Traditional models based on electroencephalographic (EEG) signals for seizure monitoring encounter difficulties in simultaneously optimizing accuracy, response latency, and computational load. These challenges hinder their deployment in edge computing environments, where real-time local inference is critical. To address these issues, we introduce a novel network architecture, designated as HengNet. This architecture integrates a Two-level Reuse Algorithm (TRA), which strategically reutilizes outputs from intermediate layers, considerably reducing the average computational load per inference - vital for scenarios requiring frequent inferences. When tested on the CHB-MIT dataset, this patient-specific model attains classification accuracies of 95.67% and 99.60% for seizure prediction and detection, respectively. Notably, it maintains an average computational load of merely 0.05 million multiply-accumulate operations (MACs) per inference and has a compact model size of 6.87 K parameters. These results represent a significant advancement compared with existing methods. Operating at a rate of 32 inferences per second, the computational load of the model for seizure prediction has been reduced by more than 19.4 times, and for seizure detection, by more than 6.4 times.

In light of the growing interest in type inference research for Python, both researchers and practitioners require a standardized process to assess the performance of various type inference techniques. This paper introduces TypeEvalPy, a comprehensive microbenchmarking framework for evaluating type inference tools. Type- EvalPy contains 154 code snippets with 845 type annotations across 18 categories that target various Python features. The framework manages the execution of containerized tools, transforms inferred types into a standardized format, and produces meaningful metrics for assessment. Through our analysis, we compare the performance of six type inference tools, highlighting their strengths and limitations. Our findings provide a foundation for further research and optimization in the domain of Python type inference.

“App stores” are online software stores where end users may browse, purchase, download, and install software applications. By far, the best known app stores are associated with mobile platforms, such as Google Play for Android and Apple’s App Store for iOS. The ubiquity of smartphones has led to mobile app stores becoming a touchstone experience of modern living. App stores have been the subject of many empirical studies. However, most of this research has concentrated on properties of the apps rather than the stores themselves. Today, there is a rich diversity of app stores and these stores have largely been overlooked by researchers: app stores exist on many distinctive platforms, are aimed at different classes of users, and have different end-goals beyond simply selling a standalone app to a smartphone user. The goal of this paper is to survey and characterize the broader dimensionality of app stores, and to explore how and why they influence software development practices, such as system design and release management. We begin by collecting a set of app store examples from web search queries. By analyzing and curating the results, we derive a set of features common to app stores. We then build a dimensional model of app stores based on these features, and we fit each app store from our web search result set into this model. Next, we performed unsupervised clustering to the app stores to find their natural groupings. Our results suggest that app stores have become an essential stakeholder in modern software development. They control the distribution channel to end users and ensure that the applications are of suitable quality; in turn, this leads to developers adhering to various store guidelines when creating their applications. However, we found the app stores operational model could vary widely between stores, and this variability could in turn affect the generalizability of existing understanding of app stores.

The present work demonstrates how drug-loaded mesoporous silica nanoparticles (MSNPs) can be prepared by a sequential flash nanoprecipitation (FNP) technique. A sequential FNP technique is developed relying on a combination of two multi-inlet vortex mixers (MIVM), by which a continuous process that involves the formation of micelle-based templates followed by an in situ formation of MSNPs is achieved. Moreover, a widely used biological nematicide, abamectin (Abm), is added during the formation of micelles, ultimately leading to Abm-loaded MSNPs with high encapsulation efficiency. The obtained Abm-loaded MSNPs show excellent stability and inhibition activity against the livability of Meloidogyne incognita. Importantly, the parameters of the resulting MSNPs, such as silica shell thickness and inner cavity size of MSNPs, can be easily controlled by tuning the compositions of the reactant streams. We believe that such a simple approach towards direct preparation of drug-loaded MSNPs would find promising up-scale applications in various fields, such as drug delivery, bioimaging, and formulation technology.

A biocompatible Dex-MA/PAA hydrogel was prepared through copolymerization of glycidyl methacrylate substituted dextran (Dex-MA) with acrylic acid (AA), which was applied as the adsorbent to remove cationic dyes from aqueous solutions. Dex-MA/PAA hydrogel presented a fast adsorption rate and the removal efficiency of Methylene Blue (MB) and Crystal Violet (CV) reached 93.9% and 86.4%, respectively within one minute at an initial concentration of 50 mg L^-1. The adsorption equilibrium data fitted the Sips isotherm model well with high adsorption capacities of 1994 mg g^-1 for MB and 2390 mg g^-1 for CV. Besides, dye adsorption occurred efficiently over the pH range 3-10 and the temperature range 20-60 °C. Moreover, the removal efficiencies for MB and CV were still >95% even after five adsorption/desorption cycles which indicates the robust nature of the Dex-MA/PAA hydrogel and its potential as an eco-friendly adsorbent for water treatment.

Chitosan has been used to cross-link poly(acrylic acid) to give three pH-sensitive hydrogels designed to control the release of the drugs amoxicillin and meloxicam. The extent of cross-linking and solution pH was found to dominate the swelling behavior of these hydrogels as shown by scanning electron microscopy and swelling time dependencies. The rates of release of amoxicillin and meloxicam from the loaded hydrogels increased with increase in pH consistent with the extent of hydrogen bonding between hydrogel components and between the hydrogel and the drugs being important determinants of release rate. Both the Korsemeyer-Peppas and Weibull models fitted release data consistent with drug release occurred through a combination of drug diffusion and hydrogel relaxation processes. These hydrogels appear to provide an ideal basis for controlled drug delivery systems.

Hollow silica nanoparticles were prepared through generating a silica layer in spherical polyelectrolyte nanogels (SPN), which consisted of a solid core of polystyrene (PS) and a shell of crosslinked poly(acrylic acid) (PAA), followed by removing the PS core via solvent dissolution. Small angle X-ray scattering (SAXS) in combination with TEM were employed to observe SPN, silica-polymer composite, and hollow silica nanoparticles. It was confirmed that SAXS is a powerful method to monitor the generation of silica layer in SPN. The density and thickness of generated silica layer in SPN were found to be tunable by controlling the crosslinking density of the templates. The porous structure and pH sensitivity of silica layer allowed the obtained hollow silica to be ideal carriers for controlled drug delivery.