Dynamic Pricing Research for Container Terminal Handling Charges based on Demand Forecast

A dynamic pricing model was established based on forecasting the demand for container handling of a specific shipping company to maximize terminal profits to solve terminal handling charges under the changing market environment. It assumes that container handling demand depends on the price and the unknown parameters in the demand model. The maximum quasi-likelihood estimation(MQLE) method is used to estimate the unknown parameters. Then an adaptive dynamic pricing policy algorithm is proposed. At the beginning of each period, through dynamic pricing, determining the optimal price relative to the estimation value of the current parameter and attach a constraint of differential price decision. Meanwhile, the accuracy of demand estimation and the optimality of price decisions are balanced. Finally, a case study is given based on the real data of Shanghai port. The results show that this pricing policy can make the handling price converge to the stable price and significantly increase this shipping company’s handling profit compared with the original “contractual pricing” mechanism

Behavioral mechanism of transfer and dispersal of Propylaea japonica in cotton adjacent to sorghum fields

Increasing crop biodiversity, such as by adjacent managed crops, is recognized as an effective biological control measure. However, few studies have focused on the mechanisms involved in how adjacent managed crops increase natural enemy populations, leading to reduced pest numbers. This study investigated the hypothesis that cotton grown adjacent to sorghum would positively influence the feeding and oviposition preferences of the ladybug Propylaea japonica, which predates cotton aphids, leading to enhanced pest control. The populations of Aphis gossypii were significantly lower and those of P. japonica were significantly higher in cotton grown adjacent to sorghum compared with monoculture cotton fields. Regardless of diet on which the larva of P. japonica were reared (Melanaphis sacchari, A. gossypii, and 50% M. sacchari + 50% A. gossypii), the adults always consumed significantly more M. sacchari compared with A. gossypii. P. japonica also showed significantly higher feeding and oviposition preferences for host plants bearing aphids to only host plants. P. japonica fed M. sacchari preferred to lay eggs on cotton, whereas those fed A. gossypii preferred to lay eggs on sorghum. These results suggest that the habitat of natural enemies can be expanded by influencing their feeding and oviposition preferences to achieve pest control in adjacent cropping systems. This research, which incorporates field and laboratory studies, suggests an approach for the successful conservation and biological control of cotton aphids using adjacent managed cotton and sorghum crops

The Involvement of Renin-Angiotensin System in Lipopolysaccharide-Induced Behavioral Changes, Neuroinflammation, and Disturbed Insulin Signaling

Brain insulin signaling is accounted for the development of a variety of neuropsychiatric disorders, such as anxiety and depression, whereas both inflammation and the activated renin-angiotensin system (RAS) are two major contributors to insulin resistance. Intriguingly, inflammation and RAS can activate each other, forming a positive feedback loop that would result in exacerbated unwanted tissue damage. To further examine the interrelationship among insulin signaling, neuroinflammation and RAS in the brain, the effect of repeated lipopolysaccharide (LPS) exposure and co-treatment with the angiotensin II (Ang II) receptor type 1 (AT1) blocker, candesartan (Cand), on anxiety and depression-like behaviors, RAS, neuroinflammation and insulin signaling was explored. Our results demonstrated that prolonged LPS challenge successfully induced the rats into anxiety and depression-like state, accompanied with significant neural apoptosis and neuroinflammation. LPS also activated RAS as evidenced by the enhanced angiotensin converting enzyme (ACE) expression, Ang II generation and AT1 expression. However, blocking the activated RAS with Cand co-treatment conferred neurobehavioral protective properties. The AT1 blocker markedly ameliorated the microglial activation, the enhanced gene expression of the proinflammatory cytokines and the overactivated NF-κB signaling. In addition, Cand also mitigated the LPS-induced disturbance of insulin signaling with the normalized phosphorylation of serine 307 and tyrosine 896 of insulin receptor substrate-1 (IRS-1). Collectively, the present study, for the first time, provided the direct evidence indicating that the inflammatory condition may interact with RAS to impede brain insulin pathway, resulting in neurobehavioral damage, and inhibiting RAS seems to be a promising strategy to block the cross-talk and cut off the vicious cycle between RAS and immune system

Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome

Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder characterized by abdominal pain or discomfort. Previous studies have illustrated that the gut microbiota might play a critical role in IBS, but the conclusions of these studies, based on various methods, were almost impossible to compare, and reproducible microorganism signatures were still in question. To cope with this problem, previously published 16S rRNA gene sequencing data from 439 fecal samples, including 253 IBS samples and 186 control samples, were collected and processed with a uniform bioinformatic pipeline. Although we found no significant differences in community structures between IBS and healthy controls at the amplicon sequence variants (ASV) level, machine learning (ML) approaches enabled us to discriminate IBS from healthy controls at genus level. Linear discriminant analysis effect size (LEfSe) analysis was subsequently used to seek out 97 biomarkers across all studies. Then, we quantified the standardized mean difference (SMDs) for all significant genera identified by LEfSe and ML approaches. Pooled results showed that the SMDs of nine genera had statistical significance, in which the abundance of Lachnoclostridium, Dorea, Erysipelatoclostridium, Prevotella 9, and Clostridium sensu stricto 1 in IBS were higher, while the dominant abundance genera of healthy controls were Ruminococcaceae UCG-005, Holdemanella, Coprococcus 2, and Eubacterium coprostanoligenes group. In summary, based on six published studies, this study identified nine new microbiome biomarkers of IBS, which might be a basis for understanding the key gut microbes associated with IBS, and could be used as potential targets for microbiome-based diagnostics and therapeutics

Curative efficacy of entomopathogenic nematodes against white grubs in honeysuckle fields

Root-feeding white grubs are one of the most serious pests of honeysuckle trees (Lonicera japonica) in China, directly damaging their roots and facilitating infection by soil pathogens. Entomopathogenic nematodes (EPNs) are considered as potential control agents against soil-dwelling insect pests. This study aimed to identify effective EPN species against white grubs through bioassay and field experiments. Among the EPN species screened against Holotrichia oblita under laboratory conditions, Steinernema feltiae and Heterorhabditis indica had low virulence, while S. longicaudum, S. glaseri, and H. bacteriophora applied at a rate of 400 IJs/larva caused a higher corrected mortality (80.00 ± 5.77%), which screened them as good candidates for future applications. The field experiments showed that both S. longicaudum and H. bacteriophora were approximately as effective in reducing white grubs as the insecticide phoxim, whereas S. glaseri caused a significantly lower reduction compared with these two EPNs and phoxim. Plant mortalities obtained from S. longicaudum, H. bacteriophora and the insecticide treatment plots were significantly lower than those observed in the water-treated control plots. All EPNs examined could establish well in the treated honeysuckle fields for 42 d, confirmed by Tenebrio molitar larvae baiting technique. Our findings suggest that EPNs could provide curative efficacy against white grubs and significantly reduce plant death in honeysuckle fields

A novel two-dimensional accordion-like titanium carbide (MXene) for adsorption of Cr(VI) from aqueous solution

Herein, we report on a novel two-dimensional (2D) material application, which shows that an accordion-like layered Ti3C2 nanomaterial (MXene) with an excellent adsorption capacity of Cr(VI) from aqueous solution was prepared by etching Al layer from Ti3AlC2 phase in hydrofluoric acid (HF) solution. Ti3C2 nanopowders were well characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and the physical property of as-obtained samples was studied by UV–Vis diffuse reflectance spectra (DRS). After HF treatment, Ti3AlC2 not only has a phase transition from one crystal to another, but surprisingly, its microstructure is also undergoing an obvious change. Ti3C2 with product of change possesses an accordion-like multilayer structure, and a relatively higher specific surface area (SSA) than untreated Ti3AlC2. Then, accordion-like Ti3C2 with a high SSA provides abundant active sites for pollutant removal and functionalization. Accordion-like Ti3C2 nanomaterial exhibits a stable adsorption capacity, and 1g as-prepared accordion-like Ti3C2 powders can remove about 80mg potassium dichromate. Therefore, the results suggest that 2D MXenes are promising as an effective nanoadsorbent in heavy metal removal from the wastewater

Spectrum-Effect Relationship-Based Strategy Combined with Molecular Docking to Explore Bioactive Flavonoids from Sceptridium ternatum

Sceptridium ternatum is a herbaceous plant with significant potential for pharmaceutical and cosmetic applications. In this study, we established a spectrum-effect relationship-based strategy to investigate the bioactive basis and tissue distribution in S. ternatum. First, a phytochemical analysis on the ethanol extracts from roots, stems, and leaves of S. ternatum was performed using the colorimetric method, high-performance liquid chromatography–ultraviolet (HPLC–UV), and high-performance liquid chromatography–electrospray ionization quadrupole time-of-flight mass spectrometry (HPLC–ESI-Q-TOF-MS/MS). Then, radical scavenging assays and the lipopolysaccharide-stimulated RAW 264.7 cell model were used to estimate the antioxidant and anti-inflammatory activities, respectively. Spectrum-effect relationship analysis and molecular docking were further employed to evaluate the correlation between the phytochemical profile and anti-inflammatory activity. Our results demonstrate that S. ternatum leaves contained the most abundant flavonoids and exerted the best biological activities. Their IC50 values for scavenging 2,2ʹ-azino-bis (3-ethylbenzthiazoline-6-sulfonic acid) and 1,1-diphenyl-2-picrylhydrazyl radicals were 2.43 ± 0.13 and 5.36 ± 0.54 mg/mL, respectively. In lipopolysaccharide-stimulated RAW 264.7 cells, the leaf extract caused the greatest reduction in nitric oxide production (38.15%) and interleukin-6 release (110.86%). Spectrum-effect relationship analysis and molecular docking indicated that quercetin 3-O-rhamnoside-7-O-glucoside possessed high anti-inflammatory activity by binding with interleukin-6. In conclusion, S. ternatum is a rich source of bioactive flavonoids with potential for exploitation in the prevention and treatment of oxidative stress and inflammation-related pathologies

Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis

BackgroundMental disorders (MDs) impose heavy burdens on health care (HC) systems and affect a growing number of people worldwide. The use of mobile health (mHealth) apps empowered by artificial intelligence (AI) is increasingly being resorted to as a possible solution. ObjectiveThis study adopted a topic modeling (TM) approach to investigate the public trust in AI apps in mental health care (MHC) by identifying the dominant topics and themes in user reviews of the 8 most relevant mental health (MH) apps with the largest numbers of reviewers. MethodsWe searched Google Play for the top MH apps with the largest numbers of reviewers, from which we selected the most relevant apps. Subsequently, we extracted data from user reviews posted from January 1, 2020, to April 2, 2022. After cleaning the extracted data using the Python text processing tool spaCy, we ascertained the optimal number of topics, drawing on the coherence scores and used latent Dirichlet allocation (LDA) TM to generate the most salient topics and related terms. We then classified the ascertained topics into different theme categories by plotting them onto a 2D plane via multidimensional scaling using the pyLDAvis visualization tool. Finally, we analyzed these topics and themes qualitatively to better understand the status of public trust in AI apps in MHC. ResultsFrom the top 20 MH apps with the largest numbers of reviewers retrieved, we chose the 8 (40%) most relevant apps: (1) Wysa: Anxiety Therapy Chatbot; (2) Youper Therapy; (3) MindDoc: Your Companion; (4) TalkLife for Anxiety, Depression & Stress; (5) 7 Cups: Online Therapy for Mental Health & Anxiety; (6) BetterHelp-Therapy; (7) Sanvello; and (8) InnerHour. These apps provided 14.2% (n=559), 11.0% (n=431), 13.7% (n=538), 8.8% (n=356), 14.1% (n=554), 11.9% (n=468), 9.2% (n=362), and 16.9% (n=663) of the collected 3931 reviews, respectively. The 4 dominant topics were topic 4 (cheering people up; n=1069, 27%), topic 3 (calming people down; n=1029, 26%), topic 2 (helping figure out the inner world; n=963, 25%), and topic 1 (being an alternative or complement to a therapist; n=870, 22%). Based on topic coherence and intertopic distance, topics 3 and 4 were combined into theme 3 (dispelling negative emotions), while topics 2 and 1 remained 2 separate themes: theme 2 (helping figure out the inner world) and theme 1 (being an alternative or complement to a therapist), respectively. These themes and topics, though involving some dissenting voices, reflected an overall high status of trust in AI apps. ConclusionsThis is the first study to investigate the public trust in AI apps in MHC from the perspective of user reviews using the TM technique. The automatic text analysis and complementary manual interpretation of the collected data allowed us to discover the dominant topics hidden in a data set and categorize these topics into different themes to reveal an overall high degree of public trust. The dissenting voices from users, though only a few, can serve as indicators for health providers and app developers to jointly improve these apps, which will ultimately facilitate the treatment of prevalent MDs and alleviate the overburdened HC systems worldwide