Value of investors’ escalation of commitment in PPP projects using real option thinking

Escalation of commitment (EOC) is a common behavior among investors who receive negative feedback (NF) in public-private partnership (PPP) projects, and this behavior typically leads to sizable losses. Recognizing this, investors set a mental threshold and track investments for escalation. Once losses reach the threshold, investors will terminate the escalation behavior, namely, they will transfer projects to governments to obtain compensation or residual asset value. This paper analyzes the maximum amount of NF that investors can sustain based on a belief-adjustment model, followed by the analysis of the greatest loss degree. Then, a threshold model for EOC is constructed using real option thinking. Different from the usual judgment criteria of the traditional option method, the threshold is less than zero in the EOC scenario. The results show that the threshold correlates with the initial generative cognition, the sunk cost level, the degree of the government guarantee and investors’ behavioral preferences as well as with total investment and return on investment. These findings serve as a reference for governments to de-escalate investors’ commitment in PPP projects

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

TANet: Robust 3D Object Detection from Point Clouds with Triple Attention

In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional noise points, the performance of existing approaches decreases rapidly. To alleviate these problems, a novel TANet is introduced in this paper, which mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression (CFR) module. By considering the channel-wise, point-wise and voxel-wise attention jointly, the TA module enhances the crucial information of the target while suppresses the unstable cloud points. Besides, the novel stacked TA further exploits the multi-level feature attention. In addition, the CFR module boosts the accuracy of localization without excessive computation cost. Experimental results on the validation set of KITTI dataset demonstrate that, in the challenging noisy cases, i.e., adding additional random noisy points around each object,the presented approach goes far beyond state-of-the-art approaches. Furthermore, for the 3D object detection task of the KITTI benchmark, our approach ranks the first place on Pedestrian class, by using the point clouds as the only input. The running speed is around 29 frames per second.Comment: AAAI 2020(Oral

In silico screening of anti-inflammatory constituents with good drug-like properties from twigs of Cinnamomum cassia based on molecular docking and network pharmacology

Purpose: To investigate by in silico screening the anti-inflammatory constituents of Cinnamomum cassia twigs. Methods: Information on the constituents of C. cassia twigs was retrieved from the online Traditional Chinese Medicines (TCM) database and literature. Inflammation-related target proteins were identified from DrugBank, Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), Genetic Association Database (GAD), and PharmGKB. The identified compounds were filtered by Lipinski’s rules with Discovery Studio software. The “Libdock” module was used to perform molecular docking; LibdockScores and default cutoff values for hydrogen bonds and van der Waals interactions were recorded. LibdockScores between the prototype ligand and target protein were set as the threshold; compounds with higher LibdockScores than threshold were regarded as active compounds. Cytoscape software was used to construct active constituent-target protein interaction networks. Results: Sixty-nine potential inflammatory constituents with good drug-like properties in C. cassia twigs were screened in silico based on molecular docking and network pharmacology analysis. JAK2, mPEGS-1, COX-2, IL-1β, and PPARγ were considered the five most important target proteins. Compounds such as methyl dihydromelilotoside, hierochin B, dihydromelilotoside, dehydrodiconiferyl alcohol, balanophonin, phenethyl (E)-3-[4-methoxyphenyl]-2-propenoate, quercetin, and luteolin each interacted with more than six of the selected target proteins. Conclusion: C. cassia twigs possess active compounds with good drug-like properties that can potentially be developed to treat inflammation with multi-components on multi-targets

Inflammation-Mediated Memory Dysfunction and Effects of a Ketogenic Diet in a Murine Model of Multiple Sclerosis

A prominent clinical symptom in multiple sclerosis (MS), a progressive disorder of the central nervous system (CNS) due to heightened neuro-inflammation, is learning and memory dysfunction. Here, we investigated the effects of a ketogenic diet (KD) on memory impairment and CNS-inflammation in a murine model of experimental autoimmune encephalomyelitis (EAE), using electrophysiological, behavioral, biochemical and in vivo imaging approaches. Behavioral spatial learning deficits were associated with motor disability in EAE mice, and were observed concurrently with brain inflammation. The KD improved motor disability in the EAE model, as well as CA1 hippocampal synaptic plasticity (long-term potentiation) and spatial learning and memory (assessed with the Morris Water Maze). Moreover, hippocampal atrophy and periventricular lesions in EAE mice were reversed in KD-treated EAE mice. Finally, we found that the increased expression of inflammatory cytokines and chemokines, as well as the production of reactive oxygen species (ROS), in our EAE model were both suppressed by the KD. Collectively, our findings indicate that brain inflammation in EAE mice is associated with impaired spatial learning and memory function, and that KD treatment can exert protective effects, likely via attenuation of the robust immune response and increased oxidative stress seen in these animals

Effect of Glow Discharge Cold Plasma Treatment on Improvement of Wheat Processing Quality

In order to improve the processing quality of wheat, newly harvested wheat was treated with glow discharge cold plasma. The changes in the physicochemical properties of wheat flour and the rheological properties of wheat flour dough after the treatment were studied, and the molecular mass distribution and secondary structure of wheat flour proteins were furthermore analyzed. The results showed that the gluten index of wheat was significantly increased after cold plasma treatment with oxygen or argon as the gas source. Dough development time and stability time were improved, and the mixographic parameters midline integral at 8 min (MTxI) and midline width at 8 min (MTxW) were significantly increased (P < 0.05), while weakening slope (WS) was significantly decreased (P < 0.05). The content of macromolecular polymeric storage protein fraction F1 was increased, and the ratio between macromolecular polymeric storage protein fraction F1 and small-molecule polymeric storage protein fraction F2 was significantly increased (P < 0.05). The protein secondary structure was transformed from β-sheet and β-turn to more ordered intermolecular β-sheet. In conclusion, glow discharge cold plasma treatment changed the molecular mass distribution and secondary structure of wheat storage proteins, significantly enhanced the elasticity and mixing tolerance of dough, and improved the tensile resistance of dough, thereby enhancing the processing quality of wheat to some extent

Government subsidies in public-private partnership projects based on altruistic theory

Nowadays, the public-private partnership (PPP) scheme has been widely adopted in infrastructure projects around the world. In PPP projects, the governments participate as a principal and the investors play the role of an agent, and therefore their behaviours and incentive strategies can be explained and designed by the principal-agent theory. As “economic men” with limited rationality, both the governments and the investors have altruistic preferences during cooperation. This paper studies how project participants’ altruistic preferences affect government subsidies based on the principal-agent theory. To this end, a principal-agent model in the presence of altruism is developed. The results show that the amount of government compensation is related to the altruistic preferences, the expected revenue, costs and investors’ efforts. Contrary to intuition, the governments’ altruism actually undermines the investors’ enthusiasm in cooperation and the risk-sharing propensity, although it increases the utilities of both parties. Moreover, when selecting the investors, governments should examine their operating capacity carefully, which has a significant impact on the sustainable development of the projects and even PPP arrangements. The findings contribute new insights into the development of incentive mechanisms between governments and private investors from the perspective of the behavioural preferences. First published online 27 January 202

A prostacyclin analogue, iloprost, protects from bleomycin-induced pulmonary fibrosis in mice

<p>Abstract</p> <p>Background</p> <p>Metabolites of arachidonic acid such as prostacyclin (PGI₂) have been shown to participate in the pathogenesis of pulmonary fibrosis by inhibiting the expression of pro-inflammatory and pro-fibrotic mediators. In this investigation, we examined whether iloprost, a stable PGI₂analogue, could prevent bleomycin-induced pulmonary inflammation and fibrosis in a mouse model.</p> <p>Methods</p> <p>Mice received a single intratracheal injection of bleomycin with or without intraperitoneal iloprost. Pulmonary inflammation and fibrosis were analysed by histological evaluation, cellular composition of bronchoalveolar lavage (BAL) fluid, and hydroxyproline content. Lung mechanics were measured. We also analysed the expression of inflammatory mediators in BAL fluid and lung tissue.</p> <p>Results</p> <p>Administration of iloprost significantly improved survival rate and reduced weight loss in the mice induced by bleomycin. The severe inflammatory response and fibrotic changes were significantly attenuated in the mice treated with iloprost as shown by reduction in infiltration of inflammatory cells into the airways and pulmonary parenchyma, diminution in interstitial collagen deposition, and lung hydroxyproline content. Iloprost significantly improved lung static compliance and tissue elastance. It increased the expression of IFNγ and CXCL10 in lung tissue measured by RT-PCR and their levels in BAL fluid as measured by ELISA. Levels of TNFα, IL-6 and TGFβ1 were lowered by iloprost.</p> <p>Conclusions</p> <p>Iloprost prevents bleomycin-induced pulmonary fibrosis, possibly by upregulating antifibrotic mediators (IFNγ and CXCL10) and downregulating pro-inflammatory and pro-fibrotic cytokines (TNFα, IL-6, and TGFβ1). Prostacyclin may represent a novel pharmacological agent for treating pulmonary fibrotic diseases.</p

MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention

Problematic smartphone use negatively affects physical and mental health. Despite the wide range of prior research, existing persuasive techniques are not flexible enough to provide dynamic persuasion content based on users' physical contexts and mental states. We first conduct a Wizard-of-Oz study (N=12) and an interview study (N=10) to summarize the mental states behind problematic smartphone use: boredom, stress, and inertia. This informs our design of four persuasion strategies: understanding, comforting, evoking, and scaffolding habits. We leverage large language models (LLMs) to enable the automatic and dynamic generation of effective persuasion content. We develop MindShift, a novel LLM-powered problematic smartphone use intervention technique. MindShift takes users' in-the-moment physical contexts, mental states, app usage behaviors, users' goals & habits as input, and generates high-quality and flexible persuasive content with appropriate persuasion strategies. We conduct a 5-week field experiment (N=25) to compare MindShift with baseline techniques. The results show that MindShift significantly improves intervention acceptance rates by 17.8-22.5% and reduces smartphone use frequency by 12.1-14.4%. Moreover, users have a significant drop in smartphone addiction scale scores and a rise in self-efficacy. Our study sheds light on the potential of leveraging LLMs for context-aware persuasion in other behavior change domains