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Abstract Introduction Landslides pose significant geological hazards, necessitating advanced prediction techniques to protect vulnerable populations. Research Gap Reviewing landslide time series analysis predictions is found to be missing despite the availability of numerous reviews. Methodology Therefore, this paper systematically reviews time series analysis in landslide prediction, focusing on physically based causative models, highlighting data preparation, model selection, optimizations, and evaluations. Key Findings The review shows that deep learning, particularly the long-short-term memory (LSTM) model, outperforms traditional methods. However, the effectiveness of these models hinges on meticulous data preparation and model optimization. Significance While the existing literature offers valuable insights, we identify key areas for future research, including the impact of data frequency and the integration of subsurface characteristics in prediction models.

Abstract To sustain agriculture for future generations and reduce the adverse impacts on soil health and the environment, there is a need to adopt sustainable and climate‐smart agricultural practices. A field experiment was conducted to study the effects of organic amendments (chicken and dairy manures and biochar) on the soil physicochemical properties, sweet corn (Zea mays) growth, and yield parameters at Prairie View A&M University, Texas. Two rates of biochar (2.5 and 5 t ha−1) and two types of manure (chicken and dairy) applied at three rates (0, 224, and 448 kg total N ha−1) were used in a factorial design with three replications. Plant height, period for each vegetative growth stage, leaf soil plant analysis development, time to reach 50% tasseling and 50% silking stage, cob length, cob diameter, sugar content, and biomass were measured. The results showed that plant biomass was significantly affected by biochar rate, while plant height, cob length, and cob diameter were significantly affected by manure rates. Sweet corn reached tasseling and silking stages earlier in chicken manure‐treated plots than the dairy manure plots. However, the sugar content was significantly affected by both biochar and manure rates. Furthermore, results revealed a strong positive correlation between plant height and cob length, diameter, and biomass; however, there was a negative correlation with tasseling and silking days. Soil phosphorus, total nitrogen, and potassium had a relatively positive correlation with plant growth parameters. Findings showed that different types and rates of amendments significantly influenced sweet corn growth parameters and soil nutrient status, highlighting the importance of adopting climate‐smart agricultural practices for improved crop yield and soil health.

Concrete bridges are the most prevalent bridge type worldwide, forming critical components of transportation infrastructure. These bridges are subjected to continuous deterioration due to environmental exposure and operational stresses, necessitating ongoing condition monitoring. Despite extensive research on condition rating and deterioration modeling of concrete bridges, a comprehensive and comparative understanding of these processes remains underexplored. This paper addresses this gap by conducting a critical scientometric and systematic review of condition rating and deterioration modeling approaches for concrete bridges to highlight their strengths and limitations. Accordingly, most of the condition rating methods were found to have a heavy reliance on qualitative visual inspections (VI) and inherent subjective assumptions. Techniques such as fuzzy logic and non-destructive evaluation (NDE) methods were identified as promising tools to mitigate uncertainties and enhance accuracy. Moreover, the performance of most deterioration models was dependent on the quality of the historical condition data. The advancement of hybrid deterioration models, such as integrating artificial intelligence (AI) with stochastic and physics-based approaches, has proven to be a powerful strategy, combining the strengths of each method to deliver enhanced condition predictions. Finally, this study offers key insights and future research directions to assist researchers and policymakers in advancing sustainable concrete bridge management practices.

A field study was conducted to investigate the effects of selected climate-smart agriculture practices on soil bulk density (ρ), porosity (β), hydraulic conductivity (Ksat), and nutrient dynamics in southeast Texas. Treatment combinations of two types of organic manure (chicken and dairy) with three rates (0, 224, and 448 kg N ha−1) and two levels of biochar (2500 and 5000 kg ha−1) were used in a factorial randomized block design. Bulk density and porosity measurements were conducted on undisturbed soil core samples collected from the topsoil (0–10 cm) of a field cultivated with sweet corn. Ksat was calculated from the steady-state infiltration measured using the Tension Infiltrometer (TI). The ANOVA results indicated that the manure application rates, and biochar levels significantly affected the soil properties. Compared to the control, β increased by 15% and 29% for the recommended and double recommended manure rates. Similarly, hydraulic conductivity increased by 25% in the double-recommended rate plots compared to the control. Also, we applied the concept of non-parametric elasticity to understand the sensitivity of soil physical and chemical properties to Ksat. ρ and β are critical physical properties that are highly sensitive to Ksat. Among soil nutrients, Boron showed the highest sensitivity to Ksat. Hydraulic conductivity can be enhanced by employing selected climate-smart practices and improving water management. Future directions for this study focus on scaling these findings to diverse cropping systems and soil types while integrating long-term assessments to evaluate the cumulative effects of climate-smart practices on soil health, crop productivity, and ecosystem sustainability.

Quantifying the impact of rutting on traffic safety contributes to the development of objective models for evaluating pavement performance. However, the existing literature shows significant discrepancies in the impact of rutting on traffic safety. To this end, this study analyzed about 40 studies to comprehensively understand the impact of rutting on traffic safety in field observations and simulation studies. This study analyzed the influence of ten factors that may impact the relationship between rutting and traffic safety, such as weather, speed, and road type. It also established rutting limits and developed machine learning-based prediction models for accident rates caused by rutting under varying conditions. These findings reveal distinct trends, with simulation studies generally suggesting a higher impact of rutting on safety compared to field observations. This discrepancy is attributed to the limitations of simulation models in capturing human factors, such as drivers’ ability to anticipate and adjust their behavior to mitigate risks. These results provide valuable insights for highway agencies and policymakers to develop more accurate rut limits and maintenance guidelines. These results also underscore the importance of considering rutting in the development of autonomous vehicles to ensure effective handling of rutting under varying conditions. This study highlights the need for more comprehensive field studies using larger datasets that account for various environmental and traffic factors. Additionally, integrating real-world driver behavior into simulation models could improve their accuracy.

Hydrostructural parameters are driven by the characterization of the soil aggregates structure using a set of thermodynamic equations. Soil aggregates structure is unique for each soil type and can be affected by its physical, chemical, and biological properties. Because it so completely represents those properties, tracking the changes in soil aggregates structure can be used to develop quantitative indicators of soil quality. This study involves a field experiment to examine pedostructure-based soil characterization under different soil management practices. It investigates the feasibility of defining a new soil quality indicator. Three organic amendments (chicken manure, dairy manure, and milorganite) were applied at 0, 168, 336, 672 kgN/ha (rate 0, 1, 2, and 3) for 36 plots of 3 m. × 1.5 m. After extracting 12 hydro-structural parameters from TypoSoil™ measurements, statistical analysis was used to evaluate the sensitivity of these parameters to management practices. Results showed that increasing the application rate from 1 to 3 showed no significant effect on hydrostructural parameters for all treatments. However, treatments significantly enhanced water content and available water in the A horizon. In the B horizon, only rate 3 affected available water for chicken and dairy manure. Comparing the three treatments, dairy manure’s significance was prominent and promising for improving soil aggregates structure in rates greater than 1. The best application rate to improve soil aggregates structure for chicken manure seems to be 1, for dairy manure rate 2, and milorganite rate 3.

Fracture-dislocations of the midfoot are relatively rare injuries. We present a case of a 20-year-old man presenting with navicular-cuneiform and calcaneal-cuboid fracture-dislocation of his right foot due to a crush injury during work. The patient's injury was treated by open reduction internal fixation.

Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry.

Globally, water is a bottleneck to food security and, as such, a new approach for water for food is needed. Food insecurity is knocking at every nation's door, including those of the most developed. Moreover, the disruptions in food supply chains that result from continued reliance on a business-as-usual approach of traditional, non-sustainable food and agricultural systems make food insecurity even more vividly present. This article explores the current relationship between food production and water resources. It attempts to better understand how we might reduce the inter-dependencies between food and fresh water by exploring new and alternative sources of water, including improving the efficiencies of green and recycled water.