bluuebunny/crossref_metadata_2025 huggingface dataset:
Movement strategy correspondence across jumping and cutting tasks after anterior cruciate ligament reconstruction
Authors: Jones Holly S. R. , Moore Isabel S. , King Enda , Stiles Victoria H. , Laudani Luca , McCarthy‐Ryan Molly , McFadden Ciarán , Daniels Katherine A. J.
Published: November 2021

There are currently a multitude of tests used to assess readiness to return to sport (RTS) following anterior cruciate ligament reconstruction (ACLR). The aim of this study was to establish the extent to which movement strategies transfer between three common assessment tasks to help improve design of athlete testing batteries following ACLR. A cohort of 127 male patients 8–10 months post‐ACLR and 45 non‐injured controls took part in the study. Three movement tasks were completed (unilateral and bilateral drop jump, and 90° pre‐planned cut), while ground reaction forces and three‐dimensional kinematics (250 Hz) were recorded. Compared to the bilateral drop jump and cut, the unilateral drop jump had a higher proportion of work done at the ankle (d = 0.29, p < 0.001 and d = −1.87, p < 0.001, respectively), and a lower proportion of work done at the knee during the braking phase of the task (d = 0.447, p < 0.001 and d = 1.56, p < 0.001, respectively). The ACLR group had higher peak hip moments than the non‐injured controls, although the proportion of work done at the ankle, knee and hip joints were similar. Movement strategies were moderately and positively related at the ankle (rs = 0.728, p < 0.001), knee (rs = 0.638, p < 0.001) and hip (rs = 0.593, p < 0.001) between the unilateral and bilateral drop jump, but there was no relationship at the ankle (rs = 0.10, p = 0.104), knee (rs = 0.106, p = 0.166) and hip (rs = −0.019, p = 0.808) between the unilateral drop jump and the cut. Clinicians could therefore consider omitting one of the drop jumps from assessment batteries but should include both jumping and cutting tasks.
Narrow‐band ultraviolet B therapy attenuates cutaneous T‐cell responses in hapten‐induced, experimental contact dermatitis in beagles
Authors: Onishi‐Sakamoto Saki , Makishi Kiichi , Takami Kazumoto , Asahina Ryota , Maeda Sadatoshi , Nagata Masahiko , Moore Peter F. , Ide Kaori , Nishifuji Koji
Published: November 2021

Background

In human medicine, narrow‐band ultraviolet B (NB‐UVB) phototherapy has been used to treat various T‐cell‐mediated skin diseases. However, the effect of NB‐UVB on inflamed canine skin remains uncertain.

Objectives

To investigate the effect of NB‐UVB phototherapy on the skin of dogs with hapten‐induced contact dermatitis.

Animals

Seven healthy beagles without skin problems.

Methods and materials

Dogs were irradiated with varying doses of NB‐UVB to determine the minimal erythema dose (MED). After determining the MEDs of six dogs (excluding one of the seven whose skin did not show a visible reaction), we investigated the effect of NB‐UVB on their inflamed skin by topically applying 2,4‐dinitrochlorobenzene (DNCB), which causes type 1 helper T cell (Th1)‐ and cytotoxic T‐cell (Tc)1‐induced skin inflammation. We then irradiated the skin with NB‐UVB. We analysed the treated skin samples via histopathological and immunohistochemical methods, and TdT‐mediated dUTP nick‐end labelling (TUNEL) to demonstrate apoptotic cells. We also analysed the cytokine gene transcription via real‐time quantitative reverse transcription PCR.

Results

The NB‐UVB MEDs caused mild inflammatory changes yet no severe epidermal exfoliations in the irradiated skin. In DNCB‐treated skin irradiated by the NB‐UVB MEDs, TUNEL‐positive dermal apoptotic cells were increased significantly compared with those of DNCB‐treated, nonirradiated skin. INF‐γ and TNF‐α transcription levels in DNCB‐treated, irradiated skin were significantly lower than those in the DNCB‐treated, nonirradiated skin.

Conclusion and clinical relevance

Phototherapy using NB‐UVB MEDs attenuated cutaneous Th1 and Tc1 cytokine responses with minimal skin damage in a canine model of hapten‐induced contact dermatitis.
Experimental Investigation of Effects of Split Diesel-Pilot Injection on Emissions From Ammonia-Diesel Dual Fuel Engine
Authors: Niki Yoichi
Published: November 2021

NH3 has been investigated for its use as an alternative fuel including for use in internal combustion engines. In NH3 combustion, emissions of unburned NH3 with toxicity and N2O as a combustion product with high global warming potential (GWP) are important issues. However, few researchers have investigated NH3 and N2O emissions from NH3 assisted diesel engines operated using NH3–diesel dual fuel. We investigate a combustion strategy to reduce these emissions with a single-cylinder diesel engine mixed NH3 gas into the intake air. We found that an early diesel pilot injection reduced unburned NH3 and N2O emissions while HC and CO emissions increased. It was also reported that NH3 and diesel fuel work as low and high reactivity fuel for reactivity controlled compression ignition combustion (RCCI), respectively. Our previous study reports the aspects of RCCI on NH3–diesel dual fuel engine to some extent. The injection timing of diesel fuel and the quantity of NH3 govern the emissions and performance on RCCI combustion. These effects need to be investigated to manipulate the RCCI combustion and reduce emissions. This paper reports the efficiency and emissions for the diesel pilot injection timing sweep at various NH3 supply quantities and the effects of a split injection on the emissions and a combustion phase. In addition, we estimated the reduction in GHG emissions using a NH3–diesel dual fuel engine, which applied the early diesel pilot injection, compared with the diesel only operation, considering the N2O GWP.
Potentiality of Yellow Oleander (Thevetia Peruviana) Seed Oil as an Alternative Diesel Fuel in Compression Ignition Engines
Authors: Kakati Jyotirmoy , Gogoi Tapan K. , Pal Sukhamoy , Saha Ujjwal K.
Published: November 2021

Biodiesel has been accepted as a clean and an eco-friendly green diesel fuel by the entire world. In India, various non-edible oils have been tested for exploring their suitability as a fuel in diesel engines. In the north eastern states of India, many oil bearing seeds such as Koroch (a variety of Pongamia glabra), Nahar (Mesua ferrea), Terminalia (Terminalia belerica Robx), Kutkura (Meyna spinosa Roxb), Amari (Amoora Wallichii King), Yellow oleander (Thevetia peruviana) and others are found in abundance. In this article, the Yellow oleander seed oil (YOSO) has been investigated for biodiesel production and characterization. The oil content in Yellow oleander seed is found to be 63.87%. The free fatty acid (FFA) content in YOSO is measured, and is found to be 32.0%; hence the two-step acid-base catalysis transesterification process has been adopted for producing biodiesel from the YOSO. YOSO contains 5.03% palmitic, 6.92% stearic, 48.14% oleic and 31.37% linoleic acid. The density, calorific value and kinematic viscosity of Yellow oleander fatty acid methyl ester (YO-FAME) are 879.7 kg/m3, 40.159 MJ/kg and 4.63 mm2/s respectively. Most of the fuel properties of YO-FAME meet ASTM D6751 and EN 14214 biodiesel standards. The YO-FAME exhibits a low sulphur content of 13.0 ppm and a high cetane number of 58.3. Fire point and pour point of YO-FAME were found to be 158°C and 5°C respectively. The physio-chemical properties of YO-FAME has been compared with FAME of Yellow oleander, Ratanjot (Jatropha curcus), Terminalia (Terminalia belerica Robx.) and Nahar (Mesua ferrea).
An Experimental Study on a Dual-Fuel Generator Fueled With Diesel and Simulated Biogas
Authors: Dev Shouvik , Stevenson David , Yousefi Amin , Guo Hongsheng , Butler James
Published: November 2021

Diesel fueled generators are widely used for power generation in remote and/or off-grid communities. In such communities, local organic waste streams can be used to generate biogas which can be used to replace diesel used by diesel generators to lower fuel cost and reduce greenhouse gas (GHG) emissions. Diesel powered generators can be easily retrofitted with a biogas dosing line in the engine intake to introduce biogas, but appropriate optimization would be of great help to further improve generator performance and reduce GHG emissions. The objective of this research is to demonstrate simplified optimization methods that can reduce GHG emissions (carbon dioxide and methane) from such retrofitted dual-fuel engines under various biogas compositions.

The study was conducted on a modern 30 kilowatt (kW) generator using an electronically controlled, four-stroke, four-cylinder, direct injection, turbo-charged diesel engine. The engine was operated with the factory electronic control unit (ECU) and a programmable ECU which allowed for control of the fuel injections and exhaust gas recirculation (EGR) valve. Biogas was simulated by using natural gas (with more than 95% methane by volume) which was diluted with either carbon dioxide or nitrogen. This study consisted of two areas. The first one was the comparison of the engine performance when operating with biogas using the factory ECU and the programmable ECU with user optimized fuel injection. The second one was the influence of volume fraction of carbon dioxide or nitrogen in the biogas.

The test results reinforced the importance of optimizing the diesel injections when the engine was operated in the biogas-diesel dual-fuel mode to ensure complete combustion and achieve a reduction in GHG emissions. Increasing nitrogen fraction had a minimal effect on the emissions, but increasing carbon dioxide fraction caused the NOx and methane emissions to decrease, and the indicated thermal efficiency to increase.
Modeling and Control of a Hybrid Opposed Piston Engine
Authors: Drallmeier Joseph , Siegel Jason B. , Middleton Robert , Stefanopoulou Anna G. , Salvi Ashwin , Huo Ming
Published: November 2021

This paper presents the modeling and control of an opposed piston (OP) engine in a novel hybrid architecture. The OP engine was selected for this work due to the inherent thermody-namic benefits and the balanced nature of the engine. The typical geartrain required on an OP engine was exchanged for two electric motors, significantly reducing friction and decoupling the crankshafts. By using the motors to control the crankshaft motion profiles, this configuration introduces capabilities to dynamically vary compression ratio, combustion volume, and scavenging dynamics. To realize these opportunities, a model of the system capturing the instantaneous engine dynamics is essential along with methodology to regulate the crankshaft’s rotational dynamics utilizing the electric motors. The modeling presented here couples a 1D model capturing the gas exchange process during scavenging and a 0D model of the crankshaft dynamics and the heat release profile due to combustion. With the use of this model, a linear quadratic controller with reference feedforward was designed to track the crankshaft motion trajectory. Experimental results are used to validate the model and controller performance. These results highlight the sensitivity to model uncertainty at points with high cylinder pressure, leading to large differences in control input near minimum volume. The proposed controller is, however, still able to maintain tracking error for crankshaft position below ± 1 degree.
A Machine Learning Based Numerical Approach for Valve Seating Velocity Control in an Electromagnetic Camless System
Authors: Tripathy Srinibas , Babu M. Mithun , M. Kanupriya , Mittal Mayank
Published: November 2021

Improving internal combustion engine performance is a significant concern over the past few decades for engine researchers and automobile manufacturers. One of the promising methods for improving the engine performance is variable valve actuation system with camless technology. In the camless system, the conventional spring-operated valve actuation mechanism is removed, and an actuator is used to independently control the valve events (lift, timing, and duration). Among different camless systems, electromagnetic variable valve actuation (EMVA) becomes more viable because of its faster valve operation. However, the major challenge is to control the valve seating velocity (velocity at which valve comes to rest during seating on the cylinder head) due to the absence of the cam mechanism. A sophisticated control system must be developed to achieve an acceptable valve seating velocity. In this study, a proportional-integral-derivative (PID) controller was used to control the EMVA system. A machine learning tool, i.e., genetic algorithm, and an iterative method, i.e., Ziegler-Nichols, were used to optimize the PID controller’s gain values. The valve lift profiles obtained using the Ziegler-Nichols method and the genetic algorithm were compared. It was found that the developed algorithm for the EMVA system can achieve faster rise time compared to the experimental results [25] utilized inverse square method. A parametric investigation was performed to verify the robustness of the PID controller with a change in temperature. It is concluded that the temperature rise may increase the resistance and inductance, but the controller with the updated gain values can control the EMVA system without affecting the performance parameter. The simulation was performed for both forward and backward strokes to investigate the valve seating velocity. It was found that the controller can achieve an acceptable valve seating velocity. Hence, the machine learning tool helps in optimizing the PID controller’s gain values to achieve faster valve operation with an acceptable valve seating velocity.
LES Study on Spray Combustion With Renewable Fuels Under ECN Spray-A Conditions
Authors: Mira Daniel , Pérez-Sánchez Eduardo J. , Surapaneni Anurag , Benajes Jesús , García-Oliver José M. , Pastor José M. , De León Daiana
Published: November 2021

Poly-Oxymethylene Dimethyl Ethers (OMEx) are being intensively investigated because of their potentially renewable synthesis path, which make them suitable as liquid fuels for low-carbon transport applications. In the present contribution, a computational study on the difference in combustion characteristics between dodecane and OMEx-type fuels under Engine Combustion Network (ECN) Spray A conditions is reported. In particular, a blend of different OMEx fuels have been investigated and compared to dodecane, which is a more conventional diesellike fuel. The modelling framework consists of a high-fidelity LES approach together with a Eulerian-Lagrangian spray model and flamelet-based turbulent combustion model. Results indicate ignition delay time and lift-off length according to the fuel reactivity properties, with the OMEx fuel performing similarly to dodecane. Flamelet calculations show that ignition of the oxygenated fuels is in general similar to that of dodecane, but it occurs at higher mixture fraction values due to the differences in stoichiometry. One of the most relevant outcomes of the study is the important effect that the oxygenated characteristics of OMEx has on the flame structure. Results show that for OMEx the reaction front is stabilized at distances closer to the nozzle than for dodecane, and that the flame shape as well as its internal structure is clearly affected.
Identification of tick species infesting livestock in Dera Ismail Khan Pakistan
Authors: Jamil Muhammad , Kashif Muhammad , Habibullah  , Mubeen Muhammad , Jelani Ghulam , Ullah Naimat , Tariq Ambrina , Rasheed Muhammad , Hussain Abid , Ali Mubarik
Published: November 2021

Ticks are blood-feeding obligate ectoparasites that belong to order Arachnida with three main families (Ixodidae, Argasidae, and Nuttalleillidae). Ticks transmit viral, bacterial, and zoonotic diseases in humans, and animals (wild and domestic). Ticks directly and indirectly affect the livestock industry which plays an important role in the rural economy of Pakistan. These are the main source of income and a source of food for poor people. The current study was conducted to identify the tick species infesting livestock in Dera Ismail Khan Pakistan. For this purpose, different farms were randomly visited and 130 ticks were collected from 200 scanned animals.  The collected ticks were identified to species level using morphological keys. In the current study, four tick species were identified as Rhipicephalus sanguineus, Hyalomma marginatum, Hyalomma anatolicum, and Dermacentor marginatus were identified. The most dominant species were Hy. anatolicum (35.39%) followed by R. sanguineus (30.00%), Hy. marginatum (23.85%) and D. marginatus (10.77%). Hy. anatolicum was the predominant tick species and accounted for 35.39% of the ticks. Tick infestation was highest in buffaloes followed by cows, sheep, and goats. Tail and ear were the most preferable sites for tick infestation than other body parts.
Automated classification of Tursiops aduncus whistles based on a depth-wise separable convolutional neural network and data augmentation
Authors: Li Lei , Qiao Gang , Liu Songzuo , Qing Xin , Zhang Huaying , Mazhar Suleman , Niu Fuqiang
Published: November 2021

Whistle classification plays an essential role in studying the habitat and social behaviours of cetaceans. We obtained six categories of sweep whistles of two Tursiops aduncus individual signals using the passive acoustic mornitoring technique over a period of eight months in the Xiamen area. First, we propose a depthwise separable convolutional neural network for whistle classification. The proposed model adopts the depthwise convolution combined with the followed point-by-point convolution instead of the conventional convolution. As a result, it brings a better classification performance in sample sets with relatively independent features between different channels. Meanwhile, it leads to less computational complexity and fewer model parameters. Second, in order to solve the problem of an imbalance in the number of samples under each whistle category, we propose a random series method with five audio augmentation algorithms. The generalization ability of the trained model was improved by using an opening probability for each algorithm and the random selection of each augmentation factor within specific ranges. Finally, we explore the effect of the proposed augmentation method on the performance of our proposed architecture and find that it enhances the accuracy up to 98.53% for the classification of Tursiops aduncus whistles.
Adaptive array invariant for source-range estimation in shallow water
Authors: Byun Gihoon , Song Heechun
Published: November 2021

The array invariant (χ) developed for source-range estimation in shallow water is based on the broadband dispersion characteristics in ideal waveguides, summarized by the waveguide invariant, β = cos2θ. This approach relies on the waveguide invariant being constant (e.g., β = 1), valid for small propagation angles, but introduces a significant range error as the propagation angle increases. In this talk, the standard array invariant is extended to fully incorporate the angle dependence of the waveguide invariant, referred to as the adaptive array invariant, denoted by χβ = χ/β. The remarkable performance enhancement is demonstrated (1) using a 56.25 m vertical array for a surface ship (200–900 Hz) in approximately 100-m shallow water and (2) using a 2.8 m vertical array for a broadband source (0.5–3.5 kHz) towed on a continental slope where the water depth varies from 87.5 to 55 m over a 5-km range.
Statistical characterization of low-porosity, additively manufactured metal foams measured in an acoustic impedance tube
Authors: Konarski Stephanie G. , Naify Christina J. , Rohde Charles A. , Roberts Scott
Published: November 2021

This work focuses on the acoustic response of aluminum foams with different porosities additively manufactured using powder bed fusion. Emerging voxel-by-voxel additive manufacturing, which further expands the achievable microstructure design space through localized spatial control of the printing properties, allows for direct fabrication of samples with varying percent porosities. Unlike traditional, rigid-frame foams with very high porosities, the samples range from fully dense to approximately 50% porosity. This work utilizes normal incidence acoustic impedance tube measurements and the Johnson–Champoux–Allard–Lafarge analytic model, which are common characterization methods for traditional rigid-frame porous media. For the reflection and transmission coefficients measured in an acoustic impedance tube, Bayesian statistical inversion is performed with the Johnson–Champoux–Allard–Lafarge model to obtain distributions of the possible values for the six required model parameters. By using a statistical characterization approach, the applicability of these common acoustic measurement and modeling methods for lower porosity additively manufactured foams is also assessed. [This work was supported by the Office of Naval Research.]
Using tone and break indices labelling to document patterns of prosodic prominence and their acoustic effects in a database of spoken Italian sentences
Authors: DeCaprio Alec , Arango Javier , Chan Ian , Di Benedetto Maria-Gabriella , De Nardis Luca , Shattuck-Hufnagel Stefanie
Published: November 2021

This study examined how tonal prominence impacts the acoustic cues to features of Italian vowels. It was one aspect of an analysis of the LaMIT (Lexical Access Model for Italian) project [Di Benedetto et al., “Speech recognition of spoken Italian based on detection of landmarks and other acoustic cues to distinctive features,” in 179th ASA Meeting, Chicago, IL, 8–12 December (2020)]. We strove to understand and systematize the quantitative changes to vowel quality provoked by prosodic and lexical stress, using the LaMIT corpus (100 Italian sentences recited twice by four speakers). Prominent vowels were classified with the TOBI (Tones and Break Indices) labelling method. Accents deemed tonally prominent, known as “pitch accents,” were starred and given a label that reflected their F0 contours. Labels were limited to those put forth by Avesani (“ToBIt: Un sistema di trascrizione per l’intonazione italiana,” in Atti 5e Giornate di Studio del Gruppo di Fonetica Sperimentale, Povo, Italy, pp. 85–98) as constitutive of the prosody of standard Italian. Labels also marked silences, elisions, and spaces between words. Results will clarify if prominence in Italian induces enhanced vowel sonority, or instead a “hyperarticulation” of distinctive segments, contributing to an ongoing debate on the subject.
Automated detection of Glottal-related acoustic cues for feature-cue-based analysis
Authors: Anupam Sagnik , Choi Jeung-Yoon , Shattuck-Hufnagel Stefanie
Published: November 2021

Detecting and interpreting individual acoustic cues to identify features that distinguish among speech sounds is thought to play a key role in automatic speech processing, modeling human speech perception, detecting and diagnosing speech disabilities (and tracking the effects of treatment for those disabilities), and studying cross-language differences. The individual acoustic cues form a bridge between transcription using phonemic symbols (phonemes) and raw acoustic measurements from the signal and allow the analysis to capture systematic language-related differences in how words are pronounced in different contexts–differences that are critical to speech recognition. Within this framework, analysis of glottal-related acoustic cues, such as aspiration and voicing, can contribute to the identification of phonemes that exhibit these acoustic cues in their respective productions. In this study, we propose an algorithm that uses time-series data processing methods and machine learning models to accurately predict the presence of the glottal cue labels from waveform analysis. We further highlight the accuracy of our model and cue-based labeling method in identifying speech modifications over phone and phoneme-based methods.
Accuracy of the Language Environment Analysis (LENA) speech processing system in identifying communicative vocalizations of young children and adults
Authors: Dilley Laura , Houston Derek
Published: November 2021

The Language Environment Analysis (LENA) system is an automated audio processing system widely used for characterizing language behaviors of children and adults for clinical and basic research. While a number of studies have assessed LENA’s reliability, its accuracy at identifying and counting speech communicative events is still not well-characterized under a range of naturalistic conditions. In two studies, we examined accuracy of LENA's speech vocalization classifications, relative to human gold standard coding for audio events, as well as word and speech vocalization counts for adults and child utterances, respectively. We found that the weighted average of accurate classification of 100-msec frames by LENA for child speech, adult female speech, and adult male speech was 57%, 61%, and 57%, respectively. Further, an analysis of LENA’s ability to accurately discriminate frames of speech vocalizations from a “key child”—a child wearing the LENA device—from speech vocalizations of other child and adult talkers and sound sources showed that LENA correctly detected key child speech vocalization frames only 41% of the time. We are currently extending this research to examine the accuracy of LENA’s child vocalization count (CVC) and conversational turn count (CTC) measures.
Gender classification from speech using convolutional networks augmented with synthetic spectrograms
Authors: Horn Skyler , Boril Hynek
Published: November 2021

Automatic gender classification from speech is an integral component of human-computer interfaces. Gender information is utilized in user authentication, speech recognizers, or human-centered intelligent agents. This study focuses on gender classification from speech spectrograms using AlexNet-inspired 2D convolutional neural networks (CNN) trained on real samples augmented with synthetic spectrograms. A generative adversarial network (GAN) is trained to produce synthetic male/female-like speech spectrograms. In limited training data experiments on LibriSpeech, augmenting a training set of 200 real samples by 800 synthetic samples reduces equal error rate of the classifier from 23.7% to 1.0%. To further test the ‘quality’ of the generated samples, in a subsequent experiment, the real training samples are progressively replaced (rather than augmented) with synthetic samples at various ratios from 0 (all original samples preserved) to 1 (all original samples replaced by synthetic ones). Depending on the system setup, substituting between 50% to 90% of the original samples with the synthetic ones is found to have a minimal impact on the classifier performance. Finally, viewing the input CNN layers as filters that select salient spectrogram features, the learned convolutional kernels and filter outputs are studied to understand which spectrogram areas receive a prominent attention in the classifier.
Emotion recognition with speech articulatory coordination features
Authors: Siriwardena Yashish M. , Seneviratne Nadee , Espy-Wilson Carol
Published: November 2021

Mental health illnesses like Major Depressive Disorder and Schizophrenia affect the coordination between articulatory gestures in speech production. Coordination features derived from Vocal tract variables (TVs) predicted by a speech inversion system can quantify the changes in articulatory gestures and have proven to be effective in the classification of mental health disorders. In this study we use data from the IEMOCAP (acted emotions) and MSP Podcast (natural emotions) datasets to understand how coordination features extracted from TVs can be used to capture changes between different emotions for the first time. We compared the eigenspectra extracted from channel delay correlation matrices for Angry, Sad and Happy emotions with respect to the “Neutral” emotion. Across both the datasets, it was observed that the “Sad” emotion follows a pattern suggesting simpler articulatory coordination while the “Angry” emotion follows the opposite showing signs of complex articulatory coordination. For the majority of subjects, the ‘Happy’ emotion follows a complex articulatory coordination pattern, but has significant confusion with “Neutral” emotion. We trained a Convolutional Neural Network with the coordination features as inputs to perform emotion classification. A detailed interpretation of the differences in eigenspectra and the results of the classification experiments will be discussed.
Heart monitoring during remote collaborative communication under noisy, reverberant and delayed communication transmission conditions
Authors: Rawool Vishakha
Published: November 2021

This study, which is part of a larger study, was designed to monitor heart rate during remote collaborative communication. Ninety (45 men and 45 women) participants were divided into 30 teams with 3 members in each team. Each team built different LEGO models under three communication transmission delay conditions in the following order: No delay, 750 ms delay and 5000 ms delay. One individual served as the LEGO builder, who did not have the instruction manual. The remaining two team members provided instructions for building the models. Participants were in separate rooms and communicated via earphones. Background white noise (50 dB SPL) was introduced in the ear canals and reverberation was applied to the transmitted communication. Participants were fitted with Empatica E4 wristbands on their wrists. ANOVA was performed on the averaged heart rate and the RMSDD (the root mean square of successive differences between normal heartbeats). The results revealed lower heart rates and increased heart rate variability in the 750 and 5000 ms delay conditions compared to the no delay condition. Detailed results will be discussed during the presentation. Acknowledgement: Graduate students Sydney Osbarn and Melissa Reyes assisted in data collection. Sydney Osbarn also assisted in pre-processing the data.
A wireless and battery-free wound infection sensor based on DNA hydrogel
Authors: Xiong Ze , Achavananthadith Sippanat , Lian Sophie , Madden Leigh Edward , Ong Zi Xin , Chua Wisely , Kalidasan Viveka , Li Zhipeng , Liu Zhu , Singh Priti , Yang Haitao , Heussler Sascha P. , Kalaiselvi S. M. P. , Breese Mark B. H. , Yao Haicheng , Gao Yuji , Sanmugam Kavitha , Tee Benjamin C. K. , Chen Po-Yen , Loke Weiqiang , Lim Chwee Teck , Chiang Grace Shu Hui , Tan Boon Yeow , Li Hao , Becker David Laurence , Ho John S.
Published: November 2021

A wireless sensor based on bioresponsive DNA hydrogel provides smartphone-based detection of wound infection.
Structure of native HIV-1 cores and their interactions with IP6 and CypA
Authors: Ni Tao , Zhu Yanan , Yang Zhengyi , Xu Chaoyi , Chaban Yuriy , Nesterova Tanya , Ning Jiying , Böcking Till , Parker Michael W. , Monnie Christina , Ahn Jinwoo , Perilla Juan R. , Zhang Peijun
Published: November 2021

High-resolution cryoET structures of native HIV-1 capsid in complex with IP6 and CypA are enabled by viral membrane perforation.
Tunable CO/H
2
ratios of electrochemical reduction of CO
2
through the Zn-Ln dual atomic catalysts
Authors: Liang Zhong , Song Lianpeng , Sun Mingzi , Huang Bolong , Du Yaping
Published: November 2021

Dual-metal atomic catalysts produce flexible ratios of syngas products.
Parity Violation in the Scattering of a Proton by Carbon and Oxygen
Authors: Milstein A. I. , Nikolaev N. N. , Salnikov S. G.
Published: November 2021

The effects of parity violation in the interaction of relativistic polarized protons with $$^{{12}}{\text{C}}$$ and $$^{{16}}{\text{O}}$$ nuclei are discussed. Within the Glauber approach, estimates are obtained for P-odd asymmetries in the total and elastic scattering cross sections, the dissociation cross section, and in the inelastic scattering cross section with meson production. Our calculations show that asymmetry should be most noticeable in the elastic cross section and in the dissociation cross section.
Intratendinous hamstring injuries: sequential MRIs as a tool to reduce the risk of reinjury in elite sport
Authors: Baldock James , Wright Steve , McNally Eugene , Wedatilake Thamindu
Published: November 2021

Hamstring injuries are the most common muscle injuries in elite football. Injuries involving the intramuscular tendon are considered more significant, with longer return to play (RTP) times and an increased risk of reinjury. MRI is the gold standard investigation for muscle injuries, but initial findings cannot accurately determine RTP times. The role of MRI in monitoring muscle and tendon healing is not well described. We present three cases of hamstring injuries with intramuscular tendon involvement, illustrating the changes seen on MRI during progressive tendon healing and describing how we utilised this information to inform safe rehabilitation progression. We conclude that intramuscular hamstring tendon healing can be accurately seen on sequential MRI scans and that this information, when combined with traditional rehabilitation markers in and elite sport environment, can be utilised by clinicians to determine the earliest but safe RTP.
Incidence and Risk Factors for Mucormycosis in Renal Transplant Patients
Authors: Downey Melissa Rachel , Taskar Varsha , Linder Daniel F , Baer Stephanie L , Waller Jennifer L , Bollag Wendy B , Kheda Mufaddal , Mohammed Azeem , Padala Sandeep
Published: November 2021

Background

Renal transplant patients are at increased risk for mucormycosis. Diabetes, neutropenia, deferoxamine therapy, and immunosuppressive medications have been associated with increased risk of mucormycosis in studies of solid organ transplant recipients. To focus on renal transplant patients, the US Renal Data System (USRDS) was queried to determine the incidence and risk factors for mucormycosis.

Methods

All renal transplant patients in the USRDS from 1988 to 2015 were queried for a diagnosis of mucormycosis after the first transplant date using ICD-9 and ICD-10 codes. The International Classification of Diseases (ICD) codes, which currently exist in the ninth and tenth revisions, are a global system of classification used to code diagnoses, procedures, and symptoms. We defined proven mucormycosis by a histopathologic or fungal stain procedure code within 7 days of the diagnosis code. Logistic regression controlling for person-years at risk was used to examine demographic and clinical diagnosis risk factors for mucormycosis.

Results

Of the 306,482 renal transplant patients, 222 (0.07%) had codes consistent with proven mucormycosis. The incidence of mucormycosis increased from 1990 to 2000 (peak 17.6 per 100,000 person-years) and subsequently demonstrated more variability. Hispanic ethnicity (OR=1.45), age 65 years or greater (OR=1.64), other or black race compared with white race (OR=1.96 and 1.74), cadaver or other donor type (OR=2.41), and receiving tacrolimus (OR=2.09) were associated with increased risk. Comorbidities associated with decreased risk of mucormycosis included female sex (OR=0.68), iron overload (OR=0.56), and receiving mycophenolate mofetil (OR=0.67) or azathioprine (OR=0.53).

Conclusions

In renal transplant patients, age, deceased donor graft transplant, tacrolimus administration, race other than white, and Hispanic ethnicity were associated with increased risk of mucormycosis. Unexpectedly, iron overload was protective. Mucormycosis is a rare infection in renal transplant patients which should be considered in patients with the above risk factors after more common infections have been ruled out.
Treatment-free survival over extended follow-up of patients with advanced melanoma treated with immune checkpoint inhibitors in CheckMate 067
Authors: Regan Meredith M , Mantia Charlene M , Werner Lillian , Tarhini Ahmad A , Larkin James , Stephen Hodi F , Wolchok Jedd , Postow Michael A , Stwalley Brian , Moshyk Andriy , Ritchings Corey , Re Sandra , van Dijck Wim , McDermott David F , Atkins Michael B
Published: November 2021

Background

Treatment-free survival (TFS) characterizes disease control after discontinuation of immune checkpoint inhibitors (ICIs) until subsequent therapy or death. We previously evaluated TFS in a pooled analysis of the CheckMate 067 and CheckMate 069 trials of the ICIs nivolumab and ipilimumab, alone or in combination, in patients with advanced melanoma after minimum follow-up of 36 months. This analysis investigated TFS differences between treatments in CheckMate 067 after a minimum follow-up of 60 months, and their relation to overall survival (OS) differences.

Methods

Data were from 937 patients who initiated treatment (nivolumab plus ipilimumab, nivolumab, or ipilimumab) in CheckMate 067 (NCT01844505). TFS was defined as the area between the Kaplan-Meier curves for time to protocol therapy cessation and time to subsequent systemic therapy initiation or death, each measured from randomization. TFS was partitioned as time with and without toxicity. Toxicity included persistent and late-onset grade ≥2 select treatment-related adverse events (ie, those of potential immunologic etiology). The area between Kaplan-Meier curves was estimated by the difference in 60-month restricted-mean times of the endpoints. Between-group differences were estimated with bootstrapped 95% CIs.

Results

At 60 months from randomization, 39%, 24%, and 11% of patients assigned to treatment with nivolumab plus ipilimumab, nivolumab, and ipilimumab, respectively, had survived and were treatment-free. The 60-month mean TFS was approximately twice as long with the combination (19.7 months) than with nivolumab (9.9 months; absolute difference, 9.8 (95% CI 6.7 to 12.8)) or ipilimumab (11.9 months; absolute difference, 7.8 (95% CI 4.6 to 11.0)). In the respective groups, mean TFS represented 33% (8% with and 25% without toxicity), 17% (2% and 14%), and 20% (3% and 17%) of the 60-month period. Compared with 36-month estimates, mean TFS over the 60-month period represented slightly greater percentages of time in the nivolumab-containing regimen groups and a lesser percentage in the ipilimumab group. TFS differences between the combination and either monotherapy increased with longer follow-up.

Conclusions

Along with improved long-term OS with the nivolumab-containing regimens versus ipilimumab, TFS without toxicity was sustained with nivolumab plus ipilimumab versus either monotherapy, demonstrating larger between-group differences with extended follow-up.
Synchronously Improving Multi-user English Translation Ability by Using AI
Authors: Zhao Xiaomei , Jiang Yubo
Published: November 2021

English has become the most widely used language in the world. Everything we do in study, life and work is closely linked with English. With the continuous development of computer technology, machine translation is becoming more and more mature. The convergence of Artificial Intelligence (AI) and language learning is getting increasingly close, which brings great impact and challenge to the language education industry, but also provides an opportunity for the synchronous promotion of the development of the language education industry. With the further development of AI, machine translation can better meet the needs of most general translation, but in the face of professional, diversified, detailed and complex communication translation tasks containing human emotion, machine translation is still difficult to replace human translation. In order to improve the English translation ability of university students, this paper uses AI to propose the innovative factor based Quantum Particle Swarm Optimization-Convolutional Neural Network (QPSO-CNN) algorithm. Through the experiment, at first, the obtained dataset can ensure the accuracy and diversity of the collected results of English translation feature samples to the maximum extent, and the trained QPSO-CNN can be used to analyze the accuracy of the English translation ability of university students. Then, by comparing the convergence curve of QPSO-CNN and back propagation-CNN (BP-CNN), it is concluded that the proposed QPSO-CNN in this paper has been greatly improved in terms of model accuracy and convergence speed.
Intelligent Analysis and Positioning of Political Public Opinion in Universities
Authors: Zhao Zhifu
Published: November 2021

With the rapid development of Internet technology, the network has become an indispensable way of life for undergraduates. The correct guidance of public opinion has also become an important thing in the ideological work of universities. Undergraduates are in an important period of formation and development of thoughts that they are easily to be incited by cyber-rumors. Therefore, it is particularly important to obtain the data of political public opinion in universities and position the hot topics for early detection of political public opinion tendency, which can also avoid the outbreak of major security incidents. With such consideration, this paper obtains multi-source political public opinion data from BBS, Tieba and Weibo of SUN YAT-SEN UNIVERSITY (SYSU) through crawler. We study a text feature extraction method based on Word2Vec & LDA (Latent Dirichlet Allocation), which improves the high-dimensional sparsity in traditional Vector Space Model (VSM) text representation. Meanwhile, based on the classical Single-pass clustering algorithm, this paper studies the Single-pass & HAC clustering algorithm. In addition, a measurement method of hot topic is defined to calculate the heat value of political public opinion. Dictionary and rule based method is used to improve the accuracy of sentiment tendency analysis. The experimental results demonstrate that the effect of topic detection and positioning based on LDA & Word2Vec and Single-pass & HAC algorithm is better than other methods.
Trouble at the source
Authors: Monroe Don
Published: November 2021

Errors and biases in artificial intelligence systems often reflect the data used to train them.
Continuum and Molecular Dynamics Studies of the Hydrodynamics of Colloids Straddling a Fluid Interface
Authors: Maldarelli Charles , Donovan Nicole T. , Ganesh Subramaniam Chembai , Das Subhabrata , Koplik Joel
Published: November 2021

Colloid-sized particles (10 nm–10 μm in characteristic size) adsorb onto fluid interfaces, where they minimize their interfacial energy by straddling the surface, immersing themselves partly in each phase bounding the interface. The energy minimum achieved by relocation to the surface can be orders of magnitude greater than the thermal energy, effectively trapping the particles into monolayers, allowing them freedom only to translate and rotate along the surface. Particles adsorbed at interfaces are models for the understanding of the dynamics and assembly of particles in two dimensions and have broad technological applications, importantly in foam and emulsion science and in the bottom-up fabrication of new materials based on their monolayer assemblies. In this review, the hydrodynamics of the colloid motion along the surface is examined from both continuum and molecular dynamics frameworks. The interfacial energies of adsorbed particles is discussed first, followed by the hydrodynamics, starting with isolated particles followed by pairwise and multiple particle interactions. The effect of particle shape is emphasized, and the role played by the immersion depth and the surface rheology is discussed; experiments illustrating the applicability of the hydrodynamic studies are also examined.
The Effect of Ultra Fine Bubbles Water on the Human skin
Authors: Kato Yuji , Matsumoto Tomoyuki , Koura Setsuko
Published: November 2021

A certain amount of water needs to be maintained in the stratum corneum of the skin in order to maintain the skin barrier function. Therefore, it is important to supply water to the stratum corneum of the skin to reduce trans epidermal water loss (TEWL). However, because normal water has large clusters, it is difficult to penetrate the stratum corneum of the skin. Therefore, it was considered that the use of Ultra-fine bubbles (UFB) water, which is said to have small water clusters, promotes penetration into the stratum corneum of the skin, and is useful for improving the skin barrier function. The artificial skin to which O2-UFB water was dripped had the highest water content and the lowest TEWL. It also had a high affinity for human skin. From these results, improvement of skin barrier function by O2-UFB water can be expected.
Research of YBCO Superconductor Coating on Austenitic Stainless Steel by Electrophoretic Deposition
Authors: Sato Shuya , Koura Setsuko
Published: November 2021

Yttrium-based superconductors (YBCO) haven’t been widely applied. One reason is because of its high cost. In this research, we replaced the substrate and the fabrication method with a lower and found the possibility of fabricating high-performance superconducting wires. Specifically, the YBCO layer was fabricated by electrophoretic deposition (EPD). However, this method requires calcination. It has been known that Fe in the substrate diffuses into the YBCO layer during calcination, and this phenomenon causes deterioration of YBCO performance. Therefore, CeO2 was fabricated as a diffusion prevention layer between the substrate and the YBCO layer. It showed the possibility of preventing the diffusion of Fe. Meanwhile, a textured CeO2 layer was fabricated to enhance the properties of the superconductor. It was also shown that controlling the current may provide preferential orientation.
A Study on the Intelligent Translation Model for English Incorporating Neural Network Migration Learning
Authors: Zhang Yanbo
Published: November 2021

Under the current artificial intelligence boom, machine translation is a research direction of natural language processing, which has important scientific research value and practical value. In practical applications, the variability of language, the limited capability of representing semantic information, and the scarcity of parallel corpus resources all constrain machine translation towards practicality and popularization. In this paper, we conduct deep mining of source language text data to express complex, high‐level, and abstract semantic information using an appropriate text data representation model; then, for machine translation tasks with a large amount of parallel corpus, I use the capability of annotated datasets to build a more effective migration learning‐based end‐to‐end neural network machine translation model on a supervised algorithm; then, for machine translation tasks with parallel corpus data resource‐poor language machine translation tasks, migration learning techniques are used to prevent the overfitting problem of neural networks during training and to improve the generalization ability of end‐to‐end neural network machine translation models under low‐resource conditions. Finally, for language translation tasks where the parallel corpus is extremely scarce but monolingual corpus is sufficient, the research focuses on unsupervised machine translation techniques, which will be a future research trend.