Short-term electrical load series forecast plays an essential role in energy demand management, however power consumption data are non-stationary, nonlinear and multi-dimensional series, leaving prediction a difficult task. Recently, fractional- order partial differential equations are attracting attention as they have been successfully utilized to describe power consumption behaviors in complex electrical systems and power grids. In this paper, a clustering fractional order predictive model called C-FGM is introduced for short-term electrical load forecast missions. The novelty of the C-FGM is that it initiates a parameter α to describe the accumulative weather trends of multiple clustering sub-series, and this parameter is also assigned to a fractional-order partial differential equation to depict the previous power series. Hyper parameters of these equations are then sent to a global optimization algorithm to reduce predictive errors. Simulation results on two electricity datasets demonstrated that our algorithm can learn from datasets hyper parameters inside equations and produce forecast values efficiently. Com- pared with contemporary models such as LSTM and the Transformer, C-FGM clearly achieved a higher accuracy (MAPE from 1.97 to 4.67%, outperforms LSTM whose average MAPE is 4.34% and Transformer whose average MAPE is 5.42%). This satisfactory performance suggests that our data-driven model can be used as an effective tool for real time forecasting missions.

Seven isolates of entomopathogenic fungi, Isaria fumosorosea (IFCF-H and IFCF-L), Beauveria bassiana s.l. (Bb02 and Bb04) and Metarhizium anisopliae s.l. (Ma01, SM076 and M09), were selected for their pathogenicity against Solenopsis invicta as well as feeding preference of S. invicta. When ants were treated with a conidial suspension at a concentration of 1 × 10⁸ conidia/ml, the median lethal times (LT₅₀) of IFCF-H, IFCF-L, Bb02, Bb04, Ma01, SM076 and M09 were 3.4, 162.6, 7.3, 2.8, 3.8, 7.3 and 2.7 days, respectively, after 10 days. The median lethal concentrations (LC₅₀) on the 10th day after inoculation were 1.20 × 10⁷, 1.56 × 10¹⁰, 4.23 × 10⁷, 3.04 × 10⁶, 6.13 × 10⁶, 2.90 × 10⁷ and 9.90 × 10⁵ conidia/ml, respectively. Furthermore, S. invicta consumed significantly less solution flavoured with Bb04 conidia than the control, which was demonstrated by the lowest preference index (PREF = 0.09). S. invicta did not have a significant feeding preference for other fungal isolates. The pathogenicity (LC₅₀) of fungal isolates was not significantly correlated (R² = 0.013) with the PREF of S. invicta. However, in the paired-choice experiments between different virulent isolates belonging to the same genera, S. invicta tended to select the solution flavoured with conidia of relatively lower pathogenic isolates such as IFCF-L, Bb02 and SM076. We conclude that the pathogenicity of congeneric fungi may affect the feeding preference of S. invicta. Red imported fire ants might adjust their feeding response to entomopathogenic fungi based on the profile of microbial volatile organic compounds.

Due to the special role of empty containers in the container transportation process, empty container repositioning is a focal point in the shipping industry. For this problem, highly efficient and feasible optimization models are critical in improving the benefit for shipping companies and increasing their market competitiveness. Operational decisions are affected by tactical ones. Aimed at this point, we propose a tactical and operational cooperative empty container repositioning optimization model. To cut the search space and obtain the optimal solution quickly, several initial solutions generation rules are extracted, based on business flow. Furthermore, the reachable shipping distance may change when the calling sequence is different. An algorithm which calculates the reachable shipping distance matrix between ports is presented to solve this problem. Simulated cases are used to test the proposed model and algorithm. The results show that the cases can cope with the tactical and operational cooperative empty container repositioning optimization model. Moreover, some interesting conclusions also are deduced about the relationships among number of calling ports, total profits, leasing cost, calling port fee, number of Empty Containers Repositioned (ECR), and laden containers. All these can guide and assist the various decisions to be made. According to the homepage of Symmetry, its subject areas include Mathematics, Computer Science, Theory, and Methods. Their branches include information theory, computer-aided design, and so on. The topic of our paper is to solve this engineering application problem by using a mathematical optimization model and computer methods. That is, applying mathematical theory and computer methods to make decision results for the empty container repositioning problem in the shipping industry. It has certain economic value and practical significance. Obviously, it is consistent with the theme of Symmetry.

The problem of empty containers repositioning (ECR) is to dispatch the empty containers coupled with laden containers transportation flow between the surplus ports and the deficit ports according to the fixed schedule. Because this problem involves many parameters, variables and constraints, how to build and solve the model is always focus point. Aiming at this problem, firstly one loop itinerary of liner is divided into several stages to analyze the changes of two typical containers located in the ports and on board with the time evolving. Secondly, for attacking the difference between supply and demand empty containers in preview stage t and the ones in next stage t + 1, one dynamic region-to-region ECR model, which adopts the dynamic across-region port set to port set redistribution strategy, is proposed to reduce the various possible costs which commonly exist in existing static port-to-port ECR policy. Finally, through some deductive instances and contrast model we analyze these two models. Results show that in any case our proposed model has absolutely more advantages than the one who uses the static port-to-port ECR strategy. Moreover, the former has significant information support for making the safety stock of ports.

Since December 2019, a novel coronavirus (COVID-19) has spread all over the world, causing unpredictable economic losses and public fear. Although vaccines against this virus have been developed and administered for months, many countries still suffer from secondary COVID-19 infections, including the United Kingdom, France, and Malaysia. Observations of COVID-19 infections in the United Kingdom and France and their governance measures showed a certain number of similarities. A further investigation of these countries COVID-19 transmission patterns suggested that when a turning point appeared, the values of their stringency indices per population density (PSI) were nearly proportional to their absolute infection rate (AIR). To justify our assumptions, we developed a mathematical model named VSHR to predict the COVID-19 turning point for Malaysia. VSHR was first trained on 30-day infection records prior to the United Kingdom, Germany, France, and Belgiums known turning points. It was then transferred to Malaysian COVID-19 data to predict this nations turning point. Given the estimated AIR parameter values in 5 days, we were now able to locate the turning points appearance on June 2^nd, 2021. VSHR offered two improvements: (1) gathered countries into groups based on their SI patterns and (2) generated a model to identify the turning point for a target country within 5 days with 90 CI. Our research on COVID-19s turning point for a country is beneficial for governments and clinical systems against future COVID-19 infections.