Miracle-dz/UniFAR-FTUs huggingface dataset:
Inoculation with Bacillus cereus DW019 Modulates Growth, Yield and Rhizospheric Microbial Community of Cherry Tomato

Plant growth-promoting rhizobacteria (PGPR) play an important role in promoting plant growth and increasing crop yield. Bacillus cereus DW019, which was previously isolated from an ion-absorbed rare-earth ore of Ganzhou in Southeastern China, has been considered as a PGPR due to its production of indole-3-acetic acid (IAA), ammonia and siderophore, but its promoting effect on plants remains poorly understood. In this study, autoclaved dead cells and viable cells of Bacillus cereus DW019 at different concentrations were inoculated into pot-cultivated cherry tomato (Lycopersicon esculentum) to investigate the promoting effect on plant growth and yield. A total of 70 days after inoculation, the plants and fruits of cherry tomato were harvested, and their growth indicators, yields, and nutrients were measured. The results showed that biomass, stem thickness, plant height and root length were significantly promoted and that the vitamin C, soluble sugar and soluble protein were significantly increased. Inoculation with Bacillus cereus also modulated the rhizospheric microbial community diversity and structure, especially the proportions of Proteobacteria and Actinobacteriota, which in turn improved the plant height, fresh weight, nutritional quality and rhizosphere soil bacterial diversity of cherry tomato. All the findings suggest that Bacillus cereus DW019 is beneficial to the growth of crops and improves the yield of cherry tomato, suggesting that Bacillus cereus DW019 could be developed into a potential biofertilizer to be used as an agricultural inoculant to increase crop yield and improve the soil ecosystem.
Role of Performance Management Systems in Employee Development and Retention: An Empirical Study of Experts’ Opinion

Systems for performance management are essential for fostering employee growth and retention. These methods assist businesses in setting clear performance standards, providing frequent feedback and coaching, identifying areas for improvement, and coordinating employee goals with the overall corporate plan. Employees' job performance, job satisfaction, and dedication to the company are all improved as a result of the ability to use performance management systems to boost skills, knowledge, and capacities. By fostering a culture of ongoing learning and growth, performance management systems can help with employee retention. Employees are more inclined to stick with the company and help it succeed if it feels that the contributions are acknowledged and recognized. Performance management systems can also assist staff members in achieving the professional goals and enhancing the level of job satisfaction by offering possibilities for growth and development. Performance management tools can also assist businesses in locating high-potential individuals and offering them specialized possibilities for growth. This can help organizations develop a solid talent pipeline for upcoming leadership positions, as well as increase these individuals' retention rates. In general, performance management systems are crucial tools that help businesses foster employee growth and retention and, eventually, accomplish the operational goals.
Cluster-assembled cubic zirconia films with tunable and stable nanoscale morphology against thermal annealing

Nanostructured zirconium dioxide (zirconia) films are very promising for catalysis and biotechnological applications: a precise control of the interfacial properties of the material at different length scales and, in particular, at the nanoscale, is therefore necessary. Here, we present the characterization of cluster-assembled zirconia films produced by supersonic cluster beam deposition possessing cubic structure at room temperature and controlled nanoscale morphology. We characterized the effect of thermal annealing in reducing and oxidizing conditions on the crystalline structure, grain dimensions, and topography. We highlight the mechanisms of film growth and phase transitions, which determine the observed interfacial morphological properties and their resilience against thermal treatments.
The Impact of Manually Derived Southern Hemisphere Sea Level Pressure Data upon Forecasts from a Global Model

Abstract Experiments have been conducted within the framework of the Australian Bureau of Meteorology's global data assimilation and prediction system to assess the forecast impact of Southern Hemisphere sea level pressure estimates [paid observations (PAOBs)] from manual analyses. Parallel assimilation cycles with and without PAOBs, for two periods each of about 4 weeks in different seasons, while inconclusive, suggest a small positive impact of PAOBs in Southern Hemisphere midlatitudes.
Random deformation of Gaussian fields with an application to Lagrange models for asymmetric ocean waves

In the stochastic Lagrange model for ocean waves the vertical and horizontal movements of surface water particles are modeled as correlated Gaussian processes. In this paper we investigate the statistical properties of wave characteristics related to wave asymmetry in the 3D Lagrange model. We present a modification of the original Lagrange model that can produce front-back asymmetry both of the space waves, i.e. observation of the sea surface at a fixed time, and of the time waves, observed at a fixed measuring station. The results, which are based on a multivariate form of Rice’s formula for the expected number of level crossings, are given in the form of the cumulative distribution functions for the slopes observed either by asynchronous sampling in space, or at synchronous sampling at upcrossings and downcrossings, respectively, of a specified fixed level. The theory is illustrated in a numerical section, showing how the degree of wave asymmetry depends on the directional spectral spreading and on the mean wave direction. It is seen that the asymmetry is more accentuated for high waves, a fact that may be of importance in safety analysis of capsizing risk.
Ocean Front Detection With Bi-Directional Progressive Fusion Attention Network

Ocean fronts are a mesoscale phenomenon in the ocean. It is important for fisheries, environmental protection, and military activities. Therefore, more and more attention has been attracted to ocean front detection. However, the distribution of front and nonfront pixels is highly unbalanced in remote-sensing images, and it is not easy to establish an effective ocean front detection algorithm with high accuracy. To alleviate these problems, we model the problem of detecting ocean fronts as an edge detection task and design a new end-to-end bi-directional progressive fusion attention network (BPFANet). Specifically, BPFANet consists of an effective backbone and a bi-directional path. The whole backbone has four-stage detection blocks (SD blocks), which capture the ocean front features at different scales. Each SD block contains a side branch structure, which includes a deep residual dilated convolution (DRDC) module to enrich multiscale edge information and an attention module (AM) to enhance the feature representation in both the channel and spatial dimensions. In addition, the bi-directional path can progressively fuse the four SD blocks of ocean front information. To evaluate BPFANet, we perform experiments on the OFDS365 dataset and show its advantages over existing ocean front detection methods.
Applications of ranking methods to multiple comparison procedures and factorial experiments

Non parametric tests based on Kruskal Wallis ranks are discussed. Problems that arise in their application to multiple comparison procedures and multifactorial analyses when the null hypothesis is false are investigated. Solutions of the former are proposed which involve re‐ranking the observations within subsets of the data. A solution to the latter is proposed based on combinations of Friedman rank sums.
Partial Discharge Detection and Recognition in Insulated Overhead Conductor Based on Bi-LSTM with Attention Mechanism

Insulated overhead conductor (IOC) faults cannot be detected by the ordinary protection devices due to the existence of the insulation layer. The failure of insulated overhead conductors is regularly accompanied by partial discharge (PD); thus, IOC faults are often judged by the PDs of insulated overhead conductors. In this paper, an intelligent PD detection model based on bidirectional long short-term memory with attention mechanism (AM-Bi-LSTM) is proposed for judging IOC faults. First, the original signals are processed using discrete wavelet transform (DWT) for de-noising, and then the signal statistical-feature and entropy-feature vectors are fused to characterize the PD signals. Finally, an AM-Bi-LSTM network is proposed for PD detection, in which the AM is able to assign the inputs different weights and highlight their effective characteristics; thus, the identification accuracy and computational complexity have been greatly improved. The validity and accuracy of the proposed model were evaluated with an ENET common dataset. The experiment results demonstrate that the AM-Bi-LSTM model exhibits a higher performance than the existing models, such as LSTM, Bi-LSTM, and AM-LSTM.
Silver Nanoparticles of Artemisia sieberi Extracts: Chemical Composition and Antimicrobial Activities

Background: Artemisia sieberi (mugwort) is a member of the daisy family Asteraceae and is widely propagated in Saudi Arabia. A. sieberi has historical medical importance in traditional societies. The current study aimed to assess the antibacterial and antifungal characteristics of the aqueous and ethanolic extracts of A. sieberi. In addition, the study investigated the effect of silver nanoparticles (AgNPs) synthesized from the A. sieberi extract. Methods: The ethanolic and aqueous extracts and AgNPs were prepared from the shoots of A. sieberi. The characteristics of AgNPs were assessed by UV–visible spectroscopy, transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and dynamic light scattering (DLS). The antibacterial experiments were performed against Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa. The fungal species used were Candida parapsilosis, Candida krusei, Candida famata, Candida rhodotorula, and Candida albicans. The antibacterial and antifungal characteristics were evaluated by measuring the diameter of growing organisms in Petri dishes treated with different concentrations of either extracts or AgNPs compared to the untreated controls. Furthermore, TEM imaging was used to investigate any ultrastructure changes in the microbes treated with crude extracts and AgNO3. Results: The ethanolic and aqueous extracts significantly decreased the growth of E. coli, S. aureus, and B. subtilis (p < 0.001), while P. aeruginosa was not affected. Unlike crude extracts, AgNPs had more substantial antibacterial effects against all species. In addition, the mycelial growth of C. famata was reduced by the treatment of both extracts. C. krusei mycelial growth was decreased by the aqueous extract, while the growth of C. parapsilosis was affected by the ethanolic extract and AgNPs (p < 0.001). None of the treatments affected the growth of C. albicans or C. rhodotorula. TEM analysis showed cellular ultrastructure changes in the treated S. aureus and C. famata compared to the control. Conclusion: The biosynthesized AgNPs and extracts of A. sieberi have a potential antimicrobial characteristic against pathogenic bacterial and fungal strains and nullified resistance behavior.
Design of error-compensating algorithms for sinusoidal phase shifting interferometry

An improved approach to interferometry using sinusoidal phase shifting balances several harmonic components in the interference signal against each other. The resulting computationally efficient phase-estimation algorithms have low sensitivity to errors such as spurious intensity noise, vibration, and errors in the phase shift pattern. Specific example algorithms employing 8 and 12 camera frames illustrate design principles that are extendable to algorithms of any length for applications that would benefit from a simplified, sinusoidal phase shift.
Singularly perturbed diffusion-advection-reactionprocesses on extremely large three-dimensional curvilinear networks with a periodic microstructure --efficient solution strategies based on homogenization theory

Boundary value problems on large periodic networks arise in many applications such as soil mechanics in geophysics or the analysis of photonic crystals in nanotechnology. As a model example, singularly perturbed elliptic differential equations of second order are addressed. Typically, the length of periodicity is very small compared to the size of the covered region. The overall complexity of the networks raises serious problems on the computational side. The high density of the graph, the huge number of edges and vertices and highly oscillating coefficients necessitate solution schemes, where even a numerical approximation is no longer feasible. Realizing that such a system depends on two spatial scales - global scale (full domain) and local scale (microstructure) - a two-scale asymptotic analysis for network differential equations is applied. The limit process leads to a homogenized model on the full domain. The homogenized coefficients cover the micro-oscillations and the topology of the periodic network and characterize the effective behaviour. The approximate model's quality is guaranteed by error estimates. Furthermore, singularly perturbed microscopic models with a decreasing diffusion part and transport-dominant problems are discussed. The effectiveness of the two-scale limit analysis is demonstrated by numerical examples of diffusion-advection-reaction problems on large periodic grids.
Closure to “Impact of sediment transport formulations on breaching modelling” by S. VAN EMELEN, Y. ZECH and S. SOARES-FRAZÃO, J. Hydraulic Res. 53(1), 2015, 60−72

Cestero, J. A. F. , Imran, J., & Chaudhry, M. H. (2015). Experimental investigation of the effects of soil properties on levee by overtopping. Journal of Hydraulic Engineering, 141(4), 04014085, 1–14. Coleman, S. E. , Andrews, D. P., & Webby, M. G. (2002). Overtopping breaching of noncohesive homogeneous embankments. Journal of Hydraulic Engineering, 128(9), 829–838. Frank, P.-J., & Hager, W. H. (2015, June). Spatial dike breach: Sediment surface topography using photogrammetry. Paper presented at 36th IAHR World Congress, The Hague, The Netherlands. Savitzky, A. , & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639. Schmocker, L. , Halldórsdóttir, B. R. , & Hager, W.H. (2011). Effect of weir face angles on circular-crested weir flow. Journal of Hydraulic Engineering, 137(6), 637–643. Schmocker, L. , & Hager, W. H. (2012a). Plane dike-breach due to overtopping: Effects of sediment, dike height and discharge. Journal of Hydraulic Research, 50(6), 576–586. Schmocker, L. , & Hager, W. H. (2012b, September). Effect of sediment diameter on plane dike breach shape and breach discharge. Paper presented at River Flow 2012, San Jose, Costa Rica. Spinewine, B. , Delobbe, A. , Elslander, L. , & Zech, Y. (2004, June). Experimental investigation of the breach growth process in sand dikes. Paper presented at River Flow 2004, Naples, Italy. Wallner, S. (2014). Influence of reservoir shape and size on the flood wave caused by progressive overtopping dam failure. Doctoral thesis, TU Wien, Vienna, Austria.
IoT-Enabled Healthcare Monitoring: A Systematic Review of Wearable Devices

The Internet of Things (IoT) has revolutionized various aspects of our daily lives, particularly in the healthcare sector. The integration of IoT with wearable devices has opened up new possibilities for healthcare monitoring, enabling the continuous tracking of patients' physiological parameters and promoting personalized medical care. This systematic review examines the current landscape of IoT-enabled wearable devices for healthcare monitoring, their potential applications, and the associated challenges. We conducted a thorough literature search to identify the most relevant and recent studies on IoT-enabled wearable devices for healthcare monitoring. Several devices were discussed, including smartwatches, fitness trackers, wearable electrocardiogram (ECG) monitors, continuous glucose monitoring systems, and smart patches for vital sign monitoring. These wearables offer numerous advantages, such as real-time monitoring, improved patient adherence, early detection of potential health issues, and enhanced patient-physician communication. The review also explores the potential drawbacks and challenges of implementing IoT-enabled wearable devices in healthcare, such as data privacy concerns, device interoperability, and the need for standardized data collection and analysis methods. Moreover, we discuss potential solutions and future research directions to overcome these challenges and promote the widespread adoption of IoT-enabled wearables for healthcare monitoring. In conclusion, IoT-enabled wearable devices have the potential to transform the healthcare sector by facilitating remote patient monitoring, improving treatment outcomes, and reducing healthcare costs. However, addressing the existing challenges and incorporating user feedback in the design and development process is essential for the successful integration of IoT-enabled wearables into the healthcare ecosystem.
Medium Access Control Techniques for Massive Machine-Type Communications in Cellular IoT Networks

A key component of the Internet of things (IoT) ecosystem is wide-area network connectivity, for which cellular network technologies are a promising option through their support of massive machine-type communications (mMTC). However, numerous devices transmitting sporadically small data packets in a highly synchronized way can generate overload on the radio access network. This situation leads to a shortage of resources, especially those associated with the random access procedure for contention, control, and data transmission, causing preamble collision, control message blocking, and data collision. As a result, mMTC traffic can jeopardize the provisioning of quality of service (QoS) to end-devices, decrease the network efficiency, and increase access latency and device energy consumption. This paper proposes medium access control (MAC) techniques for addressing problems related to the support of mMTC in cellularnetworks. First, a solution for allocating control resources with QoS provisioning and access differentiation is provided, including a resource management model, a scheduling algorithm, and a message prioritization policy. Second, a mechanism for reducing data collisions is also proposed. This solution comprises a protocol in which every device with scheduled retransmission uses a probabilistic policy to decide whether to retransmit, a novel method at the device to estimate the number of nodes trying random access, and two different retransmission policies employing this estimation. Results show that the proposals support QoS, decrease access latency, decrease the device energy consumption, and increase resource utilization under massive random access.
Influence of augmentation on the performance of the double ResNet-based model for chest X-ray classification

Purpose A pandemic disease elicited by the SARS-CoV-2 virus has become a serious health issue due to infecting millions of people all over the world. Recent publications prove that artificial intelligence (AI) can be used for medical diagnosis purposes, including interpretation of X-ray images. X-ray scanning is relatively cheap, and scan processing is not computationally demanding. Material and methods In our experiment a baseline transfer learning schema of processing of lung X-ray images, including augmentation, in order to detect COVID-19 symptoms was implemented. Seven different scenarios of augmentation were proposed. The model was trained on a dataset consisting of more than 30,000 X-ray images. Results The obtained model was evaluated using real images from a Polish hospital, with the use of standard metrics, and it achieved accuracy = 0.9839, precision = 0.9697, recall = 1.0000, and F1-score = 0.9846. Conclusions Our experiment proved that augmentations and masking could be important steps of data pre-processing and could contribute to improvement of the evaluation metrics. Because medical professionals often tend to lack confidence in AI-based tools, we have designed the proposed model so that its results would be explainable and could play a supporting role for radiology specialists in their work.
SAFELearning: Secure Aggregation in Federated Learning With Backdoor Detectability

For model privacy, local model parameters in federated learning shall be obfuscated before sent to the remote aggregator. This technique is referred to as secure aggregation. However, secure aggregation makes model poisoning attacks such as backdooring more convenient given that existing anomaly detection methods mostly require access to plaintext local models. This paper proposes a new federated learning technique SAFELearning to support backdoor detection for secure aggregation. We achieve this through two new primitives -oblivious random grouping (ORG) and partial parameter disclosure (PPD). ORG partitions participants into one-time random subgroups with group configurations oblivious to participants; PPD allows secure partial disclosure of aggregated subgroup models for anomaly detection without leaking individual model privacy. ORG is based on our construction of several new primitives including tree-based random subgroup generation, oblivious secure aggregation, and randomized Diffie-Hellman key exchange. ORG can thwart colluding attackers from knowing each other’s group membership assignment with non-negligible advantage than random guess. Backdoor attacks are detected based on statistical distributions of the subgroup aggregated parameters of the learning iterations. SAFELearning can significantly reduce backdoor model accuracy without jeopardizing the main task accuracy under common backdoor strategies. Extensive experiments show SAFELearning is robust against malicious and faulty participants, whilst being more efficient than the state-of-art secure aggregation protocol in terms of both communication and computation costs.
Design and Implementation of Montmorillonite Clay-Based Catalyst for Carbon Nanotube Synthesizing

In this study, a catalyst based on Montmorillonite clay was implemented for carbon nanotubes (CNTs) synthesizing. The kaolinite clay was used as a supporting material for iron-cobalt bimetallic catalytic nanoparticles. The CNTs have been synthesized by using atmospheric chemical vapor deposition (APCVD). To assess the quality of preparation both the catalyst and CNTs have been characterized by different techniques. The chemical bonding and interactions were verified by FT-IR. The general overview of microstructure was examined using SEM, while, the detailed structure and morphology were examined by HR-TEM, in addition to thermal analysis (DTA); surface area (BET); X-ray fluorescence (XRF), Raman spectroscopy, and XRD analysis. The results revealed that; Fe2O3 and Co3O4 NPs were uniformly assembled on the clay nanoplatelets. The specific BET surface area of the clay and catalyst was determined to be 46.12 and 57.06 m2/g respectively. Also, from XRD, the peaks at 26° and 42.7° confirm the presence of CNTs. The FTIR absorption bands, D, G, and G\ bands from the Raman spectrum confirm the hexagonal structure of the CNTs. The obtained results prove the high quality of CNTs preparation.