Miracle-dz/UniFAR-FTUs huggingface dataset:
ROOT GROWTH DYNAMICS AND CONSTRAINTS ON ABOVE GROUND GROWTH AND YIELD OF WALNUTS

There are three components to this project. The first is a minirhizotron root growth study that was installed to monitor seasonal root growth patterns in an existing Chandler pruning and irrigation trial. The peak in root growth occurred in late June in both the fourth and fifth growing seasons. In the fourth growing season, initiation of roots was greatest in the minimal pruned treatment and in the fifth growing season root initiation was greatest in the heavily pruned treatments. The peak of root growth in the minimally and heavily pruned treatments tended to be at a shallower depth compared to the peak in both the deficit and untrained/unpruned treatments. The root growth patterns observed were quite different from those in the Walnut Production Manual. Our results showed a single peak occurring right between the two peaks in the manual. The second component is a clonal rootstock replant trial that was initiated in a Howard orchard which had lost hundreds of trees to the yellow Howard problem. The third component is a a new experiment that was planted in the winter of 2013 which is looking at different rootstock and scion interactions as influenced by irrigation with the goal of understanding the yellow Howard problem. These experiments are providing some of the first field level observations of root growth patterns in walnut orchards over the season as well as with depth. PROBLEM AND ITS SIGNIFICANCE Optimization of walnut management under current and future climates requires better knowledge of root growth dynamics and root growth interactions with aboveground growth. Several current problems in walnut cultivation, such as canopy yellowing in years with wet springs and increased pathogenesis of soil borne pathogens with soil moisture (Lampinen et al., 1994), may be due to constraints on fine root growth and functioning. In Yuba and Sutter Counties where the yellowing problem was acute in 2011, yellow-leafed trees comprised 30% of many orchards. Observations collected during the 2006 and 2011 season suggest that yellow-leafed trees either had overall stunted root systems or lacked ephemeral fine roots that function in nutrient uptake and appeared unable to resume growth within the same growing season after wet spring conditions ceased. There is a need to understand if root growth (or subsequent re-growth if spring roots are lost to wet conditions) is genetically controlled by rootstocks, scions, or interactions between rootstocks and scions to insure that proper recommendations are made for new plantings of current and new clonal rootstocks and scions. Better information on root dynamics could open possibilities of making a broad range of advancements to orchard management as well as provide solutions for current problems such as the yellowing Howard problem.
DeepACSA: Automatic segmentation of anatomical cross-sectional area in ultrasound images of human lower limb muscles using deep learning

Background: Muscle anatomical cross-sectional area (ACSA) is an important parameter that characterizes muscle function and helps to classify the severity of several muscular disorders. Ultrasound is a patient friendly, fast and cheap method of assessing muscle ACSA, but manual analysis of the images is laborious, subjective and requires thorough experience. To date, no open access and fully automated program to segment ACSA in ultrasound images is available. On this basis, we present DeepACSA, a deep learning approach to automatically segment ACSA in panoramic ultrasound images of the human rectus femoris (RF), vastus lateralis (VL), gastrocnemius medialis (GM) and lateralis (GL) muscles. Methods: We trained convolutional neural networks using 1772 ultrasound images from 153 participants (25 females, 128 males; mean age = 38.2 years, range: 13-78) captured by three experienced operators using three distinct devices. We trained three muscle-specific models to detect ACSA. Findings: Comparing DeepACSA analysis of the RF to manual analysis resulted in intra-class correlation (ICC) of 0.96 (95% CI 0.94,0.97), mean difference of 0.31 cm2 (0.04,0.58) and standard error of the differences (SEM) of 0.91 cm2 (0.47,1.36). For the VL, ICC was 0.94 (0.91,0.96), mean difference was 0.25 cm2 (-0.21,0.7) and SEM was 1.55 cm2 (1.13,1.96). The GM/GL muscles demonstrated an ICC of 0.97 (0.95,0.98), a mean difference of 0.01 cm2 (-0.25, 0.24) and a SEM of 0.69 cm2 (0.52,0.83). Interpretation: DeepACSA provides fast and objective segmentation of lower limb panoramic ultrasound images comparable to manual segmentation and is easy to implement both in research and clinical settings. Inaccurate model predictions occurred predominantly on low-quality images, highlighting the importance of high image quality for accurate prediction.
The adverse effects of mobile devices on willingness to donate and online fundraising outcomes

Abstract Over the last few years, online donations have dramatically increased, and digital devices have become new channels for raising donations, replacing traditional paper forms. To better understand the effectiveness of these new digital channels, we investigate whether different device types influence donation outcomes. Overall, the results of three experimental studies consistently show a negative effect of mobile devices on willingness to donate (WTD) and donation amounts. For charitable donations, donors on a mobile (vs. non-mobile) device display less positive moods, which then lowers their WTD, resulting in lower donation amounts, suggesting a negative serial-mediation effect. Moreover, the moderated serial-mediation analysis reveals that the negative serial-mediation effect becomes stronger in a socially-distant (vs. socially-close) situation; however, for non-charitable donations (political fundraising), the mediating role of moods is attenuated while WTD still mediates the device-type effect on donation amounts. This study is the first to introduce device type as a digital medium that influences online donation outcomes and offers novel findings that the adverse effects vary not only by donors’ device type but also by social distance from their donation target. These findings help both business and non-profit organizations better understand online donors’ device usage to improve their fundraising outcomes.
First Principles Study of Elastic Properties, Mechanical Properties and Electronic Band Structures of LiGaO2 Phases

Abstract LiGaO2 (LGO) under different pressure conditions can be stable in different structures. The structures of LGO were named WZ′, HX′, BCT′ and RS′′ in analogous with the well-known phases as of ZnO. In this work, the structural, elasticity, mechanical properties and the electronic band structures of those phases were studied. We found that the band gap as well as the band gap type (direct/indirect) are strongly depending on the crystal structure. In addition, for selected crystal structures, the changes of the band gaps with respect to the pressure, i.e. the band gap deformation potentials, were studied. Our results illustrated that applying pressure in different conditions can modify the band gap of LGO. The details of elasticity and mechanical properties and electronic band structures for different phases of LGO and the pressure dependence of selected phases will be presented and discussed.
Emergence of synaptic organization and computation in dendrites

Abstract Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons’ dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.
Design and Analysis of On-chip Leakage Monitor using an MTCMOS circuit

On cutting-edge semiconductor process, leakage current varies drastically due to process variation and temperature changes. At the circuit design stage, it is difficult to estimate the amount of leakage current at every manufactured chip. Therefore an on-chip function of measuring leakage current is required. The Virtual-ground (VGND) voltage of MTCMOS circuits increases during the sleep operation, because parasitic capacitance of a VGND line is charged up by the leakage current. By applying this behavior, we design leakage monitor circuits using ASPLA 90nm technology. Simulation results show that monitor delay-time is 165ns and monitor-error is 6% under the typical process condition, 25℃ and operating frequency of 200㎒. Power dissipation of designed circuits is 29㎼ in the monitoring mode and 15㎼ in the standby mode.
Cybersecurity Leaders: Knowledge Driving Human Capital Development

Abstract Cybersecurity leaders must be able to use critical reading and thinking skills, exercise judgment when policies are not distinct and precise, and have the knowledge, skills, and abilities to tailor technical and planning data to diverse customers’ levels of understanding. Ninety-three percent of cybersecurity leaders do not report directly to the chief operating officer. While status differences influence interactions amid groups, attackers are smarter. With the aim of protecting organizations and reducing risk, knowledge about security must increase. Understanding voids are costly and increased breach chances are imminent. Burning questions exist. What are needed technological learnings for cybersecurity leaders to become smarter and remain ahead of attackers? How might these technologies hasten the understanding of the ‘what,’ ‘how,’ and ‘why’ reasons and key drivers for organizational behaviors. This article offers comparative analyses for cybersecurity leaders to engage in the questioning of practices, scrutinize entrenched assumptions about technology, customary practices, and query technology’s outputs by pursuing to comprehend all assumptions that could influence operations. Because understanding continues to rely upon progressively multifaceted epistemic technologies, outcomes of the research suggest that the salience of status distinctions is of central significance to the development of ongoing and proactive technological learning and up scaling solutions.
Fabric Image Feature Extraction Algorithm Based on InceptionV3 Transfer Learning

Abstrcat: Fabric image retrieval is of great value to the management of fabric image information in textile factories, but the diversity of fabric images makes it difficult to extract fabric image features during fabric retrieval. This paper proposes a fabric image feature extraction algorithm based on InceptionV3 transfer learning, which uses a pre-trained InceptionV3 neural network to perform transfer learning and fine-tune to extract fabric image features to build a feature database. The fabric retrieval experiment was carried out on the fabric image data set FID (Fabric Image Dataset). Experiments show that the algorithm can ameliorate the accuracy of fabric image feature extraction, with a retrieval accuracy of 84.5%. This feature extraction algorithm has practical application value.
A DenseNet-GRU Technology for Chinese Speech Emotion Recognition

The speech plays an important role in the transmission of human emotions. Voice emotion recognition has become an important part of human-machine systems, especially in specific systems that require high real-time and accuracy. In order to improve the accuracy and real-time performance of speech emotion recognition, people have done a lot of work in speech emotion recognition. This paper proposes Chinese speech emotion recognition based on DenseNet-GRU neural network. In the original Mel cepstrum coefficients feature extraction process, the selection of sampling points during the frame and window operation is performed, and the feature map size is adjusted to improve the recognition efficiency. At the same time, the DenseNet-GRU neural network structure is used for Chinese speech emotion recognition. The core is to use GRU (gated recurrent unit) to extract the association relationship between different speech emotions, and then obtain the feature changes between different speech emotions, the recognition accuracy rate is improved. The accuracy of the test set on CASIA speech emotion data reached 80%, and the model parameters were few. At the same time, white Gaussian noise enhancement is performed on the data before the neural network training, which can effectively alleviate the model overfitting.
NasalBreathInput: A Hands-Free Input Method by Nasal Breath Gestures using a Glasses Type Device

Research on hands-free input methods has been actively conducted. However, most of the previous methods are difficult to use at any time in daily life due to using speech sounds or body movements. In this study, in order to realize a hands-free input method based on nasal breath using wearable devices, we propose a method for recognizing nasal breath gestures, using piezoelectric elements placed on the nosepiece of a glasses-type device. In the proposed method, nasal vibrations generated by nasal breath are acquired as sound data from the devices. Next, the breath pattern is recognized based on the factors of breath count, time interval, and intensity. We implemented a prototype system for initial evaluation. The evaluation results for eight subjects showed that the proposed method can recognize eight types of nasal breath gestures with 0.89% of F value. Our study provides the first wearable sensing technology that uses nasal breathing for hands-free input.
Development and Implementation of an Integrated Cardiovascular Module in a PharmD Curriculum

The development and implementation of an integrated cardiovascular module occurred over a four-year period. The cardiovascular module integrated the following subject areas: pathophysiology, pharmacology, medicinal chemistry, pharmacokinetics, therapeutics and pharmacy practice. In order to accomplish this integration, a total of twelve faculty representing several major subject areas participated in the design and implementation of the module. The present manuscript discusses the developmental phases, implementation, and preliminary assessment of the course. Throughout the module, the coordinator met weekly with a focus group consisting of five randomly selected students. Immediately following the focus group meeting, the faculty involved in the course met to discuss case-reports, exam-writing assignments, and the results of the focus group discussion. At the completion of the module, traditional course evaluations as well as nominal group evaluations were used to evaluate the course. The comparison of the two different forms of course evaluations suggests that the nominal group method provides similar feedback in a more efficient manner. Finally, problems identified during the module are discussed as well as strategies that were utilized to rectify the problems. This cardiovascular module shows that cross discipline teaching can indeed work as long as faculty are willing to work together in a team fashion with each other and the students.
Voice tweets between humanization and moderation: Consequences, Challenges and Opportunities.

Nowadays, Twitter is largely used to access or share information related to a large variety of topics. In recent years, researchers have shown that Twitter data are very helpful in several fields and may have many applications. In June 2020, Twitter has announced voice tweets to create a more human experience. This paper gives an overview of possible consequences of this new feature. Furthermore, we present some challenges that researchers will face to exploit this type of data. Finally, we also introduce several opportunities for future research related to voice tweets.
Constructing Floor Plan through Smoke Using Ultra Wideband Radar

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.
Modeling and Analysis of ARINC 818 Bus Service Reliability

Since ARINC 818 digital video bus is very important for aircraft mission and even for aircraft safety, service quality of ARINC 818 bus becomes the research object of attention. Aiming at typical ARINC 818 bus system including video camera, sensor, image process unit and switch, the complex hardware failure and the soft failure in the transmission and processing of video data collection, video data fusion, switch network and video display was analyzed, the dependency, influence and transitive relation of the failure mode caused by hardware-hardware interaction and hardware-software interaction was analyzed. By transforming the service reliability analysis target, the failure model in each level and the dynamic or static relationship of the failure models to the top failure event, the middle event, the basic event and the dynamic or static gate, the data reliability, destination reliability, delay reliability and order reliability analysis dynamic fault tree model of ARINC 818 bus was established, then the service reliability of ARINC 818 bus was quantitative analyzed on the basis of the fault tree model. The ARINC 818 bus fault tree model and analysis method proposed in this paper provides a means for ARINC 818 bus system service reliability assessment and design.
Rapid Detection Solution for Fabric Printing Based on Dual Architecture Design of FPGA and Neural Network

A rapid detection solution for fabric printing based on dual architecture design of FPGA and neural network is proposed to address the problems of low accuracy and slow speed of fabric printing positioning in modern textile fabric manufacturing companies. The detection solution combines the popular deep neural networks with FPGA(Field Programmable Logic Gate Arrays) by selecting a lightweight deep neural network that is compatible with fabric print localization and using an improved method in the network training process. Then we design a hardware architecture solution that is compatible with the network to accelerate the convolutional pooling and other operations of the network, and finally building a complete fabric printing detection solution. The experimental results show that the combination of the lightweight deep neural network and the designed hardware architecture solution can effectively localize the fabric prints while maximizing the hardware resources and meeting the basic real-time requirements. The designed scheme is less powerful compared to GPU and faster compared to CPU.
Are Those Steps Worth Your Privacy?

Fitness trackers are increasingly popular. The data they collect provides substantial benefits to their users, but it also creates privacy risks. In this work, we investigate how fitness-tracker users perceive the utility of the features they provide and the associated privacy-inference risks. We conduct a longitudinal study composed of a four-month period of fitness-tracker use (N = 227), followed by an online survey (N = 227) and interviews (N = 19). We assess the users' knowledge of concrete privacy threats that fitness-tracker users are exposed to (as demonstrated by previous work), possible privacy-preserving actions users can take, and perceptions of utility of the features provided by the fitness trackers. We study the potential for data minimization and the users' mental models of how the fitness tracking ecosystem works. Our findings show that the participants are aware that some types of information might be inferred from the data collected by the fitness trackers. For instance, the participants correctly guessed that sexual activity could be inferred from heart-rate data. However, the participants did not realize that also the non-physiological information could be inferred from the data. Our findings demonstrate a high potential for data minimization, either by processing data locally or by decreasing the temporal granularity of the data sent to the service provider. Furthermore, we identify the participants' lack of understanding and common misconceptions about how the Fitbit ecosystem works.
Hunting Lovely and Serendipitous Eye Sights through Sharing Collective Human Eye Views

People usually maintain their positive and openness by watching a variety of views in the world. However, the current situation such as epidemiological situations restricts people’s physical movement and may narrow people’s abilities. Additionally, current digital platforms, especially social media platforms, confine people to a narrow territory by filtering information to only their favorites. Thus, their happiness is achieved through a very narrow view. Sharing Collective Human Eye Views is a digital platform that people can share people’s diverse eye views with the world and show various scenes through people’s eye views. In this paper, we show that Sharing Collective Human Eye Views is useful to hunt lovely and serendipitous eye sights to make people more positive and expanding their horizons through diverse eye views. The most important finding is that different categorization approaches for the theme channel significantly influence finding lovely and serendipitous eye sights.
Determining the Motivating Factors in Mobile Crowdsourcing Participation

Children that go missing is a major global problem, and finding them is imperative. One approach to addressing this problem is through crowdsourcing, in which large groups of people participate and contribute to the cause. Drawing from the lens of Self-Determination and Public Service Motivation theories, this study investigates the role of motivations in mobile crowdsourcing participation. First, this study identified the motivational factors behind participation on a mobile crowdsourcing platform to look for missing children. Next, the study examined the direct and indirect effects of these motivational factors on crowdsourcing participation. Data was collected from 264 respondents from an online survey. Results from structural equation modelling analysis revealed the motivations to volunteer have direct effects while motivations to serve the public have significant indirect effects on mobile crowdsourcing participation. This article concludes by presenting implications, limitations and future research directions.