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Loop closure detection (LCD) plays an important role in visual simultaneous location and mapping (SLAM), as it can effectively reduce the cumulative errors of the SLAM system after a long period of movement. Convolutional neural networks (CNNs) have a significant advantage in image similarity comparison, and researchers have achieved good results by incorporating CNNs into LCD. The LCD based on CNN is more robust than traditional methods. As the deep neural network frameworks from AlexNet and VGG to ResNet have become smaller while maintaining good accuracy, indoor LCD does not need robots to finish a large number of complex processing operations. To reduce the complexity of deep neural networks, this paper presents a new lightweight neural network based on MobileNet V2. We propose a strategy to use Efficient Channel Attention (ECA) to insert into Compressed MobileNet V2 (ECMobileNet) for reducing operands while maintaining precision. A corresponding loop detection method is designed based on the average distribution of ECMobileNet feature vectors combined with Euclidean distance matching. We used TUM datasets to evaluate the results, and the experimental results show that this method outperforms the state-of-the-art methods. Although the model was trained only on the indoorCVPR dataset, it also demonstrated superior performance on the TUM datasets. In particular, the proposed approach is more lightweight and highly efficient than the current existing neural network approaches. Finally, we used TUM datasets to test LCD based on ECMobileNet in PTAM, and the experimental results show that this lightweight neural network is feasible.

We herein present a weakly-coupled double bow-tie multi-ring elliptical core multi-mode fiber (DBT-MREC-MMF) supporting 22 eigenmodes for mode division multiplexing across the C+L+U band. The proposed fiber introduces a multi-ring elliptical core, bow-tie air holes, and bow-tie stress-applying areas to effectively split adjacent eigenmodes. By utilizing the finite element method (FEM), we accordingly optimized the fiber to support the 22 modes under the weakly-coupled condition. We evaluated the impact of fiber parameters on the minimum effective refractive index difference (min Δneff) between adjacent eigenmodes, model birefringence (Bm), and bending loss at a wavelength of 1550 nm. Additionally, broadband performance metrics, such as effective modal index (neff), effective index difference (Δneff), effective mode area (Aeff), differential mode delay (DMD), and chromatic dispersion (D), were comprehensively studied over the entire C+L+U band, ranging from 1530 to 1675 nm. The proposed fiber is capable of supporting 22 completely separated eigenmodes with a min Δneff between adjacent eigenmodes larger than 3.089 × 10−4 over the entire C+L+U band. The proposed DBT-MREC-MMF holds great potential for use in short-haul communication systems that require MDM to improve transmission capacity and expand bandwidth.

Depression has become a global threat to human health. In order to solve it, researchers have conducted multi-faceted studies including diet. Many food-derived bioactive substances have shown antidepressant effects. However, there are few studies on the design of industrialized food with antidepressant effect. This study aims to evaluate the antidepressant effect of a functional beverage made from several ingredients with potential antidepressant function and investigate its antidepressant mechanisms. The beverage consists of peppermint oil, active peptides derived from bovine milk casein and extract (ASE) whose active ingredient is eleutheroside. Different amounts of ASE were evaluated to determine the optimal concentration of eleutheroside in this functional beverage to deliver the best antidepressant effect through extensive behavioral testing, including preliminary acute stress experiments and further chronic unpredictable mild stress test. The results demonstrated that the beverage with 15 mg/kg of eleutheroside could significantly reduce the mice's immobility time of tail suspension test and forced swimming test, recover mice's sucrose preference and behavior changes in the open field test, improve the contents of dopamine, norepinephrine, 5-hydroxytryptamine and the activity of superoxide dismutase and reduce the content of malondialdehyde in mice's brains, which indicated that the improvement of monoamine neurotransmitter systems and antioxidation was one potential mechanism of antidepressant action. This study provides a design of antidepressant functional beverage and an efficient way for the prevention and treatment of depression.

PurposeConsidering the substantial benefits derived from the use of Building Information Modeling (BIM) in construction projects, governments and its related sectors have introduced mandatory policies requiring the use of BIM. However, little is known about the impact of mandatory policies on BIM-based project performance. Therefore, the purpose of this paper is to provide a systematical understanding on the impact of policy interventions on the implementation practice of innovative technologies.Design/methodology/approachThis paper utilizes the propensity score matching and difference in differences (PSM-DID) method to investigate the impact of policy interventions on BIM-based project performance. Using the panel data collected from 2015 to 2021 in the Hong Kong construction industry, this paper explores the impact of the first mandatory BIM policy on the BIM-based project performance of three key stakeholders.FindingsThe subjective BIM performance and BIM return on investment (ROI) have significantly improved after implementing the mandatory BIM policy. The promotion effect of mandatory BIM policy on BIM-based project performance gradually increases over time. Moreover, the promotion effect of mandatory BIM policy on BIM performance shows significant heterogeneity for different stakeholders and organizations of different sizes.Originality/valueThis study examined the impact of policy interventions on BIM-based project performance. The research findings can provide a holistic understanding of the potential implications of innovative mandatory policy in performance improvement and offer some constructive suggestions to policymakers and industry practitioners to promote the penetration of BIM in the construction industry.

SUMMARY Research background. Depression has become a global threat to human health. In order to solve it, researchers have conducted multi-faceted studies including diet. Many food-derived bioactive substances have shown antidepressant effects. However, there are few studies on the design of industrialized food with antidepressant effect. This study aimed to evaluate the antidepressant effect of a functional beverage made from several ingredients with potential antidepressant function and investigate its antidepressant mechanisms. Experimental approach. The beverage consists of peppermint oil, active peptides derived from bovine milk casein and Acanthopanax senticosus extract (ASE) whose active ingredient is eleutheroside. Different amounts of ASE were evaluated to determine the optimal concentration of eleutheroside in this functional beverage to deliver best antidepressant effect through extensive behavioral testing including preliminary acute stress experiments and further chronic unpredictable mild stress test. Results and conclusions. The results demonstrated that the beverage with 15.00 mg/kg of eleutheroside could significantly reduce the mice’s immobility time of tail suspension test and forced swimming test, recover mice’s sucrose preference and behavior changes in the open-field test, improve the contents of dopamine, norepinephrine, 5-hydroxytryptamine and the activity of superoxide dismutase and reduce the content of malondialdehyde in mice’s brains, which indicated that the improvement of monoamine neurotransmitter systems and antioxidation was one potential mechanism of antidepressant action. Novelty and scientific contribution. This study provides a design of antidepressant functional beverage and an efficient way for the prevention and treatment of depression.

Graphene modified polyimide films are partial carbonized between 500°C and 900°C in nitrogen atmosphere. Meanwhile, the structure, mechanical, thermal and electrochemical behaviors were comparatively investigated. It was shown that there was stable lamellar graphite crystal structure in part resin matrix after carbonized at 900°C. Meanwhile, according to the TG and gas adsorption experiments, there was pore structure formed in the carbonization material during the pyrolysis process above 600°C. Furthermore, the membrane carbonized at 900°C is employed as the supercapacitor electrode in the 6 M KOH aqueous electrolyte solution, which exhibits a specific capacitance of 179.5 F g-1. It also exhibits excellent stability, and the energy density was stable with the increase in the power density, suggesting it an promising electrode material for supercapacitor.

Pozadina istraživanja. Depresija je postala globalna prijetnja ljudskom zdravlju. Znanstvenici provode različita ispitivanja uključujući i analizu prehrane kako bi riješili taj problem. Mnogi bioaktivni spojevi iz hrane imaju antidepresivni učinak. No, mali se broj istraživanja bavi razvojem industrijskih prehrambenih proizvoda s antidepresivnim učinkom. Svrha je ovoga rada bila ocijeniti antidepresivni učinak i mehanizme djelovanja funkcionalnog napitka pripremljenog od nekoliko sastojaka s mogućim antidepresivnim svojstvima. Eksperimentalni pristup. Napitak se sastoji od ulja paprene metvice, aktivnih peptida iz kazeina kravljeg mlijeka i ekstrakta sibirskog ginsenga (Acanthopanax senticosus), čiji je aktivni sastojak eleuterozid. Ispitan je dodatak različitih udjela ekstrakta sibirskog ginsenga da bi se utvrdila optimalna koncentracija eleuterozida u ovom funkcionalnom napitku, radi postizanja najboljeg antidepresivnog učinka. Provedena su opsežna ispitivanja ponašanja, uključujući preliminarne testove akutnog stresa te kroničnog nepredvidljivog blagog stresa. Rezultati i zaključci. Rezultati pokazuju da napitak s 15 mg/kg eleuterozida može bitno smanjiti vrijeme mirovanja miša u testovima vješanja za rep i prisilnog plivanja, pozitivno utjecati na preferenciju saharoze i promjene ponašanja u testu otvorenog polja, povećati udjel dopamina, norepinefrina, serotonina i aktivnost superoksid dismutaze, te smanjiti udjel malondialdehida u mozgu miševa. To potvrđuje da su poboljšana neurotransmisija monoamina i antioksidacijski status mogući mehanizmi antidepresivnog učinka. Novina i znanstveni doprinos. U ovom je radu predložen sastav funkcionalnog napitka s antidepresivnim svojstvima za učinkovitu prevenciju i liječenje depresije.

This work investigated the transformation of triclosan （TCS） by the laccase produced by a pathogen isolated from rotten tomato. The pathogen was characterized as Bot~tis sp. FQ, belonging to subphylum Deuteromw＇otina. The laccase exhibited cold-adaptation with relatively high activity at 20℃. The laccase could effectively transform TCS. Approximately 62% TCS could be removed at dose of 1.0 unit·mL^-1 in 120min. The reaction rate appeared to be pseudo-first-order to the concentration of the substrate, suggesting the laccase activity remained stable during the reaction. Transformation products of TCS were analyzed by mass spectrometry and it was revealed that TCS dimers were formed via radical coupling pathways. During this process, laccase catalyzed oxidation of TCS to form a radical intermediate is the rate limiting step. However, this step can be reversed by humic acid. Overall, the laccase showed great potential in the treatment of phenolic contaminants. Since laccase is widely presented in natural environment, this study also revealed an important pathway involved in the transformation of phenolic contaminants in the environment.

Transforming growth factor-β (TGF-β) signaling has a paradoxical role in cancer progression, and it acts as a tumor suppressor in the early stages but a tumor promoter in the late stages of cancer. Once cancer cells are generated, TGF-β signaling is responsible for the orchestration of the immunosuppressive tumor microenvironment (TME) and supports cancer growth, invasion, metastasis, recurrence, and therapy resistance. These progressive behaviors are driven by an “engine” of the metabolic reprogramming in cancer. Recent studies have revealed that TGF-β signaling regulates cancer metabolic reprogramming and is a metabolic driver in the tumor metabolic microenvironment (TMME). Intriguingly, TGF-β ligands act as an “endocrine” cytokine and influence host metabolism. Therefore, having insight into the role of TGF-β signaling in the TMME is instrumental for acknowledging its wide range of effects and designing new cancer treatment strategies. Herein, we try to illustrate the concise definition of TMME based on the published literature. Then, we review the metabolic reprogramming in the TMME and elaborate on the contribution of TGF-β to metabolic rewiring at the cellular (intracellular), tissular (intercellular), and organismal (cancer-host) levels. Furthermore, we propose three potential applications of targeting TGF-β-dependent mechanism reprogramming, paving the way for TGF-β-related antitumor therapy from the perspective of metabolism.

Most deep learning-based action recognition models focus only on short-term motions, so the model often causes misjudgments of actions that are combined by multiple processes, such as long jump, high jump, etc. The proposal of Temporal Segment Networks (TSN) enables the network to capture long-term information in the video, but ignores that some unrelated frames or areas in the video can also cause great interference to action recognition. To solve this problem, a soft attention mechanism is introduced in TSN and a Spatial-Temporal Attention Temporal Segment Networks (STA-TSN), which retains the ability to capture long-term information and enables the network to adaptively focus on key features in space and time, is proposed. First, a multi-scale spatial focus feature enhancement strategy is proposed to fuse original convolution features with multi-scale spatial focus features obtained through a soft attention mechanism with spatial pyramid pooling. Second, a deep learning-based key frames exploration module, which utilizes a soft attention mechanism based on Long-Short Term Memory (LSTM) to adaptively learn temporal attention weights, is designed. Third, a temporal-attention regularization is developed to guide our STA-TSN to better realize the exploration of key frames. Finally, the experimental results show that our proposed STA-TSN outperforms TSN in the four public datasets UCF101, HMDB51, JHMDB and THUMOS14, as well as achieves state-of-the-art results.