Policy directives in several nations are focusing on the development of smart cities, linking innovations in the data sciences with the goal of advancing human well-being and sustainability on a ...highly urbanized planet. To achieve this goal, smart initiatives must move beyond city-level data to a higher-order understanding of cities as transboundary, multisectoral, multiscalar, social-ecological-infrastructural systems with diverse actors, priorities, and solutions. We identify five key dimensions of cities and present eight principles to focus attention on the systems-level decisions that society faces to transition toward a smart, sustainable, and healthy urban future.
Dry coating technique has been widely used in reducing cohesion in fine cohesive pharmaceutical powder particles and hence improving flowability. This paper aims at establishing the concept behind ...cohesion reduction by dry coating. Theory involving the mechanism of cohesion reduction by dry coating is discussed, together with tabulating the primary working principles and major operating parameters of four widely used dry coating techniques namely Comilling, Fluid Energy Milling, Magnetic Assisted Impact Coating and Mechanofusion. This review summarizes the technique of dry coating and the mechanism of cohesion reduction together with discussion of various studies undertaken to test the efficacy of the various dry coating techniques.
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We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The ...sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals D. George and E. A. Huerta, Phys. Rev. D 97, 044039 (2018); H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Phys. Rev. Lett. 120, 141103 (2018). We train a convolutional neural network with residual (shortcut) connections and compare its detection power to that of a fully coherent matched-filtering search using the Weave pipeline K. Wette, S. Walsh, R. Prix, and M. A. Papa, Phys. Rev. D 97, 123016 (2018). As test benchmarks we consider two types of all-sky searches over the frequency range from 20 to 1000 Hz: an "easy" search using T=105 s of data, and a "harder" search using T=106 s. The detection probability pdet is measured on a signal population for which matched filtering achieves pdet=90% in Gaussian noise. In the easiest test case (T=105 s at 20 Hz) the DNN achieves pdet∼88%, corresponding to a loss in sensitivity depth of ∼5% versus coherent matched filtering. However, at higher frequencies and for longer observation times the DNN detection power decreases, until pdet∼13% and a loss of ∼66% in sensitivity depth in the hardest case (T=106 s at 1000 Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.
Herein, we presented Ru‐SNS complex that serves as a useful catalyst for C‐3 alkylation of 1H‐indoles with various aliphatic primary and secondary alcohols including cyclic alcohols as well as ...benzylic alcohols. The selective synthesis of bisindolylmethane derivatives is also achieved from the same set of indole and alcohol just by altering the reaction parameters. Furthermore, the sustainable synthesis of C‐3 alkylated indoles directly from 2‐(2‐nitrophenyl)ethan‐1‐ol and alcohols catalysed by a Ru‐complex via “borrowing hydrogen” strategy is reported. This protocol provides an atom‐economical sustainable route to access structurally important compounds like arundine, vibrindole A and tryptamine based derivatives.
•Emotion is a psychological experience that is characterized by EEG signals.•The higher order statistics is used to explore emotional labeled EEG signals.•A LSTM based deep learning method is used to ...classify emotional signals.•The proposed algorithm attains 82.01.
The objective of this paper is online recognition of human emotions based on electroencephalogram (EEG) signals. The emotions are originated from the central and peripheral nervous systems. Hence, it can be adequately characterized by the EEG signal, as it directly reflects changes in the human emotional states. This paper describes an automated classification of emotions-labeled EEG signals using nonlinear higher order statistics and deep learning algorithm. The discrete wavelet transform is used to decompose the studied signal into sub-bands, known as rhythms of the EEG signal. The third-order cumulants (ToC) are used to explore the nonlinear dynamics of each sub-band signal in higher dimensional space. The data in the higher dimensional space contain repeated and redundant information due to presence of various symmetries in the ToC. Hence, an evolutionary data reduction technique, namely, the particle swarm optimization, is employed to get rid of irrelevant information. The long short-term memory based deep learning technique is used to retrieve the emotion variation from the optimized data corresponding to the labeled EEG signals. This study is carried out with the web-available DEAP dataset that yields 82.01% average classification accuracy with 10-fold cross-validation technique corresponding to four-labeled emotions classes. The achieved results have confirmed that the proposed algorithm has the potential for accurate and rapid recognition of human emotions.
Come this year I will be celebrating a milestone birthday, 25 years old. There are many significant milestones associated with that age; however, there is one milestone that I have been taking for ...granted… It is the age in which my parents’ employers drug plan will stop covering my medications. The realization that “free” medications were soon going to be a luxury of the past gave me a moment of pause, I asked myself, how does a country who prides itself on national healthcare still have people budgeting to buy life sustaining medications? The reality of that answer being the healthcare system we are so proud of is seemingly flawed.
Imaging an exotic state
Among the most intriguing of the many phases of cuprate superconductors is the so-called pair density wave (PDW) state. PDW is characterized by a spatially modulated density ...of Cooper pairs and can be detected with a scanning tunneling microscope equipped with a superconducting tip. Liu
et al.
used Josephson tunneling microscopy, modified for the task, to detect PDW in niobium diselenide, a superconductor with a layered hexagonal structure. The PDW state is expected to appear in other transition metal dichalcogenides as well.
Science
, abd4607, this issue p.
1447
Josephson tunneling microscopy is used to detect an unusual state in NbSe
2
, a layered superconductor.
Pair density wave (PDW) states are defined by a spatially modulating superconductive order parameter. To search for such states in transition-metal dichalcogenides (TMDs), we used high-speed atomic-resolution scanned Josephson-tunneling microscopy. We detected a PDW state whose electron-pair density and energy gap modulate spatially at the wave vectors of the preexisting charge density wave (CDW) state. The PDW couples linearly to both the
s
-wave superconductor and the CDW and exhibits commensurate domains with discommensuration phase slips at the boundaries, conforming those of the lattice-locked commensurate CDW. Nevertheless, we found a global
δ
Φ
≅
±
2
π
/
3
phase difference between the PDW and CDW states, possibly owing to the Cooper-pair wave function orbital content. Our findings presage pervasive PDW physics in the many other TMDs that sustain both CDW and superconducting states.
Background/Objectives
Age‐related hearing loss (ARHL) is a widely prevalent yet manageable condition that has been linked to neurocognitive and psychiatric comorbidities. Multiple barriers hinder ...older individuals from being diagnosed with ARHL through pure‐tone audiometry. This is especially true during the COVID‐19 pandemic, which has resulted in the closure of many outpatient audiology and otolaryngology offices. Smartphone‐based hearing assessment apps may overcome these challenges by enabling patients to remotely self‐administer their own hearing examination. The objective of this review is to provide an up‐to‐date overview of current mobile health applications (apps) that claim to assess hearing.
Design
Narrative review.
Measurements
The Apple App Store and Google Play Store were queried for apps that claim to assess hearing. Relevant apps were downloaded and used to conduct a mock hearing assessment. Names of included apps were searched on four literature databases (PubMed/MEDLINE, EMBASE, Cochrane Library, and CINAHL) to determine which apps had been validated against gold standard methods.
Results
App store searches identified 44 unique apps. Apps differed with respect to the type of test offered (e.g., hearing threshold test), cost, strategies to reduce ambient noise, test output (quantitative vs qualitative results), and options to export results. Validation studies were identified for seven apps.
Conclusion
Given their low cost and relative accessibility, smartphone‐based hearing apps may facilitate screening for ARHL, particularly in the setting of limitations on in‐person medical care due to COVID‐19. However, app features vary widely, few apps have been validated, and user‐centered designs for older adults are largely lacking. Further research and validation efforts are necessary to determine whether smartphone‐based hearing assessments are a feasible and accurate screening tool for ARHL.
Key Points
Age‐related hearing loss is a prevalent yet undertreated condition among older adults.
Why Does this Paper Matter?
Smartphone‐based hearing test apps may facilitate remote screening for hearing loss, but limitations surrounding app validation, usability, equipment calibration, and data security should be addressed.
ABSTRACT
SAX J1748.9−2021 is a transient accretion powered millisecond X-ray pulsar located in the globular cluster NGC 6440. We report on the spectral and timing analysis of SAX J1748.9−2021 ...performed on AstroSat data taken during its faint and short outburst of 2017. We derived the best-fitting orbital solution for the 2017 outburst and obtained an average local spin frequency of 442.361098(3) Hz. The pulse profile obtained from 3 to 7 and 7 to 20 keV energy bands suggest constant fractional amplitude ∼0.5 per cent for fundamental component, contrary to previously observed energy pulse profile dependence. Our AstroSat observations revealed the source to be in a hard spectral state. The 1–50 keV spectrum from SXT (Soft X-ray Telescope) and LAXPC (Large Area X-ray Proportional Counter) on-board AstroSat can be well described with a single temperature blackbody and thermal Comptonization. Moreover, we found that the combined spectra from XMM–Newton (EPIC-PN) and AstroSat (SXT + LAXPC) indicated the presence of reflection features in the form of iron (Fe Kα) line that we modelled with the reflection model xillvercp. One of the two X-ray burst observed during the AstroSat/LAXPC observation showed hard X-ray emission (>30 keV) due to Compton up-scattering of thermal photons by the hot corona. Time-resolved analysis performed on the bursts revealed complex evolution in emission radius of blackbody for second burst suggestive of mild photospheric radius expansion.
Objective
This study utilized a population database to investigate how social environments are associated with outcomes including stage at diagnosis, multimodal treatment, and disease‐specific ...survival for oral cavity squamous cell carcinomas.
Methods
Retrospective analysis of adults with oral cavity squamous cell carcinoma between 2007 and 2016 from the Surveillance, Epidemiology, End Results (SEER) registry was performed. The CDC's social vulnerability index (SVI) was used to characterize social vulnerability at the county level. Predictors of disease‐specific survival, stage at diagnosis, and use of multimodal therapy were identified using Cox regression and logistic regression.
Results
Our analysis included 17 043 patients. On adjusted models, patients in the highest SVI quartile (most social vulnerability) exhibited worse disease‐specific survival compared to the lowest quartile (HR 1.24, 95% CI 1.12–1.37, p < 0.001), and were more likely to be diagnosed at later stages (OR 1.24, 95% CI 1.11–1.38, p < 0.001) and less likely to receive multimodal therapy (OR 0.84, 95% CI 0.77–0.99, p = 0.037).
Conclusion
High social vulnerability was associated with worse disease‐specific survival and disease presentation in oral cavity cancer patients.
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