Risk preferences in decision-making: A construal level perspective.

This study examines the impact of construal levels on preferences for risky choices in decision-making scenarios, focusing on how situational framing moderates these effects. We explored the role of construal level as a key moderator of the influence of risky-choice framing. Our findings show significant differences in preferences for risky options between gain- and loss-framing, especially in high-level construal contexts.

Tailoring the Structure-Property Relationship of Ring-Opened Metathesis Copolymers for CO-Selective Membranes.

In this work, we explore the use of ring-opening metathesis polymerization (ROMP) facilitated by a second-generation Grubbs catalyst (G2) for the development of advanced polymer membranes aimed at CO separation. By employing a novel copolymer blend incorporating 4,4'-oxidianiline (ODA), 1,6-hexanediamine (HDA), 1-adamantylamine (AA), and 3,6,9-trioxaundecylamine (TA), along with a CO-selective poly(ethylene glycol)/poly(propylene glycol) copolymer (Jeffamine2003) and polydimethylsiloxane (PDMS) units, we have synthesized membranes under ambient conditions with exceptional CO separation capabilities. The strategic inclusion of PDMS, up to a 20% composition within the PEG/PPG matrix, has resulted in copolymer membranes that not only surpass the 2008 upper limit for CO/N separation but also meet the commercial targets for CO/H separation.

PolyMOF nanoparticles constructed from intrinsically microporous polymer ligand towards scalable composite membranes for CO separation.

Integrating different modification strategies into a single step to achieve the desired properties of metal-organic frameworks (MOFs) has been very synthetically challenging, especially in developing advanced MOF/polymer mixed matrix membranes (MMMs). Herein, we report a polymer-MOF (polyMOF) system constructed from a carboxylated polymer with intrinsic microporosity (cPIM-1) ligand. This intrinsically microporous ligand could coordinate with metals, leading to ~100 nm-sized polyMOF nanoparticles.

The Braking-Pressure and Driving-Direction Determination System (BDDS) Using Road Roughness and Passenger Conditions of Surrounding Vehicles.

A fully autonomous vehicle must ensure not only fully autonomous driving but also the safety and comfort of its passengers. However, the self-driving technology that is currently completed focuses only on perfect driving and does not guarantee the safety and comfort of passengers. This paper proposes a braking-pressure and driving-direction determination system (BDDS), which computes the brake pressure and steering angle optimized for passenger safety by utilizing more diverse information than existing autonomous vehicles.

Surface Modification of Matrimid 5218 Polyimide Membrane with Fluorine-Containing Diamines for Efficient Gas Separation.

Polyimide membranes have been widely investigated in gas separation applications due to their high separation abilities, excellent processability, relatively low cost, and stabilities. Unfortunately, it is extremely challenging to simultaneously achieve both improved gas permeability and selectivity due to the trade-off relationship in common polymer membranes. Diamine modification is a simple strategy to tune the separation performance of polyimide membranes, but an excessive loss in permeability is also generally observed.

In Situ Derived Hybrid Carbon Molecular Sieve Membranes with Tailored Ultramicroporosity for Efficient Gas Separation.

Fine control of ultramicroporosity (<7 Å) in carbon molecular sieve (CMS) membranes is highly desirable for challenging gas separation processes. Here, a versatile approach is proposed to fabricate hybrid CMS (HCMS) membranes with unique textural properties as well as tunable ultramicroporosity. The HCMS membranes are formed by pyrolysis of a polymer nanocomposite precursor containing metal-organic frameworks (MOFs) as a carbonizable nanoporous filler.

The Lightweight Autonomous Vehicle Self-Diagnosis (LAVS) Using Machine Learning Based on Sensors and Multi-Protocol IoT Gateway.

This paper proposes the lightweight autonomous vehicle self-diagnosis (LAVS) using machine learning based on sensors and the internet of things (IoT) gateway. It collects sensor data from in-vehicle sensors and changes the sensor data to sensor messages as it passes through protocol buses. The changed messages are divided into header information, sensor messages, and payloads and they are stored in an address table, a message queue, and a data collection table separately.