surface data, which is overlaid with Jakarta river map and subsequently, the data is then segmented the image. The expected result of the research is the flood potential area information, especially along the Pesanggrahan river in South Jakarta.

This article investigates a proposed new mathematical model that considers the infected individuals using various rate coefficients such as transmission, progression, recovery and vaccination. The fact that the dynamic analysis is completely determined by the basic reproduction number is established. More specifically, local and global stabilities of the disease-free equilibrium and the endemic equilibrium are proved under certain parameter conditions when the basic reproduction number is below or above unity. A realistic computer simulation is performed for better understanding of the variations in trends of different compartments after the outbreak of the disease.

This article presents, for the first time, the kinetics and the general conversion features of free radical polymerization (FRP) in a 3-component system (A/B/N), with [A] being the initiator, and [B] and [N] are additives, based on the proposed mechanism of Rahal et al. Higher FRP can be achieved by additives [B] and [N], via the dual function of (i) regeneration [A], and (ii) generation of extra radicals (R) via the radicals (S' and S) produced by N.The initiator (coumarin) shows a dual photo-oxidation and photo-reduction character for high efficacy. The FRP conversion efficacy (CE) depends not only on the property of the initiator [A], the additives [B] and [N}, but also the types of monomers. For example, when [A]=CoumC, [A]/NPG is more efficient than [A]/Iod, but revserse trend occurs in some monomers. However, 2-component systems (with CE=0% to 80%) are always less efficient than that of 3-component systems (with CE=70% to 86%, in TMPTA). Specific systems with [A]=coumarins, [B]=Iodonium salt, and N=NPG are analyzed. Analytical formulas for the role of each component concentration, light intensity and coupling rates on the conversion efficacy are derived.

During the dry season, glaciers contribute significantly to the water supply downstream, especially in areas with seasonal rainfall. It is important to have physical-based glacier models, which are more advanced in quantifying the future dynamics of glaciers and melt distribution. Toward this purpose, an investigation of different numerical modeling tools and their approach is conducted and planned further to incorporate a glacier module into a spatially distributed model called mHM (Mesoscale Hydrological Model, mhm-ufz.org). An open-source tool called MATILDA (Modeling Water Resources in Glacierized Catchments) is used beforehand to implement the glacier entities to the application catchment Maipo Basin in Chile. Due to limited time availability and source of data, the glacier melt of the MATILDA model is integrated into the mHM model to see what the implications would be if the glacier module was implemented from scratch in mHM. The analysis of the glacier hydrology resulting from the Maipo-Glacier model was carried out by comparing the modeled glacier flows with the total observed flows. To assess the credibility of the models, the results are compared based on three goodness-fit measures, specifically r^2Coefficient of Determination), NSE (Nash-Sutcliffe Efficiency), and KGE (Kling-Gupta Efficiency). Application of the mHM model in the Maipo basin in Central Chile is performed by using prepared input datasets, showing that the approach is viable as evidenced by a Kling-Gupta Efficiency of 0.80 and Nash-Sutcliffe Efficiency of 0.70 from the starting periods of 1950 to 2020. Overestimation by the mHM model occurs during the late summer season, particularly when temperature decreases. The MATILDA model is used on glacier melt distribution, forcing data is prepared from regional datasets, and a glacier profile is constructed from ice thickness datasets. According to the model, the average KGE result for the years 2012–14 and 2015–18 was respectively 0.44 and 0.41. Glacier melt produced by Matilda is in the range of 58-68 % of the total runoff and the glacier area decreased by 22-23% during the simulation period from 2012-14 and 2016-2018. A pilot sub-basin Olivares is used in the coupling mHM model with initializing MATILDA glacier melt. The new model is underestimated during the seasonal transition in the initial days of the simulation. A considerable portion of the ablation period might be missed as a result of the initial storage quality, true glacier extent, and height distribution being understated. As a conclusion, the mHM Model was capable of predicting runoff well in a mountainous region and could also serve as a monitoring tool for watershed management. There are differences in results between the two models because of the different spatial resolutions and methodologies. MATILDA have a glacier retreat routine that gives better result for the glacier characteristics such as. A new glacier dynamics model needs to be implemented in the future to develop intermediate complexity, bridge catchment, and glacier scales after using the mHM, MATILDA, and mHM with MATIDLA model for analysis of glacier and highlighting the model's deficiencies in the initial period of simulative assessments.

Accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understand their structure-function relationships and electrochemical properties. Current method of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users’ defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly / partially resolved and unresolved spectra, which is a common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper-nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.

Orbit and clock products are used in real-time GNSS precise point positioning without knowing their quality. This study develops a new approach to detect orbit and clock errors through comparing geometry-free and geometry-based wide-lane ambiguities in PPP model. The reparameterization and estimation procedures of the geometry-free and geometry-based ambiguities are described in detail. The effects of orbit and clock errors on ambiguities are given in analytical expressions. The numerical similarity and differences of geometry-free and geometry-based wide-lane ambiguities are analyzed using different orbit and clock products. Furthermore, two types of typical errors in orbit and clock are simulated and their effects on wide-lane ambiguities are numerically produced and analyzed. The contribution discloses that the geometry-free and geometry-based wide-lane ambiguities are equivalent in terms of their formal errors. Although they are very close in terms of their estimates when the used orbit and clock for geometry-based ambiguities are precise enough, they are not the same, in particular, in the case that the used orbit and clock, as a combination, contain significant errors. It is discovered that the discrepancies of geometry-free and geometry-based wide-lane ambiguities are coincided with the actual time-variant errors in the used orbit and clock at the line-of-sight direction. This provides a quality index for real-time users to detect the errors in real-time orbit and clock products, which potentially improves the accuracy of positioning.

. This article presents, for the first time, the kinetics and the general conversion features of a 3-component system (A/B/N), based on proposed mechanism of Mau et al, for both free radical polymerization (FRP) of acrylates and the free radical promoted cationic polymerization (CP) of epoxides using various new copper complex (G2) as the initiator. Higher FRP and CP conversion can be achieved by co-additive of [B] and N, via the dual function of (i) regeneration [A], and (ii) generation of extra radicals. The FRP and CP conversion efficacy (CE) are proportional to the nonlinear power of bI[A][B], where b and I are the effective absorption coefficient and the light intensity, respectively. In the interpenetrated polymer network (IPN) capable of initiating both FRP and CP in a blend of TMPTA and EPOX, (as the monomer for FRP and CP, respectively), the synergic effects due to CP include:: (i) CP can increase viscosity limiting the diffusional oxygen replenishment, such that oxygen inhibition effects are reduced; (ii) the cationic monomer also acts as a diluting agent for the IPN network , and (iii) the exothermic property of the CP. Many new findings are explored via our analytical formuals include: (i) the CE of FRP is about twice of the CE of CP, due to the extra radicals involved in FRP; (ii) the catalytic cycle enhancing the efficacy is mainly due to the regenaration of the initiator, and (iii) the nonlinear dependence of light intensity of the CE (in both FRP and CP). For the first time, the catalytic cycle, synergic effects, and the oxygen inhibition are theoretically confirmed to support the experimental hypothesis. The measured results of Mau et al are well analyzed and matching the predicted features of our modeling. .

The COVID-19 epidemic is an emerging infectious disease of the viral zoonosis type caused by the corona-virus strain SARS-CoV-2, is classified as a human-to-human communicable disease and is currently a pandemic worldwide. In this paper, we propose conceptual mathematical models the epidemic dynamics of four compartments. We have collected data from the Djibouti health ministry. We define the positivity, boundedness of solutions and basic reproduction number. Then, we study local and global stability and bifurcation analysis of equilibrium to examine its epidemiological relevance. Finally, we analyze the fit of the data in comparison with the result of our mathematical results, to validate the model and estimating the important model parameters and prediction about the disease, we consider the real cases of Djibouti from 23th March to 10th June, 2020.

XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims vs. no claims can be used to identify the determinants of traffic accidents. We compare the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contains information from an insurance company about individuals’ driving patterns – including total annual distance driven and percentage of total distance driven in urban areas. Our findings show that logistic regression is a suitable model given its interpretability and good predictive capacity. XGBoost requires numerous model-tuning procedures to match the predictive performance of the logistic regression model and greater effort as regards interpretation.

Background: According to the National Highway Traffic Safety Administration, 116 pedestrians were killed in motor vehicle crashes in Ohio in 2015. However, no study to date has analyzed crashes in Ohio exploring the factors contributing to the pedestrian injury severity resulting from motor vehicle crashes. This study fills this gap by investigating the crashes involving pedestrians exclusively in Ohio. Materials and Methods: This study uses the crash data from the Highway Safety Information System, from 2009 to 2013. The explanatory factors include the pedestrian, driver, vehicle, crash, and roadway characteristics. Both fixed- and random-parameters ordered probit models of injury severity (where possible outcomes are major, minor, and possible/no injury) were estimated. Results: The model results indicate that being older pedestrian (65 and over), younger driver (less than 24), driving under influence (DUI), being struck by truck, dark-unlighted roadways, six-lane roadways, and speed limit of 40 mph and 50 mph were associated with more severe injuries to the pedestrians. Conversely, older driver (65 and over), passenger car, crash occurring in urban locations, daytime traffic off-peak (10 AM to 3:59 PM), weekdays, and daylight condition were associated with less severe injuries. Conclusion: This study provides specific safety recommendations so that effective countermeasures could be developed and implemented by the policy makers, which in turn will improve overall highway safety.

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5–3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

This article presents, for the first time, the kinetics and the general conversion features of a 3-initiator system (A/B/N), based on proposed mechanism of Mokbel et al, for both free radical polymerization (FRP) of acrylates and the free radical promoted cationic polymerization (CP) of epoxides using copper complex as the initiator. Higher FRP and CP conversion can be achieved by co-initiators concentration [B] and [N], via the dual function of (i) regeneration [A], and (ii) generation of extra radicals S' and S. The FRP and CP conversion is proportional to, respectively, the nonlinear and linear power of bI[A][B], where b and I are the absorption coefficient and the light intensity, respectively. System in air has lower conversion than in laminate due to the oxygen inhibition effects. For thick samples (with thickness z), there is an optimal concentration [A*] which is inverse proportional (bzI), in contrast with very thin sample, in which the conversion is an increasing function of [A] and [B]. The unique feature of dark polymerization in CP conversion enables the polymerization to continue in living mode, in contrasts with that of the radical-mediated pathway in most conventional FRP. The measured results of Mokbel et al are well analyzed and matching the predicted features of our modeling.

In this paper, we consider the one-dimensional Ising model (shortly, 1D-MSIM) having mixed spin-(s,(2t−1)/2) with nearest neighbors and the external magnetic field. We establish the partition function of the model by means of the transfer matrix. Under a null boundary condition, we compute certain thermodynamic quantities for the 1D-MSIM. We find some precise formulas to determine the model’s free energy, entropy, magnetization, and susceptibility. By examining the model’s associated iterative equations, we use the cavity approach to investigate the phase transition problem. We numerically determine the model’s periodicity.

The application of warm-mixing technology brings considerable economical and environment benefits by decreasing the mixing temperature during warm asphalt mixture(WMA) production. However, the possible water residue also generates concerns in moisture susceptibility. For deep investigation on the influencing factors and mechanism of the moisture susceptibility of WMA, surface free energy(SFE) test and laboratory tests are applied in this research. A novel indicator based on SFE namely effective adhesion work is proposed to assess the asphalt-aggregate adhesion with different moisture contents. Then, given the mixing procedure of dry mixing method, an advanced three-phase model as form of asphalt-aggregate-warm mixing additive is introduced improving from the conventional two-phase asphalt-aggregate model for better reflecting the separate addition of warm mixing additive during mixing. Afterwards, the influence of aggregate types, asphalt type, aggregate moisture contents, warm-mixing agent types and warm-mixing process on the moisture susceptibility of WMA is analyzed utilizing the models and indicators proposed. Finally, the validity of the SFE indicator is verified by comparing the calculation of effective adhesion work with freeze-thaw splitting test result. The results show that all of the above factors impact the moisture susceptibility of WMA by influencing the interfacial adhesion, with the effect of moisture content being the most significant. Meanwhile, effective adhesion work and the three-phase model brought out in this research are proven to be feasible to characterize the adhesion properties of WMA, offering theoretical support to the research on warm mixing technology.

Non-alcoholic fatty liver (NAFLD) is a metabolic disorder related with a chronic lipid accumulation within the hepatocytes. This disease is the most common liver disorder worldwide and it is estimated that is present in up to 25% of the world's population. However, the real prevalence of this disease and the associated disorders is unknown mainly because reliable and applicable diagnostic tools are lacking. It is known that the level of albumin, a pleiotropic protein synthetized by hepatocytes, is correlated with the correct function of the liver. The development of a complementary tool that allow the direct, sensitive, and label-free monitoring of albumin secretion in hepatocyte cell culture can provide insight about the mechanism and drugs action in NAFLD. With this aim, we have developed a simple integrated plasmonic biosensor based on gold nanogratings from periodic nanostructures present in commercial Blu-ray optical disc. This sensor allows the direct and label-free monitoring of albumin in a 2D fatty liver disease model under flow conditions using highly specific polyclonal antibody. This technology avoids both the amplification and blocking steps showing a limit of detection within pM range (≈ 0.39 ng/mL). Thanks to this technology, we identified the optimal fetal bovine serum (FBS) concentration to maximize the lipid accumulation within the cells. Moreover, we discovered that at third day from lipids challenge, the hepatocytes increased the amount of albumin secreted. These data demonstrate the ability of hepatocytes to respond to the lipid stimulation releasing more albumin. Further investigation needed to unveil the biological significance of that cell behaviour.

The QBIT theory is an attempt toward solving the problem of consciousness based on empirical evidence provided by various scientific disciplines including quantum mechanics, biology, information theory, and thermodynamics. This theory formulates the problem of consciousness in the following four questions: (1) What is the nature of qualia? (2) How are qualia generated? (3) Why are qualia subjective? (4) Why does a quale have a particular quality or meaning?In sum, the QBIT theory proposes that (1) when a pack of quantum information is compressed beyond a certain threshold, a quale is generated; (2) a quale is a superdense pack of maximally entangled qubits in a pure state; (3) when information-theoretic certainty of a system about an external stimulus exceeds a particular level, the system becomes conscious of that stimulus; (4) subjectivity of consciousness is due to the fact that maximally entangled pure states are private and unshareable.

A rigorous model for the electron is presented by generalizing the Coulomb's Law or Gauss's Law of electrostatics, using a unified theory of electricity and gravity. The permittivity of the free-space is allowed to be variable, dependent on the energy density associated with the electric field at a given location, employing generalized concepts of gravity and mass/energy density. The electric field becomes a non-linear function of the source charge, where concept of the energy density needs to be properly defined. Stable solutions are derived for a spherically symmetric, surface-charge distribution of an elementary charge. This is implemented by assuming that the gravitational field and its equivalent permittivity function is proportional to the energy density, as a simple first-order approximation, with the constant of proportionality referred to as the Unifield Electro-Gravity (UEG) constant. The stable solution with the lowest mass/energy is assumed to represent a ``static'' electron without any spin. Further, assuming that the mass/energy of a static electron is half of the total mass/energy of an electron including its spin contribution, the required UEG constant is estimated. More fundamentally, the lowest stable mass of a static elementary charged particle, its associated classical radius, and the UEG constant are related to each other by a dimensionless constant, independent of any specific value of the charge or mass of the particle. This dimensionless constant is numerologically found to be closely related to the the fine structure constant. This possible origin of the fine structure constant is further strengthened by applying the proposed theory to successfully model the Casimir effect, from which approximately the same above relationship between the UEG constant, electron's mass and classical radius, and the fine structure constant, emerges.

High density of buildings and the large traffic volume of cities give rise to narrow spaces and high passenger flows in most subway stations. When evacuation is required during an emergency, these problems may trigger issues in operational safety. Therefore, it is imperative to comprehensively assess subway station emergency evacuation times and capacities during the operating process to ensure the design is safe, science-based, and rational. To further enhance safe evacuation capacity, this paper proposes an overall evacuation time model that considers the multielement charac-teristics of subway stations in multiple segments. The evacuation route is decomposed into five stages according to the critical nodes in the evacuation process. An overall emergency evacuation time model is established based on the diversity of bottlenecks in the five stages, integrating elements such as the horizontal movement velocity of passengers, subway equipment parameters, and human density. Taking Xi’an Wulukou Subway Station as an example, this paper verifies the outcomes of the theoretical model against the Pathfinder software and conducts additional anal-yses of the evacuation conditions of stairs and exits. The results show that the error between the theoretical emergency evacuation time and simulation evacuation time was 5.4%, The emergency evacuation model established in this study boasts strong robustness and stability.

The creation of computer models is often driven by the need to make predictions in regions where there is no data (i.e. extrapolations). This makes validation challenging as it is difficult to ensure that a model will be suitable when it is applied in a region where there are no observations of the system of interest. The current paper proposes a method that can reveal flaws in a model which may be difficult to identify using traditional approaches for model calibration and validation. The method specifically targets the situation where one is attempting to model a dynamical system that is believed to possess time-invariant calibration parameters. The proposed approach allows these parameters to vary with time, even though it is believed that they are time-invariant. The of such an analysis is to identify key discrepancies - indications that a model has inherent flaws and, as a result, should not be used to influence decisions in regions where there is no data. The proposed method isn't necessarily a predictor of extrapolation performance, rather, it is a stringent test that, the authors believe, should be applied before extrapolation is attempted. The approach could therefore form a useful part of wider validation frameworks in the future.

There has been a growing interest in incorporating psychological and neuroscientific knowledge about the development of cognitive functions in educational policies and academic practices. In this paper, we argue that the current knowledge about the interactions between these functions and their neurodevelopmental characteristics should also be considered in order to develop practices that could be better suited to pupils depending on their age. To facilitate this, we review current neuroscientific knowledge on the competitive interactions between two neural circuitry underlying distinct learning functions, their developmental trajectories and how they are linked to other functions such as cognitive control. The incorporation of this knowledge in education could help improve academic outcomes.

The organic fraction of municipal solid waste (OFMSW) is a significant environmental threat, and an economic and social challenge to manage. As such, the efficient treatment of OFMSW is a significant key factor in achieving sustainable waste management. Decentralized Composting (DC) develops a new framework of waste management. The DC Analysis Model (DCAM), proposed in our previous study, provides a powerful tool for decision makers, based on the quantification of the DC project characteristics. In this paper, we focus on the qualitative analysis as a complementary tool to support decision making, in cases where quantitative analysis is unequivocal. The qualitative analysis identifies the main players in the field, the critical stakeholders and the potential conflicts between them. It also reveals the root problems and the core competencies for the project's implementation. DCAM qualitative analysis in Shefa-Amr (case study) indicates that unresolved root problems, such as the “lack of national regulation”, “clear ownership of the project” and “lack of ongoing budget” can result in an unsustainable composting system. Countering that, “the commitment of the municipality”, together with “economic viability” and securing “suitable areas for placing composters” are among the most important core competencies for the effective implementation of DC projects.

. This article presents, for the first time, the kinetics and the general features of a photopolymerization system (under visible light) G1/ Iodonium/ TEA/gold chloride (or A/B/N/G), having initial concentrations of A0, B0, N0 and G0, based on the proposed mechanism of Tar et al. Analytic formulas are developed to explore the new features including: (i) both FRP efficacy and the production of nanogold (NG) are proportional to the relative concentration ratios of (A0+B0+N0)/G0, which may be optimized for maximum efficacy; (ii) the two competing procedure of NG production and efficacy of free radical photopolymerization (FRP) can be tailored for optimal system with nanogold in the polymer matrix;(ii) the FRP efficacy is contributed by 3 components: the coupling of the excited state of copper complex (T) with the radicals (R and S) produced by the iodonium and the amine; (iii) the production of NG is contributed by 2 components: the coupling of T and radical (S) with gold ion; (iv) NG production has a transient state value which is an increasing function of light intensity and the combined concentration ratio [(A0/G0)+(N0/(K'M0). whereas it has a steady-state independent to the light intensity.

Effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level Of Detail or LOD 2. Similarly, automated solutions for 3D modeling of individual trees at different levels of details are required for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT). The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our work in this paper is based on the assumption that, similar to the canopy shape, the structure of tree trunk and branches is also an important consideration. An algorithm for automated modeling of tree trunks is proposed. Aiming to generate 3D models of tree trunks, the suggested approach starts by extracting the trunk point cloud, which is then segmented into single stems. Finally, the individual stems are modeled by dividing them into slices. The geometry of the developed tree models is represented in a matrix format. This solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands and promoting sustainable forest management. Despite the huge variation of trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models.

Corporate Diagnosis is now recognized as an important tool by decision makers to predict and correct burgeoning problems that a corporation may face. Methods based on this model stem from the use of mathematics and are increasingly being applied in the analysis of production processes. The goal of this paper is to use a logistic regression to design a scoring model for non-financial corporations in industry. Based on the data obtained from the Registry of the Slovak Republic for 738 non-financial corporations, according to SK NACE 26, SK NACE 27, the proportional financial metrics, using the logistic regression method, were calculated. By applying these methods, two logistic regression models were found to reliably estimate the probability of bankruptcy for a firm.

The Circular Economy of plastics is a promising concept that has the potential to reduce pollution and close the loop on plastic waste. However, further research is needed to develop more efficient and environmentally friendly methods of recycling plastic. This review article discusses the Circular Economy of plastics, its potential benefits and drawbacks, and the challenges that need to be addressed to make it a reality. Some case studies are also examined to explore how the Circular Economy of plastics has been implemented across the globe.

A waterbody detection technique is an essential part of digital elevation model (DEM) generation to delineate land-water boundaries and set flattened elevations. This paper describes the technical methodology for improving the initial tile-based waterbody data that are created during production of the ASTER GDEM, because without improvement such tile-based waterbody data are not suitable for incorporating into the new ASTER GDEM Version 3. Waterbodies are classified into three categories: sea, lake, and river. For sea-waterbodies, the effect of sea ice is removed to better delineate sea shorelines in high latitude areas, because sea ice prevents accurate delineation of sea shorelines. For lake-waterbodies, the major part of the processing is to set the unique elevation value for each lake using a mosaic image that covers the entire lake area. Rivers present a unique challenge, because their elevations gradually step down from upstream to downstream. Initially, visual inspection is required to separate rivers from lakes. A stepwise elevation assignment, with a step of one meter, is carried out by manual or automated methods, depending on the situation. The ASTER GWBD product consists of a global set of 1º latitude-by-1º longitude tiles containing water body attribute and elevation data files in geographic latitude and longitude coordinates and with one arc second posting. Each tile contains 3601-by-3601 data points. All improved waterbody elevation data are incorporated into the ASTER GDEM to reflect the improved results.

Circulating cell-free DNA (cfDNA) refers to small fragments of DNA molecules released after programmed cell death and necrosis in several body fluids such as blood, saliva, urine, and cerebrospinal fluid. The discovery of cfDNA has revolutionized the field of non-invasive diagnostics in the oncologic field, in prenatal testing, and in organ transplantation. Despite the potential of cfDNA and the solid results published in recent literature, several challenges remain, represented by low abundance, need for highly sensitive assays and analytical issues. In this review, the main technical advances in cfDNA analysis are presented and discussed, with a comprehensive examination of the current available methodologies applied in each field. Considering the potential advantages of cfDNA, this biomarker is increasing its consensus among clinicians, as it consents to monitor patients’ conditions in an easy and non-invasive way, offering a more personalized care. Nevertheless, cfDNA analysis is still considered a research marker to be further validated, and very few centers are implementing its analysis in the real-life assistance. As technical improvements are enhancing the performances of cfDNA analysis, its application will transversally improve patients’ quality of life.

In this study, we present different methods of estimating fuzzy reliability of a two-parameter Rayleigh distribution via the maximum likelihood estimator, median first-order statistics estimator, quartile estimator, L-moment estimator, and mixed Thompson-type estimator. The mean-square error MSE as a measurement for comparing the considered methods using simulation through different values for the parameters and unalike sample sizes is used. The results of simulation show that the fuzziness values are better than the real values for all sample sizes, as well as the fuzzy reliability at the estimation of the Maximum

Genetic disorders are quite a major topic of discussion and debate in the recent world of biological sciences. Turner’s syndrome is one such disorder caused by a chromosome aneuploidy and it has characteristic symptoms in the patient or the affected individual. The amniotic fluid is a complex biological material found in the amniotic sac of pregnant women and they can provide valuable knowledge and understanding of the pathogenesis of this particular chromosomal abnormality. In this study, global gene expression analysis of cell-free RNA in amniotic fluid supernatant was used to detect genes/organ systems which may be significant in the pathophysiology of Turner’s syndrome. The cell-free RNA from the amniotic fluid of five mid-trimester Turner’s syndrome fetuses and five euploid female fetuses matched for age of gestation were extracted, amplified and hybridized onto Affymetrix U33 Plus 2.0. array. The paired t-test was used to identify the significantly differentially regulated genes. Biological interpretation was conducted using ingenuity pathway analysis and BioGPS gene expression atlas. Of the genes, XIST was especially downregulated and SHOX was not expressed differentially. One of the most highly represented organ systems was the hematologic/immune system, differentiating the transcriptome of Turner’s syndrome from other chromosomal aneuploidies that are discussed in this area of science. The differences in the transcriptome of the Turner’s syndrome are due to genome-wide dysregulation. The hematologic/immune system differences are significant in early-onset autoimmune dysfunction. There are other genes which have been identified that are associated with the cardiovascular and the skeletal system, as these are often seen to be affected in the female patients with turner’s syndrome. Hopefully, such knowledge gained from this study will help us to understand the deeper mechanisms of this disorder and the possible treatments of this disease.

In this paper we investigate some properties of square-free numbers. In particular, we study a function that counts the number of square-free numbers no larger than the given number n. We show that this function has asymptotics consistent with the predictions of the Riemann hypothesis and use this to obtain new and better estimates of the density of square-free numbers. Finally, we present a well-known generalization of the concept of a square-free number to monoids and research on square-free factorizations.

This paper explores the suppression of ideas within academic scholarship by academics, either by self-suppression or because of the efforts of other academics. Legal, moral, and social issues distinguishing freedom of speech, freedom of inquiry, and academic freedom are reviewed. How these freedoms and protections can come into tension is then explored by an analysis of denunciation mobs who exercise their legal free speech rights to call for punishing scholars who express ideas they disapprove of and condemn. When successful, these efforts, which constitute legally protected speech, will suppress certain ideas. Real-world examples over the past five years of academics who have been sanctioned or terminated for scholarship targeted by a denunciation mob are then explored.

The digital era offers many opportunities to the wind energy industry and research community. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration method called WeDoWind was developed in recent work. The main innovation of this method is the way it creates tangible incentives to motivate and empower different types of people from all over the world to actually share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA data based wind turbine fault detection models are investigated by carrying out a new case study, the "WinJi Gearbox Fault Detection Challenge", based on the WeDoWind Method. Six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to have a high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for repairs, replacements and inspections, provision of a clear strategy for training and test periods in advance, as well as provision of a pre-defined template or requirements for the results. These learning outcomes are used directly to define a set of best practice data sharing guidelines for wind turbine fault detection model evaluation. They can be used by the sector in order to improve model evaluation and data sharing in the future.

Considering the aging society and budget constraints, a strategy for reforming public transportation welfare policies is essential for sustainability. Among the various transportation welfare policies, fare discount policies are provided to specific age groups, and most countries offer benefits to several public transportation. South Korea provides a fare-free policy limited to subways, and the need for policy modification is emerging due to sustainability problems caused by an aging society. Hence, the scope of the policy should be expanded to all travel modes, but the discount rate should be reduced, or the target group should be reduced in consideration of the budget. This study proposes a travel mode to focus on by city through the preference of the elderly. The latent class model is employed to classfy stated preference data collected from the survey. The estimation results show a significant preference heterogeneity depending on the level of subway supply by region, and a policy focused on subways would be more reasonable in cities with sufficient subway infrastructure. Hence, regarding sustainability, it may be more appropriate to focus on specific means than to provide the same discount policy for all public transport.

Dyadic data which is also called co-occurrence data (COD) contains co-occurrences of objects. Searching for statistical models to represent dyadic data is necessary. Fortunately, finite mixture model is a solid statistical model to learn and make inference on dyadic data because mixture model is built smoothly and reliably by expectation maximization (EM) algorithm which is suitable to inherent spareness of dyadic data. This research summarizes mixture models for dyadic data. When each co-occurrence in dyadic data is associated with a value, there are many unaccomplished values because a lot of co-occurrences are inexistent. In this research, these unaccomplished values are estimated as mean (expectation) of random variable given partial probabilistic distributions inside dyadic mixture model.

Finding a good MIMO system model also major issue in Wireless Communication system. It is facing with so many problem, one of the major problem is finding good system model in terms of capacity and transmitting antenna system. In this paper, we analyze the channel capacity of various MIMO system model with some constant SNR level and outage probability. We establish a novel idea for MIMO system models as consider as 2^N- MIMO system model and find-out change in channel capacity when different transmitting antennas with constant SNR and outage probability. The channel capacity ratio CC_R is presented here on the basis of 2^N- MIMO channel capacity model. Number of transmitted antenna presented in MIMO system is increases is well-known however paper shows change in capacity in simple form.

This study aimed to compare the prognosis and characteristics of patients with advanced hepatocellular carcinoma treated with first-line atezolizumab plus bevacizumab (AB) combination therapy and hepatic artery infusion chemotherapy (HAIC). We retrospectively assessed 179 and 72 patients treated with HAIC and AB combination therapy, respectively, between January 2018 and January 2023. The progression-free survival of patients treated with HAIC was significantly superior than that of patients treated with AB (P&lt;0.05), but there was no significant difference in overall survival and objective response rate (ORR) between the two groups (P=0.1056 and P=0.137, respectively). After propensity score matching (PSM), our data revealed that there was no significant difference in PFS between patients who received AB combination therapy and HAIC therapy (P=0.8888). However, patients who received AB combination therapy had significantly better overall survival than those who received HAIC therapy (P=0.0133). In addition, after PSM, significant differences in ORR and disease control rate were not observed. In conclusion, our propensity score study reveals that patients treated with AB therapy have a significantly longer OS than those treated with HAIC.

Cell free systems are a widely used research tool in systems and synthetic biology and a promising platform for manufacturing of proteins and chemicals. In the past, cell free biology was primarily used to better understand fundamental biochemical processes. Notably, E. coli cell free extracts were used in the 1960s to decipher the sequencing of the genetic code. Since then, the transcription and translation capabilities of cell free systems have been repeatedly optimized to improve energy efficiency and protein yield. Today, cell free systems, in combination with the rise of synthetic biology, have taken on a new role as a promising technology for just in time manufacturing of therapeutically important biologics and high-value small molecules. They have also been implemented in an industrial scale for the production of antibodies and cytokines. In this review, we discuss the evolution of cell free systems, advancements in cell free protein synthesis, and cell free metabolic engineering, and conclude with discussing the importance and feasibility of mathematical modeling in cell free systems.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

Based on K-ε Standard Wall turbulence model (2-Equation) and Navier-Stokes (N-S) equations defined for incompressible fluids, fluid flow behaviour around hyperloop pods in an evacuated tube was simulated using ANSYS fluent solver assuming steady state and two dimensional conditions. In this research, to develop the case studies, using combination of different head and tail shape profile, four kind of hyperloop pods were developed with the aid of SolidWorks. These four pods have been investigated for their aerodynamic behaviour as four different case scenarios. The results of simulation depicts that an atmospheric pressure of 100 Pa with blockage ratio of 0.36 in tube provides the best possible aerodynamic behaviour for the designed hyperloop pod models. This research finds that overall aerodynamic behaviour of hyperloop pods can be varied by changing the head and tail shape profile of pods and a particular combination of head and tail shape profile can provide optimally best aerodynamic capabilities. Thus, this research paper provides a novel method of obtaining best aerodynamic capabilities in hyperloop pods by designing head profile optimally in combination with tail profile. This outcome will provide major contribution towards the development of hyperloop pods in future with better aerodynamic behaviour resulting in lesser electrical energy required to propel the hyperloop pods in evacuated tube.

Variable Angle Tow (VAT) laminates offer a promising alternative to classical straight fiber composites in terms of design and performance. However, analyzing these structures can be more complex due to the introduction of new design variables. Carrera's Unified Formulation (CUF) has been successful in previous works for buckling, vibrational, and stress analysis of VAT plates. Typically, one-dimensional (1D) and two-dimensional (2D) CUF models are used, with a linear law describing the fiber orientation variation in the main plane of the structure. The objective of this article is to expand the CUF 2D plate finite elements family to perform free vibration analysis of composite laminated plate structures with curvilinear fibers. The primary contribution is the application of Reissner's Mixed Variational Theorem (RMVT) to a CUF finite element model. The Principle of Virtual Displacements (PVD) and RMVT are both used as variational statements for the study of monolayer and multilayer VAT plates dynamic behavior. The proposed approach is compared to Abaqus three-dimensional (3D) reference solutions, classical theories and literature results to investigate the effectiveness of the developed models. Results demonstrate that mixed theories provide the best approximation of the reference solution in all cases.

The load of power system changes with the development of economy, short-term load forecasting play a very important role in dispatching and management of power system. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so a load forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, the proposed ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy, which proves the effectiveness of proposed method.

Based on the insight gained from single jet analysis performed earlier [1], CFD analysis on turbulent jets impinging on one another at an angle was performed. Multiple impingement angles were considered for this study to gain better understanding of the parameters affecting resultant jet growth and velocity distribution. From the study of single jet, it was concluded that the SST k-ω turbulence model was the ideal turbulence model capable of accurately predicting the flow physics of the jets exiting a fully developed pipe at low Reynolds number. Hence, for the study of impinging jets, SST k-ω turbulence model was used to study the velocity and jet growth characteristics. It became evident that the mesh alignment with the velocity vector at exit of the pipe domain plays a crucial role in the accuracy of the results. The parameter used to evaluate this condition was identified as False Diffusion and was observed to affect the TKE parameter significantly. Methods to reduce false diffusion are also discussed in this article. Based on the mesh obtained from the grid sensitivity study, jets impinging at 30, 45 and 60 degrees at Reynolds number of 7500 were numerically analyzed. It was observed that the profile of the resultant jet closely matched with the prediction of elliptical profile predicted by past researchers, [2] and [3]. Also, it was seen that higher jet growth was predicted in case of jets impinging at a higher impingement angle.

This paper considers a non-mixture cure model for right censored data. It utilizes the maximum likelihood method to estimate model parameters in the non-mixture cure model. The simulation study is based on Fréchet susceptible distribution to evaluate the performance of the method. Comparing with Weibull and exponentiated exponential distributions, the non-mixture Fréchet distribution is shown to be the best in modeling a real data on allogeneic marrow HLA-matched donors and ECOG phase III clinical trial e1684 data.

(1)Background: Among new anti-angiogenetic agents being developed and everchanging guidelines indications, the question of benefits/safety ratio remains unclear. (2)Methods: we have conducted a systematic review and meta-analysis of 25 randomized controlled trials (15487 patients), evaluating overall survival – OS, progression free survival– PFS and toxicity (grade ≥3 adverse effects, type and number of all adverse effects. (3)Results: analysis showed improvement of pooled-PFS (HR 0.72, 95%CI 0.66–0.78, I2 = 77%, P<0.00001) regardless of treatment settings (first-line - HR 0.83, 95%CI 0.77-0.90, P<0.00001, recurrent cancer – HR 0.61, 95%CI 0.54-0.68, P<0.00001 or maintenance – HR 0.82, 95%CI 0.67-1.00, P=0.04) and type of anti-angiogenetic drug used (VEGF inhibitors, VEGF-R inhibitors or angiopoietin inhibitors). Improved OS was also observed (HR 0.95, 95%CI 0.91–0.99, P=0.02). OS benefits were only observed in recurrent platinum-sensitive or platinum-resistant cancers. Grade≥3 adverse effects were increased across all trials. Anti-angiogenetic therapy increased the risk of hypertension, infection, thromboembolic/ hemorrhagic events, gastro-intestinal perforations but not the risk of wound related issues, anemia or posterior leukoencephalopathy syndrome. (4)Conclusions: Although angiogenesis inhibitors improve PFS, there is little to no OS benefits. Given the high risk of severe adverse reactions a careful selection of patients is required for obtaining the best results possible.

In this paper, spatial pulse position modulation (SPPM) is used as a case study to investigate the performance of the optical spatial modulation (SM) technique in outdoor atmospheric turbulence (AT). A closed-form expression for the upper bound on the asymptotic symbol error rate (SER) of SPPM in AT is derived and validated by closely-matching simulation results. The error performance is evaluated in weak to strong AT conditions. As the AT strength increases from the weak to strong, the channel fading coefficients become more dispersed and differentiable. Thus, a better error performance is observed under moderate-to-strong AT compared to weak AT. The performance in weak AT can be improved by applying unequal power allocation to make FSO links more distinguishable at the receiver. Receive diversity is considered to mitigate irradiance fluctuation and improve the robustness of the system to turbulence-induced channel fading. The diversity order is computed as half of the number of detectors. Performance comparisons, in terms of energy and spectral efficiencies, are drawn between the SPPM scheme and conventional MIMO schemes such as repetition coding and spatial multiplexing.

This paper deals with a non-contact method to identify the aerodynamic propeller parameters of the Parrot AR.Drone quadrotor. The experimental set consists in a camera recording the vehicle flights, the audio signal is extracted and is used a spectrogram analysis to estimates the propeller velocity. First, the aerial vehicle takes off and starts a hovering maneuver. The experiment is repeated with different additional masses attached to its rigid body. If the weight over the UAV increases/decreases, then the propeller must rotates faster/slower to produce a higher/lower thrust, and consequently, the sound frequency increases/decreases. Finally, this proposition is validated experimentally, and the estimated velocity is used to identify the quadrotor thrust parameters.

With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack of the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and below ground. How to map reliable 3D underground utility networks and use it in the land administration? First, to explain the importance of this work and find a possible solution, this paper observes the current issues of the existing underground utility database in Singapore. A framework for utility data governance is proposed to manage the work process from the underground utility data capture to data usage. This is the backbone to support the coordination of different roles in the utility data governance and usage. Then, an initial design of the 3D underground utility data model is introduced to describe the 3D geometric and spatial information about underground utility data and connect it to the cadastral parcel for land administration. In the case study, the newly collected data from mobile Ground Penetrating Radar is integrated with the existing utility data for 3D modelling. It is expected to explore the integration of new collected 3D data, the existing 2D data and cadastral information for land administration of underground utilities.

Atmospheric correction is a necessary step in image processing data and spectra recorded by spaceborne sensors for pure cloudless atmosphere, primarily in the visible and near-IR spectral range. We have present a fast and sufficiently accurate method of atmospheric correction based on the proposed analytical solutions describing with high accuracy the spectrum of outgoing radiation at the top boundary of the cloudless atmosphere. This technique includes the model of the atmosphere and its optical parameters that are important in terms of radiation transfer. The solution of the inverse problem for finding unknown parameters of the model is carried out by the method of non-linear least squares (Levenberg-Marquardt algorithm) for an individual selected pixel of the image (its spectrum), taking into account the adjacency effects. Using the found parameters of the atmosphere and the average surface albedo, assuming homogeneity of the atmosphere within a certain area of the hyperspectral image (or the whole frame), the spectral albedo at the Earth's surface is calculated for all other pixels. It is essential that the procedure of the numerical simulation with non-linear least squares of the direct transfer problem is based on using analytical solutions, which provides a very short calculation time of the atmospheric parameters (seconds or less) and the ability to perform atmospheric correction "on-fly." Testing methods of atmospheric correction was performed using the synthetic outgoing radiation spectra at the top of the atmosphere (TOA), obtained by numerical simulation in the LibRadTran code, as well as spectra of real space images of the Hyperion hyperspectrometer. A comparison with the results of atmospheric correction in module FLAASH of ENVI package has been performed. Finally, to validate our data obtained by the SHARK method, a comparative analysis with ground-based measurements of Radiometric Calibration Network (RadCalNet) was carried out.

There are many uncertainties and risks in residential electricity consumption during the economic development. Knowledge of the relationship between residential electricity consumption and its key determinant—income—are important to the sustainable development of electric power industry. Using panel data from 30 provinces for the 1995-2012 period, this study investigates how residential electricity consumption changes as incomes increase in China. Previous studies typically used linear or quadratic double-logarithmic models imposing ex ante restrictions on the indistinct relationship between residential electricity consumption and income. Contrary to those models, we employed a reduced piecewise linear model that is self-adaptive and highly flexible and circumvents the problem of “prior restrictions.” Robust tests of different segment specifications and regression methods are performed to ensure the conservatism of the research. The results provide strong evidence that the income elasticity was approximately one, and it remained stable throughout the estimation period. The income threshold at which residential electricity consumption automatically remains stable or slows has not been reached. To ensure the sustainable development of the electric power industry, introducing higher energy efficiency standards for electrical appliances and improving income levels are vital. And government should emphasize electricity conservation in industrial sector rather than in residential sector.

Biology is about structure. Structures within structures. Organs within animals, tissues within organs, cells within tissues, and molecules, often proteins within cells. The structures are so complex that they can only be described by numbers. No numbers are of more importance than those that describe proteins. The numbers that describe coordinates of its atoms, often determined by Patterson functions (which are inverse Fourier Transforms of intensities) of crystal diffraction. Without these numbers, structural biology would hardly exist. Without numbers, engineering would not exist. Numbers are surely needed by the engineers who produce the x-rays diffracting from atoms of protein crystals. Devices of engineering have function. They are built to implement equations. Engineering devices use structures to implement equations, when power is supplied at the right places, that produces appropriate flows. Flows are the essence of life. Equilibrium means death in most living systems. Flows in biological structures are hard to analyze because we do not know input output equations in advance. Sometimes we do not know their function. Flows, forces, and structures of life (like those of engineering) are related by field equations of conservation laws, partial differential equations, constrained by location and properties of structures. Constraints are boundary conditions located on the complicated domain of biological structure. Dealing with this complexity is simplified if one systematically analyzes structure using the most general field theory known, electricity described by the Maxwell equations, without significant known error. Currents are involved because flows of biology usually involve migration of charges, convection of water and solutes, diffusion of ions that form the plasma of life, and their interactions. Interactions can dominate function. Here I show how a few complex structures can be understood in engineering detail. This approach may be useful in dealing with biological and medical issues in many other cases. In one limited case—the clearance of a toxic waste (potassium ions) from the optic nerve—this approach seems to have succeeded.

Missing item responses are prevalent in educational large-scale assessment studies like the programme for international student assessment (PISA). The current operational practice scores missing item responses as wrong, but several psychometricians advocated a model-based treatment based on latent ignorability assumption. In this approach, item responses and response indicators are jointly modeled conditional on a latent ability and a latent response propensity variable. Alternatively, imputation-based approaches can be used. The latent ignorability assumption is weakened in the Mislevy-Wu model that characterizes a nonignorable missingness mechanism and allows the missingness of an item to depend on the item itself. The scoring of missing item responses as wrong and the latent ignorable model are submodels of the Mislevy-Wu model. This article uses the PISA 2018 mathematics dataset to investigate the consequences of different missing data treatments on country means. Obtained country means can substantially differ for the different scaling models. In contrast to previous statements in the literature, the scoring of missing item responses as incorrect provided a better model fit than a latent ignorable model for most countries. Furthermore, the dependence of the missingness of an item from the item itself after conditioning on the latent response propensity was much more pronounced for constructed-response items than for multiple-choice items. As a consequence, scaling models that presuppose latent ignorability should be refused from two perspectives. First, the Mislevy-Wu model is preferred over the latent ignorable model for reasons of model fit. Second, we argue that model fit should only play a minor role in choosing psychometric models in large-scale assessment studies because validity aspects are most relevant. Missing data treatments that countries can simply manipulate (and, hence, their students) result in unfair country comparisons.

Alzheimer’s disease is the most common neurodegenerative disease and affects persons of all races, ethnic groups, and sexes. The disease is characterized by neuronal loss leading to cognitive decline and memory loss. There is no cure and the effectiveness of existing treatments is limited and depends on the time of diagnosis. The long prodromal period, during which patients’ ability to live a normal life is not affected despite neuronal loss, often leads to a delayed diagnosis because it can be mistaken for normal aging of the brain. In order to make a substantial impact on AD patients, early diagnosis may provide a greater therapeutic window for future therapies to slow AD-associated neurodegeneration. Current gold standards for disease detection include magnetic resonance imaging and positron emission tomography scans, which visualize amyloid β and phosphorylated tau depositions and aggregates. Liquid biopsies, already an active field of research in precision oncology, are hypothesized to provide early disease detection through minimally or non-invasive sample collection techniques. Liquid biopsies in Alzheimer’s disease have been studied in cerebrospinal fluid, blood, ocular, oral, and olfactory fluids. However, most of the focus has been on blood and cerebrospinal fluid due to biomarker specificity and sensitivity attributed to the effects of the blood-brain barrier and inter-laboratory variation during sample collection. Many studies have identified amyloid β and phosphorylated tau levels as putative biomarkers, however, advances in next-generation sequencing-based liquid biopsy methods have led to significant interest in identifying nucleic acids species associated with Alzheimer’s disease from liquid tissues. Differences in cell-free RNAs and DNAs have been described as potential biomarkers for AD and hold the potential to affect disease diagnosis, treatment, and future research avenues.

This article aims to serve as a guide for the construction of supply chain simulation models designed with a lean approach, using Promodel software. To achieve this, a supply chain was designed for a fictitious company located in the City of Celaya, Guanajuato and a set of suppliers located in different cities within the same State. It was used as a google tool to define the distances between each of the companies. As a final result, a representative model of a supply chain was obtained, as well as a methodology that allows the construction of lean supply chains regardless of the number of companies that comprise it. The effect of the variability in the delivery times between suppliers was incorporated into the simulation model, as well as an equation that calculates the pollution emissions of the vehicles that integrate the network that moves the products between the companies. With this work it is possible to represent networks of supply chains of real world companies, where the variability and contamination factor is included, to facilitate the decision making regarding the number of vehicles, inventory levels, quantities to be shipped, frequency in the shipments, etc. with the purpose of contaminating as little as possible and at the same time preventing interruptions in the supply chain using the least amount of resources possible.

This paper presents the Advanced Syntactic-Graph Convolutional Model (ASGCM), a pioneering approach in Aspect-Based Sentiment Analysis (ABSA) that integrates syntactic dependency features within a graph convolution framework. ASGCM stands out for its novel use of dependency edge encoding and tag-based graph convolutions, providing a fine-grained analysis of sentiments associated with specific aspects in text. This model meticulously captures the intricacies of syntactic structures, thereby offering enhanced precision in sentiment analysis. Notably, ASGCM incorporates a dual-layer graph convolution system: one layer processes syntactic dependencies (edges), while the other interprets semantic roles (tags), ensuring a comprehensive understanding of both structural and contextual elements in text. We rigorously tested ASGCM on multiple datasets, including both English and Chinese languages, and our findings reveal a significant improvement in sentiment classification accuracy compared to existing models. The versatility of ASGCM makes it a robust tool for diverse linguistic environments, setting a new standard for ABSA methodologies.