Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the xylem-sap to unravel biological features of Xylella fastidiosa subspecies pauca ST53 in immune, resistant and susceptible crop species through metabolomics and in vitro studies

Xylella fastidiosa subsp. pauca ST53 (Xfp) is a pathogenic bacterium causing one of the most severe plant diseases currently threatening the olive-growing areas of the Mediterranean, the Olive Quick Decline Syndrome (OQDS). The majority of the olive cultivars upon infections more or less rapidly develop severe desiccation phenomena, while few are resistant (e.g. Leccino and FS17), being less impacted by the infections. The present study contributes to elucidating the basis of the resistance phenomenon by investigating the influence of the composition of the xylem sap of plant species on the rate of bacterial multiplication. Xylem saps from Xfp host and non-host species were used for growing the bacterium in vitro, monitoring bacterial growth, biofilm formation, and the expression of specific genes. Moreover, species-specific metabolites, such as mannitol, quinic acid, tartaric acid, and choline were identified by non-targeted NMR-based metabolomic analysis in olive, grapevine, and citrus. In general, the xylem saps of immune species, including grapevine and citrus, were richer in amino acids, organic acids, and glucose. The results showed greater bacterial growth in the olive cultivar notoriously susceptible to Xfp (Cellina di Nardò), compared to that recorded in the resistant cultivar Leccino. Conversely, higher biofilm formation occurred in Leccino compared to Cellina di Nardò. Using the xylem saps of two Xfp-immune species (citrus and grapevine), a divergent bacterial behavior was recorded: low planktonic growth and biofilm production were detected in citrus compared to the grapevine. A parallel evaluation of the expression of 15 genes showed that Xfp directs its molecular functions mainly to virulence. Overall, the results gained through this multidisciplinary study contribute to extending the knowledge on the host-pathogen interaction, while confirming that the host response and resistance mechanism have a multifactorial basis, most likely with a cumulative effect on the phenotype.
Soft touchless sensors and touchless sensing for soft robots

Soft robots are characterized by their mechanical compliance, making them well-suited for various bio-inspired applications. However, the challenge of preserving their flexibility during deployment has necessitated using soft sensors which can enhance their mobility, energy efficiency, and spatial adaptability. Through emulating the structure, strategies, and working principles of human senses, soft robots can detect stimuli without direct contact with soft touchless sensors and tactile stimuli. This has resulted in noteworthy progress within the field of soft robotics. Nevertheless, soft, touchless sensors offer the advantage of non-invasive sensing and gripping without the drawbacks linked to physical contact. Consequently, the popularity of soft touchless sensors has grown in recent years, as they facilitate intuitive and safe interactions with humans, other robots, and the surrounding environment. This review explores the emerging confluence of touchless sensing and soft robotics, outlining a roadmap for deployable soft robots to achieve human-level dexterity.
CNN-based estimation of pre- and post-earthquake height models from single optical images for identification of collapsed buildings

ABSTRACT In this paper, a novel approach is proposed to identify collapsed buildings after an earthquake using pre-event satellite image as well as post-event satellite image and airborne LiDAR data. In this regard, a convolutional neural network-based method is proposed for estimating a DHM from a single satellite image. A structure reconstruction strategy is designed to improve estimated height values and objects geometry by using the local features of shallow layers and employing a progressive context fusion method. The post-event images and their corresponding LiDAR data are used to train the proposed network. Subsequently, the trained network is employed to estimate a digital height model (DHM) from the pre-event satellite image. Finally, by investigating the difference image of pre- and post-event DHMs, collapsed buildings are identified. It is observed that the quality, kappa coefficient and overall accuracy of the obtained results are 84.86%, 91.15% and 98.78%, respectively, demonstrating a promising performance of the proposed approach.
Exploring the Equity Dimensions of US Bicycle Sharing Systems

Research over the past several decades has made it increasingly clear that livable communities are inextricably linked with the provision of opportunities for active and/or non-motorized transportation; i.e., walking, cycling and their variants. An emerging phenomena that is working within the broader movement of active transportation is public bicycle sharing systems (BSS). Such systems have grown considerably in the US in recent years and, in some cases, are dramatically changing the ecology of urban transport. Alongside celebrations of the early successes of US BSS, have been criticisms that these systems have not been adequately integrated into lower-income communities; a pattern that mirrors (motorized) transportation injustices--both past and present--that have burdened lower-income while simultaneously advantaging middle to higher-income communities. And while diverse communities are embracing non-motorized transportation, there is valid concern that traditionally underserved populations will again be marginalized or unable to share in the full benefits of existing and future bicycle- and pedestrian-oriented infrastructure including BSS. This research explores the spatial arrangements and allocations of US BSS and examines the extent to which lower income communities experience differential access to bike-sharing infrastructure. Spatial regression models are employed to examine the degree to which race, ethnicity and/or economic hardship explain variations in the distribution of bike-sharing stations.
Research on Optimization Algorithms for Visual Communication Systems Based on VR Technology

With the rapid development of VR technology, virtual reality has become an important tool and experience way for people in entertainment, education, training and other fields.The use of a complete and systematic visual communication system in visual recognition systems can provide people with an intuitive visual experience. As the ultimate application form of multimedia technology, VR (virtual reality) technology, with its unique immersive experience advantages, has evolved from a single visual form to a digital and dynamic one. This article studies the principles of stereo vision and optics in VR technology, and combines VR technology with teaching, A teaching system that combines teaching and entertainment in a virtual environment was designed to provide enlightening education for children. Finally, the system was tested. 88% of students believed that the virtual teaching system had strong participation, 76% were very satisfied with the virtual teaching system, and 86% believed that the virtual teaching system had better learning efficiency compared to traditional teaching models.
Statistical Analysis of the Role of Cavity Flexibility in Thermostability of Proteins

Conventional statistical investigations have primarily focused on the comparison of the simple one-dimensional characteristics of protein cavities, such as number, surface area, and volume. These studies have failed to discern the crucial distinctions in cavity properties between thermophilic and mesophilic proteins that contribute to protein thermostability. In this study, the significance of cavity properties, i.e., flexibility and location, in protein thermostability was investigated by comparing structural differences between homologous thermophilic and mesophilic proteins. Three dimensions of protein structure were categorized into three regions (core, boundary, and surface) and a comparative analysis of cavity properties using this structural index was conducted. The statistical analysis revealed that cavity flexibility is closely related to protein thermostability. The core cavities of thermophilic proteins were less flexible than those of mesophilic proteins (averaged B’ factor values, −0.6484 and −0.5111), which might be less deleterious to protein thermostability. Thermophilic proteins exhibited fewer cavities in the boundary and surface regions. Notably, cavities in mesophilic proteins, across all regions, exhibited greater flexibility than those in thermophilic proteins (>95% probability). The increased flexibility of cavities in the boundary and surface regions of mesophilic proteins, as opposed to thermophilic proteins, may compromise stability. Recent protein engineering investigations involving mesophilic xylanase and protease showed results consistent with the findings of this study, suggesting that the manipulation of flexible cavities in the surface region can enhance thermostability. Consequently, our findings suggest that a rational or computational approach to the design of flexible cavities in surface or boundary regions could serve as an effective strategy to enhance the thermostability of mesophilic proteins.
Tundra photosynthesis captured by satellite‐observed solar‐induced chlorophyll fluorescence

Accurately quantifying the timing and magnitude of respiration and photosynthesis by high‐latitude ecosystems is important for understanding how a warming climate influences global carbon cycling. Data‐driven estimates of photosynthesis across Arctic regions often rely on satellite‐derived enhanced vegetation index (EVI); we find that satellite observations of solar‐induced chlorophyll fluorescence (SIF) provide a more direct proxy for photosynthesis. We model Alaskan tundra CO2 cycling (2012–2014) according to temperature and shortwave radiation and alternately input EVI or SIF to prescribe the annual seasonal cycle of photosynthesis. We find that EVI‐based seasonality indicates spring “green‐up” to occur 9 days prior to SIF‐based estimates, and that SIF‐based estimates agree with aircraft and tower measurements of CO2. Adopting SIF, instead of EVI, for modeling the seasonal cycle of tundra photosynthesis can result in more accurate estimates of growing season duration and net carbon uptake by arctic vegetation.
Insights into the Hormone-Regulating Mechanism of Adventitious Root Formation in Softwood Cuttings of Cyclocarya paliurus and Optimization of the Hormone-Based Formula for Promoting Rooting

Adventitious root (AR) formation is vital for successful cutting propagation in plants, while the dynamic regulation of phytohormones is viewed as one of the most important factors affecting AR formation. Cyclocarya paliurus, a hard-to-root plant, is faced with the bottleneck of cloning its superior varieties in practice. In this study, ten treatments were designed to figure out the best hormone-based formula for promoting AR formation in softwood cuttings and explore their hormone-regulating mechanisms. Both the rooting process and the rooting parameters of the softwood cuttings were significantly affected by different hormone-based formulas (p < 0.05), while the greatest rooting rate (93%) and root quality index were achieved in the H3 formula (SR3:IR3 = 1:1). Significant differences in the measured phytohormone concentrations, as well as in their ratios, were detected among the cuttings sampled at various AR formation stages (p < 0.05), whereas the dynamics for each phytohormone varied greatly during AR formation. The transcriptome analysis showed 12,028 differentially expressed genes (DEGs) identified during the rooting process of C. paliurus cuttings, while the KEGG enrichment analysis indicated that a total of 20 KEGG terms were significantly enriched in all the comparison samples, with 253 DEGs detected in signal transduction. Furthermore, 19 genes with vital functions in regulating the hormone signaling pathway were identified by means of a WGCNA analysis. Our results not only optimize a hormone-based formula for improving the rooting of C. paliurus cuttings but also provide an insight into the hormonal regulatory network during AR formation in softwood C. paliurus cuttings.
Identification of a novel B-cell epitope of the African swine fever virus p34 protein and development of an indirect ELISA for the detection of serum antibodies

African swine fever (ASF) is a viral disease caused by the African swine fever virus that can be highly transmitted and lethal in domestic pigs. In the absence of a vaccine, effective diagnosis is critical for minimizing the virus’s spread. In recent years, with the decline of African swine fever virus (ASFV) virulence, antibody detection has become an important means of detection. ASFV nucleocapsid protein p34 is a mature hydrolytic product of pp220, which is highly conserved and has a high content in the structural protein of the virus. Prokaryotic cells were chosen to generate highly active and high-yield p34 protein, which was then used as an antigen for producing mouse monoclonal antibodies. The B-cell epitope 202QKELDKLQT210, which was highly conserved and found on the surface of the p34 protein, was first identified by an anti-p34 monoclonal antibody utilizing the peptide scanning technique and visualized in helix. This supported the viability of p34 protein detection even further. In addition, we established an indirect ELISA assay based on p34 to detect ASFV antibodies. The coincidence rate of this method with commercially available kits was shown to be 97.83%. Sensitivity analysis revealed that it could be detected in serum dilution as low as 1:6400, and there was no cross-reaction with other prevalent porcine epidemic diseases classical swine fever virus (CSFV), foot-and-mouth disease virus (FMDV), porcine reproductive and respiratory syndrome virus (PRRSV), and porcine circovirus 2 (PCV2). In summary, the established ELISA method and anti-P34 monoclonal antibody have demonstrated that the p34 protein has a promising application prospect for the detection of African swine fever antibodies.
Improving Accuracy in Solving Feldtkeller Equation

One major obstacle in filter and multiplexer synthesis is the challenge of accurately solving the Feldtkeller equation to compute the common denominator polynomial. In general, the numerical error can be detrimental to the direct computation using double floating precision when the network order exceeds 24. This letter introduces an iterative computational method for enhancing accuracy in solving the Feldtkeller equation without requiring extended precision in number representation. The proposed approach allows for the synthesis of high-order general asymmetric filters and multiplexers without the restriction of placing the reflection zeros (RZs) solely on the imaginary axis. Experimental results demonstrate the method’s ability to accurately compute equi-ripple filter and multiplexer characteristics with degrees up to 39.
Channel Estimation and Sparse Signal Processing in mm- Wave MIMO Systems

The use of compressive sensing (CS) for sparse channel estimation has been recognized as an effective solution to address the issue of high pilot overhead in massive MIMO systems. Therefore, the complexity of signal processing in wireless communication systems, the matrix measurement in the Channel estimates based on Carrier Sense (CS) might be “unfriendly” to channel recovery. Propose an iterative channel estimation technique for millimeter wave (mmWave) MIMO systems based on the Sparse Minimum Mean Square Error (SMMSE) and sparse message passing (SMP) algorithms. Propose a method that iteratively locates and determines the precise values of non-zero elements of the sparse channel matrix by taking use of the sparsity. The locations are found using the Sparse Message Passing (SMP) method, and values are calculated using the SMMSE at each iteration. NOMA is a competitive approach for increasing capacity in Mbps and channel gain in Signal to Noise Ratio over Additive White Gaussian Noise and other fading channels such as Rayleigh and Rician Channels. The Adaptive All Pass Filter (AAPF) is a novel methodology that is proposed for reduction in P APR for efficient NOMA 6G wireless communication to achieve efficient system. This technique meets the requirement that NOMA capacity always outperforms NOMA capacity for each user, making it suitable for use with any scheduling paradigm. The proposed methodology is compared with OFDM to prove NOMA performance is better than OFDM. The various parameters such as reduction in P APR, BER, FER and maximization in Capacity, Signal to Noise Ratio and throughput are calculated and shown as graphical representation.
What are the root causes of material delivery schedule inaccuracy in supply chains?

PurposeThis study aims to explore and empirically test variables influencing material delivery schedule inaccuracies? Design/methodology/approachA mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.FindingsThe effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.Practical implicationsThe findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.Originality/valueThe findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?
PSO Optimized Hybrid Deep Learning Model for Detection and Localization of Myocardial Infarction

Myocardial infarction (MI) damages the heart muscles irreversibly and can result in death. Hence, it is essential to diagnose MI early to save lives. However, traditional detection algorithms using an electrocardiogram (ECG) rely mainly on hand-crafted characteristics and demand a high level of subject knowledge. Hence, two hybrid deep learning (DL) models, i.e., convolutional neural network (CNN) with long short-term memory (LSTM) and CNN with the gated recurrent unit (GRU) are proposed for classifying MI. Particle swarm optimization (PSO) is employed to optimize the hyperparameters of the hybrid models for optimal performance. The hybrid models’ simulations are performed using intra- and inter-patient data from the Physikalisch-Technische Bundesanstalt (PTB)-ECG database in Python for all standard 12 leads individually for the detection and localization of MI. Among MI, normal, and non-MI, the MI is detected by employing the optimized CNN-LSTM on intra-patient data from lead-V2 that provides an accuracy of 99.96%. In contrast, the optimized CNN-GRU method offers an accuracy of 90.15% on inter-patient data from lead-V5 for MI detection. For MI localization, the optimized CNN-LSTM model on intra-patient lead-V1 data provides an accuracy of 99.87% to classify 11 classes. On the other hand, CNN-GRU provides an accuracy of 66.32% to classify five types of MI on inter-patient lead-V1 data. This study and analysis conclude that this article’s two proposed hybrid models outperform state-of-the-art methods on chest leads.
Rainbow rummy: a web-based game for vocabulary acquisition using computer-directed speech

This paper describes a new on-line game we have developed which allows learners of Chinese or English to practice speaking in a communicative setting. Game play resembles gin rummy or Mah Jong, and is intended to be sufficiently engaging to invite persistent replay. Students compete in a social game against other students at remote settings, or they can play against a robotic partner. A user study was conducted on 16 students of Chinese, to assess whether a configuration that utilizes speech recognition is as effective for learning vocabulary as a configuration that only requires the student to listen. Results show that the vocabulary learning gains averaged across subjects were greater following use of the speech-enabled version of the game compared to the listening-only version.
How will the public health committees develop after COVID-19 pandemic in China? Exploration from mixed methods study in Pingshan District, Shenzhen

Introduction The Public Health Committee has a long-standing presence in the Chinese Constitution. During the pandemic, it served as a grassroots self-governance organization and made significant contributions to China's community epidemic prevention and control system. Currently, 24 provinces in China have promoted the establishment of community public health committees. Methods To gather data, we conducted semi-structured interviews (n = 48) with the heads of superior departments of public health committees, the heads of public health committees, and the heads of community health centers, exploring aspects such as organizational structure, job responsibilities, and job security. In parallel, we administered a capacity-building survey to a sample of 23 community residents (n = 1,986) and performed regression analysis. Finally, we examined the impact of gender, age, and education level on the development of public health committees. Results Our study reveals that the development of public health committees displays features of administration across various dimensions, including personnel appointment, top-level design, medical professionalism, funding path dependence, and data path dependence. However, the decision-making function of the organization does not exhibit a significant impact. Discussion The construction of the Public Health Committee should demonstrate an “autonomization-administration” pendulum effect. Currently, due to the absence of decision-making functions within the Public Health Committee, autonomous organizations are exhibiting characteristics of administration. To prevent excessive autonomization or administrative nature in the development of committees, public health policies are continuously being refined based on the unique characteristics of public health committee construction. During exceptional circumstances or the initial stages of development, the establishment of public health committees should be primarily guided by administrative principles, utilizing political momentum to drive their progress. In contrast, during routine establishment phases or later stages of development, the establishment of public health committees should be primarily led by autonomization, restoring their capacity for self-decision making. It is essential to fully leverage the role of grassroots self-governance organizations, relying on the community to engage in self-management, self-education, and self-service within public health committees.