Non-thermal Plasma Inactivation of Bacillus Amyloliquefaciens spores

Bacterial spores have remarkable resistance to a variety of harsh conditions, causing spoilage in food industry and becoming the primary bacterial agent in biowarfare and bioterrorism. In this study, inactivation mechanisms of Bacillus amyloliquefaciens (BA) spores by non-thermal plasma (NTP) were investigated by using Fourier-transform infrared spectroscopy (FTIR) as a major tool to exam spores after NTP treatment. Chemometric techniques, such as multivariate classification models based on soft independent modeling of Class Analogy (SIMCA) and Principal Component Analysis (PCA), were employed to identify functional group changes in FTIR spectra. The IR absorbance bands correlated to dipicolinic acid (DPA) decreased after NTP treatment indicating that DPA released and then reacted with reactive species generated by NTP and it was confirmed by nuclear magnetic resonance (NMR). Also IR absorbance bands corresponding to protein structure changed. FTIR combined with UV-Vis spectroscopy was used to monitor spore germination. Large amount of DPA released in a short time when spores germinated at 50°C, showing that DPA released in response to heating. NTP treated spores could germinate with little DPA release due to sub-lethal effects induced by plasma. Also an empirical model based on Weibull distribution was established to describe the spore germination process showing that NTP treated spores exhibited abnormal germination pattern. Inactivation mechanisms of NTP with air as feed gas was compared with high-pressure, wet heat, chemical treatment using chlorine dioxide (CD) and NTP with argon as feed gas. The results showed that few chemical changes in spores after autoclave and high pressure treatments, though protein structure changed. CD and NTP with air as feed gas inactivated spores by oxidation. DPA released after NTP with argon as feed gas treatment and it is possible that UV and charged particles accounts for the inactivation. This study provides in depth insight into the inactivation mechanism of NTP and information for optimizing NTP process

Numerical investigation on rules of fracture propagation during hydraulic fracturing in heterogeneous coal-rock mass

To investigate rules of fracture propagation during hydraulic fracturing in heterogeneous coal-rock mass, a new mathematical model for hydraulic fracturing with seepage-damage coupling and its numerical algorithm are proposed. The rules of coal-rock mass heterogeneity, confining pressure, beforehand hydraulic slotting, and non-symmetric pressure gradient on fracture propagation are investigated. Numerical results show the following: (1) Compared to homogeneous coal-rock mass, the fracture propagation pattern exhibits a more zig-zag characteristic and the fracture initiation pressure is reduced in heterogeneous coal-rock mass. (2) Fracture propagation during borehole fracturing is mainly controlled by confining pressure ratio, and the fracture would propagate along the path with least resistance in coal-rock mass. (3) During hydraulic fracturing with beforehand hydraulic slotting, fracture propagation pattern would become more complex with slotting length increasing; the propagation direction of fracture is primarily controlled by principal stress difference, the larger of principal stress difference, the more difficult of oriented fracturing. (4) Non-symmetric pressure gradient can reduce breakdown pressure and influence fracture propagation pattern, which provides a beneficial guide for oriented fracturing. The simulation results are consistent with the theoretical solutions and experimental observations, which is promising to guide field operation of hydraulic fracturing to improve coalbed methane extraction

An in-depth analysis of system-level techniques for Simultaneous Multi-threaded Processors in Clouds

To improve the overall system utilization, Simultaneous Multi-Threading (SMT) has become a norm in clouds. Usually, Hardware threads are viewed and deployed directly as physical cores for attempts to improve resource utilization and system throughput. However, context switches in virtualized systems might incur severe resource waste, which further led to significant performance degradation. Worse, virtualized systems suffer from performance variations since the rescheduled vCPU may affect other hardware threads on the same physical core. In this paper, we perform an in-depth experimental study about how existing system software techniques improves the utilization of SMT Processors in Clouds. Considering the default Linux hypervisor vanilla KVM as the baseline, we evaluated two update-to-date kernel patches IdlePoll and HaltPoll through the combination of 14 real-world workloads. Our results show that mitigating they could significantly mitigate the number of context switches, which further improves the overall system throughput and decreases its latency. Based on our findings, we summarize key lessons from the previous wisdom and then discuss promising directions to be explored in the future

Non-Hermitian skin effect and nonreciprocity induced by dissipative couplings

We study the mechanism for realizing non-Hermitian skin effect (NHSE) via dissipative couplings, in which the left-right couplings have equal strengths but the phases do not satisfy the complex conjugation. Previous realizations of NHSE typically require unequal left-right couplings or on-site gain and loss. In this work we find that when combined with the multichannel interference provided by a periodic dissipative-coherent coupling structure, the dissipative couplings can lead to unequal left-right couplings, inducing NHSE. Moreover, we show that the non-Hermiticity induced by dissipative couplings can be fully transformed into nonreciprocity-type non-Hermiticity without bringing extra gain-loss-type non-Hermiticity. Thus, this mechanism enables unidirectional energy transmission without introducing additional insertion loss. Our work opens a new avenue for the study of non-Hermitian topological effects and the design of directional optical networks

A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions

In the era of growing developments in Autonomous Vehicles, the importance of Human-Vehicle Interaction has become apparent. However, the requirements of retrieving in-vehicle drivers’ multi- modal data trails, by utilizing embedded sensors, have been consid- ered user unfriendly and impractical. Hence, speculative designs, for in-vehicle multi-modal data retrieval, has been demanded for future personalized and intelligent Human-Vehicle Interaction. In this paper, we explore the feasibility to utilize facial recog- nition techniques to build in-vehicle multi-modal data retrieval. We first perform a comprehensive user study to collect relevant data and extra trails through sensors, cameras and questionnaire. Then, we build the whole pipeline through Convolution Neural Net- works to predict multi-model values of three particular categories of data, which are Heart Rate, Skin Conductance and Vehicle Speed, by solely taking facial expressions as input. We further evaluate and validate its effectiveness within the data set, which suggest the promising future of Speculative Designs for Multi-modal Data Retrieval through this approach

DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network

The rapid advances in Vision Transformer (ViT) refresh the state-of-the-art performances in various vision tasks, overshadowing the conventional CNN-based models. This ignites a few recent striking-back research in the CNN world showing that pure CNN models can achieve as good performance as ViT models when carefully tuned. While encouraging, designing such high-performance CNN models is challenging, requiring non-trivial prior knowledge of network design. To this end, a novel framework termed Mathematical Architecture Design for Deep CNN (DeepMAD) is proposed to design high-performance CNN models in a principled way. In DeepMAD, a CNN network is modeled as an information processing system whose expressiveness and effectiveness can be analytically formulated by their structural parameters. Then a constrained mathematical programming (MP) problem is proposed to optimize these structural parameters. The MP problem can be easily solved by off-the-shelf MP solvers on CPUs with a small memory footprint. In addition, DeepMAD is a pure mathematical framework: no GPU or training data is required during network design. The superiority of DeepMAD is validated on multiple large-scale computer vision benchmark datasets. Notably on ImageNet-1k, only using conventional convolutional layers, DeepMAD achieves 0.7% and 1.5% higher top-1 accuracy than ConvNeXt and Swin on Tiny level, and 0.8% and 0.9% higher on Small level.Comment: Accepted by CVPR 202

The Organic Amendment Improve the Yield and Quality of Vegetable

Using biotechnology, we can change agricultural wastes into high‐quality organic fertilizers, which leads us in the direction of the development in modern agriculture and act as substitute to the chemical fertilizers. In this chapter, five types of technologies of organic amendment are elaborated. Each method can be selected based on the specific circumstance. The effects of the technology in the production are introduced and the principles of the technologies are explained in a simple manner