First‐class accommodation: A series of coordination frameworks with different pore sizes (see structure of one; Cu blue, C gray, H white, O red) are prepared from CuII ions and carboxylate ligands of ...various lengths. Comparison of their sorption properties reveals that smaller pores allow higher densities of adsorbed H2, whereas larger pores allow higher maximum H2 storage capacities.
Network interdiction problems by upgading critical edges/nodes have important applications to reduce the infectivity of the COVID-19. A network of confirmed cases can be described as a rooted tree ...that has a weight of infectious intensity for each edge. Upgrading edges (nodes) can reduce the infectious intensity with contacts by taking prevention measures such as disinfection (treating the confirmed cases, isolating their close contacts or vaccinating the uninfected people). We take the sum of root-leaf distance on a rooted tree as the whole infectious intensity of the tree. Hence, we consider the sum of root-leaf distance interdiction problem by upgrading edges/nodes on trees (SDIPT-UE/N). The problem (SDIPT-UE) aims to minimize the sum of root-leaf distance by reducing the weights of some critical edges such that the upgrade cost under some measurement is upper-bounded by a given value. Different from the problem (SDIPT-UE), the problem (SDIPT-UN) aims to upgrade a set of critical nodes to reduce the weights of the edges adjacent to the nodes. The relevant minimum cost problem (MCSDIPT-UE/N) aims to minimize the upgrade cost on the premise that the sum of root-leaf distance is upper-bounded by a given value. We develop different norms to measure the upgrade cost. Under weighted Hamming distance, we show the problems (SDIPT-UE/N) and (MCSDIPT-UE/N) are NP-hard by showing the equivalence of the two problems and the 0–1 knapsack problem. Under weighted
l
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norm, we solve the problems (SDIPT-UE) and (MCSDIPT-UE) in
O
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n
) time by transforimg them into continuous knapsack problems. We propose two linear time greedy algorithms to solve the problem (SDIPT-UE) under unit Hamming distance and the problem (SDIPT-UN) with unit cost, respectively. Furthermore, for the the minimum cost problem (MCSDIPT-UE) under unit Hamming distance and the problem (MCSDIPT-UN) with unit cost, we provide two
O
(
n
log
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time algorithms by the binary search methods. Finally, we perform some numerical experiments to compare the results obtained by these algorithms.
Metal-organic frameworks, typically built by bridging metal centres with organic linkers, have recently shown great promise for a wide variety of applications, including gas separation and drug ...delivery. Here, we have used them as a scaffold to probe the photophysical and photochemical properties of metal-diimine complexes. We have immobilized a M(diimine)(CO)(3)X moiety (where M is Re or Mn, and X can be Cl or Br) by using it as the linker of a metal-organic framework, with Mn(II) cations acting as nodes. Time-resolved infrared measurements showed that the initial excited state formed on ultraviolet irradiation of the rhenium-based metal-organic framework was characteristic of an intra-ligand state, rather than the metal-ligand charge transfer state typically observed in solution, and revealed that the metal-diimine complexes rearranged from the fac- to mer-isomer in the crystalline solid state. This approach also enabled characterization of the photoactivity of Mn(diimine)(CO)(3)Br by single-crystal X-ray diffraction.
The drying process is fundamental for cut tobacco processing. However, there are some problems related to the drying process such as overheating, or inconsistent control of moisture content. This ...paper shows how an intelligent controller is designed for an industrial rotary drying system. This controller is applied to a tobacco production unit to reduce overdried cut tobacco and improve the overall unit performance. The proposed control system aims to keep the content of moisture at the dryer outlet as close as possible to the optimal value and improve the homogeneity of the product without any operator intervention. The study shows that, if a reduction of humidity in the cut tobacco drying process is achieved using AI, the quality of the final product improves. In particular, if compared to regulatory control, the proposed method constantly monitors and adjusts the moisture content level in order to reduce the amount of overdried product. The findings of this paper indicate that the suggested process can save at least 222.2 kg of cut tobacco for each batch in the first stage of the drying process.
A metal-bearing coordination network synthesised from Re(2,2′-bipyridine-5,5′-dicarboxylate)(CO) 3 Cl bridging ligands and Cu( ii ) nodes, {Cu(DMF)(H 2 O)LRe(CO) 3 Cl}·DMF ∞ ReCu , undergoes an ...irreversible photoinduced charge transfer process. We demonstrate using time-resolved IR spectroscopy the nature of this photoinduced process and how, under suitable conditions, it is possible to initiate irreversible modification of the crystal through induction of the charge transfer process. As a result we are able to use the photoinduced process, which arises purely as a result of the structure of the coordination network, to write on crystals.
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•A reusable bifunctional fluorescent sensor (FQ) has been prepared for detection and removal of silver ions (Ag+).•FQ showed high selectivity towards Ag+.•FQ can be regenerated by ...acid and reused at least eight cycles.
In this work, a novel bifunctional fluorescent sensor (FQ) was developed for simultaneous detection and removal of Ag+ in aqueous solutions by grafting a naphthalimide-based fluorescent probe onto the surface of the mesoporous silica nanoparticles. FQ exhibited good selectivity towards Ag+ in water samples, and showed 2.5-fold enhancement of fluorescence intensity with the addition of Ag+. Meanwhile, FQ showed a very good linearity at a low concentration of Ag+ with a detection limit of 7.2 × 10−6 M. In addition, FQ can also serve as an adsorbent for the removal of Ag+ in water samples with the maximal adsorption capacity of 14.8 mg g−1. FQ can be regenerated easily by modulating the pH value of the aqueous solution, and can be recycled at least eight times. All results demonstrate that FQ has potential applications for the detection and removal of Ag+ in aqueous samples.
Near-infrared spectroscopy (NIRS) has emerged as a key technique for rapid quality detection owing to its fast, non-destructive, and eco-friendly characteristics. However, its practical ...implementation within the formulation industry is challenging owing to insufficient data, which renders model fitting difficult. The complexity of acquiring spectra and spectral reference values results in limited spectral data, aggravating the problem of low generalization, which diminishes model performance. To address this problem, we introduce what we believe to be a novel approach combining NIRS with Wasserstein generative adversarial networks (WGANs). Specifically, spectral data are collected from representative samples of raw material provided by a formula enterprise. Then, the WGAN augments the database by generating synthetic data resembling the raw spectral data. Finally, we establish various prediction models using the PLSR, SVR, LightGBM, and XGBoost algorithms. Experimental results show the NIRS-WGAN method significantly improves the performance of prediction models, with R
and RMSE of 0.949 and 1.415 for the chemical components of sugar, respectively, and 0.922 and 0.243 for nicotine. The proposed framework effectively enhances the predictive capabilities of various models, addressing the issue caused by limited training data in NIRS prediction tasks.
Although recent developments in deep learning have brought several tasks closer to human performance, there is still a significant gap between human and machine performance in certain image ...captioning tasks. Image captioning is the process of creating a textual description of an image. Image captioning focuses on recognizing the main regions of an image, their attributes, and their relationships. It aims to generate textual descriptions that are syntactically and semantically correct. For simple image descriptions, deep learning-based techniques perform well in terms of intricacies and constraints. However, it is challenging to construct sentences when faced with complicated scenes with many entities and relationships, such as how to concurrently solve diversity, anchoring, and controllability—a seemingly simple ability for humans. Scene graphs can significantly alleviate this problem by fully mining spatial and semantic information. However, despite these promising findings, they are fragmented and do not form a systematic comparative overview. We provide a comprehensive overview of the available scene-graph-based image captioning methods in this survey. The foundations of these techniques are discussed to examine their performance, strengths, and constraints. Furthermore, we discuss the comparisons of the state-of-the-art methods, datasets, and commonly utilized evaluation measures. Finally, we conclude the survey with an in-depth discussion of the present and future research challenges. This study will assist readers in comprehending how scene graphs can be applied to image captioning.
•Identifying the main regions of an image, describing their attributes, and understanding their relationships.•Alleviating inductive bias by fully exploring spatial and semantic heterogeneity of perceptual information.•Using scene graphs to enhance the understanding of spatial and semantic information of images.•Discussing the comparisons of deep learning-based and scene graph-based methods, datasets, and commonly utilized evaluation measures.
We study the restricted inverse optimal value problem on minimum spanning tree under weighted l1 norm. In a connected edge-weighted network G(V,E,w), we are given a spanning tree T0, a cost vector c ...and a value K. We aim to obtain a new weight vector w̄ satisfying the bounded constraint such that T0 is a minimum spanning tree under w̄ whose weight is just K. We focus on minimizing the modification cost under weighted l1 norm. We first convert its mathematical model into a linear programming problem (P). Then we solve its dual problem (D) by a sub-problem (Dz∗) corresponding to the critical value z∗ which can be calculated by a binary search method. Impressively the sub-problem Dz for z∈R can be transformed into a minimum cost flow problem on an auxiliary network. Finally, we propose an O(|E|2|V|2log|V|log(|V|cmax)) algorithm, where cmax is the maximum cost of the vector c.
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