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2022 ◽  
Author(s):  
Ming Fu ◽  
Mónica Mota ◽  
Xiaofei Xiao ◽  
Andrea Jacassi ◽  
Yi Li ◽  
...  

Abstract The Raman scattering of light by molecular vibrations offers a powerful technique to ‘fingerprint’ molecules via their internal bonds and symmetries. Since Raman scattering is weak1, methods to enhance, direct and harness it are highly desirable, e.g. through the use of optical cavities2, waveguides3–6, and surface enhanced Raman scattering (SERS)7–9. While SERS offers dramatic enhancements6,15,22,2 by localizing light within vanishingly small ‘hot-spots’ in metallic nanostructures, these tiny interaction volumes are only sensitive to few molecules, yielding weak signals that are difficult to detect10 . Here, we show that SERS from 4-Aminothiophenol (4-ATP) molecules bonded to a plasmonic gap waveguide is directed into a single mode with > 99% efficiency. Although sacrificing a confinement dimension, we find > 104 times SERS enhancement across a broad spectral range enabled by the waveguide’s larger sensing volume and non-resonant mode. Remarkably, the waveguide-SERS (W-SERS) is bright enough to image Raman transport across the waveguides exposing the roles of nanofocusing11–13 and the Purcell effect14. Emulating the e-factor from laser physics15–17, the near unity Raman -factor observed exposes the SERS technique in a new light and points to alternative routes to controlling Raman scattering. The ability of W-SERS to direct Raman scattering is relevant to Raman sensors based on integrated photonics7–9 with applications in gas and bio-sensing as well as healthcare.


2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho

As the mode share of the subway in Seoul has increased, the estimation of passenger travel routes has become a crucial issue to identify the congestion sections in the subway network. This paper aims to estimate the travel train of subway passengers in Seoul. The alternative routes are generated based on the train log data. The travel route is then estimated by the empirical cumulative distribution functions (ECDFs) of access time, egress time, and transfer time. The train choice probability is estimated for alternative train combinations and the train combination with the highest probability is assigned to the subway passenger. The estimated result is validated using the transfer gate data which are recorded on private subway lines. The result showed that the accuracy of the estimated travel train is shown to be 95.6%. The choice ratios for no-transfer, one-transfer, two-transfer, three-transfer, and four-transfer trips are estimated to be 53.9%, 37.7%, 6.5%, 1.5%, and 0.4%, respectively. Regarding the practical application, the passenger kilometers by lines are estimated with the travel route estimation of the whole network. As results of the passenger kilometer calculation, the passenger kilometer of the proposed algorithm is estimated to be 88,314 million passenger kilometer. The proposed algorithm estimates the passenger kilometer about 13% higher than the shortest path algorithm. This result implies that the passengers do not always prefer the shortest path and detour about 13% for their convenience.


2021 ◽  
Vol 60 (4) ◽  
pp. 187-203
Author(s):  
Tomasz Krukowicz ◽  
Krzysztof Firląg ◽  
Aleksander Sobota ◽  
Tomasz Kołodziej ◽  
Luka Novačko

The article presents the relationship between the intensity of bicycle traffic volume and the development of bicycle infrastructure on the example of Warsaw. There has been a big increase in cycling over the last decade. At the same time, the linear and point bicycle infrastructure developed very strongly. Similar trends are also observed in other cities in Poland. The article presents the types of infrastructure available to cyclists. Then, the method of assessing the bicycle infrastructure is presented, taking into account the five features of good bicycle infrastructure: cohesion, directness, attractiveness, safety and comfort. In terms of coherence, the analysis covered the bicycle infrastructure network in the vicinity of the measurement site. The directness was tested by checking the accessibility of several dozen of the most important nodal points of the city's communication network. The attractiveness was examined by checking the availability of public bike stations, bicycle racks and bike-sharing stations. The infrastructure adjusted to the technical class of the road was adopted as a measure of safety. The comfort was checked by analyzing the quality of the road surface, which affects the driving comfort and energy expenditure. All the factors presented impact the cyclist's assessment of the infrastructure. To standardize the assessment rules, an aggregate index of the development of bicycle infrastructure was determined. The analysis was carried out for 10 sample points for four consecutive years. The points were characterized by different bicycle infrastructure, location in the city road network and different results of bicycle traffic measurements. The analysis showed a strong positive relationship between traffic and cycling infrastructure for most of the analyzed places. There was a negative dependence in the case of the construction of alternative routes in relation to the place of traffic measurements. The obtained results are the same as in the works of other authors. However, the effects of work do not allow to determine which of the examined factors is the cause and which is the effect but only show the existing relationship.


2021 ◽  
Author(s):  
Shah Mahdi Hasan ◽  
Kaushik Mahata ◽  
Md Mashud Hyder

To support the explosive growth of the Internet of Things (IoT), Uplink (UL) grant-free Non-Orthogonal Multiple Access (NOMA) emerges as a promising technology. It has the potential of offering scalable and low-cost solutions for the resource-constrained Massive Machine Type Communication (mMTC) systems. In principle, the grant-free NOMA enables small signaling overhead and low access latency time by circumventing complicated grant-access based procedures which is commonly found in the legacy wireless networks. In a UL grant-free system, a complete Multi-User Detection (MUD) algorithm not only performs the Active User Detection (AUD) but also the Channel Estimation (CE) and the Data Detection (DD). By exploiting the naturally occurring sparse user activity in the mMTC systems, the MUD problem can be solved using a wide range of Compressive Sensing based algorithms (CS-MUD). However, some alternative routes have been explored in the literature as well. The utility of these algorithms, in general, revolve around some assumptions about the channel or the availability of perfect channel information at the Base Station (BS). How these assumptions are met in a practical circumstance is, however, an important concern. In this work we devise an end-to-end MUD using Deep Neural Network (DNN) where we relax these assumptions. We approximate an ensemble of trained DNN based MUD using Knowledge Distillation (KD) to enable fast AUD at the Base Station (BS). Furthermore, using the inter-resource correlation, we estimate the channels of the active users which is an ill-posed problem otherwise. We carry out elaborate numerical investigation to validate the efficacy of the proposed approach for the UL grant-free NOMA systems.


Author(s):  
Iqra Ejaz ◽  
Salwa Naeem ◽  
Mian Seher Munir ◽  
Muhammad Usman ◽  
Sohail Zafar ◽  
...  

Objectives: To analyze impact of alternative routes and timing of dopamine and mannitol administrations to reduce negative properties of extended cardiopulmonary bypass on renal function in coronary artery operations. Methods: Set I (n: 26 individual): Mannitol (1 g/kg) has been introduced to the CPB priming solution. Set II (n: 25 patients): Even during interval among anesthetic induction and operation, 3 g/kg/min of IV dopamine was delivered. Group III (n = 25 patients): 2 g/kg/min IV dopamine was provided among anesthesia initiation and operation conclusion, and 1 g/kg mannitol were added to priming solution for CPB. Furosemide was administered to Group IV (n = 26 cases) when urine production was poor. Results: There would be a substantial rise in the post-operative urine microalbumin/creatinine ratios over all classes (p 0.06), as well as a rise in cystatin-c in Set 1, 2, and 3 (p 0.02). Conclusions: Researchers suggest that combining dopamine infusion (1 g/kg/min) and mannitol (2 g/kg) throughout CPB seems to be the more actual method for preventing detrimental possessions of CPB on renal functioning.


2021 ◽  
Vol 18 ◽  
Author(s):  
Dhwani Rana ◽  
Sagar Salave ◽  
Derajram Benival

Background: Opioid medications are an integral part in the management of acute and chronic severe pain. However, non-medical practice of these prescription drug products is emerging as a serious public health problem. To control this opioid epidemic, USFDA is encouraging pharmaceutical companies to develop Abuse Deterrent Formulations (ADFs). Abuse Deterrent Formulations are much more difficult to manipulate and abuse when compared to their conventional formulations. This feature of ADFs is due to their ability to incumber extraction of active ingredients, to prevent administration through alternative routes and making abuse of altered product less rewarding. Objective: The main objective of this review is to abridge different ADFs and various laboratory-based in vitro manipulation and extraction studies, demonstrating that these approved ADFs have capabilities to deter abuse. Methods: The method includes collection of data from different search engines like PubMed, FDA guidance documents, ScienceDirect, Google Patents to get coverage of literature in order to get appropriate information regarding ADFs. Results: Various in vitro studies demonstrate that ADFs are effective in minimizing opioid drug abuse including opioid overdose. However, real impact of these ADFs on reducing the drug abuse can be concluded only after receiving the post marketing data. Conclusion: ADFs are embracing fundamentally different paradigm in management of severe pain. We believe that development of abuse deterrent technologies would shift the architype, deterring multipill abuse and can prove as a breakthrough strategy in controlling this opioid epidemic menace.


2021 ◽  
Author(s):  
Shah Mahdi Hasan ◽  
Kaushik Mahata ◽  
Md Mashud Hyder

To support the explosive growth of the Internet of Things (IoT), Uplink (UL) grant-free Non-Orthogonal Multiple Access (NOMA) emerges as a promising technology. It has the potential of offering scalable and low-cost solutions for the resource-constrained Massive Machine Type Communication (mMTC) systems. In principle, the grant-free NOMA enables small signaling overhead and low access latency time by circumventing complicated grant-access based procedures which is commonly found in the legacy wireless networks. In a UL grant-free system, a complete Multi-User Detection (MUD) algorithm not only performs the Active User Detection (AUD) but also the Channel Estimation (CE) and the Data Detection (DD). By exploiting the naturally occurring sparse user activity in the mMTC systems, the MUD problem can be solved using a wide range of Compressive Sensing based algorithms (CS-MUD). However, some alternative routes have been explored in the literature as well. The utility of these algorithms, in general, revolve around some assumptions about the channel or the availability of perfect channel information at the Base Station (BS). How these assumptions are met in a practical circumstance is, however, an important concern. In this work we devise an end-to-end MUD using Deep Neural Network (DNN) where we relax these assumptions. We approximate an ensemble of trained DNN based MUD using Knowledge Distillation (KD) to enable fast AUD at the Base Station (BS). Furthermore, using the inter-resource correlation, we estimate the channels of the active users which is an ill-posed problem otherwise. We carry out elaborate numerical investigation to validate the efficacy of the proposed approach for the UL grant-free NOMA systems.


2021 ◽  
Author(s):  
Shah Mahdi Hasan ◽  
Kaushik Mahata ◽  
Md Mashud Hyder

To support the explosive growth of the Internet of Things (IoT), Uplink (UL) grant-free Non-Orthogonal Multiple Access (NOMA) emerges as a promising technology. It has the potential of offering scalable and low-cost solutions for the resource-constrained Massive Machine Type Communication (mMTC) systems. In principle, the grant-free NOMA enables small signaling overhead and low access latency time by circumventing complicated grant-access based procedures which is commonly found in the legacy wireless networks. In a UL grant-free system, a complete Multi-User Detection (MUD) algorithm not only performs the Active User Detection (AUD) but also the Channel Estimation (CE) and the Data Detection (DD). By exploiting the naturally occurring sparse user activity in the mMTC systems, the MUD problem can be solved using a wide range of Compressive Sensing based algorithms (CS-MUD). However, some alternative routes have been explored in the literature as well. The utility of these algorithms, in general, revolve around some assumptions about the channel or the availability of perfect channel information at the Base Station (BS). How these assumptions are met in a practical circumstance is, however, an important concern. In this work we devise an end-to-end MUD using Deep Neural Network (DNN) where we relax these assumptions. We approximate an ensemble of trained DNN based MUD using Knowledge Distillation (KD) to enable fast AUD at the Base Station (BS). Furthermore, using the inter-resource correlation, we estimate the channels of the active users which is an ill-posed problem otherwise. We carry out elaborate numerical investigation to validate the efficacy of the proposed approach for the UL grant-free NOMA systems.


2021 ◽  
Vol 4 ◽  
pp. 1-4
Author(s):  
Sean M. Kohlbrenner ◽  
Matthew K. Eager ◽  
Nilan T. Phommachanh ◽  
Christos Kastrisios ◽  
Val Schmidt ◽  
...  

Abstract. Safety of navigation is essential for the global economy as maritime trade accounts for more than 80% of international trade. Carrying goods by ship is economically and environmentally efficient, however, a maritime accident can cause harm to the environment and local economies. To ensure safe passage, mariners tend to use already familiar routes as a best practice; most groundings occur when a vessel travels in unfamiliar territories or suddenly changes its route, e.g., due to extreme weather. In highly trafficked areas, the highest risk for ships is that of collision with other vessels in the area. In these situations, a network of previously traversed routes could help mariners make informed decisions for finding safe alternative routes to the destination, whereas a system that can predict the routes of nearby vessels would ease the burden for the mariner and alleviate the risk of collision. The goal of this project is to utilize Automatic Identification System data to create a network of “roads” to promote a route planning and prediction system for ships that makes finding optimal routes easier and allows mariners on the bridge and Autonomous Surface Vehicles to predict movement of ships to avoid collisions. This paper presents the first steps taken toward this goal, including data processing through the usage of Python libraries, database design and development utilizing PostgreSQL, density map generation and visualizations through our own developed libraries, an A* pathfinding algorithm implementation, and an early implementation of an Amazon Web Services deployment.


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