Diverse Conditions Research Articles

Stress-Adaptive Stiffening Structures Inspired by Diatoms: A Parametric Solution for Lightweight Surfaces.

The intricate and highly complex morphologies of diatom frustules have long captured the attention of biomimetic researchers, initiating innovation in engineering solutions. This study investigates the potential of diatom-inspired surface stiffeners to determine whether the introduced innovative strategy is a viable alternative for addressing engineering challenges demanding enhanced stiffness. This interdisciplinary study focuses on the computer-aided generation of stress-adaptive lightweight structures aimed at optimizing bending stiffness. Through a comprehensive microscopical analysis, morphological characteristics of diatom frustules were identified and abstracted to be applied to a reference model using computer-aided methods and simulated to analyze their mechanical behavior under load-bearing conditions. Afterwards, the models are compared against a conventional engineering approach. The most promising biomimetic approach is successfully automated, extending its applicability to non-planar surfaces and diverse boundary conditions. It yields notable improvement in bending stiffness, which manifests in a decrease of displacement by approximately 93% in comparison to the reference model with an equivalent total mass. Nonetheless, for the specific load case considered, the engineering approach yields the least displacement. Although certain applications may favor conventional methods, the presented approach holds promise for scenarios subjected to varying stresses, necessitating lightweight and robust solutions.

2Memristor-1Capacitor Integrated Temporal Kernel for High-Dimensional Data Mapping.

Compact but precise feature-extracting ability is core to processing complex computational tasks in neuromorphic hardware. Physical reservoir computing (RC) offers a robust framework to map temporal data into a high-dimensional space using the time dynamics of a material system, such as a volatile memristor. However, conventional physical RC systems have limited dynamics for the given material properties, restricting the methods to increase their dimensionality. This study proposes an integrated temporal kernel composed of a 2-memristor and 1-capacitor (2M1C) using a W/HfO2/TiN memristor and TiN/ZrO2/Al2O3/ZrO2/TiN capacitor to achieve higher dimensionality and tunable dynamics. The kernel elements are carefully designed and fabricated into an integrated array, of which performances are evaluated under diverse conditions. By optimizing the time dynamics of the 2M1C kernel, each memristor simultaneously extracts complementary information from input signals. The MNIST benchmark digit classification task achieves a high accuracy of 94.3% with a (196×10) single-layer network. Analog input mapping ability is tested with a Mackey-Glass time series prediction, and the system records a normalized root mean square error of 0.04 with a 20×1 readout network, the smallest readout network ever used for Mackey-Glass prediction in RC. These performances demonstrate its high potential for efficient temporal data analysis.

Bibliometric and visual analysis of single-cell sequencing from 2010 to 2022.

Background: Single-cell sequencing (SCS) is a technique used to analyze the genome, transcriptome, epigenome, and other genetic data at the level of a single cell. The procedure is commonly utilized in multiple fields, including neurobiology, immunology, and microbiology, and has emerged as a key focus of life science research. However, a thorough and impartial analysis of the existing state and trends of SCS-related research is lacking. The current study aimed to map the development trends of studies on SCS during the years 2010-2022 through bibliometric software. Methods: Pertinent papers on SCS from 2010 to 2022 were obtained using the Web of Science Core Collection. Research categories, nations/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords were analyzed using VOSviewer, the R package "bibliometric", and CiteSpace. Results: The bibliometric analysis included 9,929 papers published between 2010 and 2022, and showed a consistent increase in the quantity of papers each year. The United States was the source of the highest quantity of articles and citations in this field. The majority of articles were published in the periodical Nature Communications. Butler A was the most frequently quoted author on this topic, and his article "Integrating single-cell transcriptome data across diverse conditions, technologies, and species" has received numerous citations to date. The literature and keyword analysis showed that studies involving single-cell RNA sequencing (scRNA-seq) were prominent in this discipline during the study period. Conclusion: This study utilized bibliometric techniques to visualize research in SCS-related domains, which facilitated the identification of emerging patterns and future directions in the field. Current hot topics in SCS research include COVID-19, tumor microenvironment, scRNA-seq, and neuroscience. Our results are significant for scholars seeking to identify key issues and generate new research ideas.

ARMOSA Model Parametrization for Winter Durum Wheat Cultivation under Diverse Cropping Management Practices in a Mediterranean Environment

In anticipation of climate changes, strategic soil management, encompassing reduced tillage and optimized crop residue utilization, emerges as a pivotal strategy for climate impact mitigation. Evaluating the transition from conventional to conservative cropping systems, especially the equilibrium shift in the medium to long term, is essential. ARMOSA, a robust crop simulation model, adeptly responds to varied soil management practices such as no tillage, minimum tillage, and specific straw management options such as chopping and incorporating crop residue into the soil (with or without prior nitrogen and water addition before ploughing). It effectively captures dynamic fluctuations in total organic carbon over an extended period. While challenges persist in precisely predicting grain yield due to climatic intricacies, ARMOSA stands out as a valuable and versatile tool. The model excels in comprehending and simulating wheat cultivar responses in dynamic agricultural ecosystems, shedding light on phenological patterns, biomass accumulation, and soil organic carbon dynamics. This research significantly advances our understanding of the intricate complexities associated with past wheat cultivation in diverse environmental conditions. ARMOSA’s ability to inform decisions on conservation practices positions it as a valuable asset for researchers, agronomists, and policymakers navigating the challenges of sustainable agriculture amidst climate change. Its real-world significance lies in its potential to guide informed decisions, contributing to global efforts in sustainable agriculture and climate resilience.

Enhancing buckling analysis of functionally graded plates using unified and generalized higher-order shear deformation theories

This paper introduces a comprehensive framework for stability analysis of functionally graded plates under diverse in-plane loading conditions while employing higher-order shear deformation theories. For this purpose, a unified model, including all shear deformation kinematics, has been proposed. This unified model incorporates any number or type of shear transverse functions, encompassing all existing plate shear deformation theories. This inclusivity is achieved through the introduction of a unique parameter, denoted by ”n” representing the number of shear deformation functions in the displacement model. Such a unified model allows great flexibility in choosing among higher order theories when modeling the behavior of plates subjected to shear deformation, making it a valuable tool for applications in engineering and materials science. Besides, this allowed us to easily introduce a fifth-order theory used for the first time in the buckling analysis of FGM plates. The critical buckling loads expressions corresponding to the employed kinematics were derived and the calculated values are compared to those computed using the numerical solver. The comparison demonstrates the effectiveness and accuracy of the formulated expressions in predicting critical buckling loads under uniaxial and biaxial loading conditions.

Effect of varying fly ash additive ratios on viscosity variation of lubricating oil at different temperatures

This study primarily aimed to conduct experimental research, focusing on examining the effects of various fly ash additive ratios, spanning from 0% to 1%, on the alterations in lubricating oil viscosity over a temperature range extending from 25°C to 85°C. The study employed viscosity measurements using the Rhotest VR2 viscometer on a commonly used lubricating oil, and carefully analyzed their behavior with varying fly ash additive concentration ratios. The results revealed that the addition of the fly ash additive increased the oil’s viscosity at all tested temperatures. The viscosity showed a proportional increase with the amount of additive up to a 0.5% weight ratio. Beyond this concentration, the viscosity began to decrease but remained higher compared to that without the additive, attributed to the thermal conductive properties of the additive’s high-hardness metal oxide particles. The viscosity of the oil mixture with a 0.5% additive ratio increases compared to the case without the additive at temperatures of 25°C, 40°C, 65°C, 75°C and 85°C, corresponding to 13.88%, 18.32%, 37.73%, 22.91% and 32.97%, respectively. These research findings provide crucial insights for fine-tuning lubricant formulations across a spectrum of applications, especially in scenarios where temperature fluctuations play a pivotal role in lubricant efficacy. By judiciously choosing the right proportion of fly ash additive, engineers and researchers can augment the lubricative attributes of oils and enhance their resilience under diverse thermal conditions. This includes factors such as increasing viscosity, load-carrying capacity and stiffness of the oil film. The result is a machinery operation that is not only more efficient but also more dependable.

Impact of geometric features of impermeable discrete bedform on hyporheic exchange

The bedform-driven hyporheic exchange plays a crucial role in mass transport within natural aquatic ecosystems like streams and rivers. This study aimed to unveil the impact of geometric features of impermeable discrete bedforms on hyporheic exchange by experimentally measuring quantitative hyporheic exchange flux data and variation characteristics in an annual flume. The experiments encompassed diverse conditions involving the ratio of bedform wavelength (λ) to wave height (h) and relative submergence. The study also analyzed the dependence of the effective diffusion coefficient on the geometric characteristics of bedform composition elements. The experimental results showed that, in comparison to a permeable flat bed, the presence of an impermeable discrete bedform tended to either attenuate or enhance hyporheic exchange, contingent on the geometric characteristics of bedform composition elements. The hyporheic exchange flux exhibited an initially increase followed by a decrease with increasing λ/h, with turbulence penetration emerging as the dominant mechanism governing hyporheic exchange for cases with relatively denser bedform composition elements (e.g., λ/h = 4.0). The effective diffusion coefficient peaked at λ/h around 6.0–8.0, owing to a significant augmentation in the relative contribution of pumping exchange to gross hyporheic exchange. Furthermore, the hyporheic exchange intensity generally increased with decreasing relative submergence, primarily attributed to the augmented relative contribution of pumping exchange to gross hyporheic exchange.

An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production

This study aims to optimize the power generation of a conventional Manzanares solar chimney (SC) plant through strategic modifications to the collector inlet height, chimney diameter, and chimney divergence. Employing a finite volume-based solver for numerical analysis, we systematically scrutinize influential geometric parameters, including collector height (hi = 1.85 to 0.1 m), chimney inlet diameter (dch = 10.16 to 55.88 m), and chimney outlet diameter (do = 10.16 to 30.48 m). Our findings demonstrate that reducing the collector inlet height consistently leads to increased power output. The optimal collector inlet height of hi = 0.2 m results in a significant power increase from 51 to 117.42 kW (~ 2.3 times) without additional installation costs, accompanied by an efficiency of 0.25%. Conversely, enlarging the chimney diameter decreases the chimney base velocity and suction pressure. However, as turbine-driven power generation rises, the flow becomes stagnant beyond a chimney diameter of 45.72 m. At this point, power generation reaches 209 kW, nearly four times greater than the Manzanares plant, with an efficiency of 0.44%. Nevertheless, the cost of expanding the chimney diameter is substantial. Furthermore, the impact of chimney divergence is evident, with power generation, collector efficiency, overall efficiency, and collector inlet velocity all peaking at an outer chimney diameter of 15.24 m (corresponding to an area ratio of 2.25). At this configuration, power generation increases to 75.91 kW, approximately 1.5 times more than the initial design. Remarkably, at a low collector inlet height of 0.2 m, combining it with a chimney diameter of 4.5 times the chimney inlet diameter (4.5dch) results in an impressive power output of 635.02 kW, signifying a substantial 12.45-fold increase. To model the performance under these diverse conditions, an artificial neural network (ANN) is effectively utilized.

Train Once, Locate Anytime for Anyone: Adversarial Learning-based Wireless Localization

Among numerous indoor localization systems, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. To push forward this approach for wide deployment, three crucial goals on high deployment ubiquity, high localization accuracy, and low maintenance cost are desirable. However, due to severe challenges about signal variation, device heterogeneity, and database degradation root in environmental dynamics, pioneer works usually make a trade-off among them. In this article, we propose iToLoc, a deep learning-based localization system that achieves all three goals simultaneously. Once trained, iToLoc will provide accurate localization service for everyone using different devices and under diverse network conditions, and automatically update itself to maintain reliable performance anytime. iToLoc is purely based on WiFi fingerprints without relying on specific infrastructures. The core components of iToLoc are a domain adversarial neural network and a co-training-based semi-supervised learning framework. Extensive experiments across 7 months with eight different devices demonstrate that iToLoc achieves remarkable performance with an accuracy of 1.92 m and >95% localization success rate. Even 7 months after the original fingerprint database was established, the rate still maintains >90%, which significantly outperforms previous works.

EhdNet: Efficient Harmonic Detection Network for All-Phase Processing with Channel Attention Mechanism

The core of harmonic detection is the recognition and extraction of each order harmonic in the signal. The current detection methods are seriously affected by the fence effect and spectrum aliasing, which brings great challenges to the detection of each order harmonic in the signal. This paper proposes an efficient harmonic detection neural network based on all-phase processing. It is based on three crucial designs. First, a harmonic signal-processing module is developed to ensure phase invariance and establish the foundation for subsequent modules. Then, we constructed the backbone network and utilized the feature-extraction module to extract deep abstract harmonic features of the target. Furthermore, a channel attention mechanism is also introduced in the weight-selection module to enhance the energy of the residual convolution stable spectrum feature, which facilitates the accurate and subtle expression of intrinsic characteristics of the target. We evaluate our method based on frequency, phase, and amplitude in two environments with and without noise. Experimental results demonstrate that the proposed EhdNet method can achieve 94% accuracy, which is higher than the compared methods. In comparison experiments with actual data, the RMSE of EhdNet is also lower than that of other recent methods. Moreover, the proposed method outperforms ResNet, BP, and other neural network approaches in data processing across diverse working conditions due to its incorporation of a channel attention mechanism.

Human autoantibodies neutralizing type I IFNs: From 1981 to 2023.

Human autoantibodies (auto-Abs) neutralizing type I IFNs were first discovered in a woman with disseminated shingles and were described by Ion Gresser from 1981 to 1984. They have since been found in patients with diverse conditions and are even used as a diagnostic criterion in patients with autoimmune polyendocrinopathy syndrome type 1 (APS-1). However, their apparent lack of association with viral diseases, including shingles, led to wide acceptance of the conclusion that they had no pathological consequences. This perception began to change in 2020, when they were found to underlie about 15% of cases of critical COVID-19 pneumonia. They have since been shown to underlie other severe viral diseases, including 5%, 20%, and 40% of cases of critical influenza pneumonia, critical MERS pneumonia, and West Nile virus encephalitis, respectively. They also seem to be associated with shingles in various settings. These auto-Abs are present in all age groups of the general population, but their frequency increases with age to reach at least 5% in the elderly. We estimate that at least 100 million people worldwide carry auto-Abs neutralizing type I IFNs. Here, we briefly review the history of the study of these auto-Abs, focusing particularly on their known causes and consequences.

Effect of Superabsorbent Polymer (SAP) Size on Microstructure and Compressive Strength of Concrete.

Superabsorbent polymers (SAPs) are hydrophilic, polymeric network materials renowned for their ability to enhance various properties of cementitious materials. This investigation examines the impact of SAP size on the hydration degree, porosity, and compressive strength of cement pastes and concrete under diverse curing conditions and ageing periods. The findings reveal that SAP addition stimulates the hydration of the C2S phase, particularly during the early curing stages, thereby favouring early strength development. However, the effect of SAPs on hydration promotion diminishes as their size increases. Conversely, the size of SAPs affects the hydration range of their action, and the 400 µm SAP demonstrates the most extensive range of hydration enhancement, reaching up to 105 µm. Additionally, SAPs effectively reduce porosity in small pores (4 nm-10 μm), with 200 μm and 400 μm SAPs exhibiting the highest efficacy. While analysing the effects of SAPs on larger pores (>10 μm), the results show that although larger SAPs result in larger average porosity, the total porosity is effectively reduced, particularly in samples incorporating 400 μm SAP. The compressive strength of cement paste, even after 28 days, is slightly reduced following the introduction of SAPs. However, the strength of concrete, due to the naturally occurring pores eliminating the negative effects of the pores produced by SAPs, is significantly increased following the introduction of SAPs, especially 400 µm SAP.

Feasibility assessment of implementing energy pile-based snowmelt system on a practical bridge deck in diverse climate conditions across China

The accumulation of snow on roads poses substantial challenges to traffic flow, incurring profound human safety concerns and considerable financial losses. To address this issue, a cost-effective and environmentally friendly solution is presented in the form of an energy pile-based snowmelt system. This research provides a comprehensive framework, which integrates the principle of bridge deck energy balance and numerical models of energy pile and practical bridge deck, aiming to evaluate the feasibility of the system in diverse climatic zones across China. First, the long-term climate and geography data pertaining to the snowmelt heat flux demand for the investigated climate regions are gathered and correlated. The Pearson Correlation Coefficients (PCCs) of each parameter (99 % confidence interval) elucidate the strongest correlation between ambient temperature and heat flux demand. Employing the proposed framework, the subsequent feasibility assessment demonstrates that the system's snow-melting effect varies with regional climate and snowmelt targets, and when real-time snow-melting is nonessential (snow-free area ratio, Ar, with values of 0 and 0.5), the system is feasible in most cities except Mohe, where the ambient temperature (Ta) reaches −28.74 °C and the snowfall rate water equivalent (s) is 1.56 mm/h. However, the system fails to meet the real-time snow-melting goal (Ar = 1) when it is located in extreme climate (Ta < −17 °C or s > 1 mm/h). The findings also reveal that snowmelt systems exhibit consistent behavior within the same climate zone. The study provides valuable insights into the practical application of energy pile-based snowmelt systems in different climate conditions, and highlights the critical role of climate considerations in the successful implementation of such innovative technologies.

Climate-smart irrigation strategy can mitigate agricultural water consumption while ensuring food security under a changing climate

North China Plain suffers from the world’s most severe water scarcity and groundwater depletion due to intensive irrigation for agricultural production. It is imperative to reduce irrigation water consumption while safeguarding crop production and food security. This study conducted a quantitative analysis with deficit irrigation strategies for winter wheat using a water-driven AquaCrop model. After model calibration and validation with field experimental data, we analyzed the irrigation water demand, crop yield, and water productivity (WP) of winter wheat under various deficit irrigation scenarios. A set of optimal irrigation schedules were proposed for different climate years, which significantly mitigated irrigation water usage while sustaining high yields and WPs. The results indicated that despite the irrigation water demand of winter wheat under the future climate scenario was slightly higher than that in the historical period, their crop water sensitive periods (reviving, jointing, and flowering) remained the same. Therefore, we recommended adopting the same deficit irrigation schedules for the historical and future periods. In wet years, adopting a 50% deficit irrigation strategy only reduced crop yields by less than 5% compared with full irrigation, but it saved 1000–1100 m3 of water per hectare and contributed a WP higher than 1.88 kg/m3. While in normal and dry years, an optimal 25% deficit irrigation could sustain over 96% of the maximum yield, meanwhile it could save 650–800 m3/ha of water and achieve almost the same WP as full irrigation. These climate-smart irrigation strategies adapting to diverse climatic conditions largely mitigate agricultural water consumption while maximizing crop productivity and water use efficiency, which are essential for achieving precision irrigation and sustainable water management under a changing climate.

Whole genome sequencing of bacteriophage NINP13076 isolated against Salmonella enteritidis

Salmonella ranks among the prominent etiological agents responsible for foodborne illnesses on a global scale. Within the scope of this investigation, a bacteriophage capable of eliminating Salmonella enteritidis was isolated using the double-layer agar overlay technique. The phage's morphological characteristics were elucidated through the application of Transmission Electron Microscopy. The genomic DNA of the phage underwent complete sequencing utilizing the MiSeq platform, with library preparation executed through the NexteraXT library prep kit method accompanied by the NexteraXT index kit. Paired-end sequencing was performed over 2 × 251 cycles read length, employing a Miseq V3 kit within the Illumina MiSeq system. Notably, the phage manifested conspicuous plaques upon S. enteritidis when subjected to the double agar overlay technique. NINP13076 displayed a 22-min latency period with a calculated average burst size of 53 PFU/cell. Phages exhibited resilience to the diverse pH conditions, manifesting no discernible impact on their viability over a storage duration of up to one week. storage at temperatures of 4 °C, 26 °C, and 37 °C demonstrated minimal effects on the phage population, with no statistically significant alterations observed. Genome assembly yielded a draft genome encompassing 161,329 base pairs with a GC content of 44.4 % and achieved coverage at a depth of 104x. Phylogenetic tree analysis unveiled a highly proximate relationship with the Salmonella Phage SSE-121 genome, demonstrating a distance score of 0.1 and signifying its classification as a novel member within the SSE121 virus group.

An improved DIC method for full-field strain measurement in tensile tests on aluminium alloys under hot stamping conditions

Digital image correlation (DIC) outperforms other strain measurement technologies due to its versatility, accuracy, non-contact nature, and capability to capture full-field strains. However, for aluminium alloys like AA6082 under high-temperature and large-deformation conditions, the shiny effects observed in the gauge area degrade the quality of the captured deformation images, leading to failures in the DIC analyses. In this study, the source of the shiny effects was investigated by analysing the evolution of AA6082 specimen surfaces painted with speckle patterns during hot stamping deformation. It was found that the shiny effects were attributed to the exposure of the reflective base material due to paint peeling and cracking. To overcome these problems, the DIC method was improved by developing a novel procedure for generating more effective speckle patterns and utilising a cross-polarisation optical system (XPOL) to mitigate the shiny effects. The improved DIC method was applied to AA6082 in both uniaxial and biaxial tensile tests under diverse hot stamping conditions, along with applications to boron steel for comparison. These improvements significantly reduced the shiny effects and increased the time over which full-field strains can be measured for AA6082, reaching over 96% (up from 75%) of the fracture time in the uniaxial tests and even 100% in the biaxial tests. The improved DIC method enables the full-field strain measurement for aluminium alloys under hot stamping conditions, allowing for the accurate determination of their thermomechanical properties.

Root architecture and rhizosphere-microbe interactions.

Plant roots fulfil crucial tasks during a plant's life. As roots encounter very diverse conditions while exploring the soil for resources, their growth and development must be responsive to changes in the rhizosphere, resulting in root architectures that are tailor-made for all prevailing circumstances. Using multi-disciplinary approaches, we are gaining more intricate insights into the regulatory mechanisms directing root system architecture. This Special Issue provides insights into our advancement of knowledge on different aspects of root development and identifies opportunities for future research.

Unstable environmental conditions constrain the fine-tune between opsin sensitivity and underwater light in an Amazon forest stream fish.

Visual adaptations can stem from variations in amino acid composition, chromophore utilization, and differential opsin gene expression levels, enabling individuals to adjust their light sensitivity to environmental lighting conditions. In stable environments, adaptations often involve amino acid substitutions, whereas in unstable conditions, differential gene expression may be a more relevant mechanism. Amazon forest streams present diverse underwater lighting conditions and experience short-term water colour fluctuations. In these environments, it is less likely for genetic and amino acid sequences to undergo modifications that tailor opsin proteins to the prevailing lighting conditions, particularly in species having several copies of the same gene. The sailfin tetra, Crenuchus spilurus, inhabits black and clear water Amazon forest streams. The long-wavelength sensitivity (LWS) is an important component for foraging and courtship. Here, we investigated LWS opsin genes in the sailfin tetra. Three copies of LWS1 and two copies of LWS2 genes were found. The maximum absorbance wavelength (λmax) estimated from the amino acid sequences of LWS1 genes exhibited variation among the different copies. In contrast, the copies of LWS2 genes showed identical expected λmax values. Although the amino acid positions affecting λmax varied among LWS genes, they remained consistent among populations living in different water colours. The relative expression levels of LWS genes differed between gene copies. While not formally tested, our results suggest that in fluctuating environments, visual adaptations may primarily stem from alterations in gene expression profiles and/or chromophore usage rather than precise genetic tuning of protein light sensitivity to environmental lighting conditions.

Strong plastic responses in aerenchyma formation in F1 hybrids of Imperata cylindrica under different soil moisture conditions.

Hybrids can express traits plastically, enabling them to occupy environments that differ from parental environments. However, there is insufficient evidence demonstrating how phenotypic plasticity in specific traits mediates hybrid performance. Two parental ecotypes of Imperata cylindrica produce F1 hybrids. The E-type in wet habitats has larger internal aerenchyma than the C-type in dry habitats. This study evaluated relationships between habitat utilisation, aerenchyma plasticity, and growth of I. cylindrica accessions. We hypothesize that plasticity in expressing parental traits explains hybrid establishment in habitats with various soil moisture conditions. Aerenchyma formation was examined in the leaf midribs, rhizomes and roots of two parental ecotypes and their F1 hybrids in their natural habitats. In common garden experiments, we examined plastic aerenchyma formation in leaf midribs, rhizomes and roots of natural and artificial F1 hybrids and parental ecotypes and quantified vegetative growth performance. In the natural habitats where soil moisture content varied widely, the F1 hybrids showed larger variation in aerenchyma formation in rhizomes than their parental ecotypes. In the common garden experiments, F1 hybrids showed high plasticity of aerenchyma formation in rhizomes, and their growth was similar to that of C-type and E-type under drained and flooded conditions, respectively. The results demonstrate that F1 hybrids of I. cylindrica exhibit plasticity in aerenchyma development in response to varying local soil moisture content. This characteristic allows the hybrids to thrive in diverse soil moisture conditions.

Enhancing the Robustness of Traffic Signal Control with StageLight: A Multiscale Learning Approach

The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area that has garnered significant interest from both researchers and practitioners is the application of deep reinforcement learning (DRL) in traffic signal control. However, DRL-based algorithms often suffer from instability due to the dynamic nature of traffic flows. Discrepancies between the environments used for training and those encountered during deployment often lead to operational failures. Moreover, conventional DRL-based traffic signal control algorithms tend to reveal vulnerabilities when faced with unforeseen events, such as sensor failure. These challenges highlight the need for innovative solutions to enhance the robustness and adaptability of such systems. To address these pertinent issues, this paper introduces StageLight, a novel two-stage multiscale learning approach, which involves learning optimal timings on a coarse time scale in stage 1, while finetuning them on a finer time scale in stage 2. Our experimental results demonstrate StageLight’s remarkable capability to generalize across diverse traffic conditions and its robustness to various sensor-failure scenarios.