The Republic of Venice was one of the longest-lived states in modern history, and its stability and survival has been studied through many different angles, and one of them is to try and find pivotal moments in its history that explain it, or its eventual demise. In this paper, through the rigorous statistical analysis of the dataset of marriages by nobles in the Republic, we will try to define a methodology for the detection of these events through mono and multivariate change point analysis, and validate that methodology through cross-validation of different procedures, as well as matching the results to historical events. Our analysis shows that these change points occur with statistical significance and that they match political and historical events. These results can be built upon for a better understanding of the historical causes of the success and failure of the Republic of Venice and, by extension, other states.

This study considers the change-point test problem for time series based on the self-normalization ratio statistic test, which is constructed using residuals obtained from a support vector regression (SVR)-autoregressive moving average (ARMA) model. Under the null hypothesis, the series is a stationary process, and our test statistic converges to a non-degenerate distribution. Under the alternative hypothesis, there are change-points in the time series, and the self-normalization test statistic diverges to infinity. The simulations show that our proposed new test has better finite sample performance than other SVR-based tests in the literature. Finally, we illustrate its usefulness by analyzing two actual data sets.

This paper is devoted to change-point detection using only the ordinal structure of a time series. A statistic based on the conditional entropy of ordinal patterns characterizing the local up and down in a time series is introduced and investigated. The statistic requires only minimal a priori information on given data and shows good performance in numerical experiments.

Sequential hypothesis test and change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. We show that for such problems, detection procedures based on sequential likelihood ratios with simple one-sample update estimates such as online mirror descent are nearly second-order optimal. This means that the upper bound for the algorithm performance meets the lower bound asymptotically up to a log-log factor in the false-alarm rate when it tends to zero. This is a blessing, since although the generalized likelihood ratio (GLR) statistics are optimal theoretically, but they cannot be computed recursively, and their exact computation usually requires infinite memory of historical data. We prove the nearly second-order optimality by making a connection between sequential analysis and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm. Numerical and real data examples validate our theory.

The study focused on two climatic variables i.e. precipitation and stream flow for analysing change in trend of rainfall for the River Ssezibwa Catchment area and stream flow of River Ssezibwa. This Catchment is found in the districts of Mukono and Buikwe in Uganda. In this area agriculture is majorly dependent on rainfall and irrigation on a small scale. However, rainfall occurrence has become unpredictable over the past few years as result of the changes in patterns of weather. This has caused severe effects on the agricultural cropping system as well as caused negative effects on the natural water resources. Stream flow data of 57 years (1960 – 2017) and rainfall data for 35 years (1982 – 2017) on a daily basis was analysed to find out the trend and detect change point. Trend analysis was done by using the non-parametric analysis while the change point detection was carried out by using the Pettit test (1979).Magnitude of trend for the time series data was carried out using Sen’s Slope estimator and Mann – Kendall test was done to determine the trend. Results from the statistical analysis highlighted that; for stream flow the trend was generally positive and change point detected to be in the year 2000 while for rainfall data analysis indicated that the trend was predominantly negative and change point was in the year 1998.

While the importance of continuous monitoring of electrocardiographic (ECG) or photoplethysmographic (PPG) signals to detect cardiac anomalies is generally accepted in preventative medicine, there remain major barriers to its actual widespread adoption. Most notably, current approaches tend to lack real-time capability, exhibit high computational cost, and be based on restrictive modeling assumptions or require large amounts of training data. We propose a lightweight and model-free approach for the online detection of cardiac anomalies such as ectopic beats in ECG or PPG signals based on the change detection capabilities of Singular Spectrum Analysis (SSA) and nonparametric rank-based cumulative sum (CUSUM) control charts. The procedure is able to quickly detect anomalies without requiring the identification of fiducial points such as R-peaks and is computationally significantly less demanding than previously proposed SSA-based approaches. Therefore, the proposed procedure is equally well suited for standalone use and as an add-on to complement existing (e.g. heart rate (HR) estimation) procedures.

In this paper, we present a relapse based demonstrating way to deal with investigate various arrangement MTC information. A commonplace use of this displaying approach incorporates three stages: first, define a model that approximates the connection between quality articulation and trial factors, with boundaries consolidated to address the exploration premium; second, utilize least-squares and assessing condition methods to gauge boundaries and their relating standard blunders; third, register test insights, P-qualities and NFD as proportions of factual criticalness. The benefits of this methodology are as per the following. To begin with, it tends to the exploration interest in a particular, precise way, and maximally uses all the information and other important data. Second, it represents both orderly and irregular varieties related with the information, and the consequences of such examination give not just quality explicit data applicable to the exploration objective, yet additionally its dependability, in this way helping agents to settle on better choices for subsequent investigations. Third, this methodology is truly adaptable, and can undoubtedly be stretched out to different sorts of MTC considers or other microarray explores by detailing various models dependent on the test plan of the investigations.

Longitudinal image registration of pulmonary computed tomography (PCT) images may serve as an essential tool for investigating the relationship between radiation dose distribution and the occurrence and phenotype of radiation-induced lung disease (RILD). Although numerous longitudinal registration algorithms have been developed for PCT, most similarity-based approaches are not suitable for PCT involving RILD due to the complex tissue variation between two PCT images. Moreover, conventional feature-based approaches might fail to find a sufficient number of matched pairs of feature points due to the disparate lung deformation caused by breathing and RILD. To overcome the challenges resulting from RILD, component structure coherence point drift (CSCPD) was proposed to establish a deformation model by decomposing the chest into several components and matching them with individual parameters based on coherence point drift (CPD). Moreover, a regional vascular point matching (RVPM) algorithm was proposed to generate a vascular subtree and to substantially increase the number of corresponding pairs between two images. Eventually, the components were recomposed and aligned by a thin plate spline algorithm. A performance assessment on 15 pairs of PCT images of patients with RILD yielded recall and precision values of 0.85 and 0.89 for RVPM, respectively. Moreover, the target registration error of CSCPD with RVPM (2.3 ± 1.79) was significantly better than that of conventional CPD with RVPM (2.95 ± 1.89) and conventional CPD (5.04 ± 2.87). Therefore, the proposed registration system is robust enough to address the disparate deformation of lungs with RILD, and it improves registration accuracy within the parenchyma.

We generalize the point information gain (PIG) and derived quantities, i.e., point information gain entropy (PIE) and point information gain entropy density (PIED), for the case of the Rényi entropy and simulate the behavior of PIG for typical distributions. We also use these methods for the analysis of multidimensional datasets. We demonstrate the main properties of PIE/PIED spectra for the real data on the example of several images, and discuss further possible utilizations in other fields of data processing.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

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

Most petrochemical plants still maintain a Proportional-Integral-Differential controller(PID) system, which is a feedback control system. However, gradually, the PID system is being extended and introduced to the Advanced Process Controller (APC) system, which is an integrated control system of feedforward and feedback that predicts external influences in advance. In the process of conducting on-site plant tests and calculating APC model parameter for the application of APC systems, a problem arises that Model Parameter are implemented differently depending on the proficiency of APC engineers. To minimize this problem, a technique for estimating APC model parameter without a plant test is required. In order to estimate the APC model parameter, it is necessary to train on dynamic interval data. In this paper, we use statistical techniques such as PELT, Linear Kernel, and Radial Basis Function Kernel of Change Point Detection (CPD) to extract dynamic data with minimum Mean Absolute Error (MAE) from time series data of a real petrochemical plant. Then, the hyper parameter is fixed and the APC model parameter is estimated by learning the dynamic section data. By applying the estimated APC model parameter to the APC Model Tool and measuring the fitting rate, it was confirmed that it is possible to estimate the APC model parameter with excellent control performance without plant test.

Identifying ambient noise-based (ANb) signatures of streams can help in the estimation of their erosive potential (EP) that promotes reverie landslides and soil losses in the fluvial valleys. This is particularly imperative on flooding or rainy days, leading to stronger erosion-prone conditions (colluvium and boulders) of the valley beds inferred from georadar attribute analysis. Developing such research direction can benefit the local communities, as is the case with the Cerrado region of Brazil, where these phenomena have high destructive potential with social, economic, and climatic implications. For the present study, a seasonal stream in the Federal District of Brazil was investigated by ANb monitoring supported by Ground Penetration Radar (GPR) for site characterization. The ANb monitoring was conducted (at a safe distance) with a seismometer over several durations of dry and rainy conditions. The power spectral density (PSDs) was computed as a function of several variables, including weather conditions (rainfall, wind speed, and pressure), time-frequency spectrograms, and ambient noise displacement root mean square (RMS). This analysis also considered the single station horizontal-to-vertical spectral ratio (HVSR), where rain, wind, pressure, river flow and anthropogenic signatures were evident (at selective frequency ranges). Multi-peaks that emerged on the HVSR curve were further analyzed to identify amplitude and frequency changes, and the three peaks shift on average to a lower position during the rainy period. The GPR amplitude and waveform variation features were attributed to the stratigraphy (i.e., the boundary between valid and invalid regions and coherence value) of the floodplain and regions susceptible to erosion (erosion-prone lithological spots). This approach provides the basis for non-destructive monitoring tools enabling the detection of 'seismic signatures' and weak spots of the fluvial channels for improving environmental management.

This work assesses sea-level rise using three different models created on Free and Open-Source Software for Geographic Information System (FOSS4GIS). Based on regional projections of Special Report on Climate Change and Oceans and Cryosphere (SROCC) of the Intergovernmental Panel on Climate Change (IPCC), the models were applied to a case of study on Rio Grande do Sul coast – Brazil under different sea-level rise scenarios by the end of this century. The End Point Rate for QGIS (EPR4Q), calculates a shoreline projection using End Point Rate method. The Uncertainty Bathtub Model (uBTM), analyses the sea-level rise impact by the uncertainty of sea-level projec-tions and vertical error of the Digital Elevation/Terrain Model (DEM/DTM). The Bruun Rule for Google Earth Engine Model (BRGM) predicts the shoreline position with sea-level rise, using topographic and bathymetric data from Unmanned Aerial Vehicles (UAV) and Coastal Modelling System (SMC – Brazil), respectively. The results indicated a maximum shoreline retreat for 2100 of -502 m and -1727 m using EPR4Q and BRGM, correspondingly. The uBTM using the land-use of Mapbiomas showed a maximum of 44.57 km2 of urban area impacted by the sea-level flood. This research highlights the possibility of performing coastal management analysis in GIS environ-ment using non-commercial software.

This paper presents a new algorithm for semi-supervised action recognition using adaptive feature analysis. We model the samples of the same action as the same manifold and those of different actions as different manifolds, and integrate feature correlation mining and visual similarity mining into a joint framework. By maximizing the separability between different classes based on the labeled data, the proposed algorithm learns multiple features from labeled data and utilizes the intrinsic geometric structure of the data distribution between labeled and unlabeled data to boost the recognition. We introduce Projected Barzilai-Borwein (PBB) to train a subspace projection matrix as classifier. Extensive experiments on three benchmark datasets demonstrate that our algorithm outperforms the existing semi-supervised action recognition algorithms when there are a few labeled training samples.

This paper explores the imminent threat posed by climate change to the unique platypus species. It delves into the ecological repercussions of global temperature rise and altered climate patterns on platypus habitats. The study emphasises the multifaceted impacts, including changes in aquatic habitat quality, food sources, and breeding rituals. Climate-induced shifts in phytoplankton spring blooms and anthropogenic pressures further endanger the platypus's habitat. The article stresses the importance of understanding these effects for both species conservation and anticipating broader ecological and socio-economic consequences. Mitigating climate change impacts requires a multidimensional approach, involving habitat manipulation and restoration strategies.

The point cloud is a form of three-dimensional data that comprises various detailed features at multiple scales. Due to this characteristic and its irregularity, point cloud analysis based on deep learning is challenging. While previous works utilize the sampling-grouping operation of PointNet++ for feature description and then explore geometry by means of sophisticated feature extractors or deep networks, such operations fail to describe multi-scale features effectively. Additionally, these techniques have led to performance saturation. And it is difficult for standard MLPs to directly "mine" point cloud. To address above problems, we propose the Detail Activation (DA) module, which encodes data based on Fourier transform after sampling and grouping. We activate the channels at different frequency levels from low to high in the DA module to gradually recover finer point cloud details. As training progresses, the proposed Point-MDA can uncover local and global geometries of point cloud progressively. Our experiments show that Point-MDA achieves superior classification accuracy, outperforming PointNet++ by 3.3% and 7.9% in terms of overall accuracy on the ModelNet40 and ScanObjectNN dataset, respectively. Furthermore, it accomplishes this without employing complicated operations, while exploring the full potential of PointNet++.

A wealth of Full Waveform (FW) LiDAR data are available to the public from different sources, which is poised to boost the extensive application of FW LiDAR data. However, we lack a handy and open source tool that can be used by potential users for processing and analyzing FW LiDAR data. To this end, we introduce waveformlidar, an R package dedicated to FW LiDAR processing, analysis and visualization as a solution to the constraint. Specifically, this package provides several commonly used waveform processing methods such as Gaussian, adaptive Gaussian and Weibull decompositions, and deconvolution approaches (Gold and Richard-Lucy (RL)) with users’ customized settings. In addition, we also developed functions to derive commonly used waveform metrics for characterizing vegetation structure. Moreover, a new way to directly visualize FW LiDAR data is developed through converting waveforms into points to form the Hyper Point cloud (HPC), which can be easily adopted and subsequently analyzed with existing discrete-return LiDAR processing tools such as LAStools and FUSION. Basic explorations of the HPC such as 3D voxelization of the HPC and conversion from original waveforms to composite waveforms are also available in this package. All of these functions are developed based on small-footprint FW LiDAR data, but they can be easily transplanted to the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) data analysis. It is anticipated that these functions will facilitate the widespread use of FW LiDAR and be beneficial for better estimating biomass and characterizing vegetation structure at various scales. The package and code examples can be found at https://github.com/tankwin08/waveformlidar.

Wetlands are one of the crucial natural resources. They provide invaluable biodiversity resources, aid in water quality improvement, support ground water recharge, help in moderating climate change and support flood control. Environment is in the other hand, where we live and something, we are very familiar with our day to day life. Geographic Information Systems (GIS), Remote Sensing and Global Positioning System (GPS) were a useful tool for wetland and environmental change analysis and to improve on the classification accuracy. This study investigates population and environmental change of Jarmet wetland and its surrounding area change analysis over the period of 1972 to 2015. The purpose of this study was to show land use/ land cover change of Jarmet wetland and its surrounding environment over years as a response to population growth. For this purpose, multi-temporal satellite imageries (Landsat MSS 1972, TM1986, ETM+ 2000, 2005 and 2015 and SRTM 2000) were obtained and used for LULC change analysis, elevation analysis and change detection analysis. ERDAS Imagine 2015, ARC GIS 10.5.1, Global Mapper11, ENVI 5.0 and DNR Garmin softwares were used to process the image data and accuracy assessment analysis. The result of LULC showed that there is spatial reduction in wetland, forest, Shrubland and grassland in the period of 43 years (1972-2015) by -1,722.8 ha, -296.2 ha, -1,718.7 ha and -661.9 ha respectively, due to increase in the farmland and plantation area as a response to overpopulation, lack of environmental policy implementation and irresponsible for natural resource degradation. The accuracy assessment of LULC change are done for recent satellite image showed the overall accuracy of 84.06% with Kappa index 75.19% this means this classification is accurately classified and handle greater than 75% of error. Finally, this study suggests that create strictly natural resource conservation law, stopping illegal expansion of farmland, educating society about the value of natural resource especially wetland and create a source of income for society rather than farming.

Myofascial Pain Syndrome (MPS) is a prevalent chronic pain disorder characterized by myofascial trigger points (MTrPs). Current diagnosis relies on manual detection of MTrPs, which has low inter-rater reliability. This study aims to enhance diagnosis through machine learning (ML) and a combination of texture-features extracted from B-mode ultrasound (B-mode-US) images. Four texture-features were investigated: statistical-features, and their combination with Gabor, local binary (LBP), and SEGL, LBP + gray-level-co-occurrence-matrices + Edge for the classification of MTrPs on the B-mode-US images. B-mode-US images of trapezius muscles (n=90) were examined for MTrPs and healthy muscle. Three methods of LBP, SEGL, and Gabor were separately calculated for each B-mode-US image. Then, the statistical-features (e.g., entropy) were calculated over the B-mode-US images and those three methods. Seven Machine learning (ML) classifiers (e.g., neural network) with the calculated statistical-features were applied to discriminate MTrPs from healthy muscle. Additionally, traditional statistical analysis (i.e., ANOVA) was calculated between statistical-features within each method. The results indicated that the combination of statistical-features effectively discriminated between MTrPs and healthy muscles, based on traditional statistical analysis. However, ML classifiers struggled due to high variation and similar mean values among them. This discrepancy between them prompts further exploration of US texture-based automated ML for MTrPs diagnosis.

The calculation of the exact value of the square root of 2 is requested. In order to obtain its infinite value, A new concept is proposed where the accumulations of the infinitely many of finity is indicated by the change in direction which means that there is a jump from finity to infinity .The meaningless for an infinite number with a decimal point is indicated by this jump because any decimal number only have meanings within a finite range values and there is only an infinite integer quantity that can not be operated by algorithms like operations of multiplication，division,addition, and subtraction . The final result of the change in direction is two quantity where the second quantity and the first quantity extend in parallel line and never intersect and the second quantity represent the size of the first quantity. The first quantity is the infinitely great that can't be talked about anything outside of it and can compress any quantities outside of it to nothing and it is the exact value of the square root of 2 .

Due to the major impact of climate change on the world's environment, political and economic systems, climate change mitigation has become a pressing priority for the international community and requires rapid action from the whole society. With the continuous advancement of artificial intelligence research, the integration of AI and other technologies makes it more and more used in the whole society. It has become a promising and innovative avenue in the field of climate change mitigation. This paper comprehensively considers the key role of AI technology in the field of climate change mitigation, such as climate modeling, the optimization of renewable energy systems, the development of intelligent solutions for sustainable practices and CSS technology, and affirms its future prospects. It also describes the challenges of AI in the field of climate change mitigation. As researchers, policymakers, and industries collaborate to refine AI methodologies and integrate them into practical applications, a concerted effort is required to establish ethical guidelines, transparency standards, and inclusive governance frameworks.

We have considered a SEIR-SEI Vector-host mathematical model which captures malaria transmission dynamics, described and built on 7-dimensional nonlinear ordinary differential equations. We compute the basic reproduction number of the model; examine the positivity and boundedness of the model compartments in a region using well established methods viz: Cauchy’s differential theorem, Birkhoff & Rota’s theorem which verifies and reveals the well-posedness, and carrying capacity of the model respectively, the existence of the Disease-Free (DFE) and Endemic (EDE) equilibrium points were determined and examined. Using the Gaussian elimination method and the Routh-hurwitz criterion, we convey stability analyses at DFE and EDE points which indicates that the DFE (malaria-free) and the EDE (epidemic outbreak) point occurs when the basic reproduction number is less than unity (one) and greater than unity (one) respectively. We obtain a solution to the model using the Variational iteration method (VIM) (an unprecedented method) to each population compartments and verify the efficacy, reliability and validity of the proposed method by comparing the respective solutions via tables and combined plots with the computer in-built Runge-kutta-Felhberg of fourth-fifths order (RKF-45). We illustrate the combined plot profiles of each compartment in the model, showing the dynamic behavior of these compartments; then we speculate that VIM is efficient and capable to conduct analysis on Malaria models and other epidemiological models.

With the advancement of photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and was used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain a final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

The study combines two critical aspects of climate research in Nigeria. The first segment focuses on improving predictive performance of global circulation models (GCMs) employing ensemble techniques. ANN ensemble displayed significant improvement in the accuracy of climate projections, particularly in high-altitude geographic areas where traditional models exhibit limitations. Performance evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Nash–Sutcliffe Efficiency (NSE) demonstrate the effectiveness of the ANN multimodal ensemble. Building on the improved climate modelling, the second segment delves into a spatial and temporal analysis of rainfall and mean temperature patterns under climate change in Nigeria. By considering two Representative Concentration Pathways (RCPs), namely RCP4.5 and RCP8.5, the study utilizes dynamically downscaled GCMs obtained from the Coupled Model Intercomparison Project phase 5 (CMIP5). The Mann-Kendall test reveals significant spatiotemporal variations in climate trends across the country. The warming trend is particularly pronounced in the northern regions, with RCP8.5 showing more severe impacts compared to RCP4.5. This integrated approach not only highlights the importance of multimodal ensembles in climate modelling but also provides valuable insights into the spatial and temporal patterns of climate change to inform effective adaptation and mitigation strategies for Nigeria.

Study of spatiotemporal dynamics of temperature is vital to assess changes in climate, especially in the Himalayan region where livelihoods of billions of people living downstream depends on water coming from the melting of snow and glacier ice. To this end, temperature trend analysis is carried out in Narayani river basin, a major river basin of Nepal characterized by three climatic regions: tropical, subtropical and alpine. Temperature data from six stations located within the basin were analyzed. The elevation of these stations ranges from 460 to 3800 m a.s.l. and the time period of available temperature data ranges from 1960–2015. Multiple regression and empirical mode decomposition (EMD) methods were applied to fill in missing data and to detect trends. Annual as well as seasonal trends were analyzed and a Mann-Kendall test was employed to test the statistical significance of detected trends. Results indicate significant cooling trends before 1970s, and warming trends after 1970s in the majority of the stations. The warming trends range from 0.028 ∘" style="position: relative;">∘C year−1" style="position: relative;">−1 to 0.035 ∘" style="position: relative;">∘C year−1" style="position: relative;">−1 with a mean increasing trend of 0.03 ∘" style="position: relative;">∘C year−1" style="position: relative;">−1 after 1971. Seasonal trends show highest warming trends in the monsoon season followed by winter, pre-monsoon, and the post-monsoon season. However, difference in warming rates between different seasons was not significant. An average temperature lapse rate of −0.006 ∘" style="position: relative;">∘C m−1" style="position: relative;">−1 with the steepest value (−0.0064 ∘" style="position: relative;">∘C m−1" style="position: relative;">−1) in pre-monsoon season and least negative (−0.0052 ∘" style="position: relative;">∘C m−1" style="position: relative;">−1) in winter season was observed for this basin. A comparative analysis of the gap-filled data with freely available global climate datasets show reasonable correlation thus confirming the suitability of the gap filling methods.

The aim of this article is to extend the local as well as the semi-local convergence analysis of multi-point iterative methods using center Lipschitz conditions in combination with our idea, of the restricted convergence region. It turns out that this way a finer convergence analysis for these methods is obtained than in earlier works and without additional hypotheses. Numerical examples favoring our technique over earlier ones completes this article.

Lake Chapala is a natural ecosystem of Mexico, declared Ramsar site, currently, is an area with importance in the agricultural sector, tourism, and important source of drinking water in the Metropolitan area of Guadalajara, considered as a thermo regulatory body. This study used tools such as Rhtest, as well as analyzing extreme climate indices using RClimdex. In addition, the relationship with the change in water surface of Lake Chapala was assessed through the NDWI index, using Landsat images, between 1985 and 2018. According to the obtained results, the signs of climatic variability are detected in the study region, which, directly affects the basin runoff and water levels in the lake, as well as, the factor of water overexploitation in the basin, so it is considered that the lake conditions mostly depend on the type of management that is given to the resource. Finally, the results will help the understanding of climate variability and its water resources influence of the Lerma Chapala basin, which will assist in future research.

With the increased use of laterite nickel ore, the impact of high Al2O3 slag on blast furnace smelting has gradually increased. In this paper, the effects of slag basicity and Al2O3 content on slag viscosity and enthalpy change under constant temperature conditions was investigated. The changes in slag structure were analyzed by activation energy and Fourier Transform Infrared (FT-IR) spectroscopy. The relationship between slag components and slag temperature and viscosity when slag heat is reduced was investigated. The results showed that the viscosity first slightly decreased and then significantly increased with increasing basicity at constant temperature. With the addition of Al2O3 content, the viscosity of the slag increases. The activation energy increases with increasing slag basicity and Al2O3. With increasing basicity, the [SiO4]4- tetrahedral unit trough depth becomes shallow, the [AlO4]5- asymmetric stretching band migrates to lower wave numbers, and the slag structure depolymerizes. With the increase of Al2O3 content, the trough of [SiO4]4- tetrahedra deepens and the center of the symmetric stretching band moves to a higher wave number. The [AlO4]5- asymmetric stretching band becomes obvious, indicating the complexity of the slag structure. When the heat decreases, the slag temperature increases as the basicity increases, and the slag thermal stability is better at the basicity of 0.95-1.05. As the Al2O3 content increases, the thermal stability of the slag becomes worse.

Assessment of meadow restoration benefits from understanding the connection between geomorphology, hydrology and vegetation, and multispectral imagery captured from unpiloted aerial vehicles (UAV) may provide the method most suitable in terms of cost, spatial resolution, support for vegetation indices, and frequency of acquisition. Our field studies of northern Sierra Nevada montane meadows (with <40 km2 watersheds) in various stages of restoration includes GPS + laser-leveling channel survey, laser-leveled cross section, aerial LiDAR, vegetation sampling, soil measurements, and UAV imaging using a sensor capturing calibrated blue (465-485 nm), green (550-570 nm), red (663-673 nm), near infrared (820-860 nm), and red edge (712-722 nm) bands at 5.5 cm resolution, providing multispectral false-color images and vegetation indices such as normalized difference vegetation index (NDVI) and red-edge Chlorophyll index (Clre). This fine-scale imagery extends our morphometric assessment of post-restoration channel scouring exhibiting bedform patterns and sinuosity related to Carex-influenced soil properties and Salix copses, but also provided a view of groundwater-related drainage effects including sinuous Carex-vegetated zones evident from spring snowmelt images as well as NDVI and Clre in growing to senescent phenological stages. Carex in particular was significantly associated with low bulk density and high soil moisture, NDVI, and Clre at lower elevations.

The relocation of Indonesia's capital to the IKN (Ibukota Negara) Nusantara in East Kalimantan is leading to significant changes in land use, shifting from natural vegetation and agriculture to urban infrastructure. This transition brings about economic diversification and urban expansion, but it also raises concerns about its impact on society, the economy, and the environment. The rapid development affects biodiversity conservation, food security, and the livelihoods of rural and indigenous communities, leading to conflicts across social and economic dimensions. This research uses qualitative and quantitative data to examine the socio-economic and environmental changes in the IKN Nusantara area from 2003 to 2023. The findings show a notable increase in built-up areas, indicating urbanization and a decrease in agricultural land. The study discusses the implications for local populations and ecosystems, emphasizing the need for inclusive governance, community participation, and conflict resolution. It also proposes a comprehensive policy framework that promotes sustainable land management, recognizes indigenous and local rights, and fosters inclusive economic growth to respect Indonesia's rich environmental and cultural heritage.

This study assessed farmers’ perception of climate change, estimated the determinants of, and evaluated the relationship among adaptation practices using the multivariate probit model. A survey in 300 agricultural households was carried out covering 10 sample districts considering five agro-ecological zones and a vulnerability index. Four adaptation choices (change in planting date, crop variety, crop type and investment in irrigation) were deemed as outcome variables and socioeconomic, demographic, institutional, farm-level and perceptions variables were deployed as explanatory variables. Their marginal effects were determined for three climatic variables—temperature, precipitation and drought. Age, gender and education of head of household, credit access, farm area, rain-fed farming and tenure, are found to be more influential compared to other factors. All four adaptation-options are found to be complimentary to each other. Importantly, the intensity of impact of dependent variables in different models, and for available adaptation-options, are found to be unequal. Therefore, policy options and support facilities should be devised according to climatic variables and adaptation options to achieve superior results.

This paper deals with the structural decomposition input-output for the economic analysis of agriculture, forestry and fishing in six EU-28 countries (Austria, Belgium, France, Germany, Italy and Spain) in the 2010-2015 period. The objective is to determine the causes of changes in production in these sectors with a particular focus on disaggregation technological change by distribution factors associated with a specific normalisation of the Leontief inverse. In calculating the net multipliers, an attempt was made to exclude sectors' own consumption appropriately. However, the treatment of own consumption upon introducing a time factor requires further investigation to avoid questionable quantifications. In general, typical characteristics of primary sectors include the accumulation of a significant amount of their own consumption, facilitated by symmetric accounting. Therefore, attention is drawn to these sectors so as to reveal possible analysis techniques that will provide nuance or validate existing techniques.

These days, numerous organizations and Analysis Centers (AC) offer various Ambiguity Resolution (AR) products using various methodologies. To use it for time-frequency transfer, there is no associated study. This paper chooses 16 MGEX stations with external high-precision atomic clocks to constitute 15 international time comparison links and uses AR products data from CNES, SGG, CODE, and PRIDE laboratory, using three ambiguity-fixed strategies, to thoroughly evaluate the effects of various strategies and AR products for high-precision time-frequency transfer. We reach the following results by using the IGS final clock product as a reference and comparing it to ambiguity-float. With various ambiguity-fixed procedures, the time stability STD of time transfer is increased for a single GPS, and the improvement ranges from 10% to 40%. The frequency stability has barely improved; up to 40%, the most notable improvement comes from FCB with GRM products. The time stability STD of combinations has improved after the addition of the Galileo system compared to the single GPS, and the improvement ranges from 2% to 9%. Most strategies have improved, while a few techniques have weakened with the GEC (GPS+Galileo+BDS) combination. We feel that the stability has not significantly increased with the system's increase within short-term stability after comparing multiple groups of linkages.

In this paper, Lyapunov-Razumikhin technique, design of state-dependent switching laws, a fixed point theorem and variational methods are employed to derive the existence and the unique existence results of (globally) exponentially stable positive stationary solution of delayed reaction-diffusion cell neural networks under Dirichlet zero boundary value, including the global stability criteria \textbf{in the classical meaning}. Next, sufficient conditions are proposed to guarantee the global stability invariance of ordinary differential systems under the influence of diffusions. New theorems show that the diffusion is a double-edged sword in judging the stability of diffusion systems. Besides, an example is constructed to illuminate that any non-zero constant equilibrium point must be not in the phase plane of dynamic system under Dirichlet zero boundary value, or it must lead to a contradiction. Next, under Lipschitz assumptions on active function, another example is designed to prove that the small diffusion effect will cause the essential change of the phase plane structure of the dynamic behavior of the delayed neural networks via a Saddle point theorem. Finally, a numerical example illustrates the feasibility of the proposed methods.

The aim of this paper is to investigate whether multifractal analysis can be applied to study choroidal blood vessels and help ophthalmologists for the early diagnosis of retinitis pigmentosa (RP). In a case study, we use Spectral Domain Optical Coherence Tomography (SDOCT), which is a non-invasive and highly sensitive imaging technique of the retina and choroid. The image of a choroidal branching pattern can be regarded as a multifractal. Therefore, we calculate generalized Renyi point-centered dimensions, which is considered a measure of inhomogeneity of data, to prove that it increases in the patients with RP with respect to the control group.

This paper addresses the issues of how we can quantify structural information for nonparametric distributions and how we can detect its changes. Structural information refers to an index for a global understanding of a data distribution. When we consider the problem of clustering using a parametric model such as a Gaussian mixture model, the number of mixture components (clusters) can be thought of as structural information in the model. However, there does not exist any notion of structural information for nonparametric modeling of data. In this paper we introduce a novel notion of {\em kernel complexity} (KC) as structural information in the nonparametric setting. The key idea of KC is to combine the information bias inspired by the Gini index with the information quantity measured in terms of the normalized maximum likelihood (NML) code length. We empirically show that KC has a property similar to the number of clusters in a parametric model. We further propose a framework for structural change detection with KC in nonparametric distributions. With synthetic and real data sets we empirically demonstrate that our framework enables us to detect structural changes underlying the data and their early warning signals.

Recently, federated learning (FL) has been receiving great attention as an effective machine learning method to avoid the security issue in raw data collection as well as to distribute the computing load to edge devices. However, even though wireless communications is a essential part for implementing FL in edge networks, there have been a few works that analyze the effect of wireless networks on FL. In this paper, we investigate FL in small-cell networks where multiple base stations (BSs) and users are located according to homogeneous Poisson point process (PPP) with different densities. We comprehensively analyze the effects of geographic node deployment on the model aggregation in FL on the basis of stochastic geometry-based analysis. We derive the closed-form expressions of coverage probability with tractable approximations and discuss the minimum required BS density for achieving a target model aggregation rate in small-cell networks. Our analysis and simulation results provide insightful information for understanding the behaviors of FL in small-cell networks, and it can be exploited as a guideline for designing the network facilitating the wireless FL.

Climate change is affecting the planet, compromising survival and altering forest ecosystems. Investigating climatic variations in all regions is important. The aim of this research was to assess such variations in the W and Pendjari Biosphere Reserves in Benin from 1985 to 2015 and predict climate up to 2045. Drought frequency was assessed by using the Standardized Precipitation and Evapotranspiration Index (SPEI) and correlation was performed between climatic variables. Then a predictive analysis was computed. From 1985 to 2015, there was change in the climate with few severe dry and wet years while no extreme climatic condition was noticed. Most of the years showed nearly normal climatic conditions. The predicted climate indicates an absence of extreme conditions up to 2045 but an increase in the annual humidity is expected. A low decrease in annual mean temperature with high confidence is expected. Decision-makers can better organize adaptation programs and sensitize people in the reserves on their role in environmental conservation and mitigation of climate change using these outputs.

The interest in renewable energy to replace fossil fuel is increasing as the problem caused by climate change become more severe. Small hydropower (SHP) is evaluated as a resource with high development value because of its high energy density compared to other renewable energy sources. SHP may be an attractive and sustainable power generation environmental perspective because of its potential to be found in small rivers and streams. The power generation potential could be estimated based on the discharge in the river basin. Since the river discharge depends on the climate conditions, the hydropower generation potential changes sensitively according to climate variability. Therefore, it is necessary to analyze the SHP potential in consideration of future climate change. In this study, the future prospect of SHP potential is simulated for the period of 2021 to 2100 considering the climate change in three hydropower plants of Deoksong, Hanseok, and Socheon stations, Korea. As the results, SHP potential for the near future (2021 to 2040) shows a tendency to be increased and the highest increase is 23.4% at the Deoksong SPH plant. Through the result of future prospect, we have shown that hydroelectric power generation capacity or SHP potential will be increased in the future. Therefore, we believe that it is necessary to revitalize the development of SHP in order to expand the use of renewable energy. Also, a methodology presented in this study could be used for the future prospect of the small hydropower potential.

Sufficient evidence is currently available to demonstrate the reality of the warming of our planet's climate system. Global warming has different effects on climate at the regional and local levels. The detection of changes in extreme events using instrumental data provides further evidence of such warming and allows for the characterization of its local manifestations. The present study analyzes changes in temperature and precipitation extremes in the Mexican state of Zacatecas using climate change indices developed by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDI). We studied a 40-year period (1976-2015) using annual and seasonal time scales. Maximum and minimum temperature data were used, as well as precipitation statistics from the Mexican climatology database (CLICOM) provided by the Mexican meteorological service. Weather stations with at least 80% of data availability for the selected study period were selected; these databases were subjected to quality control, homogenization, and data filling using Climatol, which runs in the R programming language. These homogenized series were used to obtain daily grides of the three variables at a resolution of 1.3 km. Results reveal important changes in temperature-related indices, such as the increase in maximum temperature and the decrease in minimum temperature. Irregular variability was observed in the case of precipitation, which could be associated with low-frequency oscillations such as the Pacific Decadal Oscillation and the El Niño–Southern Oscillation. The possible impact of these changes in temperature and the increased irregularity of precipitation could have a negative impact on the agricultural sector, especially given that the state of Zacatecas is the largest national bean producer. The most important problems in the short term will be related to the difficulty of adapting to these rapid changes and the new climate scenario, which will pose new challenges in the future.

Several sun models suggest the radioactive balance where the concentration of greenhouse gases and the albedo effect are related to the Earth's surface temperature. There is a considerable increment of greenhouse gases due to anthropogenic activities. Climate change correlates with this alteration in the atmosphere and an increase in surface temperature. Efficient forecasting of climate change and its impacts of 1.5°C global warming above pre-industrial levels could be helpful to respond to the threat of c.c. and develop sustainably. Many studies have predicted the temperature change in the coming years. The global community has to create a model that can realize good predictions to ensure the best way to deal with the warming. Thus, we propose a finite-time thermodynamic (FTT) approach in the present work. The FTT can solve problems such as the faint young sun paradox. In addition, we use different machine learning models to evaluate our method and compare the experimental prediction and results.

Like the rest of the globe, Forests in Sub-Saharan Africa (SSA) continue to play a vital role when it comes to food security from the perspective of forest function of climate regulation, water provision, and soil protection. Nevertheless, most of the recent deforestation practices in various countries indicate that the region could face severe food insecurity in the near future since there are already signs of shortage in food production. This study, therefore, examines deforestation, climate change, and food security nexus in SSA while exploring a wide range of examples of food insecurity in the region. Content analysis and a synthetic literature study were conducted using data from scientific data banks. The study links deforestation, climate change to food security while citing examples from various SSA countries such as Cameroon, Nigeria, Kenya to mention but a few. More so, the study investigates how deforestation contributes to climate change, and how such change directly affects agricultural output and hence food security. Lastly, the study discusses the various implication of deforestation in relation to food security.

This study compared two object-oriented land use change detection methods—detection after classification (DAC) and classification after detection (CAD) —based on a digital elevation model, slope data, and multi-temporal Landsat images (TM image for 2000 and ETM image for 2010). We noted that the overall accuracy of the DAC (86.42%) was much higher than that of the CAD (71.71%). However, a slight difference between the accuracies of the two methods exists for deciduous broadleaf forest, evergreen coniferous forest, mixed wood, upland, paddy, reserved land, and settlement. Owing to substantial spectrum differences, these land use types can be extracted using spectral indexes. The accuracy of DAC was much higher than that of CAD for industrial land, traffic land, green shrub, reservoir, lake, river, and channel, all of which share similar spectrums. The discrepancy was mainly because DAC can completely utilize various forms of information apart from spectrum information during a two-stage classification. In addition, the change-area boundary was not limited at first, but was adjustable in the process of classification. DAC can overcome smoothing effects to a great extent using multi-scale segmentations and multi-characters in detection. Although DAC yielded better results, it was more time-consuming (28 days) because it uses a two-stage classification approach. Conversely, CAD consumed less time (15 days). Thus, a hybrid of the two methods is recommended for application in land use change detection.

In Bangladesh, climate change is a major concern because of its geophysical location and climate dependent agriculture. As sessile organisms, crops plants have to face difficulties often in this environmentally vulnerable country. Therefore, this study examines the seasonal trend of two climatic parameters viz. temperature (maximum and minimum) and rainfall over a period of 1983 to 2013. Besides, this study provides insight into the relationship between climatic parameters and crop yield of two major crops viz. rice and wheat during 1997-2013. To assess the relationship of climatic parameters with time and yield using Pearson correlation analysis, time series data used at an aggregate level. SPSS software utilized for this analysis. The cropping seasons such as rice growing seasons Aus (summer rice), Aman (autumn rice) and Boro (winter rice) exhibited a significant increase in maximum and minimum temperature. Rainfall found to have a decreasing trend for all the seasons. This study also revealed that the climatic parameters had significant effects on rice yield, but these results varied among three rice crops. Maximum temperature had positive effects on all rice yields, especially on Aus and Aman. Minimum temperature had a negative effect on Aman rice yield but a positive effect on Aus rice yield. Wheat yield negatively associated with temperature. Rainfall exhibited negative relation with both rice and wheat yield.

Abstract: In recent years, the concentration of fine particulate matter (PM2.5) in China has decreased significantly, whereas the concentration of surface ozone (O3) has increased. The formation regime of ozone is closely related to the ratio of volatile organic compounds (VOCs) to nitrogen oxides (NOx). To reveal the reasons for this increase in ozone, we determined the sensitivity of ozone generation by determining the regional threshold of the ratio of formaldehyde to nitrogen dioxide (HCHO/NO2) in the satellite troposphere. The different FNR(HCHO/NO2) ratio ranges indicate three formation regimes: VOC-limited, transitional, and NOx-limited. The range of the transitional regime plays a crucial role in identifying the regime of ozone formation. Currently, the key threshold for ozone generation in China remains unclear. Polynomial fitting models were used to determine the threshold range for the transitional regime in the BTH region [2.0,3.1]. The ozone formation regime in the BTH region mainly exhibited a transitional and NOx-limited regime, and the overall concentration changes of the HCHO and NO2 columns in the BTH region showed a fluctuating trend from 2019 to 2022. However, compared to 2019, the ozone precursors and FNR showed varying degrees of decline in 2022. The concentration changes of NO2 were high in winter and low in summer, whereas the trend of HCHO and FNR changes was the opposite to that of NO2, being high in summer and low in winter. The concentrations of HCHO and NO2 in the BTH region showed a trend of urban agglomeration areas>urban expansion areas>non-urban areas in different land types from 2019 to 2022, whereas the FNR showed an opposite trend in urban agglomeration areas<urban expansion areas<non-urban areas.

The article presents the author's method of land use change assessment in the context of sustainable development and the results of its application based on the transformations that occurred in individual areas of Europe in the years 2012–2018. This method is based on data from the CORINE Land Cover program and local government units presenting the degree of urbanization (DEGURBA). The authors evaluate the transformations taking place in space, reducing them to economic, social and environmental dimensions. They then analyse the results in terms of space (covering the entire Europe) and in terms of division into: large cities, small towns as well as suburbs and rural areas. It has been shown that: development of the economic dimension most often takes place at the expense of natural resources; the higher the population density and more important function in the functional system of a given country, the greater the sustainable development differentiation level in the analysed dimensions, of which the social dimension is characterized by the lowest differentiation and the economic dimension is the highest; development of rural areas is less sustainable than in case of large urban centres. The result interpretation also leads to the conclusion that the areas of Europe are very diverse in terms of sustainable development. However, the method itself, despite the imperfections observed by the authors, may be used in further or similar studies.

Risk attitudes are relevant factors affecting the production and investment decisions at farm level. They are key factors that are related to farmers’ attitudes towards environment and climate change. Several methodological approaches are available to measure the level of stated risk of an economic agent. The Multiple Price List (MPL) method is one of the methods that is gaining relevance. In this study we apply the MPL and relate the risk outcomes with farmers’ characteristics and their perception towards environment and climate change. Data was collected using a face to face survey carried out for a group of 370 agricultural producers of the irrigation district located in northwest of Mexico. Results showed an average risk of about 0.32, locating the agricultural producers of the region in a group with risk aversion, according to the MPL scale. The heterogeneity analysis showed that the socioeconomic factors and the perceptions towards climate change are related to the farmers´ stated risk level. Farmers who are young women with propensity to use public support to invest were shown to be greater risk lovers. Farmers in the region have perceived climate change to a greater extent like floods, hail, diseases and pests, and changing vegetation.

Having sustained, over the course of more than two decades, record-breaking natural catastrophe losses, American insurers and reinsurers are justifiably questioning the potential linkage between anthropogenic climate change and extreme weather. Here, we explore issues pertaining to this linkage, looking at both the likely short-term implications for the insurance industry, as well as potential longer-term impacts on financial performance and corporate resilience. We begin our discussion with an overview of the implications that climate change is likely to have on the industry, especially as it relates to how catastrophic risks are construed, assessed, and managed. We then present the rudiments of an econometric analysis that explores the financial resilience of the property/casualty (P/C) industry in the face of both natural and man-made catastrophes. In this analysis, we explore the profitability consequences of several illustrative scenarios involving large-scale losses from extreme weather—specifically, a sequence of storms like those striking the U.S. in 2004—and a scenario that explores the prospect of a Katrina-scale storm in combination with a mass terror attack on the scale of 9/11. At systemic levels of aggregation, our analysis suggests a high degree of macro-resilience for the insurance industry. Moreover, we find that insurer resilience is higher for larger impacts, considering both the speed of recovery, as well as the inverse of the area under the unaffected system profile. We conclude with a summary of our findings and a closing commentary that explores the potential implications of these results for P/C insurers moving forward.

This paper performs non-parametric Mann Kendall (MK) trend analysis of historical hydroclimatic data (1961-2016), an ensemble climate model validation and a computation of 16 Expert Team on Climate Change Detection and Indices (ETCCDI) temperature and rainfall extremes indices. The climate indices are evaluated using MK test and annual trend analysis for two Representative Concentration Pathways (RCP4.5 & RCP8.5) future scenarios from 2020 to 2045 over Mono River Basin (MRB) in Togo. The annual and seasonal trend analyses are assessed on historical potential evapotranspiration, mean temperature, rainfall and discharge data. Results show positive and negative trends of hydroclimatic data over MRB from1961 to 2016. Mean temperatures increase significantly in most of the stations while a negative non-significant trend is noticed for rainfall. Meanwhile, the discharge presents a significant seasonal and annual trend for three gauge stations (Corrokope, Nangbéto and Athiémé). Validation of the ensemble climate models reveals that the model under-estimates observations at Sokode, Atkakpamé and Tabligbo stations, however linear regression and spatial correlation coefficients are higher than 0.6. Moreover, the percentage of bias between climate model and observations are less than 15% at most of the stations. Finally, the computation of extreme climatic indices under RCP4.5 and RCP8.5 scenarios shows a significant annual trend of some extreme climatic indices of rainfall and temperature at selected stations between 2020 and 2045 in the MRB. Therefore, relevant governmental politics are needed to elaborate strategies and measures to cope with projected climate changes impacts in the country.

The boreal Hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change expectations. The continental extension of the phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this context, the satellite is the only system that can satisfy the need for global coverage. The main objective we have addressed in the present paper is the capability of infrared satellite observations helping to monitor the vegetation stress due to increasing drought and heatwaves in summer. We have designed and implemented a new water deficit index (wdi) that exploits satellite observations in the infrared to retrieve humidity, air temperature, and surface temperature simultaneously. These three parameters are combined to provide the water deficit index. The index has been developed based on the Infrared Atmospheric Sounder Interferometer or IASI, which covers the infrared spectral range 645 to 2760 cm-1 with a sampling of 0.25 cm-1. The index has been used to study the 2017 heatwave, which hit Continental Europe from May to October. In particular, we have examined Southern Italy, where Mediterranean forests suffer from climate change. We have computed the index's time series and show that it can be used to indicate the atmospheric background conditions associated with meteorological drought. We have also found a good agreement with soil moisture, which suggests that the persistence of anomalously high water deficit index was an essential driver of the rapid development and evolution of the exceptionally severe 2017 droughts.

The world is facing several global issues such as food and energy crisis, climate change and greenhouse gases emissions. To subdue these issues, many entities from academia and industries have innovated alternate techniques of performing regular activities which cause such problems. One of these innovations is the introduction of vertical and zero acreage farming in the field of sustainability. These carry the potential to solve one of the most important affairs of food security in most countries of the world. But, this technology has been in its nascent stage for many years. This paper uses a comprehensive framework proving the feasibility of initiating vertical farming on university campuses to feed the staff and students, which could also set an example to the rest of the world into using this technique on a wider scale. The study chose Huazhong University of science and technology (HUST) in Wuhan city, China for accessing the return on investment and food sufficiency if vertical farming is implemented. Using Central Limit Theorem, a statistical model was developed, and various scenarios were analyzed. The results indicated that if a separate vertical farm is constructed, the breakeven can be achieved in a range of 10-20 depending on parameters such as type of operation, number of floors and amount of vegetation. The study has shown that the use of vertical farming cannot only bring in revenue for the campus but also aid in mitigating climate change.

Emerging countries in Southeast Asia are facing considerable challenges in addressing rising motorisation and its negative impact on air quality, traffic, energy security, liveability, and greenhouse gas emissions. Indeed, even as initial policies to address these issues are being agreed and implemented, current trends are incompatible with sustainable development and long-term climate change targets. This paper presents a comparative analysis of the approach and status of sustainable, low-carbon transport policy in ASEAN countries and identifies differences and similarities, with the aim of helping assessment of feasibility of future policies and informing future studies on policy innovations and cross-country learning. The methodology is based on the taxonomy of policy components developed by Howlett and Cashore, and our data on comprehensive country studies for Indonesia, the Philippines, Thailand and Vietnam and interviews. We find that each country has a specific set of goals, objectives and targets that support sustainable transport, and, directly or indirectly, climate change mitigation. In terms of specific instruments and calibrations, which we analyse based on the Avoid-Shift-Improve approach, there are notable differences between the countries, for example in fuel economy policy.

Single-layer core corrugated sandwich panels generally consist of a corrugated core and two layers of panels, while multi-layer core corrugated sandwich panels are formed by stacking multiple layers of panels with multiple layers of core layers. In this study, integrated multilayer core corrugated sandwich panels with different shapes of corrugated cores (triangular, trapezoidal, and rectangular) and the different number of core layers were fabricated using 3D printing technology, and the mechanical behavior of such multilayer core corrugated sandwich panels under quasi-static three-point bending was investigated using experiments and numerical simulations. The effects of core shape and number of core layers on the bending deformation process, damage mode, load carrying capacity, and bending energy dissipation capacity of multilayer core sandwich panels are discussed. Parametric design of multilayer triangular core corrugated sandwich panels was also carried out by finite element software ABAQUS. It was found that a new multilayer corrugated sandwich panel with a multi-layer core is better than the single core shape multilayer corrugated sandwich panel in terms of bending load capacity, energy dissipation capacity and deformation capacity can be obtained through the combination design of different core shapes.

Nowadays, there are implemented devices whose purpose is to perform massive computations by saving resources at the time they reduce the latency of arithmetic operations. These devices are usually GPUs, FPGAs and other specialised devices such as "Coral". Neural networks, digital filters and numerical simulators take advantage of the massively parallel operations of such devices. One way to reduce the amount of resources used is to limit the size of the registers that store data. This has led to the proliferation of numeric formats with a length of less than 32 bits, known as short floating point or SFP. We have developed several SFP’s for use in our neural network accelerator design, allowing for different levels of accuracy. We use a 16-bit format for data transfer and different formats can be used simultaneously for internal operations. The internal operations can be performed in 16-bit, 20-bit and 24-bit. The use of registers larger than 16-bit allows the preservation of fractional information while increasing precision. By leveraging some of the FPGA’s arithmetic resources, our design outperforms designs implemented from scratch and is competitive with specialized arithmetic circuits already implemented in the FPGA.

Uganda possesses natural rainforests that serve enormous environmental ecosystems and biodiversity services. Moreover, the country is known for its various tropical rainforest hardwoods, birds, and animal species. Over the years, the trend in the natural forest land has declined at an alarming rate; hence need to investigate the possible drivers. The loss of such biodiversity and ecosystems risks desertification and extreme climatic condition. As the world moves towards Zero Deforestation 2030, understanding the determinants of deforestation and forest degradation is paramount. Therefore, the main objective of this study was to understand the impact and relationships between net forest conversion, energy emission, agriculture, and forest production of Roundwood. We used data from FAO for the period 2004-2016. Using the ADF and KPSS test, we checked for the unit root presence in the variables. Also, the study used two different regression models: multiple linear regression and dynamic linear model. To analyze the determinants of deforestation, we used net forest conversion in Uganda. There was 94 % variation in the dependent variable (Net Forest conversion). The outcome of the dynamic linear regression showed that agriculture and energy emission positively impact net forest conversion. Based on our findings, this study recommended the modernization of agriculture by the government of Uganda to stop cutting down the forests on a big scale. Also, the study suggested that the government strictly legislate Roundwood and wood fuels/charcoal and firewood to reduce huge dependency on forests toward Zero-Deforestation by 2030. If well-structured and implemented, government policies could solve the unnecessary over dependency on the rainforest, the heart of the region's climatic conditions.

Climate change presents an urgent and impending threat to virtually all global biological systems. In recent years, there has been a surge in research endeavors aimed at investigating the potential relationship between changes in climate patterns and the spread of infectious diseases. This scoping review included studies on global warming and infectious diseases from 2019 to 2023, The studies included in this research were obtained from reputable academic databases such as Web of Science and google scholar. A systematic keyword search was conducted to identify relevant studies, and a predefined set of inclusion criteria was applied to review and select the appropriate studies for analysis. This review found that climate change has a remarkable impact on the dissemination and incidence of many infectious diseases, it increases microbial resistance and may increase the prevalence the arthropods-borne diseases with mild effect on fungal infections, also, the low to middle income countries may be affected more than other countries from these virulent pathogens, this study concluded that a future research in the field of climate change and infectious diseases should involve a thorough examination of the transmission of diseases, considering both direct and indirect modes of transmission

Climate change is expected to increase the occurrence of droughts with the hydrology in alpine systems being largely determined by snow dynamics. In this paper we propose a methodology to assess the impact of climate change on both meteorological and hydrological droughts taking into account the dynamics of the snow cover area (SCA). We will also analyse the correlation between these types of droughts. We have generated ensembles of local climate scenarios based on regional climate models (RCMs) representative of potential future conditions. We have considered several sources of uncertainty: different historical climate databases, simulations obtained with several RCMs, and some statistical downscaling techniques. We then used a stochastic weather generator (SWG) to generate multiple climatic series preserving the characteristics of the ensemble scenario. These were simulated within a cellular automata (CA) model to generate multiple SCA future series. They were used to calculate multiple series of meteorological drought indices, the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)) and a novel hydrological drought index (Standardized Snow Cover Index (SSCI)). A linear correlation analysis was applied to both types of drought to analyse how they propagate and the time delay between them. We applied the proposed methodology to the Sierra Nevada (southern Spain) where we estimated a general increase in meteorological and hydrological drought magnitude and duration for the horizon 2071-2100 under the RCP 8.5 emission scenario. The SCA droughts also revealed a significant increase in drought intensity. The meteorological drought propagation to SCA droughts is reflected in an immediate or short time (1 month), obtaining significant correlations in lower accumulation periods of drought indices (3 and 6 months). This will allow us to obtain information about meteorological drought from SCA deficits and vice versa.

The high transmissibility and replication of SARS-CoV-2 have been attributed to enhanced protein functions which are dependent on protein folding. Our in silico study endeavored to scrutinize SARS-CoV-2 ORF1ab by analyzing the conserved folding patterns of its transcribed proteins. Accordingly, the findings indicated that SARS-CoV-2 ORF1ab shares domain-specific fold-fingerprints with a spectrum of unrelated organisms. Closer observation revealed slight changes in folding patterns engendered with small variation in the intrinsic amino acid sequence. By correlating with the evolvability-potential of RNA-viruses and COVID-19 pandemic, we hypothesize that SARS-CoV-2 could undergo fast recombination with the host, SARS-CoV-2 minor variants and other viral species resulting in a reservoir of SARS-CoV-2 quasispecies. It is highly possible that natural selection will cause a future emergence of evolved SARS-CoV-2-descendants. Nonetheless, we hope that this insightful study will assist in elucidating SARS-CoV-2 protein functionalities, development of vaccines, and the possibility and nature of future emergence.

In recent years, organic agriculture has gained more popularity, yet its approach to food production and its potential impact on consumers’ health and various environmental aspects remain to be fully discovered. The goal of organic farming practices is to maintain soil health, sustain ecological systems, maintain fairness in its relationship with the environment and protect the environment in its entirety. Various health benefits have been associated with higher consumption of organic foods. This review identified some of these health benefits including a reduction in obesity and body mass index (BMI), improvements in blood nutrient composition as well as a reduction in maternal obesity and pregnancy-associated preeclampsia risk. Furthermore, organic food consumption can reduce the development of non-Hodgkin lymphoma and colorectal cancers. Upon reviewing existing literature regarding the nutritional value of organic foods, it was found that organic food contained higher levels of iron, magnesium, and vitamin C. However, the evidence available to draw definitive generalizations remains limited. In this review, we provided essential insights to support sustainable organic farming and highlighted the potential of organic food consumption that could play a pivotal role in positively impacting human health.

Based on the recorded watershed characteristics, the future conditions on the basin system can be predicted using a different method. In this study, dynamic land-use change and its impacts on the streamflow for the Dabus watershed were predicted using ANN-CA based method. The model performance for accurate prediction of the future land-use change on the Dabus River watershed has been checked by validation of the simulated value with the actual value, hence the overall kappa value (k) = 0.83 for the simulated 2016-LULC validated with actual 2016-LULC. Then, 2026-LULC was predicted based on the 2004 and 2009-LULC. The streamflow for the case of 2004 and 2009-LULC has been simulated using the SWAT model. The value of NSE = 0.87 and 0.90 was attained during validation of simulated streamflow for 2004 and 2009-LULC data cases, respectively. The agreement of simulated value of streamflow with the observed data is indicated as R2 = 0.91 and 0.96 for 2004-LULC and 2009-LULC. The effects of the dynamic land-use change on streamflow for the predicted land use(2026-LULC) catchment were evaluated by T-test analysis. Hence, T-stat =0.04 and -0.002 in the case of simulated streamflow used 2004-LULC and 2009-LULC, respectively compared with simulated value using 2026-LULC.

In this paper, existence and stability results of non-local integro-differential equation with Hilfer derivative of order ω∈(1,2) have been investigated. In this derivative is applied in RLC circuit equation of various fractional order. To proposed problem the existence solutions are derived using Schaefer’s fixed point theorem and the Banach contraction principle is using for uniqueness results. To demonstrate the effectiveness and applicability of our theoretical conclusions and two-step Lagrange polynomial interpolation were used to solve four numerical results.

The demand of high data rate and ubiquitous coverage in heterogeneous cellular (HetNets) is increasing progressively. In order to meet this demand, sophisticated model having applied interference reduction scheme and cell association technique is needed. The small base station (sBS)are deployed inside the broadcasting area of macro base station (mBS), in heterogeneous cellular networks (HetNets). Since mBS has high transmission power therefore a large number of users get connected with mBS. This causes disproportion of load distribution across the HetNets. For load balancing users from high power mBS are migrated to low power sBS to increase network capacity and to decrease the load from mBS. This results in interference in the communication signal because of strong mBS Interference. Hence, we need interference management technique to mitigate interference and user association and to efficiently use sBSs’ resources. Inter-cell interference (ICI) limit the HetNets’ performance. Additionally, there exist deliberate jamming interference which depends on jammers transmission power and its proximity with the target, which notably degrades the network performance. In this paper, we employ reverse frequency allocation scheme (RFA) to reduce inter cell interference, deliberate jamming interference and to accomplish load balancing. The proposed setup is analyzed inquisitively and with the help of simulations. The result shows reduction in interferences as well as balance of load distribution in the network achieved by employing RFA scheme together with cell association.

Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific IgG antibody lateral flow assays that enables rapid and accurate evaluation of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in over 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.

Blood gas analysis plays a central role in modern medicine. Advances in technology have expanded the range of available parameters and increased the complexity of their interpretation. By applying user-centered design principles, it is possible to reduce the cognitive load associated with interpreting blood gas analysis. In this international, multicenter study, we explored anesthesiologists' perspectives on Visual Blood, a novel visualization technique for presenting blood gas analysis results. We conducted interviews with participants following two computer-based simulation studies: the first utilizing Virtual Reality (VR) (50 participants) and the second without VR (70 participants). Employing the template approach, we identified key themes in the interview responses and formulated six statements, which were rated using Likert scales: from 1 (strongly disagree) to 5 (strongly agree) in an online questionnaire. The most frequently mentioned theme was the positive usability features of Visual Blood. The online survey revealed that participants found Visual Blood to be an intuitive method for interpreting blood gas analysis (median 4, IQR 4-4, p<0.001). Participants noted that minimal training was required to effectively learn how to interpret Visual Blood (median 4, IQR 4-4, p<0.001). However, adjustments are necessary to reduce visual overload (median 4, IQR 2-4, p<0.001). Overall, Visual Blood received a favorable response. The strengths and weaknesses derived from these data will help optimize future versions of Visual Blood to improve the presentation of blood gas analysis results.

The advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, resulting in identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence there is no “silver bullet” for the treatment of a cancer type. This reveals the importance of developing a pipeline to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types. In this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in a significant portion of samples to identify cancer subtypes. We applied our pipeline to 12270 samples collected from the International Cancer Genome Consortium (ICGC), covering 19 cancer types. Here we identified 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways, in which, for most of them, targeted treatment options are currently available. This new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. We also comprehensive study the causes of mutations among samples in each subtype by mining the mutational signatures, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on “gene-motif” suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer.

The purpose of this study was to elucidate patterns and mechanisms driving seasonal and inter-annual variations of carbon and nitrogen stable isotopes in the zooplankton crustacean commu-nity of Lake Maggiore (Italy), during the period 2009-2020. Different zooplankton taxa and groups revealed different ranges of d13C signatures, giving an insight into food sources and niche partition. In particular, cyclopoids had a restricted range with most negative d13C‰ values and increase in d13C fractionation with the establishment of water thermal vertical stratification, high-lighting the importance of vertical distribution as key factor for taxa coexistence in a vertically heterogenous environment. d13C of the zooplankton community and of Daphnia were positively related to water temperature (R2=0.58 p <0.0001 and R2=0.68 p <0.0001, respectively) and d13C Daphnia signature to chlorophyll-a (R2=0.32, p < 0.0001). Decomposition of time series data for zooplankton carbon and nitrogen signatures and environmental parameters identified increasing trends in water temperature, chlorophyll-a and water conductivity and decrease in nitrate, that matched changes in carbon isotopic signature trend in some zooplankton taxa (Bosmina, Daphnia and diaptomids). Overall, the observed patterns in zooplankton isotopic signatures were interpreted as integrations of climate warming effects in Lake Maggiore, affecting both food sources availability and environmental conditions.

Coronavirus disease 2019 (COVID-19) was declared a pandemic in March 2020, which led to the implementation of non-pharmaceutical interventions that had inadvertent physical, mental and social effects. The purpose of this study was to examine the experiences and responses of Canadians to these interventions on Twitter using the Kübler-Ross Change Curve (KRCC) during the first six months of the pandemic. Tweets were analyzed using sentiment analysis, thematic content analysis and KRCC. The findings highlight that many Canadians attempted to adapt to the changes but had a predominantly negative outlook on policies due to the financial and social repercussions

Background: Pressure ulcers (PU) affect millions of people worldwide and always occur over bony areas of the body where pressure and tissue distortion is greatest. The national pooled prevalence of pressure ulcer remains unknown. Hence, the aim of this meta-analysis was to determine the effect of position change on pressure ulcer among hospitalized clients in Ethiopia. Methods: Studies were retrieved through search engines in PubMed, Scopus, WHO afro library, Google Scholar, Africa journal online, PsycINFO and web-science following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Analysis was done using STATA version 14 software. We checked the between-study heterogeneity using the I² and examined a potential publication bias by visual inspection of the funnel plot and Egger’s regression test statistic. The random-effect model was fitted to estimate the summary effects, odds ratios (ORs), and 95% confidence interval (CIs) across studies. Results: Out of the reviewing 401 studies, 7 studies fulfilled the inclusion criteria and were included in the meta-analysis. The estimated pooled prevalence of pressure ulcer in Ethiopia was 11.7% (95% CI (7.28, 16.13%)). Based on the subgroup analysis, the estimated magnitude of pressure ulcer was 15.89% (95% CI: 35.34, 54.04) among studies their sample size were greater than or equals to 250. Those clients who have position change during hospitalization were 85% less likely to develop pressure ulcer [(OR 0.15, 95%CI (0.06, 0.4)] than their counter part. Conclusion: The overall prevalence of pressure ulcer in Ethiopia was relatively high. Position change of the client during hospitalization had paramount benefit to reduce the burden of pressure ulcer. Therefore, policymakers could give special attention to minimize the magnitude of pressure ulcer in order to improve the overall quality of healthcare service. Further meta-analysis study is need to identify individual and health care service related factors to the occurrence of pressure ulcer.

1). As on-the-ground projects come into existence and continue to expand to adapt to climate change and empower women, it is important to understand their location within TANs. Using the Bhungroo technology as a case study, we aim to assess the potential of TANs to increase the scope and scale of local projects as well as the ability of similar and emerging projects to create social change at local levels. 2). We do so by analyzing interview and earned media hits data from the UNFCCC Momentum for Change. 3). We find that while TANs may help increase the scale and scope of projects, increasing their ability to effectively reach more people and areas is still up for debate 4). We conclude by considering how women’s political participation may be enhanced by similar projects.

In this work commercial 18650 lithium-ion cells with LiMn₂O₄, LiFePO₄ and Li(Ni_0.33Mn_0.33Co_0.33)O₂ cathodes were exposed to external heating in an Accelerating Rate Calorimeter (es-ARC, THT Company) to investigate the thermal behavior under abuse conditions. New procedures for measuring external and internal pressure change of cells were developed. The external pressure was measured utilizing a gas-tight cylinder inside the calorimeter chamber in order to detect venting of the cells. For internal pressure measurements, a pressure line connected to a pressure transducer was directly inserted into the cell. During the thermal runaway experiments, three stages (low rate, medium rate and high rate reaction) have been observed. Both pressure and temperature change indicated different stages of exothermic reactions, which produced gases or/and heat. The onset temperature of thermal runaway was estimated according to temperature and pressure changes. Moreover, the different activation energies for the exothermic reactions could be derived from Arrhenius plots.

We are living in a business context characterized by change, which today behaves with an unprecedented intensity, driven by the technological revolution and increased competition. As a result, increasingly large and complex companies are facing profound transformation processes in which the role of the human factor and of managers in particular, due to their high capacity to impact the organization, is essential. In response to this reality, the literature has developed a large number of approaches from different areas of knowledge: from business economics to psychology, including technical disciplines. This article offers a classification of these approaches that provides an overview of research on the role of managers in change management.

3D measurement is a high-throughput method that can record a large amount of information. In this study, we have developed new methods that could be used for morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The correlation coefficients between manual measurements and the scale-related methods were all above 0.9. In particular, the differences in LC calculated from the normal vector data allowed us to visualize and quantify the concavity and convexity of leaves. Furthermore, we also found a difference in the time point at which leaf blistering began to develop among the cultivars. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment.

One of the most problematic forms of nature protection in Poland relates to landscape parks. On the one hand, they include the most valuable landscapes; on the other hand, the areas within the landscape park still have economic uses. Therefore, the monitoring of landscape changes within landscape parks is necessary in order to properly manage these forms of protection. The main objective of the study was to monitor the scale and nature of landscape transformations within the boundaries of landscape parks in Poland during the period 2000–2018 and to assess the possibility of using the landscape change index (LCI) to monitor the intensity of landscape transformations within this type of protected area. Filling a gap in the research on landscape changes, I developed and verified the possibility of using LCI for monitoring the intensity of landscape changes using the example of 12 landscape parks in the Lower Silesia region. Preliminary analyses of the transformations within all landscape parks in Poland showed an upward trend, both in terms of the number of types of identified landscape changes as well as their area. In spite of the large diversity and degree of transformation in landscape parks, several dominant processes can be observed. The largest number and area of changes during each of the analyzed periods were found in transformations within forest landscapes (temporary and permanent deforestation and forest maturation), which constitute the dominant type of land cover within most of the landscape parks. In open landscapes, changes mainly relate to afforestation and natural succession in meadows, pastures and arable land, as well as the transformation of arable land into mining areas. Twelve case studies, covering all landscape parks of the Lower Silesia, have shown that the LCI is an excellent tool for monitoring the intensity of landscape changes, but it is dependent on the accuracy of the source data. The analyses confirmed that, during the study periods, the changes in all 12 Lower Silesian landscape parks were at a low level, but their particular intensification took place in the years 2012–2018. The highest LCI was found in the area where a natural disaster had occurred (air tornado), which destroyed huge areas of forest in landscape parks. After changes in the forest landscape, the most frequently identified type of change in 2006–2012 is the transformation of non-forest landscapes into forest landscapes. The main reason for such changes was the expansion of forest into abandoned arable land, meadows and pastures. The use of the Corine Land Cover database to calculate LCI and monitor the intensity of landscape change revealed a low usability of the database for the year 2000 and a high usability for data from 2006–2018.

The energy crisis is currently a major topic in scientific and managerial discussions in the building and construction industry. While employing low-cost strategies for reducing energy usage is beneficial for principals and decision-makers, the primary purpose of this study is to recommend the use of phase change materials (PCM) to improve cooling and heating load management in buildings. In order to accomplish this, the study applies design-builder simulations to a developing country case study. The results indicate that integrating PCM in buildings can reduce energy usage by around 6 percent. Although this value may be lower than other techniques, it is nevertheless important for reaching green building goals in both developed and developing nations. The case study, which takes place in a four-season region, demonstrates the significant potential of the PCM system for both cooling and heating energy management. In addition, the reciprocal performance improvement of PCMs is a remarkable characteristic, especially when contrasted to some conventional systems that focus on a single energy management objective in buildings. Lastly, it should be underlined that the suggested platform is consistent with Sustainable Development Goal 7 (Affordable and Clean Energy Targets).

Cleaning wastewater and using it again for secondary purposes is a measure to address water scarcity in urban areas. However, upscaling of recycled water schemes is challenging due to the possible emergence of various barriers. Based on a review of the governance literature we suggest that a set of five governance conditions is necessary for a successful upscaling of recycled water schemes; (1) policy leadership, (2) policy coordination, (3) availability of financial resources, (4) awareness of a problem, and (5) the presence of a public forum. In order to elaborate on the practical relevance of these conditions we studied a recycled water scheme currently being upscaled in Sabadell, Spain. We reviewed policy documents, conducted a set of 21 semi-structured interviews, and attended two policy meetings about the subject. Our results suggest that Sabadell meets the required conditions for upscaling reused water to a certain extent. However, the presence of a public forum is lacking. We discuss the implications of the absence of the venue and procedures for public participation in Sabadell and how it could be strengthened. Following this discussion, we conclude with some lessons for other cities that plan to upscale their recycled water schemes.

To explore impact of weight change (WC) on risk of atherosclerosis measured by cIMT, 20,700 participants from the CLSA follow-up were included in analysis. WC was defined as the difference of weight measured at follow-up and baseline, then quartered into four groups (Q1–Q4). cIMT>1.0 mm was defined as high-risk for atherosclerosis. Adjusted odds ratio (OR, [95% CI]) from logistic regression models were used to evaluate the association between WC and risk of atherosclerosis. At follow-up, participants have gained 0.118 kg weight, on average, and 16.4% of them were at high-risk for atherosclerosis. The mean levels of cIMT were comparable between participants from Q1 to Q4. Compared to Q2 (reference), the ORs (95% CI) were 1.00 (0.86, 1.15), 1.19 (1.03,1.38), and 1.25 (1.08,1.45) for Q1, Q3, and Q4, respectively. A similar pattern was observed when analyses conducted by <65 vs. 65+ years separately, but it was weaker for those aged 65+ years. Results from the jointed distribution analyses indicated that weight loss might increase risk for atherosclerosis among participants with obese at baseline, but not for those with cardiovascular events status at baseline. Weight gain, however, would increase risk for atherosclerosis regardless of cardiovascular events status, or overweight/obese at baseline.

This research analyses extreme precipitation events in the Huaihe River Basin in China, a densely populated region with a history of human settlements and agricultural activities. This study aims to explore the impact of extreme precipitation index changes and provide decision-making suggestions for flood early warning and agricultural development in the Huaihe River Basin. The study utilises the NEX-GDDP-CMIP6 climate models dataset and the daily value dataset (V3.0) from China's national surface weather stations to investigate temporal and spatial changes in extreme precipitation indices from 1960 to 2014 and future projections. At the same time, this study adopted the RclimDex model, Taylor diagram and Sen+Mann-Kendall trend analysis research methods to analyse the data. The results reveal a slight increase in extreme precipitation indices from northwest to southeast within the basin, except for CDD, which shows a decreasing trend. Regarding spatial, the future increase of extreme precipitation in the Huaihe River Basin will show a spatial variation characteristic that decreases from northwest to southeast. These findings suggest that extreme precipitation events are intensifying in the region. Understanding these trends and their implications is vital for adaptation strategy planning and mitigating the risks associated with extreme precipitation events in the Huaihe River Basin.

Scope, time, and cost permanently effects each other and most of Information Technology projects fails due to these three factors. Scope shifting mostly occur due to time and cost. At project start, lack of understanding of project and product scope is focal involvement that leads to unsuccessful projects. Complete software scope definition determines quality of project. Defining the customer requirement and the definite scope of project has key role for implementation of project management. The complications originates when systems are developed from impractical expectations and misunderstanding requirements. These problems are cause of many changes, occurs in system development and leads to poor scope management. Scope creep is one of the momentous prompting parameter on the success of project. The failure in manage scope creep leads for 80 percent of software projects failure. However, using agile approach the impact of scope creep on projects become insignificant. A correctly distinct scope tends us to develop a quality product, within identified plans and decided cost to the stake-holders.

Panama possesses multiple wetlands designated as protected areas and zones of internationally recognized importance, one amongst them, the Bahía de Panamá wetland Ramsar site, which represents approximately 21% of the national mangrove surface coverage. Due to the major ecological and economic relevance of mangrove ecosystems and their vulnerability to the effects of a changing climate it is considered necessary to perform in situ studies that evaluate the changes in sensitive meteorological parameters. This research was carried out with the objective of measuring air temperature and relative humidity values in a section of the Panama Bay wetland for the 2017-2018 period. This section, neighboring urban development projects, houses since the year 2015 the first tower for determining eddy covariance and measuring micrometeorological parameters in mangrove forests in Panama. We determined monthly and yearly mean values for the studied variables and determined the correlation between them with the use of Pearson’s correlation coefficient. Results show that both parameters still remain at tolerable levels for this type of ecosystems, with both years having very similar annual mean temperatures (27.47 ± 0.32°C for 2017 and 27.48 ± 0.67°C for 2018) and the annual mean relative humidity for 2018, being 85.35%.

Building envelope can play a crucial role in building improvement efficiency and the adoption of Phase Change Materials (PCMs) coupled with transparent elements may (i) allow a better control of the heat flows from/to the outdoor environment, (ii) increase the exploitation of solar energy at building scale and (iii) modulate the light transmission to avoid glare effects. Starting from a literature review of experimental works, this research identifies the main possible integration of PCM in transparent/translucent building envelope components (in glazing, in shutters and in multilayer façade system) drawing a global picture potentials and limitations of these technologies. Transparent envelopes with PCMs have been classified from the simplest “zero” technology, which integrates the PCM into the double glass unit (DGU), to more complex solutions – with different numbers of cavities of the glass (TGU), different position of PCM layer (internal/external shutter), and in combination with other materials (TIM, aerogel, prismatic solar reflector, PCM curtain controlled by an electric pump). The results of the analysis are resumed in a SWOT (strengths, weakness, opportunities and threats) analysis table to underline strengths and weaknesses of transparent building envelope components with PCMs, and to indicate opportunities and threats for future research and building application.

Several extinctions have already been attributed, at least in part, to global warming, as climate change constitutes a serious threat for species living in isolated ecosystems and thus unable to track habitat changes. However, in all these cases extinction was due to human impacts, often directly but generally also through exotic invasive species. For two arboreal land snails in Indian Ocean islands a link has been proposed with decreasing rainfall. The decline (but probably not extinction) of Pachnodus velutinus, a specialist of moist forests on the summits of northwestern Mahé, was most likely caused instead by invasive plants altering its habitat and alien predators decimating the population in the small remaining moist forests. An alternative explanation assuming genetic swamping through hybridization with a species from lower elevations has no basis, as the presumed hybrid constitutes a distinct species able to survive in the altered, dryer habitat. On Aldabra Atoll, the endemic Rhachistia aldabrae was claimed to have been the first extinction due to climate change, but is still extant. No relationship can be detected between number of sightings and annual rainfall, although a weighted measure that takes into account rainfall in previous years suggests a limited impact of weather. Analysis of the sighting record in various ways yields a probability of survival over time that never dropped below 0.3. The decline was caused instead by intense impacts of exotic invasive species. Alternative shortcuts to evaluate extinction rates among poorly known species are shown to be unreliable. Although no contemporary extinction can still be attributed to climate change, indirect and synergistic impacts on biodiversity are expected, especially through promoting biological invasions.

With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, filters for identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined dis-tance from such surface. We present a new filter, Multi-view and shift rasterization algorithm (MVSR) is based on a different principle, i.e., on the identification of just the lowest points in in-dividual grid cells, shifting the grid along both planar axis and subsequent tilting of the entire grid. The principle is presented in detail and compared both visually and numerically to other commonly used ground filters (PMF, SMRF, CSF, ATIN) on three sites with different ruggedness and vegetation density. Visually, the MVSR filter showed the smoothest and thinnest ground profiles, with ATIN the only filter performing comparably. The same was confirmed when comparing ground filtered by other filters with the MVSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSD) of the points from the original cloud found below the MVSR-generated surface (ranging, depending on site, between 0.6-2.5 cm). The MVSR filter performed outstandingly at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation/above-ground points. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived rather as a benefit than as a disadvantage.

With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, universal filters for accurate identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined distance from such surface. In this paper, we present a new filter. This Multi-view and shift rasterization algorithm (MVSR) is based on an entirely different principle, i.e., on the identification of just the lowest points in individual grid cells, shifting the grid along both planar axis and subsequent tilting of the entire grid – after each of these steps, one lowest point per cell is detected. The principle is presented in detail and compared both visually and numerically to other commonly used ground filters (PMF, SMRF, CSF, ATIN) on three sites with different ruggedness and vegetation density. Visually, the MVSR filter showed the smoothest and thinnest ground profiles, with ATIN the only filter performing comparably (although the profiles were somewhat thicker and not as complete as MVSR-acquired ground). The same was confirmed when comparing ground filtered by other filters with the MVSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSD) of the points from the original cloud found below the MVSR-generated surface (ranging, depending on site, between 0.6-2.5 cm). ATIN again performed closest to MVSR, with RMSDs of ground filtered points found above MVSR-based surface at individual sites ranging between 4.5-7.4 cm. The remaining filters performed comparable in the simplest flat area but poorly in rugged and much-vegetated sites, with RMSDs above the MVSR surface ranging at such sites from 21 to 95 cm. In conclusion, the novel filter presented in this paper performed outstandingly at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation/above-ground points. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived rather as a benefit than as a disadvantage.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

This paper proposes a method to utilize weather and land cover models to generate future environmental scenarios, and presents the watershed models to simulate the hydrological impact on watershed-scale hydrology. The Weather Generator model and General Circulation Model were applied to produce rainfall and local temperature under different climate conditions, and the Conservation and Land Use and its Effects model was incorporated to simulate future land cover variability. The circumstances of future climate and land cover changes were used as inputs to drive the HEC-HMS rainfall runoff model for obtaining surface runoff in a mountainous area. The WASH123D model was then utilized for the entire watershed simulation. Modeling results were then examined to discuss hydrological impacts on three different time periods: near future (2020-2039), future (2050-2069), and distant future (2080-2099). The Fengshan Creek basin in northern Taiwan was selected as study site. Simulations results indicated that the influence of climate change revealed more relevant effects when compared to local land cover changes. The ground water levels tended to diminish as the land cover area changed. In addition, both river and groundwater levels reveal that it is drier in dry season and wetter in wet season in future.

Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies showed that genomic alterations represent the most common mechanism for molecular alterations that cause the development and progression of PC. Great efforts have been done to identify common protein-coding genetic variations; however, the impact of non-coding variations including regulatory genetic variants is not still well understood. To gain an understanding of the functional impact of genetic variants, particularly, regulatory variants in PC, we developed an integrative pipeline (AGV) that used whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 of them significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants may affect 131 coding and non-coding genes. Interestingly, our study showed the 131 protein-coding genes are involved in disease-related pathways including Reactome and MSigDB in which for most of them targeted treatment options are currently available. Together, our results provide a comprehensive map of genomic variants in PC and revealed their potential contribution to prostate cancer progression and development.

This work exclusively focuses on the assessment of the thermal performance of a multifunctional facade panel incorporating PCM in foam layers, recurring to a hot box heat flux meter method to determine the thermal transmittance (U-value). The experimental setup is based on the steady-state approach using climatic chambers, assuring a stable thermal environment. Even small fractions of PCM achieved a small reduction in thermal amplitude. Numerical simulations using Ansys Fluent were developed to evaluate the performance of PCM use over a wide range of temperature boundary conditions and operating modes. These numerical models were calibrated and validated using the results of experimental tests, achieving a correlation factor of 0.9674, thus accurately representing a real-world scenario. The decrement factor (f) was used to analyse the data. It was identified that the efficiency of the panel and size of the optimum region increased with the PCM fraction growth. The simulated behaviour was optimum when the input mean temperature is of 20 °C for a room temperature of between 18-20 °C. The results show the significant potential of the multi-layered panel and the thermal regulator effect of the PCM incorporated on indoor space temperature to reach good thermal comfort levels.

Based on the daily observation data of 2254 meteorological stations in China from 1961 to 2021 after homogenization and correction, the extreme value analysis method that combines relative threshold and absolute threshold was used to identify extreme climate events in the mainland of China, namely extreme high temperature, extreme low temperature, extreme drought, extreme precipitation and extreme typhoon events. The frequency and linear variation trends of individual extreme events in the past 60 years were investigated. On this basis, the multi extreme events composite risk grade index (MXCI) was constructed, which can objectively reflect the comprehensive characteristics of multi extreme climate events in different regions. The results show that the high value areas of MXCI were mainly located in Southeast China and Southwest China. The MXCI presented a decreasing trend in most of the western and southeastern regions of China, while an increasing trend from Southwest China to the south of Northeast China, due to the frequent occurrence of extreme high temperature, extreme drought and other extreme events in these regions.

The main objective of this paper is to assess pro-environmental behavior, climate change perception, anxiety, hope and despair in different political orientations. Specific aims included to validate all the instruments; to assess whether the factor structure of the scales were valid across political orientations; to evaluate their reliability; to assess differences concerning age, gender and political orientation; to know the variables that explain pro-environment behavior; and to evaluate the moderating role of climate change perception, despair and hope in the relationship between climate change anxiety and pro-environmental behavior. Confirmatory factor analysis (CFAs), multi-group CFAs (to measurement invariance), multiple linear regressions and moderations were performed. Results showed that pro-environment behavior and climate change hope achieved the four levels of invariance across different political orientations; climate change anxiety achieved the three first levels of invariance; climate change perception and climate change despair achieved configural invariance. Climate change anxiety personal experience, climate change perception total, reality and consequences present higher values in left political orientation that in right or center. Climate change anxiety variables contribute most to explaining pro-environmental behaviors. Hope, despair and climate change perception consequences moderate de relationship between climate change anxiety and pro-environment behavior. These results open up new avenues of investigation, namely, to understand why high levels of anxiety lead to more pro-environment al behaviors.

This study investigates proximate drivers of cropland and forest degradation in Kloto district (Togo, West Africa) as, way of, exploring integrated sustainable landscape approaches in respect to socio-economic and environmental needs and requirements. Net change analysis of major cash and food crops based on three time steps Landsat data (1985–2002, 2002–2017 and 1985–2017) and quantitative analysis from participatory survey data with farmers and landowners are used. Study underlines poor agricultural systems and cassava farming as major impediments to alarming forest losses between 1985–2017. Significant net loss in forests cover by 23.6% and surface areas under cultivation of cocoa agroforestry and maize by 12.99 and 10.1% from 1985 to 2017, due to, intensive cassava cropping (38.78%) and settlement expansions (7.84%). Meanwhile, loss in forest cover between 2017 and 2002 was marginal (8.36%) compared to the period 1985–2002 for which the loss was considerable (15.24%). Based on participatory surveys, majority of agricultural lands are threatened by erosion or physical deterioration (67.5%), land degradation or salt deposits and loss of micro/macro fauna and flora at 56.7%, declining in soil fertility (32.5%), soil water holding capacity (11.7%) and changes in soil texture (3.3%). Majority of farmers adhere to the adoption of the proposed climate smart practices with emphasis on cost effective drip irrigation systems (45.83%), soil mulching (35%) and adoption of drought resilient varieties (29.17%) to anticipate drought spells adverse. The study concludes that low adoption of improved soil conservation, integrated water management and harvesting systems and low productive and adaptive cultivars entail extreme degradation of croplands and crops productivity decline. Therefore, farmers are forced to clear more forests in search of stable and healthy soils for production and extraction of forest products to meet their food demands and improve their livelihoods conditions. Capacity building on integrated pathways of soil and land management practices are therefore needed to ensure sustainable and viable socio-ecological systems at local scale.

This study aims to evaluate the climate-change response of greening persistently vacant houses and barren sites in deteriorated high-density residential areas in old downtown metropolitan areas. The current status of building demolition sites in Ami-dong and Chojang-dong, Busan Metropolitan City, was examined. In this area, of the 340 vacant houses from 2017, 246 (72.35%) remained persistently vacant till 2020. In 2020, 213 barren sites were neglected after building demolition. Amongst these sites, 177 (83.10%) were paved with concrete. Persistently vacant houses and barren sites were afforested, and the climate-change response was quantified in terms of flood reduction and CO2 mitigation in four scenarios (current, concrete, ground cover plants, and trees). Runoff analysis was performed using the Korea low-impact development model (K-LIDM) to assess flood mitigation. The tree scenario showed average runoff mitigations of 1.71%, 2.38%, and 3.06% in the long-term, 30-year, and 2-year rainfall scenarios, respectively. Additionally, 3058 deciduous broadleaf trees may be planted in the study area to promote CO2 mitigation. Thus, an additional annual carbon fixation of 62,123.78 kg CO2 may be expected. The greening of vacant houses and demolished sites was effective in all aspects of the climate-change response, and tree-based greening exhibited the most significant effect.

Acetylcholine (ACh) is the best one characterized neurotransmitter. Its central roles in the cholinergic areas and central nervous system (CNS) and peripheral (PNS) synapses are well known. It was the first molecule identified as a neurotransmitter and appears to be phylogenetically the oldest one signaling molecules. ACh was detected in bacteria, protozoa, fungi, algae and primitive plants, indicating that the cholinergic system was widely distributed in living organisms before its appearance in the nervous system. The autonomous nervous system (ANS) is an integral part animal kingdom history. It determines fight-escape reactions as well as outlining the vital and cognitive functions bio-organization rhythms. The virus (Covid-19) extraneous effects acting on it could help to better understand its functions. The Blast results show some factors like protein that can be implicated such as bungaro-toxins, phospholipase A2 and the similar prothrombin activator protein. A positive correlation with different toxins is obtained and the Covid-19 double pathogenic mechanism theory is proposed. The process allows us to outline the possibility about toxic-like factors presence.

Previous camera self-calibration methods have exhibited certain notable shortcomings. On one hand, they either exclusively emphasized scene cues or solely focused on vehicle-related cues, resulting in a lack of adaptability to diverse scenarios and a limited number of effective features. Furthermore, these methods either solely utilized geometric features within traffic scenes or exclusively extracted semantic information, failing to comprehensively consider both aspects. This limited the comprehensive feature extraction from scenes, ultimately leading to a decrease in calibration accuracy. Additionally, conventional vanishing point-based self-calibration methods often required the design of additional edge-background models and manual parameter tuning, thereby increasing operational complexity and the potential for errors. Given these observed limitations, and in order to address these challenges, we propose an innovative roadside camera self-calibration model based on the Transformer architecture. This model possesses a unique capability to simultaneously learn scene features and vehicle features within traffic scenarios while considering both geometric and semantic information. Through this approach, our model can overcome the constraints of prior methods, enhancing calibration accuracy and robustness while reducing operational complexity and the potential for errors. Our method outperforms existing approaches on both real-world dataset scenarios and publicly available datasets, demonstrating the effectiveness of our approach.

Most real-time terrain point cloud rendering techniques do not address the empty space between the points but rather try to minimize it by changing the way how the points are rendered by either rendering them bigger or with more appropriate shapes such as paraboloids. In this work, we propose an alternative approach to point cloud rendering, which addresses the empty space between the points and tries to fill it with appropriate values to achieve the best possible output. The proposed approach runs in real time and outperforms several existing point cloud rendering techniques in terms of speed and render quality.

In this article, we introduce a relatively new concept of multi-valued (θ;R)-contractions and utilize the same to prove some xed point results for a new class of multi-valued mappings in metric spaces endowed with an amorphous binary relation. Illustrative examples are also provided to exhibit the utility of our results proved herein. Finally, we utilize some of our results to investigate the existence and uniqueness of a positive solution for the integral equation of Volterra type.

Background: Reelin is a large extracellular glycoprotein secreted by Cajal–Retzius cells and has a main role during brain development, especially in neuronal migration. Reelin is comprised of N-terminal F-spondin like domain, eight tandem repeats, and a highly conserved basic C-terminal region (CTR). The CTR main role in the secretion of Reelin has been investigated by advertently inducing deletion in whole or a part of this region; however, the role of CTR point mutations on the secretion of Reelin is shrouded in mystery. Materials and Methods: In this study, we performed experimental analyses on a subregion of Human Reelin containing 5th and 6th repeats (R5-R6), a part of 8 th repeat and the CTR which were amplified from cDNA of K562 and HEPG2 cells and cloned into a mammalian expressional plasmid (pVP22/myc-His). Bioinformatics investigation was performed on the CTR at both level of nucleotide and amino acid as well as mutant type. Random mutagenesis by error-prone PCR method was utilized to induce mutation in the CTR. The secretion efficiency of recombinant wild-type and mutant Reelin constructs compared in cell lysate and supernatant isolated from the transiently transfected HEK 293T cells using 6XHistag ELISA method. Results: In-vitro study demonstrated that the CTR alteration(S3440P) leads to impairment of Reelin secretion even after overexpression. Conclusions: Our results indicate that S3440P substitution in highly conserved structure of the CTR has an important effect on Reelin secretion.

This paper aims at tackling with the task of fusion feature from images and its corresponding point clouds for 3D object detection in autonomous driving scenarios basing on AVOD, an Aggregate View Object Detection network. The proposed fusion algorithms fuse features targeted from Bird’s Eye View (BEV) LIDAR point clouds and its corresponding RGB images. Differs in existing fusion methods, which are simply the adoptions of concatenation module, element-wise sum module or element-wise mean module, our proposed fusion algorithms enhance the interaction between BEV feature maps and its corresponding images feature maps by designing a novel structure, where single level feature maps and another utilizes multilevel feature maps. Experiments show that our proposed fusion algorithm produces better results on 3D mAP and AHS with less speed loss comparing to existing fusion method used on the KITTI 3D object detection benchmark.

Assuming the Multiple Point Principle (MPP) as a new law of Nature, we considered the existence of the two degenerate vacua of the Universe: a) the first Electroweak (EW) vacuum at $v_1\approx 246$ GeV—“true vacuum”, and b) the second Planck scale “false vacuum” at $v_2 \sim 10^{18}$ GeV. In these vacua, we investigated different topological defects. The main aim of the paper is an investigation of the black-hole-hedgehogs configurations as defects of the false vacuum. In the framework of the $f(R)$ gravity, described by the Gravi-Weak unification model, we considered a black-hole solution, which corresponds to a “hedgehog”—global monopole, that has been “swallowed” by the black-hole with mass core $M_{BH}\sim 10^{18}$ GeV and radius $\delta\sim 10^{-21}$ GeV$^{-1}$. Considering the results of the hedgehog lattice theory in the framework of the $SU(2)$ Yang-Mills gauge-invariant theory with hedgehogs in the Wilson loops, we have used the critical value of temperature for the hedgehogs’ confinement phase ($T_c\sim 10^{18}$ GeV). This result gave us the possibility to conclude that the SM shows a new physics (with contributions of the $SU(2)$-triplet Higgs bosons) at the scale $\sim 10$ TeV. This theory predicts the stability of the EW-vacuum and the accuracy of the MPP.

The purpose of this article is to characterize the Caristi type mapping by the absolute derivative. The equivalences of the Caristi mapping with contraction mapping is discussed too. In addition, it was shown that the contraction mapping can be characterized by its absolute derivative.