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This study examined: (1) the value of using the statistical metric, kurtosis [β(t)], along with an energy metric to determine the hazard to hearing from high level industrial noise environments, and (2) the accuracy of the International Standard Organization (ISO-1999:1990) model for median noise-induced permanent threshold shift (NIPTS) estimates with actual recent epidemiological data obtained on 240 highly screened workers exposed to high-level industrial noise in China. A cross-sectional approach was used in this study. Shift-long temporal waveforms of the noise that workers were exposed to for evaluation of noise exposures and audiometric threshold measures were obtained on all selected subjects. The subjects were exposed to only one occupational noise exposure without the use of hearing protection devices. The results suggest that: (1) the kurtosis metric is an important variable in determining the hazards to hearing posed by a high-level industrial noise environment for hearing conservation purposes, i.e., the kurtosis differentiated between the hazardous effects produced by Gaussian and non-Gaussian noise environments, (2) the ISO-1999 predictive model does not accurately estimate the degree of median NIPTS incurred to high level kurtosis industrial noise, and (3) the inherent large variability in NIPTS among subjects emphasize the need to develop and analyze a larger database of workers with well-documented exposures to better understand the effect of kurtosis on NIPTS incurred from high level industrial noise exposures. A better understanding of the role of the kurtosis metric may lead to its incorporation into a new generation of more predictive hearing risk assessment for occupational noise exposure.

In recent years, researchers have taken the many-objective optimization algorithm, which can optimize 5, 8, 10, 15, 20 objective functions simultaneously, as a new research topic. However, the current research on many-objective optimization technology also encounters some challenges. For example: Pareto resistance phenomenon, difficult diversity maintenance. Based on the above problems, this paper proposes a many-objective evolutionary algorithm based on three states (MOEA/TS). Firstly, a feature extraction operator is proposed. It can extract the features of the high-quality solution set, and then assist the evolution of the current individual. Secondly, based on Pareto front layer, the concept of “individual importance degree” is proposed. The importance degree of an individual can reflect the importance of the individual in the same Pareto front layer, so as to further distinguish the advantages and disadvantages of different individuals in the same front layer. Then, a repulsion field method is proposed. The diversity of the population in the objective space is maintained by the repulsion field, so that the population can be evenly distributed on the real Pareto front. Finally, a new concurrent algorithm framework is designed. In the algorithm framework, the algorithm is divided into three states, and each state focuses on a specific task. The population can switch freely among these three states according to its own evolution. The MOEA/TS algorithm is compared with 7 advanced many-objective optimization algorithms. The experimental results show that the MOEA/TS algorithm is more competitive in many-objective optimization problems.

In the design of media access control (MAC) mechanism in underwater acoustic sensor networks (UASNs), due to the propagation characteristic of low-speed underwater acoustic signals, it is necessary to solve the spatial–temporal uncertainty problem. In order to avoid the multi-user access conflict in underwater networks, reduce the energy cost and improve the throughput and fairness across the network, a dynamic channel negotiation MAC mechanism based on spatial–temporal mapping of receiving queue (DCN-MAC) was proposed. DCN-MAC uses a duty cycle mechanism and implements a network management based on dynamic single node wake-up. The awakening node collects the request to send (RTS) and network status information in the network to solve the problem of space-temporal uncertainty and the highly dynamic needs of network access nodes and access services. The simulation results show that in different network scenarios, especially in those featuring high density and heavy network load, compared with the traditional underwater acoustic network MAC protocols, this protocol can effectively improve the network throughput and reduce the packet loss probability caused by multi-node conflict.

This study was conducted to compare the effects of Eucommia ulmoides leaves (EL) in different forms (EL extract, fermented EL, and EL powder) with antibiotics on growth performance, intestinal morphology, and the microbiota composition and diversity of weanling piglets. Compared to the control group, the antibiotics and EL extract significantly increased the average daily gain and decreased the feed: gain ratio as well as the diarrhea rate (P < 0.05). The EL extract significantly decreased the crypt depth and increased the ratio of villus height to crypt depth (P < 0.05), while the fermented EL group did the opposite (P < 0.05). The crypt depth in the antibiotics group was of similar value to the EL extract group, and was lower than the fermented EL and EL powder groups (P < 0.05). Compared to the control and antibiotics groups, the jejunul claudin-3 mRNA expression and the concentrations of total VFA, Chao 1, and ACE were significantly augmented in the EL extract group of piglets (P < 0.05). The EL extract groups also showed elevated Shannon (P < 0.05) and Simpson (P = 0.07) values relative to the control and antibiotics groups. At the phylum level, the EL extract group exhibited a reduced abundance of Bacteroidetes and an enhanced abundance of Firmicutes. At the genus level, the abundance of Prevotella was augmented in the EL extract group. Moreover, compared with the antibiotic group, the acetate concentration was enhanced in the EL extract and fermented EL groups. Overall, dietary supplementation with the EL extract, but not the fermented EL or EL powder, improved growth performance, jejunul morphology and function, as well as changed colonic microbial composition and diversity, which might be an alternative to confer protection against weanling stress in weanling piglets.

Graph-based learning and estimation are fundamental problems in various applications involving power, social, and brain networks, to name a few. While learning pair-wise interactions in network data is a well-studied problem, discovering higher-order interactions among subsets of nodes is still not yet fully explored. To this end, encompassing and leveraging (non)linear structural equation models as well as vector autoregressions, this paper proposes autoregressive graph Volterra models (AGVMs) that can capture not only the connectivity between nodes but also higher-order interactions presented in the networked data. The proposed overarching model inherits the identifiability and expressibility of the Volterra series. Furthermore, two tailored algorithms based on the proposed AGVM are put forth for topology identification and link prediction in distribution grids and social networks, respectively. Real-data experiments on different real-world collaboration networks highlight the impact of higher-order interactions in our approach, yielding discernible differences relative to existing methods.

J‐aggregation and H‐aggregation are identified as two classical models of functionally oriented non‐covalent interactions, and significant attention has been drawn by researchers. However, due to the scarcity of single‐crystal examples of H‐aggregation, a comprehensive understanding of the relationship between its stacking mode and optical behaviour has been hindered. In recent studies, two polyaromatic Schiff base compounds, Cl‐Salmphen and H‐Salmphen, were successfully synthesized, and both were found to exhibit H‐aggregation. In the findings, H‐Salmphen was shown to display typical C─H···π interactions, characteristic of Aggregation‐Induced Emission (AIE) active molecules, whereas its halogenated counterpart was identified as behaving similar to Aggregation‐Caused Quenching (ACQ) active molecules. These types of results suggest that identical intermolecular interactions can produce differing optical behaviours. Light was shed, at least in part, on the formation mechanisms of H‐type aggregates and their luminescence properties from these observations. Additionally, the high optical signal‐to‐noise ratio inherent to H‐aggregates was utilized for the exploration of water content detection. As an outcome, a high‐performance fluorescent filter paper was developed, enabling easy real‐time detection using a smartphone. The Cl‐Salmphen and H‐Salmphen are isomers with H‐aggregation characteristics, despite their primary structures comprising multiple aromatic rings, no conventional π–π interactions associated with H‐aggregation were identified. Instead, the interplay between adjacent molecules appears to be orchestrated by C─H···π bonds Moreover, the non‐halogenated H‐Salmphen displayed AIE behaviour dominated by C─H···π interactions, its halogenated counterpart, Cl‐Salmphen, exhibited C─H···π‐dominated ACQ activity.

With the advancements in wireless sensor networks and the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have attracted much attention, which has also been widely used in marine engineering exploration and disaster prevention. However, UASNs still face many challenges, including high propagation latency, limited bandwidth, high energy consumption, and unreliable transmission, influencing the good quality of service (QoS). In this paper, we propose a routing protocol based on the on-site architecture (SROA) for UASNs to improve network scalability and energy efficiency. The on-site architecture adopted by SROA is different from most architectures in that the data center is deployed underwater, which makes the sink nodes closer to the data source. A clustering method is introduced in SROA, which makes the network adapt to the changes in the network scale and avoid single-point failure. Moreover, the Q-learning algorithm is applied to seek optimal routing policies, in which the characteristics of underwater acoustic communication such as residual energy, end-to-end delay, and link quality are considered jointly when constructing the reward function. Furthermore, the reduction of packet retransmissions and collisions is advocated using a waiting mechanism developed from opportunistic routing (OR). The SROA realizes opportunistic routing to choose candidate nodes and coordinate packet forwarding among candidate nodes. The scalability of the proposed routing protocols is also analyzed by varying the network size and transmission range. According to the evaluation results, with the network scale ranging from 100 to 500, the SROA outperforms the existing routing protocols, extensively decreasing energy consumption and end-to-end delay.

Underwater acoustic sensor networks (UASNs) have become a hot research topic. In UASNs, nodes can be affected by ocean currents and external forces, which could result in sudden link disruption. Therefore, designing a flexible and efficient link disruption restoration mechanism to ensure the network connectivity is a challenge. In the paper, we propose a glider-assisted restoration mechanism which includes link disruption recognition and related link restoring mechanism. In the link disruption recognition mechanism, the cluster heads collect the link disruption information and then schedule gliders acting as relay nodes to restore the disrupted link. Considering the glider’s sawtooth motion, we design a relay location optimization algorithm with a consideration of both the glider’s trajectory and acoustic channel attenuation model. The utility function is established by minimizing the channel attenuation and the optimal location of glider is solved by a multiplier method. The glider-assisted restoration mechanism can greatly improve the packet delivery rate and reduce the communication energy consumption and it is more general for the restoration of different link disruption scenarios. The simulation results show that glider-assisted restoration mechanism can improve the delivery rate of data packets by 15–33% compared with cooperative opportunistic routing (OVAR), the hop-by-hop vector-based forwarding (HH-VBF) and the vector based forward (VBF) methods, and reduce communication energy consumption by 20–58% for a typical network’s setting.

The safety of workers in modular construction remains a concern due to the dynamic hazardous work environments and unawareness of the potential proximity of equipment. To avoid potential contact collisions and to provide a safe workplace, workers’ trajectory prediction is required. With recent technology advancements, the study in the area of trajectory prediction has benefited from various data-driven approaches. However, existing data-driven approaches are mostly limited to considering only the movement information of workers in the workplace, resulting in poor estimation accuracy. In this study, we propose an environment-aware worker trajectory prediction framework based on long short-term memory (LSTM) network to not only take the individual movement into account but also the surrounding information to fully exploit the context in the modular construction facilities. By incorporating worker-to-worker interactions as well as environment-to-worker interactions into our prediction model, a sequence of the worker’s future positions can be predicted. Extensive numerical tests on synthetic as well as modular construction datasets show the improved prediction performance of the proposed approach in comparison to several state-of-the-art alternatives. This study offers a systematic and flexible framework to incorporate rich contextual information into the prediction model in modular construction. The observation of how to integrate construction data analytics into a single framework could be inspiring for further future research to support robust construction safety practices.

In oceans, the limited acoustic spectrum resource is heavily shared by marine mammals and manmade systems including underwater sensor networks. In order to limit the negative impact of acoustic signal on marine mammals, we propose an environmentally friendly power control (EFPC) scheme for underwater sensor networks. EFPC allocates transmission power of sensor nodes with a consideration of the existence of marine mammals. By applying a Nash Equilibrium based utility function with a set of limitations to optimize transmission power, the proposed power control algorithm can conduct parallel transmissions to improve the network’s goodput, while avoiding interference with marine mammals. Additionally, to localize marine mammals, which is a prerequisite of EFPC, we propose a novel passive hyperboloid localization algorithm (PHLA). PHLA passively localize marine mammals with the help of the acoustic characteristic of these targets. Simulation results show that PHLA can localize most of the target with a relatively small localization error and EFPC can achieve a close goodput performance compared with an existing power control algorithm while avoiding interfering with marine mammals.