Experimental Environment Research Articles

Probing an LSTM-PPO-Based reinforcement learning algorithm to solve dynamic job shop scheduling problem

With the growth of personalized demand and the continuous improvement in social productivity, the large-scale and few-variety centralized production model is gradually transitioning towards a personalized model of small batches and multiple varieties, which makes the manufacturing process of the job shop increasingly complex. Furthermore, disruptive events such as machinery failures and rush orders in the job shop increase the uncertainty and variability of the production environment. Traditional scheduling methods are usually based on fixed rules and heuristic algorithms, which are difficult to adapt to constantly changing production environments and demands. This may lead to inaccurate scheduling decisions and hinder the optimal allocation of job shop resources. To solve the dynamic job shop scheduling problem (JSP) more effectively, this paper proposes a Reinforcement Learning (RL) optimization algorithm integrating long short-term memory (LSTM) neural network and proximal policy optimization (PPO). It can dynamically adjust scheduling strategies according to the changing production environment, achieving comprehensive status awareness of the job shop environment to make optimal scheduling decisions. First, a state-aware network framework based on LSTM-PPO is proposed to achieve real-time perception of job shop state changes. Then, the state and action space of the job shop are described within the context of the state-aware network framework. Finally, an experimental environment is established to verify the algorithm’s effectiveness. Training the LSTM-PPO algorithm makes it feasible to achieve better performance than other scheduling methods. By comparing the initial planning time with the actual completion time of the rescheduling decision under different dynamic disturbances, the efficiency of the proposed algorithm is verified for the dynamic JSP

Efficient Semantic Segmentation for Large-Scale Agricultural Nursery Managements via Point Cloud-Based Neural Network

Remote sensing technology has found extensive application in agriculture, providing critical data for analysis. The advancement of semantic segmentation models significantly enhances the utilization of point cloud data, offering innovative technical support for modern horticulture in nursery environments, particularly in the area of plant cultivation. Semantic segmentation results aid in obtaining tree components, like canopies and trunks, and detailed data on tree growth environments. However, obtaining precise semantic segmentation results from large-scale areas can be challenging due to the vast number of points involved. Therefore, this paper introduces an improved model aimed at achieving superior performance for large-scale points. The model incorporates direction angles between points to improve local feature extraction and ensure rotational invariance. It also uses geometric and relative distance information for better adjustment of different neighboring point features. An external attention module extracts global spatial features, and an upsampling feature adjustment strategy integrates features from the encoder and decoder. A specialized dataset was created from real nursery environments for experiments. Results show that the improved model surpasses several point-based models, achieving a Mean Intersection over Union (mIoU) of 87.18%. This enhances the precision of nursery environment analysis and supports the advancement of autonomous nursery managements.

Illuminating Salient Contributions in Neuron Activation with Attribution Equilibrium.

With the remarkable success of deep neural networks, there is a growing interest in research aimed at providing clear interpretations of their decision-making processes. In this paper, we introduce Attribution Equilibrium, a novel method to decompose output predictions into fine-grained attributions, balancing positive and negative relevance for clearer visualization of the evidence behind a network decision. We carefully analyze conventional approaches to decision explanation and present a different perspective on the conservation of evidence. We define the evidence as a gap between positive and negative influences among gradient-derived initial contribution maps. Then, we incorporate antagonistic elements and a user-defined criterion for the degree of positive attribution during propagation. Additionally, we consider the role of inactivated neurons in the propagation rule, thereby enhancing the discernment of less relevant elements such as the background. We conduct various assessments in a verified experimental environment with PASCAL VOC 2007, MS COCO 2014, and ImageNet datasets. The results demonstrate that our method outperforms existing attribution methods both qualitatively and quantitatively in identifying the key input features that influence model decisions.

Genotype-by-Trait (GT) biplot analysis and ranking of rice genotypes in salinity stress conditions

Estimation of plant tolerance values for agronomic traits, in addition to being genetically influenced, can also be influenced by the plant’s growing environment. Effective screening at the generative stage can only be carried out on land directly affected by salinity. However, the challenge lies in the fact that salinity levels vary widely on farmers’ land and are difficult to predict. The GT biplot analysis is a statistical method used in plant breeding to evaluate the performance of different genotypes based on various supporting traits. e three groups of biplots used are genotype by trait evaluation, genotype ranking analysis, and mega-environment analysis. There were differences in genotype responses based on yield component variables in each experimental environment. IRRI-IL12 and IRRI-IL09 are stable salinity-tolerant rice genotypes. IRRI-IL07 and IRRI-IL16 are adaptive in the optimum environment (L1), IRRI-IL07 and IRRI-IL10 are adaptive in the greenhouse salinity environment (L2), and IRRI-IL13 is adaptive in the salinated intrusion (L3).

Performance of game sessions in VR vs standard 2D monitor environment. an EEG study

Nowadays studies using Virtual Reality (VR) are gaining high popularity due to VR being a better approximation of the ecological environment for visual experiments than standard 2D display settings. VR technology has been already applied in medicine in the therapy of mental disorders, neurorehabilitation, and neurofeedback. However, its effectiveness compared to the standard 2D procedure is still not fully documented and limited information about the neurophysiological underpinnings of VR is provided. In this study, we tested participants’ performance during several sessions of the computer game in two different environments, VR vs. 2D monitor display. Participants performed three 25 min gaming sessions of adapted Delay Match-To-Sample task during EEG recording. The results showed that the VR group outperformed the 2D display group in the first session and then maintained its performance level throughout the remaining two sessions while the 2D group increased performance in each session eventually leveling up in the last one. Also group differences in the EEG activity were most profound only in the first session. In this session, the VR group was characterized by stronger and more synchronized neuronal activity, especially in delta, theta, and gamma bands. The VR group was less impacted by visual arousals as indicated by the theta/beta2 ratio in parietal electrodes.

Study on the Applicability of Deterioration Detection Techniques for Sandstone Heritage in Multi Environment using MLP-Attention Model

Abstract. Stone cultural heritage, encompassing a broad spectrum of artifacts such as stone artworks, buildings, tools, and utensils, represents one of the most significant categories of cultural heritage. However, the conservation of these cultural heritage faces challenges from the process of deterioration. This degradation not only compromises the structural integrity of the heritage but also results in the loss of invaluable historical information. Thus, there emerges a critical demand for effective methods to detect and assess the condition of stone cultural heritage, enabling timely and precise conservation interventions. Here we obtained sandstone samples under different deteriorative environments through laboratory-simulated deterioration experiments and employed a variety of detection technologies to capture a series of changes in the deterioration detection parameters during the sandstone deterioration process. Subsequently, a deep learning model was established to correlate the detection parameters with the degree of stone deterioration. A SHAP analysis was then conducted to determine the contribution of various parameters to the degree of stone weathering under different experimental environments, providing recommendations for selecting appropriate detection technologies and indicators adapted to different deterioration environments. To further analyze the deterioration processes of the stone samples, XRD analysis was conducted to observe changes in mineral composition throughout the deterioration process. SEM images were utilized to examine the changes in micro-morphology and the internal pore structure associated with deterioration. This study provides a basis for the scientific design of deterioration detection schemes by selecting the most suitable testing technology for optimal deterioration assessment under specific environmental conditions.

Distributed Multi-Robot SLAM Algorithm with Lightweight Communication and Optimization

Multi-robot SLAM (simultaneous localization and mapping) is crucial for the implementation of robots in practical scenarios. Bandwidth constraints significantly influence multi-robot SLAM systems, prompting a reliance on lightweight feature descriptors for robot cooperation in positioning tasks. Real-time map sharing among robots is also frequently ignored in such systems. Consequently, such algorithms are not feasible for autonomous multi-robot navigation tasks in the real world. Furthermore, the computation cost of the global optimization of multi-robot SLAM increases significantly in large-scale scenes. In this study, we introduce a novel distributed multi-robot SLAM framework incorporating sliding window-based optimization to mitigate computation loads and manage inter-robot loop closure constraints. In particular, we transmit a 2.5D grid map of the keyframe-based submap between robots to promote map consistency among robots and maintain bandwidth efficiency in data exchange. The proposed algorithm was evaluated in extensive experimental environments, and the results validate its effectiveness and superiority over other mainstream methods.

Stochastic‐gradient‐based control algorithms for power quality enhancement in solar photovoltaic interfaced three‐phase distribution system

AbstractHere, stochastic‐gradient‐based adaptive control algorithms have been discussed and employed for power quality enhancement in a Photovoltaics (PV) integrated distribution system. Least mean square (LMS), least mean fourth (LMF), sign‐error LMS, and ‐normalised LMS (‐NLMS) have been implemented as control algorithms for the estimation of fundamental load current. The performances of these adaptive algorithms are compared under steady‐state and dynamic conditions under the non‐linear load conditions in a closed‐loop three‐phase system. The main aim of implementing these algorithms is reactive power compensation, power quality enhancement, and load balancing in a single‐stage three‐phase grid‐tied PV system. The hysteresis current control (HCC) technique is used to generate switching pulses for the three‐phase Distribution Static Power Compensator (DSTATCOM). An MPPT is also employed to ensure maximum power delivery from the solar PV array. PV integrated three‐phase single‐stage distribution system with adaptive control algorithms is implemented in MATLAB/Simulink environment as well as in experimental environment to achieve the goals per standard IEEE‐519.

UWB/IMU integrated indoor positioning algorithm based on robust extended Kalman filter

Abstract As a fundamental method for integrated indoor positioning, Ultra wideband (UWB)/ Inertial Measurement Unit (IMU) offers more stable performance compared to separate UWB sensor and IMU sensor positioning and uses the least squares (LS) and extended Kalman filter (EKF) for positioning processing, however, the performance of conventional LS and EKF will be seriously affected when the contamination rate of measurement noise is high. To address this problem, this paper proposes a robust extended Kalman filter (REKF) based algorithm for robot-integrated indoor positioning, which combines UWB and IMU positioning methods to obtain a more robust system performance and optimal positioning accuracy. In this paper, a vehicle equipped with a UWB/IMU sensor and four anchor points is set up to perform linear, circular, and trajectory motions in an experimental environment with visible obstacles. The experimental results show that compared with EKF, REKF improves the localization precision and accuracy in the three motions by 20.98%, 35.51%, and 47.06%, respectively. REKF effectively improves the accuracy and robustness of the robot’s indoor positioning, and provides an improved and practical method for integrated indoor positioning algorithms to make indoor positioning more accurate and reliable.

HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.

EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging. In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects. Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% ± 1.25% (SEED), 98.93% ± 1.45% (DEAP-arousal), and 98.57% ± 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology. The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition.

Study on Static Biomechanical Model of Whole Body Based on Virtual Human.

Material handling tasks often lead to skeletal injury of workers. The whole-body static biomechanical modeling method based on virtual humans is the theoretical basis for analyzing the human factor index in the lifting process. This paper focuses on the study of humans' body static biomechanical model for virtual human ergonomics analysis: First, the whole-body static biomechanical model is constructed, which calculates the biomechanical data such as force and moment, average strength, and maximum hand load at human joints. Secondly, the prototype model test system is developed, and the real experiment environment is set up with the inertial motion capture system. Finally, the model reliability verification experiment and application simulation experiment are designed. The comparison results with the industrial ergonomic software show that the model is consistent with the output of the industrial ergonomic software, which proves the reliability of the model. The simulation results show that under the same load, the maximum joint load and the maximum hand load are strongly related to the working posture, and the working posture should be adjusted to adapt to the load. Upright or bent legs have less influence on the maximum load capacity of the hand. Lower hand load capacity is due to forearm extension, and the upper arm extension greatly reduces the load capacity of the hand. Compared with a one-handed load, the two-handed load has a greater load capacity.

Formation of flake-like DAAF crystals with tunable size realized on a microfluidic platform for binder-free initiator explosives

Flake-like energetic crystal has a great potential to achieve initiator explosive with high reactivity, low initiation threshold, high safety, and facile post-processing, but it has been limited for 3,3′-diamino-4,4′-azoxyfurazan (DAAF) by the difficulties in controlling crystal morphology. Here reported is a microfluidic preparation of flake-like DAAF crystals (f-DAAF) with tunable size by combined effect of the adding of facet-controlling agent (nigrosin alcohol soluble) and tuning of the external crystallization conditions. The results show that the crystal system of f-DAAF belong to monoclinic system, the (−201) facet is the largest exposed face. The f-DAAF possess high crystallinity, tunable flake aspect ratio from 6.15 to 66.8 and specific surface area from 2.57 to 5.67 m2·g−1. Molecular dynamics simulations were performed to investigate the selective interactions between the two molecules (facet-controlling agent and solvent) and different crystal faces of DAAF in a real experiment environment. Combined with the experimental results, a possible growth mechanism of the flake-like morphology was discussed. It is found that the f-DAAF not only possess lower impact sensitivity itself, but also can reduce the impact sensitivity of other explosives with high impact sensitivity. The electric explosion derived flyer initiation experiments show that the initiation sensitivity of f-DAAF is much lower than that of raw DAAF in micron size, and comparable to that of ultrafine DAAF. This work demonstrates the promising application potential of f-DAAF as high-performance initiator explosives with low initiation threshold and high safety, and also provides an effective way for their preparation.

Straddling the Line Between In Vitro and Ex Vivo Investigations.

Tissue engineering research fundamentally relies on experiments to advance knowledge, utilizing various models for research on both humans and animals. With scientific progress, experimental models have become increasingly complex over time. This complexity sometimes blurs the distinction between categories, making terminology less consistent. In biomedical research, three overarching terms are commonly used to characterize experimental environments: in vitro, ex vivo, and invivo. While in vitro translates from Latin as "in glass," referring historically to experimental conditions in a test tube or petri dish, invivo experiments occur within a living organism's natural environment. Conversely, ex vivo originates from living tissue outside its host environment while striving to maintain conditions as close to the host surroundings as possible. In the tissue engineering and regenerative medicine (TE&RM) community, there needs to be more clarity between in vitro and ex vivo terminology, with historical definitions sometimes disregarded and new terms often introduced without rigorous scientific justification. At this juncture, the question arises of when to refer to experiments as in vitro or ex vivo or whether the terms may be used synonymously in some instances. Moreover, what criteria must ex vivo experiments meet to be legitimately defined as such? This perspective is intended to address questions that would assist the TE&RM community in better understanding the differences between in vitro and ex vivo models. Impact Statement In the tissue engineering & regenerative medicine literature, the terms "in vitro" and "ex vivo" are often used interchangeably to describe experiments. This interchangeable usage can lead to a compromised interpretation of research results and, consequently, misleading scientific conclusions and teachings. This perspective aims to provide clarity on the various definitions of experimental designs. It also highlights the issue of using terms with inconsistent meanings that have origins dating back to the distant past. It's important to note that scientific definitions constantly evolve, and there is a scientifically rooted responsibility to evaluate and rethink the current state of affairs critically.

Near‐Infrared Spectroscopy‐Based Chilled Fresh Lamb Quality Detection Using Machine Learning Algorithms

ABSTRACTTraditionally methods for assessing mutton quality rely on physical and chemical examination analyses that necessitate precise experimental environment conditions and specialized knowledge, often resulting in the compromise of the sample's structural integrity. To address these challenges, this study explores the application of near‐infrared spectroscopy (NIR) as a non‐destructive alternative for mutton quality evaluation, leveraging its operational simplicity, rapid analysis capabilities, and minimal requirement for technical expertise. Among various spectral data preprocessing techniques evaluated, multiple scattering correction (MSC) was found to significantly enhance model detection performance. Furthermore, principal component analysis (PCA) combined with the Mahalanobis Distance method was utilized for outlier identification. Finally, a mutton freshness detection model is constructed based on stacking ensemble learning, yielding an impressive accuracy rate of 0.976, outperforming other advanced approaches. In conclusion, our findings establish a robust theoretical framework for the rapid and non‐destructive assessment of meat freshness, contributing to advancements in meat quality detection.

Experimental study on the plate-type fuel melting behavior based on alternative materials

In this paper, low-temperature experiments are carried out on the visualized experimental device to study the melting behavior of plate-type fuel in severe accidents of the reactor. In the experiments, the plate-type fuel with different sizes made of nickel–chromium alloy, zinc and aluminum was used to carry out the visualized experiments in air, argon, and vacuum environment. It was found that both the size of the plate and the experimental environment have a significant influence on the melting behavior in this study. And the temperature distribution, melting behavior characteristic, the key parameters such as blistering position, blistering size, breaking position and breaking size were also obtained. Based on the experimental data, the physical phenomena and processes related to the blistering and melting of the fuel plates are analyzed in this paper, which provides experimental data support for the development of analysis model and formulating perfect mitigation strategies for severe accidents.

An improved whale optimization algorithm for UAV swarm trajectory planning

For the problem of trajectory planning in the small-scale unmanned aerial vehicle (UAV) swarms, classical intelligent algorithms and newly emerged bio-inspired algorithms often suffer from being trapped in local optima, leading to suboptimal solutions. Moreover, these algorithms fail to ensure optimal solutions and convergence speed in high-speed dynamic UAV networking scenarios. In response to these challenges, this paper proposes an Improved Whale Optimization Algorithm (IWOA) that considers the global perspective. To address the issue of initial solution diversity, an opposition-based learning method is introduced by constructing a reverse population, enhancing the diversity of the initial population. To overcome the problem of the algorithm getting trapped in local optima, random convergence factors, and end-point random neighborhood perturbations are incorporated to accelerate the global convergence speed of the IWOA and prevent it from getting stuck in local optima. Additionally, a grid digital elevation model is employed when constructing the experimental environment model, considering various physical constraints of the UAV swarm. This ensures that the simulation validation of the IWOA algorithm is closer to real-world scenarios. Simulation results indicate that, compared to commonly used algorithms, the IWOA can generate more optimal trajectories for drone swarm planning under constraints resembling real-world scenarios. It exhibits better performance in trajectory evaluation, convergence speed, and stability, thereby meeting the requirements of trajectory planning for small-scale UAV swarms. The proposed IWOA enhances UAV swarm coordination and efficiency, significantly impacting real-world applications in various fields.

Analysis and Prospects of Lubrication and Friction Characteristics in Cold Strip Rolling: A Review

Lubrication and friction are the core processes in cold strip rolling, which are the key links to realizing the stable production of high‐quality strips, reflecting country advanced level in rolling technology. This review investigates the crucial properties of lubrication and friction in cold strip rolling by analyzing the mechanism, characterization methods, and influencing factors. The lubricant forms an oil film on the surface of rolls and strips based on the lubrication dynamics and tribology theory, changing the asperity contact state in the loaded roll gap, leading to changes in friction characteristics. Oil film thickness and coefficient of friction (COF) are crucial parameters for characterizing and judging the characteristics of lubrication and friction, theoretical calculations have become the core analytical method, and the study of the measurement method is still a great challenge. Parameters like rolling speed and oil viscosity affect the lubricant distribution and asperity contact in the loaded roll gap, changing the lubrication and friction state. The influence of parameters is strongly linked to the experimental or rolling environment. Prospective research trends on lubrication and friction in cold strip rolling are presented based on the status of research on lubrication and friction characterization.

Exploring the potential of pantomime games in medical education: Non-verbal exercise

The primary educational challenge in medical education is the lack of focus on non-verbal communication skills, such as body language, facial expressions, and gestures. This gap is crucial, particularly in multicultural and multilingual contexts where non-verbal cues can bridge language barriers and enhance understanding between healthcare professionals and patients. To address this, our wave explores the potential of integrating art, specifically pantomime games, into medical education as a non-verbal exercise. Inspired by ancient drama and role-play, pantomime games offer an innovative methodology for enhancing non-verbal communication skills. These games help students develop observation, empathy, and ambiguity tolerance—skills essential for effective healthcare delivery. Incorporating art and game-based learning into medical curricula has improved visual perception, personal reflection, and student engagement. The benefits of this integration extend globally, fostering emotional intelligence, creative thinking, and cross-cultural understanding. Educational games create a safe environment for students to experiment, learn from mistakes, and gain practical experience, ultimately reducing training time and instructor load. Moreover, non-verbal communication training can enhance professor-student interactions, improving feedback quality and learning outcomes. To implement these innovations, clear guidelines and effective facilitation are crucial. Educators should provide supportive environments for experimentation and learning, using minimal materials such as open spaces and simple props. High-resource settings can leverage AI technologies for feedback and create mobile applications to modernize the approach. This wave advocates for a student-centered, multimodal communication and learning environment, highlighting the transformative potential of integrating performing arts into medical education to prepare future healthcare providers better.

Experiments of Ultrasonic Positioning System with Symmetrical Array Used in Jiangmen Underground Neutrino Observatory

The experimental environment of the JUNO is a spherical container filled with a liquid scintillator (alkylbenzene) with a diameter of about 35 m. To observe neutrino interactions in alkylbenzene with photomultiplier tubes (PMTs) and to precisely measure the neutrino properties in this environment, it is necessary to design a high-precision localization system for the PMT device. In this paper, we report the design of an ultrasonic localization system with a symmetrical receiving array, based on the construction of an experimental setup that reproduces the configuration of JUNO’s environment. We show through positioning consistency and accuracy measurements that the ultrasonic localization system has a high localization accuracy and can perform effective localization in an alkylbenzene solution with 98% purity.

Studying the Effects of Inhaled Environmental Pollutants on Olfactory Function in Mice.

Olfactory impairment is a significant public health problem and independently predicts the risk of neurodegenerative diseases. Inhaled environmental pollutants exposure may impair olfaction; thereby, there is an urgent need for methods to evaluate the effects of inhaled environmental pollutants exposure on olfaction. Mice are ideal models for olfactory experiments because of their highly developed olfactory system and behavioral characteristics. To assess the effects of inhaled environmental pollutants exposure on olfactory function in mice, a detailed buried food test and social odor discrimination experiment is provided, including the experiment preparation, the selection and construction of experimental facilities, the testing process, and indexes of time. Meanwhile, timekeeping equipment, operational details, and the experimental environment are discussed to ensure the success of the assay. Zinc sulfate is used as the treatment to demonstrate the feasibility of the experimental approach. The protocol provides a simple and clear operational process for assessing the effects of inhaled environmental pollutants on olfactory function in mice.