bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of hyperparameters in Sentiment Analysis of Movie Reviews using Bi-LSTM
Authors: Singh Amankumar , Thapliyal Riya , Vanave Ritika , Shedge Rajashree , Mumbaikar Snehal
Published: May 2022

Movie reviews are an important factor in determining a film’s success because instead of depending solely on the number of views as a parameter for the success of the movie, movie reviews are used to acquire additional insights into the movie. Existing systems use LSTM for sentiment analysis but there is no study available how various hyperparameters affect the performance of the model. Bi-LSTM along with dropout layers provide good accuracy in sentiment analysis. The suggested method outperforms CNN and Natural Language Toolkit in terms of accuracy.The proposed model is tested using different hyper parameters including dropout rate,number of Bi-LSTM layers and Bi-LSTM nodes. 64 LSTM nodes, 2 Bi-directional Layers, and a 0.2 Dropout rate should be used for optimal accuracy. Effect of different text vectorization algorithms and activation functions was also studied. The combination of Tf-idf text vectorization and the ReLU activation function yields the best results.
Algovis – An Enhanced Way to Visualize Algorithms
Authors: Lanjewar Rahul , Mehra Nidhish , Doshi Aashka , Mane Yogita
Published: May 2022

It has been observed that learning using visual aids is less complicated than learning using traditional methods. As they say, “Words usually cannot do a picture’s justice". Keeping the above fact in mind, we have come up with ‘Algovis’. The aim of this project is to build a website that enables a user to visualize the steps and iterations involved in Data Structures and Algorithms using custom input and various animations. The program is menu driven to fit to the convenience of the user, depending on how many inputs he wants to enter, and implement the algorithm of his/her choice. The website also aims to become a convenient pedagogical tool. Algovis helps in visualizing various Data structure algorithms using graphical animations and with 100% accuracy.
Smart QR-based Restaurant Dine-in System with Sales Analysis
Authors: Singh Riteshkumar , Sonje Rupal , Salkar Soham , Jadhav Ashish
Published: May 2022

The modern-day customer has new priorities when it comes to dine-out and the top of them is safety. Most of the restaurant’s meal orders rely on the interaction with waiters to place orders into the kitchen. Due to the pandemic situation, there happens to be discomfort interacting with waiters and other staff members of the restaurant. To solve such problems this system is developed. This system covers the whole order process of a restaurant including the interaction between the customer, the waiter, the kitchen, and the cashier through a web application. Additionally, restaurant owners will be able to enhance sales by using this system to analyze data gathered. This system will attempt to replace the traditional manual ordering process.A better user experience that includes food recommendations can indirectly boost customer loyalty to the restaurant. It is a complete product for managing restaurant services with minimal human interaction while providing maximum contactless service.
Music Genre Classification Using Neural Network
Authors: Shaikh Tarannum , Jadhav Ashish
Published: May 2022

Music Genre classification on Neural Network is presented in this article. The research work uses spectrogram images generated from the songs timeslices and given as input to NN to do classification of songs to their respective musical genre. The research work focuses on analyzing the parameters of the model. Using two different datasets and implementing NN technique we have achieved an optimized result. The Convolutional Neural Network model presented in this article classifies 10 classes of Music Genres with the improved accuracy.
Hand gesture based X-ray image controlling using Convolutional Neural Network
Authors: Mhatre Rutika , Dhage Bhakti , Kwatra Vishesh , Chavan Pallavi
Published: May 2022

This paper proposes a novel computer vision based system that allows doctors, surgeons and other physicians to control X-Ray images just by using simple gestures thus eliminating the need of traditional devices like mouse and keyboard. This will help reduce the risk of contamination in sterile environments like those found in the hospitals and it will also help in preventing the spread of covid by not allowing contact with contaminated surfaces. It is implemented using CNN model. CNN is specially used for image recognition as well as processing. The system detects gestures through in-built webcam and converts it into corresponding computer commands to perform its associated tasks.
Loan Prediction System Using Machine Learning
Authors: Shinde Anant , Patil Yash , Kotian Ishan , Shinde Abhinav , Gulwani Reshma
Published: May 2022

As the needs of people are increasing, the demand for loans in banks is also frequently getting higher every day. Banks typically process an applicant’s loan after screening and verifying the applicant’s eligibility, which is a difficult and time-consuming process. In some cases, some applicants default and banks lose capital. The machine learning approach is ideal for reducing human effort and effective decision making in the loan approval process by implementing machine learning tools that use classification algorithms to predict eligible loan applicants.
Deepfake Video Detection using Neural Networks
Authors: Patel Nimitt , Jethwa Niket , Mali Chirag , Deone Jyoti
Published: May 2022

In today’s era, software tools based on deep learning have made the people work easier to make credible faces exchanges in video with little signs of manipulation, nicknamed “DeepFake” videos. Manipulation in digital media has been performed for decades through the appropriate use of visual effects; nevertheless, current breakthroughs occurred in deep learning have resulted in a significant rise to gain reality of fake material or contents using the simple ways. This are Artifical Intelligence-generated media (known as DF). Using tools of artificial intelligence to create the DF is an easy task. However, detecting these DF poses a significant barrier. Because it is difficult to teach the algorithm to detect the DF. Using Convolutional Neural Networks and Recurrent Neural Networks, we have made progress in detecting the DF. The system employs a Convolutional Neural network (CNN) on frame level to extract features. These observations are noted and this can train a Recurrent Neural Network (RNN), which has the ability to learn and classify whether or not a video has been tampered with and identified the temporal irregularities in the frame introduced by DF tools. We demonstrate how utilizing a simple architecture, our system may get competitive outcomes in this job.
Supervisory framework for threat detection withmultilayer processing in CNN
Authors: Mule Omkar , Pandit Abhijeet , Karmakar Sujoy , Kavale Atharva , Waghmode Mr. Uttam
Published: May 2022

Face recognition has received huge acknowledgement due to its various uses in Internet communication, security, access control, surveillance, PC entertainment and law enforcement. Conventional methods of recognition based on the ownerships of identity-cards or full knowledge such as a security number or password are not totally solid. Physical ID cards can be lost or forged, passwords can be hacked or forgotten but a face is undoubtedly connected to its owner. It cannot be stolen, borrowed or easily forged. Our current system has a lot of weaknesses wherever it is simply taken and merged. The focus of this paper is to help users for development of the security by utilizing face identification and recognition. The proposed framework principally comprises of subsystems specifically picture capture, face identification and detection, email alerts and metal detection. Furthermore, the improvement in Computer Vision through Deep Learning algorithms has been an impressive achievement, especially with the Convolutional Neural Network algorithm. A convolutional neural network is a feed-forward neural network that is by and large used to break down visual pictures by using grid-like topology. It is also called as ConvNet. The objects in a picture are distinguished and arranged using convolutional neural network. CNN detects various simple complex patterns in images and data in its different layers of Convolution Layer, Max Polling Layer and Fully Connected Layer .This field intends to allow and configure machines to see the world as people do, and utilize the information for doing tasks (such as Image Analysis, Image Recognition and Classification, etc).
Automated Digital Presentation Control using Hand Gesture Technique
Authors: Powar Salonee , Kadam Shweta , Malage Sonali , Shingane Priyanka
Published: May 2022

In today’s digital world Presentation using a slideshow is an effective and attractive way that helps speakers to convey information and convince the audience. There are ways to control slides with devices like mouse, keyboard, or laser pointer, etc. The drawback is one should have previous knowledge about the devices in order to manage them. Gesture recognition has acquired importance a couple of years prior and are utilized to control applications like media players, robot control, gaming. The hand gesture recognition system builds the use of gloves, markers and so on However, the utilization of such gloves or markers expands the expense of the system. In this proposed system, Artificial intelligence-based hand gesture detection methodology is proposed. Users will be able to change the slides of the presentation in both forward and backward directions by just doing hand gestures. Use of hand gestures cause connection simple, helpful, and doesn’t need any additional gadget. The suggested method is to help speakers for a productive presentation with natural improved communication with the computer. Specifically, the proposed system is more viable than utilizing a laser pointer since the hand is more apparent and thus can better grab the attention of the audience.
Hate Classifier for Social Media Platform Using Tree LSTM
Authors: Raut Sahil , Mhatre Nikhil , Jha Sanskar , Chhabria Aditi
Published: May 2022

Social-media without a doubt is one of the most noteworthy developments ever. From associating with individuals across the globe for sharing of data and information in an infinitesimal of a second, online media stages have enormously altered the method of our lives. This is joined by a steadily expanding utilization of social media, less expensive cell phones, and the simplicity of web access which have additionally prepared for the huge development of social media. To place this into numbers, according to an ongoing report, billions of individuals all over the planet presently utilize web-based media every month, and a normal amount of almost 2 million people new clients are going along with them consistently. While web-based media stages have permitted us to interface with others and fortify connections in manners that were not conceivable previously. Unfortunately, they have additionally turned into the default gatherings for can’t stand discourse. Online hate is a wild issue, with the adverse result of disallowing client support in web-based conversations and causing mental mischief to people. Since hate is pervasive across friendly, media stages, our objective was to foster a classifier that is feasible to train classifiers that can identify hateful remarks with strong execution and with the portion of misleading up-sides and negatives staying inside sensible limits.
Predicting students’ academic grade using machine learning algorithms with hybrid feature selection approach
Authors: Paddalwar Snehal , Mane Vanita , Ragha Leena
Published: May 2022

Data plays an important role where any prediction is to be made. Due to the advancement in technology there have been significant ways of collecting students’ data. Educators use lots of supportive digital system to teach and collect data, such as Learning Management System, Student Information system, etc. The data collection through these system is huge and nevertheless it can be called as a big data. Lots of research is being done in how to improve the students’ performance and overall educational performance and to tap key areas that may help in student progress. Traditional analysis of data includes sampling, whether it be a climate prediction or any performance prediction. In this research, the data collected through classroom observation, academic performance or learning management systems is used to predict the performance of the students. Lots of factors were present in the collected data but the main factors that add relevance for appropriate prediction are only few. This paper proposes how more than one feature selection algorithm results can be combined to improve the predictions with machine learning algorithm.
Predicting E-commerce Sales & Inventory Management using Machine Learning
Authors: Agnani Divyansh , Bavkar Abhishek , Salgar Samarth , Ahir Sheetal
Published: May 2022

Due to prevalent transition from visiting physical stores to online shopping, predicting customer behaviour in the e-commerce market is gaining increasing importance. Over the past few years, e-commerce marketplaces such as Flipkart and Amazon have observed a manifold increase in both sales and market share of retail products sold. During this period, many traditional retailers and wholesalers have set up their e-commerce portfolios to stay relevant in the marketplace. However, due to lack of technology penetration in the Medium, Small, & Micro Enterprises (MSMEs) sector, the technological tools required by them are largely absent. One key issue for small businesses in e-commerce has been to forecast how many products their business will sell, thereby introducing another big issue of predicting how much inventory of a product they need to hold, to match up with demand. This is partly due to the massive scale and opportunities opening for them by participating in an open, large marketplace such as e-commerce. The COVID-19 pandemic has also drastically changed consumer shopping trends and behaviours: consumers have changed their preference to buying online. Thus, Indian small businesses need to have the ability to forecast demand more accurately than ever before.
Analysis of Multiple Factors Influencing COVID-19 Induced Mental Health
Authors: Chauhan Neha , Dubey Niranjan , Iyer R. Kamatchi , Zade Nilima
Published: May 2022

The pandemic has adversely affected human lives. People have lost their loved ones, their jobs and even their homes in some cases. People have suffered from anxiety, frequent frustration, distress and loneliness due to the pandemic. With many of us facing challenges and battles in our lives, it is natural to feel frustrated and distressed. The spread of rumours and misinformation has led to anger, disorder and stress among people. In a country like India, where the population has suffered immensely due to the lack of healthcare infrastructure, Mental Health conditions are also deteriorated considerably due to different factors, we can confirm this by looking at the rise in suicide cases not only in India but also all around the world. We surveyed a few people across India including the working class and students from various age groups as the Covid-19 pandemic situation has brought everyone into renewed focus. The result indicates the level of suffering and the factors that have influenced their recovery.
Product Review Based on Facial Expression Detection
Authors: Bera Chirag , Adhav Prathmesh , Amati Shridhar , Singhaniya Navin
Published: May 2022

The research presents a method for assessing public acceptability of items based on their brand by analysing the facial expression of a consumer who intends to purchase the product at a supermarket. In such circumstances, face expression detection is crucial in product evaluation. Emotions are conveyed through facial expressions. Sentimental analysis is a type of natural language processing that may be used for a variety of purposes. As a result, several techniques to classifying human emotional states have been proposed. The extraction of feature points via a cascade classifier is used to identify facial expressions, which minimizes the time complexity. The owner can view the feedback of the the reviewed product. This product ranking will assist the business owner in increasing product sales while also ensuring that the top products are available for the customers.
A Novel Method of Deepfake Detection
Authors: Hande Rutuja , Goon Sneha , Gondhali Aaditi , Singhaniya Navin
Published: May 2022

Deep-Fake is a novel artificial media technology that uses the likeness of someone else to replace people in existing photographs and films. Deep Learning, as the name implies, is a type of Artificial Intelligence that is used to create it. It is critical to develop counter attacking approaches for detecting fraudulent data. This research examines the Deep-Fake technology in depth. The Deep-Fake Detection discussed here is based on current datasets, such as the Deep-Fake Detection Challenge (DFDC) and Google’s Deep-Fake Detection dataset (DFD). The creation of a bespoke dataset from high-quality Deep-Fakes was utilised to test models. The results of both with and without Transfer Learning were analysed. Finally, the trained models were used to spot well-known deep-fakes of former US President Barack Obama and well-known actor Tom Cruise. A comparison study was performed on all three models. The findings show that the detection are generally domain-specific tasks, however that using Transfer Learning considerably improves the model performance parameters, whereas convolutional RNN gives sequence detection advantage.
Regularization algorithms for linear copositive problems
Authors: Kostyukova Olga I. , Tchemisova Tatiana V.
Published: May 2022

The paper is devoted to the regularization of linear Copositive Programming problems which consists of transforming a problem to an equivalent form, where the Slater condition is satisfied and therefore the strong duality holds. We describe regularization algorithms based on a concept of immobile indices and on the understanding of the important role that these indices play in the feasible sets' characterization. These algorithms are compared to some regularization procedures developed for a more general case of convex problems and based on a facial reduction approach. We show that the immobile-index-based approach combined with the specifics of copositive problems allows us to construct more explicit and detailed regularization algorithms for linear Copositive Programming problems than those already available.
Tinnitus Handicap Inventory (THI) as a Hearing Aid Outcome Measure
Authors: Surr Rauna K. , Kolb Jennifer A. , Cord Mary T. , Garrus Nancy P.
Published: May 2022

This study assessed the effects of hearing aids on the perception of tinnitus using the Tinnitus Handicap Inventory (THI). THI benefit scores (unaided-aided) were examined in relation to hearing aid benefit as measured with the Abbreviated Profile of Hearing Aid Benefit (APHAB) inventory. The THI benefit was also related to the users' ratings of overall satisfaction with their hearing aids. Thirty-four novice hearing aid users with complaints of hearing loss and tinnitus participated in the study. Outcome measures were obtained 6 weeks after the hearing aid fittings. The results showed that hearing aid use reduced tinnitus handicap significantly, but, typically, the effect was small. The association between overall satisfaction ratings and THI benefit scores was weak. In contrast, the overall satisfaction ratings were strongly related to benefit on the speech subscales (average of Ease of Communication, Reverberation, and Background Noise) but not on the Aversiveness subscale of the APHAB. The weak relationship observed between THI benefit and benefit on the speech subscales of the APHAB suggested that the two inventories were not redundant. The results of the study suggest that the THI can make a useful contribution to the overall profile of hearing aid benefit for new hearing aid users with tinnitus.

Abbreviations: AA&SC = Army Audiology and Speech Center, APHAB = Abbreviated Profile of Hearing Aid Benefit, AV = Aversiveness of Sounds subscale, BN = Background Noise subscale, C = Catastrophic subscale, cd = critical difference, E = Emotional subscale, EC = Ease of Communication subscale, E = Emotional subscale, F = functional subscale, RV = Reverberation subscale, THI = Tinnitus Handicap Inventory
Just how giant is “giant” electrostriction?
Authors: Patrick Chris
Published: May 2022

Materials exhibiting anomalously large electrostriction may be able to replace toxic lead-based piezoelectrics in electromechanical applications
Observation of breakdown wave mechanism in avalanche ionization produced atmospheric plasma generated by a picosecond CO2 laser
Authors: Welch E. , Matteo D. , Tochitsky S. , Louwrens G. , Joshi C.
Published: May 2022

Understanding the formation and long-timescale evolution of atmospheric plasmas produced by ultrashort, long-wavelength infrared (LWIR) pulses is an important but partially understood problem. Of particular interest are plasmas produced in air with a peak laser intensity ∼1012 W/cm2, the so-called clamping intensity observed in LWIR atmospheric guiding experiments where tunneling and multi-photon ionization operative at near-IR or shorter wavelengths are inoperative. We find that avalanche breakdown on the surface of aerosol (dust) particles can act to seed the breakdown of air observed above the 200 GW/cm2 threshold when a train of 3 ps 10.6 μm laser pulses separated by 18 ps is used. The breakdown first appears at the best focus but propagates backward toward the focusing optic as the plasma density approaches critical density and makes forward propagation impossible. The velocity of the backward propagating breakdown can be as high as 109 cm/s, an order of magnitude greater than measured with ns pulse-produced breakdown, and can be explained rather well by the so-called breakdown wave mechanism. Transverse plasma expansion with a similar velocity is assisted by UV photoionization and is observed as a secondary longitudinal breakdown mechanism in roughly 10% of the shots. When a cm-size, TW power beam is propagated, interception of aerosol particles is guaranteed and several (40 cm−3) breakdown sites appear, each initially producing a near-critical density plasma. On a 10 ns–1 μs timescale, shockwaves from each site expand radially and coalesce to produce a large hot gas channel. The radial velocity of the expansion agrees well with the prediction of the blast wave theory developed for ultrafast atmospheric detonations.
Self-triggering topology for high-power nanosecond pulse generators based on avalanche transistors Marx bank circuits and linear transformer driver
Authors: Deng Zichen , Yuan Qi , Ding Weidong , Wang Yanan , Ren Linyuan , Wan Zhenbo
Published: May 2022

In recent years, several novel avalanche transistor-based power synthesis topologies have been proposed to improve the output performance of pulse generators based on avalanche transistors. The most promising is the topology based on avalanche transistors Marx Bank Circuits (MBCs) and linear transformer driver (LTD). However, it suffers from the same problems as other semiconductor switch-based LTD generators. The greater the number of LTD modules, the higher the requirements for synchronization and drive capability of the trigger system. This paper proposes a new self-triggering topology for pulse generators based on avalanche transistors MBCs and LTD, which significantly simplifies the entire generator's requirement for trigger system synchronization and driving capability. First, the circuit topology and its operation principle are introduced. Then, three prototypes with one trigger LTD module and three self-triggering LTD modules are developed. The output characteristics are experimentally investigated. The results verify the feasibility of the proposed topology. Finally, the output amplitude and the rise time are 3.35 kV/3.7 ns, 4.12 kV/3.7 ns, and 4.88 kV/4.0 ns on a 25 Ω resistive load, respectively. All generators can operate at 1 kHz. The topology proposed in the article maximally simplifies the requirements for synchronization and drive capability of the trigger system for generators based on avalanche transistor MBCs and LTD.
Remoteness, models of primary care and inequity: Medicare under-expenditure in the Northern Territory
Authors: Zhao Yuejen , Wakerman John , Zhang Xiaohua , Wright Jo , VanBruggen Maja , Nasir Rus , Duckett Stephen , Burgess Paul
Published: May 2022

Objective To analyse Medicare expenditure by State/Territory, remoteness, and Indigenous demography to assess funding equality in meeting the health needs of remote Indigenous populations in the Northern Territory. Methods Analytic descriptions of Medicare online reports on services and benefits by key demographic variables linked with Australian Bureau of Statistics data on remoteness and Indigenous population proportion. The Northern Territory Indigenous and non-Indigenous populations were compared with the Australian average between the 2010/2011 and 2019/2020 fiscal years in terms of standardised rates of Medicare services and benefits. These were further analysed using ordinary least squares, simultaneous equations and multilevel models. Results In per capita terms, the Northern Territory receives around 30% less Medicare funds than the national average, even when additional Commonwealth funding for Aboriginal medical services is included. This funding shortfall amounts to approximately AU$80 million annually across both the Medicare Benefits Schedule and Pharmaceutical Benefits Scheme. The multilevel models indicate that providing healthcare for an Aboriginal and Torres Strait Islander person in a remote area involves a Medicare shortfall of AU$531–AU$1041 less Medicare Benefits Schedule benefits per annum compared with a non-Indigenous person in an urban area. Indigenous population proportion, together with remoteness, explained 51% of the funding variation. An age–sex based capitation funding model would correct about 87% of the Northern Territory primary care funding inequality. Conclusions The current Medicare funding scheme systematically disadvantages the Northern Territory. A needs-based funding model is required that does not penalise the Northern Territory population based on the remote primary health care service model.
We have to set the bar higher: towards consumer leadership, beyond engagement or involvement
Authors: Scholz Brett
Published: May 2022

Understanding of the benefits of consumer-led health policy, practice, research and education has been developing for decades. However, barriers to genuine, non-tokenistic consumer leadership remain across the health sector. While recent calls to align Australian consumer engagement practices with those in the UK and elsewhere may sound progressive, doing so would be problematic for three reasons. First, Australia has been at the forefront of consumer leadership scholarship and advocacy for decades, and we should not ignore the work consumers and allies have done in improving our health systems. Second, although there have been positive outcomes from consumer engagement and inclusion practices (as often required in other jurisdictions), they are open to tokenism and continue to position consumers’ experiential expertise as ‘lesser’ compared to other health sector stakeholders’ knowledge. Last, compared to consumer leadership, engagement or inclusion are ‘lower bars’ for health professionals to aim for. If we settle for engagement or inclusion in cases where consumer leadership would have been possible, then we lose not only our position at the forefront of consumer leadership, but also the expertise of consumers. Three propositions are provided: (1) we should support consumer-led development policy for consumer leadership in health, (2) we should ensure consumer leadership in oversight over as well as conduct of health and medical research, (3) we should encourage honest claims about the extent to which projects or initiatives are led by or with consumers.
Does the relationship between sow body composition change in lactation and re-breeding success still exist?
Authors: Muller T. L. , Hewitt R. J. E. , Plush K. J. , Souza D. N. D , Pluske J. R. , Miller D. W. , van Barneveld R. J.
Published: May 2022

Sow body composition largely reflects the amount of lean and fat tissue stores in the body, and is measured, managed and reported because traditionally when sows mobilise body tissues in lactation to support piglet growth, adverse consequences in subsequent reproduction may be observed. These consequences are largely driven by metabolic changes exerting negative influences on the reproductive axes through luteinising hormone and follicle stimulating hormone and direct impact on the ovary. This results in sows that take longer to ovulate, have lower ovulation rates and shed poorer-quality oocytes, translating to delayed wean to service intervals, higher pregnancy failure and lower litter sizes. Sow management needs to meet both the needs of the piglet (adequate colostrum and milk intake for survival and growth) and the needs of the sow (successful re-breeding). The way pork producers tackle this is through diets designed to match sow requirements at different production stages. We have recently observed, despite efforts (nutritional challenges), that we are unable to induce fat or lean tissue mobilisation in lactating sows, which is a novel finding, although pig populations such as in the EU, UK and USA would appear to be experiencing similar outcomes. Despite our lower reproductive performance than in much of the rest of the world as a consequence of having a closed genetic herd, the specific genetic selection programs that exist within Australia, and the resultant leaner, more efficient sows, may be a potential explanation. This, coupled with the high lactation feed intakes now commonly reported, means that sows are less likely to become catabolic and so suffer from poor re-breeding outcomes. A conclusion from these findings may mean that we now have an opportunity to feed the lactating sow to better meet the growth and survival needs of piglets, with the knowledge that we will not compromise subsequent reproduction.
A multi-layered approach uncovers overlooked taxonomic and physiological diversity in Alpine subterranean spiders (Araneae: Linyphiidae:
Authors: Isaia Marco , Arnedo Miquel A. , Mammola Stefano
Published: May 2022

The integration of multiple lines of evidence in the delimitation of taxa is becoming the gold standard in modern taxonomy and systematics. However, multi-layered taxonomy is still incipient when it comes to species description within a mega-diverse group of organisms (e.g. arthropods), especially those inhabiting secluded environments such as caves. This may represent a significant shortcoming, because species in subterranean habitats generally show deep convergence in their somatic traits, perplexing our ability to delimit species by traditional approaches. The genus Troglohyphantes Joseph, 1882 (Araneae: Linyphiidae) is presently known to include 129 species and 5 subspecies, all of which are primarily distributed in subterranean habitats of the European mountain ranges. Recent studies on Italian alpine species covered taxonomy and faunistic, biogeography and ecology. However, our knowledge about the actual species diversity of Troglohyphantes spiders is far from complete, and it is expected that significant levels of overlooked diversity might exist within the genus. In this work, we combined morphological data with target gene molecular data, functional trait analysis and thermal physiology to explore overlooked diversity in Troglohyphantes vignai auct., a species of subterranean spider in the Western Italian Alps with an apparently broader distribution than its congeners. Our approach proved effective in uncovering overlooked diversity and further supporting the split of T. vignai auct. into three distinct species whose distribution ranges how striking resemblances with that of other subterranean spiders. We here describe the new species and discuss their origins in light of their geographical vicariance, niche convergence, thermal ecology and divergence time.
Sand characteristics do not influence hatching success of nests at the world’s largest green turtle rookery
Authors: Booth David T. , Staines Melissa N. , Reina Richard D.
Published: May 2022

Raine Island, located in the northern Great Barrier Reef, hosts the largest green turtle nesting aggregation in the world, but typically experiences low hatching success (20–60%, depending on the number of females visiting the island to nest). To determine whether the low hatching success of green turtle eggs at Raine Island might be explained by local sand characteristics, we investigated the physical properties of Raine Island sand and compared it to sand from other eastern coast Australian sea turtle nesting beaches that have high hatching success (>80%). We also measured the water, salt and organic material content of sand within nests at Raine Island to see whether any of these variables were correlated with the proportion of early embryo death or hatching success. The physical characteristics of Raine Island sand were similar to those of other eastern coast Australian nesting beaches, so it seems unlikely that inherent physical sand properties, water content, salt or organic matter explain the relatively low hatching success observed on Raine Island compared to other Australian green turtle nesting beaches. However, we found that nests that were inundated twice with seawater during spring high tides at the end of their first week of incubation experienced greater early development mortality and lower hatching success than did non-inundated nests, suggesting that embryos drowned during the inundation. Last, we found that hatching success declined towards the end of the nesting season, suggesting that the beach sand in the nesting areas of Raine Island changes in some way, and/or that egg quality decreases as the nesting season progresses.
Transient learning degrees of freedom for introducing function in materials
Authors: Hagh Varda F. , Nagel Sidney R. , Liu Andrea J. , Manning M. Lisa , Corwin Eric I.
Published: May 2022

Significance

Many protocols used in material design and training have a common theme: they introduce new degrees of freedom, often by relaxing away existing constraints, and then evolve these degrees of freedom based on a rule that leads the material to a desired state at which point these new degrees of freedom are frozen out. By creating a unifying framework for these protocols, we can now understand that some protocols work better than others because the choice of new degrees of freedom matters. For instance, introducing particle sizes as degrees of freedom to the minimization of a jammed particle packing can lead to a highly stable state, whereas particle stiffnesses do not have nearly the same impact.
Autophagy induced by taurolidine protects against polymicrobial sepsis by promoting both host resistance and disease tolerance
Authors: Huang Jie , Ita Michael , Zhou Huiting , Zhao He , Hassan Fara , Bai Zhenjiang , O’Leary D. Peter , Li Yiping , Redmond H. Paul , Wang Jiang Huai , Wang Jian
Published: May 2022

Significance

Disease resistance and tolerance are evolutionarily conserved yet distinct defense strategies that protect the host against microbial infection. Here, we report that taurolidine administered before the start of infection confers protection against polymicrobial sepsis by promoting resistance and tolerance. Notably, taurolidine given after the onset of infection also rescues mice from sepsis-associated lethality by enhancing disease tolerance to organ damage. This protection relies on an intact autophagy pathway, as taurolidine fails to protect autophagy-deficient mice against microbial sepsis. Specifically, taurolidine induces light chain 3-associated phagocytosis, but not xenophagy, in macrophages, resulting in an augmented bactericidal activity with enhanced cellular resistance to infection. These results highlight the importance of autophagy induction for taurolidine-augmented host resistance and disease tolerance and subsequent protection.
A long range sighting and terrestrial refraction
Authors: Giudici Sergio
Published: May 2022

The sighting of a distant island fixed on a digital image allowed us to measure the refraction coefficient and probe terrestrial refraction models. The measurement has been proposed to first-year students of Physics on behalf of the geometrical optics course held at Pisa University. The argument is inspired by a passage from Snell’s seminal treatise on geodesy raising the issue of terrestrial refraction, and it is developed in learning by doing context in which students are solicited to (re)construct a theory and compare measurements with simulation.
Parameter identification for elliptic boundary value problems: an abstract framework and applications
Authors: Hoffmann Heiko , Wald Anne , Nguyen Tram Thi Ngoc
Published: May 2022

Parameter identification problems for partial differential equations are an important subclass of inverse problems. The parameter-to-state map, which maps the parameter of interest to the respective solution of the PDE or state of the system, plays the central role in the (usually nonlinear) forward operator. Consequently, one is interested in well-definedness and further analytic properties such as continuity and differentiability of this operator w.r.t. the parameter in order to make sure that techniques from inverse problems theory may be successfully applied to solve the inverse problem. In this work, we present a general functional analytic framework suited for the study of a huge class of parameter identification problems including a variety of elliptic boundary value problems with Dirichlet, Neumann, Robin or mixed boundary conditions in Hilbert and Banach spaces and possibly complex-valued parameters. In particular, we show that the corresponding parameter-to-state operators fulfill, under suitable conditions, the tangential cone condition, which is often postulated for numerical solution techniques. This framework particularly covers the inverse medium problem and an inverse problem that arises in terahertz tomography.
Imaging conductivity from current density magnitude using neural networks*
Authors: Jin Bangti , Li Xiyao , Lu Xiliang
Published: May 2022

Conductivity imaging represents one of the most important tasks in medical imaging. In this work we develop a neural network based reconstruction technique for imaging the conductivity from the magnitude of the internal current density. It is achieved by formulating the problem as a relaxed weighted least-gradient problem, and then approximating its minimizer by standard fully connected feedforward neural networks. We derive bounds on two components of the generalization error, i.e., approximation error and statistical error, explicitly in terms of properties of the neural networks (e.g., depth, total number of parameters, and the bound of the network parameters). We illustrate the performance and distinct features of the approach on several numerical experiments. Numerically, it is observed that the approach enjoys remarkable robustness with respect to the presence of data noise.
Barriers to macroscopic superfluidity and insulation in a 2D Aubry–André model
Authors: Johnstone Dean , Öhberg Patrik , Duncan Callum W
Published: May 2022

We study the ground state phases of interacting bosons in the presence of a 2D Aubry–André (AA) potential. By using a mean-field percolation analysis, we focus on several superlattice and quasicrystalline regimes of the 2D AA model, including generalisations that account for a tilting or skewing of the potential. We show that barriers to the onset of macroscopic phases naturally arise from weakly modulated domains in the 2D AA model. This leads to the formation of extended crossover domains, in which the macroscopic properties are dominated by a minority of the system. The phase diagrams then exhibit substantially different features when compared against crystalline systems, including a lobe-like or wave-like appearance of the Bose glass, sharp extrusions and thin, percolating clusters. By studying the 2D AA model across multiple regimes, we have shown that these extended crossover domains are not distinct to a small set of parameters.
Tuning the electronic structure of layered Co-based serpentine nanosheets for efficient oxygen evolution reaction
Authors: Yang Baopeng , Zhang Ning
Published: May 2022

As low-cost and abundant catalysts, layered cobalt-based hydroxides that are composed of Co–OH octahedron slabs have promising prospect in electrocatalytic oxygen evolution reaction (OER). However, the layered hydrotalcite and brucite structure hydroxides usually possess inferior activity and poor stability. Herein, we developed layered cobalt-based serpentine nanosheets with a tuned electronic structure and stable active phase by using Ge–O and Si–O tetrahedron slabs to coordinate with Co–(O)OH octahedron slabs for efficient OER. Theoretical calculations reveal that Co–(O)OH octahedron slabs that coordinate with Ge–O tetrahedron slabs possess an optimal electronic structure and smaller reaction energy barriers for OER. Inspired by the calculations, the layered Co3Ge2O5(OH)4, Co3Si2O5(OH)4 and brucite Co(OH)2 nanosheets are synthesized. The coordination of Ge–O tetrahedron decreases the size and crystallinity of Co–(O)OH octahedron slabs, simultaneously enhancing the stability of Co–(O)OH octahedron slabs during OER, thus improving the activity and stability. As a result, Co3Ge2O5(OH)4 nanosheets present an outstanding OER activity (overpotential is 287 mV at 10 mA cm–1, lower than that of Co3Si2O5(OH)4 and Co(OH)2) and a remarkable stability for water splitting (working continuously more than 100 h without decay, much higher than that of Co(OH)2 (<36 h)). The solar driven water splitting process achieves a high Faradaic efficiency of 98.8%, and the solar-to-hydrogen energy conversion efficiency is 7.2%. Such a strategy of tuning the electronic structure of Co–O(OH) octahedron by coordination with a Ge–O tetrahedron offers an effective method for designing highly efficient Co-based electrocatalysts.
A novel designed 3D multi-microhole plasma jet device driven by nanosecond pulse at atmospheric pressure
Authors: Liu Zhijie , Wang Sitao , Pang Bolun , Gao Yuting , Li Qiaosong , Xu Dehui , Liu Dingxin , Zhou Renwu
Published: May 2022

A novel designed three-dimensional (3D) multi-microhole helium plasma jet device excited by nanosecond pulse is developed in atmospheric air. Systematical investigations about the discharge characteristics are carried out to get insights into the formation mechanisms of 3D plasma jets. Results show that the 3D plasma jets originated from the branching of a single ionization wave to present a hexagonal-like structure including bottom jet (BJ) and side jets (SJ1 and SJ2). The BJ always keeps a stable performance while the SJ displays a remarkably turbulent mode. The dynamic evolution, and the propagation velocity and distance for BJ and SJ demonstrate significant differences, with a delay effect between BJ and SJ bullets propagation being observed. The optical emission spectra show that 3D jets possess a high yield of the spatial distribution of reactive species in jet plumes. The formation mechanism of 3D jets is determined by the hydrodynamic (He flow distribution) and electrical interactions (distribution of electric field force) in the discharge tube emerging from individual holes with different radial directions. The direct treatment of water using this developed underwater 3D plasma device results in the production of plasma activated water with a lower pH value, higher conductivity, and greater concentrations of reactive oxygen and nitrogen species, compared to the indirect treatment. Especially, the concentration of H2O2 can remarkably increase 141 folds from 1.43 to 202.12 μM within 5 min after the direct 3D plasma treatment. This novel-designed 3D jets-based technique is a promising platform for 3D application scenarios, especially in the case of underwater microbubble discharge, which is of great significance for water activation in emerging applications.
Time-resolved electron energy distribution functions at the substrate during a HiPIMS discharge with cathode voltage reversal
Authors: Huber Wolfgang , Houlahan Thomas , Jeckell Zachary , Barlaz David , Haehnlein Ian , Jurczyk Brian , Ruzic David N
Published: May 2022

The time-dependent plasma properties of a high-power impulse magnetron sputtering plasma are investigated which include a positive ‘kick’ pulse on the sputtering target 2 μs after the main negative pulse, this reversing the voltage on the cathode. At a substrate 15 cm distant from the magnetron, the time-dependent electron energy distribution function (EEDF), plasma potential, potential commute time and plasma diffusion properties are measured using a single Langmuir probe. Results show that the positive pulse on the target expels plasma and raises the plasma potential across the chamber on the order of 1 to 2 μs, which is the time scale of the electron diffusion. The EEDF at the substrate fits a Druyvesteyn distribution during the main negative pulse rising slightly in average energy over time. The distribution is still Druyvesteyn and at the very start of the positive pulse, but then loses the higher energy electrons and drops in average electron energy as the positive pulse progresses. A Boltzmann equation solver, BOLSIG+, was used to predict the EEDF at the substrate during the positive pulse and it agrees best with the measurements assuming a value of 0.2 Td for the E/N (electric field/gas number density).
Chaotic dynamics of Bose–Einstein condensate induced by density-dependent gauge field
Authors: Chen Lei , Zhu Qizhong
Published: May 2022

In this work we study the effect of density-dependent gauge field on the collective dynamics of a harmonically trapped Bose–Einstein condensate (BEC), beyond the linear response regime. The density-dependent gauge field, as a backaction of the condensate, can in turn affect the condensate dynamics, resulting in highly nonlinear equations of motion. The dipole and breathing oscillations of the condensate along the direction of gauge field are coupled by this field. We find that, in the presence of this coupling, the collective motion of a quasi-one-dimensional condensate is still regular, i.e., periodic or quasiperiodic. In contrast, for a quasi-two-dimensional condensate, the collective dynamics of the condensate can become chaotic, when the density-dependent gauge field is strong. The mechanism is that the gauge field can also induce a Hall effect, manifested as an additional coupling between dipole and breathing oscillations in perpendicular direction, and chaotic motion is resulted from the interplay between these oscillations. Our findings reveal an important effect of dynamical gauge field on the nonlinear dynamics of a BEC.