Managing blood cholesterol levels is important for the treatment and prevention of diabetes, cardiovascular disease, and obesity. An easy-to-use, portable cholesterol blood test will accelerate more frequent testing by patients and at-risk populations. We aim to evaluate the performance of smartphone-based point-of-care cholesterol blood tests as compared to that of hospital-grade laboratory tests. We used smartphone systems that are already familiar to many people. Because smartphone systems can be carried around everywhere, blood can be measured easily and frequently. We compared the results of cholesterol tests with those of existing clinical diagnostic laboratory methods. We found that smartphone-based point-of-care lipid blood tests are as accurate as hospital-grade laboratory tests (N=116, R>0.97, P<0.001 for all 3 cholesterol blood tests: total cholesterol, high density lipoprotein, and triglyceride). Our system will be useful for those who need to manage blood cholesterol levels to motivate them to track and control their behavior.

Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. Nevertheless, some works don’t ensure sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs. In this paper, we propose a novel method to accurately and automatically reconstruct tree branches from laser scans. We first extract an initial tree skeleton from the input tree point cloud, then simplify the skeleton through iteratively removing redundant components. A global-optimization approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. Experiments on various types of trees from different data sources demonstrate the effectiveness and robustness of our method. The resulted tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc.

The Kanchenjunga Conservation Area (KCA) is a high mountain protected area in eastern Nepal undergoing socio-economic and environmental changes that are unprecedented in the region’s history. In the following paper, we discuss recent changes involving new road construction, climate change, adventure tourism, and the wildlife/forest cover/medicinal plant sectors. Our assessments are based on the collective results of past and recent field research, remote sensing analysis, oral histories, and literature reviews. While similar changes are occurring in mountain protected areas (MPA) throughout Nepal, it is suggested that the KCA differs from other regions in that it still retains the option of conducting detailed feasibility, environmental, and impact assessments prior to the implementation of major change-associated projects, particularly the construction of roads. In turn, the KCA might avoid many of the environmental, social, and economic problems experienced elsewhere in Nepal, while enhancing its status, income-generating capacities, and sustainability as a global destination for adventure, nature, and cultural tourism.

Climate and land use changes are major factors affecting runoff in regional basins. Understanding variation by considering interactions among hydrological components is an important process for water resource management. This study aimed to assess the variation of future runoff in the Upper Chi Basin, Northeastern Thailand. QSWAT hydrological model was integrated to 3 CMIP6 GCMs including ACCESS-CM2, MIROC6, and MPI-ESM1-2-LR under SSP245 and SSP585 scenarios during 2023 – 2100. Land Change Modeler (LCM) was also used for future land use simulation. The results revealed that future average of long-term precipitation and temperature tended to increase while forest land tended to decrease and be replaced by sugarcane plantations. The accuracy assessment of baseline year runoff calculation by QSWAT during 1997 – 2022 showed acceptable result as can be seen from R2, NSE, RSR, and PBIAS indices. This result could lead to temporal and spatial simulation of future runoff. Likewise, runoff of 2 SSPs scenarios tended to increase consecutively, especially in SSP585 scenario. In addition, in case of long-term spatial changes in the subbasins scale, over 90% of the area, from upstream to outlet point, tended to get higher due to 2 major factors including future increased precipitation and changes in cultivation, which would be influential to groundwater and interflow components respectively. Methodology and result of this study can be useful to stakeholders in understanding changes in hydrological system so that they can apply it to develop a strategy for water resource management and handling factors affecting different dimensions properly and sustainably.

Urban cities are the major drivers of economic growth and development. Economic growth and development however results in considerable land cover land use dynamics. This study assessed the dynamics in land cover land use that have occurred in New Braunfels, Texas in the last 7 years (2013 - 2020) to observe areas in the city that had experienced considerable shifts in land cover and land use. A 30-meter resolution Landsat images were used to examine possible changes in land cover land use. New Braunfels was observed to have experienced significant changes in land use especially in developed areas. This change can be attributed to the influx of people into the city, contributing to the need for increased urban development. Analysis of this study shows that about 16% (about 553 hectares) of forest land cover class and 28% (about 1,139 hectares) of grassland class in time 1 (August 31, 2013) changed to built-up land use class in time 2 (November 5, 2020). A limitation to this study was that of the spatial resolution of images used. Higher spatial resolution images could impact the producers, users, and overall accuracy assessment. Results from this study can aid in supporting better decision-making for sustainable urban development and climate change mitigation.

Global change is affecting vegetation cover and processes through multiple pathways. Long time series of surface land surface properties derived from satellite remote sensing offer a unique abilities to observe these changes, particularly in areas with complex topography and limited research infrastructure. Here, we focus on Nepal, a biodiversity hotspot where vegetation productivity is limited by moisture availability (dominated by a summer monsoon) at lower elevations and by temperature at high elevations. We analyze normalized difference vegetation index (NDVI) from 1981 to 2015 semimonthly, at 8 km spatial resolution. We use a random forest (RF) of regression trees to generate a statistical model of NDVI as a function of elevation, land use, CO2 level, temperature, and precipitation. We find that NDVI has increased over the studied period, particularly at low and middle elevations and during fall (post-monsoon). We infer from the fitted RF model that the NDVI linear trend is primarily due to CO2 level (or another environmental parameter that is changing quasi-linearly), and not primarily to temperature or precipitation trends. On the other hand, interannual fluctuation in NDVI is more correlated with temperature and precipitation. RF accurately fits the available data and shows promise for estimating trends and testing hypotheses about their causes.

This study presents established methods, along with new algorithmic developments, to automate the point cloud processing within the Field Information Modeling (FIM)™ framework. More specifically, given an n-D designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of the point clouds to their corresponding elements within the designed model is considered. The methods addressed two classes of field conditions, namely, (i) negligible construction errors; and (ii) existence of construction errors. The emphasis was given to describing and defining the assumptions in each method and shed light on some of their potentials and limitations in practical settings. Considering the shortcomings of current point cloud processing frameworks, three new and generic algorithms were developed to help solve the point cloud to model assignment in field conditions with both negligible, and existence (or speculation) of construction errors. The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results.

The international literature shows a polarised debate on the impacts of climate change on migration. Some studies find a positive linkage, whereas others find a negative one. It is, without a doubt, a complex process better considered case-specific. There is no available information on the relationship between climate change and migration in Colombia, despite past research exploring each of these subjects independently. This study intends explicitly to investigate this linkage gap. Consequently, this paper's essential contribution is that it builds a bridge between climate change scenarios and migratory science for the first time in Colombia. Despite their limitations, the theoretical and methodological framework suggested by IOM (2009b pp. 86, Section B, Chapter II) is demonstrated in this study to be very valid since it provides a methodology to predict where future flows will occur (based on past evidence). The methodological approaches of SLA and NELM explained in section A in the IV Chapter are also valuable for analysing and approaching this study's conclusions. The primary conclusions of this study indicate that the "Coffee Region," Valle, and Atlantic (or Caribbean Coast) provinces that mainly send emigrants to Spain and the US are the key internal regions responsible for most of the international migration from Colombians. The same areas are especially vulnerable to the impacts of upcoming climate change in the A1B scenario produced by the IDEAM (2010) for 2040 and 2100. Thus, future flows of migrants are expected from these regions (2040-2100). However, issues such as visa requirements or the costs associated with migration constitute international barriers to this flow. The sensitivity of these regions can also be associated with internal migration flows, more armed conflict, and forced displacement in a cyclical process. Theoretically, a resurgence of Colombia's armed conflict and displacement due to climate change can be expected. However, the need for empirical studies in Colombia to support this analysis is imperative and is the most crucial recommendation arising from this study.

The paper will analyze the pressures and vulnerabilities of the consolidated city from two perspectives: technical and social. Some design and pragmatic experiences conducted by the author in his teaching and research experience first at the Department of Urbanism of TUDelft in the Netherlands and currently at the PDTA Department of La Sapienza University of Rome will be introduced and analyzed. In the first research activity, whose case study is Rotterdam, all urban vulnerabilities related to climate change will be analyzed while in the second one, conducted in Viterbo, the vulnerability related to the hull of social inclusion, poor accessibility and psycho-social stress that plague our established cities will be treated. The two areas of study, different in size and spatial governance tools, are comparable because they allow deciphering the city's risks through lines of intervention that could serve as best practices and serve the urban planning disciplinary update also allowing to define a reflection on morphology and fabrics and on the shape of the city itself. Both teaching and research activities in which the author is involved allow the topic of urban vulnerability to be addressed with a broad exploratory scope that, in the final stage, hypothesizes design intervention on the neighborhood scale, identified as the most appropriate to provide plausible climate and social adaptation and mitigation responses.

The impact of warming on the phenology of grapevine (Vitis vinifera L.) in conditions of Central Europe was evaluated at the locality of Dolné Plachtince in the Slovakian wine region. In Welschriesling and Pinot Blanc model varieties there was observed onset of phenophases as defined in BBCH scale over 1985–2018 period. Based on the data obtained there was evaluated the influence of average and average maximum temperature and GDD on the onset of phenophases. The results observed indicate earlier budburst by 5–7 days, earlier beginning of flowering by 7–10 days, and earlier berry softening by 18 days, and harvest dates advanced by 8–10 days on average. In both varieties there was found the highest influence of the average monthly temperature in March on budburst, the highest influence of the average monthly temperature and the average maximum temperature in May on the beginning of flowering, and the highest, statistically significant influence of the average maximum temperature in June on grape veraison. The warming observed in moderate climate conditions of northern wine regions in Central Europe (Slovakia) has not caused yet the changes in the grapevine phenology stable enough to require serious adaptation measures.

Development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods are either methods with adjustable parameters or machine learning algorithms, both requiring a database of experimentally measured folding and binding free energy changes. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residue on the above-mentioned thermodynamics characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variant (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases, may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases and the distribution of the free energy changes are not the same. Furthermore, the Pearson correlation coefficients (PCCs) obtained on cases involving SNVs are less impressive than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. All methods are found to underestimate the energy changes by roughly a factor of 2.

Nature-based solutions (NbS) - working with and enhancing nature to address societal challenges - are increasingly being featured in climate change adaptation policy and plans. While there is growing evidence that NbS can reduce vulnerability to climate change impacts in general, there is a lack of understanding on the mechanisms through which this can be achieved, particularly in the Global South. To address this, we analyse 85 nature-based interventions in rural areas across the Global South, and factors mediating their effectiveness, based on a systematic map of peer-reviewed studies encompassing a wide diversity of ecosystems, climate impacts, and intervention types. We develop and apply an analytical framework of people’s social-ecological vulnerability to climate change, in terms of six pathways of vulnerability reduction: social and ecological exposure, sensitivity, and adaptive capacity. Most cases (95%) report a reduction in vulnerability, primarily by lowering ecosystem sensitivity to climate impacts (73% of interventions), followed by reducing social sensitivity (52%), reducing ecological exposure (36%), increasing social adaptive capacity (31%), increasing ecological adaptive capacity (19%) and/or reducing social exposure (14%). An analysis of mediating factors shows that social dimensions are equally important as technical factors in NbS to achieving equitable and effective outcomes. Attention to the distinct social and ecological pathways through which vulnerability is reduced helps to harness the multiple benefits of working with nature in a warming world.

The conservation of coastal wetland ecosystems, like mangrove forests and salt marshes, represents a critical strategy for mitigating atmospheric emissions and climate change in the 21st century. Yet the existence of these environments is threatened by human-induced disturbances, namely deforestation and accelerated sea-level rise. Coastal systems maintain surface elevation in response to sea-level rise through a combination of physical and biological processes both above and below the ground surface. The quantification and relative contribution of belowground process controls (e.g., seasonal water content, organic matter decomposition) on surface elevation change is largely unexplored but crucial for informing coastal ecosystem sustainability. To address this knowledge deficit, we integrated measurements of surface elevation change of the live root zone (0.5 to 1 m depth) with geotechnical data from co-located sediment cores in the Sundarbans mangrove forest (SMF) of southwest Bangladesh. Core data reveal that the primary belowground controls on surface elevation change include seasonal fluctuations in pore-water content and the relative abundance of fine-grained sediments capable of volumetric expansion and contraction, supporting an elevation gain of ~2.42 ± 0.26 cm yr⁻¹. In contrast to many mangrove environments, the soils of the SMF contain little organic matter and are dominantly composed (>90%) of inorganic clastic sediments. The mineral-rich soil texture likely leads to less compaction-induced subsidence as compared to organic-rich substrates and facilitates surface equilibrium in response to sea level rise. Despite a relatively high soil bulk density, soil carbon (C) density of the SMF is very low owing to the dearth of preserved organic content. However, rates of C accumulation are balanced out by locally high accretion rates, rendering the SMF a greater sink of terrestrial C than the worldwide mangrove average. The findings of this study demonstrate that C accumulation in the SMF, and possibly other alluvial mangrove forests, is highly dependent on the continued delivery of sediment to the mangrove platform and associated settings.

Leaders are failing to respond to the climate and environmental urgency the world is facing. A growing action gap, clearly visible during the recent CoP25, has been fueled by leaders' inability to respond efficiently to the mounting threats scientists—and increasingly society—are concerned about. Bridging this gap and tackling the growing polarization within society calls for leaders to accept the full complexity of the issues the world is facing. This will require them to question their understanding of these geopolitical affairs and embrace the dynamics at play, and avoid falling back on simplistic cognitive models. We propose a heuristic to convey the pathways available to decision-makers to make their way out of the current inaction impasse. By breaking free of this deadlock, a social transition will have the potential to take place, helping us to avoid crossing the climate system tipping points.

Monge-Ampere equations have important research significance in many fields such as geometry, convex geometry and mathematical physics. In this paper, under some superlinear and sublinear conditions, the existence of nontrivial solutions for a system arising from Monge-Ampere equations with two parameters is investigated based on Guo-Krasnosel'skii fixed point theorem. In the end, two examples are given to illustrate our main results.

The study of dog point following has been increasingly popular, at least partially due to the initial claims that dogs outperform apes and the suggestion that our domestication process caused dogs to have a biological capacity to communicate with humans. We take the view that dog pointing is more complicated than was initially reported, and in this study, we explore several potential variables that may make a difference. Our variables include point type (ipsilateral (same-side) vs contralateral (cross-body) points), position (standing vs seated), and distance (proximal vs distal). We find that most dogs follow ipsilateral points while less than half of the dogs in our sample follow contralateral points, and that position and distance also affect dogs’ success. We suggest that dogs acquire ipsilateral points via associative mechanisms, but that contralateral points require the dogs to understand the humans’ communicative intent.

The consumption of fiber in the human diet is a global recommendation to ensure a healthy diet. Quinoa (Chenopodium quinoa Willd.), a gluten-free grain, and chia (Salvia hispanica), a seed, contain high fiber content and both have the potential to be explored in the development of nutraceutical and pharmaceutical formulations. An interesting characteristic of chia is its ability to form viscous mucilage when in contact with water, bringing the prospect of a binder in solid formulations. To produce tablets with a high fiber content, it is more appropriate to transform the material into granules using a wet process, with the help of a tool that exploits this possibility. Therefore, the objective of the present work was to study, using the Mixer Torque Rheometer, the feasibility of using chia as a binder to produce granulated quinoa and the development of chewable tablets. To this end, an experimental design was carried out to evaluate the impact of variables on the rheometer results for subsequent granulation. It was possible to obtain the granulation point for the formulations and after producing the granules, physical tests were carried out in which we noticed improvements in the flow. In the end, a formulation was selected for the development of chewable tablets containing quinoa and chia fibers.

Recently, Li et al [1] investigated the nonlinear Langevin equations existence including two fractional orders with infinite-point boundary conditions. It has shown that the outcomes given it does count on the solution form for which it has boundary values. It means that their solution needs more to be in the final step. In current work, we will have discussion about the existence and uniqueness for the same boundary conditions by investigating the closely explicit solution form that has no boundary values. Numerical example is supported to have more vision to compare the between new and previous results. It turns out that our results are superior.

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.

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++.

The link of fracture analysis of large ships is an essential part of the structural safety of ships. However, the finite element analysis of large structure according to the traditional method not only has high requirements on the computer, but also the influence of the mesh encryption at special locations makes further requirements on the accuracy of the ship. In this paper, based on the extended finite element method (XFEM) and the multi-point constraint method (MPC), the dynamic crack expansion process of the rectangular plate containing cracks under the multi-scale model is comparatively studied by using ABAQUS simulation software Finally, the applicability of the method is verified by applying the multi-scale model of ship nodes. The results show that the computational effort of the multi-scale model is significantly reduced with the same accuracy and allowable error, which proves the accuracy and efficiency of the method.

Ammonia known as harmful gas that could impact on health and environment. Typically, ammonia gas emitted by fertilizer industry, rubber factory, etc. There are many options for advance industry to control ammonia gas pollution, absorption tower using water scrubber and using activated carbon as adsorption reaction could be an option. But for middle- and lower-class industry, it is common in Indonesia, as seen on many rubber factories, ammonia gas pollution is serious problem to control, actually they have used wet scrubber but eficiency is lower about 47%. This study measure performance for ammonia elimination as gas pollutant using absorption tower and adsorption tower in laboratory scale. Using electrochemical gas sensor, we measure both ammonia concentration for inlet and outlet simultaneously to settle saturation point of these two types of ammonia emission control. In conclusion, Ammonia absorbed in water proportional to saturation time and absorbent volume. Highest absorbed ammonia concentration 1.538 mg/L on 4000 mL absorbent. For Adsorption system, saturation time proportional and correlated to adsorbent weight and Adsorption capacity reverse correlated to adsorbent weight. Optimum adsorption point can be achieved from intersection curve between saturation time and adsorption capacity which is 1200 grams adsorbent

This work involves results of research on short-term and long-term DC-drifts in electro-optical modulators based on annealed proton exchange waveguides in LiNbO3 crystals after wafer pre-annealing. The relaxation time of the DC-drift of the operating point for a short-term drift is minutes, and for a long-term drift, hours and days. DC-drift was measured by applying bias voltage and changing crystal temperature. Obtained results shows significant impact on stability of operating point in EO-modulators after treatment of defective structure of the near-surface layer of a LiNbO3 crystal. Treatment of the disturbed near-surface layer of a LiNbO3 crystal results in twice reduction of short-term DC-drift and increase of operation stability of electro-optical modulators during long-term measurement of temperature by activation energy calculation.

In this paper, we consider with a class of system of differential equations whose argument transforms are involution. In this an initial value problem for a differential equation with involution is reduced to an initial value problem for a higher order ordinary differential equation. Than either two initial conditions are necessary for a solution, the equation is then reduced to a boundary value problem for a higher order ODE.

Abstract: This study was aimed to determine the influence of the volume fraction of steel fibers and on fracture parameters of concrete. Fifty notched steel fiber reinforced concrete (SFRC) beams and ordinary concrete beams with dimensions of 100mm×100mm×515mm were cast and tested via three-point bending test. Among them, the type of steel fiber is milling type (MF), and the volume fraction of steel fiber added is 0%, 0.5%, 0.5%, 1.5%, 1.5%, 2%, respectively. The effects of the steel fiber volume fraction (VF) on the critical stress intensity factor (KIC), fracture energy (GF), the deflection at failure(δ0), the critical crack mouth opening displacement (CMODC) and the critical crack tip opening displacement (CTODC)were studied. Through the analysis of test phenomena and test data such as load-deflection (P-δ) curve, load-crack mouth opening displacement (P-CMOD) curve and load-crack tip opening displacement (P-CTOD) curve following conclusions are drawn: With the increase of steel fiber volume fraction, some fracture parameters increase gradually and maintain a certain linear growth. The gain ratio of fracture parameters increases significantly, and the gain effect is obvious. Through this law of growth, the experimental statistical formulas of fracture energy and critical stress intensity factor are summarized.

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They provide useful information on signal strength to be processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing some of their most relevant infrastructure components. Included are the arrangement of APs over the environment, the number of reference points (RPs), and the number of samples per location estimation test. A simulation environment is also proposed, in which the impact of key influencing factors on system accuracy is analyzed. Finally, a case study is simulated to validate an optimal combination of design parameters and its compliance with the requirements of localization error and the limited number of access points. Our simulation results clearly show that the desired localization accuracy, which is set as a goal, can be achieved while maintaining the factors already mentioned at minimal levels, which decreases both system deployment costs and computational effort.

COVID-19, the disease caused by the new coronavirus, SARS-CoV-2, has caused significant human and economic burden. Of the three major methods to mitigate the effect of the virus, diagnostics have become the focal point because vaccines and therapeutics, despite the intense effort of scientists worldwide, will take months to develop, test for safety and efficacy, scaleup and distribute to millions. This perspective aims to clarify some of the issues related to COVID-19 diagnostics and highlights some of the challenges key stakeholders including policymakers, businesses, managers, scientists and individuals face during this evolving pandemic.

Simultaneous measurement of different substances from a single sample is an emerging issue to achieve efficient and high-throughput detection in several fields of application. Although immunoanalytical techniques have well-established and prevailing advantages over alternative screening analytical platforms, one of the incoming challenges for immunoassay is exactly multiplexing. The Lateral Flow Immunoassay (LFIA) is a leading immunoanalytical technique for onsite analysis thanks to its simplicity, rapidity and cost-effectiveness. Moreover, LFIA architecture is adaptable to multiplexing and therefore candidates as a possible answer to the pressing demand of multiplexing point-of-need analysis. The review present an overview of diverse approaches for multiplex LFIA, with a special focus on strategies based on new types of magnetic, fluorescent and colored labels

Recent advances in open data initiatives allow us to free access to a vast amount of open LiDAR data in many cities. However, most of these open LiDAR data over cities are acquired by airborne scanning, where the points on façades are sparse or even completely missing due to the viewpoint and object occlusions in the urban environment. Integrating other sources of data, such as ground images, to complete the missing parts is an effective and practical solution. This paper presents an approach for improving open LiDAR data coverage on building façades by using point cloud generated from ground images. A coarse-to-fine strategy is proposed to fuse these two different sources of data. Firstly, the façade point cloud generated from terrestrial images is initially geolocated by matching the SFM camera positions to their GPS meta-information. Next, an improved Coherent Point Drift algorithm with normal consistency is proposed to accurately align building façades to open LiDAR data. The significance of the work resides in the use of 2D overlapping points on the outline of buildings instead of limited 3D overlap between the two point clouds and the achievement to a reliable and precise registration under possible incomplete coverage and ambiguous correspondence. Experiments show that the proposed approach can significantly improve the façades details of buildings in open LiDAR data and improving registration accuracy from up to 10 meters to less than half a meter compared to classic registration methods.

The present work is focused on the numerical solution of the complete energy equation used in fluid film lubrication. The work was motivated by the fact the complete energy equation has no analytic solution that could be used for validations. Its accuracy and computation time are related to the employed numerical method and to the grid resolution. The natural discretization method (NDM) applied on different grids is systematically compared with the spectral method (the Lobatto Point Colocation Method or LPCM) with different polynomial degrees. A one dimensional inclined slider is used for the numerical tests and the energy equation is artificially decoupled from Reynolds. This approach enables to focus all the attention on the numerical solution of the energy equation. The results show that the LPCM is one or two orders of magnitudes more efficient than the NDM in terms of computation time. The energy equation is then coupled with Reynolds equation in a thermo-hydrodynamic analysis of the same 1D slider; the numerical results confirm again the efficiency of the LPCM. A thermo-hydrodynamic analysis of a two-lobe journal bearing is then presented as a practical application.

A numerical application of linear-molecule symmetry properties, described by the D_∞h point group, is formulated in terms of lower-order symmetry groups D_nh with finite n. Character tables and irreducible representation transformation matrices are presented for D_nh groups with arbitrary n-values. These groups are subsequently used in the construction of symmetry-adapted ro-vibrational basis functions for solving the Schrödinger equations of linear molecules as part of the variational nuclear motion program TROVE. The TROVE symmetrisation procedure is based on a set of "reduced" vibrational eigenvalue problems with simplified Hamiltonians. The solutions of these eigenvalue problems have now been extended to include the classification of basis-set functions using ℓ, the eigenvalue (in units of ℏ) of the vibrational angular momentum operator ${\widehat{L}}_z$ . This facilitates the symmetry adaptation of the basis set functions in terms of the irreducible representations of D_nh. ¹²C₂H₂ is used as an example of a linear molecule of D_∞h point group symmetry to illustrate the symmetrisation procedure.

A methodology for both accurate and smooth contour line generation from Light Detection and Ranging (LiDAR) point clouds is proposed in this paper. In order to improve the accuracy of contour lines in the area of forests, constrained triangulation networks with break lines are then constructed to generate contour lines. In break line extraction, a bi-threshold method for edge line detection is used to extract both complete and reliable break lines. A point clouds elevation adjustment with constrain of break lines and an interpolator considering a contour interval is proposed to improve the smoothness of contour lines. The proposed interpotator is also can avoid contour line intersection when contour lines are interpolated. Statistical parameters and shape index are then used to evaluate quantitatively the accuracy and smoothness of the resultant contour lines, which fill in the blank of contour lines evaluation in theory. The experiments show that high-quality contours in terms of smoothness and accuracy can be generated from LiDAR point clouds.

The study explores the value of indigenous knowledge in mitigating and adapting to the effects of climate change in rural communities of Uganda. It provides an overview of how rural farmers use traditional and indigenous knowledge to predict weather changes to inform their adaptation and mitigation plans. The study intended to investigate how climate change effects were managed by the rural communities of Bakiga using indigenous knowledge and practices. Specifically, it sought to establish how local farmers’ innovations and practices were used to mitigate the effects of climate change and enhance food security. A case study research design with triangulation of data sources was employed. The study was conducted in Rukiga District, southwestern Uganda, where rural farmers were interviewed to explore their practices for reducing the effects of climate change to enhance food security. Thematic data analysis was used to get participants’ experiences and views regarding indigenous knowledge and effects of climate change mitigation. The findings indicate that rural farmers have indigenous practices of mitigating the effects climate change and contributing to the Sustainable Development Goals (SDGs).

The prefabricated houses supply in Chile was analyzed from web platforms, public market, social media, and Internal Revenue Service, using indicators according to regulatory compliance, complexity, and sustainability attributes, which are essential in advancing to industrialization, and climate change adaptability. The 80% is concentrated in construction, and manufacturing companies, 83% of them are legally registered,with the capacity of meeting technical requirements. To delve deeper into this, 54% has low level, 35% medium level, and 11% high level. The sustainability was measured in 5 levels: 2.7% (1), 37,5% (2), 58,6% (3), 1,1% (4) and 0% (5), which is the highest one. This attribute was determined as the weakest one. The proposed evaluation, based on indicators by attribute, is objective and relevant to consideration since there is still a lack of capacity to supply the housing deficit, and there is not attributes associated to security in habitability to address the climate change, and environment threats, with a lack of action by the state to promote this productive sector, therefore focusing more in provide products, than taking responsibility of the site, not advancing to become a real state agency, which could be improved if management, and regulation were incorporated.

Abstract: Increasing population and urbanization are affecting human health and comfort. In order to get rid of these affects, mankind is changing its enviroment and looking for new life areas. This study investigates the influence of Land Cover Change (LCC) and Normalisied Densly Vegetation Index (NDVI) on Land Surface Temperature (LST) of Fethiye-Göcek Specially Protected Area (SEPA) in easterm mediterranean basin. In the study LCC, NDVI and LST were drived from landsat 5, 7 and 8 satellite image of resolation at 30x30 m acquired between 1995-2020. LST were computed based on Land Use/Land Cover (LULC) types. The Corine Index were used for determination of land uses. The results indicated that water, forest and maquia lands decreasing while urban fabric and bare lands are increasing depend upon the urbanization and forest fires in the basin. These changes in LULC widened the temperature differences between the urban and rural areas. The change in LST is associated with changes in constructional materials in urban land and in vegetation abundance both in the urban and rural areas. Vegetation has an important factor in the temperature of different land covers. That produces warming trend in temperetaure in built-up areas it causes to keep other lands warmer in cold weather. Another important result is affective Urban Heat Island (UHI) on climate change based on the impact of urbanization and land cover changes. Significantly possitive correlation were found between the urbanization rate, population and built-up area and warming rate of average air temperature and so the LST.

Corresponding to the principles of biological synapses, an essential prerequisite for hardware neural networks using electronics devices is continuous regulation of conductance. We implemented artificial synaptic characteristics in a (GeTe/Sb2Te3)16 iPCM with a superlattice structure under optimized identical pulse trains. Based on atomically controlling the Ge switch in the phase transition that appears in the GeTe/Sb2Te3 superlattice structure, multiple conductance states were implemented by applying the appropriate electrical pulses. Furthermore, we found that the bidirectional switching behavior of a (GeTe/Sb2Te3)16 iPCM can achieve a desired resistance level using the pulse width. Therefore, we also fabricated a Ge2Sb2Te5 PCM and designed a pulse scheme based on the phase transition mechanism to compare to the (GeTe/Sb2Te3)16 iPCM. We designed an identical pulse scheme that implements linear and symmetrical LTP and LTD based on the iPCM mechanism. As a result, the (GeTe/Sb2Te3)16 iPCM showed relatively excellent synaptic characteristics by implementing gradual conductance modulation, a nonlinearity value of 0.32, and LTP/LTD 40 conductance states using identical pulses trains. Our results demonstrate the general applicability of the artificial synaptic device for potential use in neuro-inspired computing and next generation non-volatile memory.

Land use/land cover (LULC) and climate changes are two main factors directly affecting hydrologic conditions. However, very few studies in Vietnam have investigated changes in hydrological process under the impact of climate and land use changes on a basin scale. The objective of this study is to assess the individual and combined impacts of land use and climate changes on hydrological processes for the Nam Rom river basin, Northwestern Viet Nam using Remote Sensing (RS) and Soil and Water Assessment Tools (SWAT) model. SWAT model was used for hydrological process simulation. Results indicated that SWAT proved to be a powerful tool in simulating the impacts of land use and climate change on catchment hydrology. The change in historical land use between 1992 and 2015 strongly contributed to increasing hydrological processes (ET, percolation, ground water, and water yield), whereas, climate change led to significant decrease of all hydrological components. The combination of land use and climate changes significantly reduced surface runoff (-16.9%), ground water (-5.7%), water yield (-9.2%), and sediment load (-4.9%). Overall climatic changes had more significant effect on hydrological components than land use changes in the Nam Rom river basin during the 1992–2015. Under impacts of projected land use and climate change scenarios in 2030 on hydrological process of the upper Nam Rom river basin indicate that ET and surface flow are more sensitive to the changes in land use and climate in the future. In conclusion, the findings of this study will basic knowledge of the effects of climate and land-use changes on the hydrology for future development of integrated land use and water management practices in Nam Rom river basin.

The key anthropogenic effects on climate include the changes in land use and emission of greenhouse gases into the atmosphere. Depletion of vegetation poses serious threat that speeds the process of climate change and reduces carbon sequestration by the environment. Thus, the preservation of natural environment in urban areas is an essential component of the garden city model, proposed by Sir Ebenezer Howard in 1898, to ensure ecological balance. Recent Landsat images showed that Kumasi does not have the required percentage of green vegetation as was stipulated in the garden city model on which the city was built. It was observed that most parts of Kumasi's green vegetation have been lost to built environments. This study was conducted to assess the impact of urbanization on the garden city status and its effect on the micro-climate of the city. Significant changes in the vegetation cover of the city was evaluated from Landsat-TM imagery and analysis of a long term climatic data of Kumasi carried out over a 55-year period (1960 to 2015). It was observed that, climatic conditions have slightly changed, as mean surface temperature of has increased by 1.2 °C/ 55 years, due to the significant landuse changes from development of non-transpiring, reduced evaporative urban surfaces. However, the impact is not greatly felt due to the geographical location of the city on the globe despite the evidence of a considerable temperature change. Green vegetation conservation for the city is recommended as a top priority in future for city authorities and planners.

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.

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.

Carbon (C) is one of the most abundant elements. It is part of all living organisms and is found in all states of matter (solid, liquid, gas, and plasma). It can take the form of one of the hardest and transparent materials (diamond) or it can be flexible and dark ( graphite), with many carbonaceous materials at both extremes. In gaseous form, C is a component of several greenhouse gas emitted during the combustion of fossil fuels. Carbon movement between the atmosphere, land (biosphere and lithosphere), and ocean (hydrosphere) can alter total amounts in each pool. Human activities accelerate C movement into the atmosphere, causing increases in climate change. This shift from terrestrial and oceanic C pools to the atmosphere cause an increase in the intensity and duration of droughts, hurricanes, snowstorms, and other natural phenomenon. Although society hears about C emissions there is a lack of understanding of its importance and the need to decrease C in the atmospheric pool to avoid exacerbating climate change. Forests and biochar are two biological methods to retain C in the terrestrial pool for long time and at very low cost. This review details where C comes from, its relationship with forests, and biochar and their roles in increasing carbon sequestration to limit the impacts of climate change and continue having future sustainable forests and ecosystems services that we enjoy today.

Puerto Rico is a neotropical island located in the Caribbean. Like other neotropical regions, Puerto Rico streams discharges can vary with the seasons, atmospheric events, and human activities. The Lapa River is part of the Puerto Rico Heritage Rivers Program. This program identifies and manages the protection of hydrographic systems that maintain their natural conditions. The necessity of increasing the knowledge of the natural processes of the Lapa River and providing valuable information for the development of a future management plan for the ecosystem, the following objectives were established in this research: (1) to describe the temporal variations in water discharges in the Lapa River and (2) to describe the impact of atmospheric events on the water discharges at the Lapa River. This study concluded that the highest annual discharge averages recorded from 1989 to 2021 in the Lapa River occurred in 2011 and 2017. 2011, the annual mean discharge was 31.20 (ft3/s); in 2017, the average was 40.44 (ft3/s). In terms of atmospheric events, this study concludes that, during the evaluated period, the highest discharge event occurred after the impact of hurricanes Irma and Maria, with an average discharge of 164.40 ft3/s in October and 182.60 ft3/s in September. This event set a precedent in the river Lapa’s discharges, impacting the river basin’s flora, fauna, and morphology from its headwaters downstream.

Climate changes caused by anthropogenic actions can directly and indirectly affect living beings, including parasites and their hosts. Changes such as water temperature, pH, distribution of nutrients in the aquatic environment are some examples that can interfere with the fish community, whether in open water or in production systems. In this review, we will show how climate change can affect the adaptation of hosts and parasites in the aquatic environment and how these parasites can be used for environmental monitoring.

Introduction. Environmental Health in a Global World at New York University was re-designed as a class participatory effort, challenging undergraduate students to understand environmental hazards and the resultant adverse health outcomes by embracing the inherent complexity of environmental risks and proposing solutions. Methods. Following introductory lectures, students are placed into teams and assigned a specific perspective, or avatar, which includes learning to see the challenge from the perspective of a technical expert such as a biologist, an engineer, or an anthropologist. The teams then design specific systems maps to visualize the complex interactions that lead to adverse health outcomes after a given environmental exposure. The maps highlight potential leverage points, where relatively minor interventions can provide a disproportionate benefit in health outcomes. The teams then explore potential interventions and identify the potential unintended consequences of those actions, develop and advocate for innovative new strategies to mitigate risk and improve outcomes. Results and Discussion. Over the past 5 years we have taught this methodology to over 680 students with strong, student-oriented results. The teams created and presented more than 100 strategies, addressing a diverse set of environmental challenges that include water contamination, gun violence, air pollution, environmental justice, health security, and climate change. Developing the strategies helped the students understand environmental threats in a more holistic way, provided them with some agency in finding solutions, and offered an opportunity for them to improve their presentation skills. The responses in course evaluations have been enthusiastic, with many students reporting a deep impact on their college experience.

The study aims to investigate the impacts of climate change on butterfly populations Thimphu district of Bhutan. Butterflies are indicators of environmental factors and can act as an early warning of impending changes in local flora and fauna. The study will involve citizen science with participants using iNaturalist and Seek applications to capture butterfly data in various habitat areas in the Thimphu district. The data collected will be analyzed using Statistics Kit for Social Sciences (SPSS), Nvivo, and Geographical Information System (GIS) programs. The results of the research will be disseminated through awareness campaigns to increase public understanding of the value of butterfly protection in the conservation of healthy environments. The study's findings will be used to develop conservation initiatives to maintain biodiversity and assess the impacts of climate change.

Turmeric is highly tolerant to several climatic changes and can grow under high temperatures and moderate drought conditions. This herb is very much dependant on optimum rainfall, optimum heat with less chilling or freezing conditions. These conditions if are more than normal would tend to reduce the yields of the crops and also effect the productivity. To reduce such drastic yield losses certain conventional plant breeding methods were employed but were very less effective compared to plant biotechnology. To reduce these loses by stresses, extensive and effective molecular biology methods were employed which identifies the genes that are stress responsive along with certain methods like gene transfer, genetic engineering was also known to be effective. All these methods are quite helpful in mitigating the yield losses and promoting healthy growth in the plants. The maintenance of rhizome size, curcumin content, essential oils etc. is very much necessary for the turmeric crop because of its role, especially in the medical field. Therefore, the yield losses are reduced to a maximum extent so that development of smart turmeric is easy and crop designing is possible only with the advanced techniques involved in agriculture biotechnology.

In contrast to animals, adult organs in plants are not determined during embryogenesis but gen-erated from meristematic cells as plants advance through development. Plant development in-volves a succession of different phenotypic stages and the transition between these stages is termed phase transition. Phase transitions need to be tightly regulated and coordinated to ensure they occur under optimal seasonal, environmental conditions. Polycarpic perennials transition through vegetative stages and the mature, reproductive stage many times during their lifecycles and, in both perennial and annual species, environmental factors and culturing methods can re-verse the otherwise unidirectional vector of plant development. Epigenetic factors regulating gene expression in response to internal cues and external (environmental) stimuli influencing the plant’s phenotype and development have been shown to control phase transitions. How develop-mental and environmental cues interact to epigenetically alter gene expression and influence these transitions are not well understood and understanding this interaction is important considering the current climate change scenarios, since epigenetic maladaptation could have catastrophic con-sequences for perennial plants in natural and agricultural ecosystems. Here we review studies focussing on the epigenetic regulators of the vegetative phase change and highlight how these mechanisms might act in exogenously induced plant rejuvenation and regrowth following stress.

This research analyzes the Physical Education students’ degree of academic performance with the incorporation of active methodologies, specifically the flipped classroom mixed learning, restricted to evaluation periods in the months of June and September. The study focuses on whether there are significant differences in this variable through the scores obtained. Through a simple random sampling, 131 students participated in this empiric-analytic research, using an ex-post-facto study with a retrospective design with quasi control group. A robust test of averages comparison, multiple linear regressions and an evaluation of the relative importance of predictors is conducted. The results show how flipped classroom methodology linearly and positively influences academic performance and correlationally motivation and support. As main conclusion, in a hybrid and digitalized learning context, the value of the consideration of active methodologies (flipped classroom) based on emerging pedagogies, allows improving students’ achievement and competence development, providing critical, significant, ubiquitous, transformational and especially motivating experiences.

Land cover and climate changes greatly influence hydrologic responses of a basin. However, the response vary from basin to basin depending on the nature and severity of the changes and basin characteristics. Moreover, the combined impacts of the changes affect hydrologic responses of a basin in an offsetting or synergistic manner. This study quantified the separate and combined impacts, and the relative contributions of land cover and climate changes on multiple hydrological regimes (i.e., surface runoff, streamflow, groundwater recharge evapotranspiration) for the Dhidhessa Subbasin. Land cover and climate change data were obtained from a recent study completed for the basin. Calibrated Soil and Water Analysis Tool (SWAT) was used to quantify the impacts. The result showed that SWAT model performed well for the Dhidhessa Subbasin in predicting the water balance components. Substantial land cover change as well as an increasing temperature and rainfall trends were reported in the river basin during the past three decades. In response to these changes, surface runoff, streamflow and actual evapotranspiration (AET) increased while groundwater recharge declined. Surface runoff was more sensitive to land cover than to climate changes whereas streamflow and AET were more sensitive to climate change than to land cover change. The combined impacts played offsetting effect on groundwater recharge and AET while inconsistent effects within study periods for other hydrologic responses. Overall, the predicted hydrologic responses will have negative impacts on agricultural production and water resources availability. Therefore, the implementation of integrated watershed management strategies such as soil and water conservation and afforestation could reverse the negative impacts.

The paper presents the results of high precision control survey carried out in the underground mining of Oyu Tolgoi project using terrestrial laser scanning technology. The purpose of the measurements is to ensure the heading, total length and volume of the penetration and excavation works are being implementing in accordance with the planned and designed dimensions. We established a network of 18 reference points in the tunnel of the selected underground level which were repeatedly measured 4 times by two round readings of the Trimble S7 tacheometry and processed by using the Trimble Business Center software. All points except the one was within the permissible error. Based on these points the excavation length and volumes were determined using the Trimble SX12 laser scanner. The difference between the planned work quantity provided by the Oyu Tolgoi project and the actual values measured by the laser scanning was calculated using the comparative method. The TLS partial control measurements in the excavations made through the ore bodies revealed the length of 20285.72 m and volume of 441128.80 m3 for underground 7 mine levels while their project values were 16158.26 m length and 422837.50 m3 volume, respectively.

The research of object classification and part segmentation is a hot topic in computer vision. A considerable number of studies have been carried out about deep learning on 3D point clouds. However,it is challenging to achieve effective feature learning due to sparsity of point clouds. Recently, a variety of Transformers have been adopted to improve point cloud processing and display great potential. Nevertheless, large numbers of Transformer layers tend to incur huge computational and memory costs. PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks, this does not mean that PointNet++ has no potential.Thus, this paper offer two major contributions that significantly improve PointNet++ performance. Firstly, we introduce a novel contextual feature extraction (CFE) block that significantly enhances the feature extraction capabilities of PointNet++ networks. Secondly, to further enhance feature fusion, we seamlessly integrate a mutual learning (ML) block into the network architecture. By embedding these two innovative blocks within each layer of the network, we not only enrich the network's functionality but also impart it with greater robustness and adaptability. The specific experiments were conducted on the S3DIS (6-fold cross-validation) and Modelnet40 datasets with 86.5% and 92.7% accuracy, respectively, which proved that our model is comparable or even better than most existing methods for classification and segmentation tasks.

In this paper, a new full-Newton step feasible interior point method for convex quadratic programming is presented and analyzed. The idea behind this method is to replace the complementarity condition with a non-negative variable weight vector and use the algebraic equivalent transformation for the obtained equation. Under the selection of appropriate parameters, the quadratic rate of convergence of the new algorithm is established. In addition, the iteration complexity of the algorithm is obtained. Finally, some numerical results are presented to demonstrate the practical performance of the proposed algorithm.

The process of blood coagulation, wherein circulating blood transforms into a clot in response to internal or external injury, is a critical physiological mechanism. Monitoring this coagulation process is vital to ensure that blood clotting occurs neither too rapidly nor too slowly. Anticoagulants, a category of medications designed to prevent and treat blood clots, require meticulous monitoring to optimise dosage, enhance clinical outcomes, and minimise adverse effects. This review article delves into the various stages of blood coagulation, explores commonly used anticoagulants and their targets within the coagulation enzyme system, and emphasises the electrochemical methods employed in anticoagulant testing. Electrochemical sensors for anticoagulant monitoring are categorised into two types. The first type focuses on assays measuring thrombin activity via electrochemical techniques. The second type involves modified electrode surfaces that either directly measure the redox behaviour of anticoagulants or monitor the response of standard redox probes in the presence of these drugs. The review provides a comprehensive list of different electrode compositions and their detection and quantification limits. Additionally, it discusses the potential of employing a universal calibration plot to replace individual drug-specific calibrations. The insights presented are anticipated to significantly contribute to the sensor community's efforts in this field.

This study aimed to investigate the effect of adding Mirror Visual Feedback Therapy (MFT) after Dry Needling (DDN) on sensitivity and motor performance in patients diagnosed with Post-Needling pain from Myofascial Trigger Points (MTrPs) in lateral elbow pain patients. A randomized, controlled, two-arm pilot trial following Consolidated Standards of Reporting Trials (CONSORT) was conducted at the European University of Madrid between February 1, 2023, and June 2, 2023. A total of 49 patients (F=23, M=26) were randomly assigned to the Experimental Group Deep Dry Needling, Ischemic Compression, Cold Spray, Stretching, and Mirror Therapy (n=25), and the others receiving the same interventions without Mirror Therapy (n=24). Pre- and post-treatment assessments encompassed Post-needling pain (VAS), pressure pain threshold (PPT), two-point discrimination threshold (TPDT), and maximum hand grip strength (MHGS). Post-needling pain intensity in the EG showed statistically significant reductions (MD = 0.400, SEM = 0.271, W = 137.00, p = 0.047) following the addition of MT to the DDN protocol. Similarly, PPT exhibited significant improvements (MD = 0.148 Kg/cm2, SEM = 0.271, W = 262.00, p < 0.001). However, TPDT and MHGS did not show significant differences between initial and post-treatment measurements. The addition of MFT to DDN for MTrPS in lateral elbow pain reduced Post-needling pain and influenced on sensory perception in lateral elbow pain patients with Myofascial Trigger Points. Despite observed clinical effects not reaching statistical significance, these findings offer valuable insights for future research considerations.

More than one million curable sexually transmitted infections occur every day. Trichomonas vaginalis is one of the main responsible for these epidemiological data, however, the diagnosis of this protozoan is still mainly based on microscopic and culture identification. The commerciali-zation of immunological tests and the development of molecular techniques have improved the sensitivity of classical methods. Nevertheless, the fact that trichomoniasis is a neglected parasitic infection, hinders the development of novel techniques and their implementation in routine di-agnosis. This review article shows the different methods developed to identify T. vaginalis in population and the difficulties to diagnose male and asymptomatic patients. The importance of including this parasite in routine gynecological screening, especially in pregnant women, and the importance of considering T. vaginalis as an indicator of high-risk sexual behavior is also discussed.

The paper proposes a new implementation of the PID algorithm in digital hardware. The proposed circuit implements an advanced PID formula, containing a non ideal derivative component, and weighting coefficients, which enable reducing influence of setpoint changes in the proportional and derivative components. The implementation operates on standard single precision (32 bit) floating-point numbers. The proposed circuit structure is optimized for cost. It uses just one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequential manner. The circuit was implemented in a Cyclone V FPGA device from Intel, using the Quartus Prime software. Proper operation of the circuit was verified by simulation. The proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on standard single precision floating-point numbers, while being significantly cheaper. However, it outperforms by several orders of magnitude the speed of any software-based implementation, including solutions using PLCs, and CPU/MCUs. The proposed circuit structure, together with the overall regulator device concept, suit well the SoC (System on Chip), or SoPC (System on Programmable Chip) idea, i. e. a device, that contains a CPU core immersed in “FPGA fabric” - logic resources characteristic for FPGA devices.

Versatility, sensitivity and accuracy have made the real-time polymerase chain reaction (qPCR) a crucial tool for research as well as diagnostic applications. However, for point-of-care (PoC) use traditional qPCR faces two main challenges: long run times mean results are not available for half an hour or more and the requisite high-temperature denaturation requires more robust and pow-er-demanding instrumentation. This study addresses both issues and revised primer and probe designs, modified buffers and low ∆T protocols which, together, speed up qPCR on conventional qPCR instruments and will allow the development of robust, point-of-care devices. Our approach, called "FlashPCR", also allows efficient reverse transcription as part of a one-step RT-qPCR pro-tocol, making it universally applicable for both rapid research and diagnostic applications.

In this paper, a positive answer to the open question of Fabino et al. is given, and the concept of I\c sik type set valued contractions is introduced and fixed point theorem for such contractions is established. Examples to support main theorems are given, and an application to integral inclusion is given.

This study introduces a multilayer perceptron (MLP) error compensation method for real-time camera orientation estimation, leveraging a single vanishing point and road lane lines within a steady-state framework. The research emphasizes cameras with a roll angle of 0°, predominant in autonomous vehicle contexts. The methodology estimates pitch and yaw angles using a single image and integrates two Kalman filter models with inputs from image points (u, v) and derived angles (pitch, yaw). Performance metrics, including AE, MINE, MAXE, SSE, and STDEV, were utilized, testing the system in both simulator and real-vehicle environments. The outcomes indicate that our method notably enhances the accuracy of camera orientation estimations, consistently outpacing competing techniques across varied scenarios. This method’s potency is evident in its adaptability and precision, holding promise for advanced vehicle systems and real-world applications.

In recent years, there has been significant growth in the ubiquity and popularity of three dimensional(3D) point clouds, with an increasing focus on the classification of 3D point clouds. To extract richer features from point clouds, many researchers have turned their attention to various point set regions and channels within irregular point clouds. However, this approach has limited capability in attending to crucial regions of interest in 3D point clouds and may overlook valuable information from neighboring features during feature aggregation. Therefore, this paper proposes a novel 3D point cloud classification method based on global attention and adaptive graph convolution. The method consists of two main branches: the first branch com-putes attention masks for each point, while the second branch employs adaptive graph convolu-tion to extract global features from the point set. It dynamically learns features based on point interactions, generating adaptive kernels to effectively and precisely capture diverse relationships among points from different semantic parts. Experimental results demonstrate that the proposed model achieves 93.8% in overall accuracy and 90.8% in average accuracy on the ModeNet40 dataset.

To solve the problem of complex sensor installation and easy damage in contact displacement measurement, a non-contact measurement method of brace displacement in bridge swivel construction based on binocular vision is proposed in this study. Combined with monitoring data in a swivel construction interchange project, binocular vision system was compared and analyzed with the traditional displacement meter and total station. Results showed that force versus displacement variation plots were obtained by binocular visual measurement in the weighing test, the inflection point can be clearly distinguished, and the position of the inflection point was the same as that of the displacement meter; monitoring data of binocular vision measurement and total station were compared and analyzed in the bridge swivel process, the average error was no more than 0.18 mm, the maximum error was no more than 0.42 mm, and the standard deviation was no more than 0.12. The method proposed in this study can be used as an alternative to displacement meter and total station measurement methods in bridge swivel construction, the problem of complex sensor installation and easy damage is solved in brace displacement measurement, equipment and labor costs can be effectively saved.

Background Ultrasonographic measurement of the optic nerve sheath diameter (ONSD) is helpful in the assessment of intracranial pressure. The aim of this study was to determine whether the Trendelenburg position during laparoscopic urological surgery changed the ONSD and whether changes correlated with postoperative complications. Methods In this prospective study, we measured ONSD before patients were placed in the Trendelenburg position. Then the Trendelenburg position was established by tilting the operating table to 20-25 degrees. After surgery, the patients were placed in a horizontal position, and the second measurement of ONSD was obtained. Results The study included 69 patients. The average preoperative ONSD in the right eye was 5.8 ± 0.7 mm and 5.8 ± 0.8 mm in the left eye. The average postoperative ONSD in the right eye was 6.6 ± 0.8 mm and 6.6 ± 0.7 mm in the left eye. The differences between postoperative and preoperative values in the right and left eyes were statistically significant (p <0.000001). ONSD increases above the norm did not correlate with the incidence of postoperative nausea (p = 0.94), delirium (p = 0.81), or the time to patient awakening (p = 0.29). Conclusion ONSD increased in patients who underwent surgical procedures performed in the Trendelenburg position, but the increase did not correlate with postoperative complications.

With the rise of the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), an abundant amount of trajectory data has made research on location prediction more popular. The check-in data shared through LBSN hides information related to life patterns, obtaining this information is helpful for location prediction. However, the trajectory data recorded by mobile devices is different from check-in data that has semantic information. In order to obtain the user’s semantic, relevant studies match the stay point to the nearest Point of Interest (POI), but location error may lead to wrong semantic matching. Therefore, we propose A Self-Attention for Next Location Prediction based on Semantic Mining to predict the next location. When calculating the semantic feature of a stay point, the first step is to search for the k nearest POI, then use the reciprocal of the distance from the stay point to the k nearest POI and the number of categories as weights. Finally, we use the probability to express semantic without losing other important semantic information. Furthermore, this research combined with sequential pattern mining can bring richer semantic features. In order to better perceive the trajectory, temporal features learn periodicity of time series by sine function. In terms of location features, we build a directed weighted graph and regard the frequency of users visiting locations as the weight, so the location features are rich in contextual information. We then adopt the Self-Attention model to capture long-term dependencies in long trajectory sequences. Experiments in Geolife show that the semantic matching of this study improved by 45.78% in TOP@1 compared with the closest distance search for POI. Compared with the baseline, the model proposed in this study improved by 2.5% in TOP@1.

We develop a quantitative microscopic theory of decaying Turbulence by studying the dimensional reduction of the Navier-Stokes loop equation for the velocity circulation. We have found an infinite dimensional manifold of solutions of the Navier-Stokes loop equation\cite{M93, M23PR} for the Wilson loop in decaying Turbulence in arbitrary dimension $d >2$. This family of solutions corresponds to a fractal curve in complex space $\mathbb C^d$, described by an algebraic equation between consecutive positions plus a nonlinear periodicity condition. We derive the constrained SDE for the evolution of the fractal curve at a fixed moment of physical time as a function of an auxiliary stochastic time. We expect this stochastic process to cover our fixed manifold of the solutions of the decaying Turbulence. The energy density of the fluid decays as $\mathcal E_0/t$, where $\mathcal E_0$ is an initial dissipation rate. Presumably, we have found a new phase of extreme Turbulence yet to be observed in real or numerical experiments.

Inflammatory bowel disease (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC), defines as an idiopathic, chronic, relapsing, inflammatory disease affecting the gastrointestinal tract and leading to chronic damage. Endoscopy with biopsies is considered the gold standard for IBD diagnosis, whereas magnetic resonance (MRI) for CD extension and complication assess-ment. However, the latter are invasive procedures; thus, the need for a reliable, accessible and noninvasive way to perform IBD diagnosis and monitoring in the tight control era, like intestinal ultrasound (IUS) is. Compared to endoscopy and MRI, IUS has shown reliable diagnostic accuracy in assessing CD and UC severity and extension. Performing IUS in a point-of-care setting (POCIUS) can guide the clinician in driving the diagnostic and therapeutic pathway, thus accel-erating clinical decisions. POCIUS performed with pocket devices can represent the future of physical examination in outpatient or inpatient examination. Accordingly, we performed a liter-ature review about the application of IUS in CD and UC diagnosis. Furthermore, we explored the technical aspects of this imaging technique and its application in a point-of-care setting.

In this paper, we prove some common fixed point theorems on complex partial metric space. The presented results gener- alize and expand some of the literature well-known results. We also explore some of the application of our key results.

This article has two objectives. Firstly, we will use the vector variational-like inequalities problems to achieve local approximate (weakly) efficient solutions of Vector Optimization Problem within the novel field of the Hadamard manifolds. Previously, we will introduce the concepts of generalized approximate geodesic convex functions and illustrate them with examples. We will see the minimum requirements under which critical points, solutions of Stampacchia and Minty weak variational-like inequalities and local approximate weakly efficient solutions can be identified, extending previous results from the literature for linear Euclidean spaces. Secondly, we will show an economical application, using again solutions of the variational problems to identify with Stackelberg equilibrium points on Hadamard manifolds and under geodesic convexity assumptions.

A run-of-the-mill remote sensor arrange comprises numerous vitality compelled sensor hubs that are arbitrarily sent is the sensor field. Vitality is one of the most significant parts of planning an information dispersal convention for applications, for example, war zone checking, living space observing, and so on. We present EGDD, an Energy-effective Grid-based Data Dissemination plot for arbitrarily conveyed remote sensor systems. The proposed conspire is vitality effective for dealing with both sink and source portability. EGDD depends on a virtual framework-based foundation for information scattering from various versatile sources to different portable sinks. The virtual lattice of square size is built by the main source showing up in the sensor field dependent on the remaining vitality of the sensor hubs. Moreover, we proposed an askew sending calculation for inquiry and information sending which guarantees that just a single spread hub advances the question and information at once. In EGDD, substitute scattering hubs are chosen ahead of time during the lattice development procedure and the least limit vitality level is characterized for spread hubs. When the vitality of a dispersal hub arrives at the base edge esteem, it is supplanted by another spread hub. The reenactment results show that the proposed EGDD conspire is more vitality proficient when contrasted with other information scattering conventions.

In this study, we investigate the form of solutions, stability character and asymptotic behavior of the following four rational difference equations x_{n+1} = (1/(x_{n}(x_{n-1}±1)±1)), x_{n+1} = ((-1)/(x_{n}(x_{n-1}±1)∓1)), such that their solutions are associated with Tribonacci numbers.

In the framework of a multiplicative metric space. We have discuss some Properties of convex structures in pseudo multiplicative metric space and have applied these properties toiobtain alfixed point results in complete pseudo. It would be interesting to prove some further results in such metric spaces with completeness property.

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.

To improve the effect of multi-point stretch forming of sheet metal, it is proposed in this paper to replace fixed ball head with swinging ball head. According to the multi-point dies with different arrangements, this research establishes the finite element models of the following stretch forming, i.e. fixed ball heads with conventional arrangement, swinging ball heads with conventional arrangement, swinging ball heads with declining staggered arrangement, and swinging ball heads with parallel staggered arrangement and then numerical simulation is performed. The simulation results show that by replacing fixed ball head with swinging ball head, the surface indentation of the formed part was effectively suppressed, the stress and tension strain distribution of the formed part was improved and the forming quality was improved; that the thickness of the elastic pad was reduced, the springback was reduced and the forming accuracy was improved; and that when the ball head was applied to multi-point die with staggered arrangement, better forming result was achieved, where the best forming result was achieved in combining the swinging ball heads with the multi-point die with parallel staggered arrangement. The forming experiments were carried out, and the experimental results were consistent with the trend of numerical simulation results, which verified the correctness of the numerical simulation.

Despite increased awareness of and attention to the need for sustainable agriculture, fertilizers and compost application in excess of crop requirements remain common agricultural practices in South Korea, causing eutrophication of freshwater and coastal ecosystems. In this study, a phosphorus (P) budget was developed to quantify P inputs, outputs, and retention in a forested- agricultural watershed. The P budget showed that chemical fertilizers and organic compost were the largest source of P (97.6% of the total) followed by atmospheric deposition (2.1% of the total P), whereas forest export (0.2% of the total) and sewage treatment plants (STPs) (0.1% of the total) were negligible. The dominant P outputs were crop harvesting and hydrologic export to surface water. The P balance showed a significant accumulation of P in the watershed; approximately 87% of the total P input was retained in the soils within the watershed. However, P concentrations in drainage water were still high enough to cause eutrophication of downstream reservoirs. The results provide useful information on the proportion of P export and retention in soils and will help support efforts to improve water quality and design better management of agricultural non-point source pollution.

Model-based design has received considerable attention in biological and chemical industries over the last two decades. However, the parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies were introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computation expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints. We also suggest employing the information from global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies where one is to design a heating/cooling profile for the essential part of a continuous production process and the other is to optimize the feeding profile for a fed-batch reactor of the penicillin fermentation process. The results reveal that the proposed approach can be used successfully for complex (bio)chemical problems in model-based design.

Since the systematic evolution of ligands by exponential enrichment (SELEX) method was developed, aptamers have made significant contributions as bio-recognition sensors. Microdevice systems allow for low reagent consumption, high-throughput of samples, and disposability. Due to these advantages, there has been an increasing demand to develop microfluidic-based aptasensors for analytical technique applications. This review introduces the principal concepts of aptasensors and then presents some advanced applications of microdevice-based aptasensors on several platforms. Highly sensitive detection techniques such as electrochemical and optical detection have been integrated into lab-on-a-chip devices and researchers have moved towards the goal of establishing point-of-care diagnoses for target analyses.

In our previous works we have proposed design equations that can predict with reasonable accuracy the transition point from homogeneous to heterogeneous regime as well as the gas holdup and the mean Sauter diameter at the homogeneous regime. The validity of the proposed correlations was checked with data obtained using different geometrical configurations and several Newtonian and non-Newtonian liquids as well as the addition of surfactants. However, in all the experiments the gas phase was atmospheric air. This work investigates the effect of gas phase properties by conducting experiments employing various gases (i.e., air, CO₂, He) that cover a wide range of physical property values. Experiments revealed that only the use of low-density gas (He) has a measurable effect on bubble column performance. More precisely, when the low-density gas (He) is employed, the transition point shifts to higher gas flow rates and the gas holdup decreases, a fact attributed to the lower momentum force exerted by the gas. In view of the new data, the proposed correlations have been slightly modified to include the effect of gas phase properties and it is found that they can predict the aforementioned quantities with an accuracy of ±15%. It has been also proved that CFD simulations are an accurate means for assessing the flow characteristics inside a bubble column.

In the last two centuries, land use change (LUC) has been the most important direct changes driver for terrestrial ecosystems. To contrast the consequent ecosystems degradation, forward-looking spatial policies and target landscape and land-use planning processes, promoting a sustainable land use change, are needed. The present paper proposes a framework of action including different landscape planning and ecological approaches: from the spatial modelling to recognize the LUC and build different scenarios, to the ecosystem services (ESs) assessment to evaluate the possible environmental impacts. Three different scenarios were built: Trend, No-Tillage and Energy crops. The Sediment Delivery Ratio and Carbon Storage and Sequestration ESs were assessed and compared for each scenario. The aim of the paper is to support decision-makers and local communities into the landscape planning process. Results show that a regional development in line with past trend could lead to further land degradation. Instead, the two scenarios proposed in compliance with EU policies, could bring benefits only if related to moderate LUCs and respecting the naturally grass-vegetated land. From the local to global scale, a guided and shared LUC management allows implementing sustainable development, basing on a deep knowledge of physical-environmental but also social and economic issues.

Natural ecosystem of Islands and coastal region are vulnerable to climate change phenomena such as increasing temperature, fluctuating rainfalls, ocean acidification and tsunami. Andaman and Nicobar group of islands lies in Bay of Bangal facing such extreme climate phenomena. A spatial-temporal analysis of forest cover of middle Andaman region of the Andaman and Nicobar group of islands was done from 1990 to 2019 with an interval of 5-10 years. Support vector machine classifier, spectral indices such as Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index were used for the analysis of greenery, water resources, and urban land. Land surface temperature was estimated using split window algorithm for Landsat 8 and mono window algorithm for Landsat 5. The data showed relative contribution of forest region toward rising temperature in the island region. The research also showed that subsurface hydrology linked to interconnected lineaments provides a stable zone for forest cover. The open forest showed maximum fluctuation while minimum change was observed in Evergreen Forest. The spectral characteristics analysis using indices showed significant change except in 2005 due to Tsunami occurred in 2005. The land surface temperature showed fluctuation near to 30° C from 1990 to 2019.

The practice of nurse health coaching (NHC) draws from the art and science of nursing, behavioral sciences, and evidence-based health coaching methods. This secondary analysis of the audio-recorded natural language of participants during NHC sessions of our recent 8-week RCT evaluates improvement over time in cognitive-behavioral outcomes: Change Talk, Resiliency, Self-Efficacy/Independent Agency, Insight & Pattern Recognition, and Building Towards Sustainability. We developed a measurement tool for coding, Indicators of Health Behavior Change (IHBC), that was designed to allow trained health coach experts to assess the presence and frequency of the indicators in the natural language content of participants. We used a two-step method for randomly selecting the 20-minute audio-recorded session that was analyzed at each time point. Fifty-six participants had high-quality audio recordings of the NHC sessions. Twelve participants were placed in the social determinants of health (SDH) group based on the following: low income (<$20,000/year), early-onset hypertension, and social disadvantages. Our analyses significantly improved Change Talk and the other four factors over time. Our factor analyses indicated two distinct factors at each measurement point of the study, demonstrating the stability of the outcome measures over time. Our newly developed measurement tool, IHBC, proved stable in structure over time and sensitive to change. This NHC program shows promise in improving cognitive-behavioral indicators associated with health behavior change in both non-SDH and SDH individuals.

Aim: The main purpose of this feasibility study was to estimate compliance and acceptability rate of a 9-Steps mobile app preparation program. Subsequently, to assess its impact regarding attitudes towards smoking cessation. Methods: The program was only available on versions above iOS 13.3. The ecological study collected user’s data via the app from 04/07/2021 to 28/07/2021. Results: A total of 2331 ecological users completed onboarding; 1926 users (73.9%) completed the step 0 and only 265 users continued in step 1. On average, participants actively engaged with intra-steps activities (85,1%). Most users were highly satisfied at the end of each step. On average 86% found the content of each step comprehensive and relevant. Mann-Whitney tests demonstrated positive changes in attitudes toward smoking cessation on the less confident users: willing (3 to 5, p <.05); confidence (3 to 5, p <.001) and readiness to quit (3 to 5, p <.001). Conclusions: This ecological study corroborates the expected dropout between first use and regular use of health mobile apps. However, engagement and satisfaction rates support the feasibility and acceptability of a 9-step preparation program. Additional research is needed to better understand determinants of regular use of mobile apps and its impact on the target behavior.

Climate change, biodiversity loss, and other social and environmental problems pose grave risks. Progress so far has been incremental and insufficient, and as a result scientists, global policy experts, and the general public increasingly conclude that bold change is required across all sectors of society. At least two kinds of bold change are conceivable: reform of existing societal systems (e.g., financial, economic, legal, and governance systems), including their institutions, policies, rules, and priorities; and transformation, understood as the de novo development of and migration to new, improved systems. This paper is the second in a series of three that together propose a novel science-driven research and development program aimed at societal transformation. Moreover, the series advances a conceptual framework and formal mechanics by which societal transformation might be approached. Two of the underlying hypotheses are that new societal systems can be developed in a science-driven process to be fit for purpose, and system fitness can be compared across designs. Societies are viewed as superorganisms, and systems are viewed as a societal cognitive architecture. The first paper in the series provides definitions, aims, hypotheses, and a worldview. This paper discusses motivations, the role of science in societal transformation, a theory of change, and fitness metrics. The proposed R&amp;D program and theory of change are sound, viable, and affordable. The local-global-viral strategy invites the global science community to play a unique co-leadership role with local communities in the development, testing, and monitoring of new societal systems. Systems are implemented via a novel civic club model, where participation is voluntary. Clubs grow and replicate based on merit and aided by club networks, whose systems are also viewed as societal cognitive architectures. Benefits of the program and strategy are discussed.

Abstract: Changes in forest landscapes have been connected with human activity for centuries, which can be considered as one of the main driving forces of change in the global perspective. The spatial distribution of forests changes along with the geopolitical situation, demographic changes, intensification of agriculture, urbanization or changes in the land use policy. However, due to the limited availability of historical data, the driving forces of changes in forest landscapes are most often considered in relation to recent decades, without taking into account long-term analyzes. The aim of this paper is to determine the impact of natural and socio-economic factors on changes in forest landscapes within the protected area – Ślęża Landscape Park and its buffer zone in the aspect of long-term analyzes covering the period of 140 years (1883-2013). The comparison of historical and current maps, demographic data on 4 different periods as well as natural and location factors by using the ArcGIS software allowed analyzing selected driving forces of forest landscape transformations. We took into account natural factors like altitude, slope, exposure of the hillside and socio-economic drivers like population changes, distances to centers of municipalities, main roads and built-up areas.

Chronic kidney disease (CKD) is a progressive condition that affects more than 10% of the world's population. Monitoring urine albumin and creatinine ratio (µACR) has become the gold standard for nephropathy diagnosis and control. The objective of the present study was to develop a simple, accurate, sensitive, and rapid point-of-care test (PoCT) device - MYμALBUMIN that community healthcare professionals can use in resource-limited facilities and by patients in their own homes for screening the risk and monitoring the progress of CKD. Albumin and creatinine concentrations in urine samples were determined using spectrophotometric dye (tetrabromophenol blue)-binding and colorimetric Jaffe’assay, respectively. Urine samples were diluted with distilled water (1:80) and mixed separately with albumin and creatinine reaction mixture. The creatinine reaction was incubated at room temperature (25oC) for 30 minutes before analysis. Optical density (OD) was measured at the wavelengths of 625 nm (albumin) and 515 nm (creatinine). All calibration curves (2-60 mg/L and 0.25-2 mg/dL for albumin and creatinine) yielded linear relationships with correlation coefficients (r2) of &gt; 0.999. Good accuracy (%DMV &lt; + 2.88%) and precision (%CV &lt; 3.0%) were observed from both the intra- or interday assays for the determination of albumin and creatinine by MYµALBUMIN. The limit of quantification (LOQ) of albumin and creatinine in urine samples determined by MYµALBUMIN and laboratory spectrophotometer were 2 mg/L and 0.1 mg/dL, respectively, using 37.5 μL urine samples. The device was well applied with clinical samples from 13 CKD patients. The median (range) of %DMV of the central (hospital) laboratory method (immune-based assay) was 3.18 (-10.11 to 14.12)%, with a high correlation coefficient (r2 &gt;0.99). In conclusion, MYμALBUMIN showed satisfactory test performance in terms of accuracy, reproducibility, sensitivity, and specificity. The device would have significant economic benefits besides obviously improving clinical decision-making processes.

Accurate clinical diagnosis of patients presenting to primary care settings with acute sore throat remains challenging, often resulting in the over-prescribing of antibiotics. Using point-of-care tests (POCTs) to differentiate between respiratory infections is well-accepted, yet evidence on the application within primary care is sparse. We assessed the application of testing patients (n=160) from three family practices with suspected Streptococcal infections using rapid molecular tests (ID NOW Strep A2, Abbott). In addition to comparing clinical evaluation and prescription rates with either usual care or with testing, patients and staff completed a questionnaire about their experience of molecular POCT in primary care. The immediate availability of the result was important to patients (100%) and staff (≈90%) stated that molecular testing improved quality of care. Interestingly, only 22.73% of patients with a Centor score >2 tested positive for Strep A and overall, less than 50% of Centor scores 3 and 4 tested positive for Strep A with the ID NOW testing platform. The addition of rapid molecular POCTs to clinical assessment resulted in a 55- 65% reduction in immediate and deferred antibiotic prescriptions. The intervention was popular with patients and medical staff but was associated with increased cost and a longer appointment length.

This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection And Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the crown point cloud. Each part is then processed using specific techniques to create a 3D model and determine its volume. The trunk point cloud is segmented based on individual stems, with each stem further divided into slices that are modeled as cylinders. On the other hand, the crown point cloud is analyzed by calculating its footprint and gravity center. The footprint is further divided into angular sectors, with each being used to create a rotating surface around the vertical line passing through the gravity center. All models are represented in a matrix format, simplifying the process of minimizing and calculating the tree’s upper biomass, consisting of crown biomass and trunk biomass. To validate the proposed approach, both terrestrial and airborne datasets are utilized. A comparison with existing algorithms in the literature confirms the effectiveness of the new method. The study shows that the proposed algorithm achieves an average fit between 0.01 m and 0.49 m for individual trees.

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

Interactions between mobile robots and human operators in common areas require a high safety especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection the crossings of planned trajectories, their uncertainty based on model fluctuations, system noise and sensor noise,play an outstanding role. This paper discusses the calculation of expected areas of interactions du ring human-robot navigation with respect to fuzzy and noisy information. Expected crossing points of possible trajectories are nonlinearily associated with positions and orientations of robot and human. The nonlinear transformation of a noisy system input, such as directions of motion of human and robot, to a system output, the expected area of intersection of their trajectories, is done by two methods: statistical linearization and the sigma-point-transformation. For both approaches fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from given output distribution parameters.

Point defects induced by doping rare earth elements RE (Nd, Er) into the magnesium oxide host were investigated by classical atomistic simulations utilising the General Utility Lattice Program (GULP). Formation and association energies were calculated for the potential defect structures. Both isolated defects and defect complexes were considered. The most energetically favourable structures of defect complexes were found for rare earth doped and Li co-doped systems. The correlation between the association energy and the structure of the defect complex was investigated. The influences of Li were revealed with respect to energy and structure. The simulation results contribute to the understanding of the point defects of doped MgO and how Li influences the doping of rare earth elements in the MgO host.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

We investigated the development of a simple and sensitive assay for the identification of minute amounts of animal and human biological material. The suggested system uses isothermal amplification of DNA in a rapid assay format, which returns the results in as little as 40 minutes from sampling. The assay used for the proof of concept employs the novel handheld SaLux19 device (Max-Planck Innovation prototype, Germany). This well-established infield detection system could also be utilized for forensic scenarios. The results presented here demonstrated that the assay is sufficiently specific and sensitive and can detect the presence of Sus scrofa nucleic acids.

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a hand-held laser scanning system for the extraction of tree position, diameter at breast height (DBH) and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%) compared to 2.54 cm (9.46%) and 0.23 cm (0.84%). Tree height extraction accuracy varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and height were underestimated. Linear mixed effects models (LME) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data. Other fixed factors of plot area, tree density and trajectory length didn’t have significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

The most commonly used real-time augmentation services in China are International GNSS Service (IGS) real-time service, PPP-B2b service, and BeiDou Satellite-Based Augmentation System (BDSBAS) dual-frequency augmentation service. However, the research on the performance evaluation, comparison and application scope of these three products is still unknown and unrevealed. This article introduces methods for obtaining real-time augmentation information and real-time orbit and clock offset recovery. Based on real-time orbit and clock offset accuracy, positioning accuracy, and positioning availability, it systematically evaluates the performance of CNES, PPP-B2b, and BDSBAS augmentation information. The results indicate that the radial accuracy of CNES and PPP-B2b real-time orbit products is consistent, and the Root Mean Square (RMS) is better than 5 cm. CNES can achieve centimeter-level accuracy in both along-track and cross-track components, surpassing the decimeter-level accuracy of PPP-B2b. Both services demonstrate consistent accuracy in real-time clock offset, with PPP-B2b showing similar standard deviations (STD) of 0.16 ns for different satellites. However, for CNES, the STD of real-time clock offset varies for GPS, BDS-3 MEO, and BDS-3 IGSO satellites, with values of 0.10 ns, 0.19 ns, and 0.60 ns, respectively. The accuracy and availability of the two PPP services exhibit the same levels, with centimeter-level accuracy achieved after convergence and positioning availability exceeding 99%. Therefore, the difference between the two services in application areas depends on the acquisition of augmentation information. However, BDSBAS, which concentrates on code observations, demonstrates lower accuracy in real-time orbit and clock offset, positioning accuracy, and positioning availability compared to the other two services. Its primary application is in the aviation and maritime domains, where there is a greater need for service integrity, continuity, and reliability.

Rational delineation of urban–rural boundaries is a foundational prerequisite for holistic urban and rural development planning and rational resource allocation. However, the results of division of urban–rural boundaries extracted using a single data source are non-comprehensive. To address this problem, the present study proposes a method for using multiple sources such as population data, nighttime light data, land use, and points of interest (POI) data to extract urban–rural boundaries. Considering Guizhou Province for a case study, we here present a two-step method for identifying urban–rural boundaries. First, the random forest model was combined with the dasymetric mapping method to obtain the population spatialization data with a 30-m resolution in the studied province. Second, using the breaking point method, we extracted the urban–rural boundary for Guizhou Province in 2020 based on the spatialized population. This method fully integrated the benefits of various data and judiciously extracted the boundaries of the main urban areas and small- and medium-sized towns of each city in the study province at the same spatial scale. The stratified random sampling method revealed that the average overall accuracy was 88.05%. The method proposed has certain universality and application value and allows identifying the urban–rural boundaries more accurately and practically.

The management and classification of solid waste is one of the most important challenges around the world, in order to sustain economic growth and preserve the environment. The objective is to propose the use of Smart Ecological Points as a strategy to address the problem related to the solid waste management system at the source, which has become one of the biggest problems globally and Colombia is no exception. The article describes the current state of the problem in the country and in turn, presents the possibility of developing a prototype corresponding to a low-cost Smart Ecological Point supported by the use of an experimental capacitive sensor and Machine Learning algorithms, which will reduce the time necessary for the classification of recyclable and non recyclable waste, minimizing the health risks and increasing the percentage of waste that can be reused, by reducing the probability of being contaminated at the source, an aspect that is very common when done manually. According to the results obtained, it was evident that the proposed prototype made an adequate classification of waste, generating the possibility of being manufactured with existing technology in the environment, in order to promote adequate waste classification at the source.

Although relatively rare, affecting 8% of the general population, autoimmune disorders are causative linked with chronic diseases and morbidity. Control of the course of the disease is closely dependent on the ability to monitor its onset as well as response to treatment. In the present report, we review the progress in the development of biosensor-based approaches and related tools for the Point-of-Care diagnosis and monitoring of biomarkers related to several autoimmune diseases, such as Myasthenia Gravis, Rheumatoid Arthritis, Multiple Sclerosis, Systemic Erythematosus Lupus, Crohn’s Disease, Diabetes Mellitus, Behcet’s disease, Psoriasis and Celiac Disease. Various biosensing technologies are discussed, including electrochemical, optical and mechanical ones, along with the use of advanced nanomaterials and immobilization techniques for the biorecognition elements. The need for innovative devices with unique features of rapid, low-cost, real-time detection is considered in the context of preventing permanent organ and tissue damage from chronic autoimmune diseases.

High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is of great significance for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a Park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the Filter algorithm, this article proposed a triangular grid filter based on Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground points extraction, this method has good accuracy, stability and achieves good results in separating ground points from non-ground points. At first, using Slope Filter to remove some non-ground points, to reduce the error of taking ground points as non-ground points; Secondly, established a triangular grid based on the triangular relationship between each point, the violation-triangle can determin through the grid, and then the corresponding violation points were found in the violation-triangle; Thirdly, according to the three-point collinear method to extract the regular points, used these points to extract the regular landmarks by KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm; Finally, removed disperse points and irregular landmarks by Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 sample data provided by ISPRS, and obtained an average error of 3. 46%. The results showed that the algorithm in this article have a high processing efficiency and accuracy, which can greatly improve the processing efficiency of point cloud data in practical applications.

3D point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we design an augmented sampling and grouping (ASG) module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to mine point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first try to use a linearly-normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module is adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that our method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which was 0.5% higher than PCT. Our network also archived 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT.

The paper's main goal is to introduce a novel type of interpolative convex contraction and build up some fresh findings for it using the interpolative convex contractions' progressive approach. We have established certain conclusions using orbitally S-complete and Suzuki type contractions in F-metric spaces. My research aims to examine the fixed point method's efficacy in solving fractional differential equation with boundary conditions.

Spatial approach based on the deformation measurement of volcanic dome and crater rim is key to evaluate the activity of a volcano, such as Merapi volcano where associated disaster risk is regularly taking lives. Within this framework, this study aime to detect localized deformation and change in the summit area that has occurred concomitantly with the dome growth and explosion reported. The methodology was focused on two sets of data, one LiDAR-based dataset of 2012 and one UAV-dataset of 2014. The results show that during the period 2012-2014, the crater walls are 100 m to 120 m high above the crater floor at its maximum (North to East-South-East sector), while the West and North sector presents a topographic range of 40 to 80 m. During the period 2012 – 2014, the evolution of the crater rim around the dome is generally stable (no large collapse). The opening of a new vent on the surface of the dome has displaced an equivalent volume of 2.04 E+04 m3 corresponding to a maximum -9 m (+/- 0.9 m) vertically. This concludes that during the period 2012 – 2014 when the dome of Merapi experienced phreatic or phreatomagmatic explosions, the topography around the dome rose. This rise does not seem to be related to large wall collapses, and it is likely that modification in the subsurface have triggered those changes.

Accumulated data show the utility of diagnostic multi-organ point-of-care ultrasound (PoCUS) in the assessment of patients admitted to an internal medicine ward. Assess whether multi-organ PoCUS (lung, cardiac, and abdomen) provides relevant diagnostic and/or therapeutic information in patients admitted for any reason to an internal medicine ward. Prospective, observational, and single-center study, at a secondary hospital. Multi-organ PoCUS was performed during the first 24 hours of admission. The sonographer had access to the patients’ medical history, physical examination, and basic complementary tests performed in the ED (laboratory, X-ray, electrocardiogram). We considered a relevant ultrasound finding if it implied a significant diagnostic and/or therapeutic change. In the second semester of 2019, 310 patients were enrolled (48.7% men, mean age 70.5 years). Relevant ultrasound findings were detected in 86 patients (27.7%) and in 60 (19.3%) triggered a therapeutic change. These findings were associated with older age (Mantel-Haenszel 2 = 25.6; p&lt; .001) and higher degree of dependency (Mantel Haenszel 2 = 5.7; p = .017). Multi-organ PoCUS provides relevant diagnostic information, complementing traditional physical exam, and facilitates therapy adjustment, regardless of the cause of admission. Multi-organ PoCUS to be useful need to be systematically integrated into the decision-making process in internal medicine.