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Marine environment monitoring has attracted more and more attention due to the growing concern about climate change. During the past couple of decades, advanced information and communication technologies have been applied to the development of various marine environment monitoring systems. Among others, the Internet of Things (IoT) has been playing an important role in this area. This paper presents a review of the application of the Internet of Things in the field of marine environment monitoring. New technologies including advanced Big Data analytics and their applications in this area are briefly reviewed. It also discusses key research challenges and opportunities in this area, including the potential application of IoT and Big Data in marine environment protection.

This paper presents a multi-agent reinforcement learning (MARL) algorithm to address the scheduling and routing problems of multiple automated guided vehicles (AGVs), with the goal of minimizing overall energy consumption. The proposed algorithm is developed based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, with modifications made to the action and state space to fit the setting of AGV activities. While previous studies overlooked the energy efficiency of AGVs, this paper develops a well-designed reward function that helps to optimize the overall energy consumption required to fulfill all tasks. Moreover, we incorporate the e-greedy exploration strategy into the proposed algorithm to balance exploration and exploitation during training, which helps it converge faster and achieve better performance. The proposed MARL algorithm is equipped with carefully selected parameters that aid in avoiding obstacles, speeding up path planning, and achieving minimal energy consumption. To demonstrate the effectiveness of the proposed algorithm, three types of numerical experiments including the ϵ-greedy MADDPG, MADDPG, and Q-Learning methods were conducted. The results show that the proposed algorithm can effectively solve the multi-AGV task assignment and path planning problems, and the energy consumption results show that the planned routes can effectively improve energy efficiency.

To control autonomous vehicles (AVs) in urban unsignalized intersections is a challenging problem, especially in a hybrid traffic environment where self-driving vehicles coexist with human driving vehicles. In this study, a coordinated control method with proximal policy optimization (PPO) in Vehicle-Road-Cloud Integration System (VRCIS) is proposed, where this control problem is formulated as a reinforcement learning (RL) problem. In this system, vehicles and everything (V2X) was used to keep communication between vehicles, and vehicle wireless technology can detect vehicles that use vehicles and infrastructure (V2I) wireless communication, thereby achieving a cost-efficient method. Then, the connected and autonomous vehicle (CAV) defined in the VRCIS learned a policy to adapt to human driving vehicles (HDVs) across the intersection safely by reinforcement learning (RL). We have developed a valid, scalable RL framework, which can communicate topologies that may be dynamic traffic. Then, state, action and reward of RL are designed according to urban unsignalized intersection problem. Finally, how to deploy within the RL framework was described, and several experiments with this framework were undertaken to verify the effectiveness of the proposed method.

Self-assembly of monolayers of functional molecules on dielectric surfaces is a promising approach for the development of molecular devices proposed in the 1970s. Substrate chemically bonded self-assembled monolayers of semiconducting conjugated molecules exhibit low mobility. And self-assembled monolayer molecular crystals are difficult to scale up and limited to growth on substrates terminated by hydroxyl groups, which makes it difficult to realize sophisticated device functions, particularly for those relying on n-type electron transport, as electrons suffer severe charge trapping on hydroxyl terminated surfaces. Here we report a gravity-assisted, two-dimensional spatial confinement method for bottom-up growth of high-quality n-type single-crystalline monolayers over large, centimeter-sized areas. We demonstrate that by this method, n-type monolayer molecular crystals with high field-effect mobility of 1.24 cm 2  V −1  s −1 and band-like transport characteristics can be grown on hydroxyl-free polymer surface. Furthermore, we used these monolayer molecular crystals to realize high-performance crystalline, gate-/light-tunable lateral organic p–n diodes. New methods for obtaining large-area monolayer molecular crystals (MMCs) on hydrophobic surfaces are needed to realize the full potential of MMCs for organic electronics. Here, the authors demonstrate bottom-up growth of high-grade n-type MMCs, which show superior performance in device applications.

Turbomachinery from a life cycle perspective involves sustainability-oriented development activities such as design, production, and operation. Digital Twin is a technology with great potential for improving turbomachinery, which has a high volume of investment and a long lifespan. This study presents a general framework with different digital twin enabling technologies for the turbomachinery life cycle, including the design phase, experimental phase, manufacturing and assembly phase, operation and maintenance phase, and recycle phase. The existing digital twin and turbomachinery are briefly reviewed. New digital twin technologies are discussed, including modelling, simulation, sensors, Industrial Internet of Things, big data, and AI technologies. Finally, the major challenges and opportunities of DT for turbomachinery are discussed.

Background Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across China. Methods We obtained daily confirmed cases of COVID-19, air particulate matter (PM 2.5 , PM 10 ), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the Generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM 10 , PM 2.5 and MSI on daily confirmed COVID-19 cases. Results We found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 ~ 1.28) was observed at lag 014. In PM analysis, we found each 10 μg/m 3 increase in the concentration of PM 10 and PM 2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM 10 and PM 2.5 were at lag 014, and the RRs of each 10 μg/m 3 increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), respectively. Conclusions Population mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.

Long noncoding RNAs (lncRNAs) have recently emerged as important biological regulators, and the aberrant expression of lncRNAs has been reported in numerous diseases. However, the expression of lncRNAs in peripheral blood mononuclear cells (PBMCs) in rheumatoid arthritis (RA) has not been well documented. We applied a microarray analysis to profile the lncRNA and mRNA expression in 3 pairs of samples. Each sample was mixed with equivalent PBMCs from 9 female RA patients and 9 corresponding healthy controls, and the data were validated via qPCR using another cohort that comprised 36 RA patients and 24 healthy controls. A bioinformatic analysis was performed to investigate the potential functions of differentially expressed genes. Overall, 2,099 lncRNAs and 2,307 mRNAs were differentially expressed between the RA patients and healthy controls. The bioinformatic analysis indicated that the differentially expressed lncRNAs regulated the abnormally expressed mRNAs, which were involved in the pathogenesis of RA through several different pathways. The qPCR results showed that the expression levels of ENST00000456270 and NR_002838 were significantly increased in the RA patients, whereas the expression levels of NR_026812 and uc001zwf.1 were significantly decreased. Furthermore, the expression level of ENST00000456270 was strongly associated with the serum levels of IL-6 and TNF-a and the Simplified Disease Activity Index (SDAI) of the RA patients. Our data provided comprehensive evidence regarding the differential expression of lncRNAs in PBMCs of RA patients, which shed light on the understanding of the molecular mechanisms of lncRNAs in the pathogenesis of RA.

The cornerstones of emerging high-performance organic photovoltaic devices are bulk heterojunctions, which usually contain both structure disorders and bicontinuous interpenetrating grain boundaries with interfacial defects. This feature complicates fundamental understanding of their working mechanism. Highly-ordered crystalline organic p–n heterojunctions with well-defined interface and tailored layer thickness, are highly desirable to understand the nature of organic heterojunctions. However, direct growth of such a crystalline organic p–n heterojunction remains a huge challenge. In this work, we report a design rationale to fabricate monolayer molecular crystals based p–n heterojunctions. In an organic field-effect transistor configuration, we achieved a well-balanced ambipolar charge transport, comparable to single component monolayer molecular crystals devices, demonstrating the high-quality interface in the heterojunctions. In an organic solar cell device based on the p–n junction, we show the device exhibits gate-tunable open-circuit voltage up to 1.04 V, a record-high value in organic single crystalline photovoltaics. Realizing organic p–n junctions based on ordered crystalline materials with dimensions comparable to the exciton diffusion length of most organic semiconductors remains a challenge. Here, the authors report a strategy to form molecular monolayer crystal-based p–n junctions with thickness below 5 nm.

Background Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence. Methods Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted. Results A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC. Conclusion Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

The rapid changes in market demand are driving a transition from traditional mass production to high-mix, low-volume production, emphasizing the need for customization and rapid response. Reconfigurable Manufacturing Systems (RMS) are crucial in this shift, providing a flexible platform to meet diverse production requirements, and forming an essential component of next-generation manufacturing. Reconfigurable Machine Tools (RMTs), the core of RMS, enable dynamic configuration adjustments through auxiliary modules (AMs), enhancing both flexibility and efficiency. However, optimizing the allocation of limited AMs, considering non-negligible assembly and disassembly times, remains a significant challenge. This paper focuses on the flexible job shop scheduling problem with machine reconfigurations (FJSP-MR) and proposes an improved genetic algorithm with a two-stage neighborhood search (IGA-TNS) to minimize total weighted tardiness (TWT). Initially, a mixed-integer linear programming (MILP) model is formulated to comprehensively represent the problem. To enhance search efficiency, a two-stage neighborhood search strategy is developed: the first stage extends the k-insertion search to facilitate operation movement across different machine configurations, while the second stage refines operations within the same configuration. Furthermore, a trend-detection-based neighborhood search activation strategy is introduced to accelerate convergence and reduce computational costs. Experimental results on extended benchmark instances demonstrate that the proposed IGA-TNS effectively addresses the FJSP-MR, outperforming other algorithms in solution quality and computational efficiency. Finally, a industrial FJSP-MR case is studied, demonstrating that the proposed IGA-TNS is applicable to large-scale problems.