学科核心素养视角下的高中数学新教材使用研究——以湘教版必修第一册为例

随着新课程的不断推进,新的教材已经普遍应用于高中课程。一线数学教师需要通过深入研究新课程、挖掘新教材、探索新高考,才能适应新课程、应对新高考。本研究立足新课标,着力数学学科核心素养,在湘教版教材的使用过程中,结合新教材编写规律和使用感受,从教材的编排、引言、栏目、信息技术与学科融合、数学文化的渗透、学科间的交融六个方面进行了探讨研究与举例,并从单元教学、数学文化背景、信息技术促进课堂发展三个方面提出教学启示。

High Wall-Plug Efficiency 1060 nm High Power Semiconductor Laser

By analyzing the factors which affect the wall-plug efficiency of semiconductor Laser Diodes (LDs), a high efficiency 1060 nm LD was designed, including active region, waveguide layers, and cladding layers. The simulation result shows that the component of In in InGaAs in the active region cannot be too small, otherwise the thickness of InGaAs active layer will exceed the critical thickness, meanwhile the asymmetric large optical cavity can decrease the cavity loss effectively. The epitaxial structure was grown by MOCVD, experimental results of varying cavity length showed that the internal quantum efficiency reached 98.57%, and the cavity loss was only 0.273 cm?1. Devices with 4 mm-cavity-length and 100 μm-strip-width were fabricated, 47.4% wall-plug efficiency was reached under QCW pulse condition at room temperature, and the peak wavelength was 1059.4 nm.

Dynamic time prediction for electric vehicle charging based on charging pattern recognition

Overcharging is an important safety issue in the charging process of electric vehicle power batteries,and can easily lead to accelerated battery aging and serious safety accidents.It is necessary to accurately predict the vehicle’s charging time to effectively prevent the battery from overcharging.Due to the complex structure of the battery pack and various charging modes,the traditional charging time prediction method often encounters modeling difficulties and low accuracy.In response to the above problems,data drivers and machine learning theories are applied.On the basis of fully considering the different electric vehicle battery management system(BMS)charging modes,a charging time prediction method with charging mode recognition is proposed.First,an intelligent algorithm based on dynamic weighted density peak clustering(DWDPC)and random forest fusion is proposed to classify vehicle charging modes.Then,on the basis of an improved simplified particle swarm optimization(ISPSO)algorithm,a high-performance charging time prediction method is constructed by fully integrating long short-term memory(LSTM)and a strong tracking filter.Finally,the data run by the actual engineering system are verified for the proposed charging time prediction algorithm.Experimental results show that the new method can effectively distinguish the charging modes of different vehicles,identify the charging characteristics of different electric vehicles,and achieve high prediction accuracy.

Targeting slug-mediated non-canonical activation of c-Met to overcome chemo-resistance in metastatic ovarian cancer cells

Metastasis-associated drug resistance accounts for high mortality in ovarian cancer and remains to be a major barrier for effective treatment. In this study, SKOV3/T4, a metastatic subpopulation of ovarian cancer SKOV3 cells, was enriched to explore potential interventions against metastaticassociated drug resistance. Quantitative genomic and functional analyses were performed and found that slug was significantly increased in the SKOV3/T4 subpopulation and contributed to the high resistance of SKOV3/T4. Further studies showed that slug activated c-Met in a ligand-independent manner due to elevated levels of fibronectin and provoked integrin α V function, which was confirmed by the significant correlation of slug and p-Met levels in 121 ovarian cancer patient samples. Intriguingly,c-Met inhibitor(s) exhibited greatly enhanced anti-cancer effects in slug-positive ovarian cancer models both in vitro and in vivo. Additionally, IHC analyses revealed that slug levels were highly correlated with reduced survival of ovarian cancer patients. Taken together, this study not only uncovers the critical roles of slug in drug resistance in ovarian cancer but also highlights a promising therapeutic strategy by targeting the noncanonical activation of c-Met in slug-positive ovarian cancer patients with poor prognosis.

Smart heightened-containment biological laboratory:Technologies,modules,and aims

With the rapid development of intelligent technology,the smart heightened-containment biological laboratory(sHCBL)has moved from concept to reality.Experimental activities and laboratory construction,operation,and management will undoubtedly lead to disruptive changes.Conventional laboratories are increasingly being replaced by smart laboratories;however,the key technologies involved in this transition remain at an exploratory stage.It is necessary for HCBLs to absorb the advanced ideas of smart laboratories to guarantee the establishment of biosafety and biosecurity in a more automated way.This study examines in detail sHCBL module structures,the functions of each module,laboratory operation processes,and the advanced nature of smart laboratories.It may provide a theoretical foundation for the future transformation and smart construction of sHCBLs.

Similar aerosol emission rates and viral loads in upper respiratory tracts for COVID-19 patients with Delta and Omicron variant infection

Dear Editor,In 2021,the Delta variant of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)caused the coronavirus disease 2019(COVID-19)pandemic to spread in the UK,Nepal,southeast Asia and elsewhere,which seems to be approximately 60%more transmissible than the already highly infectious Alpha variant in late 2020(Callaway,2021).In addition to the high efficiency of the invading infection and the acquired immune escape ability,changes in the aerodynamic characteristics of SARS-CoV-2 aerosols may be another important reason for the Delta variant spread.Previously,it was identified that SARS-CoV-2 can spread through close contact and airborne routes(Guo et al.,2020;Morawska and Milton,2020).

Constructing vertical phase separation of polymer blends via mixed solvents to enhance their photovoltaic performance

A polymer blend comprising poly(3-hexylthiophene)(P3HT)donor and poly[2,7-(9,9′-octyl-fluorene)-alt-5,5-(4′,7′-di-2-thienyl-5′,6′-bis(hexyloxy)-2′,1′,3′-benzothiadiazole)](PFDTBT-OC6)acceptor is used as the active layer to fabricate all-polymer solar cells.The blend morphology variance processed with pure and mixed solvents,and the related photovoltaic performance,are investigated in detail.It is found that,due to its low surface energy,a thin P3HT enrichment layer on the top surface of the active layer greatly increases bimolecular recombination and results in S-kinks of the illuminated current density-voltage curves.With the incorporation of p-xylene(a marginal solvent of P3HT)in the blend solution,the P3HT enrichment atop the active layer surface is effectively decreased because the high boiling-point p-xylene suppresses the diffusion of P3HT chains toward the top surface during the film-drying process.The bimolecular recombination was thus improved and the S-kinks of the photovoltaic curves were completely removed.The overall power conversion efficiencies of the devices are strongly boosted(from 0.88%to 1.41%)when chlorobenzene:p-xylene mixed solvent is used to replace pure chlorobenzene.