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Global challenges such as soil degradation and water scarcity necessitate sustainable agricultural practices, particularly in regions where saline water is increasingly used for irrigation. This study investigates the effects of four compost treatments, including surface-applied mulch compost (MC), Johnson–Su biologically active compost incorporated into soil (JCI), mulch compost incorporated into soil (MCI), and no compost as control (NC), on soil fertility, microbial activity, and Capsicum annuum (chili pepper) growth. Greenhouse experiments were conducted using soil from two different sites (New Mexico State University’s (NMSU) agricultural research plots and agricultural field-testing site at the Brackish Groundwater National Desalination Research Facility (BGNDRF) in Alamogordo, New Mexico) and two irrigation water salinities (brackish at ~3000 µS/cm and agricultural at ~800 µS/cm). The Johnson–Su compost treatment demonstrated superior performance, due to its high soil organic matter (41.5%), nitrate (NO3−) content (82.5 mg/kg), and phosphorus availability (193.1 mg/kg). In the JCI-treated soils, microbial biomass increased by 40%, and total microbial carbon reached 64.69 g/m2 as compared to 64.7 g/m2 in the NC. Plant growth parameters, including chlorophyll content, root length, and wet biomass, improved substantially with JCI. For instance, JCI increased plant height by 20% and wet biomass by 30% compared to NC treatments. The JCI treatment also effectively mitigated soil salinity, reducing Na+ accumulation by 60% and Cl− by 70% while enhancing water retention and soil structure. Principal Component Analysis (PCA) revealed a distinct clustering of JCI treatments, demonstrating its ability to increase nutrient retention and minimize salinity stress. These results indicate that biologically active properties, such as fungi-rich compost, are critical to providing an effective, environmentally resilient approach for enhancing soil fertility and supporting sustainable crop production under brackish groundwater irrigation, particularly in regions facing freshwater scarcity. 

Zero liquid discharge (ZLD) and minimal liquid discharge (MLD) are brine management approaches that aim to reduce the environmental impacts of brine discharge and recover water for reuse. ZLD maximizes water recovery and avoids the needs for brine disposal, but is expensive and energy-intensive. MLD (which reduces the brine volume and recovers some water) has been proposed as a practical and cost-effective alternative to ZLD, but brine disposal is needed. In this Review, we examine the concepts, technologies and industrial applications of ZLD and MLD. These brine management strategies have current and potential applications in the desalination, energy, mining and semiconductor industries, all of which produce large volumes of brine. Brine concentration and crystallization in ZLD and MLD often rely on mechanical vapour compression and thermal crystallizers, which are effective but energy-intensive. Novel engineered systems for brine volume reduction and crystallization are under active development to achieve MLD and/or ZLD. These emerging systems, such as membrane distillation, electrodialytic crystallization and solvent extraction desalination, still face challenges to outcompete mechanical vapour compression and thermal crystallizers, underscoring the critical need to maximize the full potential of reverse osmosis to attain ultrahigh water recovery. Brine valorization has potential to partially offset the cost of ZLD and MLD, provided that resource recovery can be integrated into treatment trains economically and in accordance with regulations. 

Photocatalytic processes offer promising solutions for environmental remediation and clean energy production, yet their efficiency under the visible light spectrum remains a significant challenge. Here, we report a novel silver–graphene (Ag-G) modified TiO2 (Ag-G-TiO2) nanocomposite photocatalyst that demonstrates remarkably enhanced photocatalytic activity for both dye wastewater degradation and hydrogen production under visible and UV light irradiation. Through comprehensive characterization and performance analysis, we reveal that the Ag-G modification narrows the TiO2 bandgap from 3.12 eV to 1.79 eV, enabling efficient visible light absorption. The nanocomposite achieves a peak hydrogen production rate of 191 μmolesg−1h−1 in deionized (DI) water dye solution under visible light, significantly outperforming unmodified TiO2. Intriguingly, we observe an inverse relationship between dye degradation efficiency and hydrogen production rates in dye solutions with tap water versus DI water, highlighting the critical role of water composition in photocatalytic processes. This work not only advances the understanding of fundamental photocatalytic mechanisms but also presents a promising photocatalyst for solar-driven environmental remediation and clean energy production. The Ag-G-TiO2 nanocomposite’s enhanced performance across both visible and UV spectra, coupled with its dual functionality in dye degradation and hydrogen evolution, represents a significant step towards addressing critical challenges in water treatment and sustainable energy generation. Our findings highlight the complex interplay between light absorption and reaction conditions, offering new insights for optimizing photocatalytic systems. This research paves the way for developing more efficient and versatile photocatalysts, potentially contributing to the global transition towards sustainable technologies and circular economy in waste management and energy production. 

This study explores the effects of alternating current-induced electromagnetic field (EMF) on mitigating brackish water irrigation and soil salinization impacts. Greenhouse experiments were conducted to evaluate the effect of EMF on plant growth, soil properties, and leaching of ions under different conditions, including using brackish water and desalinated water for irrigation and soil compost incorporation. The experiment was performed with four types of irrigation water using soil columns representing field soil layers. EMF-treated brackish water maintained a sodium adsorption ratio of 2.7 by leaching Na+ from the soil. EMF-treated irrigation columns showed an increase in soil organic carbon by 7% over no EMF-treated columns. Compost treatment reduced the leaching of NO3− from the soil by more than 15% using EMF-treated irrigation water. EMF-treated brackish water and compost treatment enhanced plant growth by increasing wet weight by 63.6%, dry weight by 71.4%, plant height by 22.8%, and root length by 115.8% over no EMF and compost columns. EMF-treated agricultural water without compost also showed growth improvements. The findings suggest that EMF treatment, especially combined with compost, offers an effective, low-cost, and eco-friendly solution to mitigate soil salinization, promoting plant growth by improving nutrient availability and soil organic carbon. 

Increasing soil salinity and degraded irrigation water quality are major challenges for agriculture. This study investigated the effects of irrigation water quality and incorporating compost (3% dry mass in soil) on minimizing soil salinization and promoting sustainable cropping systems. A greenhouse study used brackish water (electrical conductivity of 2010 µS/cm) and agricultural water (792 µS/cm) to irrigate Dundale pea and clay loam soil. Compost treatment enhanced soil water retention with soil moisture content above 0.280 m3/m3, increased plant carbon assimilation by ~30%, improved plant growth by >50%, and reduced NO3− leaching from the soil by 16% and 23.5% for agricultural and brackish water irrigation, respectively. Compared to no compost treatment, the compost-incorporated soil irrigated with brackish water showed the highest plant growth by increasing plant fresh weight by 64%, dry weight by 50%, root length by 121%, and plant height by 16%. Compost treatment reduced soil sodicity during brackish water irrigation by promoting the leaching of Cl− and Na+ from the soil. Compost treatment provides an environmentally sustainable approach to managing soil salinity, remediating the impact of brackish water irrigation, improving soil organic matter, enhancing the availability of water and nutrients to plants, and increasing plant growth and carbon sequestration potential. 

We present a deep learning method for composite and task-driven motion control for physically simulated characters. In contrast to existing data-driven approaches using reinforcement learning that imitate full-body motions, we learn decoupled motions for specific body parts from multiple reference motions simultaneously and directly by leveraging the use of multiple discriminators in a GAN-like setup. In this process, there is no need of any manual work to produce composite reference motions for learning. Instead, the control policy explores by itself how the composite motions can be combined automatically. We further account for multiple task-specific rewards and train a single, multi-objective control policy. To this end, we propose a novel framework for multi-objective learning that adaptively balances the learning of disparate motions from multiple sources and multiple goal-directed control objectives. In addition, as composite motions are typically augmentations of simpler behaviors, we introduce a sample-efficient method for training composite control policies in an incremental manner, where we reuse a pre-trained policy as the meta policy and train a cooperative policy that adapts the meta one for new composite tasks. We show the applicability of our approach on a variety of challenging multi-objective tasks involving both composite motion imitation and multiple goal-directed control. Code is available at https://motion-lab.github.io/CompositeMotion. 

Motivated by humans' ability to adapt skills in the learning of new ones, this paper presents AdaptNet, an approach for modifying the latent space of existing policies to allow new behaviors to be quickly learned from like tasks in comparison to learning from scratch. Building on top of a given reinforcement learning controller, AdaptNet uses a two-tier hierarchy that augments the original state embedding to support modest changes in a behavior and further modifies the policy network layers to make more substantive changes. The technique is shown to be effective for adapting existing physics-based controllers to a wide range of new styles for locomotion, new task targets, changes in character morphology and extensive changes in environment. Furthermore, it exhibits significant increase in learning efficiency, as indicated by greatly reduced training times when compared to training from scratch or using other approaches that modify existing policies. Code is available athttps://motion-lab.github.io/AdaptNet. 

Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions. However, despite significant advancements, it is still challenging for existing approaches to capture the uncertainty and multimodality of human navigation decision making. In this paper, we propose SocialVAE, a novel approach for human trajectory prediction. The core of SocialVAE is a timewise variational autoencoder architecture that exploits stochastic recurrent neural networks to perform prediction, combined with a social attention mechanism and a backward posterior approximation to allow for better extraction of pedestrian navigation strategies. We show that SocialVAE improves current state-of-the-art performance on several pedestrian trajectory prediction benchmarks, including the ETH/UCY benchmark, Stanford Drone Dataset, and SportVU NBA movement dataset.