Quantum Control of Heat Current

We investigate the local thermal transport in a quantum trimer of harmonic oscillators connected to two thermal baths. The coupling between them are augmented by complex phases which leads to the quantum control of the local atypical heat current between two oscillators connected to the same heat bath. Our study reveals that this atypical heat current is a consequence of the lifting of the dark mode and the modulation of this current is due to variation in system bath correlations. The proposed quantum system may find application in quantum thermal and memory devices by leveraging the heat current

Scaling of transition temerature and CuO_2 plane buckling in the cuprate superconductors

Recently it is seen\cite{Nature},that both $\rm{T}_c$ and the buckling of the $CuO_2$ planes goes through a maximum at the same doping level. We show that only for optimal doping concentration the Fermi surface touches the M($0,\pi$) point in the BZ, where the matrix element for interlayer pair tunneling amplitude is largest, so that the gain in delocalization energy by tunneling(in pairs) along the $c$ axis is largest. Buckling of the planes on the other hand modulates the separation between the planes and thereby modulates the interlayer pair tunneling amplitude. That is why both $\rm{T}_c$ and buckling angle(Oxygen atom displacement out of the plane) scales the same way with doping concentration. We have calculated $\rm{T}_c$ and buckling angle for various doping concentration. The agreement with experiment is remarkably good. We also point out the possible reason for large(about 1 percent) change of the buckling mode phonon frequency, accross the transition temperature. scatteringComment: Summitted to Physics C, Journal of Superconductivity, 6 pages Tex file with 4 postscript files attache

TDLR: Top (\u3cem\u3eSemantic\u3c/em\u3e)-Down (\u3cem\u3eSyntactic\u3c/em\u3e) Language Representation

Language understanding involves processing text with both the grammatical and common-sense contexts of the text fragments. The text “I went to the grocery store and brought home a car” requires both the grammatical context (syntactic) and common-sense context (semantic) to capture the oddity in the sentence. Contextualized text representations learned by Language Models (LMs) are expected to capture a variety of syntactic and semantic contexts from large amounts of training data corpora. Recent work such as ERNIE has shown that infusing the knowledge contexts, where they are available in LMs, results in significant performance gains on General Language Understanding (GLUE) benchmark tasks. However, to our knowledge, no knowledge-aware model has attempted to infuse knowledge through top-down semantics-driven syntactic processing (Eg: Common-sense to Grammatical) and directly operated on the attention mechanism that LMs leverage to learn the data context. We propose a learning framework Top-Down Language Representation (TDLR) to infuse common-sense semantics into LMs. In our implementation, we build on BERT for its rich syntactic knowledge and use the knowledge graphs ConceptNet and WordNet to infuse semantic knowledge

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans

Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

Spatial pattern in macroinvertebrate communities in headwater streams of New Zealand and a multivariate river classification system : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Ecology at Massey University, Palmerston North, New Zealand

Macroinvertebrate data collected from 120 headwater streams in New Zealand were used to test the ability of the Freshwater Environments of New Zealand River Classification (FWENZ) to explain spatial variation in unimpacted stream invertebrate communities. FWENZ is a GIS based multivariate river environment classification of the sections of national river network. The classification performance of the FWENZ was examined to determine the optimum classification level which could be used for the purpose of conservation and biomonitoring of New Zealand rivers and streams. The classification performance of the FWENZ was also compared to those of two other river classification systems, the ecoregions and the River Environment Classification (REC). Results of the analysis of similarity (ANOSIM) test showed that discrimination of the study sites based on interclass differences in macroinvertebrate community composition was optimal at FWENZ 100 class level which classifies the New Zealand rivers and streams into 100 different groups. The FWENZ 100 class level distinguished the biological variation of the study sites at a finer spatial scale than the REC Geology level. Although performance of the ecoregions classification was stronger than both the river environmental classifications, the REC and the FWENZ, but it was unable to explain the variation in local assemblage structures. Multivariate analyses of the macroinvertebrate abundance data and the associated environmental variables at three different spatial scales (upstream catchment, segment, and reach) were used to identify environmental predictors of assemblage patterns. Catchmentscale measures of climatic, topographic and landcover factors were more strongly correlated with macroinvertebrate community structures than segment scale measures, whereas reachscale measures of instream physicochemical factors and riparian characteristics had the least association with assemblage patterns. Despite the strong influences of cathment-scale factors on macroinvertebrate communities, local factors like water temperature, stream velocity, reach elevation, percent canopy cover and percent moss cover were also involved in explaining the within-region variation in assemblage patterns, which indicates the importance of considering regional as well as local factors as surrogates of stream invertebrate communities to provide a base for stream bioassessment programmes at multiple scales

PROCESS VALIDATION OF BETA-SITOSTEROL HAIR GEL FORMULATION AND EVALUATION OF 5 ALPHA REDUCTASE INHIBITION IN VITRO FOR THE TREATMENT OF ANDROGENETIC ALOPECIA

Objective: The present study was aimed to develop topical gel containing β-sitosterol using carbopol 940 as gelling agent and to investigate 5 alpha reductase (5α-reductase) inhibitory activity of suitable gel formulation and compare it with a commercial product used topically for alopecia. Methods: Three different batches of β-sitosterol hair gel formulation were manufactured and evaluated. Additionally, the 5α-reductase inhibitory activity of the prepared formulation, finasteride as a positive control, was evaluated and compared to the commercial herbal formulation used. Results: According to the analytical findings of three different batches, the gel formulation is good in appearance, homogeneous, and easily spreadable. Based on findings from HPLC and HPTLC, the amount of β-sitosterol in those formulations complies with the label claim. By checking different critical parameters of those batches, we established the manufacturing process method validation and the process reproducibility. In-vitro results showed the good 5α-reductase inhibitory potential of prepared gel formulation and then commercial product. The IC50 value of the prepared formulation was 118.960 ± 0.634 (µg/ml) and standard beta-sitosterol 88.854 ± 0.70 (µg/ml), whereas Finasteride (positive control) 224.372 ± 3.103 (ng/ml). Conclusion: Thus, β-sitosterol formulation utilises a straightforward, low-cost production, less time-consuming process with minimal facility and equipment requirements. The formulation may be a promising candidate for future investigation into their antiandrogenic activities

Carbon Dots -A Turn-On Probe for Neurological Disorder

The creation of novel methods is essential for the early diagnosis and treatment of neurological illnesses (NDs). The blood-brain barrier (BBB), which can prevent substances from accessing the central nervous system, is the most difficult obstacle to overcome in the development of neural medication delivery systems (CNS). For several biological applications, carbon dots (CDs) have emerged as highly outstanding and promising agents, including the treatment of brain tumours, ND, and bioimaging research. Because of their great qualities, they have a lot of potential for a range of scientific disciplines due to their biocompatibility, tiny size, tunable optical properties, photostability, and straightforward fabrication processes. This article's goal is to provide a summary of current CD research and to make recommendations for future work on creating neural drug delivery systems that can penetrate the BBB and reach the central nervous system. The two main subjects of this review are CD toxicity and unique optical properties. For a variety of neurological illnesses, a unique CD-based drug delivery system is designed in detail. This study also explores the possible applications of CDs for neurodegenerative disease therapies and imaging of brain tumours. The final section provides a summary of present CD sensing applications and projected future developments