Electronic Modulation of Nickel Disulfide toward Efficient Water Electrolysis

Developing highly efficient earth‐abundant nickel‐based compounds is an important step to realize hydrogen generation from water. Herein, the electronic modulation of the semiconducting NiS₂ by cation doping for advanced water electrolysis is reported. Both theoretical calculations and temperature‐dependent resistivity measurements indicate the semiconductor‐to‐conductor transition of NiS₂ after Cu incorporation. Further calculations also suggest the advantages of Cu dopant to cathodic water electrolysis by bringing Gibbs free energy of H adsorption at both Ni sites and S sites much closer to zero. It is noteworthy that water dissociation on Cu‐doped NiS₂ (Cu‐NiS₂) surface is even more favorable than those on NiS₂ and Pt(111). Thus, the prepared Cu‐NiS₂ shows noticeably improved performance toward alkaline hydrogen and oxygen evolution reactions (HER and OER). Specifically, it requires merely 232 mV OER overpotential to drive 10 mA cm^?2; in parallel with Tafel slopes of 46 mV dec^?1. Regarding HER, an onset overpotential of only 68 mV is achieved. When integrated as both electrodes for water electrolysis, Cu‐NiS₂ needs only 1.64 V to drive 10 mA cm^?2, surpassing the state‐of‐the‐art Ir/C–Pt/C couple (1.71 V). This work opens up an avenue to engineer low‐cost and earth‐abundant catalysts performing on par with the noble‐metal‐based one for water splitting.     

Supply chain coordination under trade credit and retailer effort

In this paper, we study the role of trade credit in coordinating a Capital Constrained Supply Chain in the presence of retailer Effort (CCSCE), essentially because of the impact of its related default risks on the relationship between the chain’s members. We consider a CCSCE consisting of a supplier and a retailer where the retailer may exert costly promotional efforts to increase the market demand but has limited capital and no access to bank financing due to low credit rating. Conversely, the supplier has adequate funds to offer trade credit to the retailer without borrowing from external channels. We then examine whether the existing coordination contracts can still coordinate the CCSCE under trade credit. Our result shows that these contracts can achieve coordination of the supply chain when the interest rate of trade credit is competitively priced. Nevertheless, this position cannot always be reached. That’s why we propose a generalised contract based on risk compensation to coordinate the CCSCE. Using our proposed coordinating contract, the supplier perfectly coordinates the retailer’s decisions for the largest joint profit, and arbitrarily allocates the maximised joint profit among supply chain members. Finally, the numerical study allows to verify this finding. From managerial insights, our results provide the supply chain managers with novel insights on how to combine trade credit with the existing coordination contracts in order to improve the profitability of the entire supply chain as well as the individual member.     

Heuristic regularization methods for numerical differentiation

In this paper, we use smoothing splines to deal with numerical differentiation. Some heuristic methods for choosing regularization parameters are proposed, including the $L$-curve method and the de Boor method. Numerical experiments are performed to illustrate the efficiency of these methods in comparison with other procedures.     

Efficient DC programming approaches for the asymmetric eigenvalue complementarity problem

In this paper, we propose nonlinear programming (NLP) formulations and difference of convex functions (DC) programming approaches for the asymmetric eigenvalue complementarity problem (EiCP). The EiCP has a solution if and only if these NLP formulations have zero global optimal value. We reformulate the NLP formulations as DC programs which can be efficiently solved by a DC algorithm. Some preliminary numerical results illustrate the good performance of the proposed methods.     

Small bowel image classification based on Fourier-Zernike moment features and canonical discriminant analysis

In this paper, we propose a novel method for the classification of small bowel images into normal or abnormal class for automatic detection of cancers. We extract the Fourier features from the input small bowel image, and then the Zernike moment features are computed from the Fourier features. We then use the canonical discriminant analysis (CDA) to classify the small bowel images to normal or abnormal class. Experimental results show that the proposed method achieves the highest correct classification rate 100% for this problem. Our method is computationally very efficient. It can be used to automate the classification of capsule endoscopic images and to reduce the cost of interpreting those images that are acquired in clinical setting.     

Visual Analytics for the Exploration of Tumor Tissue Characterization

Tumors are heterogeneous tissues consisting of multiple regions with distinct characteristics. Characterization of these intra‐tumor regions can improve patient diagnosis and enable a better targeted treatment. Ideally, tissue characterization could be performed non‐invasively, using medical imaging data, to derive per voxel a number of features, indicative of tissue properties. However, the high dimensionality and complexity of this imaging‐derived feature space is prohibiting for easy exploration and analysis ‐ especially when clinical researchers require to associate observations from the feature space to other reference data, e.g., features derived from histopathological data. Currently, the exploratory approach used in clinical research consists of juxtaposing these data, visually comparing them and mentally reconstructing their relationships. This is a time consuming and tedious process, from which it is difficult to obtain the required insight. We propose a visual tool for: (1) easy exploration and visual analysis of the feature space of imaging‐derived tissue characteristics and (2) knowledge discovery and hypothesis generation and confirmation, with respect to reference data used in clinical research. We employ, as central view, a 2D embedding of the imaging‐derived features. Multiple linked interactive views provide functionality for the exploration and analysis of the local structure of the feature space, enabling linking to patient anatomy and clinical reference data. We performed an initial evaluation with ten clinical researchers. All participants agreed that, unlike current practice, the proposed visual tool enables them to identify, explore and analyze heterogeneous intra‐tumor regions and particularly, to generate and confirm hypotheses, with respect to clinical reference data.     

Automatically improving image quality using tensor voting

A novel corrupted region detection technique based on tensor voting is proposed to automatically improve the image quality. This method is suitable for restoring degraded images and enhancing binary images. First, the input images are converted into layered images in which each layer contains objects having similar characteristics. By encoding the pixels in the layered images with second-order tensors and performing voting among them, the corrupted regions are automatically detected using the resulting tensors. These corrupted regions are then restored to improve the image quality. The experimental results obtained from automatic image restoration and binary image enhancement applications show that our method can successfully detect and correct the corrupted regions.     

On ruin probability minimization under excess reinsurance

The problem of ruin probability minimization in the Cramer-Lundberg risk model under excess reinsurance is studied. Together with traditional maximization of the Lundberg characteristic coefficient R is considered the problem of direct calculation of insurer’s ruin probability ? _r (x) as an initial-capital function x under the prescribed level of net-retention r. To solve this problem, we propose the excess variant of the Cramer integral equation which is an equivalent to the Hamilton-Jacobi-Bellman equation. The continuation method is used for solving this equation; by means of it is found the analytical solution to the Markov risk model. We demonstrated on a series of standard examples that with any admissible value of x the ruin probability ? _x (r): = ? _r (x) is usually a unimodal function r. A comparison of the analytic representation of ruin probability ? _r(x) with its asymptotic approximation with x → ∞ was conducted.     

Proposing the Affect-Trust Infusion Model (ATIM) to explain and predict the influence of high and low affect infusion on Web vendor trust

We adapt the Affect Infusion Model and propose the Affect-Trust Infusion Model (ATIM), which explains and predicts how and when cognition, through perceived Web site performance (PWP), and positive emotion (PE) each influence Web vendor trust. The ATIM explains the underlying causal mechanisms that determine the degree of affect infusion and the subsequent processing strategy that a user adopts when interacting with a new Web site. Under high affect infusion, PE acts as a mediator between PWP and vendor trust; under low affect infusion, PWP primarily affects trust, and PE is disintermediated. We review two distinct, rigorously validated experiments that empirically support the ATIM. To conclude, we detail several promising research opportunities that can leverage the ATIM and show how the ATIM can help to guide user-centered design (UCD) as an example practical application.     

Reductive Amination of 5-Hydroxymethyl-2-furaldehyde Over Beta Zeolite-Supported Ruthenium Catalyst

Beta-zeolite supported ruthenium catalysts for reductive amination of 5-hydroxymethyl-2-furaldehyde (HMF) with an aqueous solution of ammonia (NH₃ aq.) and molecular hydrogen (H₂) are examined to synthesize the corresponding primary amine of 5-aminomethyl-2-furylmethanol (FAA). Various SiO₂/Al₂O₃ (Si/2Al) ratios of the beta-zeolite support were used to prepare the Ru-based catalysts. It was observed that the Si/2Al ratio was contributed to the catalytic activity, and the Si/2Al?=?150 of beta-zeolite was found to be the most active for Ru catalyzed reductive amination of HMF, affording ca. 70% yield. Characterization techniques were taken to analysis the factors that influence the reactivity of catalysts, and which revealed that not only the ruthenium nanoparticle size but also the ratio of RuO₂ against metallic Ru species were crucial factors for the reactivity of reductive amination of HMF to FAA.

Graphical Abstract