Search

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1–5 min) but low spatial resolution (≥2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with deep learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel accuracy using an affine motion model to capture the camera platform motion. The frames are externally upsampled using single-image interpolation. The interpolated frames are then fused with the custom EWF layer, employing subpixel registration information to give more weight to pixels with less interpolation error. Realistic image acquisition conditions are simulated to generate training and testing datasets with corresponding ground truths. The observation model captures optical degradation from diffraction and detector integration from the sensor. The experimental results demonstrate the efficacy of EFIF-Net using both simulated and real camera data. The real camera results use authentic, unaltered camera data without artificial downsampling or degradation.

This review paper discusses the development trends of agricultural autonomous all-terrain vehicles (AATVs) from four cornerstones, such as (1) control strategy and algorithms, (2) sensors, (3) data communication tools and systems, and (4) controllers and actuators, based on 221 papers published in peer-reviewed journals for 1960–2023. The paper highlights a comparative analysis of commonly employed control methods and algorithms by highlighting their advantages and disadvantages. It gives comparative analyses of sensors, data communication tools, actuators, and hardware-embedded controllers. In recent years, many novel developments in AATVs have been made due to advancements in wireless and remote communication, high-speed data processors, sensors, computer vision, and broader applications of AI tools. Technical advancements in fully autonomous control of AATVs remain limited, requiring research into accurate estimation of terrain mechanics, identifying uncertainties, and making fast and accurate decisions, as well as utilizing wireless communication and edge cloud computing. Furthermore, most of the developments are at the research level and have many practical limitations due to terrain and weather conditions.

This study uses an Integrated Agent-Centered (IAC) framework to investigate the socio-psychological drivers of Iranian farmers’ unsustainable groundwater management practices. Empirical land use change analysis of US Geological Survey Landsat satellite images of the Jaz-Murian wetland region for 1990, 2010, and 2022, is combined with community surveys conducted with randomly selected farmers in five townships within the region (n = 356). Visual analysis reveals dramatic increases in agricultural land coverage, diminished water bodies, and increased salt lands over the 32-year sampled period. We use survey data to explain the socio-psychological drivers of unsustainable groundwater use that lead to these adverse environmental changes. In the IAC survey analysis, we find that variables for “expectation” and “subjective culture” have a negative influence on pro-environmental “intention”. “Intention” and “habit” have a positive influence and “contextual factors” have a negative influence on the drivers of “unsustainable water use behavior”. We conclude that situational influences, habitual process, intentional process, and normative processes must be considered together to alleviate pressure on wetland ecosystems. Policy makers must provide effective agricultural extension training, deliberative dialogue amongst farmer networks, well-governed local water markets and financial support to shift farmer short-termist economic gain-thinking towards socially-supported pro-environmental habits over the longer term.

In drylands, poor rains combined with high evaporation rates increase the risks of soil salinization in addition to drought stress. Here, we determined the values of the parameters in the Feddes root water uptake function for sesame (Sesamum indicum L.) under drought and salinity stresses in a pot experiment using “Lebap-55”, which has been bred for the drylands of the Aral Sea Basin but is moderately sensitive to salinity stress. We measured the hourly values of the transpiration, soil moisture, and salinity in the upper and lower soil layers in pots, solar radiation, and root distribution. The values were quantified by two methods. The bulk method uses only daily pot weight data, and the average soil water content and salt concentration are back-calculated from the mass balance. The inverse method uses the monitored values of the soil water content and salinity as well as daily weight data and solar radiation. Both methods could successfully estimate all the parameter values for both stresses. The bulk method performed better under drought stress, even without the measured soil water content or root distribution. It also had satisfactory accuracy in estimating the values under salinity stress. Both methods performed better under drought stress than under salinity stress. The parameter values determined here could be used for irrigation scheduling and salinity management using numerical models for the studied crop.

Rebar cages are the skeletons of reinforced concrete structures. These temporary structures consist of longitudinal and transverse reinforcing bars connected by tie-wires. Given the relatively low strength of tie-wire connections, replacing tie-wires with mechanical connectors such as U-bolts can improve the stability and strength of rebar cages. This paper aims to understand the behavior of large prefabricated rebar cages reinforced with mechanical U-bolt connectors through experimental and analytical investigations. Twenty-six full-scale experimental tests are performed on five different underground pile-shaft rebar cages with tie-wire and U-bolt connectors to determine their behavior during different site handling conditions. The data obtained from the experiments are used to develop and calibrate detailed finite element models that can predict the complex response behavior of rebar cages reinforced with U-bolt connectors. The results show that U-bolt connectors can effectively ensure rebar cage integrity even under extreme loading conditions. Additionally, it is concluded that the presence of U-bolt connectors allows for the elimination of internal stiffening elements, which are common for large rebar cages, adding to the simplicity and efficiency of the construction process. The results of this study can be used as a basis for establishing analysis, design, fabrication, and handling guidelines for rebar cages.

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations, followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation, together with the TG heat smoke release scheme, leads to a more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved.

Over the last few years and with increasing global climatic change, the international energy crisis and shortage of freshwater resources have raised many inquiries about global water security and energy. Therefore, finding out alternative and sustainable energy sources has become an important universal requirement. Here, we assessed the viability of exploiting municipal wastewater (WW) as a nutrient-rich growth medium for cultivating the pollution-tolerant coccoid green microalga Chlorococcum sp. (Chlorophyceae) to simultaneously remove nutrients and produce biodiesel. Chlorococcum sp. was isolated from municipal wastewater sampled from Menoufia Governorate, Egypt. Under the standard growth conditions and until reaching the late exponential growth phase, it was cultivated at different concentrations (25%, 50%, 75%, and 100%) of the secondary treated WW, and the findings were compared to the control (grown in BBM). The study results revealed that the 50% WW treatment was the most suitable approach for removing NO3−, NH4+, and TP with percentages of 96.9%, 98.4%, and 90.1%, respectively. Moreover, the 50% WW treatment produced the highest algal biomass (1.97 g L−1) and productivity (82 mg L−1 day−1). In addition, it showed the highest lipid production (600 mg L−1), with 25 mg L−1 day−1 lipid productivity and lipid yield with 30.5% of the cell dry weight (CDW). The gas chromatography–mass spectrometry (GC-MS) technique was applied to characterize fatty acid profiling, and it was found that oleic (C18:1) and palmitic (C16:0) fatty acids were present in much higher concentrations in Chlorococcum sp. cells grown in 50% WW as compared to the control, i.e., 44.43% and 27.38% vs. 36.75% and 21.36%, respectively. No big difference was present in linoleic (C18:2) fatty acid concentrations. Importantly, the biodiesel properties of our Chlorococcum sp. grown in 50% WW were consistent with the international biodiesel standards. In light of our findings, Chlorococcum sp. has a great potential for utilization as a biodiesel feedstock and for bioremediation of wastewater.

This study presents a two-step FE model updating approach for health monitoring and damage identification of prestressed concrete girder bridges. To reduce the effects of modeling error in the model updating process, in the first step, modal-based model updating is used to estimate linear model parameters mainly related to the stiffness of boundary conditions and material properties. In the second step, a time-domain model updating is carried out using acceleration data to refine parameters accounting for the nonlinear response behavior of the bridge. In this step, boundary conditions are fixed at their final estimates using modal-based model updating. To prevent the convergence of updating algorithm to local solutions, the initial estimates for nonlinear material properties are selected based on the first-step model updating results. To validate the applicability of the two-step FE model updating approach, a series of forced-vibration experiments are designed and carried out on a pair of full-scale decommissioned and deteriorated prestressed bridge I-girders. In the first step, parameters related to boundary conditions, including stiffness of supports and coupling beams, as well as material properties, including initial stiffness of concrete material, are estimated. In the second step, concrete compressive strength and damping properties are updated. The final estimates of the concrete compressive strength are used to infer the extent of damage in the girders. The obtained results agree with the literature regarding the extent of reduction in concrete compressive strength in deteriorated concrete structures.

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels.

Understanding how nutrients accumulate and change throughout the developmental stages according to the BBCH scale can be a very effective technique for developing fertilization programmes for high-quality, marketable grape production. Accordingly, the macro- and micronutrient concentrations at different growth stages were analyzed for two commercial grape cultivars, viz., “Anab-e-Shahi” and “Perlette”, by using an extended BBCH scale. The results show nitrogen content was reduced during the sampling period (D1 to D6) from 1.76 percent to 1.09 percent. Potassium concentration in developing berries kept on increasing from 0.15%, at principal growth stage 7 with BBCH code 73 (D1), to 0.26% at principal growth stage 8 code 89 (D6). The lowest phosphorus content of 253.88 ppm and the highest of 338.43 ppm were found in growth stages D1 and D6, respectively. The berry Ca however showed an increase first and thereafter decreased to 225.18 ppm at harvest. Mg content also recorded a similar trend from stages D1 to D6 of the BBCH scale and decreased to 116.08 ppm at D6. B and Cu concentrations increased from D1 to D6, and Mn and Zn increased first and decreased thereafter until harvest. No specific trend was recorded in Fe concentration. The concentration of nutrients in berries at different developmental stages can be used as the standard reference for growing berries using proper fertilization.

An accurate seismic response simulation of civil structures requires accounting for the nonlinear soil response behavior. This, in turn, requires understanding the nonlinear material behavior of in situ soils under earthquake excitations. System identification methods applied to data recorded during earthquakes provide an opportunity to identify the nonlinear material properties of in situ soils. In this study, we use a Bayesian inference framework for nonlinear model updating to estimate the nonlinear soil properties from recorded downhole array data. For this purpose, a one-dimensional finite element model of the geotechnical site with nonlinear soil material constitutive model is updated to estimate the parameters of the soil model as well as the input excitations, including incident, bedrock, or within motions. The seismic inversion method is first verified by using several synthetic case studies. It is then validated by using measurements from a centrifuge test and with data recorded at the Lotung experimental site in Taiwan. The site inversion method is then applied to the Benicia–Martinez geotechnical array in California, using the seismic data recorded during the 2014 South Napa earthquake. The results show the promising application of the proposed seismic inversion approach using Bayesian model updating to identify the nonlinear material parameters of in situ soil by using recorded downhole array data.

We present a finite element model updating technique for soil–structure system identification of the Millikan Library building using the seismic data recorded during the 2002 Yorba Linda earthquake. A detailed finite element (FE) model of the Millikan Library building is developed in OpenSees and updated using a sequential Bayesian estimation approach for joint parameter and input identification. A two-step system identification approach is devised. First, the fixed-base structural model is updated to estimate the structural model parameters (including effective elastic modulus of structural components, distributed floor mass, and Rayleigh damping parameters) and some uncertain components of the foundation-level motion. Then, the identified structural model is used for soil–structure model updating wherein the Rayleigh damping parameters, the stiffness and viscosity of the soil subsystem (modeled using a substructure approach), and the foundation input motions (FIMs) are estimated. The identified model parameters are compared with state-of-practice recommendations. While a specific application is made for the Millikan Library, the present work offers a framework for integrating large-scale FE models with measurement data for model inversion. By utilizing this framework for different civil structures and earthquake records, key structural model parameters can be estimated from the real-world recorded data, which can subsequently be used for assessing and improving, as necessary, state-of-the-art seismic analysis and structural modeling techniques. This paper presents an effort towards using real-world measurements for large-scale FE model updating in the soil and structure, uniform soil time domain for joint parameter and input estimation, and thus paves the way for future applications in system identification, health monitoring, and diagnosis of civil structures.

Understanding material behavior is key to discovering innovative applications in any field. Regardless of the exciting mechanical properties of polyurea, there has been a limited effort in studying the use of polyurea for structural retrofit and strengthening applications. This study aims to understand the behavior of polyurea under different tensile loading conditions to provide critical information towards enabling the future use of polyurea in structural applications. Several standard coupons are tested under various tensile loading conditions to understand the mechanical behavior of eight different commercial polyureas. The study provides the full stress–strain characteristic curves that can be used for constitutive modeling purposes. The results show that polyurea has a wide range of properties from low strength flexible nature to high strength rigid nature. All tested polyureas displayed some level of rate dependency, i.e., ultimate strength is a function of loading rates. The high-strength polyureas tested only show slight rate dependency and good strength retention under cyclic and fatigue tensile loading, suggesting that polyureas have promising mechanical properties for potential structural applications.

The demand for water and energy in today’s developing world is enormous and has become the key to the progress of societies. Many methods have been developed to desalinate water, but energy and environmental constraints have slowed or stopped the growth of many. Capacitive Deionization (CDI) is a very new method that uses porous carbon electrodes with significant potential for low energy desalination. This process is known as deionization by applying a very low voltage of 1.2 volts and removing charged ions and molecules. Using capacitive principles in this method, the absorption phenomenon is facilitated, which is known as capacitive deionization. In the capacitive deionization method, unlike other methods in which water is separated from salt, in this technology, salt, which is a smaller part of this compound, is separated from water and salt solution, which in turn causes less energy consumption. With the advancement of science and the introduction of new porous materials, the use of this method of deionization has increased greatly. Due to the limitations of other methods of desalination, this method has been very popular among researchers and the water desalination industry and needs more scientific research to become more commercial.

Disturbance events can happen at a temporal scale much faster than wildland fire fuel data updates. When used as input for wildland fire behavior models, outdated fuel datasets can contribute to misleading forecasts, which have implications for operational firefighting, mitigation, and wildland fire research. Remote sensing and machine learning methods can provide a solution for on-demand fuel estimation. Here, we show a proof of concept using C-band synthetic aperture radar and multispectral imagery, land cover classes, and tree mortality surveys to train a random forest classifier to estimate wildland fire fuel data in the East Troublesome Fire (Colorado) domain. The algorithm classified over 80% of the test dataset correctly, and the resulting wildland fire fuel data was used to simulate the East Troublesome Fire using the coupled atmosphere—wildland fire behavior model, WRF-Fire. The simulation using the modified fuel inputs, where 43% of original fuels are replaced with fuels representing dead trees, improved the burn area forecast by 38%. This study demonstrates the need for up-to-date fuel maps available in real time to provide accurate prediction of wildland fire spread, and outlines the methodology based on high-resolution satellite observations and machine learning that can accomplish this task.

Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agreement between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism.

The present study aimed to investigate the enzymatic potential of Silybum marianum leaves to bioconvert phenolic acids produced in S. marianum callus into silymarin derivatives as chemopreventive agent. Here we demonstrate that despite the fact that leaves of S. marianum did not accumulate silymarin themselves, expanding leaves had the full capacity to convert di-caffeoylquinic acid to silymarin complex. This was proven by HPLC separations coupled with electrospray ionization mass spectrometry (ESI-MS) analysis. Soaking the leaf discs with S. marianum callus extract for different times revealed that silymarin derivatives had been formed at high yield after 16 h. Bioconverted products displayed the same retention time and the same mass spectra (MS or MS/MS) as standard silymarin. Bioconversion was achieved only when using leaves of a specific age, as both very young and old leaves failed to produce silymarin from callus extract. Only medium leaves had the metabolic capacity to convert callus components into silymarin. The results revealed higher activities of enzymes of the phenylpropanoid pathway in medium leaves than in young and old leaves. It is concluded that cotyledon-derived callus efficiently produces compounds that can be bio-converted to flavonolignans in leaves tissue of S. marianum.

Optimization of water use with consideration of salinity control is a crucial task for crop production. A new scheme, “optimized irrigation”, was recently presented to determine irrigation depth using WASH_1D/2D which are numerical simulation models of water flow and solute transport in soils and crop growth. In the scheme, irrigation depth is determined such that net income is maximized considering the price of water and weather forecasts. To evaluate whether the optimized irrigation is also able to restrict salinity stress and avoid salinization without any intentional leaching, we carried out a numerical experiment for winter wheat grown in northern Sudan under the following scenarios: (1) Available water in the root zone is refilled using freshwater (0.17 g/L of NaCl) at every five days; (2) available water in the root zone is refilled using saline water (1.7 g/L) at every five days; (3) optimized irrigation using fresh water at 7-days interval; (4) optimized irrigation on a weekly basis using saline water; and (5) same as scenario 2, except for leaching is carried out at the middle of the growing season and leaching depth is optimized such that net income is maximized. The results showed that the optimized irrigation scheme automatically instructs additional water required for leaching at each irrigation event and maximizes the net income even under saline conditions.

Date palm midribs enjoy a long heritage among rural builders and craftsmen in Egypt for their abundance and low-cost. This familiarity encouraged previous studies to tackle the question of using date palm midribs in wide-span construction to provide simple, quick, cost efficient shaded structures. The design of tri-arched space truss was aimed to utilize date palm midribs in cost-efficient wide-span construction with minimal processing and maximum structural efficiency. However, the validated mechanical properties, the workability, the short-term and the long-term structural behaviors of the system are yet to be investigated. This paper investigates the structural behavior of a proposed tri-arched system using 1:3 scale specimens. The long-term environmental effects are also studied in one of the specimens. The specimens experienced high flexibility and gradual failure. A finite element model was created to predict the behavior of the specimens. The validated model was used to determine the structural behavior of the system with 12 m span. The system was found to be safe under the loads of wind and roofing. This paper is a part of a continuous process of validation that aims to utilize date palm midribs in contemporary wide-span construction to match the spirit of the youth in rural communities.

In this research, artemisia selengensis was used to produce biochar via microwave pyrolysis. The influence of pyrolysis temperature, heating rates, temperature holding time and additive on the biochar yield and adsorbability were all investigated. The results suggest that the biochar yield decreased with the increase of pyrolysis temperature while the adsorbability of the biochar increased with an increase of the pyrolysis temperature; the biochar yield and its adsorbability could achieve the desired value when the heating rate and temperature holding time were in a specific scope; the biochar yield decreased when an additive was added; the adsorbability of the biochar could be increased by adding ZnCl2 (metal chloride) and Na2CO3 (metal carbonate). According to the orthogonal experiments, the optimal conditions for biochar production were: pyrolysis temperature 550 °C, heating rate 2 °C/s, temperature holding time 15 min, without additive.

The functionalization of nanoparticles (NPs) with DNA has proven to be an effective strategy for self-assembly of NPs into superlattices with a broad range of lattice symmetries. By combining this strategy with the DNA origami approach, the possible lattice structures have been expanded to include the cubic diamond lattice. This symmetry is of particular interest, both due to the inherent synthesis challenges, as well as the potential valuable optical properties, including a complete band-gap. Using these lattices in functional devices requires a robust and stable lattice. Here, we use molecular simulations to investigate how NP size and DNA stiffness affect the structure, stability, and crystallite shape of NP superlattices with diamond symmetry. We use the Wulff construction method to predict the equilibrium crystallite shape of the cubic diamond lattice. We find that, due to reorientation of surface particles, it is possible to create bonds at the surface with dangling DNA links on the interior, thereby reducing surface energy. Consequently, the crystallite shape depends on the degree to which such surface reorientation is possible, which is sensitive to DNA stiffness. Further, we determine dependence of the lattice stability on NP size and DNA stiffness by evaluating relative Gibbs free energy. We find that the free energy is dominated by the entropic component. Increasing NP size or DNA stiffness increases free energy, and thus decreases the relative stability of lattices. On the other hand, increasing DNA stiffness results in a more precisely defined lattice structure. Thus, there is a trade off between structure and stability of the lattice. Our findings should assist experimental design for controlling lattice stability and crystallite shape.

This paper describes an industry laboratory implementation of local and remote automation monitoring and control of several product testing stands for status, safety, and an efficient use of resources, as well as the purpose and inter-relationship between the tests, product reliability estimations, customer applications, and possible solutions to test specification issues. Like an airport departure/arrival board, the summary statuses of several life stands are reported on a monitor using a mobile application interface. Detailed data analysis reports and emergency shutdown statuses are emailed to authorized-interested parties. The life stands include submersion tanks and flow rigs at different fluid temperatures (steady-state, slow cycling, thermal shock). To reduce equipment costs and lab space requirements, some life test stands are combined to share heating/cooling transfer fluids and pumping resources that are automatically controlled to direct different fluid temperature ranges through different flow paths. A heater/chilling system cycles between −80 °C and +140 °C with automated diverter valves to route −80 °C to +85 °C fluid through a set of products; when the fluid temperature is increased to > +85 °C, diverter valves route the > +85 °C fluid through a different set of products that are tested only between fluid temperatures of +85 °C to +140 °C.

Measles remains a highly contagious and potentially severe infectious disease, necessitating high vaccine coverage. However, misinformation and measles vaccine hesitancy/resistance have posed significant challenges to achieving this goal. The COVID-19 pandemic further exacerbated these challenges, leading to a measles outbreak in Jordan in 2023. This study aimed to investigate the acceptance of the measles rubella (MR) vaccine among parents in Jordan and to identify its associated determinants. This cross-sectional questionnaire-based study was conducted using a previously Arabic-validated version of the Parental Attitudes towards Childhood Vaccines (PACV) survey instrument. Data collection took place in October 2023, and the final study sample comprised a total of 391 parents, with mothers representing 69.8% of the participants ( n = 273). The majority of participating parents expressed either resistance ( n = 169, 43.2%) or hesitancy ( n = 168, 43.0%) towards MR vaccination, while only 54 participants (13.8%) expressed MR vaccine acceptance. Multivariate analysis revealed that trust in vaccine safety/efficacy, behavior, and having fewer offspring were significantly associated with MR vaccine acceptance. The current study revealed a concerning level of MR vaccine hesitancy/resistance among parents in Jordan, which could signal a public health alarm in the country. Urgent and targeted interventions are strongly recommended to address this issue, including mass campaigns aimed at building trust in the MR vaccine's safety/efficacy. Additionally, there is an urgent need for effective public health initiatives to ensure sufficient measles vaccine coverage to prevent future outbreaks of this serious disease.