Performance In Various Environments Research Articles

Research progress on the preparation of irradiation-resistant coating based on PVD technology

Nuclear energy is essential for the future development of countries. However, both structural and functional components of nuclear power equipment are facing severe challenges of nuclear irradiation damage after experiencing irradiation growth and irradiation creep. How to avoid irradiation damage to nuclear power equipment has become a hotspot in international research and development of surface protection technology. Deposition of protective coatings on the underlying object surface or in bulk materials has been considered as a near-term solution to enhanc functional components. Different substrate materials are selected according to other service conditions within the reactor. Suitable material selection combined with relevant optimization can significantly increase the service life of materials. This review summarizes recent research on several categories of anti-irradiation coatings prepared by physical vapor deposition technology for current industrial applications. These includes metallic, ceramic, composite and high entropy alloy coatings. The review endeavors to impart a thorough understanding of the properties of these selected anti-irradiation coatings, from the fundamental aspects of their substrate materials to their practical applications across diverse settings. It explores not only the current research progress but also the potential avenues for future advancements. Additionally, the intricate relationships between coating formulations, their resistance to irradiation, and their ultimate performance in various environments are illuminated in this paper.

Biobased Polybutyrolactam Nanofiber with Excellent Biodegradability and Cell Growth for Sustainable Healthcare Textiles.

The white pollution caused by unsustainable materials is a significant challenge around the globe. Here, a novel and fully biobased polybutyrolactam (PBY) nanofiber membrane was fabricated via the electrospinning method. As-spun PBY nanofiber membranes have good thermal stability, high porosity of up to 71.94%, and excellent wetting behavior. The biodegradability in soil, UV aging irradiation, and seawater was investigated. The PBY nanofiber membrane is almost completely degraded in the soil within 80 days, showing excellent degradability. More interestingly, γ-aminobutyric acid, as a healthcare agent with intrinsic hypotensive, tranquilizing, diuretic, and antidiabetic efficacy, can be detected in the degradation intermediates. In addition, the PBY nanofiber membrane also exhibits antibacterial ability against Escherichia coli. As a fully biomass-derived material, the PBY membrane has excellent biodegradable performance in various environments as well as negligible cytotoxicity and commendable cell proliferation. Our PBY nanofiber membrane shows great potential as biodegradable packaging and in vitro healthcare materials.

IoT based MPPT techniques for photovoltaic frameworks management under different environmental conditions: a review

<p>Solar energy (SE) is the most attractive form of renewable energy (RE) source for electrification. To harness SE, the photovoltaic (PV) system is required towards converting sunlight into direct electricity. The PV frameworks can be placed in areas with high energy potential. The performance of PV frameworks is complex work which depends on various parameters of the frameworks and their operations. The performance of PV frameworks can be evaluated using MATLAB/Simulink platform and real-time implementation. In this research article, the internet of things (IoT) is investigated to regulate and monitor PV system performance in various environments. IoT-based maximum power point tracking (MPPT) technology improves the response of real-time operating characteristics which makes it possible to control remote PV systems management, quickly diagnose problems and maintain them effectively. Additionally, it allows for recording production and performance data for analysis.</p>

Yield stability among potato varieties suitable for different agroecological regions of Bangladesh

Multilocation trials are usually performed in breeding and variety evaluation programs to identify stable genotype(s) with similar crop performance in various environments. The present study evaluated the stability of six selected potato varieties (BARI Alu-7, BARI Alu-8, BARI Alu-25, BARI Alu-28, BARI Alu-36, and BARI Alu-41) suitable for multiple locations (Barishal, Bogura, Cumilla, Jamalpur, Jashore, Munshiganj, Mymensingh, and Rajshahi) in Bangladesh from 2014 to 2019. The study considered genotype and environment as treatments, year as replications and used a randomized complete block design (RCBD) with to construct the genotype plus genotype-vs-environment interaction (GGE) model. The joint analysis of variance revealed significant differences among the genotypes and environments (GE). The scores of PC1 (principal component 1) and PC2 (principal component 2) cumulatively explained approximately 63 % of the total variation in GE interactions and were used to construct the GGE biplot. BARI Alu-8 and BARI Alu-28 were the best genotypes, with high average yields and high stability across the locations. Jamalpur and Munshiganj was identified as the desired locations among the tested environments for growing all the genotypes. This study will help potato growers select highly stable high-performance varieties for a particular environment to achieve maximum tuber production.

An RGB‐D object detection model with high‐generalization ability applied to tea harvesting robot for outdoor cross‐variety tea shoots detection

AbstractDetecting tea shoots is the first and most crucial step in achieving intelligent tea harvesting. However, when faced with thousands of tea varieties, establishing a high‐quality and comprehensive database comes with significant costs. Therefore, it has become an urgent challenge to improve the model's generalization ability and train it with minimal samples to develop a model capable of achieving optimal detection performance in various environments and tea varieties. This paper introduces a model named You Only See Tea (YOST) which utilizes depth maps to enhance model's generalization ability. It is applied to detect tea shoots in complex environments and to perform cross‐variety tea shoots detection. Our approach differs from common data augmentation strategies aimed at enhancing model generalization by diversifying the data set. Instead, we enhance the model's learning capability by strategically amplifying its attention towards core target features while simultaneously reducing attention towards noncore features. The proposed module YOST is developed upon the You Only Look Once version 7 (YOLOv7) model, utilizing two shared‐weight backbone networks to process both RGB and depth images. Then further integrate two modalities with feature layers at the same scale into our designed Ultra‐attention Fusion and Activation Module. By utilizing this approach, the model can proficiently detect targets by capturing core features, even when encountering complex environments or unfamiliar tea leaf varieties. The experimental results indicate that YOST displayed faster and more consistent convergence compared with YOLOv7 in training. Additionally, YOST demonstrated a 6.58% enhancement in AP50 for detecting tea shoots in complex environments. Moreover, when faced with a cross‐variety tea shoots detection task involving multiple unfamiliar varieties, YOST showcased impressive generalization abilities, achieving a significant maximum AP50 improvement of 33.31% compared with YOLOv7. These findings establish its superior performance. Our research departs from the heavy reliance on high‐generalization models on a large number of training samples, making it easier to train small‐scale, high‐generalization models. This approach significantly alleviates the pressure associated with data collection and model training.

Towards an Environmentally Robust Speech Assistant System for Emergency Medical Services.

Automated speech recognition technology with robust performance in various environments is highly needed by emergency clinicians, but there are few successful cases. One main challenge is the wide variety of environmental interference involved during a typical prehospital care emergency service such as background noises and overlapping speech. To solve this problem, we try to establish an environmentally robust speech assistant system with the help of the proposed personalized speech enhancement (PSE) method, which utilizes the target physician's voiceprint feature to suppress non-target signal components. We demonstrate its potential value using both general public test set and our real EMS test set by evaluating the objective speech quality metrics, DNSMOS, and the recognition accuracy. Hopefully, the proposed method will raise EMS efficiency and security against non-target speech.

IS-CAT: Intensity-Spatial Cross-Attention Transformer for LiDAR-Based Place Recognition.

LiDAR place recognition is a crucial component of autonomous navigation, essential for loop closure in simultaneous localization and mapping (SLAM) systems. Notably, while camera-based methods struggle in fluctuating environments, such as weather or light, LiDAR demonstrates robustness against such challenges. This study introduces the intensity and spatial cross-attention transformer, which is a novel approach that utilizes LiDAR to generate global descriptors by fusing spatial and intensity data for enhanced place recognition. The proposed model leveraged a cross attention to a concatenation mechanism to process and integrate multi-layered LiDAR projections. Consequently, the previously unexplored synergy between spatial and intensity data was addressed. We demonstrated the performance of IS-CAT through extensive validation on the NCLT dataset. Additionally, we performed indoor evaluations on our Sejong indoor-5F dataset and demonstrated successful application to a 3D LiDAR SLAM system. Our findings highlight descriptors that demonstrate superior performance in various environments. This performance enhancement is evident in both indoor and outdoor settings, underscoring the practical effectiveness and advancements of our approach.

Balanced Q-learning: Combining the influence of optimistic and pessimistic targets

The optimistic nature of the Q−learning target leads to an overestimation bias, which is an inherent problem associated with standard Q−learning. Such a bias fails to account for the possibility of low returns, particularly in risky scenarios. However, the existence of biases, whether overestimation or underestimation, need not necessarily be undesirable. In this paper, we analytically examine the utility of biased learning, and show that specific types of biases may be preferable, depending on the scenario. Based on this finding, we design a novel reinforcement learning algorithm, Balanced Q-learning, in which the target is modified to be a convex combination of a pessimistic and an optimistic term, whose associated weights are determined online, analytically. Such a balanced target inherently promotes risk-averse behavior, which we examine through the lens of the agent's exploration. We prove the convergence of this algorithm in a tabular setting, and empirically demonstrate its consistently good learning performance in various environments.

Visual-Based Simultaneous Localization and Mapping (VSLAM) Techniques for Robots: A Scientific Review

The main problem facing autonomous robots is to navigate in an environment with the ability to determine its location and simultaneously build a map, SLAM technique can formulate this requirement efficiently. In this paper Filter-based, Graph-based, and AI-based Visual-SLAM techniques have been reviewed. The review shows that the first method suffers from high computations when the number of landmarks increases. The Graph-based algorithms are exposed to drift-error problems which cause a delocalization and require optimization. The AI-based vSLAM has the advantage of not-having complicated mathematical models in the algorithm, and it shows an efficient performance in various environments. The reviewed algorithms utilize different cameras including mono, stereo, and RGB-D cameras. The low-cost RGB-D cameras encourage implementation in modern autonomous robots. This work introduces a scientific-based overview of vSLAM to the reader, by explaining all phases of SLAM, the state-of-the-art algorithms, highlighting the strengths and weaknesses of each paradigm.

RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments

Camera relocalization has various applications in autonomous driving. Previous camera pose regression models consider only ideal scenarios where there is little environmental perturbation. To deal with challenging driving environments that may have changing seasons, weather, illumination, and the presence of unstable objects, we propose RobustLoc, which derives its robustness against perturbations from neural differential equations. Our model uses a convolutional neural network to extract feature maps from multi-view images, a robust neural differential equation diffusion block module to diffuse information interactively, and a branched pose decoder with multi-layer training to estimate the vehicle poses. Experiments demonstrate that RobustLoc surpasses current state-of-the-art camera pose regression models and achieves robust performance in various environments. Our code is released at: https://github.com/sijieaaa/RobustLoc

Promoting Transfer of Robot Neuro-Motion-Controllers by Many-Objective Topology and Weight Evolution

The ability of robot motion controllers to quickly adapt to new environments is expected to extend the applications of mobile robots. Using the concept of transfer optimization, this study investigates the capabilities of neuro-motion-controllers, which were obtained by simultaneously solving several source problems, to adapt to target problems. In particular, the adaptation comparison is carried out between specialized controllers, which are optimal for a single source motion problem, and nonspecialized controllers that can solve several source motion problems. The compared types of controllers were simultaneously obtained by a many-objective evolution search that is tailored for the optimization of the topology and weights of neural networks. Based on the examined problems, it appears that nonspecialized solutions, which are “good enough” in all the source motion problems, show significantly better transfer capabilities as compared with solutions that were optimized for a single source motion problem. The proposed approach opens up new opportunities to develop controllers that have good enough performances in various environments while also exhibiting efficient adaptation capabilities to changes in the environments.

Tuning the Parameters of Cu–WS2 Composite Production via Powder Metallurgy: Evaluation of the Effects on Tribological Properties

Metal matrix self-lubricating composites exhibit outstanding performance in various environments, reaching the required properties by modifying the reinforcement–matrix ratio and the production method. The present research investigated the effects on tribological performance and electrical properties of different pressure loads, maintaining pressing time, and sintering temperatures during the production of copper–10 wt% tungsten disulfide (Cu–WS2) composite via powder metallurgy. Moreover, additional thermo-mechanical treatments were evaluated, namely second pressing and second sintering steps. The density and the hardness of the produced composites were measured, as well as the electrical resistivity, considering sliding electrical contacts as possible employment. The outputs of the wear tests were considered together with the analysis of the wear track via scanning electron microscopy and confocal laser scanning microscopy to understand wear mechanisms. Different production routes were compared in terms of electrical resistivity, wear coefficient, and specific wear rate, calculated by the confocal laser scanning microscopy, and friction coefficient, measured during the wear test. The main results highlighted that the increase in sintering temperature was detrimental to the hardness and tribological properties; higher load and additional pressing step determined a general improvement in the tested properties.

The Contribution of Nitrate Dissimilation to Nitrate Consumption in narG- and napA-Containing Nitrate Reducers with Various Oxygen and Nitrate Supplies.

Nitrate reducers containing narG or napA play an important role in the nitrogen cycle, but little is known about their functional differentiations in relation to environmental changes. In this study, three types of nitrate reducers in the genus Pseudomonas, including strains containing narG (G type), napA (A type) and both narG and napA (GA type), were selected to explore their functional performances under varied nitrate and oxygen concentrations. Their growth characteristics, nitrate consumption, and dissimilatory nitrate-reducing activity were investigated. Growth and nitrate consumption of all three types of strains were generally promoted with increasing oxygen and nitrate concentrations. However, their dissimilatory nitrate-reducing activities were restricted by oxygen supply. When supplied with 0.25 mM KNO3, A-type strains showed a higher growth rate but lower activity of dissimilatory nitrate reduction (DNR) than G-type strains, regardless of oxygen concentration. However, when nitrate concentration increased to 0.75 mM or 5 mM, G-type strains displayed stronger capability of nitrate consumption and DNR than A-type strains under anaerobic conditions, whereas under oxygenated conditions, A-type strains exhibited higher growth and nitrate consumption but weaker DNR than G-type strains. The GA-type strains appeared similar to G type under anaerobic conditions but performed more similarly to A type in aerobic environments. In summary, the nitrate consumption of narG-containing nitrate reducers is mainly caused by DNR in both anaerobic and aerobic environments, while the large proportion of nitrate consumption in A-type nitrate reducers under the aerobic condition is attributed to the assimilation by cell growth. IMPORTANCE Nitrate reducers containing narG or napA are ubiquitous, but little is known about their functional performance in various environments. Our study provides an important clue that the nitrate consumption of narG-containing strains is mainly caused by dissimilatory reduction in the environments, while that of napA-containing nitrate reducers under anaerobic conditions is ascribed to nitrate dissimilation but under the aerobic condition is attributed to the assimilation by cell growth. This finding broadens the understanding of aerobic nitrate reduction in the nitrogen cycle and highlights the important role of narG-containing bacteria in nitrate reduction under aerobic conditions.

Motion characteristics and gait planning methods analysis for the walkable lunar lander to optimize the performances of terrain adaptability

Lunar environmental adaptability and gait control are significant for developing a walkable lunar lander (WLL). However, gait planning for the WLL is challenging due to the optimization process required for the stable margin sequence. To overcome this concern, this study develops a new Center of Gravity (COG) trajectory planning algorithm appropriate for a WLL that will enhance walking performance. Specifically, our method designs an adaptive back-stepping controller (ABSC) based on the established kinematic model to compensate for the influence of parameter uncertainties on the single-leg motion system. Compared to the classic PID control, our method's motion trajectory accuracy on the joint drive position is improved by 3.2 times. Additionally, various experiments on the prototype device verify the foot's motion trajectory. Furthermore, a new COG trajectory planning algorithm is proposed that combines the Jacobian COG and the centroid of a support polygon, including a foot contact constraint. This strategy effectively balances optimization and search operations, improving the lander's passing ability and stability in complex terrains. Moreover, this work considers the longitudinal stability margin (LSM) method and minimum stability margin of the lander as a sequential optimization problem. Hence, the hierarchical control architecture is established to compensate for the mission and environment changes, improving computational efficiency. Finally, the developed method is applied to the simulation prototype to verify its performance in various environments. The experimental results demonstrate that the proposed method allows the WLL to move in various environments and improves its stability in simulation experiments. At the same time, compared with the foot endpoint trajectory gait control method, our method can improve the walking efficiency by 8.56% and 6.58%, respectively.

Attention-calibration based double-branch cross-domain person re-identification

Existing cross-domain pedestrian re-identification methods tend to utilize domain adaptation or domain generalization strategies to eliminate the differences between domains, but these methods fail to fully characterize the characteristics of unknown domain samples, resulting in poor recognition performance in unknown domain. By learning specific features and invariant features of different domains, this paper proposes a new attention-calibration double-branch cross-domain pedestrian re-identification network (ACDBNet). Firstly, the attention calibration module is utilized to extract the channel information and spatial information of an image, and locate the distinguishable local features; then, the global branch and local branch are introduced to fully learn the domain specific feature and domain invariant feature respectively, and the features in these two branches are linearly weighted fused. Experimental results on a large number of public datasets demonstrate that the proposed method has excellent performance in various environments.

VULoc

Ultra-WideBand (UWB) localization has shown promising prospects in both academia and industry. However, accurate UWB localization for a large number of tags (i.e., targets) is still an open problem. Existing works usually require tedious time synchronization and labor-intensive calibrations. We present VULoc, an accurate UWB localization system with high scalability for an unlimited number of targets, which significantly reduces synchronization and calibration overhead. The key idea of VULoc is an accurate localization method based on passive reception without time synchronization. Specifically, we propose a novel virtual-Two Way Ranging (V-TWR) method to enable accurate localization for an unlimited number of tags. We theoretically analyze the performance of our method and show its superiority. We leverage redundant ranging packets among anchors with known positions to infer a range mapping for auto-calibration, which eliminates the ranging bias arising from the hardware and multipath issues. We finally design an anchor scheduling algorithm, which estimates reception quality for adaptive anchor selection to minimize the influence of NLOS. We implement VULoc with DW1000 chips and extensively evaluate its performance in various environments. The results show that VULoc can achieve accurate localization with a median error of 10.5 cm and 90% error of 15.7 cm, reducing the error of ATLAS (an open-source TDOA-based UWB localization system) by 57.6% while supporting countless targets with no synchronization and low calibration overhead.

Assessment of nine gridded temperature data for modeling of wheat production systems

Temperature plays a critical role for plant development and growth, and maximum and minimum temperature are two of the key inputs for the application of crop simulation models. The lack of good observed weather data is a challenging issue in many agricultural regions. Recent studies have used gridded weather data as an alternative to observed weather data for application in agricultural systems modeling. The aim of this study was to identify the most appropriate gridded temperature data (GTD) to be applied in crop simulation modeling. Therefore, nine global GTDs were evaluated for rainfed and irrigated wheat production for six sites across Iran using the Cropping System Model (CSM)-NWheat model. The GTDs included AgCFSR, AgERA5, AgMERRA, CPC, GLDAS, NCEP RII, PGF, POWER, and S14FD. The observed data from the weather stations located in the selected sites were used to evaluate the GTDs. To increase the robustness of the final results, six different rainfed and irrigated wheat cultivars were considered for modeling. Results showed that AgCFSR, AgMERRA, PGF, and AgERA5 had the best performance, while NCEP and GLDAS had the weakest performance. Using AgCFSR and AgERA5, grain yield was simulated with NRMSE ≤ 11.5% and NSE ≥ 0.7. The overall performance of AgCFSR, AgMERRA, and PGF was better that for AgERA5; however, these three GTDs are not recommended for agricultural modeling applications, especially if current weather data are needed. AgCFSR and AgMERRA are only available until 2010, and PGF is available until 2016. Therefore, AgERA5 can be recommended for temperature-related agricultural simulations for the study area in Iran and similar environmental regions, due to its overall performance compared to the observed temperature data and with availability since 1979 to present. Further studies are necessary to assess differences in performance for various environments.

Synchronous microbial V(V) reduction and denitrification using corn straw as the sole carbon source

The coexistence of nitrate and V(V) in groundwater aquifers poses potential threats to ecological environment and public health. However, much remains to be elucidated about how the complex microbial community coupled nitrate and V(V) simultaneous bio-reduction with carbon source oxidation. For the first time, it was demonstrated that denitrification and V(V) bio-reduction occur by using corn straw as the sole carbon and energy source. Corn straw was proved to have efficient denitrification and V(V) bio-reduction performance in various environments, especially at V(V) concentrations of 100 mg/L for optimal V(V) reduction rate (19.25 mg/L·d) and at pH of 11 with the best nitrate reduction rate (3.12 d−1). In addition, an interesting phenomenon was found that the release of V(V) occurred when the carbon source was insufficient and the competitive electron acceptor (NO3−-N) existed. Metagenomic analysis showed that the addition of corn straw increased the abundance of genes related to metal resistance, cytochrome and dimethyl sulfoxide, and increased the abundance of glycolytic process, which may play a vital role in facilitating the reduction of V(V). These findings can provide basic suggestions for improving the mechanism of V(V) reduction pathway and provide guidance for the remediation of groundwater polluted by nitrate and V(V).

Conjugate gradient-based FLANN algorithms in nonlinear active noise control

The conjugate gradient (CG) method exhibits fast convergence speed than the steepest descent, which has received considerable attention. In this work, we propose two CG-based methods for nonlinear active noise control (NLANC). The proposed filtered-s Bessel CG (FsBCG)-I algorithm implements the functional link artificial neural network (FLANN) as a controller, and it is derived from the Matérn kernel to achieve enhanced performance in various environments. On the basis of the FsBCG-I algorithm, we further develop the FsBCG-II algorithm, which utilizes the Bessel function of the first kind to constrain outliers. As an alternative, the FsBCG-II algorithm has reduced computational complexity and similar performance as compared to the FsBCG-I algorithm. Moreover, the convergence property of the algorithms is analyzed. The proposed algorithms are compared with some highly cited previous works. Extensive simulation results demonstrate that the proposed algorithms can achieve robust performance when the noise source is impulsive, Gaussian, logistic, and time-varying.

Bioinoculants-Natural Biological Resources for Sustainable Plant Production.

Agricultural sustainability is of foremost importance for maintaining high food production. Irresponsible resource use not only negatively affects agroecology, but also reduces the economic profitability of the production system. Among different resources, soil is one of the most vital resources of agriculture. Soil fertility is the key to achieve high crop productivity. Maintaining soil fertility and soil health requires conscious management effort to avoid excessive nutrient loss, sustain organic carbon content, and minimize soil contamination. Though the use of chemical fertilizers have successfully improved crop production, its integration with organic manures and other bioinoculants helps in improving nutrient use efficiency, improves soil health and to some extent ameliorates some of the constraints associated with excessive fertilizer application. In addition to nutrient supplementation, bioinoculants have other beneficial effects such as plant growth-promoting activity, nutrient mobilization and solubilization, soil decontamination and/or detoxification, etc. During the present time, high energy based chemical inputs also caused havoc to agriculture because of the ill effects of global warming and climate change. Under the consequences of climate change, the use of bioinputs may be considered as a suitable mitigation option. Bioinoculants, as a concept, is not something new to agricultural science, however; it is one of the areas where consistent innovations have been made. Understanding the role of bioinoculants, the scope of their use, and analysing their performance in various environments are key to the successful adaptation of this technology in agriculture.