Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent progress on jet printing of oxide-based thin film transistors

Among the printing technologies for printed electronics, jet printing-based methods provide promising solutions to the fabrication of oxide-based thin film transistors (TFTs) for flexible and non-flexible electronics. Their direct patterning capability, non-vacuum and solution-based material formation process have attracted a significant amount of interests from both the academic and industry sectors. This topical review summarizes recent progress on the development of jet printed metal oxide TFTs with special attention given to the surface related effects, ink preparation and post-printing treatments, which are the critical aspects of obtaining high performance printed TFTs. Firstly, a brief introduction is made on the state of art jet printing technologies: the piezoelectric/thermal inkjet printing, the newly emerged electro-hydrodynamic jet printing and aerosol jet printing. Then, surface related issues of wettability and coffee-ring effect are discussed. Finally, jet printing of oxide-based TFTs is reviewed according to the classification of printed material types: metal oxide semiconductors, gate dielectrics and conductive electrodes. The extensive review of low temperature annealing methods make this work particularly interesting to the printing of oxide-based TFTs on flexible substrates.
Local Oscillator Phase Shifting and Harmonic Mixing-Based High-Precision Phased Array for 5G Millimeter-Wave Communications

This paper presents a 28-GHz low-cost high-precision phased array for fifth-generation millimeter-wave communications. The local oscillator (LO) phase shifting approach and the subharmonic mixing technique are adopted to achieve a full 360° phase-shifting range with up to 10-bit phase resolution and ultralow magnitude deviation. Each phased channel in this array contains a 1-bit 180° intermediate frequency phase shifter and a low-voltage varactor-tuned reflective-type phase shifter at LO path. The LO phase shifting is used to achieve fine phase shifting with low-magnitude variation. The subharmonic mixing enables lower phase range and lower operation frequency at the LO path. The measured root-mean-square phase error and magnitude variation are around 0.3° and 0.1 dB, respectively. The over-the-air phased array calibration and measurement are also performed. The eight-element phased array achieves +/−50° beam scanning angle and fine beam resolution less than 1° step. The gain flatness of the phased array is less than +/−1dB at 1-GHz signal bandwidth. The eight-element array achieves an effective isotropic radiated power of 41 dBm at 10-dB power backoff. The measured error-vector-magnitude is 1.72% with a 500-MHz orthogonal-frequency-division-multiplexing QAM-64 signal.
Impacts of salinity stress on crop plants: improving salt tolerance through genetic and molecular dissection

Improper use of water resources in irrigation that contain a significant amount of salts, faulty agronomic practices such as improper fertilization, climate change etc. are gradually increasing soil salinity of arable lands across the globe. It is one of the major abiotic factors that inhibits overall plant growth through ionic imbalance, osmotic stress, oxidative stress, and reduced nutrient uptake. Plants have evolved with several adaptation strategies at morphological and molecular levels to withstand salinity stress. Among various approaches, harnessing the crop genetic variability across different genepools and developing salinity tolerant crop plants offer the most sustainable way of salt stress mitigation. Some important major genetic determinants controlling salinity tolerance have been uncovered using classical genetic approaches. However, its complex inheritance pattern makes breeding for salinity tolerance challenging. Subsequently, advances in sequence based breeding approaches and functional genomics have greatly assisted in underpinning novel genetic variants controlling salinity tolerance in plants at the whole genome level. This current review aims to shed light on physiological, biochemical, and molecular responses under salt stress, defense mechanisms of plants, underlying genetics of salt tolerance through bi-parental QTL mapping and Genome Wide Association Studies, and implication of Genomic Selection to breed salt tolerant lines.
The current status of global river discharge monitoring and potential new technologies complementing traditional discharge measurements

River discharge is one of the most accurately measured components of the hydrological cycle, but it is rarely utilized in climate system studies. Collection, archiving and distribution of river discharge data globally is limited and often restricted. Besides the difficulties in accessing discharge data globally, the currently operating network is clearly inadequate in many parts of the Earth and is still declining. The present paper gives an assessment of the current state of discharge monitoring. The paper also provides a coarse estimate about the network density needed for climate system studies and discusses some of the emerging new technologies, which may provide supplemental information about river discharge in the future.
GTCO: Graph and Tensor Co-Design for Transformer-Based Image Recognition on Tensor Cores

Deep learning frameworks or compilers optimize the operators in computation graph using fixed templates via significant engineering efforts, which may miss potential optimizations such as operator fusion. Therefore, automatically implementing and optimizing the emerging new combinations of operators on a specific hardware accelerator is of importance. In this article, we introduce GTCO, a tensor compilation system designed to accelerate transformer-based vision models’ inference on GPUs. GTCO tackles the operator fusion techniques in the transformer-based model using a novel dynamic programming algorithm and proposes a search policy with new sketch generation rules for the fused batch matrix multiplication and softmax operators. Tensor programs are sampled from an effective search space, and a hardware abstraction with hierarchical mapping from tensor computation to domain-specific accelerators (Tensor Cores) is formally defined. Finally, our framework can map and transform tensor expression into efficient CUDA kernels with hardware intrinsics on GPU. Our experimental results demonstrate that GTCO improves the end-to-end execution performance by up to $1.73\times $ relative to the cutting-edge deep learning library TensorRT on NVIDIA GPUs with Tensor Cores.
ASHL: An Adaptive Multi-Stage Distributed Deep Learning Training Scheme for Heterogeneous Environments

With the increment of data sets and models sizes, distributed deep learning has been proposed to accelerate training and improve the accuracy of DNN models. The parameter server framework is a popular collaborative architecture for data-parallel training, which works well for homogeneous environments by properly aggregating the computation/communication capabilities of different workers. However, in heterogeneous environments, the resources of different workers vary a lot. Some stragglers may seriously limit the whole speed, which impacts the overall training process. In this paper, we propose an adaptive multi-stage distributed deep learning training framework, named ASHL, for heterogeneous environments. First, a profiling scheme is proposed to capture the capabilities of each worker to reasonably plan the training and communication tasks on each worker, and lay the foundation for the formal training. Second, a hybrid-mode training scheme (i.e., coarse-grained and fined-grained training) is proposed to balance the model accuracy and training speed. The coarse-grained training scheme (named AHL) adopts an asynchronous communication strategy, which involves less frequent communications. Its main goal is to make the model quickly converge to a certain level. The fine-grained training stage (named SHL) uses a semi-asynchronous communication strategy and adopts a high communication frequency. Its main goal is to improve the model convergence effect. Finally, a compression-based communication scheme is proposed to further increase the communication efficiency of the training process. Our experimental results show that ASHL reduces the overall training time by more than 35% to converge to the same degree and has better generalization ability compared with state-of-the-art schemes like ADSP.
On the taxonomic status of Dina ratschaensis Kobakhidze, 1958 with a description of two new species – Dina imeretiensis sp. nov. and D. samegreloensis sp. nov. (Annelida, Hirudinida: Erpobdellidae)

Two species of leeches were described from Georgia in the past, Dina ratschaensis Kobakhidze, 1958 from the Racha-Lechkhumi and Kvemo Svaneti region and Trocheta ariescornuta Grosser, Barjadze & Maghradze, 2021 from the Samegrelo-Zemo Svaneti region. Both species were the only known typical representatives of cave leeches in Georgia. Recently, two more species of the genus Dina R. Blanchard, 1894 have been found in karst caves in this country. These leeches are morphologically similar to D. ratschaensis, from which they differ in the shape of the reproductive system, primarily the shape of the cornua of the genital atrium and the shape and extension of the vasa deferentia and ovisacs, justifying the description of two new species, Dina imeretiensis Grosser, Barjadze & Maghradze sp. nov. from the Imereti region and Dina samegreloensis Grosser, Barjadze & Shavadze sp. nov. from the Samegrelo-Zemo Svaneti region. Data on the cave dwelling invertebrate communities in the studied caves are provided. Molecular data are provided, and a phylogenetic tree based on Cox1 sequences of Dina spp. and related genera is provided and discussed.
Better than expected: the gap between self-reported and objective measures of cognitive performance in remitted bipolar disorder

Background Studies comparing objective and self-reported cognitive functioning as well as influencing factors in individuals with remitted bipolar disorder are scarce and contradictory. Methods The aim of this study was to compare executive functioning and other objective and self-reported cognitive impairment between 26 individuals with remitted bipolar disorder (15 BD I) and 24 healthy controls using a cross-sectional design. Executive functions were measured by the TAP Go/No-go subtest as well as the Stroop Task. Self-rated functioning was assessed using the Attention Deficit Experience Questionnaire. In addition, possible predictors of self-reported and objective cognitive functioning were examined to perform regression analyses. Results Individuals with remitted bipolar disorder did not differ significantly in executive functions or other objective cognitive domains from the healthy control group, but showed a significantly lower level of self-reported cognitive functioning and self-esteem. While self-esteem was the strongest predictor in healthy controls for self-reported cognitive functioning, severity of illness and subthreshold depressive mood were the most important predictors in individuals with remitted bipolar disorder. Conclusion The results once again demonstrate the cognitive heterogeneity in bipolar disorder. In the treatment of cognitive deficits, factors such as subthreshold depressive symptomatology and self-esteem should be focused on in addition to cognitive training in remitted patients.
Long- and Short-Term Climate Influences on Southwestern Shrublands

Ahstract-N ew findings raise questions about long and short-term climatic effects on Southwestern shrublands. Millennial-scale climate variability during the last glacial may have quickened the pace of evolution for genetically variable shrub species. During the last deglaciation, greater plant water use efficiency due to 30 percent CO2 enrichment was not enough to offset Holocene aridity, and desertscrub replaced woodlands over much of the Southwestern United States. Likewise, the degree to which present CO2 enrichment is driving C3 shrubs into C4 grasslands is probably a function of interdecadal climate variability. Shrub encroachment accelerated during the 1950's drought, when both winters and summers went dry, and continues under the wet winter-summer dry regime since 1976. Climate is also modulating the spread of exotic annual grasses and introduction of a vigorous fire regime in native shrublands, where none existed before. A combination of biogeographic, historical, experimental, modeling, and monitoring approaches is proposed to predict the ecological outcomes of climate change and direct human impacts.
Numerical Analyses on Liquid-Metal Magnetohydrodynamic Flow in 180°-Turn Channel

Numerical calculations have been performed on liquid-metal magnetohydrodynamic (MHD) flow in a 180°-turn (i.e. hairpin-shaped) channel, in order to contribute to design of a fusion reactor blanket. A magnetic field is applied in a direction perpendicular to an inlet channel, a turning channel (turning section) and an outlet channel. The continuity equation, the momentum equation and the induction equation have been solved numerically. In this study, attention is focused on pressure drops along the channels and pressure distribution in the turning channel. The Hartmann number (indicating magnetic field strength), the Reynolds number and the channel aspect ratio, in the present calculations, cover 100 to 500, 1000 to 5000 and 1 to 1/4, respectively. The following things have become clear from calculation results. The total MHD pressure drop from a channel inlet to a channel outlet agrees approximately with that for the total channel length, meaning that the loss coefficient for the turning channel is nearly zero or small. For large Reynolds numbers, the pressure in the peripheral region of the turning channel becomes larger than that at the channel inlet, due to the centrifugal force acting in the turning channel. It is considered that this pressure increase should be taken into account in designing a fusion reactor blanket.
Improving feedback analysis: Deep learning approach to college customer satisfaction assessments

Establishing consumers' views via text-based feedback in a questionnaire is crucial for organizations, include education, since it gives a summary of significant areas that help administrators plan, regulations, and decision making. Through surveys, academic organizations have gathered huge quantities of textual data all over the years. For the organization, it is still difficult to analyse the vast quantities of unstructured feedback from customers to understand their concerns and opinions generally. In this study, we propose deep learning (DL) based technique called topic modelling that utilizing Naive Bayesian (NB) to automatically summarize text and retrieve ideas from this raw data. Additionally, it discusses the text mining procedure used to extract relevant information from the vast volume of text-based data. The most significant issues obtained through feedback from customers were subsequently identified. The findings showed particular issues for workplaces, including environment, staffing, IT infrastructure, and customer feedback system. The feedbacks also prominently highlight difficulties with the attitude of student assistance and security staff as well as the library's management and operations.
Eu3+ Complex-Based Superhydrophobic Fluorescence Sensor for Cr(VI) Detection in Water

Cr(VI) compounds are bioaccumulative and highly toxic pollutants, and there is a need for simple and fast detection methods to monitor their trace levels. In this work, we developed a Eu3+ complex-based fluorescence sensor to easily detect Cr(VI) in water droplets. Our sensor consists of a nanofibrous membrane electrospun with a blend of polyvinylidene fluoride (PVDF), silica particles, and Eu3+ complex. Upon modifying the membrane surface with fluoroalkyl chemistry, the sensor displayed superhydrophobicity. When a water droplet with Cr(VI) was placed on such a superhydrophobic fluorescence sensor, the overlapping absorption of Cr(VI) and Eu3+ complex facilitated the inner filter effect, allowing the selective detection of Cr(VI) down to 0.44 µM (i.e., 45.76 µg L−1). We proposed and designed of new inexpensive and fast sensor for the detection of Cr(VI).
Hippocampal volume loss in individuals with a history of non‐fatal opioid overdose

Incidence of opioid‐related overdoses in the United States has increased dramatically over the past two decades. Despite public emphasis on overdose fatalities, most overdose cases are not fatal. Although there are case reports of amnestic syndromes and acute injury to the hippocampus following non‐fatal opioid overdose, the effects of such overdoses on brain structure are poorly understood. Here, we investigated the neuroanatomical correlates of non‐fatal opioid overdoses by comparing hippocampal volume in opioid use disorder (OUD) patients who had experienced an opioid overdose (OD; N = 17) with those who had not (NOD; N = 32). Voxel‐based morphometry showed lower hippocampal volume in the OD group than in the NOD group, which on post hoc analysis was evident in the left but not the right hippocampus. These findings strengthen the evidence that hippocampal injury is associated with non‐fatal opioid overdose, which is hypothesized to underlie overdose‐related amnestic syndrome.
Exploration of Spontaneous Speech Corpus Development in Urban Agriculture Instructional Videos

Video transcription can be obtained automatically based on the original language translation of the video maker's speech, but the quality of the transcription depends on the quality of the audio signal and the natural voice of the speaker. In this study, Deep Speech is used to predict letters based on acoustic recognition without understanding language rules. The Common Voice multilingual corpus helps Deep Seech to transcribe Indonesian. However, this corpus does not accommodate the special topic of urban agriculture, so an additional corpus is needed to build acoustic and language models with the urban agriculture domain. A total of 15 popular videos with closed captions and nine E-Books with the theme of Horticulture (fruit, vegetables and medicinal plants) were curated. The video data were extracted into audio and transcription according to specifications as training data, while the agricultural text data were transformed into language models, which were used to predict recognition results. The evaluation results show that the number of epochs has an effect on improving the transcription performance. The language model score used during prediction improved WER performance as it interpreted words with agricultural terms. Another finding was that the model was unable to predict short words with informal varieties and located at the end of the sentence.
ADVANCEMENT OF CROP PRODUCTIVITY VIA CRISPR-NANOPARTICLE INTERFACE

Plant improvement strategies involve diverse techniques, ranging from traditional to marker-assisted methods, as well as chemical and radiation treatments. However, these methods can introduce imprecise changes in plant DNA. Accelerating plant enhancement is crucial to meet global food demand, but current methods are time-consuming. Scientists are revolutionizing plant breeding by employing various techniques to develop crops with specific attributes, such as increased yield and pest resistance, aligning with environmental and societal needs. While these methods offer substantial advantages, they often face challenges and can be less precise than desired. Innovative methods, such as gene editing using CRISPR, offer enhanced precision. CRISPR technology enables precise modifications to a plant's DNA, allowing for targeted improvements without unintended consequences. While CRISPR shows great potential, ensuring its safe and accurate implementation is a priority. Scientists are exploring diverse methods, both viral and non-viral, to effectively deliver CRISPR components into plant cells, with non-viral approaches gaining traction due to their safety and versatility. Nanoparticles play a pivotal role in these advancements by serving as delivery vehicles for CRISPR tools. These particles safeguard and transport the necessary components to specific locations within plants, bolstering growth, yield, and disease resistance. Despite challenges, the synergy of nanotechnology and CRISPR holds promise for revolutionizing plant improvement while safeguarding the environment. This integrated approach offers the potential to enhance crop growth and quality while upholding ecological balance.
Optimistic narrative future visions: a communication tool for promoting sustainable (plastic) behavior

Plastic pollution is a pressing global issue, necessitating a focus on consumer behavior to curb this problem at its source. To effectively promote sustainable practices, communication strategies that employ future visions have gained attention. This study examines the effects of a narrative video intervention depicting an optimistic future vision concerning single-use plastic bag consumption in South Africa, compared to a representation of the prevailing status quo. Using a preregistered within-subject design, we assess the psychological and emotional responses to two scenarios of which one is illustrating adaptive behaviors toward reduced plastic bag usage, and the other showcasing current consumption patterns. Parametric analyses revealed a shift in emotional states, characterized by a greater experience of positive emotions and a reduced experience of negative emotions following the exposure to the optimistic future scenario video, as compared to the status quo video. Moreover, engagement with the optimistic future scenario was associated with higher levels of perceived behavioral control and behavioral intentions. No significant changes were found regarding sense of responsibility. These findings point to the potential of optimistic future visions to influence individuals at psychological and emotional levels. This renders optimistic future vision communication as an effective tool for sustainable behavior change, particularly in relation to the sustainable use of plastic shopping bags.