Reactive Crystallization of Paracetamol in a Continuous Oscillatory Baffled Reactor

In this article, we report, for the first time, a reactive seeded cooling crystallization of paracetamol successfully performed in a continuous oscillatory baffled reactor (COBR). The synthesis of acetaminophen took place in the first part of the COBR and generated the solvent for crystallization via a specific ratio of starting materials. Seeded cooling crystallization was immediately commenced after the completion of the reaction in the second part of the COBR. We investigated the seeding strategy of three seed masses (10, 15, and 20 w/w%) at a fixed size in this work, enabling smooth and encrustation-free runs. We also estimated agglomeration by comparing the ideal to actual normalized product/seed sizes, which was confirmed by SEM images. The effect of mixing intensity on crystal properties was reported. Samples at two ports a known distance apart along the COBR enabled the study of steady states of the paracetamol concentration and crystal mean size in both temporal and spatial domains as well as the determination of the local and overall growth rates. Form I paracetamol particles were consistently produced with an average purity of 99.96%

A Comparative Evaluation of Hydrogenation of 3-Butyn-2-ol over Pd/Al₂O₃ in an Oscillatory Baffled Reactor and a Commercial Parr Reactor

A multiphase catalytic hydrogenation of 3-butyn-2-ol over Pd/Al₂O₃ to produce 3-buten-2-ol was for the first time investigated in both an oscillatory baffled reactor (OBR) and a commercial stirred tank reactor (Parr). Catalytic control was first identified in the Parr by evaluating the effects of mixing, initial molar ratio of 3-butyn-2-ol/Pd, and temperature on reaction rate. These conditions were then used as the benchmark for a comparable evaluation of the OBR in terms of reactor efficiency (power consumption) and hydrogen utilization (H₂ efficiency). Our systematic investigations demonstrate that, due to the enhanced mass transfer in the OBR, it consumes 5 times less power to achieve the same reaction rate and delivers 2.3 times higher H₂ efficiency and 50% less reaction time in comparison to the Parr

SEM-ACSIT:Secure and Efficient Multiauthority Access Control for IoT Cloud Storage

Data access control in a cloud storage system is regarded as a promising technique for enhanced efficiency and security utilizing a ciphertext-policy attribute-based encryption (CP-ABE) approach. However, due to a large number of data users as well as limited resources and heterogeneity of data devices in Internet of Things (IoT), existing access control schemes for the cloud storage are not effectively applicable to IoT applications. In this article, we construct a new CP-ABE-based storage model for data storing and secure access in a cloud for IoT applications. Our new framework introduces an attribute authority management (AAM) module in the cloud storage system functioned as an agent that provides a user-friendly access control and highly reduces the storage overhead of public keys. Then, we propose a novel secure and efficient multiauthority access control scheme of the cloud storage system for IoT, namely, SEM-ACSIT, which obtains both backward security and forward security when an attribute of a user is revoked. By exploiting encryption outsourcing, simplified key structuring and the AAM module, the computational overhead of a user is immensely decreased. Moreover, a user access control list (UACL) in the cloud server is constructed newly to support authorization access for a specific user. The analysis and simulation results demonstrate that our SEM-ACSIT scheme achieves powerful security with less computational overhead and lower storage costs than the existing schemes

Characterization and modelling of antisolvent crystallization of salicylic acid in a continuous oscillatory baffled crystallizer

Using antisolvent crystallisation of salicylic acid as the model process, we report our experimental investigation into the temporal and spatial steady states of solution concentration and mean crystal size in a continuous oscillatory baffled crystallizer. The evolutions of the two parameters over time and distance along the crystallizer are measured for a variety of operating conditions. The results show that the attainment of long term temporal and spatial stabilities (>100 residence times) for the solute concentrations are easily achieved, whereas the temporal steady states of the mean crystal size are more difficult to accomplish, even though the spatial steady states have been obtained. A simplified population balance model is applied to the experimental data for the determination of nucleation and growth kinetic parameters. From which both the solution concentration and the mean size were predicted and matched to experimental values reasonably well. In addition, we have identified and executed the conditions of long term steady states for extended operation of 6.25. h to produce close to 1. kg of crystal product with minimal variation in crystal size (±3.01. μm)

UAV-Assisted Wireless Powered Cooperative Mobile Edge Computing:Joint Offloading, CPU Control, and Trajectory Optimization

This article investigates the unmanned-aerial-vehicle (UAV)-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have no computing task. An optimization problem is formulated to minimize the total required energy of UAV by jointly optimizing the CPU frequencies, the offloading amount, the transmit power, and the UAV’s trajectory. To tackle the nonconvex problem, a successive convex approximation (SCA)-based algorithm is designed. Since it may be with relatively high computational complexity, as an alternative, a decomposition and iteration (DAI)-based algorithm is also proposed. The simulation results show that both proposed algorithms converge within several iterations, and the DAI-based algorithm achieve the similar minimal required energy and optimized trajectory with the SCA-based one. Moreover, for a relatively large amount of data, the SCA-based algorithm should be adopted to find an optimal solution, while for a relatively small amount of data, the DAI-based algorithm is a better choice to achieve smaller computing energy consumption. It also shows that the trajectory optimization plays a dominant factor in minimizing the total required energy of the system and optimizing acceleration has a great effect on the required energy of the UAV. Additionally, by jointly optimizing the UAV’s CPU frequencies and the amount of bits offloaded to UAV, the minimal required energy for computing can be greatly reduced compared to other schemes and by leveraging the computing resources of idle SDs, the UAV’s computing energy can also be greatly reduced

Effect of a booster dose of influenza vaccine in patients with hemodialysis, peritoneal dialysis and renal transplant recipients: A systematic literature review and meta-analysis

Booster influenza vaccination has been recommended for patients with chronic renal disease in order to enhance the immune response to the influenza vaccine; however, the efficacy of a booster influenza vaccination is a matter of controversy. Therefore, we made a meta-analysis to determine the efficacy in patients with hemodialysis (HD), peritoneal dialysis (PD) and renal transplant recipient (RT). The sero-protection rate was used as a serologic parameter to describe the immune response to the vaccine. Statistical analysis was performed to calculate the pooled rate difference (RD) and 95% confidence interval (CI). The pooled RD for the H1N1, H3N2 and B influenza vaccines was 0.02 (95% CI: −0.02–0.06), 0.05 (95% CI: −0.01–0.11), 0.04 (95% CI: −0.02–0.10), respectively. We concluded that a booster dose of the influenza vaccine did not effectively enhance immunogenicity. Therefore, a booster dose of vaccine is not recommended for patients with hemodialysis, peritoneal dialysis and renal transplant recipients.Natural Science Foundation of Guangdong Province, Chin

Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models

Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments. The recent emergence of foundation models such as ChatGPT marks a revolutionary shift in the fields of machine learning and artificial intelligence. Their unparalleled capabilities in contextual understanding, problem solving, and adaptability across a wide range of tasks suggest that integrating these models into urban domains could have a transformative impact on the development of smart cities. Despite growing interest in Urban Foundation Models~(UFMs), this burgeoning field faces challenges such as a lack of clear definitions, systematic reviews, and universalizable solutions. To this end, this paper first introduces the concept of UFM and discusses the unique challenges involved in building them. We then propose a data-centric taxonomy that categorizes current UFM-related works, based on urban data modalities and types. Furthermore, to foster advancement in this field, we present a promising framework aimed at the prospective realization of UFMs, designed to overcome the identified challenges. Additionally, we explore the application landscape of UFMs, detailing their potential impact in various urban contexts. Relevant papers and open-source resources have been collated and are continuously updated at https://github.com/usail-hkust/Awesome-Urban-Foundation-Models