In this review, we discuss the insulin resistance (IR) and its development in the insulin target tissues that leads to diabetes. Also, we highlight the use of induced pluripotent stem cells (iPSCs) to understand the mechanisms underlying the development of IR. IR is associated with several metabolic disorders, including type 2 diabetes (T2D). The development of IR in insulin target tissues involves genetic and acquired factors. Persons at genetic risk for T2D tend to develop IR several years before glucose intolerance. Although there are currently several mouse models for both IR and T2D that had provided a lot of information about the disease, these models cannot recapitulate all the aspects of this complex disease as seen in each individual. Patient-specific iPSCs can overcome the hurdles faced with the classical mouse models for studying IR. iPSC technology can generate cells genetically identical to IR individuals, which can help in distinguishing between genetic and acquired defects in insulin sensitivity. Combining the technologies of the genome editing and iPSCs may provide important information about the inherited factors underlying the development of different forms of IR. Further studies are required to fill the gaps in understanding the pathogenesis of IR and diabetes.

Power disaggregation aims at determining the appliance-by-appliance electricity consumption leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models have been proven remarkable results on energy disaggregation. Nevertheless, those procedures have various weaknesses: there is a scalability problem as the number of devices to observe raises and the algorithmic complexity of the inference step is severe. DNN architectures, such as Convolutional Neural Networks, have demonstrated to be a viable solution to deal with FHMMs shortcomings. Nonetheless, there are two significant limitations: a complicated and time-consuming training system based on back-propagation has to be employed to estimates the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome those limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. Experiment evaluation has been conducted using the UK-DALE corpus. We find that the suggested approach achieves similar performances to recently proposed ANN-based methods and outperforms FHMMs. Besides, our solution generalises well to unseen houses.

Remote sensing technology provides a new way to explore the earth for geoscience applications. In the context of continuous deepening of geological research and the vigorous development of geospatial information science, 3D geological modeling technology has become a research hotspot in the intersection of earth science and information science. The traditional 3D geological modeling technology refers to underground 3D modeling using geological data. This modeling method can only display the underground scene in 3D visualization, but cannot display the surface in detail. In this study, remote sensing technology is adopted to improve the traditional 3D geological modeling method, so that geological modeling can be integrated with remote sensing image, and a new 3D geological modeling method based on remote sensing technology is developed. This method can perform integrated 3D visualization of underground and aboveground scenes, which provides a new way for disaster prevention and reduction, geological prospecting and tectonic interpretation. The new 3D geological modeling method is mainly applied in the geological field, and has made new progress in multi-source heterogeneous geological data fusion, large-scale high-precision modeling, geological grid subdivision, attribute modeling technology, remote sensing image fusion and other aspects. Taking the Ya'an area as an example, this paper makes use of the new generation of 3D geological modeling technology to carry out 3D geological modeling and visual display. The 3D visualization in Ya'an area verifies the feasibility and effectiveness of the new 3D geological modeling method based on remote sensing technology in the current data environment.

There is a continuous growth of heat pump installations in residential buildings in Germany. The heat pumps were not only used for space heating and domestic hot water consumption but also to offer flexibility to the grid. the high coefficient of performance and the low cost of heat storages made the heat pumps an optimal candidate for the power to heat applications. Thus, several questions are raised about the optimal integration and control of the heat pump system with buffer storages to maximize its operation efficiency and minimize the operation costs. In this paper, an experimental investigation is performed to study the performance of a ground source heat pump (GSHP) with a combi-storage under several configurations and control factors. The experiments were performed on an innovative modular testbed that is capable of emulating a ground source to provide the heat pump with different temperature levels at different times of the day. Moreover, it can emulate the different building loads such as the space heating load and the domestic hot water consumption in real-time. The data gathered from the testbed and different experimental studies were used to develop a simulation model based on Modelica that can accurately simulate the dynamics of a GSHP in a building. The model was validated based on different metrics. Energetically, the difference between the developed model and the measured values was only 3.08\% and 4.18\% for the heat generation and electricity consumption, respectively.

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. These regulatory networks can then be used to predict biological and cellular behavior by system perturbations and in silico simulations. Today, broad sets of these interactions are being made available in a variety of biological knowledge resources. Moreover, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. Therefore, data users can find it challenging to efficiently explore resources of causal interaction and to be aware of recorded contextual information that ensures valid use of the data. This manuscript presents a review of public resources collecting causal interactions and the different views they convey, together with a thorough description of the export formats established to store and retrieve these interactions. Our goal is to raise awareness amongst the targeted audience, i.e., logical modelers, but also any scientist interested in molecular causal interactions, about existing data resources and how to get familiar with them.

Tsunami modeling and simulation has changed in the past few years more than it had in decades, especially so with respect to coastal inundation. Among other things, this change is supported by the approaching era of exa-scale computing, whether via GPU or more likely forms of hybrid computing whose presence is growing across the geosciences. For reasons identified across this review, exa-scale computing efforts will impact the on-shore, highly turbulent r\'egime to higher degree than the 2D shallow water equations used to model tsunami propagation in the open ocean. This short review describes the different approaches to tsunami modeling from generation to impact and underlines the limits of each model based on the flow r\'egime. Moreover, from the perspective of a future comprehensive multi-scale modeling infrastructure to simulate a full tsunami, we underline the current challenges associated with this approach and review the few efforts that are currently underway to achieve this goal. A table of existing tsunami software packages is provided along with an open Github repository to allow developers and model users to update the table with additional models as they are published and help with model discoverability.

Information regarding the cost of a construction project is available to the investor and project participants in order to determine the subsequent success of a project, given that the information they collect has an impact on the decisions they make. Cost calculations, especially in the initial phase of a project, often generate large errors. This paper presents the new approach based on a combination of the Case Based Reasoning method (CBR) with the originally selected criteria for the description of a construction project (as a result of Pearson correlation coefficient and Spearman's rank correlation coefficient) and Building Information Modeling (BIM) technology. The CBR method fulfils expectations for a simple and fast system supporting the cost estimation process. It does not require any specialist knowledge, so it will be comprehensible to cost estimation practitioners. The BIM-based model gives the opportunity for the calculation of quantity take-offs and enables the use of the information contained in the BIM model in the cost estimation process. In order to prepare the model an appropriate relational database had to be developed. With extensive research, a database of 173 construction projects, including the construction of a sports field, was obtained. There were 14 variables defined originally by authors; however, only 10 (as a result of the correlation analysis) were used for the calculation. Data related to the project were collected in the BIM model. Results estimating the project’s unit price, using the CBR method, were presented and discussed. The Mean Absolute Estimate Error was used to evaluate the model.

GNSS receivers are nowadays commonly used in monitoring applications, e.g., in estimating crustal and infrastructure deformations. This is basically due to the recent improvements in GNSS instruments and methodologies that allow high precision positioning, 24 h availability and semiautomatic data processing. In this paper, GNSS estimated deformations on a dam structure have been analyzed and compared with pendulum data. This study has been carried out for the Eleonora D’Arborea (Cantoniera) dam, which is in the Sardinia Island. Time series of pendulum and GNSS over a time span of 2.5 years have been aligned so to be comparable. Analytical models fitting these time series have been estimated and compared. Those models were able to properly fit pendulum data and GNSS data, with standard deviation of residuals smaller than one millimeter. This encouraging results led to the conclusion that GNSS technique can be profitably applied to dam monitoring allowing a denser description, both in space and time, of the dam displacements than the one based on pendulum observations.

Purpose: The aim of the present experimental 3D-finite element study was to evaluate the influence of an augmented sinus lift with an additional inserted bone graft block. The bone graft block stabilizes the implant in addition to conventional augmented bone. We placed the block in three different positions. The implants were loaded with axial force and forces secondary to laterotrusion and protrusion. Material and Methods: A simplified U-shaped 3D finite element model of the upper jaw and a more complex anatomical model of the left maxilla were created. The bone graft block was placed in three positions: in the lower third in contact with the sinus floor, the middle, and the upper third of the implant. Van Mises’ stress distribution was calculated and analyzed for the different models. We also compared the complex anatomical model with the simplified one. Results: The position of the bone graft block significantly influences the magnitude of stress distribution. A bone graft block positioned in the upper third or middle of the implant reduces the quantity of stress compared to the reference model without a bone graft block. The low bone graft block position is clearly associated with lower stress distribution in compact bone. We registered no significant differences in stress in compact bone with regard to laterotrusion or protrusion. Conclusions: Maximum values of von Mises stresses in compact bone can be reduced significantly by using a bone graft block. The reduction of stress is nearly the same for positions in the upper third and the middle of the implant. It is much more pronounced when the bone graft block is in the lower third of the implant near the sinus floor, which appeared to be the best position in the present study.

Mineral dust is one of the most important aerosol in mass over the world, affecting health and dynamics. This aerosol is mainly emitted over arid areas but may be long-range transported, impacting the local budget of air quality in many cities. While models were extensively used to study a single specific event, or making a global analysis at coarse resolution, the goal of our study is to simultaneously focus on several areas - Europe, North America, Central Asia, East China and the Caribbean area - for a one month period, March 2014, avoiding any parameter fitting to better simulate a given dust outbreak. The simulation is performed for the first time with the hemispheric version of the CHIMERE model, with a horizontal resolution of 10km. In this study, an overview of several simultaneous dust outbreaks over the north hemisphere is proposed to assess the capability of such modelling tools to predict dust pollution events. A quantitative and qualitative evaluation of the most striking episodes is presented with comparisons to satellite data, ground based Particulate Matter and calcium measurements. Despite some overestimation of dust concentrations far from emission areas, the model is able to simulate the timing of the arrival of dust outbreaks on observational sites. For instance, several spectacular dust storms in the US and China are perfectly captured by the models. The high resolution allows to better describe and understand the orographic effects and the long-range transport of dust plumes.

This article takes an approach to explaining the behavioral manifestations of the decision making in US companies’ offer of health insurance that is grounded not only on their cost minimizing behavior, but also in a humanness dimension based on the African concept of Ubuntu. In this way, we define an Ubuntu based Random Utility modeling framework, describing the choice process as a tripartite decision making, and implemented using a nationally representative random sample of 1,061 American companies from the Dunn and Bradstreet Business data, supplied by Survey Sampling International to the Associated Press-NORC Center for Public Affairs Research. The results from the three sequentially implemented specifications showed that the relationship between management culture and health plan offering strategy is dependent on other relevant co-variates, which when left out, leads to the problem of omitted variables bias. However, when all variables are included but assumed to enter the relationship exogenously, this results in management culture not having any statistically significant effect on companies' decisions about scope of health plan offering. When the exogeneity assumption is relaxed through a recursively Bivariate Probit model, the system of two equations produces a highly significant management culture effect. In fact, in this later case we see that companies with groups and formal committee management culture are 1.58 times less likely to choose a multiple plan strategy over a single plan strategy, hence failing to show the more wholesome plan offering that would theoretically prevail under Ubuntu style management.

In the context of climate change in West Africa characterized by a reduction of precipitation, this study was conducted to evaluate the impact of climate change on water resources from now to the end of the 21st century in the transboundary watershed of the Sassandra River shared by Guinea and Côte d’Ivoire. Historical and future climate (Representative Concentration Pathways or RCPs 4.5 and 8.5 scenarios) data were projected with the model. The Abdus Salam ICTP RegCM4 was used. The hydrological modeling of the river basin was carried out with the conceptual hydrological model, GR2M. This model is a monthly time steps model that allows the assessment of the discharge of the Sassandra River for each climate scenario according to the 2030 (2021–2040), 2050 (2041–2060), 2070 (2061–2080), and 2090 (2081–2100) horizons. The results showed a reduction of the annual discharge when compared to the baseline (1961–1980). For the RCP 4.5, the observed values went from –1.2% in 2030 to –2.3% in 2070 and rose to –2.1% in 2090. Concerning the RCP 8.5, we saw a variation from –4.2% to –7.9% in the 2030 and 2090 horizons, respectively. With the general decrease of rainfall in West Africa, it is appropriate to assess the impact on water resources on the largest rivers (Niger, Gambia, and Senegal) that irrigate the Sahelo-Saharian zone.

A mathematical model is proposed for quantitative description of the outbreak of novel coronavirus COVID-19 Although the model is relatively simple, the comparison with the public data shows that an exact solution solution of the model (with the correctly-specified parameters) leads to the results, which are in good agreement with the measured data in China and Austria. Prediction of the total number of the COVID-19 cases is discussed and examples are presented using the measured data in Austria, France and Poland.

Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

Current study aims to evaluate the possibilities to increase part strength by optimizing the FFF process parameters. Five different CAD models of parts with same coupling dimensions but different shape inherited from a recent study were converted into test samples with Ultimaker 2 3D printer. The main measure of success was the sample strength, defined as the load at which the first crack in the stressed area of the part appeared. Three different modifications to the FFF process with verified positive effect on interlayer bonding were applied. First modification included raising the extrusion temperature and disabling printed part cooling. Second modification consisted in reduction of the layer thickness. Third modification combined the effects of the first and the second ones. For four out of five shapes tested applied process modifications resulted in significant strengthening of the part. The shape that exhibited the best results was subject to further research by creating special printing mode. The mode included fine-tuning of three technological parameters on different stages of the part fabrication. As result it was possible to increase the part strength by 108% only by tuning printing parameters of the best shape designed with increasing its weight by 8%.

Lightning strike can cause a considerable damage in aircraft parts made from semiconducting materials such as Carbon Fiber Reinforced Plastics (CFRPs). Therefore, in recent years, the lightning strike phenomenon has attracted the interest of the academic community and the aircraft industry. Until now, the problem has been addressed mainly experimentally, while the reported numerical works are very limited. In the present work, a coupled electro-thermal FE model has been developed using the ANSYS commercial FE code to simulate the lightning strike damage in unidirectional CFRP laminates due to the Joule heat flux phenomenon. The model is based on the SOLID69 thermoelectric element and applies a non-linear, time-transient analysis. The main input to the model is the thermal-electrical properties of the composite material which vary with temperature. Using the model, a parametric study on the effect of mesh density and peak intensity on the thermal damage has been performed. Three electrical lightning strikes of low (10 kA), medium (30 kA) and high peak intensity (40 kA) have been applied according to the SAE ARP 5412 standard. The electro-thermal model has been validated against a numerical model from the literature. The numerical results reveal that the increase of peak intensity leads to the increase of the area and penetration depth of matrix thermal damage (pyrolysis) as well as to the increase of the area of fiber damage (deterioration and ablation). Through progressive damage modeling, the residual tensile strength of the CFRP plate after being subjected to lightning strike of different peak intensity has been predicted. Lightning strike initial damage has been simulated by translating the thermal field into degradation of elastic properties of the lamina. The results show an increase in the accumulated matrix damage and a decrease of tensile strength due to the initial lightning strike damage. For the maximum peak intensity of 40 kA, a decrease in tensile strength of 4.8% has been predicted

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

Aims: Angiotensin-converting enzyme 2 (ACE2) plays an important role in the entry of coronaviruses into host cells. This paper described how carnosine, a naturally occurring supplement, can be an effective drug candidate for coronavirus disease (COVID-19) on the basis of molecular docking and modeling to host ACE2 co-crystallized with COVID-19 spike protein. Methods: First, the starting point was ACE2 inhibitors and their structure-activity relationship (SAR). Next, chemical similarity (or diversity) and PubMed searches made it possible to repurpose and assess approved or experimental drugs for COVID-19. In parallel, at all stages, authors performed bioactivity scoring to assess potential repurposed inhibitors at ACE2. Finally, investigators performed molecular docking and modeling of the identified drug candidate to host ACE2 co-crystallized with COVID-19 spike protein. Results: Carnosine emerged as the best known drug candidate to match ACE2 inhibitor structure. Preliminary docking was more optimal to ACE2 than the known typical angiotensin-converting enzyme 1 (ACE1) inhibitor (enalapril) and quite comparable to known or presumed ACE2 inhibitors. Viral spike protein elements binding to ACE2 were retained in the best carnosine pose in SwissDock at 1.75 Angstroms. Out of the three main areas of attachment expected to the co-crystallized protein structure, carnosine bind with higher affinity to two compared to the known ACE2 active site. LibDock score was 92.40 for site 3, 90.88 for site 1, and inside the active site 85.49. Conclusion: Carnosine has promising inhibitory interactions with host ACE2 co-crystallized with COVID-19 spike protein and hence could offer potential mitigating effect against current COVID-19 pandemic.

The knowledge of human body proportion and segmental properties of limbs, head and trunk are of fundamental importance in biomechanical research. As many methods are employed, it is important to know which they are currently available, which data on human body masses, lengths, center of mass (COM) location, weights and moment of inertia are available and which methods are most suitable for a specific research purposes. The present review examines the literature concerning human body segments properties for biomechanical purposes. It emerges that data obtained in studies on cadaveric specimens are still the most accurate, whilst technological tools currently available are manifolds, each one with proper advantages and disadvantages. Classical studies were focused mainly on white men, while in recent year the available data of body segments has been extended to children, woman, and other races. Also, data on special population (obese, pregnant women) are starting to appear in the scientific literature.

Temperature field is an essential attribute of metal additive manufacturing in view of its bearings on the prediction, control, and optimization of residual stress, part distortion, fatigue, balling effect, etc. This work provides an analytical physics-based approach to investigate the effect of scan strategy parameters including time delay between two irradiations and hatching space on thermal material properties and melt pool geometry. This approach is performed through the analysis of the distribution of material properties and temperature profile in three-dimensional space. The moving point heat source approach is used to predict the temperature field. To predict the temperature field during the additive manufacturing process some important phenomena are considered. 1) Due to the high magnitude of temperature in the presence of the laser, the temperature gradient is usually high which has a crucial influence on thermal material properties. Consequently, the thermal material properties of stainless steel grade 316L are considered to be temperature-dependent. 2) Due to the repeated heating and cooling, part usually undergoes several melting and solidification cycles. This physical phenomenon is considered by modifying the heat capacity using the latent heat of melting. 3) The multi-layer aspect of metal AM process is considered by incorporating the temperature history from the previous layer since the interaction of the successive layers has an impact on heat transfer mechanisms. 4) Effect of heat affected zone on thermal material properties is considered by the superposition of material properties in regions where the temperature fields of two consecutive irradiations have an overlap since the consecutive irradiations change the behavior of the material properties. The goals are to 1) investigate the effects of temperature-sensitive material properties and constant material properties on the temperature field. 2) Study the behavior of thermal material properties under different scan strategies. 3) Study the importance of considering the effect of heat affected zone on thermal material through the prediction of melt pool geometry. 4) Investigate the effect of hatching space on melt pool geometry. This work is purely employed physics-based analytical models to predict the behavior of material properties and temperature field under different process conditions, and no finite element modeling is used.

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. We show that a fire forecast system based on a numerical weather prediction (NWP) model coupled with a wildland fire behavior model can provide this forecast. This is illustrated with the Chimney Tops II wildland fire responsible for large socio-economic impacts. The system is run at high horizontal resolution (111 m) over the region affected by the fire to provide a fine representation of the terrain and fuel heterogeneities and explicitly resolve atmospheric turbulence. Our findings suggest that one can use the high spatial resolution winds, fire spread and smoke forecast to minimize the adverse impacts of wildland fires.

In the present time, the biggest problem of the world is the outbreak of novel coronavirus. Novel coronavirus (COVID-19), this one name has become a part of our daily lives over the past few months. Beyond the boundaries of medical science, coronavirus is now the main subject of research in all fields like Applied Mathematics, Economy, Philosophy, Sociology, Politics upto living room. The epidemic has brought unimaginable changes in our traditional habits and daily routines. Thousands of people in our country are fighting with the rest of the world to survive in various new situations. There are different kinds of coronavirus appeared in different times. In this time, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is responsible for the coronavirus disease of 2019 (COVID-19). This virus was first identified towards the end of 2019 in the city of Wuhan in the province of Hubei in China. Within very short duration of time and very fast, it has spread throughout a large part of the world. In this study, the main aim is to investigate the spreading rate, death rate, recovery rate due to corona virus infection and to study the future of the coronavirus in India by using mathematical modeling based on the previous data. Mathematical models, in this situation, are the important tools in recruiting effective strategies to fight this epidemic. India is at high risk of spreading the disease and is facing many losses in socio-economic aspects. With current infection rates and existing levels of personal alertness, the number of infected people in India will increase at least in the next three months. Proper social awareness, maintain of social distance, large rate of testing and separation may break the chain of the Coronavirus-2.

To date, several independent methods and algorithms exist exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives, (ii) the identification of gene target-sets according to logical gene-protein-reaction associations, (iii) minimization of the number of network perturbations, and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

Helmeted hornbill (Rhinoplax vigil) is a protected wildlife in Indonesia according to enactment no. 5, 1999 about Conservation of Natural Resources and its Ecosystems and Government Regulation no. 9, 1999 about plant and wildlife preservation. Helmeted Hornbill habitats spread in five country regions: Myanmar, Thailand, Malaysia (Malayan Peninsula and Serawak), Brunei, and Indonesia (Sumatra and Borneo). Silokek Geopark which located in Sijunjung Regency, West Sumatra Province, Indonesia is an identified location of Helmeted Hornbill habitat existence. Beside its uniqueness in physics, this bird also have an ecological function as seed dispersal in nature. The utilization of Remote Sensing (RS) technology and and Geographic Information System (GIS) is highly useful in identification the Helmeted Hornbill habitat distribution in this research. Geographic dateset used in this research are Landsat OLI 8 imagery, Shuttle Radar Topographic Model (SRTM), Coordinate points of Helmeted Hornbill existence and location assesment, and other dataset related to administration boundary in Silokek Geopark. This research aims to find conservation priority zone of Helmeted Hornbill in Silokek Geopark. By utilizing Maximun Entropy (MaxEnt) algorithm with finding points and location assessment, we can determine the distribution of Helmeted Hornbill habitat in Silokek Geopark based on habitat likeness. This research produces the model of conservation priority zones in geopark silokek which are distributed in hilly protected forest area and the distributions are concentrated in the center and noth east part of our researc area. This model is highly influenced by forest texture (25.7%), distance of patches (24.3%), and distance of settlement.

A position sensing glove, called SmartScan, that creates a 3D virtual model of a real object is presented. The data from the glove is processed by a volume minimization algorithm to validate the position sensor data. This allows only data from the object’s surface to be retained. The data validation algorithm allows the user to progressively improve an image by repeatedly moving their hand over the object. In addition, the user can choose their own balance between feature resolution and invalid data rejection. The SmartScan glove is tested on a foot model and is shown to be robust against motion artifacts, and has a mean accuracy of 2.9 mm (compared to a 3D model generated from optical imaging) without calibration.

Despite recent developments in monitoring and visualizing construction progress, the data exchange between construction jobsite and office lacks automation and real-time recording. To address this issue, a near real-time construction work inspection system called iVR is proposed; this system integrates 3D scanning, extended reality, and visual programming to visualize interactive onsite inspection for indoor activities and provide numeric data. iVR comprises five modules: iVR-location finder (finding laser scanner located in the construction site) iVR-scan (capture point cloud data of jobsite indoor activity), iVR-prepare (processes and convert 3D scan data into a 3D model), iVR-inspect (conduct immersive visual reality inspection in construction office), and iVR-feedback (visualize inspection feedback from jobsite using augmented reality). An experimental lab test is conducted to validate the applicability of iVR process; it successfully exchanges required information between construction jobsite and office in a specific time. This system is expected to assist Engineers and workers in quality assessment, progress assessments, and decision-making which can realize a productive and practical communication platform, unlike conventional monitoring or data capturing, processing, and storage methods, which involve storage, compatibility, and time-consumption issues.

Force reduction is one of the most important benefit of applying ultrasonic vibration on milling. However, most of studies so far are limited to experimental investigation. In the current study, an analytical predictive model on cutting forces in ultrasonic vibration-assisted milling is proposed. The three types of tool-workpiece criteria are considered based on the instantaneous position and velocity of tool center. Type I criterion indicates that there is no contact if the instantaneous velocity is opposite to tool rotation direction. Type II criterion checks whether the vibration displacement is larger than the instantaneous uncut chip thickness. Type III criterion considers the overlaps between current and previous tool paths due to vibration. If none of these criteria is satisfied, milling forces are nonzero. Then the calculation is performed by transforming milling and tool geometry configuration to orthogonal cutting at each instant. The orthogonal cutting forces are predicted through the exhaustive search of shear angle and calculation of shear flow stress on tool-chip interface. The axial force is then calculated based on tool geometry, and the milling forces in feed, cutting, and axial directions are calculated after coordinate transformation. The proposed predictive force model in ultrasonic vibration-assisted milling is validated through comparison to experimental measurements on Aluminum alloy 2A12. The predicted values are able to match the measured milling forces with high accuracy of average difference of 13.6% in feed direction and 13.8% in cutting direction.

A sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural or other contexts in a sustainable way. The process of sustainable business model construction forms an innovative part of business strategy. Different industries and business types have utilized sustainability business models to satisfy their economic, environmental and social goals simultaneously. This study is conducted to present the state of the art of sustainable business models in various application areas. The business models are classified and reviewed in different application groups. To do so, a review is conducted, and the findings reveal that the application of sustainability business models can be classified in 16 unique categories. The key contribution of this study is providing an insight about the state of the art of sustainable business models in various application areas and its research path.

The present reality about the Nigerian economy calls for investment and development in the non-oil sector. This becomes necessary as a result of fall in the oil price in the global market. This paper examined the Bayesian Vector Autoregression (BVAR) modeling and forecasting of the dynamic interrelationship between Economic growth and revenue from the oil and non-oil sectors in Nigeria. To achieve this, annual data on Gross Domestic Product (GDP), revenue from oil and non-oil sectors were collected from Central Bank of Nigeria (CBN) bulletin, the sample from 1981 to 2008 was used for analysis, while sample from 2009 to 2014 was used for model validation. Six (6) versions of Sims-Zha BVAR models were compared for out-of-sample forecast, the result revealed the superiority of the BVAR6 model over the other BVAR models. Lastly, evidence from the decomposition forecast errors revealed that revenue of oil sector contributed 7.69% to GDP while revenue from non-oil sector contributed 0.12% to GDP in Nigeria. This paper therefore recommended that the present government should encourage investment that is geared toward development in the non-oil sector, of which it has the capacity to improve the Economic growth of the Nigerian economy.

This work studied the corrosion of welded pipes and how welding destroyed surface film of pipes. Surface reaction of a welded pipe is key to understanding phenomena and important factors during the corrosion. This paper presents experiment and CFD modeling approaches of a welded pipe corrosion under salt vapor condition. The pipes were welded at currents of 60 A,70 A and 80 A to observe the effect of welding current on corrosion. A welded pipe is a stainless-steel ASTM A312 grade 304L and period of experiment about 0-600 hours that they are tested in vertical and horizontal alignments. In CFD software, there is not direct model of corrosion but it can use surface reaction and create add-on species and chemical reaction technique for imitating the corrosion mechanism. The modeling approaches of corrosion have presented in 3-dimensional transient times in CFD simulation. Surface reactions were performed by Species Model which involve site species. Site species in Species Model took place at gas-solid interfaces and in this case are salt vapor and surface pipe. Chemical reaction rate on the surface controls lost weight of a welded pipe and the model can be validated with experiment. In conclusion, in period 0-600 hours error between CFD modeling and experiment have error trend decreased. The error at 600 hours is 6% both of vertical pipe and horizontal pipe test. The modeling approaches closely with the performed experiment and can be accepted. Moreover, the model is able to predict corrosion of a welded pipe of different sizes and their lost weight after 600 hours without experiment. Also the model can predict lifetime of pipe.

Multiphase systems are important in minerals processing, and usually include solid-solid and solid-fluid systems. Examples of operations in multiphase systems include flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization, in multiphase systems, are analyzed. Different scales of size and time are included, and therefore the analysis includes modeling at the molecular level and unit operation level, and the application of optimization for the design of a plant. New strategies for the modeling and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The paper finishes with tools to study the uncertainty, both epistemic and stochastic, which is present in all mineral processing operations. It is shown that all these areas are very active and can help to understand, operate, design, and optimize mineral processing that involves multiphase systems.

In the contemporary construction management, poor project performance, cost overrun, and low-quality construction have become a central point of attention for improving project performance including information, interaction and communication management. virtual design and construction and building information modelling play indispensable role in improving the project performance. The aim of this study is to propose a Virtual Information Modeling as a novel information and communication technology method with potential to overcome problems in the construction industry. Using one-sample t test with Kolmogorov-Smirnov approach, we analysed data collected from structured questionnaire survey involving project managers working at architecture, engineering and construction industries in Tehran, Iran. The results suggest that the most important benefits and challenges of using Virtual Information Modeling are related to the area of integration, and implementation of Virtual Information Modeling has the greatest impact on this area of project management knowledge. Thus, integration should be considered a top priority for construction companies implementing Virtual Information Modeling in their projects. Policymakers should understand that careful management of virtual information is essential for improving the performance of projects and enhancing the process efficiency.

Design and advancement of the durable urban train infrastructures are of utmost importance for reliable mobility in the smart cities of the future. Given the importance of urban train lines, tunnels, and subway stations, these structures should be meticulously analyzed. In this research, two-dimensional modeling and analysis of the soil-structure mass of the Alan Dasht station of Mashhad Urban Train are studied. The two-dimensional modeling was conducted using Hashash’s method and displacement interaction. After calculating the free-field resonance and side distortion of the soil mass, this resonance was entered into PLAXIS finite element program, and finally, stress and displacement contours together with the bending moment, shear force and axial force curves of the structure were obtained.

In this contribution, a method based on a solid solution theory of clathrate hydrate for multiple cage occupancy, host lattice relaxation and guest-guest interactions has been presented to estimate hydrate formation conditions of binary and ternary gas mixtures. We have performed molecular modeling of structure, guest distribution, and hydrate formation conditions for the CO₂ + CH₄, and CO₂ + CH₄ + N₂ gas hydrates. In all considered systems with and without N₂, at high and medium content of CO₂ in the gas phase we have found that CO₂ is more favorable to occupy clathrate hydrate cavities than CH₄ or N₂. Addition of N₂ to the gas phase increases ratio concentration CO₂ in compressing with concentration CH₄ in clathrate hydrates and makes gas replacement more effective. The mole fractions of CO₂ in CO₂ + CH₄ + N₂ gas hydrate rapidly increases with the growth of its content in the gas phase. And the formation pressure of CO₂ + CH₄ + N₂ gas hydrate rises in comparison with the formation pressure of CO₂ + CH₄ gas hydrate. Obtained results agree with the known experimental data for simple CH₄, CO₂ gas hydrates and mixed CO₂ + CH₄ gas hydrate.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

It is currently difficult to obtain accurate fine dust information in residential areas due to the insufficient number of air quality monitoring systems and spatial imbalances. Therefore, a detailed particulate matter dispersion model including factors such as land use and meteorological information was developed in this study and used to create fine dust concentration distribution maps. The fine dust concentration distribution maps currently available to citizens were compared with those obtained by dispersion modeling, and population distribution data were employed to compare the populations exposed to fine dust according to the two methods. The results of the existing method and the developed particulate matter dispersion model differed significantly. For instance, the PM2.5 concentrations in Daejeon, South Korea, on February 17, 2018, were 56% “Good” and 44% “Moderate,” according to the existing method, while they were 31% “Good,” 26% “Moderate,” 28% “Unhealthy,” and 15% “Very Unhealthy,” according to the dispersion model. Furthermore, the existing method indicated that no portion of the population was exposed to poor fine dust concentrations, while the proposed model revealed that over 170 thousand people were exposed to such concentrations. These results on fine dust distributions will contribute to sustainable urban and environmental planning.

Finely ground glass has the potential for pozzolanic reactivity and can serve as a supplementary cementitious material (SCM). Glass reaction kinetics depends on both temperature and glass composition. Microstructural modeling is a helpful approach to get better understanding of cement hydration and microstructure development. Mechanical and performance properties of concrete are directly related to the development of concrete microstructure, which is the consequence of progress in cement hydration. This study initially provides a comprehensive background about cement hydration process and microstructural modeling of the hydration. It then utilizes results of experimental studies, i.e. isothermal calorimetry and thermogravimetric analysis, to find kinetics equation parameters called “Avrami Constants”. For the first time, these constants were found for three main components of cement, i.e. C₃S, C₂S, and C₃A, and also for glass particles smaller than 25 µm. Although modeling of cement hydration and cementitious systems containing single glass particles showed promising results, simulations of combined glass types and sizes showed that more work on microstructural models is needed to properly model the reactivity of mixed glass particle systems.

This paper relies on accounting-based measures of country risk to investigate U.S. global banks' exposure to foreign country risk over the 2017 fiscal year as measured by the sum of cross-border risk, foreign office risk, and derivative risk claims. We achieve this using panel linear modeling methods with country level heterogeneity and time fixed effects, along with a constructed panel data of 284 observations on 71 countries distributed across 6 world regional blocks, and observed over 4 consecutive quarters starting from 4th quarter 2016 and ending with 3rd quarter 2017. The results show that on average, over the four quarters, a 1% increase in foreign banking sector's claims significantly increases U.S. global banks cross border risk exposure by 0.34%, while reducing derivative risk exposure by 0.22%, but have no significant impact on foreign office risk exposure. Similar results are observed with public sector claims which significantly increase banks' exposure to cross border risk by 0.21%, while reducing derivative risk exposure by 0.19%. Conversely however, non-bank financial sector claims are found to have no significant affect on cross-border risk exposure, but significantly reduce foreign office risk exposure by 0.09%, while increasing derivative risk exposure by 0.06%. These results indicate the presence of sectoral heterogeneities in U.S. banks' exposure to foreign counterparties' risk, and also that overall, over the course of 2017 the level of U.S. global banks' cross-border risk exposure increased, while their level of derivative risk exposure decreased, and the level of foreign office risk exposure remained relatively unchanged.

The concept of transit-oriented development (TOD) has been widely recognized in recent years for its role in reducing car traffic, improving public transportation, and enhancing traffic sustainability. This paper conducts empirical research on a developed rail transit network, using Shanghai as a case study. In addition to traditional TOD features, other factors based on urban rail transit are introduced, including multi-level modeling (MLM), which is used to analyze the possible factors influencing rail patronage. To avoid the bias of research results led by the correlation between independent variables, factors are divided into two levels. The first level includes three groups of variables: the built environment, station characteristics, and socioeconomic and demographic characteristics. The second level includes a set of variables which are regional characteristics. Results show that the most significant impact on train patronage is station location in the business district area. Other factors that have a positive effect on promoting rail transit travel include the number of service facilities around the station, degree of employment around the station, economic level, intensity of residential development, if the station is a transfer station, the operating period of the station, and the size of the large transportation hub around the station.

Wave energy converter (WEC) arrays deployed in coastal regions may create physical disturbances potentially resulting in environmental stresses. Presently, limited information is available on the nature of these physical disturbance or the resultant effects. A quantitative Spatial Environmental Assessment Tool (SEAT) for evaluating potential effects of wave energy converter (WEC) arrays on nearshore hydrodynamics and sediment transport is presented for the central Oregon coast (USA) through coupled numerical model simulations of an array of WECs. Derived climatological wave conditions were used as inputs to the model to allow for the calculation of risk metrics associated with various hydrodynamic and sediment transport variables such as maximum shear stress, bottom velocity, and change in bed elevation. The risk maps provided simple, quantitative, and spatially-resolved means of evaluating physical changes in the vicinity of a hypothetical WEC array in response to varying wave conditions. Near-field risk of sediment mobility was determined to be moderate in the lee of the densely spaced array, where the potential for increased sediment deposition could result in benthic habitat alteration. Modifications to the nearshore sediment deposition and erosion patterns were observed near headlands and topographic features, which could have implications for littoral sediment transport. The results illustrate the benefits of a risk evaluation tool for facilitating coastal resource management at early market marine renewable energy sites.

Umbu is a tropical fruit with high content of bioactive compounds. However, maturation causes significant losses on nutrient density reducing the nutritional value of this highly appreciated fruit. Thus, the objective of the present work was to encapsulate the Umbu fruit bioactive compounds using spray drying and identify the variables affecting the biomolecules preservation. A Box-Behnken experimental design with 3 factors was set varying inlet temperature, atomization flow rate, and maltodextrin concentration for process otimization. Then the powder physicochemical and chemical properties were characterized, and results were modeled using a polynomial equation. Results revealed that the droplet size and maltodextrin concentration had a significant influence on the conservation of the biomolecules. Drying kinetics favoring fast formation of a particle crust increases encapsulation efficiency. Bioactive compounds retention was achieved by increasing maltodextrin even at high temperatures, where a matrix is formed hindering chemical degradation. Process optimization was validated and revealed low inlet temperatures (122ºC), high atomization flow rate (5kg/h) and high maltodextrin concentration (20%) to be the most desirable conditions for bioactive compounds retention.

Current paper investigates the influence of hardware setup and parameters of a 3D printing process based on fused filament fabrication (FFF) technology on resulting sample strength. Three-point bending of samples printed with long side oriented along Z axis was used as a measure of the interlayer bonding strength. The same CAD model was converted into NC-programs through same slicing software to be run on four different desktop FFF 3D printers, out of filament of same brand and color. Within all the printers same ranges of layer thickness values from 0.1 to 0.3 mm and feed rates from 25 to 75 mm/s were planned to be varied. All the machines demonstrated statistically almost identical values of maximum flexural strength, however the different machines exhibited maximum sample strength with different combinations of varied parameters. Among all the hardware factors observed, the most important was proved to be extruder type, direct or Bowden. This feature fundamentally changes the nature of studied parameters influence onto the resulting strength of the FFF process. For the extruders of Bowden type the length of flexible guiding tube is of great importance.All the machines demonstrated statistically almost identical values of maximum flexural strength, however the different machines exhibited maximum sample strength with different combinations of varied parameters. Among all the hardware factors observed, the most important was proved to be extruder type, direct or Bowden. This feature fundamentally changes the nature of studied parameters influence onto the resulting strength of the FFF process. For the extruders of Bowden type the length of flexible guiding tube is of great importance.

Project complexity is usually considered as one of main causes of cost overruns, resulting in poor performance and thus project failure. However, empirical studies focused on evaluating its effects on project cost remain lacking. Given this circumstance, this study attempts to develop the relationships between project cost and the multidimensional project complexity elements. We establish complexity as a multidimensional factor including the task, organization, market, legal, and environment complexities. This study uses an empirical evidence-based structural model to account for the relationships between project cost and project complexity. By doing so, a quantitative assessment of multi-dimensional project complexity has been developed. The findings suggest that task and organization complexities have direct effects on project cost, while market, legal and external environment complexities have indirect effects on project cost. The practical contribution is that the findings can improve the understanding of which dimension of complexity significantly influence project cost, and the need to focus efforts on strategically addressing that complexities.

Studies performed at single-cell resolution have demonstrated the physiological significance of cell-to-cell variability. Various types of mathematical models and systems analyses of biological networks have further been used to gain a better understanding of the sources and regulatory mechanisms of such variability. In this work, we present a novel sensitivity analysis method, called molecular density function perturbation (MDFP), for the dynamical analysis of cellular heterogeneity. The proposed analysis is based on introducing perturbations to the density or distribution function of the cellular state variables at specific time points, and quantifying how such perturbations affect the state distribution at later time points. We applied the MDFP analysis to a model of signal transduction pathway involved in TRAIL (tumor necrosis factor-related apoptosis-inducing ligand)-induced apoptosis in HeLa cells. The MDFP analysis showed that caspase-8 activation regulates the timing of the switch-like increase of cPARP (cleaved poly(ADP-ribose) polymerase), an indicator of apoptosis. Meanwhile, the cell-to-cell variability in the commitment to apoptosis depended on mitochondrial outer membrane permeabilization (MOMP) and events following MOMP, including the release of Smac (second mitochondria-derived activator of caspases) and cytochrome-C from mitochondria, the inhibition of XIAP (X-linked inhibitor of apoptosis) by Smac and the formation of apoptosome.

The “Crisis of Reproducibility” has received considerable attention both within the scientific community and without. While factors associated with scientific culture and practical practice are most often invoked, I propose that the Crisis of Reproducibility is ultimately a failure of generalization with a fundamental scientific basis in the methods used for biomedical research. The Denominator Problem describes how limitations intrinsic to the two primary approaches of biomedical research, clinical studies and pre-clinical experimental biology, lead to an inability to effectively characterize the full extent of biological heterogeneity, which compromises the task of generalizing acquired knowledge. Drawing on the example of the unifying role of theory in the physical sciences, I propose that multi-scale mathematical and dynamic computational models, when mapped to the modular structure of biological systems, can serve a unifying role as formal representations of what is conserved and similar from one biological context to another. This ability to explicitly describe the generation of heterogeneity from similarity addresses the Denominator Problem and provides a scientific response to the Crisis of Reproducibility.

In this paper, we introduce the evolve-then-filter (EF) regularization method for reduced order modeling of convection-dominated stochastic systems. The standard Galerkin projection reduced order model (G-ROM) yield numerical oscillations in a convection-dominated regime. The evolve-then-filter reduced order model (EF-ROM) aims at the numerical stabilization of the standard G-ROM, which uses explicit ROM spatial filter to regularize various terms in the reduced order model (ROM). Our numerical results based on a stochastic Burgers equation with linear multiplicative noise. It shows that the EF-ROM is significantly better results than G-ROM.

This paper considers a new approach to the modeling of the vacuum infusion process at the manufacturing three-dimensional composite parts of complex shape. The developed approach and numerical methods are focused on the reliable prediction with the needed accuracy and elimination the unrecoverable defect of composite structure such as the dry spots. The paper presents some experimental results, which demonstrate two cases of dry spots formation in large aircraft composite panels, and analyses the reasons of these defects arising. Our numerical technique is based on the vacuum infusion of the liquid resin into porous preform as the two phase flow, which is described by the phase field equation coupled with the Richards equation describing the fluid motion in unsaturated soils with spatially varied pressure dependant porosity and saturation. This problem statement allowed to correctly reconstruct the resin front motion and formation of inner and outer dry spots depending on its movement. For the rapid detection of preform zones that are suspicious for defect formation two indicators calculated during process simulation are proposed and tested at the numerical experiments. The auxiliary program tool has been developed in MATLAB environment to correctly detect the times of formation, localization and dimensions of the arising dry spots by using the results of the finite element model simulation.

In this study, I extend the Fama and French five-factor asset pricing model with a sixth factor, namely, carbon risk, to investigate its impact on equity returns. To measure carbon risk, a new factor ‘pollutant minus green,’ is developed using the difference between the weighted average returns of pollutant and green firms across 51 developed and emerging countries across four categories—North America, Europe, Emerging Markets, and the Asia Pacific. The results reveal that North America, Europe, and Asia Pacific markets have a carbon risk premium that gets eliminated in small-cap firms. The carbon risk factor is further tested in left-hand side (LHS) test asset portfolios and found to be more pronounced with size-effect anomaly; specifically, small stock firms report greater declining average returns because of more exposure than the mega-cap stocks to carbon dioxide emissions. Furthermore, size-effect anomaly prevails with profitability and investment factors across firms. Therefore, high profitability, as well as high investment small firms, show a greater decline than the big stock firms in average returns when their carbon dioxide emissions increase. The asset pricing model evaluation is carried out through the Gibbons, Ross, and Shanken test. The six-factor model directed at capturing carbon risk patterns in average equity returns performs better than the three-factor and five-factor models of Fama and French (1993 and 2015) in the majority of categories under 3x3 sorting and compete with both Fama and French model under 2x4x4 sorted LHS portfolios. The finding of this study offers various useful applications for investors, policymakers, brokers, corporations, governmental pollution abatement institutions, and other stakeholders who wish to obtain carbon risk premium.

Flooding and overflows are recurring problems in several Brazilian cities, which usually undergo disorderly development. Their causes vary from increased impervious surface areas, deficiency/inefficiency of drainage structures and their maintenance, siltation of rivers, channel obstructions, and climatic factors. This situation is aggravated in the major cities. The Anhangabau watershed lies in the central portion of the city of Sao Paulo – Brazil and covers a drainage area of 5.4 km². The region is highly urbanized and crossed by a major north-south road connection. During heavy rain events, portions of this interconnection passage become compromised, disrupting the flow of vehicles, creating a chaotic situation for the population, as well as losses to the national economy. Observed rainfall records and an existing IDF (intensity duration frequency) curve for the region are used to obtain design storms. To account for climate change, a well know procedure, the equidistance quantile matching method for updating IDF curves under climate change, was applied to the existing historical data. Several different global climate models (GCM) and one regional model were applied to obtain and update rainfall design storm. The GCMs and future scenarios used were from the IPCC Assessment Report 5 (AR5) and two future projections: RCP (representative concentration pathway) 4.5 and 8.5. Alternatives previously proposed to solve to flooding issue are briefly reviewed. On one of the latest studies [1], a few modern concepts of water resources management are presented, and the linear retention measure was found to offer higher potential to mitigate the flooding problem in the lower valley of the watershed. Therefore, this alternative was used to evaluate different design storms scenarios combined with return periods of 25 and 100-years as well as the updated IDF under climate change for RCP 4.5 and RCP 8.5. To model the complex network, representing both road and drainage systems and their interconnections, PCSWMM/SWMM software was applied. Results are presented as flooding maps and show the impacts of the proposed linear retention measure based on the existing IDF curves and the updated IDF curves under climate change for two different drainage system conditions, current and improved with the use of linear retention reservoirs. Results show that the prosed changes on the drainage system help reduce the risk and damage to flooding. The climate change scenarios, however, impose a significant threat and need immediate attention from city planners and stakeholders.

Floods in Ukraine is a common natural phenomenon that repeats periodically and in some cases it becomes disastrous signs. In an average year in the rivers of Volyn passes from one to three floods with going beyond the limits of the floodplain. Floodplain of Styr river is located in the historical center of Lutsk city, that`s why issues of research and forecasting of floods are very important for a given city. Using modern technologies of geodesy and remote sensing allows to quickly determine and predict the floodplain area of settlements. The research results of water level changes on the Styr River and flood zones within the limits of Lutsk is proposed. The mathematical model of short-term forecasting of water level in flood period on the river Styr with implementation of geoinformation modeling of flooded areas using remote sensing data is proposed.

The sun bear is conservation mammal in Indonesia. The ecological problems as conflict among mammals and human often happen around wildlife, the one of the case is the conflict between sun bear (helarctos malayanus) with human. In Pasaman where around the anthropogenic landscape is bounded by natural forest as sun bear habitat. In this decade, the story has recorded about ±16 incidents. The aim of this research is to geospatial modeling the area of potential conflict between sun bear- human. The method in this research is to use natural logarithmic and regression logistic. The tool is geographical information system and maximum entropy. The result of this research, there has found the distribute energy each variable landscape ecosystem. The statistical model of the potential conflict it has spread on landscape ecosystem. The average value of AUC prediction in this model is at number of 0.91. The variable contribution which affect are forest edge at number of 39.2%, the alternative food (plantation) at number of 31.4%, and forest fragmentation at number of 16.9%.

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

Ecologists use the net biotic interactions among plants as a major factor to predict other ecosystem features, such as species diversity, community structure, or plant atmospheric carbon uptake. By adopting this approach, ecologists have built a giant body of theory founded on observational evidence. However, growing evidence points out that this may not be the right approach. The literature addressing the biophysical mechanisms underlying the plant interactions is much scarcer. A rising number of scientists claim the need for a mechanistic understanding of plant interactions due to the limitations that a phenomenological approach raises both in empirical and theoretical studies. Scattered studies have recently taken such a mechanistic approach, but we still lack a general theoretical framework to study mechanistically plant interactions. In this review, we first recapitulate the elementary units of plant interactions, i.e., all the known biophysical processes affected by the presence of an influencing plant and the possible phenotypic responses of plants influenced by those processes. Second, we discuss how a net interaction between two plants emerges from the simultaneous effect of these elementary units. Third, we touch upon the spatial and temporal variability of the net interaction and discuss the links between this variability and the underlying biophysical processes. We conclude by discussing how to integrate these processes into a mechanistic framework for plant interactions that must necessarily focus on the individual scale and explicitly incorporate the spatial structure of the community and environmental factors: the plant interaction models (PIM). A PIM incorporates a pair or few plants interacting with their physical environment so that the biotic interaction is not imposed but emerges from the model. This type of model can provide concise, mechanistic hypotheses to be tested empirically. This review calls for a paradigm shift in the ecology of plant interactions, from the classic species interaction study towards a more mechanistic individual-level approach. It also presents a comprehensive foundation for studying the mechanisms underpinning the net interaction between two plants.

The article presents the Base Point Split (BPSplit) algorithm for generating a polygon skeleton based on sets of vector data describing lakes and rivers. A key feature of the BPSplit algorithm is that it is dependent on base points representing the source or mouth of a river or a stream. The input values of base points determine the shape of the resulting skeleton. Various skeletons can be generated with the use of different base points. Base points are applied to divide polygon boundaries into segments. Segmentation supports the selection of TIN edges inside polygons. The midpoints of selected TIN edges constitute a basis for generating a skeleton. The algorithm handles polygons with numerous holes, and it accounts for all holes. This article proposes a method for modifying a complex skeleton with numerous holes. In the discussed approach, skeleton edges that do not meet the preset criteria (e.g. that the skeleton is to be located between holes in the center of the polygon), are automatically removed. A simple algorithm for smoothing zigzag lines was proposed.

By the study of the pattern book Ying Zao Fa Shi (building regulations of Song Dynasty, 1103 AD), while analyzing the combining and dimensioning rule of timber framework and tile work, a model self-generating program has been compiled for the first time. The operating framework has been firstly defined, while solving the issues of clustering principle, connecting method, output classification, etc. with the detailed description of algorithm theory. Taking the corner bracket set and nine-ridge roof for example, after the compilation and debug by Grasshopper, according to various input parameters, various models have been generated automatically by the plugin, proving the velocity and the veracity of the algorithm.

The study aims to investigate Gen Z's intentions to utilize the Waze mobile application as a solution for sustainable traffic management in Meto Manila. The extended Technology Acceptance Model (TAM) was used to define user behavior and technology usage to determine the factors influencing Filipino drivers' application acceptance of Waze. The study’s data results have revealed that the System Quality, Perceived Location Accuracy, Perceived Usefulness, and Perceived Ease of Use affect Filipinos' intentions to use traffic navigation applications, particularly Waze. The study utilized a survey and was given to 300 Filipino drivers traveling within the National Capital Region to accurately measure the factors and their connections to usage intention and actual use. The analytical approach used for this investigation was partial least squares structural equation modeling (PLS-SEM). The study's results can be applied as a theoretical framework for future researchers of Waze mobile applications, Waze route accuracy, and Waze overall application performance, as well as the features of the Waze application, to evaluate how the use of the Waze mobile application could help in the effective management of traffic situations in Metro Manila. The study was able to assess the effectiveness of the Waze navigation app as a platform for efficiently managing traffic situations in Metro Manila. Moreover, this study also highlighted the application performance of Waze and its acceptance by Filipino drivers in Metro Manila. Furthermore, the study implores a critical review of Waze's application performance. The study recommends that similar future studies must consider samples from a more diverse geographic background to obtain a more accurate representation of Filipinos traveling. Future researchers are also advised to adopt a qualitative research design to gain more detailed insights of Filipinos regarding their experience with the Waze application.

The Physics Informed Neural Networks framework is applied to the understanding of the dynamics of Coronavirus of 2019. To provide the governing system of equations used by the framework, the Susceptible-Infected-Recovered-Death mathematical model is used. The study focused on finding the patterns of the dynamics of the disease which involves predicting the infection rate, recovery rate and death rate; thus predicting the active infections, total recovered, susceptible and deceased at any required time. The study used data that was collected on the dynamics of COVID-19 from the Kingdom of Eswatini between March 2020 and September 2021. The obtained results showed less errors thus making highly accurate predictions.

Among many other themes, Leonardo da Vinci’s Manuscript B contains several drawings of centrally planned churches, some of which are represented using a plan view paired with a bird’s-eye view. The use of a bird’s-eye instead of an elevation represents an innovative depiction technique, which allows to combine the immediacy of the perspective view with the measurability of the façades, and therefore to describe the three-dimensionality of the buildings. To understand the reasons behind the use of this original technique, the edifices’ shape and classify them we decided to use 3D digital reconstruction techniques, for their ability to avoid misunderstandings in the reconstruction process and in the results. This article describes the method to create the digital models of sixteen churches. A Visual programming language (VPL) script was used as 3D base for modelling the churches achieved from a classification code expressing the aggregative rules of the churches. Then, the geometric process for the construction of the plan and its relationship with the elevation measures was studied for each church. Finally, this information was used for the completion of the 3D models, distinguishing more output variants each time there was an inconsistency between plan and perspective view, a variability of one architectural element or an uncertainty.

Rapid increase in digital data coupled with advances in deep learning algorithms is opening unprecedented opportunities for incorporating multiple data sources for modeling spatial dynamics of human infectious diseases. We used Convolutional Neural Networks (CNN) in conjunction with satellite imagery-based urban housing and socio-economic data to predict disease density in a developing country setting. We explored both single (uni) and multiple input (multimodality) network architectures for this purpose. We achieved maximum test set accuracy of 81.6 per cent using a single input CNN model built with one convolutional layer and trained using housing image data. However, this fairly good performance was biased in favor of specific disease density classes due to an unbalanced data set despite our use of methods to address the problem. These results suggest CNN are promising for modeling spatial dynamics of human infectious diseases, especially in a developing country setting. Urban housing signals extracted from satellite imagery seem suitable for this purpose, under the same context.

The author demonstrates a stable Lagrangian solid modeling method, tracking the interactions of solid mass particles rather than using a meshed grid. This numerical method avoids the problem of tensile instability often seen with smooth particle applied mechanics by having the solid particles apply stresses expected with Hooke's law, as opposed to using a smoothing function for neighboring solid particles. This method has been tested successfully with a bar in tension, compression, and shear, as well as a disk compressed into a flat plate, and the numerical model consistently matched the analytical Hooke's law as well as Hertz contact theory for all examples. The solid modeling numerical method was then built into a 2-D model of a pressure vessel, which was tested with liquid water particles under pressure and simulated with smoothed particle hydrodynamics. This simulation was stable, and demonstrated the feasibility of Lagrangian specification modeling for fluid–solid interactions.

The purpose of this study was to assess the extent to which self-esteem and depression mediated the influence of internalized stigma on life satisfaction among Korean out-of-school youths. Cross-sectional data on 318 youths provided information on perceived stigma, self-esteem, depression, life satisfaction, and personal characteristics. A multinomial logistic regression analysis was followed by structured path analysis to investigate the mediation effects. Internalized stigma was negatively associated with life satisfaction. Self-esteem significantly mediated the influence of stigma on depression and the influence of depression on life satisfaction. Further, stigma directly and significantly influenced depression. This study demonstrated that self-esteem and depression were important to the relationship between internalized stigma and life satisfaction. Implications for possible policies and programs with the aim of helping out-of-school youths to integrate and lead successful satisfying lives are discussed.

Whenever vegetated areas are monitored over time, phenological changes in land cover should be decoupled from changes in acquisition conditions, like atmospheric components, sun and satellite heights, and imaging instrument. This especially holds when the multispectral (MS) bands are sharpened for spatial resolution enhancement by means of a panchromatic (Pan) image of higher resolution, a process referred to as pansharpening. In this paper, we provide evidence that pansharpening of visible/near-infrared (VNIR) bands takes advantage from a correction of the path radiance term introduced by the atmosphere, during the fusion process. This holds whenever the fusion mechanism emulates the radiative transfer model ruling the acquisition of the Earth’s surface from space, that is, for methods exploiting a multiplicative, or contrast-based, injection model of spatial details extracted from the panchromatic (Pan) image into the interpolated multispectral (MS) bands. The path radiance should be estimated and subtracted from each band before the product by Pan is accomplished. Both empirical and model-based estimation techniques of MS path radiances are compared within the framework of optimized algorithms. Simulations carried out on two GeoEye-1 observations of the same agricultural landscape at different dates highlight that the de-hazing of MS before fusion is beneficial for an accurate detection of seasonal changes in the scene, as measured by the normalized differential vegetation index (NDVI).

Background: New open-source electric-grid planning models have the potential to improve power system planning and bring a wider range of stakeholders into the planning process for next-generation, high-renewable power systems. However, it has not yet been established whether open-source models perform similarly to the more established commercial models for power system analysis. This reduces their credibility and attractiveness to stakeholders, postponing the benefits they could offer. In this paper, we report the first model intercomparison between an open-source power system model and an established commercial production cost model. Results: We compare the open-source Switch 2.0 to GE Energy Consulting’s Multi Area Production Simulation (MAPS) for production-cost modeling, considering hourly operation under 17 scenarios of renewable energy adoption in Hawaii. We find that after configuring Switch with similar inputs to MAPS, the two models agree closely on hourly and annual production from all power sources. Comparing production gave a coefficient of determination of 0.996 across all energy sources and scenarios, indicating that the two models agree on 99.6% of the variation. For individual energy sources, the coefficient of determination was 69–100. Conclusions: Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here. Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here.

Recent advancements in cloud-computing and social-networking are influencing how we communicate professionally, work collaboratively, and approach data-science tasks. Here we show how the groundwater modeling field is well positioned to benefit from these advancements. We present a case study detailing a vertically-integrated, collaborative modeling framework jointly developed by participants at the American Samoa Power Authority and at the University of Hawaii Water Resources Research Center. The framework components include direct collection and analysis of climatic and streamflow data, development of a water budget model, and initiation of a dynamic groundwater modeling process. The framework is entirely open-source and applies newly available data-science infrastructure using Python-based tools compiled with Jupyter Notebooks and cloud computing services such as GitHub. These resources allow for seamless integration of multiple computational components into a dynamic cloud-based workflow that is immediately accessible to stakeholders, resource managers, or anyone with an internet connection

The energy yield of the Vehicle-integrated photovoltaic (VIPV) differs from that of the standard photovoltaics (PV). It is mainly by the difference of the solar irradiance onto the car-roof and car-bodies as well as its curved-shape. Both meaningful and practical modeling and measurement of the solar irradiance for VIPV are needed to be newly established, not the extension of the current technologies. The solar irradiance was modeled by a random distribution of the shading objects and car-orientation with the correction of the curved surface of the PV modules. The measurement of the solar irradiance onto the car-roof and car-body was done using five pyranometers in five local axes on the car for one year. The measured dynamic solar irradiance onto the car-body and car-roof was used for validation of the solar irradiance model in the car.

After 40 years of the discovery of metallocene catalysts, there are still several aspects that remain unresolved, especially when the “conventional” alkylaluminum activators are not used. Herein, we systematically investigate the synthesis of PE via three different zirconocene catalysts, with different alkyl substituents, activated via different organoboron compounds. The polymerization behavior, as well as the properties of the materials, are evaluated. The results demonstrate that the highest catalytic activity is shown by Bis(cyclopentadienyl)dimethylzirconium activated by trityl tetra(pentafluorophenyl)borate. Also finding that toluene is the optimum solvent for these systems and at these reaction conditions. Moreover, to validate our experimental results, a comprehensive mathematical model is developed on the basis of thermodynamic and kinetic principles. The concentration of ethylene transferred to the solvent phase (toluene) in a liquid-vapor equilibrium (LVE) system is estimated based on the Duhem’s theorem. Arrhenius expressions for the kinetic rate constants of a proposed kinetic mechanism are estimated by a kinetic model, in which the rate of polymerization is fitted by a least-square optimization procedure and the molecular weight averages by the method of moments. The simulations of the coordination polymerization suggest the presence of two types of active sites, principally at low temperatures, and the reactivation of the deactivated sites via a boron-based activator. However, the effect of the temperature on the reactivation step is no clear; a deeper understanding via designed experiments is required.

The epithelial-mesenchymal (E/M) hybrid state has emerged as an important mediator of elements of cancer progression, facilitated by epithelial mesenchymal plasticity (EMP). We review here evidence for the presence, prognostic significance, and therapeutic potential of the E/M hybrid state in carcinoma. We further assess modelling predictions and validation studies to demonstrate stabilised E/M hybrid states along the spectrum of EMP, as well as computational approaches for characterising and quantifying EMP phenotypes, with particular attention to the emerging realm of single-cell approaches through RNA sequencing and protein-based techniques.

This is the second of a two-paper series describing a multi-scale modeling approach developed to simulate crack sensing in polymer fibrous composites by exploiting interruption of electrically conductive carbon nanotube (CNT) networks. The approach is based on the finite element (FE) method. FE models at three different scales, namely the micro-scale, the meso-scale and the macro-scale, have been developed using the ANSYS PDL environment. In the present paper, the meso- and macro-scale analyses are described. In the meso-scale, a two-dimensional model of the CNT/polymer matrix reinforced by carbon fibers is used to develop a crack sensing methodology from a parametric study which relates the crack position and length with the reduction of current flow. In the meso-model, the effective electrical conductivity of the CNT/polymer computed from the micro-scale is used as input. In the macro-scale, the final implementation of the crack sensing methodology is performed on a CNT/polymer/carbon fiber composite volume using as input the electrical response of the cracked CNT/polymer derived at the micro-scale and the crack sensing methodology. Analyses have been performed for cracks of two different lengths. In both cases, the numerical model predicts with good accuracy both the length and position of the crack. These results highlight the prospect of conductive CNT networks to be used as a localized structural health monitoring technique.

The today competitive advantage of Ready-made garment industries depends on the ability to improve the efficiency and effectiveness of resource utilization. Ready-made garment industries have long historically adopted fewer technological and process advancement as compared to automotive, electronics and semiconductor industries. Simulation modeling of garment assembly line system has attracted a number of researchers as one way for insightful analysis of system behaviour and improving its performance. However, most of simulation studies have considered ill-defined experimental design which cannot fully explore the assembly line design alternatives and does not uncover the interaction effects of the input variables. Simulation metamodeling is an approach to assembly line design which has recently been of interest to so many researchers. However, its application in garment assembly line design has never been well explored. In this paper, simulation metamodeling of trouser assembly line with 72 operations has been demonstrated. The linear regression metamodel technique with resolution-V design was used. The effects of five factors: bundle size, job release policy, task assignment pattern, machine number and helper number on the production throughput of the trouser assembly line were studied. The increase of 28.63% of the production throughput was achieved for the best factors’ setting of the metamodel.

Physics-informed neural networks are a promising method to yield surrogate models of flow fields. We present a metamodeling technique for variable geometries based on physics-informed neural networks. The method was applied to the DU99W350 airfoil at a Reynolds number of 1×10⁵. The model predicted the Reynolds-averaged velocity and pressure field around the airfoil for arbitrary angles of attack between 10.0° and 17.5°. The model was trained with data from CFD simulations for a limited set of angles of attack. Additionally, satisfaction of the a priori known boundary conditions as well as the Reynolds-averaged Navier-Stokes equations were trained. A sensitivity analysis concerning the Reynolds number, the amount and distribution of training data, and the turbulence model was conducted showing the superiority of the pseudo-Reynolds stress method and the demand of labeled training data in the domain. The trained network was capable of predicting the developing flow separation on the suction surface and exhibited excellent agreement with CFD results even in the proximity to the wall for interpolations as well as extrapolations from the labeled data set.

U.S. Navy Surface Ship depot-level maintenance periods of performance were studied to develop a method for predicting maintenance durations. The need for the method has been highlighted by Navy leadership in recent media posts describing unacceptable maintenance delays and this research provides practitioners and decisionmakers with a reliable estimating tool. This study helps by putting forth a method that defines the rate of work accomplishment based on relevant variables. Using ordinary least squares models, this research revealed that the size of the contract obligation and the amount of shipyard work occurring simultaneously in the market are key variables in determining depot maintenance durations. With the knowledge found here, the next logical step is an optimization model for each U.S. Navy surface ship homeport.

The number of applications for nanobodies is steadily expanding, positioning these molecules as fast-growing biologic products in the biotechnology market. Several of their applications require protein engineering, which in turn would greatly benefit from having a reliable structural model of the nanobody of interest. However, as with antibodies, structural modeling of nanobodies is still a challenge. With the rise of artificial intelligence (AI), several methods have been developed in recent years that attempt to solve the problem of protein modeling. In this study, we have compared the performance in nanobody modeling of several state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2, OmegaFold, ESMFold and Yang-Server, or specifically designed for antibody modeling, such as IgFold, and Nanonet. While all these programs performed rather well in constructing the nanobody framework and CDRs 1 and 2, modeling of CDR3 sill represents a big challenge. Interestingly, tailoring an AI method for antibody modeling does not necessarily translate into better results for nanobodies.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

Abstract: Polylactic acid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the producibility of fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDM-printed tensile test samples with the desired part thickness. To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering layer thickness, infill percentage, and extruder temperature as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The honeycomb internal fill pattern is applied for the production of light-weight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using ANN and ANN-GA techniques for developing an accurate estimation for toughness, part thickness, and production cost as dependant variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid ML technique could successfully improve the accuracy of modeling about 7.5, 11.5 and 4.5 % for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. In the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which develops the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing fused deposition modeling setup without changing the functional hardware/software of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.

The efficacy of the product development process is measured by the ability to launch a project with product and production process specifications that could guarantee that the manufacturing can produce it with the least impact. If a problem is detected late, they bring consequences beyond the high cost of the solution, if related to physical ergonomics, which will influence the well-being of operators, productivity, and quality. Virtual Reality (VR) and Digital Human Modeling (DHM) are ones of the enabling technologies of Industry 4.0 and has already been applied on a large scale in industries such as automotive, construction, and aeronautics. However, even though the huge applications, these technologies are not yet applied by these industries for the analysis of physical ergonomics during product development phases. This study aims to characterize the state of the art and technology about the application of Virtual Reality and Digital Human Modeling for the physical ergonomics analysis in the during product development phases in the industry through a systematic review of the literature and patents. In patent documents recovery, we used Derwent Innovation database. The research is based on searching the selected terms in the title, summary, and claims of the documents through a search strategy containing IPC code and keywords. In articles recovery, we searched ScienceDirect, Springer, and IEEExplore databases for scientific publications. The search resulted in 311 patents documents and 16 articles in the scientific database. This study analyzed the patents to map out the technological progress in this area, where we found in the charts and data an increasing number of publications per year and a spread application with a considerable number of new technologies presented in these recent patents. The literature review indicated that Virtual Reality technology complements the Digital Human Modeling during physical ergonomics analysis for manufacturing process already designed. The majority of research on the use of VR and DHM technologies for physical ergonomics analysis focus on the automotive industry and the ergonomic assessment of workstations and current processes. Further research is needed to investigate how Virtual Reality and Digital Human Modeling might assist in the understanding of physical ergonomics in certain tasks throughout the product development process, such as the simulation of worker posture or effort when assembling parts.

As of not long ago, understanding the administrative conduct of cells has been sought after through autonomous investigation of the transcriptome or the proteome. In view of the focal creed, it was commonly accepted that there exist an immediate correspondence between mRNA records and produced protein articulations. In any case, late examinations have demonstrated that the relationship among's mRNA and Protein articulations can be low because of different factors, for example, unique half lives and post record apparatus. In this manner, a joint investigation of the transcriptomic and proteomic information can give valuable experiences that may not be translated from singular examination of mRNA or protein articulations. This article audits the current significant methodologies for joint investigation of transcriptomic and proteomic information. We order the various methodologies into eight primary classes dependent on the underlying calculation and last investigation objective. We further present analogies with different spaces and talk about the current exploration issues around there.

As recent history has shown, changing climate not only threatens to increase the spread of known disease, but also the emergence of new and dangerous phenotypes. This occurred most recently with West Nile virus: a virus previously known for mild febrile illness rapidly emerged to become a major cause of mortality and long-term disability throughout the world. As we move forward, into increasingly uncertain times, public health research must begin to incorporate a broader understanding of the determinants of disease emergence – what, how, why, and when. The increasing mainstream availability of high-quality open data and high-powered analytical methods presents promising new opportunities. Up to now, quantitative models of disease outbreak risk have been largely based on just a few key drivers, namely climate and large-scale climatic effects. Such limited assessments, however, often overlook key interacting processes and downstream determinants more likely to drive local manifestation of disease. Such pivotal determinants may include local host abundance, human behavioral variability, and population susceptibility dynamics. The results of such analyses can therefore be misleading in cases where necessary downstream requirements are not fulfilled. It is therefore important to develop models that include climate and higher-level climatic effects alongside the downstream non-climatic factors that ultimately determine individual disease manifestation. Today, few models attempt to comprehensively address such dynamics: up until very recently, the technology simply hasn’t been available. Herein, we present an updated overview of current perspectives on the varying drivers and levels of interactions that drive disease spread. We review the predominant analytical paradigms, discuss their strengths and weaknesses, and highlight promising new analytical solutions. Our focus is on the prediction of arboviruses, particularly West Nile virus, as these diseases represent the pinnacle of epidemiological complexity – solution to which would serve as an effective “gatekeeper”. We present the current state-of-the-art with respect to known drivers of arbovirus outbreak risk and severity, differentially highlighting the impact of climate and non-climatic drivers. The reality of multiple classes of drivers interacting at different geospatial and temporal scales requires advanced new methodologies. We therefore close out by presenting and discussing some promising new applications of AI. Given the reality of accelerating disease risks due to climate change, public health and other related fields must begin the process of updating their research programs to incorporate these much needed, new capabilities.

Discovery of inhibitors for endothelial-related transcription factors can contribute to the development of angiogenic therapies that treat diseases ranging from cardiovascular to cancer. The role of transcription factor Vezf1 in vascular development and regulation of angiogenesis has been defined by several earlier studies. Through construction of a computational model for Vezf1, work here has identified a novel small molecule drug capable of inhibiting Vezf1 from binding to its cognate DNA binding site. Using structure-based design and virtual screening of the NCI Diversity Compound Library, 12 shortlisted compounds were tested for their ability to interfere with the binding of Vezf1 to DNA using electrophoretic gel mobility shift assays. We identified one compound, T4, which has an IC50 of 20uM. Using murine endothelial cells, MSS31, we tested the effect of T4 on endothelial cell viability and angiogenesis by using tube formation assay. Our data show that addition of T4 in cell culture medium does not affect cell viability at concentrations lower or equal to its IC 50 but strongly inhibits the network formation by MSS31 in the tube formation assays. Given its potential efficacy, this inhibitor has significant therapeutic potential in several human diseases raging from wound healing to cancer.

In this work, the thermodynamic data basis and EOS modeling necessary to simulate the fractionation of organic liquid products (OLP), a liquid reaction product obtained by thermal catalytic cracking of palm oil at 450ºC, 1.0 atmosphere, with 10% (wt.) Na2CO3 as catalyst, in multistage countercurrent absorber/stripping columns using SC-CO2 as solvent, with Aspen-Hysys was systematically investigated. The chemical composition of OLP was used to predict the physical (), thermo-physical properties (Tb, Tc, Pc, Vc), and acentric factor () of all the compounds present in OLP by applying the group contribution methods of Marrero-Gani, Han-Peng, Marrero-Pardillo, Constantinou-Gani, Joback and Reid, and Vetere. The RK-Aspen (EOS) used as thermodynamic fluid package, applied to correlate the experimental phase equilibrium data of binary systems organic liquid products compounds (OLP)-i/CO2 available in the literature. The group contribution methods selected based on the lowest relative average deviation by computing Tb, Tc, Pc, Vc, and . For n-alkanes, the method of Marrero-Gani selected for the prediction of Tc, Pc and Vc, and that of Han-Peng for . For alkenes, the method of Marrero-Gani selected for the prediction of Tb and Tc, Marrero-Pardillo for Pc and Vc, and Han-Peng for . For unsubstituted cyclic hydrocarbons, the method of Constantinou-Gani selected for the prediction of Tb, Marrero-Gani for Tc, Joback for Pc and Vc, and the undirected method of Vetere for . For substituted cyclic hydrocarbons, the method of Constantinou-Gani selected for the prediction of Tb and Pc, Marrero-Gani for Tc and Vc, and the undirected method of Vetere for . For aromatic hydrocarbon, the method of Joback selected for the prediction of Tb, Constantinou-Gani for Tc and Vc, Marrero-Gani for Pc, and the undirected method of Vetere for . The regressions show that RK-Aspen EOS was able to describe the experimental phase equilibrium data for all the binary pairs undecane-CO2, tetradecane-CO2, pentadecane-CO2, hexadecane-CO2, octadecane-CO2, palmitic acid-CO2, and oleic acid-CO2, showing average absolute deviation (AADx) between 0.8% and 1.25% for the liquid phase and (AADy) between 0.01% to 0.66% for gaseous phase.

RAS oncogenes are amongst the most commonly mutated proteins in human cancers. They regulate a wide range of effector pathways that control cell proliferation, survival, differentiation, migration and metabolic status. Including aberrations in these pathways, RAS dependent signaling is altered in more than half of human cancers. Targeting mutant RAS proteins and their downstream oncogenic signaling pathways has been elusive. However, recent results comprising detailed molecular studies, large scale omics studies and computational modeling have painted a new and more comprehensive portrait of RAS signaling that helps us to understand the intricacies of RAS, how its physiological and pathophysiological functions are regulated, and how we can target them. Here, we review these efforts particularly trying to relate the detailed mechanistic studies with global functional studies. We highlight the importance of computational modeling and data integration to derive an actionable understanding of RAS signaling that will allow us to design new mechanism based therapies for RAS mutated cancers.

Summary: Studying biological systems generally relies on computational modeling and simulation, e.g., for model-driven discovery and hypothesis testing. Progress in standardization efforts led to the development of interrelated file formats to exchange and reuse models in systems biology, such as SBML, the Simulation Experiment Description Markup Language (SED-ML), or the Open Modeling EXchange format (OMEX). Conducting simulation experiments based on these formats requires efficient and reusable implementations to make them accessible to the broader scientific community and to ensure the reproducibility of the results. The Systems Biology Simulation Core Library (SBSCL) provides interpreters and solvers for these standards as a versatile open-source API in Java™. The library simulates even complex bio-models and supports deterministic Ordinary Differential Equations (ODEs); Stochastic Differential Equations (SDEs); constraint-based analyses; recent SBML and SED-ML versions; exchange of results, and visualization of in silico experiments; open modeling exchange formats (COMBINE archives); hierarchically structured models; and compatibility with standard testing systems, including the Systems Biology Test Suite and published models from the BioModels and BiGG databases. Availability and implementation: SBSCL is freely available at https://draeger-lab.github.io/SBSCL/.

Additive manufacturing (AM) are used in several industries such as automotive, aerospace, and medical sciences. One of the most common devices used in additive manufacturing is fused deposition modeling (FDM) 3D printers. This fabrication method has different inputs that affect the quality of the parts. In this research, the bending fatigue properties of polylactic acid (PLA) biomaterial made with a 3D printer are investigated. To demonstrate the influence of printing parameters on fatigue lifetime, standard specimens with nozzle diameters of 0.2-0.6 mm, extruder temperature of 180-240°C, and print speed of 5-15 mm/s were printed. After performing fully-reversed bending fatigue tests, it was found that printed specimens at 180°C have the best fatigue lifetime in most cases. Accordingly, fatigue behavior improved by reducing the nozzle diameter. Printing at lower temperatures also improved fatigue lifetime. The printing speed affected the slope of the S-N diagram, known as the fatigue strength exponent.

For water-stressed regions like Iran improving the effectiveness and productivity of agricultural water-use is of utmost importance due to climate change and unsustainable demands. Therefore, a hydro-economic model has been developed here for the Zarrine River Basin with the central concept of that demands are value-sensitive functions, where quantities of water-uses at different locations and times have a changeable economic benefits. To do this, the potential crop yields and the surface and groundwater resources, especially Boukan Dam inflow are simulated using the hydrologic model, SWAT, based on predicted climatic scenarios i.e. quantile mapping-downscaled projections. Then, to allocate the agricultural water based on the agro- economic crop water productivity (AEWP) of crops, a basin-wide water management tool, MODSIM, is customized. Next, a simulation- optimization model has been developed using a coupled CSPSO-MODSIM, to optimize the total AEWP, considering climatic impact and crop pattern scenarios, for 2020-2038, 2050-2068 and 2080-2098 periods. Finally, the optimum crop pattern and crop water irrigation depths are presented for different RCPs and periods. The results indicated that this approach will improve considerably the AEWPs and decrease the agricultural water-use up to 40%. Thus, this integrated model is able to support water authorities and other stakeholder in a water-scarce basin, as is the study area.

With advances in Building Information Modeling (BIM), Virtual Reality (VR) and Augmented Reality (AR) technologies have many potential applications in the Architecture, Engineering, and Construction (AEC) industry. However, the AEC industry, relative to other industries, has been slow in adopting AR/VR technologies, partly due to lack of feasibility studies examining the actual cost of implementation versus an increase in profit. The main objectives of this paper are to understand the industry trends in adopting AR/VR technologies and identifying gaps within the industry. The identified gaps can lead to opportunities for developing new tools and finding new use cases. To achieve these goals, two rounds of a survey at two different time periods (a year apart) were conducted. Responses from 158 industry experts and researchers were analyzed to assess the current state, growth, and saving opportunities for AR/VR technologies for the AEC industry. The findings demonstrate that older generations are significantly more confident about the future of AR/VR technologies and they see more benefits in AR/VR utilization. Furthermore, the research results indicate that Residential and commercial sectors have adopted these tools the most, compared to other sectors and institutional and transportation sectors had the highest growth from 2017 to 2018. Industry experts anticipated a solid growth in the use of AR/VR technologies in 5 to 10 years, with the highest expectations towards healthcare. Ultimately, the findings show a significant increase in AR/VR utilization in the AEC industry from 2017 to 2018.

Humans are incapable of judging the temporal order of visual events at brief temporal separations with perfect accuracy. Their performance---which is of much interest in visual cognition and attention research---can be measured with the temporal-order judgment task, which typically produces S-shaped psychometric functions. Occasionally, researchers reported plateaus within these functions, and some theories predict such deviation from the basic S shape. However, the centers of the psychometric functions result from the weakest performance at the most difficult presentations and therefore fluctuate strongly, leaving existence and exact shapes of plateaus unclear. This study set out to investigate whether plateaus disappear if the data accuracy is enhanced, or if we are ``stuck on a plateau'', or rather with it. For this purpose, highly accurate data were assessed by model-based analysis. The existence of plateaus is confidently confirmed and two plausible mechanisms derived from very different models are presented. Neither model, however, performs well in the presence of a strong attention manipulation, and model comparison remains unclear on the question which of the models describes the data best. Nevertheless, the present study includes the highest accuracy in visual TOJ data and the most explicit models of plateaus in TOJ studied so far.

Selective laser melting is an emerging Additive Manufacturing (AM) technology for metals. Intricate three-dimensional parts can be generated from the powder bed by selectively melting the desired location of the powders. The process is repeated for each layer until the part is built. The necessary heat is provided by a laser. Temperature magnitude and history during SLM directly determine the molten pool dimensions, thermal stress, residual stress, balling effect, and dimensional accuracy. Laser-matter interaction is a crucial physical phenomenon in the SLM process. In this paper, five different heat source models are introduced to predict the three-dimensional temperature field analytically. These models are known as steady state moving point heat source, transient moving point heat source, semi-elliptical moving heat source, double elliptical moving heat source, and uniform moving heat source. The analytical temperature model for all of the heat source models are solved using three-dimensional differential equation of heat conduction with different approaches. The Steady state and transient moving heat source are solved using separation of variables approach. However, the rest of models are solved by employing the Green’s functions. Due to the high magnitude of the temperature in the presence of the laser, the temperature gradient is usually high which has a substantial impact on thermal material properties. Consequently, the temperature field is predicted by considering the temperature sensitivity thermal material properties. Moreover, due to the repeated heating and cooling, the part usually undergoes several melting and solidification cycles, this physical phenomenon is considered by modifying the heat capacity using latent heat of melting. Furthermore, the multi-layer aspect of metal AM process is considered by incorporating the temperature history from the previous layer since the interaction of the layers have an impact on heat transfer mechanisms. The proposed temperature field models based on different heat source approaches are validated using experimental measurement of melt pool geometry from independent experimentations. The detailed explanation of the comparison of models is also provided. Moreover, the effect of process parameters on the balling effect is also discussed.

It is challenging to apply heat flow through a thermal bridge, which requires the analysis of 2D or 3D heat transfer to building energy simulation(BES). Research on the dynamic analysis of thermal bridges has been underway for many years, but their utilization remains low in BESs. This paper proposes a thermal bridge modeling and a dynamic analysis method that can be easily applied to BESs. The main idea begins with an analogy of the steady-state analysis of thermal bridges. As with steady-state analysis, the proposed method first divides the thermal bridge into a clear wall, where the heat flow is uniform, and the sections that are not the clear wall (the thermal bridge part). For the clear wall part, the method used in existing BESs is applied and analyzed. The thermal bridge part (TB part) is modeled with the linear time-invariant system (LTI system) and the system identification process is performed to find the transfer function. Then, the heat flow is obtained via a linear combination of the two parts. This method is validated by comparing the step, sinusoidal and annual outdoor temperature response of the finite differential method(FDM) simulation. When the thermal bridge was modeled as a third-order model, the root mean square error(RMSE) of annual heat flow with the FDM solution of heat flow through the entire wall was about 0.1W.

The Virtual Blade Model (VBM) is the implementation of the Blade element model (BEM). This was done by coupling the Blade Element Momentum theory equations to simulate rotor operation with the Reynolds Averaged Navier-Stokes (RANS) equation to simulate rotor wake and the turbulent flow field around it. Exclusion of actual geometry of blades causes lower computational cost (about 10 to 100 times). Also, due to simplifications in the meshing procedure, VBM is easier to set up than the models that consider the actual geometry of blades. One of the main unaddressed limitations of the VBM code was the constraint of modeling up to ten rotor zones within one computational domain. This paper provides a detailed and well-documented general methodology to develop a virtual blade model for simulation of ten-plus turbines within one computational domain to remove the limitation of this widely used and robust code. It is strongly believed that the technical contribution of this paper, combined with the current sky-rocketing advancement of available computational resources and hardware, would open the gates to simulate various engineering problems in the field of aerospace, clean energy, and many more.

Linear elastic fracture modeling coupled with empirical material tension data result in good quantitative agreement with experimental measurements of fracture failure for both brittle and tough epoxy nanocomposites. The nanocomposites comprise diglycidyl ethers of bisphenol A cured with O,O’ bis (2-aminopropylpropylene glycol) (Jeffamine D230) and doped with rubber nanoparticles of varying concentrations. Toughness, critical load, and critical displacement in quasi-static single edge-notched three-point bending are predicted accurately using both surface-based cohesive zone (CZS) and extended finite element (XFEM) methods implemented in Abaqus software. Fracture initiation within a crack is taken at the yield stress from uniaxial tension data. Prediction of fracture processes using a generalized truncated linear traction-separation law was significantly improved by considering the case of a linear softening function. There are no adjustable parameters in the XFEM model. The CZS model requires only optimization of the element displacement at fracture parameter. Thus, these continuum methods describe these materials in mode I fracture with a minimum number of independent parameters.

Decomposition of natural and synthetic polymeric materials (peat, humates, biochar, strongly swelling hydrogels and other soil conditioners) in a biologically- and chemically- active soil environment inevitably leads to their degradation and ability to improve the structure, water-retention, absorptive capacity and actual fertility of artificial soil constructions in urbanized ecosystems and agro landscapes (constructozems). Quantitative assessment of the biodegradation process using field and laboratory incubation experiments, as well as mathematical modeling, showed the possibility of significant (up to 30-50% per year) losses of organic matter of constructozems and a corresponding deterioration in their quality. Incubation experiments with the analysis of carbon dioxide emission for polymeric materials under given thermodynamic conditions allow to estimate the potential rates of their decomposition (half-life) and their dependence on the dose of inhibitors of microbial activity. Special nomographs provide an opportunity to determine the optimum depth of the arrangement of organic components in soil constructions to ensure their stable functioning during a fixed operating time in urban conditions. The results of the study are useful for geo-engineers and landscaping practitioners.

Flooding is a prevalent natural disaster with both short and long-term social, economic, and infrastructure impacts. Changes in intensity and frequency of precipitation (including rain, snow, and rain on snow) events create challenges for the planning and management of resilient infrastructure and communities. While there is general acknowledgement that new infrastructure design should account for future climate change, no clear methods or actionable information is available to community planners and designers to ensure resilient design considering an uncertain climate future. This research used climate projections to drive high-resolution hydrology and flood models to evaluate social, economic, and infrastructure resilience for the Snohomish Watershed, WA, U.S.A. The proposed model chain has been calibrated and validated. Based on the established model chain, the peaks of precipitation and streamflows were found to shift from spring and summer to earlier winter season. The nonstationarity of peak discharges was discovered with more frequent and severe flood risks projected. The peak discharges were also projected to decrease for a certain period in the near future, which might be due to the reduced rain-on-snow events. This research was expected to provide a clear method for the incorporation of climate science in flood resilience analysis and to also provide actionable information relative to the frequency and intensity of future precipitation events.

Object. Flooding in Ukraine is a common natural phenomenon that repeats periodically and in some cases it becomes disastrous. In an average year floods on the rivers of Volyn region take place from one to three times which extend beyond the limits of the floodplain. The floodplain of Styr river is located in the historical center of Lutsk city, that`s why issues of research and forecasting of floods are very important for a given city. Methodology. Using modern technologies of geodesy and remote sensing allows to quickly determine and predict the floodplain area of settlements. Based on the statistical data of the Volyn Regional Center for Hydrometeorology during the 7 year period 2011-2017 about water levels of the river Styr. We conducted mathematical modeling of fluctuations of water levels within the territory of Lutsk, based on creating a partial Fourier series for discrete values of middle-ten-day water levels values. The post hydrological measurements of Styr river water levels in the territory of Lutsk located on the Shevchenko Street comply with an altitude 172.87 meters. Based on the data of short-term flood forecasting in February and March, and relief data from the Department of Architecture and Urban Development of Volyn State Administration, we conducted visualization of the results using geographic information system QGIS. Results. The results of mathematical processing were the basis for geoinformation simulation of flooded areas using remote sensing data that are publicly available. Use of statistical and geospatial data in this article has great potential for further application in modeling the processes of natural and technogenic origin. Scientific novelty. The mathematical model of short-term forecasting of water levels during the flood period on the river Styr with implementation of geoinformation modeling of flooded areas using remote sensing data is proposed. Practical significance. The research results of water level changes on the Styr River and flood zones within the limits of Lutsk is proposed. The spring flood in February-March 2018, with the maximum water level 5.33 m, corresponds to an absolute mark of 178.20 m, which is forecasted in this article.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with themselves, their environment, and hosts via metabolites and signaling molecules. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metagenomics methods, and modeling formalisms. Furthermore, the review explains model building, examples of microbiome model applications for prediction, optimization, and control. It concludes with guidelines, software, and repositories for modeling. Each section provides entry-level information, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby animal welfare, productivity, and sustainability of the farming industry.

Coincident changes in abundance and behavior pose a challenge for interpreting abundance data from monitoring programs. In the San Francisco Estuary, long-term monitoring documented the declines of many species including the anadromous Longfin Smelt (Spirinchus thaleichthys). We identified seasonal patterns in reginal presence of Longfin Smelt through its life cycle using monitoring data and generalized additive modelling. We then investigated the year-to-year variability in the seasonal patterns of presence using functional data analysis (FDA). FDA separated the variability due to population size from variability due to differences in timing of presence. We found that Longfin Smelt have consistent seasonal distribution patterns and that two trawl types were needed to accurately describe those patterns. After accounting for variability due to year-class strength, shifts in the timing of presence were evident in three regions. The most variable period for the upstream regions Suisun Bay and West Delta was for age-0 fish in summer and for the downstream region Central Bay was for age-0 fish in late fall. This manifested as a delay in the typical fall re-occupation of upstream regions that comprise the study area for another monitoring study (Fall Midwater Trawl). Thus, a portion of the recent reductions in Fall Midwater Trawl abundance of Longfin Smelt resulted from changes in behavior rather than a decline in abundance. The presence of multiple monitoring surveys allowed analysis of distribution from one data set to aid interpretation of patterns in abundance from another monitoring survey. This study highlights how identifying portions of the life cycle with the most and least variability in distribution can help inform the types of management strategies that will be most effective. It also illustrates an analytical method that can be used to address the problem of confounded effects of abundance and behavior on patterns in monitoring data.

Renewable sources and electric distribution network can produce or make available a surplus of electric and thermal energies. The Intermediate Temperature Solid Oxide Electrolyzer (IT-SOE) fed by CO2-steam mixtures can store these electric and thermal energies producing CO-H2 mixtures with high conversion efficiency. Inside the IT-SOE, the CO2-steam mixtures are converted to CO-H2 mixtures and O2 through the cathode and anode electrochemical reactions and reverse water gas shift chemical reaction. In this article an IT-SOE stack fed by different types of steam mixtures was tested at different operating temperatures and the stack polarization and electric power curves were detected experimentally. At the highest hydrogen production operating temperature of the stack fed by steam mixtures, the experimental polarization and electric power curves of the stack fed by steam and CO2-steam mixtures were compared. A simulation model of the IT-SOE system (stack and furnace) fed by CO2-steam mixtures was formulated ad hoc and implemented in a Matlab environment and experimentally validated. At the highest hydrogen production stack operating temperature, the IT-SOE system thermal equilibrium current was evaluated through the simulation model. Moreover, the influence of this current on the IT-SOE system efficiency and the CO-H2 mixture degree of purity was highlighted.

Additive manufacturing (AM) is an attractive technology for manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for production of end-use parts. Prediction of mechanical properties can be one of the possible ways to improve the repeatability of the results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress and elongation at break for polyamide 2200 (also known as PA12). EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models have prediction accuracy higher than 80% only for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about material properties, these models need to be improved in the future based on additional experimental work.

This study aimed at the investigation of the effect of substrate preheating on residual stress in laser powder bed fusion using a physics-based analytical model. In this study, an analytical model is proposed to predict the residual stress through the calculation of preheating affected temperature profile and thermal stress. The effect of preheating is super-positioned with initial temperature in the modeling of temperature profile using a moving heat source approach; the resultant temperature gradient is then employed to predict the thermal stress from a point body load approach. If the thermal stress exceeds the yield strength of the material, then the residual stress under cyclic heating and cooling will be calculated based on the incremental plasticity and kinematic hardening behavior of metal. IN718 is used as a material example to pursue this investigation. To validate the predicted residual stress, experimental measurements are conducted using X-ray diffraction on IN718 samples manufactured via laser powder bed fusion under different process conditions. Results showed that preheating of the substrate could reduce the residual stress in an additively manufactured part due to the reduction in temperature gradient and resultant shrinkage stresses. However, the excessive preheating could have an opposite impact on residual stress accumulation. Moreover, the results confirm that the proposed model is a valuable tool for the prediction of residual stress- eliminating the costly experiments and time-consuming finite element simulations.

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. Machine learning algorithms have achieved state-of-the-art material classification performance on benchmark hyperspectral data sets; however, these techniques often do not consider varying atmospheric conditions experienced in a real-world detection scenario. To reduce the impact of atmospheric effects in the at-sensor signal, atmospheric compensation must be performed. Radiative Transfer (RT) modeling can generate high-fidelity atmospheric estimates at detailed spectral resolutions, but is often too time-consuming for real-time detection scenarios. This research utilizes machine learning methods to perform dimension reduction on the transmittance, upwelling radiance, and downwelling radiance (TUD) data to create high accuracy atmospheric estimates with lower computational cost than RT modeling. The utility of this approach is investigated using the instrument line shape for the Mako long-wave infrared hyperspectral sensor. This study employs physics-based metrics and loss functions to identify promising dimension reduction techniques. As a result, TUD vectors can be produced in real-time allowing for atmospheric compensation across diverse remote sensing scenarios.