Miracle-dz/UniFAR-FTUs huggingface dataset:
Sputtered Piezoelectric Films as Materials for Flexible Devices and Sensors: A Review

The demand for innovative and versatile technologies in wearable electronics has catalyzed research into functional materials that can seamlessly integrate with complex and curved surfaces. Recent advances in sputtering technology have not only enabled precise control over the thin-film composition and crystal structure on various substrates, including flexible ones, but also made it easier to optimize a sensor’s performance for specific applications. Piezoelectric thin films produced by sputtering techniques are recognized for their exceptional electromechanical properties and offer a promising approach for developing low-cost, high-performance flexible sensors. These sensors have a wide range of applications, from wearable devices to environmental monitoring. In this article, we present and discuss the use of sputtered piezoelectric thin films as materials for flexible devices and sensors. The growth of these thin films through sputtering, their piezoelectric properties, and the sensor fabrication processes are detailed, with a focus on challenges that remain to be addressed. We also discuss the difficulties in optimizing film performance while ensuring mechanical durability, emphasizing the importance of material engineering. Additionally, the broad spectrum of applications for flexible devices and sensors is highlighted.
Imputation method for single-cell RNA-seq data using neural topic model

Abstract Single-cell RNA sequencing (scRNA-seq) technology studies transcriptome and cell-to-cell differences from higher single-cell resolution and different perspectives. Despite the advantage of high capture efficiency, downstream functional analysis of scRNA-seq data is made difficult by the excess of zero values (i.e., the dropout phenomenon). To effectively address this problem, we introduced scNTImpute, an imputation framework based on a neural topic model. A neural network encoder is used to extract underlying topic features of single-cell transcriptome data to infer high-quality cell similarity. At the same time, we determine which transcriptome data are affected by the dropout phenomenon according to the learning of the mixture model by the neural network. On the basis of stable cell similarity, the same gene information in other similar cells is borrowed to impute only the missing expression values. By evaluating the performance of real data, scNTImpute can accurately and efficiently identify the dropout values and imputes them accurately. In the meantime, the clustering of cell subsets is improved and the original biological information in cell clustering is solved, which is covered by technical noise. The source code for the scNTImpute module is available as open source at https://github.com/qiyueyang-7/scNTImpute.git.
An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology

Abstract In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases. Our methodology encompasses a step-by-step process, beginning with the acquisition of digital color images of diseased leaves, followed by denoising using the mean function and enhancement through Contrast-limited adaptive histogram equalization. The subsequent stages involve segmentation through the Fuzzy C Means algorithm, feature extraction via Principal Component Analysis, and disease classification, employing Particle Swarm Optimization (PSO) in conjunction with SVM, Backpropagation Neural Network, and Random Forest algorithms. The results of our study showcase the exceptional performance of the PSO SVM algorithm in accurately classifying and detecting fig leaf disease, demonstrating its potential for practical implementation in agriculture. This innovative approach not only underscores the significance of advanced image processing techniques but also highlights their substantial contributions to sustainable agriculture and plant disease mitigation. In conclusion, the integration of image processing and SVM-based classification offers a promising avenue for advancing crop disease management, ultimately bolstering agricultural productivity and global food security.
Distinct impacts of feeding frequency and warming on life history traits affect population fitness in vertebrate ectotherms

Abstract Body size shifts in ectotherms are mostly attributed to the Temperature Size Rule (TSR) stating that warming speeds up initial growth rate but leads to smaller size when food does not limit growth. Investigating the links between temperature, growth, and life history traits is key to understand the adaptive value of TSR, which might be context dependent. In particular, global warming can affect food quantity or quality which is another major driver of growth, fecundity, and survival. However, we have limited information on how temperature and food jointly influence life history traits in vertebrate predators and how changes in different life history traits combine to influence fitness and population demography. We investigate (1) whether TSR is maintained under different food conditions, (2) if food exacerbates or dampens the effects of temperature on growth and life history traits and (3) if food influences the adaptive value of TSR. We combine experiments on the medaka with Integral Projection Models to scale from life history traits to fitness consequences. Our results confirm that warming triggers a higher initial growth rate and a lower adult size, reduces generation time and increases mean fitness. A lower level of food exacerbates the effects of warming on growth trajectories. Although lower feeding frequency increased survival and decreased fecundity, it did not influence the effects of warming on fish development rates, fecundity, and survival. In contrast, feeding frequency influenced the adaptive value of TSR, as, under intermittent feeding, generation time decreased faster with warming and the increase in growth rate with warming was weaker compared to continuously fed fish. These results are of importance in the context of global warming as resources are expected to change with increasing temperatures but, surprisingly, our results suggest that feeding frequency have a lower impact on fitness at high temperature.
Association between flaunting behaviors on social media and among the general population in Bangladesh: A cross‐sectional study

The number of social media users is growing with each passing day at full tilt, keeping pace with digitalization and technological advances. Despite several advantages, there are also certain negative aspects to using social networking sites (SNS) for communication, amusement, self‐expression, impression management, and other purposes. This study sought to investigate the association between mental health status and flaunting behaviors in social media among the general population in Bangladesh.
Satellite mapping of red snow on North American glaciers

Red snow caused by blooms of microalgae darkens the surface of summer snowfields, increasing snowmelt. To assess the contribution of red snow to supraglacial snowmelt in northwestern North America, we systematically mapped the 2019–2022 distribution of blooms by applying supervised classification to 6158 satellite images. Blooms occurred on 5% of the total glaciated area, heavily affecting many glaciers in years of prolonged snow cover duration. Individual glaciers had up to 65% of their surface area affected by bloom in one melt season, which we estimate caused as much as 3 cm of snow meltwater equivalent averaged across the glacier surface. These results demonstrate appreciable snowmelt caused by red snow albedo over vast areas of North American glaciers.
Comparative analysis of classification techniques for topic-based biomedical literature categorisation

Introduction: Scientific articles serve as vital sources of biomedical information, but with the yearly growth in publication volume, processing such vast amounts of information has become increasingly challenging. This difficulty is particularly pronounced when it requires the expertise of highly qualified professionals. Our research focused on the domain-specific articles classification to determine whether they contain information about drug-induced liver injury (DILI). DILI is a clinically significant condition and one of the reasons for drug registration failures. The rapid and accurate identification of drugs that may cause such conditions can prevent side effects in millions of patients. Methods: Developing a text classification method can help regulators, such as the FDA, much faster at a massive scale identify facts of potential DILI of concrete drugs. In our study, we compared several text classification methodologies, including transformers, LSTMs, information theory, and statistics-based methods. We devised a simple and interpretable text classification method that is as fast as Naïve Bayes while delivering superior performance for topic-oriented text categorisation. Moreover, we revisited techniques and methodologies to handle the imbalance of the data. Results: Transformers achieve the best results in cases if the distribution of classes and semantics of test data matches the training set. But in cases of imbalanced data, simple statistical-information theory-based models can surpass complex transformers, bringing more interpretable results that are so important for the biomedical domain. As our results show, neural networks can achieve better results if they are pre-trained on domain-specific data, and the loss function was designed to reflect the class distribution. Discussion: Overall, transformers are powerful architecture, however, in certain cases, such as topic classification, its usage can be redundant and simple statistical approaches can achieve compatible results while being much faster and explainable. However, we see potential in combining results from both worlds. Development of new neural network architectures, loss functions and training procedures that bring stability to unbalanced data is a promising topic of development.
Impact of pH on the activity of novel cephalosporin cefiderocol in human urine

Abstract Background Antimicrobial activity of antibiotics can be impacted by pH, enhancing or reducing their bactericidal properties. Cefiderocol, a novel cephalosporin antibiotic that is among others indicated for the treatment of complicated urinary tract infections (cUTIs), lacks data on activity in urine. Methods Pooled human urine (iron levels ∼0.05 mg/L/24 h), CAMHB and iron-depleted CAMHB (ID-CAMHB) at pH 5, 7 and 8 served as media. MIC testing was done according to EUCAST with the broth microdilution method for 17 clinical isolates of Escherichia coli and ATCC 25922 (including isolates with ESBL activity), 17 clinical isolates of Klebsiella pneumoniae and ATCC 700603 (also with ESBL), and 6 clinical isolates of Pseudomonas aeruginosa and ATCC 27853. Time–kill curves (TKCs) were performed for selected strains at pH 5, 7 and 8 in urine. Results MIC values in urine, CAMHB and ID-CAMHB exhibited isolate-specific variations when assessed under identical pH conditions, ranging from a 1-fold dilution to changes of up to 4-fold dilutions in either direction. Median MICs of cefiderocol were up to 50-fold higher in pH 5 than in pH 7 for P. aeruginosa isolates and 32-fold higher in E. coli and K. pneumoniae isolates. TKCs with 650 and 1300 mg/L cefiderocol in urine showed that ATCC strains were efficiently eradicated despite the pH set. Conclusions Acidic pH had a significant negative impact on cefiderocol activity. Yet, after a recommended IV administration of 2 g cefiderocol every 8 h, a concentration of approximately 1300 mg/L can be achieved in urine, suggesting that efficient killing of all tested pathogens could have been possible even under acidic conditions in vivo.
A Qualitative Investigation on Chronic Disease Management and Prevention Among Older Adults During the COVID-19 Pandemic

Purpose To qualitatively describe experiences of chronic disease management and prevention in older adults (age ≥65 years) during COVID-19. Approach Qualitative descriptive approach. Setting Data collected online via telephone and video-conferencing technologies to participants located in various cities in British Columbia, Canada. Data analyzed by researchers in the cities of Vancouver and Kelowna in British Columbia. Participants Twenty-four community-living older adults (n = 24) age ≥65 years. Methods Each participant was invited to complete a 30-to-45-minute virtual, semi-structured, one-on-one interview with a trained interviewer. Interview questions focused on experiences managing health prior to COVID-19 and transitioning experiences of practicing health management and prevention strategies during COVID-19. Audio recordings of interviews were transcribed verbatim and analyzed thematically. Results The sample’s mean age was 73.4 years (58% female) with 75% reporting two or more chronic conditions (12.5% none, 12.5% one). Three themes described participants’ strategies for chronic disease management and prevention: (1) having a purpose to optimize health (i.e., managing health challenges and maintaining independence); (2) internal self-control strategies (i.e., self-accountability and adaptability); and (3) external support strategies (i.e., informational support, motivational support, and emotional support). Conclusion Helping older adults identify purposes for their own health management, developing internal control strategies, and optimizing social support opportunities may be important person-centred strategies for chronic disease management and prevention during unprecedented times like COVID-19.
The relationship between the spatial distribution of vegetation and soil environmental factors in the tidal creek areas of the Yellow River Delta

The tidal creeks are widely distributed in the Yellow River Delta,accompanying with many distinctive types of vegetations.We set 28 sampling sites in the study area and collected the data of vegetations and soil environmental factors.By TWINSPAN and canonical correspondence analysis(CCA),we analyzed the relationship between the spatial distribution of vegetations and soil environmental factors in the tidal creek areas in the Yellow River Delta;and found that there had 7 types of vegetation communities in the study area.The result indicated that(1) the soil salinity was mainly in the form of Cl-and Na+;(2) the distance from the sea and the water content in the soil both influenced the amount of the soil salinity;(3) the distribution of Suaeda salsa and Tamarix chinensis were mainly affected by the soil salinity,the content of Na+ and the distance from the sea,the distribution of Limonium bicolor,Apocynum venetum,Suaeda glauca and Phragmites australis were related with total phosphorus(TP),total nitrogen(TN),organic matters,Ca2+ and Mg2+ and other types of vegetations were mainly distributed in the areas far away from the sea.
Key CT markers for predicting haemorrhagic transformation after ischaemic stroke: a prospective cohort study in China

Objectives Limited studies have systematically addressed the CT markers of predicting haemorrhagic transformation (HT). We aimed to (1) investigate the predictive ability of the imaging factors on multimodal CT for HT and (2) identify the key CT markers that can accurately predict HT while maintaining easy and rapid assessment in the early stage of stroke. Design and setting This was a prospective cohort study conducted in a tertiary hospital in Southwest China. Participants Patients with ischaemic stroke admitted within 24 hours after onset were included. Outcome measures The primary outcome was measured as the overall HT. The secondary outcomes were the presence of parenchymal haematoma, symptomatic HT and spontaneous HT. Results A total of 763 patients were included. The early hypodensity >1/3 of the middle cerebral artery (MCA) territory, Alberta Stroke Programme Early CT Score≤7, midline shift, hyperdense middle cerebral artery sign (HMCAS), poor collateral circulation, infarct core and penumbra was independently associated with the increased risk of HT (all p < 0.05). The sensitivity of midline shift for predicting HT was only 3.5%, whereas its specificity was 99.8%. The combination of the early hypodensity >1/3 of the MCA territory, midline shift and HMCAS showed a good predictive performance for HT (area under the curve 0.80, 95% CI 0.75 to 0.84). Conclusions Seven imaging factors on multimodal CT were independently associated with HT. The high specificity of midline shift suggests the need to consider it as an imaging indicator when assessing the risk of HT. The early hypodensity >1/3 of the MCA territory, midline shift and HMCAS was identified as the key CT markers for the early prediction of HT. The coexistence of the three key factors might be a valuable index for identifying individuals at high bleeding risk and guiding further treatments.
On the Benefit of GOSAT Observations to the Estimation of Regional CO2 Fluxes

We assessed the utility of global CO{sub 2} distributions brought by the Greenhouse gases Observing SATellite (GOSAT) in the estimation of regional CO{sub 2} fluxes. We did so by estimating monthly fluxes and their uncertainty over a one-year period between June 2009 and May 2010 from (1) observational data collected in existing networks of surface CO2 measurement sites (GLOBALVIEWCO2 2010; extrapolated to the year 2010) and (2) both the surface observations and column-averaged dry air mole fractions of CO{sub 2} (X{sub CO2}) retrieved from GOSAT soundings. Monthly means of the surface observations and GOSAT X{sub CO2} retrievals gridded to 5{sup o} x 5{sup o} cells were used here. The estimation was performed for 64 subcontinental-scale regions. We compared these two sets of results in terms of change in uncertainty associated with the flux estimates. The rate of reduction in the flux uncertainty, which represents the degree to which the GOSAT X{sub CO2} retrievals contribute to constraining the fluxes, was evaluated. We found that the GOSAT X{sub CO2} retrievals could lower the flux uncertainty by as much as 48% (annual mean). Pronounced uncertainty reduction was found in the fluxes estimated for regions in Africa, South America, and Asia, where the sparsitymore » of the surface monitoring sites is most evident. « less
How Exactly does Literary Content Depend on Genre? A Case Study of Animals in Children's Literature

The content of literary 昀椀ction at least partly depends on literary tradition. The dependence is attested quantitatively in the association of genre with lexical statistical patterns. This short paper is a step to formal modeling of the content-moderating processes associated with literary genres. The idea is to explain prevalence of the particular lemmas in a literary text by the genre-dependent accessibility of the semantic category during the creative process. Data on animals mentioned in various sub-genres in a corpus of Russian children’s literature is used as an empirical case. Vocabulary growth models are applied to infer genre-related di昀昀erences in overall diversity of animal vocabularies. A constrained topic model is employed to infer preferences for particular animal lemmas displayed by various genres. Results demonstrate the models’ potential to infer genre-related content preferences in the context of high variance and data imbalance.
Asynchronous interference alignment

A constant three-user interference channel wherein users are not symbol synchronous is considered. A novel practical interference alignment scheme for this channel is proposed wherein aligning interfering signals at each receiver is performed with the help of asynchronous delays which inherently exist among the received signals at each receiver node. Due to the existence of asynchronism among the users, the underlying quasi-static flat fading links are converted into frequency selective and accordingly into time varying channels with correlated coefficients across the time. This solves the lack of channel variation required for interference alignment in quasi-static scenarios. It is proved that the proposed scheme achieves the total degrees of freedom equal to 3/2 of this channel almost surely in the limit of codeword's length. Application of the proposed scheme is not limited to high signal to noise ratio (SNR) regime and it can be used in any range of SNR.
A 3.3-V 12-b 50-MS/s A/D converter in 0.6-μm CMOS with over 80-dB SFDR

A 12-b analog-to-digital converter (ADC) is optimized for spurious-free dynamic range (SFDR) performance at low supply voltage and suitable for use in modern wireless base stations. The 6-7-b two-stage pipeline ADC uses a bootstrap circuit to linearize the sampling switch of an on-chip sample-and-hold (S/H) and achieves over 80-dB SFDR for signal frequencies up to 75 MHz at 50 MSample/s (MSPS) without trimming, calibration, or dithering. INL is 1.3 LSB, differential nonlinearity (DNL) is 0.8 LSB. The 6-b and 7-b flash sub-ADCs are implemented efficiently using offset averaging and analog folding. In 0.6-/spl mu/m CMOS, the 16-mm/sup 2/ ADC dissipates 850 mW.
Ultra-Fast Bloom Filters using SIMD techniques

The network link speed is increasing at an alarming rate, which requires all network functions on routers/switches to keep pace. Bloom filter is a widely-used membership check data structure in network applications. It also faces the urgent demand of improving the performance in membership check speed. To this end, this paper proposes a new Bloom filter variant called Ultra-Fast Bloom Filters, by leveraging the SIMD techniques. We make three improvements for the UFBF to accelerate the membership check speed. First, we develop a novel hash computation algorithm which can compute multiple hash functions in parallel with the use of SIMD instructions. Second, we change a Bloom filter's bit-test process from sequential to parallel. Third, we increase the cache efficiency of membership check by encoding an element's information to a small block which can easily fit into a cache-line. Both theoretical analysis and extensive simulations show that the UFBF greatly exceeds the state-of-the-art Bloom filter variants on membership check speed.
Studies on Seasonal Variations of Ground Water Quality in Major Cities of Southern Part of India (Chennai, Bangalore and Hyderabad)

Three major cities in India such as Chennai, Bangalore and Hyderabad were selected for this study. The study mainly focused on assessment of physico-chemical as well as biological properties of ground water on four different seasons like spring, summer, autumn and winter in the year 2012. The physico-chemical parameters like pH, turbidity, TDS, TH, Ca 2+ , Mg 2+ , TA, Cl – , SO4 2– , NO3 – , F – , Fe and biological parameters like total coliforms were analyzed using standard procedures and all the parameters were expressed in milligram/L except turbidity which expressed in NTU and total coliforms which expressed in MPN/100 mL. Based on the results obtained from this study, clear evidence on seasonal wise variations on ground water quality was observed in Southern part of India. Further the suitable reverse osmosis technology is recommended for accessing the safe drinking water from ground water.