Tailoring the properties of carbon molecular sieves membranes for the separation of propionic acid from aqueous solutions

In the fermentative production of propionic acid (PA), the major problem with batch fermentation systems is the strong inhibitory effect of PA on the production yield; one way to increase the yield is the in-situ removal of PA by using pervaporation. Acetic acid (AA) is the most important by-product in the fermentation; therefore, the membrane should be able to remove selectively PA from an aqueous solution containing AA. Considering that PA is more hydrophobic than AA and their kinetic diameter are 0.480 and 0.436 nm respectively, hydrophobic membranes with main pores in the range of around 0.5–0.6 nm with high permeation are required. Supported thin Carbon Molecular Sieve Membranes (CMSM) were prepared by the dip coating a porous alumina support into a solution containing resorcinol phenolic resin as carbon source. The hydrophobicity was obtained by carbonizing the polymer at temperatures higher than 750 °C and adding polyvinyl butyral (PVB) as pore forming agent and carbon contributor. PA with 88 % of purity was obtained by pervaporation of an aqueous solution containing 5 % of PA and 5 % of AA using a CMSM carbonized at 850 °C containing 1 % of PVB in the dipping solution.</p

Guidance for goal achievement in knowledge-intensive processes using intuitionistic fuzzy sets

Throughout the execution of a knowledge-intensive process (KiP), knowledge workers need to make critical decisions such as skipping a task or canceling a process instance. These decisions significantly impact the efficiency and effectiveness of KiP execution and should, therefore, be made in a well-informed manner. When historical data, such as event logs, is available, it can be leveraged to support knowledge workers in making these decisions. However, KiPs often lack useful historical data, as each KiP instance is unique and hardly repeatable. To address this issue, this paper proposes the novel concept of potential goal achievement, i.e., the extent to which a goal can be achieved at the end of the process, considering the collected (but incomplete) data, to support knowledge workers in efficiently executing KiPs. An approach based on Intuitionistic Fuzzy Sets (IFSs) is introduced to calculate the potential goal achievement without relying on historical data. The use of potential goal achievement in supporting knowledge workers’ decisions is demonstrated, and the effectiveness of the approach is evaluated through simulations. The results demonstrate that modeling and calculating potential goal achievement support knowledge workers in achieving goals more efficiently.</p

Characterization of traffic dynamics in non-equilibrium ride-hailing mobility networks:A mesoscopic approach

Ride-hailing vehicles, private vehicles, and passengers are integral components of ride-hailing markets. Accurately characterizing the traffic dynamics driven by the spatio-temporal variations of these traffic flows is crucial for formulating operational strategies to realize sustainable ride-hailing services. From the mesoscopic perspective, we develop an integrated simulation model with high spatio-temporal resolutions. In a multi-class cell transmission model, we embed aggregate-ratio based decision-making mechanisms and bilateral matching between waiting passengers and idle vehicles in a large-scale non-equilibrium ride-hailing mobility network. At the individual level, the simulation model can capture the entire trip chain of passengers. Simultaneously, it can describe the cruising strategy of idle vehicles and the routing strategy of reserved/occupied/private vehicles. At the network level, it can depict the real-time space distribution of these multi-class traffic flows in the ride-hailing mobility network. We use empirical data, including road network density data, ride-hailing order, and trajectory data, to calibrate and verify the proposed simulation model. Sensitivity analyses based on simulation experiments indicate that the matching strategy, fleet size, and background traffic have diverse and significant effects on the operation performance of ride-hailing services.</p

A 0.1–3.2 GHz Reconfigurable LPF With Peaking Reducing and Selectivity Enhancement Using Adaptive Impedance Transformation Technique

This article presents an on-chip low-pass filter (LPF) for cognitive radio (CR) in a 130-nm SiGe BiCMOS technology. The LPF achieves a broad tuning range of 0.1–3.2 GHz with a 100-MHz step. To address gain variation caused by the frequency-peaking effect, we propose an adaptive impedance transformation (AIT) technique, achieving a remarkable ripple of less than 3.01 dB across the entire 0.1–3.2 GHz frequency band. This technique also enhances passband selectivity. Furthermore, we introduce a novel operational amplifier (OPAMP) featuring a four-stage heterojunction bipolar transistor (HBT)–complementary metal–oxide–semiconductor (CMOS) transistor composite pair. Leveraging the inherent advantages of both HBT and CMOS technologies, this OPAMP elevates the gain-bandwidth (GBW) product from 0.834 GHz of a CMOS-only topology to an impressive 8.33 GHz. The LPF requires only 8 mA in the low-power mode at 1.5 V and 17.5 mA in the high-power mode at 2 V. With such wide-tuning range, the proposed LPF is suitable for CR applications

Geometrical Pruning of the First Order Regular Perturbation Kernels of the Manakov Equation

We propose an approach for constraining the set of nonlinear coefficients of the conventional first-order regular perturbation (FRP) model of the Manakov Equation. We identify the largest contributions in the FRP model and provide geometrical insights into the distribution of their magnitudes in a three-dimensional space. As a result, a multi-plane hyperbolic constraint is introduced. A closed-form upper bound on the constrained set of nonlinear coefficients is given. We also report on the performance characterization of the FRP with multi-plane hyperbolic constraint and show that it reduces the overall complexity of the FRP model with minimal penalties in accuracy. For a 120 km standard single-mode fiber transmission, at 60 Gbaud with DP-16QAM, a 93% reduction in modeling complexity with a penalty below 0.1 dB is achieved with respect to FRP M=15.</p

The influence of macroscopic pedestrian structures on train boarding efficiency

A deeper understanding of pedestrian dynamics is essential to improve crowd flows in public spaces such as train stations. It is essential to understand both the physical and the psychological processes present in this context. However, current research on train boarding behavior is limited in scope and mainly focuses on how group level variables such as number of boarders/deboarders influence train boarding efficiency. Viewing pedestrian dynamics through a psychological lens is important for a detailed understanding of the train boarding context and to recognize target areas for improvement. At Dutch train stations, boarders follow a social norm of waiting at the train door until deboarding is complete. Although people generally adhere to this norm, the way it is executed may not be optimal for deboarding efficiency. We investigate how waiting boarders form a deboarding channel (a corridor where deboarders exit the train) and how this channel in turn influences the efficiency of deboarding. Analyzing a dataset with 3278 boarding events at Utrecht Centraal Station in the Netherlands from 2017 to 2020 (subset of a trajectory dataset capturing 100,000 trajectories per day), we found that higher numbers of boarders and a higher ratio of boarders to deboarders, reduced the width of the deboarding channel, and a lower width was associated with lower deboarding efficiency. These results shift the focus from group level variables to identifying macroscopic structures that are formed when pedestrians interact within a social system and provide specific target areas where nudges/behavioral interventions could be implemented

ERIES-Pollution, Atmospheric Boundary layer, Interaction and Ship Emission Data

The dispersion of pollutants exhausted from a generic service offshore vessel are investigated in the closed-circuit atmospheric boundary layer wind tunnel (ABLWT) at Eindhoven University of Technology. The vessel is replicated at a geometric scaling ratio of 1:100 and exposed to maritime neutral atmospheric flow conditions. For most measurements, an approach flow velocity of 10 m/s (full-scale) is set at the exhaust height. The majority of conducted measurements relate to 25% of maximum continuous rating (MCR) engine load, which results in a release velocity of Ue = 8.8 m/s (full-scale). Emissions are released from an exhaust with circular shaft of 0.76 m in diameter (De). To match the densimetric Froude number (Fr) in experiments, and reality, a velocity scaling ratio of 1:10 is chosen for the wind tunnel measurements. Main focus of pollutant concentration measurements is set on locations at 1.5 m height on the vessel’s deck to assess the possibility of workers’ exposure to emitted pollutants. Measurements are valid for the following conditions: • Standard atmospheric conditions (15 degrees Celsius and 1013.25 hPa), resulting in an ambient air density of ρa = 1.225 kg/m3. • Emission temperature of 350 degrees Celsius (corresponding to an emission density of 0.567 kg/m3) This results in a densymetric Froude number of Fr = 4.396069 (based on the diameter of the exhaust stack (De) and Ue = 8.8 m/s