This paper numerically investigates a 3D mesh independency study of a straight blade horizontal axis tidal turbine modelled using Computational Fluid Dynamics (CFD). The solution was produced by employing two turbulence models, the standard k-ε model and Shear Stress Transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis. It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15% to 20% lower than the accuracy of the SST model. It can also be demonstrated that the torque coefficient increases with the increasing Tip Speed Ratio (TSR), but drops drastically after TSR = 5 and k-ε model failing to capture the non-linearity in the torque coefficient with the increasing TSR.

Lake sediment records are of great importance for understanding the evolution of watershed environments. Various studies have been carried out to constrain the depositional ages of lake sediments and examine their physical, chemical, and biological characteristics, aiming to construct historical vegetation, environment and climate patterns in Chinese lake watersheds. In this study, we obtained relevant literature on lake sediment records by searching the key word 'age-depth' from the databases – Web of Science and China National Knowledge Infrastructure (CNKI, the largest Chinese academic database). We analyzed the literature based on its type (published in a Chinese/English journal or as a Master’s/PhD thesis), period of publication, journal (if published in a journal), key authors, study area, dating scale, and main aims. The results suggest that the lakes in the plateau regions are the most popular research topic typically covering 100–200 years (short-term) and 500–30,000 years (long-term). The literature focuses on a wide range of topics, from past environmental evolution in the watershed to lake ecology, and provides a solid foundation for better understanding regional climate change and the preservation of lake environments and ecosystems. In the future, resulting data from environmental reconstruction with lake sediments will be needed to integrate with emerging information processing technologies (e.g., artificial intelligence and meta-analysis), to disentangle the complex interplay between Earth surface processes and global climate change; furthermore, strengthening interdisciplinary collaboration will deepen our comprehension of man-land relationship and promote the sustainable management of lake ecosystems in the context of global climate change.

The aim of the study was to identify limiting factors for reusing wood through the recycling of window frames by conducting research under fully controlled conditions. The research involved manufacturing new window frames, seasoning them, and then shredding them into wood particles to prepare a three-layer particleboard. The proportion of wood particles in recycling was 0, 5, 10, 25, 50, and 100 parts by weight of the manufactured particleboard. Mechanical property tests were conducted: modulus of elasticity (MOE) and modulus of rupture (MOR), internal bond (IB), screw withdrawal resistance (SWR), and physical properties: density profile (DP), thickness swelling (TS) after water immersion, water absorption (WA), as well as formaldehyde emission and total volatile organic compounds (TVOC) tests. The conducted research indicates a significant potential for utilising wood from this sector of the wood industry, particularly considering variants with a higher proportion of recycled wood. MOR and MOE results are most promising for variants above 50 parts by weight of recycled wood. Based on the results obtained, it is clear that the production process should be improved or the raw material modified to enhance the internal bonding of particleboard, as these results were the weakest. Thus, recycled wood from window joinery has the potential to be reincarnated as particleboard, which continues to be widely used in their production.

The prevalence of hepatitis B and delta viruses (HBV/HDV) among people who use drugs (PWUD) remains largely unknown. In the context of one Philadelphia-based harm reduction organization (HRO), this study aimed to assess HBV/HDV prevalence and facilitate linkage to care. Participants completed a demographic HBV/HDV risk factor survey and were screened for HBV and reflexively for HDV if positive for HBV surface antigen or isolated core antibody. Fisher’s exact tests and regression were used to understand relationships between risks and HBV blood markers. Of the 498 participants, 126 (25.3%) were susceptible to HBV, 52.6% had been vaccinated against HBV, and 17.9% had recovered from a past infection. Eleven (2.2%) participants tested positive for isolated HBV core antibody, 10 (2.0%) for HBV surface antigen, and one (0.2%) for HDV antibody. History of incarceration was associated with current HBV infection, while transactional sex and experience of homelessness were predictive of previous exposure. This study found high rates of current and past HBV infection, and a 10% HBV/HDV co-infection rate. Despite availability of vaccine, one quarter of participants remained vulnerable to infection. Findings demonstrate the need to improve low-threshold HBV/HDV screening, vaccination, and linkage to care among PWUD. The study also identified gaps in the HBV/HDV care cascade, including lack of point-of-care diagnostics and lack of support for HROs to provide HBV services.

This paper introduces a three-layer system, proposing a comprehensive model of granular mixture transport over a mobile sloped bed in a steady flow. This system, consisting of the bottom, contact and upper zones, provides complete, continuous sediment velocity and concentration vertical profiles. The model considers gravity’s effect on sediment transport in the bottom (dense) layer, a crucial factor often overlooked in previous studies. The induced movement in the mobile sublayer of the dense layer causes an increase in velocity at the upper boundary of this layer, significantly affecting the vertical distributions of velocity and concentration above this layer. We described the interactions between fractions and an intensive sediment mixing and sorting process in the contact layer. The proposed shear variation, with an increase in its maximum value at the bed, adds to the novelty of our approach. The data sets used for the model’s validation cover various conditions, including slopes, grain diameters, densities and grain mobility conditions, from incipient motion to a fully mobilised bed. This extensive validation process instils confidence in the theoretical description and its applicability to real-world scenarios.

(1) Background: Second Victims are health care workers negatively affected by adverse events and are prone to developing dysfunctional coping strategies compromising not only their own, but also patient safety. An effective way to address this issue is the implementation of peer support programs. This paper reports on the effectiveness of a peer support program at the Austrian Hietzing Clinic (KHI). (2) Methods: Telephone interviews were carried out after peer support sessions using a questionnaire. Descriptive analysis was carried out reporting frequencies and proportions for nominal variables, means with standard deviations for ordinal and interval-scaled variables. (3) Results: So far, 45 peer supporters have completed telephone interviews after a peer support session. The main reasons for the session were bad news from the private sphere and death of a patient. The peer supporters rated the helpfulness of the sessions for their colleagues as very high while not feeling very burdened themselves afterwards. (4) Conclusions: Underutilization of the program is probable. However, the high rating of helpfulness of the carried-out sessions by the peer supporters in combination with them not feeling very burdened, shows that the program is safe for the peer supporters and an upscaling is warranted.

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

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

Extruded polystyrene (XPS) is frequently used in the construction of many different structures. It is therefore necessary to appropriately characterize its mechanical properties to ensure the safety of said structures. Among the available characterization tests, static bending tests are simple and easy to perform; owing to these characteristics, they should be performed more frequently than other tests. However, in static bending tests on XPS, there are several challenges owing to the high flexibility of XPS, and the chosen testing method and sample configuration affect the accuracy of characterization. In this study, three bending properties (Young’s modulus, proportional limit stress, and bending strength) of samples cut from an XPS panel were determined using three-point bending (TPB), four-point bending (FPB), and compression bending (CB) tests with varying sample span/depth ratios, and statistical analyses were performed to determine the relevance of the tests. The experimental results suggest that the TPB and CB tests were more feasible than the FPB test when the span/depth ratio was appropriately determined. However, clear differences were observed in the sample bending properties determined in these tests. In light of these findings, further studies should be conducted to elucidate these differences.

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

In the coming years, digital applications and services that continue to use the country's native cloud systems will be huge. By 2023, that will exceed 500 million, according to IDC. This corresponds to the sum of all applications developed in the last 40 years. If you are the one you answered, yes! This article is for you!

Beliefs that maintain patriarchal principles may affect individuals’ willingness to avoid conflicts in their intimate partner relationships and eventually are likely to affect chances of intimate partner economic violence. Nonetheless, research has yet to fully explore these associations. This study used a sample of 321 adults in the ultra-Orthodox Jewish community—a patriarchal and traditional culture—to examine associations in beliefs supporting patriarchal principles at the micro (gendered domestic roles), meso, and macro ( institutional power of men and inherent inferiority of women) levels; conflict avoidance and economic violence; and sex differences in these factors. Descriptive statistics were used to explore gender differences in research variables, and path analysis examined the correlations between research variables for men and women. Beliefs in patriarchal ideologies were moderate at all levels, although slightly higher among men. Men tended to avoid conflict with their intimate partner significantly more than women did. Beliefs in support of patriarchal ideologies predicted conflict avoidance, especially among women. Finally, in opposition to prior research findings, this study revealed nonsignificant sex differences in the prevalence of economic violence victimization. These findings, however, do not suggest that economic violence victimization is not rooted in sex issues. We discuss the findings and the meanings assigned to conflict avoidance by men and women while considering gender disparities of power and control. We suggest that the contribution of men’s tendency to avoid conflict likely moderated their chances to perpetrate economic violence.

Researches on the runoff and sediment reduction effects of soil and water conservation measures are being on the rise, which is of great significance for carrying out strategic on soil and water conservation in the Yellow River Basin. Through the analysis of various soil and water conserva-tion measures, runoff, sediment, and rainfall datas in the Sanchuan River Basin from 1960 to 2019, we use the lagrange multiplier method to determining the threshold year of soil and water con-servation measures on runoff and sediment processes. The preliminary conclusion is as follows: The trend of flood season rainfall and annual rainfall in the Sanchuan River Basin is consistent. The 10-year average rainfall in each periods indicates that before 2000, it gradually decreased and then significantly increased. The 10-year average runoff showed a gradual decrease before 2010, followed by a sudden increase, while the 10-year average sediment has shown a decreasing trend years by years, especially after the centralized treatment period. Terrace and dam measures show a trend of first decreasing, then increasing, and then approaching stability during different treat-ment periods, while the trend of forest and grass measures is slightly decreasing and then in-creasing rapidly. The runoff and sediment reduction effects of various soil and water conservation measures during different treatment periods indicate that the runoff reduction effect reached its peak in 2005, while the sediment reduction benefit reached its best in 2015. Based on the compre-hensive benefits of runoff and sediment regulation, 2015 are considered as the threshold year for various soil and water conservation measures, with each measure areas being 4.86 × 104, 17.91 × 104, 1.20 × 104, and 0.83 × 104hm2, respectively. The research results will have certain reference significance for the high-quality development of soil and water conservation in the Yellow River Basin.

Traumatic brain injury (TBI) is an important global clinical issue, requiring not only prevention but also effective treatment. Following TBI, diverse parallel and intertwined pathological mechanisms affecting biochemical, neurochemical, and inflammatory pathways can have severe impact on the patient’s quality of life. The current review summarizes evidence for utility of amantadine in TBI in connection to its mechanism of action. Amantadine combining multiple mechanisms of action may offer both neuroprotective and neuroactivating effects in TBI patients. Indeed, use of amantadine in TBI has been encouraged by several clinical practice guidelines/recommendations. Amantadine is also available as infusion which may be of particular benefit in unconscious patients with TBI, due to immediate delivery to the central nervous system and the possibility of precise dosing. In other situations, orally administered amantadine may be used. There are several questions that remain to be addressed: Can amantadine be effective in disorders of consciousness requiring long-term treatment and in combination with drugs approved for treatment of TBI? Do the observed beneficial effects of amantadine extend to disorders of consciousness due to factors other than TBI? Well controlled clinical studies are warranted to ultimately confirm its utility in the TBI and provide answers these questions.

The information on the land use and soil conservation practice based on year 2006, 2010 and 2014, hence offering an opportunity to model the impacts of land use change on erosion, deposition and surface water runoff. Limitation in the use of hydrological models had been their inability to handle the large amount of input data that describe the heterogeneity of the natural system. In this study, a procedure that takes into account soil conservation practice based on the land use change, the response of soil erosion and sediment export from the George Town Conurbation catchment area, and average annual sediment yields were estimated for each grid cell of the watershed to identify the critical erosion areas of rural and urban planning proposes. Average annual sediment yield and data on a grid basis estimated using Universal Soil Loss Equation (USLE) and an emerging technology represented by Geographic Information System (GIS) used as a tool to produce a map for erosion rate. The changing of the land use from forest to agriculture and then to an urban area is a challenging task to research on land use demand for population, and environmental impact assessment is important for the planning of natural resources management, allowing research the modification of land use properly and implement more sustainable for long term management strategies. The challenge is to formulate strategies that would promote an integrated approach to the land use planning at an appropriate level as to address the issues that arose. Modelling for creating urban growth boundary for the George Town Conurbation must have to be controlled surface runoff and soil loss and sediment export from land use of the George Town Conurbation catchment.

Sediment plumes, released to the Bering Sea from the delta front of the Yukon River, Alaska, are initiated mainly by glacier-melt sediment runoffs in the glacierized regions of the Yukon River drainage basin. The surface sediment plumes are extended around the fan-shaped Yukon River delta, which is followed by the northwestward dispersion. During continuous measure-ments of the Yukon River discharge and sediment load, behaviors of the sediment plumes were explored by shipboard observations in the Bering Sea offshore from the Yukon delta. At the high river sediment load of ca. 3000 kg/s, the plume partially plunged into the sea bottom layer. The plunging probably originated in the nepheloid-layer formation from the flocculation of river-suspended sediment, of which more than 90 %wt. is silt and clay (grain size d < 0.063 mm). In order to numerically obtain the area of the surface sediment plumes, a satellite image analy-sis was performed by using three near-infrared bands in MODIS/Aqua or MODIS/Terra. The plume area was significantly correlated (R2=0.735, p<0.01) to the sediment load averaged for the two days with time lags of 20 days and 21 days to the date of a certain satellite image. Hence, the dispersion of plume-suspended sediment appears to be controlled by the sediment runoff events in the Yukon River rather than the northward “Alaskan Coastal Water”.

The task of weakly supervised point cloud semantic segmentation has received widespread attention and also has been widely used in autonomous driving, robotics, and modern industries. Due to the high dimensionality and large volume of point cloud data, many technical difficulties appeal to weakly supervised semantic processing, especially segmentation. However, weakly-supervised schemes usually provide only partial labelling information of the underlying point cloud data and thus need to effectively extract local features, geometric information, and contextual relationships only using these limited labelled data for supervised learning. To improve weakly-supervised semantic segmentation, we propose a novel segmentation network through the boundary-based feature aggregation based on a K-NN algorithm with down-sampling operation, and also introduce the smoothness loss and Siamese loss for effective segmentation. The experiments on public datasets demonstrate that our presented segmentation method exceeds most of the existing fully supervised and weakly-supervised methods in terms of mIoU. Specifically, our network has high segmentation accuracy on the labels of objects with similar geometrical structures, such as ceiling, wall, floor, chair and table, reaching 91.2%, 98.8%, 83.3%, 75.3% and 80.2%, respectively. Extensive experiments also illustrate its robustness, effectiveness, and generalization of the proposed weakly supervised segmentation network.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierar-chical path planning approach driven by dynamic 3D point cloud is proposed in this article. The developed path planning model is essentially constituted of two layers: global path planning layer and local path planning layer. At the global path planning layer, a new method is proposed for calculating terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision area and a strategy for temporary target point selection is developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experi-mental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maxi-mum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is short-ened by 20%, more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

Poultry farming in Slovakia has a tradition and an irreplaceable position to ensure rational nutrition for the population. In terms of a nutritional value, poultrymeat (Galliformes) contains a high amount of proteins, essential amino acids, essential unsaturated fatty acids, minerals, vitamins and a low fat content. Poultrymeat production has a relatively short reproductive cycle and thus allows regulating the demand and supply. The poultry market also influences the development of domestic consumption. According to the Statistical Office of the Slovak Republic, the consumption of meat has witnessed a persistent increase over the last 10 years. [1] According to the 2023 AVEC (The Voice of European Meat Sector) annual report, the consumption of poultrymeat in Slovakia was 20.0 kg per person per year. The average consumption in the EU-27 (2021) was 23.4 kg, with the countries with the highest consumption being Portugal (37.8 kg), Ireland (36.0 kg), Hungary (33.5 kg) and Spain (29.9 kg). On the other hand, the countries with the lowest consumption of poultrymeat, apart from Slovakia, also include Austria and Greece with a consumption of 21.5, resp. 21.0 kg [2]. The breeding of broiler chickens, as well as the production and placing on the market of poultrymeat must comply with the requirements laid down by legislation, while the compliance is regularly checked by the competent authorities.

Microbial composition across substrates in mangroves, particularly in the Middle East, remains unclear. This study characterized bacterial communities in sediment, water, Terebralia palustris snail guts, and plastic associated with Avicennia marina mangrove forests in two coastal lagoons in the Sea of Oman using 16S rDNA gene MiSeq sequencing. The genus Vibrio dominated all substrates except water, which featured unique genera Sunxiuqinia and Pseudomonas. There were significant differences in bacterial communities on different substrata, in particular plastic. Snail guts harbored the highest number of unique Operational Taxonomic Units (OTUs) in both lagoons. Plastic in the polluted Sawadi lagoon with low salinity harbored distinct genera like Vibrio, Aestuariibacter, Zunongwangia, and Jeotgalibacillus absent in Qurum lagoon with higher salinity and low pollution. Sawadi lagoon exhibited higher species diversity in sediment and plastic substrates, Qurum lagoon demonstrated lower overall species diversity. The PCA indicates that environmental factors like salinity, pH, and nutrient levels significantly influence bacterial community composition across substrates. Variations in organic matter and potential anthropogenic influences, particularly from plastics, further shape bacterial communities. This study highlights the complex microbial communities in mangrove ecosystems, emphasizing the importance of considering multiple substrates in mangrove microbial ecology studies.

The purpose of this paper is to examine the role and impact of public governance on European supranational public policies that seek to foster scientific and economic growth within innovation ecosystems (IEs) through the PITCCH initiative. The PITCCH is a European innovation development initiative funded by Horizon 2020 under the 'Innovation in SMEs' measure.The research methodology for this study is based on a 10-month real-time ethnographic field study of the PITCCH project, which allowed for the observation of the project governance and the capture of the perspectives of innovation-driven small and medium enterprises (i-SMEs) in their engagement with IE stakeholders.The findings of this research demonstrate the role of public governance in providing result-oriented practical support to the IE through the lens of i-SMEs, and they contribute to the existing literature on regional economic growth supported by supranational public policies. These findings have both academic and practical applications. They enrich the current literature on this topic and provide insights for policymakers and governing representatives seeking to implement effective public policies.

The global process of urbanization, and the modification of social interaction determined by the pandemic crisis poses the issue of the place of vulnerable users, and in particular children, within the contemporary city. This research aims to elaborate a theoretical and methodological framework, based on the concepts of affordance and capability, for analyzing the potential of public spaces to enable and support children’s independent activities. This potential, or meaningful usefulness, is expressed by the Index of Meaningful Usefulness of public Urban Spaces (IUIS). The latter is calculated via the tool ‘Opportunities for Children in Urban Spaces’ (OCUS). This methodology is applied to the analysis of significant public spaces within the historic center of the city of Iglesias in Sardinia, Italy. The results reveal adequate usefulness of the selected spaces, while underlining criticalities related to intrinsic spatial and physical attributes. The application to the case study confirms the validity of the theoretical and methodological framework embodied in the OCUS tool for supporting urban design and planning by orienting place-shaping processes towards the acknowledgement of children’s needs.

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

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.

Nasal polyposis is defined by a Th2-driven chronic inflammation of the nose and sinus with polyps visible in the nasal fossae. It is a prevalent disease with a significant impact on the HRQL. Allergy, allergic rhinitis, asthma, and aspirin intolerance are frequently associated. The management is individual. The first line of treatment is a long-term treatment with intranasal corticosteroids. Oral corticosteroids should be used with caution. When the medical treatment fails the patient is eligible for sinus surgery. In case of symptomatic recurrence after both medical treatment and ethmoidectomy, biologics are nowadays a very promising treatment effective on all the respiratory tract. Dupilumab seems in the literature the molecule of choice. However, besides the international guidelines published by EPOS and Euforea, the molecule prescribed depends also on the availability of it in each country and the criteria edited by the health authorities to get reimbursement. Long treatment is mandatory. Traditional medical treatment is necessary as a complement to biologics.

Health and social needs exist along a dynamic continuum. Recognizing that health status is inextricably impacted by social determinants of health, community paramedicine has opportunities and a responsibility to reduce inequities. The objective of this scoping review was to investigate peer-reviewed and grey literature to explore how community paramedicine supports community needs along a health and social continuum. We conducted a scoping review of English language literature using the JBI Scoping Review methodology. We searched CINAHL, EMBASE, MEDLINE, Google Scholar, and organisational websites. 30 peer-reviewed and 13 grey literature articles met inclusion criteria. The findings describe the ways community paramedicine models evolved from minimising system pressures on emergency health services to addressing health and social needs. A key recommendation across the literature was the need to meaningfully engage communities early in program development to understand how best to implement and co-design integrated service models addressing specific community needs, though there was a lack of evidence to guide this approach. There is a notable lack of evidence pertaining to optimising technologies in program design and implementation. Results highlight opportunities to determine best practices for conducting holistic community needs assessments that include equitable stakeholder engagement and enhancing education to prepare paramedics for expanded roles. Community paramedicine provides opportunities to better meet the needs of structurally marginalised communities. There is a social responsibility and opportunity to engage communities to co-design service delivery, advance paramedic education, and enhance interprofessional collaboration to better support community needs and generate upstream solutions for individuals and communities

The accelerated development of AI technology has brought about revolutionary changes in various fields of society. Recently, it has been emphasized that Fairness, Accountability, Transparency, Ethics (Explainability) (FATE) should be considered to support the reliability and validity of AI-based decision-making. However, in the case of autonomous driving technology, which is directly related to human life and requires real-time adaptation and response to various changes and risks in the real world, environmental adaptability must be considered in a more comprehensive and converged manner. In order to derive definitive evidence for each object in a convergent autonomous driving environment, it is necessary to transparently collect and provide various types of road environment information for driving objects and driving assistance, and to construct driving technology that is adaptable to various situations by considering all uncertainties in the real-time changing driving environment.This allows unbiased and fair results based on flexible contextual understanding, even in situations that do not conform to rules and patterns, by considering the convergent interactions and dynamic situations of various objects that are possible in a real-time road environment. The transparent, environmentally adaptive, and fairness-based outcomes provide the basis for the decision-making process and support clear interpretation and explainability of decisions. All of these processes enable autonomous vehicles to draw reliable conclusions and take responsibility for their decisions in autonomous driving situations. Therefore, this paper proposes an Adaptability, Fairness, Transparency, Explainability and Accountability (AFTEA) Framework to build a stable and reliable autonomous driving environment in dynamic situations. This paper explains the definition, role, and necessity of AFTEA in artificial intelligence technology and highlights its value when applied and integrated into autonomous driving technology. The AFTEA framework with environmental adaptability will support the establishment of a sustainable autonomous driving environment in dynamic environments, and aims to provide a direction for establishing a stable and reliable AI system that adapts to various real-world scenarios.

In this paper, we investigate a class of hierarchical variational inequalities (HVIPs, i.e., strongly monotone variational inequality problems defined on the solution set of multiple-set split common fixed point problems) with quasi-pseudocontractive mappings in real Hilbert spaces, which special cases can be found in many important engineering practical applications such as image recognizing, signal processing and machine learning. In order to solve HVIPs of potential application value, inspired by the primal-dual algorithm, we propose a novel accelerated cyclic iterative algorithm that combines the inertial method with a correction term and a self-adaptive step-size technique. Our approach eliminates the need for prior knowledge of the bounded linear operator norm. Under appropriate assumptions, we establish strong convergence of the algorithm. Finally, we apply our novel iterative approximation to solve multiple-set split feasibility problems and verify the effectiveness of the proposed iterative algorithm through numerical results.

Human activities for exploitation and utilization of coastal zones have transformed coastline morphology and severely changed regional flow fields, underwater topography, and sediment distribution in the sea. In this study, single-beam bathymetry coupled with sediment sampling and analysis was carried out to ascertain submarine topography, geomorphology and sediment distribution patterns and explore sediment provenance in Qinzhou Bay, China. The results show the following. 1) The underwater topography in Qinzhou Bay is complex and variable, with water depths in the range of 0–20 m. It can be divided into four underwater topographic zones: the central (outer Qinzhou Bay), eastern (Sanniang Bay), western (east of Fangcheng Port), and southern (outside of the bay) parts. 2) Based on geomorphological features, the study area comprises four major submarine geomorphological units (i.e., tide-dominated delta, tidal sand ridge group, tidal scour troughs, and underwater slope) and two intertidal geomorphological units (i.e., tidal flat and abrasion platforms). 3) Sandy sediments are widely present in Qinzhou Bay, accounting for 70% of the total sediments. From the mouth of the Maowei Sea to the offshore, the sediments gradually become coarser, shifting from sandy mud to muddy sand, and then to fine sand and medium–coarse sand. The detrital minerals contained in the sediments mainly consist of quartz, feldspar, debris, ilmenite, leucosphenite, and tourmaline, whereas the clay minerals are dominated by kaolinite, followed by illite and smectite. The sediment provenance is mainly terrigenous input from near-source rivers. With sea reclamation and dam construction, outer Qinzhou Bay has experienced enormous morphological variation of its coastline. Together with the channel effect where the velocity of ebb tide is greater than that of flood tide, the underwater topography is characterized by increased scale and height difference of troughs and ridges as well as enhanced offshore deposition.

Human activities for exploitation and utilization of coastal zones have transformed coastline morphology and severely changed regional flow fields, underwater topography, and sediment distribution in the sea. In this study, single-beam bathymetry coupled with sediment sampling and analysis was carried out to ascertain submarine topography, geomorphology and sediment distribution patterns and explore sediment provenance in Qinzhou Bay, China. The results show the following. 1) The underwater topography in Qinzhou Bay is complex and variable, with water depths in the range of 0–20 m. It can be divided into four underwater topographic zones: the central (outer Qinzhou Bay), eastern (Sanniang Bay), western (east of Fangcheng Port), and southern (outside of the bay) parts. 2) Based on geomorphological features, the study area comprises four major submarine geomorphological units (i.e., tide-dominated delta, tidal sand ridge group, tidal scour troughs, and underwater slope) and two intertidal geomorphological units (i.e., tidal flat and abrasion platforms). 3) Sandy sediments are widely present in Qinzhou Bay, accounting for 70% of the total sediments. From the mouth of the Maowei Sea to the offshore, the sediments gradually become coarser, shifting from sandy mud to muddy sand, and then to fine sand and medium–coarse sand. The detrital minerals contained in the sediments mainly consist of quartz, feldspar, debris, ilmenite, leucosphenite, and tourmaline, whereas the clay minerals are dominated by kaolinite, followed by illite and smectite. The sediment provenance is mainly terrigenous input from near-source rivers. With sea reclamation and dam construction, outer Qinzhou Bay has experienced enormous morphological variation of its coastline. Together with the channel effect where the velocity of ebb tide is greater than that of flood tide, the underwater topography is characterized by increased scale and height difference of troughs and ridges as well as enhanced offshore deposition.

Anthropogenic activities such as the damming of rivers have resulted in the formation of depositional systems for sediments that are naturally transported along river channels all over the world. The steady accumulation of sediments has profound effects on how reservoirs function. As the reservoir ages, the impact of sedimentation becomes more pronounced. In an alluvial channel, the dynamics of sediment movement, scour, and deposition are exceedingly complicated. The quantitative and qualitative management of river engineering heavily relies on sediment motion. One of the key concerns affecting reservoir storage capacity is reservoir sedimentation. Understanding the form and behavior of the river or reservoir is necessary for a scientific approach to sedimentation. An overview of reservoir sedimentation processes, and models of reservoir and shallow water bodies is provided in this article. More research in the developing fields is advised for continuing studies on reservoir sedimentation due to climate change effects

With the development of salt cavern gas storage construction technology, the construction of large-scale gas storage salt caverns using sediment voids is expected to solve the problem of low effective volume formation rate and poor construction economy of high-impurity salt mines. There are few studies on the long-term operation mechanical behavior of salt cavern gas storage under the influence of sediment accumulation currently. This paper studies the influence of sediment accumulation height, particle gradation, and operating pressure on the stability of salt caverns by constructing a coupling model of sediment particle discontinuous medium and surrounding rock continuous medium. The continuous-discontinuous coupling algorithm is suitable for analyzing the influence of particle accumulation height and particle gradation on the creep shrinkage of salt caverns. The increase of sediment accumulation height slows down the creep shrinkage of the bottom of the salt cavern, strengthens the restraining effect on the surrounding rock of the cavern, and moves the position of the maximum displacement of the surrounding rock to the upper part of the cavern; the sediment particle gradation has little effect on the cavern volume shrinkage rate, and the greater the coarse particle content, the smaller the cavern volume shrinkage rate; the greater the operating pressure, the more conducive to maintaining the stability of the cavern, slowing down the upward movement of the sediment body and increasing the gas storage space in the upper part of the cavern. This research results can provide a reference for evaluating the long-term operation stability of sediment-type high-impurity salt cavern gas storage.

For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

This study introduces a hardware accelerator to support various Post-Quantum Cryptosystem (PQC) schemes, addressing the quantum computing threat to cryptographic security. PQCs, while more secure, also bring significant computational demands, especially problematic for lightweight devices. Previous hardware accelerators are typically scheme-specific, which is inefficient given the National Institute of Standards and Technology (NIST)'s multiple finalists. Our approach focuses on the shared operations among these schemes, allowing a single design to accelerate multiple candidate PQCs at the same time. This is further enhanced by allocating resources according to performance profiling results. Our compact, scalable hardware accelerator supports four of NIST PQC finalists, achieving an area efficiency of up to 81.85\% compared to the current state-of-the-art multi-scheme accelerator while supporting twice as many schemes. The design demonstrates average throughput improvements ranging from 0.97$\times$ to 35.97$\times$ across the four schemes and their main operations, offering an efficient solution for implementing multiple PQC schemes within constrained hardware environments.

The aim of the article is to identify and illustrate the most used psychological techniques in the field of cystic fibrosis (CF) and to help clinicians choosing the appropriate strategy among the various possibilities. The disease and its medical treatments can be difficult to tolerate and can cause anxiety about health status or feelings of hopelessness and stress. The prevalence of depression and anxiety is 2.3 times higher in adults with CF than in community samples. A strong correlation has been identified between elevated psychological distress and unfavorable health outcomes, including, among others, impaired lung function, reduced BMI, an increased incidence of pulmonary exacerbations, and an elevated risk of transplantation. The use of psychological interventions is useful in addressing these common distress in CF patients. Aware of the necessity to identify efficacious interventions for all levels of depression and anxiety in CF patients, this study presents an overview of the research on psychological interventions for patients with CF, in order to complement the treatments suggested by the international guidelines on mental health in CF cases. In fact, the aim of this study is to conduct a review and quantitative synthesis of the psychological intervention techniques that are currently available for individuals with CF.

Oculomotor Dysfunctions (OMDs) are problems with eye-musculature relating to coordination and accuracy of eye movements. Eye-tracking (ET) technologies show great promise in the identification of OMDs. Current computer technologies for vision screening are specialized devices or laptop based technologies with limited by screen size and the inability to measure depth. In this experimental study, we examine the possibilities of immersive virtual reality (VR) technologies for increased user experiences, presence, immersiveness, and the use of serious games. Further results of an experimental study present increased interest in VR-based screening, its ability to focus better, and aspects motivating for its use, despite the actual limitations of current technologies. These limitations currently include lower performance and confidence in results of the used HMDs. Some users also describe being more focused when screening in VR, free from outside distractions. Using serious games for screening in VR is also estimated to have great potential for developing a more robust vision screening tool, especially for younger children.

Traditional agricultural extension services rely on extension workers, especially in countries with large agricultural areas. In order to increase adoption of sustainable agriculture, the recommendations given by such services must be adapted to local conditions and be provided in a timely manner. The AgroTutor mobile application was built to provide highly specific and timely agricultural recommendations to farmers across Mexico and complement the work of extension agents. At the same time, AgroTutor provides direct contributions to the United Nations Sustainable Development Goals, either by advancing their implementation or providing local data systems to measure and monitor specific indicators such as the proportion of agricultural area under productive and sustainable agriculture. The application is freely available and allows farmers to geo-locate and register plots and the crops grown there, using the phone’s in-built GPS, or alternatively, on top of very high-resolution imagery. Once a crop and some basic data such as planting date and cultivar type have been registered, the app provides targeted information such as weather, potential and historical yield, financial benchmarking information, data-driven recommendations as well as commodity price forecasts. Farmers are also encouraged to contribute in-situ information, e.g., soils, management, and yield data. The information can then be used by crop models, which, in turn, would send tailored results back to the farmers. Initial feedback from farmers and extension agents has already improved some of the app’s characteristics. More enhancements are planned for inclusion in the future to increase the app’s function as a decision support tool.

The Casiquiare River is a natural channel that connects two of the greatest rivers in the world, the Orinoco and the Amazon in the South American Continent. The aim of this paper is to present a review and synthesis of the hydrological and sedimentological knowledge of the Casiquiare River, including the first hydro-sedimentary balance of the Casiquiare fluvial system conducted at the bifurcation and at the mouth on September 9-12, 2000, during the expedition ‘Humboldt-Amazonia 2000’. Bathymetric, flow discharge and physico-chemical measurements were made at the inlet and at the outlet of the Casiquiare Channel. The main conclusions of this study indicate that Casiquiare is taking a significant proportion of flow (20 to 30%) from the Upper Orinoco basin to the Amazon basin. Throughout its 356 km-course, this chameleon channel undergoes significant morphological, hydrological and bio-geochemical variations between the inlet and the outlet, whose most visible witnesses are the increase in its width (3 to 4 times), flow (7 to 9 times) and its change in water color (white to black water), under the influence of tributaries coming from vast forest plains.

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

A software defined radio (SDR) is a communication system that makes use of components that can be configured through software, in contrast to traditional systems where these components are variable through hardware, these radio devices are much more versatile, this article describes the factors that must be considered when implementing a communication system based on Software Defined Radios (SDR), in order to reduce the attenuation factors and thus obtain the maximum distance for a transmission of data effectively in the UHF band. The calculations made for the first Fresnel zone and for the design of the Ground Plane type antennas used in the transmission/reception stages of the x40 bladeRF platforms are also presented. The tests were carried out at the facilities of the Huarangal Nuclear Center of the Peruvian Institute of Nuclear Energy, obtaining favorable results that allow ratifying the versatility and performance of the SDRs.

The meaning of a good social outcome in land use transport policy/planning has been relatively underdeveloped, compared to economic and environmental sustainability goals, but this is now changing. Cities are increasingly prioritizing reducing social exclusion, with the allied intent of providing all residents with equitable access to the benefits of their city. However, in jurisdictions that use cost-benefit analysis to guide government decision-making, this poses a challenge: monetization of the benefits of reducing social exclusion is poorly developed. An evaluation gap thus confronts benefit-cost based assessment of initiatives intended to reduce social exclusion, compared to initiatives directed towards societal economic and environmental goals. This is a particular problem for public transport services that primarily enable people at risk of mobility-related social exclusion to access more opportunities in their society (social transit). The benefits of reducing social exclusion associated with these services have not been monetized, so social transit initiatives are poorly placed to argue their merits against competing initiatives where benefit monetization is more advanced (e.g., mass public transport and roads). The paper summarizes Australian research that has developed monetized measures for the value of increased trip making, bridging/bonding social capital, sense of community and several conceptions of wellbeing, and of reducing neighbourhood disadvantage, as these contribute to reducing social exclusion. New Singaporean research on the value of trip making to support reduced exclusion is also presented, affirming the Australian trip values. Trip values are then used in case study examples to show they can make a material difference to the societal worth of initiatives intended to reduce exclusion. Using these trip values, and other pathway values linked to reducing social exclusion, closes or at least substantially reduces the evaluation gap confronting social goal achievement in land use transport policy and planning, strengthening the case for reducing exclusion. These values will assist jurisdictions that are seeking to provide their residents with more equitable access to the benefits of their city.

Good eyesight belongs to the most-valued attributes of health, and diseases of the eye are a significant health care burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PK) and pharmacodynamics (PD). Ocular quantitative systems pharmacology (QSP) models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug-response in specific segments of the eye. In particular, ocular physiologically-based pharmacokinetic (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models employing molecular data from specific cellular systems are needed to develop dose-response correlations. We here describe the current status of PK/PBPK as well as PD models for the eye and describe cellular systems, data repositories as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for post-operative fibrosis after glaucoma surgery.

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.

In interceptive treatments in which impacted teeth (incisor or first permanent molar) should be included into the dental arch, the main clinical problem is the provision of an anchorage area and a suitable appliance. The use of laser sintered metal orthodontic grids, designed and printed digitally, has expanded the boundaries of orthodontic treatment, especially in its interceptive phase. Our aim is to describe a digital model for providing anchorage during orthodontic-surgical traction of impacted teeth in the mixed dentition period. We present the treatment of three clinical cases with digitally projected and printed metal grids used for anchorage. In the described clinical cases, individually designed anchorage systems were used. They were created through design software and after that the metal grids were printed. The devices are placed in the oral area and don’t disturb the aesthetics of the patient in any way. The advantages of individual designed metal support grids allow to accommodate the features of relief of the hard palate and alveolar bone. This makes the grids comfortable for the patient and rigid enough to perform orthodontic tractions at the same time. The printed additional grids are applicable in mixed dentition and in complicated clinical cases in permanent dentition. Their individuality allows segmental treatments or treatments in cases requiring additional anchorage further from the active force.

In this paper is discussed sediment transport as a mechanical process that characterises a natural stream or channel flow regime. The objective of experimental work presented in this paper is to recall and to give another prospect of well-known Meyer-Peter and Müller approach for estimation of Shield’s number (θ_c,θ) in laboratory conditions, and calibration of dimensionless MPM number (A). For this purpose two different experiments are conducted, during the first experiment water amount flushed on the flume and bed slope was changed simultaneously until equilibrium state is achieved, meanwhile is estimated the critical Shield’s number (θ_c). While, during the second experiment, water amount was kept constant, only bed slope of flume was continuously tilted, meanwhile sediment, discharge and Shield’s number (θ) was determined for given hydraulic conditions. In addition calibration of dimensionless MPM number (A) was performed, where several iteration were considered until for (A=3.42), sediment discharge measured become almost equal with sediment discharge computed by using MPM formula. After these experiments, is concluded that MPM formula can be used also for other certain initial condition and similar procedure may be adopted to calibrate the dimensionless MPM number (A) .

Measurements of optical properties have been used for decades to study particle distributions in the ocean. They have been found useful to constrain suspended mass concentration as well as particle-related properties such as size, composition, packing (particle porosity or density) and settling velocity. Optical properties, however, provide measurements that are biased, as certain particles (based on size, composition, shape or packing) contribute to a specific property more than others. Here we study this issue both theoretically as well as by contrasting different optical properties collected simultaneously in a bottom boundary layer, to highlight the utility of such measurements as well as the biases we are likely to encounter using different optical properties to study suspended particles. In particular, we investigate the possibility to infer settling velocity from vertical profiles of optical measurements, finding that the effects of aggregation dynamics can seldom be ignored.

As the penetration of renewable energies increases, their acceptance of the power grid system is becoming an important issue. In the case of South Korea, a 22.9kV transformer is frequently utilized to reduce the power loss of the generated renewable energy transmission, so the power quality at the point of common connection (PCC) is inevitably influenced by the nonlinearity of the transformer and the uncertainty of the inductance and resistance of the transmission line. In this paper, we present a point of common connection voltage-modulated direct power control (PCCVM-DPC) system that enhances power quality at the PCC. First, we present the mathematical dynamics of active and reactive power at the PCC side, which contains the step-up transformer’s nonlinearity, the grid voltage harmonics, and the parameter uncertainties. By analyzing the disturbance terms of the PCCVM-DPC system, we present the disturbance observer (DOB) for the PCCVM-DPC system. To assess the efficacy of our approach, we perform comparative studies of the PCCVM-DPC without DOB and PCCVM-DPC with DOB by constructing the simulation environment based on the commercial step-up transformer and ESS inverter datasheet. We have validated that the active and reactive power control performance of the PCCVM-DPC with DOB outperforms the PCCVM-DPC without DOB from the observation that the current total harmonic distortion reduced by more than 40% compared to the PCCVM-DPC without the DOB.

Spatiotemporal variations of sediments transported along rivers play a crucial role in a wide spectrum of uses, such as navigation, recreation, habitats or hydropower production. The advancement in technology has made it possible to use various indirect techniques to study and evaluate the transport of suspended sediment in fluvial environments. To investigate large-scale phenomena, remote sensing is becoming a largely utilized approach, as it allows to combine spatially-distributed and local information. Dam-induced sediment reduction occurs in large and small rivers worldwide, having profound implications on the fluvial systems. However, the systematic change of suspended sediment concentration (SSC) and its dynamic processes are not well known, and generally investigated only at the very local scale using field information. In this work, SSC maps were created by combining satellite images with local monitoring SSC data, using the Zhijiang -Chenglingji reach of the Changjiang River (China) as a case study, and analyzing how SSC dynamics changed over the period 2017-2022 in correspondence of extreme events. Multiple relationships between measured SSC and bands reflectance were tested, showing the potential of the best-performing one (R2=0.43) in mapping SSC spatiotemporal variations over an extent of dozens of kilometres, eventually providing new insights into the SSC dynamics at hotspots of the river systems, such as confluence zones, barriers, and reservoirs.

This paper presented a two-dimensional, well-balanced hydrodynamic and sediment transport model based on the solutions of variable density shallow water equations (VDSWEs) for sediment-laden flows, and the Exner equation for bed changes. Those equations are solved in a coupled way by the first-order Godunov-type finite volume method. The Harten-Lax-van Leer-Contact (HLLC) Riemann solver is extended to find the local Riemann fluxes in order to maintain the exact balance between the momentum term and the bed slope term. The advantage of a well-balanced scheme over an unbalanced scheme is demonstrated by the synthetic standing contact-discontinuity test case. Then, the model is employed to simulate two laboratory experiments. At last, a field case, the 1996 Lake Ha! Ha! flood event (Canada), is simulated. Results of cross sectional geometries and profiles of longitudinal thalweg agree well with measurements. The accuracy and simplicity of the numerical model, together with the robust implementation, make the model a good candidate for practical engineering applications.

This study presents results of the sediment yield Watershed of Ribeirão do Pinhal, in the city of Limeira, São Paulo, conducted under three different scenarios of the land use: 1 – land use in 2008, 2 – with reforested protection areas, 3 – the increase sugarcane's areas over grassland and citrus areas. The Soil and Water Assessment Tool (SWAT) was used as a hydro-sedimentological modeling tool to simulate these scenarios. This watershed was divided into five sub-watersheds according to the prevalent use, and adjustments were made in soils database parameters and cultures that are in SWAT, to adapt it to the Brazilian reality. Was used climatological data of eight years (2007-2014) for this modeling. Different sediment yield could be noted in the different types of land use, sub-watersheds and scenarios. The uses that presented higher sediment yield, in ascending order, were: natural vegetation, sugarcane, citrus, annual crop e grassland. Sub-watershed number 2 had the highest sediment yield (~10Mg.ha^-1), while the sub-watershed 5, the lowest (5.51Mg.ha^-1). This remained in the three scenarios. Overall, scenario 2 had the lowest sediment yield (7.74Mg.ha^-1). Scenario 1 had 8.37 Mg.ha^-1 and scenario 3, 8.58Mg.ha^-1. SWAT was considered efficient to simulate the sediment yield by scenarios and by use, combining different thematic and tabular data when performing the necessary adjustments on their original database.

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.

Preventing the rebound of black and odorous water bodies is critical for improving the ecological environment of water bodies. This study investigated the effect and mechanism of in-situ improvement of sediment microenvironment by nitrate in tailwater of wastewater treatment plants combined with aerobic denitrifying bacteria under low-DO regulation (TailN+CFM+LDO). On the 60th day of remediation, the dissolved oxygen and oxidation-reduction potential in the overlying water increased to 5.6 mg/L and 300 mV, respectively, the acid volatile sulfide in the sediment decreased by 70.4%, and the organic matter in the sediment was reduced by 62.7%, in which the HFOM was degraded from 105 g/kg to 56 g/kg, and the potential risk of water reverting to black and odorous conditions significantly decreased. Amplicon sequencing analysis revealed that the relative abundance of electroactive bacteria Thiobacillus and Pseudomonas with denitrification capacity was found to be significantly higher in the TailN+CFM+LDO group than in the other remediation groups. Functional prediction of 16S sequencing results indicated that both the quantity and activity of critical microbial enzymes involved in nitrification and denitrification processes could enhance in the TailN+CFM+LDO group. These results improved our understanding of the improvement of the sediment microenvironment and could thus facilitate its application.

The wave energy converter (WEC) technology constantly develops new designs and models. To encourage its development within the energy sector, research is required to improve current designs and create durable and effective devices. However, it is also crucial to investigate the suitability of a WEC to a site-specific sea condition as this can impact its optimal power absorption and constructability. This study evaluates and compares the efficiency of a heaving buoy with varied geometries in one of the most energetic places along the Mexican coast. A statistical analysis of the wave climate on the coast of Ensenada during the last 42 years is performed to define the conditions to which the device is subjected. The location of the WEC in shallow waters is chosen using a computational model which solves the modified mild slope equation in its elliptic shape. The heaving buoy floater is studied using three distinct geometries: a semi-sphere, a cylinder, and a proposed rounded semi-rectangle. The hydrodynamic response of the three geometries is then analysed in the frequency and time domain using ANSYS AQWA. The hydrodynamic study involves the assessment of the floating body dynamics, exerted forces, the power absorbed as well as the suitability of the proposed power take-off (PTO) system. Findings reveal that the proposed geometry absorbs the most energy, with an annual power of 135.11 MW, and that the transmission PTO design is appropriate for this type of technology.

Today in times of increasing inequality, climate change, and major social challenges, education is the best way to equip citizens, scholars and leaders to implement meaningful change and prevent future crises. Biologi education and science will solve these problems to supporting sustainable development goals especially in soil remediation, clean water, education quality and clean and affordable energy. This paper will describe how biological education could solve these problems. New biology can solve the problem about hunger use biotech, use synthetic biological material to find new advance material. New biology could driving intersectorial, interdisciplinary and international connectivity, and the leveraging of existing investments in synthetic biology, materials science, allied science and technology areas, are the major challenges in delivering the Materials from Biology vision.

This paper attempts to alter a prevailing assumption that buildings use energy to an understanding that in fact, people use energy. Therefore, to successfully accelerate the transition to a low-carbon society and economy more emphasis should be on motivating people and increasing their awareness by making them energy conscious building users and therefore active players in the energy transition process. In this context, this paper provides insights from the Horizon 2020 MOBISTYLE project. It demonstrates research and development approaches, highlights the main project objectives, and presents findings of an ethnographic (qualitative) study of users’ habits, practices, and needs. The aim of the project is to motivate behavioural change by raising consumer awareness through the provision of attractive personalized information on user’s energy use, indoor environment and health, all enabled by an integrated information and communication technology (ICT) service. In this context, the anthropological people-centred approach is integrated into the MOBISTYLE approach putting users at the centre of the ICT tools development process. The main quantitative objective of the project is a reduction of energy use for at least 16 % prompted by the provision of combined information and feedback systems on energy, indoor environmental quality (IEQ) and health. The most relevant motivational factors and key performance indicators (KPIs) for encouraging a more energy conscious and healthy lifestyle were defined by means of a people-centred approach, adopting anthropological inquiries in different settings. Information about users’ lifestyles and their needs was collected in focus groups with potential users in five case studies, located in different European Union (EU) countries. Behaviour change is achieved through awareness campaigns, which encourage users to be pro-active about their energy consumption and to simultaneously improve health and well-being.

The intrinsic non-linearity and complexity of suspended sediment dynamics, which are impacted by the geographical variability of basin parameters and temporal climatic patterns, make it difficult to estimate suspended sediment concentration (SSC) accurately in hydrological processes. Deep neural networks (DNNs), a cutting-edge modeling method that can capture the innate non-linearity in hydrological systems, have emerged as a solution to this problem. Using primary data on discharge, SSC, and turbidity, the long short-term memory method of DNNs was employed in this study to simulate the discharge-suspended sediment connection for the Esopus Creek, NY, USA as the first objective. To develop effective modeling strategies, multiple scenarios of feature selection, including combinations of discharge, turbidity and SSC for preceding days, were considered. Secondly, the variations in SSC and discharge between the three USGS gages along Esopus Creek that were upper stream, lower stream, and downstream, adjacent to the pourpoint were analyzed. Statistical metrics and scenarios of feature importance were used to evaluate the effectiveness of the DNN-based models. The study shows that among 24 DNN approaches feature selection is crucial in simulating the daily SSC, and discharge and presenting novel research directions for utilizing machine learning algorithms in water quality.

Soils provide important regulating ecosystem services and have crucial implications for human well-being and environmental conservation. However, soil degradation and particularly soil erosion jeopardize the maintenance and existence of these services. This study explores the spatio-temporal relationships of soil erosion to understand the distribution patterns of sediment retention services in mainland Portugal. Based on Corine Land Cover maps from 1990 to 2018, the InVEST Sediment Delivery Ratio (SDR) model was used to evaluate the influence of sediment dynamics for soil and water conservation. Spatial differences in the sediment retention levels were observed within the NUTS III boundaries, showing which areas are more vulnerable to soil erosion processes. Results indicated that the Region of Leiria, Douro and the coastal regions have decreased importantly sediment retention capacity over the years. However, in most of the territory (77.52%) changes in sediment retention were little or not important (i.e., less than 5%). The statistical validation of the model proved the consistency of the results, highlighting the usefulness of this methodology to analyse the state of soil erosion in the country. These findings can be relevant to support strategies for more efficient land use planning regarding soil erosion mitigation practices.

A two-link model of exoskeleton with variable-length links for supporting the lower limbs of the human musculoskeletal system is proposed in the article. The researched model differs from the existing ones by the variable-length links, and by the angle calculation method. While in the existing models, the angles are calculated from the regular direction – from vertical, or from horizontal, - in the proposed research they are calculated between the links. As for practical exoskeleton implementation, the proposed method of angle calculation is appropriate to the actual working conditions of the electrical motors with the reduction gears installed in the hinges, which change the angles between the links. The mathematical model in the form of the system of Lagrange differential equations of the second kind is obtained. The obtained mathematical model is examined for existence and uniqueness of the Cauchy solution. The kinematic trajectory of the link motion has been synthesized. The controlling actions required for its implementation have been found.

This paper presents the latest developments in microCT, both globally and locally, for supporting the additive manufacturing industry. There are a number of recently developed capabilities which are especially relevant to the non-destructive quality inspection of additive manufactured parts; and also for advanced process optimization. These new capabilities are all locally available but not yet utilized to their full potential, most likely due to a lack of knowledge of these capabilities. The aim of this paper is therefore to fill this gap and provide an overview of these latest capabilities, showcasing numerous local examples.

This paper proposes a novel single-stage buck-boost three-Level neutral-point-clamped (NPC) inverter with two independent dc sources coupled for the grid-tied photovoltaic (PV) application, which can effectively solve the unbalanced operational conditions generally appeared between two coupled independent PV sources induced by the unequal irradiation and temperature distribution. The proposed control scheme can simultaneously guarantee the maximum power point (MPP) operation of both PV sources and maintain the output waveform quality. Compared to the traditional two-stage PV inverter, the proposed NPC inverter could reduce the PV array voltage requirement and dc-link capacitors’ voltage rating, meanwhile show the advantage in operational efficiency. MATLAB simulations and the captured experimental results are presented to show the performance of the proposed three-level inverter.

This study is part of a project designed to simulate the long-term landform equilibrium of a rehabilitated mine site. The project utilizes event fine suspended sediment (FSS) fluxes in a receiving stream following a rainfall event as an indicator of landform stability. The aim of this study was to use HEC-HMS to determine sediment and discharge quantity upstream to determine how it affects the downstream development of the catchment landform, in terms of sediment changes and geomorphology. Thus, the study focused on hydrology and sediment modelling of the upper catchment with HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) to determine daily discharge and sediment output at the catchment outlet. HEC-HMS was used to calibrate the stream discharge and FSS quantities at the catchment outlet to observed continuous discharge and FSS values. Calibration of the HEC-HMS model was done for two water years and then the same model parameters were used to validate the model for a third water year. Catchment discharge and FSS were calibrated and validated for continuous rainfall events against observed discharge and FSS data at the catchment outlet. The model was then run for a projected rainfall of 50 years. The denudation rate predicted by the model falls in the range previously determined for the region. The simulated sediment output was compared to the rainfall trends over the years. As a result, the sediment spikes following a rainfall-runoff event gradually decreased over time. Reducing FSS spikes indicate that the landform gradually attains stability. This modeling study can be used for long-term simulations to determine erosion equilibrium over the years and to quantify sediment yield in catchments for projected wet and dry rainfall scenarios.

The sustainability of agriculture depends as much on the natural resources required for production as it does on the stakeholders that manage those resources. It is thus essential to understand the variables that influence the decision-making process of agricultural stakeholders to design educational programs, interventions, and policies geared towards their specific needs, a required step to enhance agricultural sustainability. We examined the perceptions, experiences, and priorities that influence management decisions of five major groups of agricultural stakeholders (conventional small grain producers, organic small grain producers, organic vegetable producers, extension agents and agro-industry crop consultants, and researchers) across the Montana, United States. Results revealed that while stakeholder groups have distinct perceptions, experiences, and priorities, there were similarities across groups. Specifically, organic vegetable and organic small grain producers showed similar responses that were, in turn, divergent of conventional producers, researchers, and crop consultants. Conventional small grain producers and researchers showed overlapping response patterns while crop consultants formed an isolated group. Our results reinforce the need for agricultural education and programs that address unique and shared experiences, priorities, and concerns of multiple stakeholder groups. This study endorses the call for a paradigm shift from the traditional top-down agricultural extension model to one that accounts for participants’ socio-ecological contexts to facilitate the adoption of sustainable agricultural systems that support environmental and human wellbeing.

This study examines change detection techniques in dense point clouds for the purpose of Cultural Heritage preservation, with a particular focus on the San Pietro Barisano Bell Tower in Matera, Italy. Dense point clouds, obtained via advanced laser scanning, offer detailed 3D representations of heritage structures, facilitating the precise monitoring changes over time. The investigation uses a variety of change detection algorithms, including the Iterative Closest Point (ICP) algorithm, which is renowned for its robust registration capabilities in aligning point clouds with high accuracy. The combination of ICP with deviation analysis and feature-based methods allows for the effective identification of alterations, including deformations, material loss, and surface degradation. This methodology establishes a comprehensive framework for the monitoring of Cultural Heritage, thereby enabling timely and targeted preservation efforts. The results emphasise the substantial contribution of dense point cloud analysis to the enhancement of heritage management and the safeguarding of vulnerable architectural sites.

The COVID-19 pandemic has brought about unprecedented changes to human lifestyles across the world. The virus and associated social restriction measures have been linked to an increase in mental health conditions. A considerable body of evidence shows that spending time in and engaging with nature can improve human health and wellbeing. Our study explores nature’s role in supporting health during the COVID-19 pandemic. We created web-based questionnaires with validated health instruments and conducted spatial analyses in a geographic information system (GIS). We collected data on people’s patterns of nature exposure, associated health and wellbeing responses, and potential socioecological drivers such as relative deprivation, access to greenspaces, and land-cover greenness. We applied a range of statistical analyses including bootstrap resampled correlations and binomial regression models, adjusting for several potential confounding factors. We found that respondents significantly changed their patterns of visiting nature as a result of the COVID-19 pandemic. People spent more time in nature and visited nature more often during the pandemic. People generally visited nature for a health and wellbeing benefit and felt that nature helped them cope during the pandemic. Greater land-cover greenness within a 250 m radius around a respondent’s postcode was important in predicting higher levels of mental wellbeing. There were significantly more food-growing allotments within 100 m and 250 m of respondents with high mental wellbeing scores. The need for a mutually-advantageous relationship between humans and the wider biotic community has never been more important. We must conserve, restore and design nature-centric environments to maintain resilient societies and planetary health.

Where mining activities cause disturbance in catchments, streams are often impacted by heavy loads of fine eroded material. Since geomorphological processes are very slow, it is expected that during rehabilitation typically hundreds of years are required for a mine landform to return back to stability. A sensitive approach to analyzing post-mining landform stability in tropical regions is to assess the quantity of fine suspended sediments (FSS = silt + clay (0.45 µm < diameter < 63 µm)) leaving the catchment where the mine resides and entering the receiving streams in response to storm events. Continuous stream discharge and FSS quantities upstream and downstream of the catchment where the mine resides were modeled using HEC-HMS (Hydrologic Engineering Centre-Hydrologic Modeling System). Once calibrated, the model was run for a thousand years to predict continuous stream discharge and FSS quantities for various predicted rainfall scenarios. Short-term erosion and deposition across the mine catchment were also evaluated using a cali-brated landform evolution model, CAESAR-Lisflood. This paper reviews watershed soil erosion measurements and modeling research. It compares the previous research to approach above that assesses mine landform erosion and stability in terms of fine-suspended sediments. This approach can be used to determine mine landform erosion dynamics, predict achieving stability equilib-rium, and as a post-mining rehabilitation assessment tool.

The objective of the research was to evaluate the concentration of organochlorine pesticides in sediment of the Alvarado Veracruz lagoon system, Mexico. To define the risk that causes to the public health the consumption of organisms that inhabit in sediments of this ecosystem. In 20 out of 41 stations analyzed, 11 prohibited organochlorine pesticides were identified, such as hexachlorocyclohexane, lindane, aldrin, dieldrin, endrin, among others. The highest concentrations were: aldrin 46.05; β-HCH 42.11; α-HCH 38.44; γ-HCH (lindane) 34.20; δ-HCH 31.61; methoxychlor 29.40; heptachlor epoxide 25.70; heptachlor 24.11; dieldrin 22.13; endrin 21.23; and endrin aldehyde 12.40 ng g-1. Concentrations reported are prohibited in international standards. In Mexico it is necessary to establish permissible limits in the environmental legislation for this matrix. There is a strong need to demonstrate, with scientific studies, the level of concentration reported by impact of compounds widely used in agricultural-livestock activities.

Municipal Solid Waste Management (MSWM) struggles with significant policy and operational challenges, particularly concerning recyclables collection routes and fleet composition. Within the European Union, phasing out traditional fuel-based vocational vehicles, like garbage trucks, in favor of zero-emission alternatives is mandatory to achieve sustainable development objectives. Integrating electric solid waste collection vehicles into municipal fleets presents challenges due to differences in range, capacity, and noise levels. The emission is even more crucial in the systems where recyclables are collected separately, which requires a greater number of trips to be performed. This study addresses MSWM’s strategic aspect by minimizing the total cost of replacing waste collection vehicles while meeting emission constraints. The Fleet Transition Problem (FTP) was formulated and solved using a MARKAL-based approach. The objective of the FTP and a mixed integer linear program used to solve it is minimizing the overall cost of fleet modernization throughout a multi-phase planning horizon so that the sustainable transition of the fleet can be assured. Computational experiments affirmed the model’s effectiveness in strategizing fleet transition. This research outlines a multi-period model for transitioning to a zero-emission fleet and demonstrates the FTP’s potential for strategic decision-making. Notably, the study observes a consistent reduction in permissible emissions across the planning horizon.

With the swift advancement of wearable electronic devices and the electronic textile industry, the energy storage components of these devices must possess the capability to maintain stable mechanical and chemical properties even after undergoing multiple bending or tensile deformations. This circumstance has expedited research efforts toward novel materials, processing techniques, and designs for flexible energy storage devices. Among the myriad of materials investigated thus far, biomass-derived architectures have garnered significant attention due to renewability, cost-effectiveness, and abundance in earthly resources. Notably, flexible carbon membrane/ monolithic derived from biomass serve as a particularly promising option for self-supporting electrodes, align seamlessly with the requirements of next-generation electronic devices, and obviate the need for additional costs, unnecessary weight, and the high contact resistance that additive insulating adhesives may entail. This review delves into the comprehensive analysis of biomass feedstocks and methodologies employed in the synthesis of flexible self-supporting carbon electrodes. Subsequently, the advancements in their application in energy storage devices are elucidated. Finally, an outlook on the potential of biomass self-supported carbon architectures and the challenges they face is provided.

Public health agencies are increasingly using pathogen whole genome sequencing (WGS) to support surveillance and epidemiologic investigations. As access to WGS has grown, greater amounts of molecular data have helped improve our ability to detect outbreaks, investigate transmission chains, and explore large-scale population dynamics, such as the spread of antibiotic resistance. However, the wide adoption of WGS also poses challenges due to the amount of data generated and the need to transform raw data prior to analysis. This complexity means that public health agencies may need more advanced computational infrastructure, a broader technical workforce, and new approaches to data management and stewardship. As both a guide for how this development could occur, and a place to initiate discussion, we describe ten proposals for developing and supporting an informatics infrastructure for public health.

We investigate the impact of aging on exchangeable inter-arrival times in mixed renewal processes, exploring its implications for reliability and survival analysis. In this study, first, we revisit the definition of renewal point processes, where inter-event time intervals are considered as exchangeable non-negative random variables. Then, we define the concept of statistical aging as latency in the observational process of event counting. Latency affects event detection but preserves exchangeability. However, it may alter the statistical properties of inter-event time intervals. Our analytical and numerical assessments highlight the significance of aging in exchangeable lifetimes, offering insights into key metrics such as the failure survival function, renewal function, and hazard rate function. Through archetypal examples and empirical findings, we illustrate the implications of aging on renewal processes. In particular, employing a Bayesian perspective, we analyze high-frequency currency exchange rate data to assess the impact of aging on volatility risk evaluation. This study contributes novel insights to the literature of renewal theory and survival analysis, emphasizing the role of latency aging in stochastic point processes under exchangeability assumption.

We consider a Cauchy problem for differential equations in a Hilbert space $X$. The problem is stated in a time interval $I$, which can be finite or infinite. Weuse a fixed point argument for history-dependent operators to prove the unique solvability of the problem. Then, we state and prove convergence criteria for both a general fixed point problem and thecorresponding Cauchy problem. These criteria provide necessary and sufficient conditions on a sequence $\{u_n\}$ which guarantee its convergence to the solution of the corresponding problem, in the space of both continuous and continuously differentiable functions. We then specify our results in the study of a particular differential equation governed by two nonlinear operators. Finally, we provide an application in viscoelasticity and give a mechanical interpretation of the corresponding convergence result.

First came the digital banks without offices, and then the “fintech” companies that combined technology and finance to achieve greater efficiency and new business models. Later was the turn of transport and logistics, understood as the sector whose economic activities derived from the transport of people and goods, while on the one hand, it has had a new spring thanks to factors such as online sales. Then, perhaps catalyzed by the pandemic scenario, the turn has reached education, where years ago was only a complement of traditional education, now is a central part with multiple modern methodologies. Numerous studies exist about using or implementing these e-learning procedures, but technical analysis is barely found in references. This work aims to analyze the digital transformation of the main sectors of our society, including finances, transport, and education, together with several examples of how technology was helping to fight the pandemic scenario.

The Rupat Strait, a part of the Malacca Strait, is recognized as semi-closed waters and shows a high activity; thus, discovering the transport sediment mechanism of the strait as a consequence of ambient and anthropogenic forces is essential. Hydrodynamic and sediment transport modelling was constructed using the 2-Dimensional Explicit method which is averaged over depth. The results show that the dispersion of sediment at high tide is longer than that at low tide. This follows the hydrodynamic model in which current velocity at high tide is greater than the ocean current at the low tide. The previous sediment observation supports the results of transport sediment modelling, indicating that the anthropogenic factors are highly associated with the sedimentation in the Rupat strait

Reservoirs are characterized by accumulation of sediments where micropollutants may concentrate, with potential toxic effects on downstream river ecosystems. However, sediment management such as flushing is needed to maintain storage capacity. Climate change is expected to increase sediment loads, but potential effects on their quality are scarcely known. In this context, sediment contamination by trace elements (As, Cd, Cr, Cu, Hg, Ni, Pb, Zn) and organics (PAHs, PCBs, C>12) was analyzed in 20 reservoirs located in Italian Central Alps. A strong As and a moderate Cd, Hg and Pb enrichment was emphasized by Igeo, with potential ecotoxicological risk according to PEC quotients. Siltation, granulometry, total organic carbon (TOC) and altitude resulted as the main drivers governing pollutant concentrations in sediments. According to climate change models, expected increase of rainfall erosivity will enhance soil erosion and consequently the sediment flow to reservoirs, potentially increasing coarse grain fractions and thus potentially diluting pollutants. Conversely, increased weathering may enhance metal fluxes to reservoirs. Increased vegetation cover will potentially result in higher TOC concentrations, which may contrast contaminant bioavailability and thus toxicity. Our results may provide elements for a proper management of contaminated sediments in a climate change scenario aiming at preserving water quality and ecosystem functioning.

The dynamics of suspended sediment involves inherent non-linearity and complexity as a result of the presence of both spatial variability of the basin characteristics and temporal climatic patterns. As a result of this complexity, the conventional sediment rating curve (SRC) and other empirical methods produce inaccurate predictions. Deep neural networks (DNNs) have emerged as one of the advanced modeling techniques capable of addressing inherent non-linearity in hydrological processes over the last few decades. DNN algorithms are used to perform predictive analysis and investigate the interdependencies among the most pivotal water quantity and quality parameters i.e., discharge, suspended sediment concentration (SSC), and turbidity. In this study, the Long short-term memory (LSTM) algorithm of DNNs is used to model the discharge-suspended sediment relationship for the Stony Clove Creek. The simulations were run using primary data on discharge, SSC and turbidity. For the development of the DNN models and examining the effects of input vectors, combinations of different input vectors (namely discharge, and SSC) for the current and previous days are considered. Furthermore, a suitable modelling approach with an appropriate model input structure is suggested based on model performance indices for the training and testing phases. The performance of developed models is assessed using statistical indices such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Statistically, the performance of DNN-based models in simulating the daily SSC performed well with observed sediment concentration series data. The study demonstrates the suitability of the DNN approach for simulation and estimation of daily SSC, opening up new research avenues for applying hybrid soft computing models in hydrology.

Sediment transport is highly sensitive to flow conditions, showing significant increase during flood events. Based on this principle, this study set out to rank flood events occurring along river Trotuș (Romania) based on the amount of transported sediment and event duration. The 77 flood events recorded from 2000 to 2017 were ranked into 4 classes: type A (4%); type B (16%), type C (14%) and type D (66%). The sediment transport specific for the 4 types of flood events was related to the flow discharge (sediment rating curve and hysteresis effect), the specific stream power and the energy expenditure of these events. More than 60% of the hysteresis loops typical for flood events were clockwise, thus singling out the channel as the main sediment source. Ca. 74% of the total sediment yield was transported at stream power values higher than the 300 Wm-2 threshold, which was exceeded in less than 1% of the investigated timeframe. The changes occurring in the sediment transport rates after major floods show that these events are significant thresholds in the hydrogeomorphic evolution of river channels.

The issues mothers face in the academy have been discussed for decades. Routinely, new studies report significant differences between women and men at comparable career stages with respect to salary, service demands, publications, grant submissions, and overall funding rates. The COVID-19 pandemic is further exposing these inequalities as women scientists who are parenting while also engaging in a combination of academic related duties are falling further behind. COVID-19 is shaking the very foundations of our society and laying bare the many inequalities that defined our pre-COVID world. We can solve these inequities by investing strategically in creative solutions, thereby making the most of women’s contributions to scientific endeavors. Here we describe strategies that would make the academy more equitable for working mothers now and into the future. Importantly, while the data are clear that mothers are being disproportionally impacted by COVID-19, many groups could benefit from these same ideas. Now is the time to act. Rather than rebuilding what we once knew, let us be the architects of a new world.

Lymphedema is a chronic and debilitating disease that affects up to 250 million patients worldwide. Recent advances in understanding its pathophysiology, along with improved diagnosis and microsurgical techniques, have enhanced our ability to cope with the challenging task of treating this disease. This review provides an overview of the disease from a surgeon's point of view, including existing imaging modalities used for preoperative assessment, as well as surgical procedures used in its treatment. Advantages and drawbacks of various existing modalities used for pre or intraoperative assessment of lymphatic vessels are discussed. Lymphedema treatment has shifted from palliative debulking procedures (liposuction and direct excision) to those aimed at restoring lymphatic flow and countering the pathophysiology of the disease (lymphaticovenous anastomosis and vascularized lymph node transfer). A combination of both approaches can result in a synergistic benefit for patients and are discussed in this review. Despite recent advances, some controversies persist, and further studies are needed to better define surgical treatment algorithms.

The impact of ponds on basins has recently started to receive its well–deserved scientific attention. In this study, pond–induced impacts on soil erosion and sediment transport were investigated at the scale of the French Claise basin. In order to determine erosion and sediment transport patterns under current conditions, the CORINE erosion and SWAT models were used. The impact of ponds on the studied processes was revealed by means of land cover change scenarios, using ponded versus pondless inputs. Results show that under current conditions (pond presence), 12.48% of the basin corresponds to no–erosion risk zones (attributed to the dense pond network), while 65.66% corresponds to low–erosion risk, 21.68% to moderate–erosion risk and only 0.18% to high–erosion risk zones. The SWAT model revealed that ponded sub–basins correspond to low sediment yields areas, in contrast to the pondless sub–basins, which yield appreciably higher erosion rates. Under the alternative scenario, erosion risks shifted to 1.12%, 0.52%, 76.8% and 21.56% for no, low, moderate and high–erosion risks, respectively, while the sediment transport pattern of the Claise completely shifted to higher sediment yield zones. This approach solidifies ponds as powerful man–induced modifications to hydro/sedimentary processes.

Lake Rawa Besar is an urban lake surrounded by dense settlements and market area. Currently Lake Rawa Besar is experiencing physical and ecology strain. This research’s objectives are to determine the levels of sediment and nutrient, the distribution, also the relation to land use and human activities producing pollutant. Field surveys with 30 sample points and observations was needed scattered within the lake. Measuring the value of each parameter is carried out in national standardized laboratory. The result shows that sediment load of TDS is still below the standard limit for clean water, while TSS levels in the middle of lake exceed the standard limit. Nutrient loads, spesifically nitrate and phospate levels is below the standard limit. While turbidity and BOD levels have a uniform distribution pattern in the lake, exceed the standard limit for clean water, and have a positive correlation. High levels of turbidity and BOD are caused by household waste and human activities producing organic waste such as tofu factory, fowl manure, and garbage dump. Small sewage goes into the lake mediates pollutants inflow. Attention is needed by nearby people, also for the government, to sustain the ecological condition of the lake.

Dementia is a major issue for healthcare systems worldwide, necessitating the development of creative and effective strategies for its management. This paper examines the use of Artificial Intelligence (AI) technologies in caring for and managing people with dementia. AI-driven solutions can improve diagnosis, personalize care, optimize medication management, and reduce the burden on caregivers. This paper discusses the implementation of an AI-based framework for creating videos related to people’s memories to support train-therapy or travel- therapy, a non-pharmacological intervention for Alzheimer’s disease patients

This study explores the role of information technology (IT) in supporting sustainability efforts in Saudi Arabia. Through an analysis of four case studies, we examine how technology has been implemented by Saudi Arabian companies to advance sustainability goals. The findings demonstrate a notable shift towards green initiatives in the country, with the adoption of technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence. These technologies have been instrumental in enhancing energy efficiency and waste reduction. The study highlights the significance of policy interventions and public-private partnerships in achieving both environmental sustainability and economic growth. To ensure widespread adoption of sustainable practices, it is crucial for the government to focus on building digital capacity in companies and removing technological barriers. The case studies of four companies serve as illustrative examples of sustainable practices and the corresponding technological support in Saudi Arabia. The research underscores the importance of leveraging information technology and collaboration to accelerate progress towards the nation's sustainability goals.

Biodiversity loss has been identified as one of the environmental impacts where humankind has been tres-passing planetary boundaries most significantly. Going beyond the pressures causing damages (calling them ‘direct drivers’) and analysing their underlying driving forces, IPBES, the Intergovernmental Sci-ence-Policy Platform for Biodiversity and Ecosystem Services, also identified a series of indirect drivers. The Montreal-Kunming Global Biodiversity Framework GBF including its suggested monitoring approach is intended to and claims to be a policy response to such analyses. However, to assess the human impact on ecosystems as a basis for planning conservation and restoration, as foreseen in the GBF, monitoring ecosystem typologies (in the GBF with reference to the UN statistical standard SEEA ES, which in turn refers to the IUCN ecosystem classification) is not enough. It needs to be complemented with data on the severity of human impacts, and on the history of places, i.e. how and when the current ecosystem status was brought about. In this conceptual paper we suggest LUI, a deliberately simple ordinal scale index for land use intensity changes, to address these two gaps. It is based on the hemeroby concept, measuring the human impact as deviation from naturalness. This makes it an information collection and presentation tool for those working in landscape planning and management. LUI’s simple and intuitively understandable structure makes it suitable for citizens’ science applications, and thus for participative monitoring when extensive statistical data gathering is not feasible, and past data are not available. Of course is can also be used as a simple too for communicating when detailed statistical data series are available. While the aggregate index is expected to communicate well, its components are more relevant to motivate and help policy makers to prioritise their decisions according to the severity of recent anthropogenic ecosystem disturabances.

Smart air filters are beneficial to provide highly efficient particle removal, treat multiple contaminants simultaneously and conserve energy during air filtration processes. Herein, a type of self-supporting smart air filter (SSSAF) was fabricated by sandwiching the VOC-responsive PZT/PVDF electrospun membrane with two metal mesh electrodes. Besides the high filtration efficiency for sub-micron particles, the SSSAF showed good responses to pressure drop in the range of 0 to 500 Pa via the electroactivity of PZT/PVDF membrane. In addition, the SSSAF achieved VOC sensing function via the swelling properties of PZT/PVDF membrane in organic vapors, demonstrated by its signal to 50 to 200 ppm ethanol gases. The SSSAF based nanogenerator was employed to harvest wind energy, which was further applied to inhibit bacterial growth without the need of additional power input. The results provide new insight into development of all-in-one smart air filters.

Background: Having a laboratory renal profile for medical imaging examinations requiring contrast media (CM) administration is strongly advised. Creatinine helps identify patients at risk of contrast-induced nephropathy (CIN). GEM® Premier™ ChemSTAT (Werfen) is a point-of-care (POC) analyzer with 12 emergency parameters including creatinine assay. The aim of this study consists to compare ChemSTAT with the central analytical solution of the University Hospital of Clermont-Ferrand and to evaluate the interest of the POC creatinine in the emergency department (ED) in optimizing the flow of patients, especially if CM used. Methods: More than 200 whole blood (WB) samples from the ED were evaluated on the ChemSTAT analyzer. As comparative methods, the plasma aliquots from the same samples were assayed on an Atellica® CH (Siemens Healthineers). The clinical concordance was assessed ac-cording the decision cut-offs of the French Society of Radiology for the risk of CIN. The availability times of biological results between ChemSTAT and the central laboratory were studied. Results: WB results from ChemSTAT correlated well with those from Atellica® CH, except for tCO2 (known bias between Siemens and Cobas Roche methods predicting ChemSTAT values). The results of creatinine assay allow to have identical medical decisions compared to the renal risk cut-offs. The availability of biological results is reduced by 50 min on average with ChemSTAT vs the central laboratory. Computed tomography was performed for 44.7% of patients including the injection of the CM in 68% of cases. For these patients, the availability of creatinine results relative to imaging time is faster with the ChemSTAT on average of 45.2 min. Conclusions: Great analytical and clinical correlations for creatinine assay allow safe iden-tification of patients at risk of CIN and improve patient flow in ED especially for those requiring computed tomography with use of CM.

Schizoaffective disorder and its classification have always been a topic of discrepancy among researchers. The overlapping presentations of schizoaffective disorder, schizophrenia and psychotic bipolar disorder makes it difficult to classify and diagnose. This study is a narrative review of literatures and genetic and epidemiological research evidences, done to determine the similarities and differences between schizoaffective and psychotic bipolar disorder. Beginning from the introduction of schizoaffective disorder as "schizoaffective psychosis" by Kasanin in 1933, various researchers have proposed different views regarding the diagnosis and classifications for schizoaffective disorder. DSM-III considered schizoaffective disorder as a diagnosis of exclusion as it fails to fit the definite diagnostic criteria of schizophrenia, major affective disorders, or schizophreniform disorder. DSM-V updated the criteria of schizoaffective disorder and stated it as a life time illness rather than being an episodic disorder as in DSM-IV and hence is more reliable. However, some studies show that schizoaffective disorder patients are more like schizophrenia than mood disorder. It still remains unclear whether schizoaffective disorder is a heterogenous disease, a form of schizophrenia, a form of mood disorder or a midpoint in the continuation spectrum of schizophrenia and psychotic bipolar disease.

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

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

Vienna Atomic Line Database (VALD) contains data on atomic and molecular energy levels and parameters of spectral lines required for stellar spectra analysis. Hundreds of millions lines for fine spectral synthesis and for opacity calculations are collected in present version of VALD (VALD3). Critical evaluation of the data and the diversity of extraction tools support high popularity of VALD among users. The data model of VALD3 incorporates obligatory links to the bibliography making our database more attractive as publishing platform for data producers. The VALD data quality and completeness are constantly improving allowing better reproduction of stellar spectra. To illustrate continuous evolution of the data content we present a comparative analysis of the recent experimental and theoretical atomic data for Fe-group elements, which will be included in the next VALD release. This release will also include a possibility for extracting the line data with full isotopic and hyperfine structures.

For the purpose of clean energy hydropower development, the construction of reservoirs has been continuously promoted in the Jinsha River basin for many years, and how the reservoirs affect the water and sediment process is a very necessary topic to study. This study is based on the gauged water and sediment data (span from the 1960’s to 2020) at Shigu, Panzhihua, and Xiangjiaba stations downstream located in the trunk channel of the river, and uses Mann-Kendall trend test method and double cumulative curve method to comprehensively judge the variation trends of runoff and suspended sediment load and reveal their credible mutation years. The linear regression method is used to reveal the variation characteristics of the relationship between water and sediment before and after the abrupt change years. The results show that the variations in runoff at Shigu and Panzhihua stations have significant and relatively obvious increasing trends, respectively, and that at Panzhihua station has a mutation year of 1985. The runoff at Xiangjiaba Station slightly increased but not significantly. The variation of suspended sediment load showed a temporal and spatial differentiation. The variation of sediment discharge at Shigu Station showed an increasing trend with a mutation year of 1997. For Panzhihua Station, it showed an increasing trend before 1998, but has significantly decreased since 1998. The fluctuation of sediment transport at Xiangjiaba Station was significant before 1998, but the trend is unclear. In the period of 1998−2020, it showed a significant decreasing trend, especially since 2013, when the mean annual suspended sediment load only accounted for 0.61% of its multi-year average. The variations of mean annual sediment concentration and sediment inflow coefficient at the hydrological stations is consistent with the variation trend of sediment transport. Before 2013, the correlation between water and sediment was strong, but thereafter it was extremely weak. The two sudden years of 1998 and 2013 are consistent with the year when large reservoirs were built in the river basin. The construction of large reservoirs and their large amount of sediment retention are the key reasons for the sudden changes in the water-sediment relationship and the sharp decrease in sediment transport in the downstream reach of the reservoir dam. The climate and underlying surface changes in the study area are not significant, and their impact on the water and sediment processes in the watershed is limited.

Fungi play important roles in the process of material cycling and energy transfers in aquatic ecosystems. Yet little is known about the fungal community in lake sediment. In this study, sediment samples from five habitat types in Baiyangdian Lake (BYD Lake) were collected across three seasons. High-throughput sequencing techniques were used to determine the compositions of fungal communities. Fungi are highly diverse in the sediment of BYD Lake, although some important fungi have not been accurately identified. The fungal diversity was highest in winter and lowest in summer, while the variations of species richness among sampling sites were not significant. The compositions of fungal community differed among seasons and habitats. Physicochemical properties of sediments were measured and the influence of the environmental factors on fungal communities were analyzed. Temperature, P, N, and heavy metals explained 48.98% of the variations of fungal communities across three seasons. Human activities have affected the species and biomass of fungi to some extent. Temperature is the most influential factor and is negative to fungal diversity. Nutrient element in different forms have different effects on shaping the fungal community. The effect of heavy metals is not clear.

A three-dimensional, unstructured grid, hydrodynamic and suspended-sediment transport model (i.e., SELFE-SED) was developed to simulate temporal and spatial variations of suspended sediment and was applied to the subtropical subalpine Tsuei-Feng Lake (TFL) of Taiwan. The model was validated with measured water level and suspended‑sediment concentration in 2009, 2010, and 2011. The overall model simulation results are in quantitative agreement with the observational data. The validated model was then applied to explore the most important parameter that affects the suspended-sediment concentration and to investigate the effect of wind stress on the mean current and suspended‑sediment distribution in this shallow lake. Modeling results of sensitivity analysis reveal that the settling velocity is a crucial parameter and erosion rate is less important in the suspended-sediment transport model. Remarkable lake circulation was found based on the strength of wind speed and wind direction. Strong wind would result in higher mean current in the top layer and suspended-sediment distribution in the top and bottom layers. This study demonstrated that the wind stress played a significant influence on mean circulation and suspended-sediment transport in a shallow lake.

Objective: Studies in Sports Psychology and Sociology have validated causality in team-sport athletes by using emotional intelligence as a variable. This study aimed to examine the causal relationship between the types of autonomy support coaching, emotional intelligence, and interruption intention as psychosocial variables among current taekwondo athletes in Korea.Methods: In this study, 217 adult or university athletes registered in the Korea Taekwondo Association in 2020 were evaluated for the type of autonomy support coaching, emotional intelligence, and interruption intention. Results: Autonomy support coaching recognized by taekwondo athletes has a negative and positive effect on interruption intention and emotional intelligence, respectively. Moreover, emotional intelligence has a negative effect on interruption intention. which revealed that autonomy support coaching has a negative effect on interruption intention through emotional intelligence.Conclusions: Such outcomes can serve as a foundation for athletes to have the opportunity to participate in sports in a mature manner and promote positive changes in sports culture. In other words, the sensibility of the athletes can be harmoniously symmetry.

Objective: Studies in Sports Psychology and Sociology have validated causality in team-sport athletes by using emotional intelligence as a variable. This study aimed to examine the causal relationship between the types of autonomy support coaching, emotional intelligence, and interruption intention as psychosocial variables among current taekwondo athletes in Korea. Methods: In this study, 217 adult or university athletes registered in the Korea Taekwondo Association in 2020 were evaluated for the type of autonomy support coaching, emotional intelligence, and interruption intention. Results: Autonomy support coaching recognized by taekwondo athletes has a negative and positive effect on interruption intention and emotional intelligence, respectively. Moreover, emotional intelligence has a negative effect on interruption intention. which revealed that autonomy support coaching has a negative effect on interruption intention through emotional intelligence. Conclusions: Such outcomes can serve as a foundation for athletes to have the opportunity to participate in sports in a mature manner and promote positive changes in sports culture. In other words, the sensibility of the athletes can be harmoniously symmetry.

Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality worldwide, including in Croatia. Since most patients have multiple disorders and diseases caused largely by the same risk factors, and as it is essential to approach each patient as a person with all disorders, today we are talking about a new paradigm - cardio-renal-metabolic (CKM) syndrome and cardio-renal-metabolic health, which necessarily includes brain health. Elevated systolic blood pressure, LDL cholesterol, smoking, obesity, diabetes, impaired renal function or chronic kidney disease, which all stem from insufficient physical activity, unhealthy diet with excessive intake of table salt, and air pollution, are the leading causes of overall morbidity and mortality from CKM diseases, especially mortality from CVD. Experts from various fields key to CKM health have written this document with the aim of integrating it as part of the national plan for the prevention of chronic non-communicable diseases with a focus on CVD, which should become mandatory and be based on the existing guidelines of professional societies.

Microplastics are a fast emerging group of contaminants. Their worldwide occurrence in water, sediment, and aquatic fauna raises questions and concerns as to their probable effects on aquatic life and ecology. This study investigates for the first time presence, abundance, and types of microplastics in water and sediment from a lagoon bordering the large urban agglomeration of Lagos in Nigeria, and renders additional information about grain size and sediment composition. Water and sediment samples were collected from four locations in the Lagos lagoon and a tributary. The abundance and distribution of microplastics in four range classes were determined for the sampled locations. Plastic particles were counted using digital microscopy, and identified with FTIR and pyrolysis GC-MS. The abundance of microplastics ranged from 310-2319 microplastic particles/kg in sediment, and 139-303 particles/m³ in water. The large discrepancy in the sediments can be explained by sediment characteristics as more microplastics were detected in the fine-grained sediments of Makoko. Fibres were the predominant shape found in all samples followed by fragments and few films whereas spheres were missing. Fibres were more abundant in water (92.6 %) than in sediments (32.5 %) while more fragments and foils occurred in sediments. The most commonly used polymers polypropylene and polyethylene were also the most detected ones in both matrices. Compared to other studies in Nigeria, our findings especially in the coarser sediments were lower while the fine-grained site revealed similar results.

Densimetric Froude (Fr) is the minimum velocity required to prevent sediment deposition in pipes. Prediction of Fr is of utmost importance in numerous applications in civil engineering. In this paper through using a new hybrid method. Genetic Algorithm (GA) is used for optimum selection of membership functions of Adaptive Neuro-Fuzzy Inference System (ANFIS), and Singular Value Decomposition (SVD) method is used to compute the linear parameters of ANFIS’s results section (ANFIS-GA/SVD). Also, two different target functions are known as training error (TE) and prediction error (PE) by Pareto curve, the trade-off between these functions is selected as the optimal modeling point. First, different models will be presented using the parameters affecting Fr prediction, classifying them in different groups; then the Fr parameter will be predicted for all the different models through utilizing three different sets of data and the ANFIS-GA/SVD technique. The results of the models indicate that the best Fr prediction is obtained when independent parameters such as volumetric sediment concentration (CV), ratio of median diameter of particle size to pipe diameter (d/D), ratio of median diameter of particle size to hydraulic radius (d/R) and overall friction factor of sediment (λs) use as input variables in prediction of Fr. A sensitivity analysis is also conducted for the purpose of examining the effect of each of the dimensionless parameters on Fr prediction accuracy. Comparing the results of the suggested models with the existing regression-based equations shows that ANFIS-GA/SVD (R²=0.986, MAPE=4.397, RMSE=0.206, SI=0.053, ρ=0.026, BIAS=-0.025) is more accurate than the rest of the models.

The late universe contains a wealth of information about fundamental physics and gravity, wrapped up in non-Gaussian fields. To make use of as much information as possible it is necessary to go beyond two-point statistics. Rather than going to higher order N-point correlation functions, we demonstrate that the probability distribution function (PDF) of spheres in the matter field (a one-point function) already contains a significant amount of this non-Gaussian information. The matter PDF dissects different density environments which are lumped together in two-point statistics, making it particularly useful for probing modifications of gravity or expansion history. Our approach in Cataneo et. al. 2021 extends the success of Large Deviation Theory for predicting the matter PDF in ΛCDM in these “extended” cosmologies. A Fisher forecast demonstrates the information content in the matter PDF via constraints for a Euclid-like survey volume combining the 3D matter PDF with the 3D matter power spectrum. Adding the matter PDF halves the uncertainties on parameters in an evolving dark energy model, relative to the power spectrum alone. Additionally, the matter PDF contains enough non-linear information to substantially increase the detection significance of departures from General Relativity, with improvements up to six times the power spectrum alone. This analysis demonstrates that the matter PDF is a promising non-Gaussian statistic for extracting cosmological information, particularly for beyond ΛCDM models.

Current commitments by European governments seek to improve energy consumption as a means to reduce carbon emissions from building stock by 2050. Within such context, retrieving reliable three-dimensional contours from point clouds becomes an important step in developing facade retrofitting solutions since facade retrofitting projects often make use of as-built 3D models to help reduce inaccuracies by narrowing interpretation and measurement errors. This work aims to provide a method that uses topology-based parametric modelling for reconstructing building envelopes from point clouds. Through a semi-automated process that gives permanent visual feedback, the user adjusts parameters to custom standards of acceptability. A solution under the form of a Grasshopper definition delivers building envelope 3D contours in various file formats as a means for increasing interoperability. The main contributions of this work consist of a parametric reconstruction workflow capable of solving building topology for retrieving 3D contours, a strategy to bypass point cloud occlusion, and a strategy for converting those contours into an IFC model directly from the parametric modelling environment.

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