Why indexing works

We develop a simple stock selection model to explain why active equity managers tend to underperform a benchmark index. We motivate our model with the empirical observation that the best performing stocks in a broad market index often perform much better than the other stocks in the index. Randomly selecting a subset of securities from the index may dramatically increase the chance of underperforming the index. The relative likelihood of underperformance by investors choosing active management likely is much more important than the loss those same investors take due to the higher fees of active management relative to passive index investing. Thus, active management may be even more challenging than previously believed, and the stakes for finding the best active managers may be larger than previously assumed.     

Risk externalities and too big to fail

This paper establishes the case for a fallacy of economies of scale in large aggregate institutions and the effects of scale risks. The problem of rogue trading and excessive risk taking is taken as a case example. Assuming (conservatively) that a firm exposure and losses are limited to its capital while external losses are unbounded, we establish a condition for a firm not to be allowed to be too big to fail. In such a case, the expected external losses second derivative with respect to the firm capital at risk is positive. Examples and analytical results are obtained based on simplifying assumptions and focusing exclusively on the risk externalities that firms too big to fail can have.     

Self‐assembly of Carbon Particles as a Heat Sink Coating to Promote Radiative Cooling

Novel composite carbon particles are developed that can self‐assemble as a coating on a substrate without a binder. These carbon particles were used as a coating to enhance thermal dissipation and their thermal conductivity, surface emissivity and cooling performance were measured. Carbon particles with both thiol and epoxy functional groups self‐assembled to form a coating on the surface of a heat sink without a binder, which greatly improved the thermal conductivity of the coating. Coating a heat sink with the carbon particles yielded a higher thermal conductivity and emissivity than could be obtained with the addition of binder in the conventional approach, and significantly enhanced the cooling performance. In addition, the cooling performance of the carbon nanotube outperformed all other particles when coated on a substrate, because it had the highest thermal conductivity and good radiation emissivity. We developed an equation to describe the various parameters affecting the cooling performance of the thermally dissipative coating. This equation was confirmed by the experimental data.     

Time-dependent optimization of a multi-item uncertain supply chain network: A hybrid approximation algorithm

We consider the uncertain least cost shipping problem. The input is a multi-item supply chain network with time-evolving uncertain costs and capacities. Exploiting the operational law of uncertainty theory, a mathematical model of the problem is established and the indeterminacy factors are tackled. We use the scaling idea together with transformation approach and uncertainty programming to develop a hybrid algorithm to optimize and obtain the uncertainty distribution of the total shipping cost. We analyze the practical performance of the algorithm and present an illustrative example.     

Concise Synthesis of (3Z)-Dodecen-12-olide,Pheromone Component of the Emerald Ash Borer

Emerald ash borer (EAB), Agrilus planipennis Fairmaire, is an invasive insect that has killed millions of ash trees in Canada and the USA. (3Z)-Dodecen-12-olide is a known female-produced pheromone of this insect, and a concise, three-step synthesis of a 2:1 blend of (3Z)-dodecen-12-olide and (3E)-dodecen-12-olide starting from commercially available (2-carboxyethyl)triphenylphosphonium bromide and 10-bromo-1-decene is described. The key steps in this synthesis are a lithium-salt-free Wittig reaction and an intramolecular S_N2 esterification. Both of these macrocyclic lactones are behaviorally active toward EAB, and the 2:1 blend whose synthesis is described here has the potential to be a detection agent, mating disruptor, or mass trapping agent, which could be used in the control of EAB.     

A short review on latest developments in catalytic depolymerization of Poly (ethylene terephathalate) wastes

Chemical recycling of plastic wastes is top among the effective management of the solid wastes. Particularly the post-consumer polyethylene terephthalate (PET) plastic wastes mainly generated from the disposal of beverage bottles and placed third most produced polymeric waste. However, PET wastes could be chemically recycled using several types of homo-/heterogeneous acid or base catalysts, and an effective recycling process has yet to be achieved. Therefore, the present short review is intended to display recent reports on the depolymerization of PET polymer wastes. The review aimed to cover glycolysis and aminolytic depolymerization using various catalytic systems. There is a wide spectrum of catalytic systems such as metal oxides, ionic liquids, organic bases, nanoparticles, porous materials and microwave-assisted rapid depolymerization methods have been developed toward the yield enhancement of the depolymerized products. Ideologically, the present review would benefit the researchers in familiarizing themselves with the latest developments in this field.     

针对理论-实验教学相融合的微助教辅助实验教学实践——以滴定分析实验教学为例

研究生助教培养是高校化学本科生实验教学的重要组成部分,对提高实验课程的教学水平具有重要的推动作用。为解决助教培训时间仓促、定位不清、内容不连续、工具性倾向明显,以及在助教聘用、考核、激励、教学质量评估等方面规范性和系统性不足等问题,仪器分析实验室以《GB/T 19028–2018质量管理人员参与和能力指南》为理论依据,探索提高研究生助教深度参与实验教学的方法。连续三年追踪助教培养改进效果反馈数据表明,实验室采取的一系列改进措施有效提升了研究生助教的教学能力,促进了教学质量的提高,实现助教管理的改革创新与“过程增值”。     

Amide Moieties Modulate the Antimicrobial Activities of Conjugated Oligoelectrolytes against Gram-negative Bacteria

Cationic conjugated oligoelectrolytes (COEs) are a class of compounds that can be tailored to achieve relevant in vitro antimicrobial properties with relatively low cytotoxicity against mammalian cells. Three distyrylbenzene-based COEs were designed containing amide functional groups on the side chains. Their properties were compared to two representative COEs with only quaternary ammonium groups. The optimal compound, COE2−3C−C3-Apropyl , has an antimicrobial efficacy against Escherichia coli with an MIC=2 μg mL⁻¹, even in the presence of human serum albumin low cytotoxicity (IC₅₀=740 μg mL⁻¹) and minimal hemolytic activity. Moreover, we find that amide groups increase interactions between COEs and a bacterial lipid mimic based on calcein leakage assay and allow COEs to readily permeabilize the cytoplasmic membrane of E. coli. These findings suggest that hydrogen bond forming moieties can be further applied in the molecular design of antimicrobial COEs to further improve their selectivity towards bacteria.     

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications.