Crop cultivation planning with fuzzy estimation using water wave optimization.

In a complex agricultural region, determine the appropriate crop for each plot of land to maximize the expected total profit is the key problem in cultivation management. However, many factors such as cost, yield, and selling price are typically uncertain, which causes an exact programming method impractical. In this paper, we present a problem of crop cultivation planning, where the uncertain factors are estimated as fuzzy parameters.

Integration of machine learning prediction and heuristic optimization for mask delivery in COVID-19.

The novel coronavirus pneumonia (COVID-19) has created huge demands for medical masks that need to be delivered to a lot of demand points to protect citizens. The efficiency of delivery is critical to the prevention and control of the epidemic. However, the huge demands for masks and massive number of demand points scattered make the problem highly complex.

A robust multi-objective model for healthcare resource management and location planning during pandemics.

In this study, we consider the problem of healthcare resource management and location planning problem during the early stages of a pandemic/epidemic under demand uncertainty. Our main ambition is to improve the preparedness level and response effectiveness of healthcare authorities in fighting pandemics/epidemics by implementing analytical techniques. Building on lessons from the Chinese experience in the COVID-19 outbreak, we first develop a deterministic multi-objective mixed integer linear program (MILP) which determines the location and size of new pandemic hospitals (strategic level planning), periodic regional health resource re-allocations (tactical level planning) and daily patient-hospital assignments (operational level planning).

Predicting Demands of COVID-19 Prevention and Control Materials via Co-Evolutionary Transfer Learning.

The novel coronavirus pneumonia (COVID-19) has created great demands for medical resources. Determining these demands timely and accurately is critically important for the prevention and control of the pandemic. However, even if the infection rate has been estimated, the demands of many medical materials are still difficult to estimate due to their complex relationships with the infection rate and insufficient historical data.

Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control.

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands.

Real-time neural network scheduling of emergency medical mask production during COVID-19.

During the outbreak of the novel coronavirus pneumonia (COVID-19), there is a huge demand for medical masks. A mask manufacturer often receives a large amount of orders that must be processed within a short response time. It is of critical importance for the manufacturer to schedule and reschedule mask production tasks as efficiently as possible.

Tridirectional Transfer Learning for Predicting Gastric Cancer Morbidity.

Our previous study has constructed a deep learning model for predicting gastrointestinal infection morbidity based on environmental pollutant indicators in some regions in central China. This article aims to adapt the prediction model for three purposes: 1) predicting the morbidity of a different disease in the same region; 2) predicting the morbidity of the same disease in a different region; and 3) predicting the morbidity of a different disease in a different region. We propose a tridirectional transfer learning approach, which achieves the abovementioned three purposes by: 1) developing a combined univariate regression and multivariate Gaussian model for establishing the relationship between the morbidity of the target disease and that of the source disease together with the high-level pollutant features in the current source region; 2) using mapping-based deep transfer learning to extend the current model to predict the morbidity of the source disease in both source and target regions; and 3) applying the pattern of the combined model in the source region to the extended model to derive a new combined model for predicting the morbidity of the target disease in the target region.

Quarantine Vehicle Scheduling for Transferring High-Risk Individuals in Epidemic Areas.

In a large-scale epidemic outbreak, there can be many high-risk individuals to be transferred for medical isolation in epidemic areas. Typically, the individuals are scattered across different locations, and available quarantine vehicles are limited. Therefore, it is challenging to efficiently schedule the vehicles to transfer the individuals to isolated regions to control the spread of the epidemic.

Correlations Study Between F-FDG PET/CT Metabolic Parameters Predicting Epidermal Growth Factor Receptor Mutation Status and Prognosis in Lung Adenocarcinoma.

This study assessed the ability of metabolic parameters from Fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) and clinicopathological data to predict epidermal growth factor receptor (EGFR) expression/mutation status in patients with lung adenocarcinoma and to develop a prognostic model based on differences in expression status, to enable individualized targeted molecular therapy. Metabolic parameters and clinicopathological data from 200 patients diagnosed with lung adenocarcinoma between July 2009 and November 2016, who underwent F-FDG PET/CT and mutation testing, were retrospectively evaluated. Multivariate logistic regression was applied to significant variables to establish a prediction model for mutation status.

Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods.

Morbidity prediction can be useful in improving the effectiveness and efficiency of medical services, but accurate morbidity prediction is often difficult because of the complex relationships between diseases and their influencing factors. This study investigates the effects of food contamination on gastrointestinal-disease morbidities using eight different machine-learning models, including multiple linear regression, a shallow neural network, and three deep neural networks and their improved versions trained by an evolutionary algorithm. Experiments on the datasets from ten cities/counties in central China demonstrate that deep neural networks achieve significantly higher accuracy than classical linear-regression and shallow neural-network models, and the deep denoising autoencoder model with evolutionary learning exhibits the best prediction performance.

An unprecedented pentanary chalcohalide with Mn atoms in two chemical environments: unique bonding characteristics and magnetic properties.

Mixed-anion chalcohalides have attracted significant attention lately, attributable to their unique structure compositions and captivating physicochemical properties. Herein, an unprecedented pentanary chalcohalide, Cs2[Mn2Ga3S7Cl] (1), was discovered by solid-state reaction at 1223 K. It is constructed by alternately stacked layers, each of which is made by a 2D [Ga3S9]9- ribbon embedded with 1D [Mn2S8Cl]13- chains.

Generative adversarial network based telecom fraud detection at the receiving bank.

Recently telecom fraud has become a serious problem especially in developing countries such as China. At present, it can be very difficult to coordinate different agencies to prevent fraud completely. In this paper we study how to detect large transfers that are sent from victims deceived by fraudsters at the receiving bank.

Coexistence of Strong Second Harmonic Generation Response and Wide Band Gap in AZn Ga S (A=K, Rb, Cs) with 3D Diamond-like Frameworks.

Mid-infrared (MIR, 2-20 μm) second-order nonlinear optical (NLO) materials with outstanding performances are of great importance in laser science and technology. However, the enormous challenge to design and synthesize an excellent MIR NLO material lies in achieving simultaneously a strong second harmonic generation (SHG) response [d >0.6 × AgGaS (AGS)] and wide band gap (E >3.

Two excellent phase-matchable infrared nonlinear optical materials based on 3D diamond-like frameworks: RbGaSnSe and RbInSnSe.

Mid- and far-infrared (MFIR) nonlinear optical (NLO) crystals with excellent performances are critical to laser frequency-conversion technology. However, the current commercial MFIR NLO crystals, including AgGaS (AGS), AgGaSe and ZnGeP, suffer from certain intrinsic drawbacks and cannot achieve a good balance between large second-harmonic generation (SHG) efficiency and high laser-induced damage thresholds (LIDTs). Herein, we report two new phase-matchable MFIR NLO chalcogenides, specifically RbXSnSe (X = Ga, In), which were successfully synthesized by high-temperature solid-state reactions.

Histone Acetyltransferase Activity of MOF Is Required for Leukemogenesis.

Chromatin-based mechanisms offer therapeutic targets in acute myeloid leukemia (AML) that are of great current interest. In this study, we conducted an RNAi-based screen to identify druggable chromatin regulator-based targets in leukemias marked by oncogenic rearrangements of the gene. In this manner, we discovered the H4K16 histone acetyltransferase (HAT) MOF to be important for leukemia cell growth.

BaCuInS: a quaternary semiconductor with a unique 3D copper-rich framework and ultralow thermal conductivity.

A novel quaternary sulfide, BaCuInS (1), has been successfully synthesized via a high-temperature solid-state reaction. It contains CuSS clusters as basic building blocks, which are connected to one another by discrete In ions to generate a 3D copper-rich framework, where the Ba cations reside. Interestingly, such large clusters that are fused by five crystallographically independent Cu sites with three different chemical environments result in the increase of phonon scattering, which is the crucial factor to the exceptionally low lattice thermal conductivity (ca.

Fireworks Algorithm with Enhanced Fireworks Interaction.

As a relatively new metaheuristic in swarm intelligence, fireworks algorithm (FWA) has exhibited promising performance on a wide range of optimization problems. This paper aims to improve FWA by enhancing fireworks interaction in three aspects: 1) Developing a new Gaussian mutation operator to make sparks learn from more exemplars; 2) Integrating the regular explosion operator of FWA with the migration operator of biogeography-based optimization (BBO) to increase information sharing; 3) Adopting a new population selection strategy that enables high-quality solutions to have high probabilities of entering the next generation without incurring high computational cost. The combination of the three strategies can significantly enhance fireworks interaction and thus improve solution diversity and suppress premature convergence.

Tailored synthesis of nonlinear optical quaternary chalcohalides: BaGeSCl, BaSiSeCl and BaGeSeCl.

Three novel zero-dimensional quaternary chalcohalides, BaGeSCl, BaSiSeCl and BaGeSeCl, which crystallize in the polar noncentrosymmetric space group P6 (no. 173), have been rationally synthesized by a tailored approach on the basis of unique [MQ] (M = Ge, Q = S; M = Ge/Si, Q = Se) units with Ba cations and Cl anions occupying the interspaces. The [MQ] units which consist of three Q-corner-sharing [MQ] tetrahedra, are arranged along the 6 screw axis.

Non-centrosymmetric Selenides AZn In Se (A=Rb, Cs): Synthesis, Characterization and Nonlinear Optical Properties.

Two new non-centrosymmetric polar quaternary selenides, namely, RbZn In Se and CsZn In Se , have been synthesized and structurally characterized. They exhibit a 3D diamond-like framework (DLF) consisting of corner-shared MSe (M=Zn/In) tetrahedra, in which the A ions are located. Both compounds are thermally stable up to 1300 K and exhibit large transmittance in the infrared region (0.

AXXTe (A = Rb, Cs; X = Mn, Zn, Cd; X = Ga, In): quaternary semiconducting tellurides with very low thermal conductivities.

The discovery of novel materials with very low thermal conductivity is paramount to improving the efficiency of thermoelectric devices. Here we present a series of quaternary semiconducting tellurides AXXTe (A = Rb, Cs; X = Mn, Zn, Cd; X = Ga, In) with three-dimensional (3D) diamond-like frameworks (DLFs) and they exhibit a very low thermal conductivity (ca. 0.

Pb5Ga6ZnS15: a noncentrosymmetric framework with chains of T2-supertetrahedra.

A novel noncentrosysmmetric sulfide, Pb5Ga6ZnS15 (1), was synthesized for the first time. Its crystal structure revealed the presence of [GaS2](-)∞ (chain 1, chains of [GaS4](5-) tetrahedra) and [Ga4S9](6-)∞ (chain 2, chains of T2-supertetrahedra) connected by isolated [ZnS4](6-) tetrahedra. Structure correlation with the network constructed solely using chain 1 (Pb4Ga4GeQ12-type) is discussed.

CsBi4Te6: a new facile synthetic method and mid-temperature thermoelectric performance.

CsBi4Te6 is one of the best performing low-temperature thermoelectric (TE) materials. However, it has not received worldwide intensive investigation due to the limitation of synthetic methods. Here we report a new facile approach by not using the reactive Cs metal and the mid-temperature TE properties have been studied for the first time.

Syntheses, structures, physical and electronic properties of quaternary semiconductors: Cs[RE9Cd4Se18] (RE = Tb-Tm).

Five new quaternary rare-earth selenides, Cs[RE9Cd4Se18] (RE = Tb-Tm), which are the first examples with closed cavities in the quaternary A/RE/Cd/Q (A = alkali metal; RE = rare earth metal; Q = chalcogenide) system, have been synthesized by high-temperature solid state reactions with a modified reactive CsCl flux. Single crystal X-ray diffraction analyses show that these isostructural materials adopted a known BaV13O18-structure type in the trigonal space group R3[combining macron] (no. 148) with cell parameters of a = 17.

Electrophoresis of randomly and vertically embedded graphene nanosheets in activated carbon film as a counter electrode for dye-sensitized solar cells.

A new approach has been developed to randomly and vertically embed the graphene nanosheets (GNs) in the activated carbon (AC) film in an applied electric field. The activated carbon (AC) nanoparticles in suspension during electrophoresis play an important role in supporting the GNs perpendicular to the FTO (fluorine-doped tin oxide) glass. Insufficient amount of AC nanoparticles might result in a deposition of GNs parallel to the FTO glass, leading to incomplete utilization of the surface area accessible to electrolyte ions.