Ethyl cellulose/gelatin/ß-cyclodextrin/curcumin nanofibrous membrane with antibacterial and formaldehyde adsorbable capabilities for lightweight and high-performance air filtration.

Functionalization of bio-based nanofibers is the development tendency of high-performance air filter. However, the conventional structural optimization strategy based on high solution conductivity greatly hinders the development of fully bio-based air filter, and not conducive to sustainable development. This work fabricated fully bio-based nanofibrous membrane with formaldehyde-adsorbable and antibacterial capabilities by electrospinning low-conductivity solution for high-performance air filtration and applied to lightweight mask. The "water-like" ethyl cellulose (EC) was selected as the base polymer to "nourish" functional materials of gelatin (GE), ß-cyclodextrin (ßCD), and curcumin (Cur), thus forming a solution system with high binding energy differences and electrospinning into ultrafine bimodal nanofibers. The filtration efficiency for 0.3 µm NaCl particles, pressure drop, and quality factor were 99.25 %, 53 Pa, and 0.092 Pa-1, respectively; the bacteriostatic rates against Escherichia coli and Staphylococcus aureus were 99.9 % and 99.4 %, respectively; the formaldehyde adsorption capacity was 442 µg/g. This is the first report on antibacterial and formaldehyde-adsorbable high-performance air filter entirely made from bio-based materials. This simple strategy will greatly broaden the selection of materials for preparing high-performance multifunctional air filter, and promote the development of bio-based air filter.

Coaxial Electrospun Tai Chi-Inspired Lithium-Ion Battery Separator with High Performance and Fireproofing Capacity.

Organic flame-retardant-loaded battery separator offers a new opportunity for battery safety. However, its poor thermal stability still poses serious safety issues. Inspired by Tai Chi, an "internal-cultivating and external-practicing" core-shell nanofibrous membrane was prepared by coaxial electrospinning, wherein the shell layer was a mixture of polyvinylidene fluoride, silicon dioxide (SiO2), and graphene oxide (GO) and the core layer contained triphenyl phosphate (TPP). SiO2 and GO enhanced the thermal stability and electrochemical performance. The encapsulated TPP prevented heat transfer and the degradation of electrochemical performance caused by its direct dissolution. This separator exhibited outstanding thermal stability and flame retardancy: it did not burn and remained relatively intact (91.2%) in an open flame for 15 s. The battery assembled with a composite separator showed excellent performance: the initial capacity reached 164 mA h/g and maintained 95% after 100 charge-discharge cycles. This novel strategy endows high-performance lithium batteries with relatively higher safety.

Hotspots Reduction for GALS NoC Using a Low-Latency Multistage Packet Reordering Approach.

Traffic splitting enabled by Globally Asynchronous Locally Synchronous (GALS) Network-on-chip (NoC) brings multipath routing capability, which significantly increases link bandwidth at the cost of out-of-order packet delivery. Solving the packet reordering problem is one of the keys to ensure the quality of service (QoS) for NoC. However, traditional packet reordering approaches rely on local reorder buffer, causing on-chip hotspots, which aggravates chip aging and even leads to interconnection failures. In this paper, we present a multistage packet reordering (MPR) approach, which cannot only reduce the transmission latency but also effectively reduces hotspots caused by local reordering. Specifically, we propose multistage reordering buffer (MRB) by reusing channel buffers for implementing MPR. Experimental results show that our proposed approach achieved improved thermal efficiency with reduced hardware resource consumption.

Vessel extraction from non-fluorescein fundus images using orientation-aware detector.

The automatic extraction of blood vessels in non-fluorescein eye fundus images is a tough task in applications such as diabetic retinopathy screening. However, vessel shapes have complex variations, and accurate modeling of retinal vascular structures is challenging. We have therefore developed a new approach to accurately extract blood vessels in non-fluorescein fundus images using an orientation-aware detector (OAD). The detector was designed according to the intrinsic property of vessels being locally oriented and having linearly elongated structures. We employ the OAD to extract vessel shapes with no assumptions on parametric orientations of vessel shapes. The orientations of vessels can be efficiently modeled by the energy distribution of Fourier transformation. Accordingly, both wide and thin vessels can be extracted with two-scale segmentation in which line operators are applied in large scale and the Gabor filter bank is applied in small scale. A post-processing technique, based on the path opening operation, is applied to eliminate false responses to nonvascular areas, such as retinal structures (optic disc and macula) and pathologies (exudates, hemorrhages,and microaneurysms). This makes the detector robust and structure-aware. By achieving a competitive CAL measurement of 80.82% for the DRIVE database and 68.94% for the STARE, the experimental results demonstrated that the OAD approach outperforms existing segmentation methods. Furthermore, the proposed approach effectively works with non-fluorescein fundus images and proves highly accurate and robust in complicated regions such as the central reflex, close vessels, and crossover points, despite a high level of illumination noise in the original data.

A novel self-organizing E-Learner community model with award and exchange mechanisms.

How to share experience and resources among learners is becoming one of the hottest topics in the field of E-Learning collaborative techniques. An intuitive way to achieve this objective is to group learners which can help each other into the same community and help them learn collaboratively. In this paper, we proposed a novel community self-organization model based on multi-agent mechanism, which can automatically group learners with similar preferences and capabilities. In particular, we proposed award and exchange schemas with evaluation and preference track records to raise the performance of this algorithm. The description of learner capability, the matchmaking process, the definition of evaluation and preference track records, the rules of award and exchange schemas and the self-organization algorithm are all discussed in this paper. Meanwhile, a prototype has been built to verify the validity and efficiency of the algorithm. Experiments based on real learner data showed that this mechanism can organize learner communities properly and efficiently; and that it has sustainable improved efficiency and scalability.