Miracle-dz/UniFAR-FTUs huggingface dataset:
A Calcineurin Regulator MoRCN1 Is Important for Asexual Development, Stress Response, and Plant Infection of Magnaporthe oryzae

The calcium/calcineurin signaling pathway plays a key role in the development and virulence of plant pathogenic fungi, but the regulation of this signaling pathway is still not clear. In this study, we identified a calcineurin regulator MoRCN1 in the plant pathogenic fungus Magnaporthe oryzae and found it is important for virulence by regulating the calcineurin pathway. MoRCN1 deletion mutants were severely decreased in colony growth and conidia formation. More importantly, the deletion of MoRCN1 led to a significant reduction in virulence due to defects in appressorium formation and invasive growth. The ΔMorcn1 mutants were more sensitive to different stresses and induced host ROS accumulation, suggesting a role of MoRCN1 in stress adaptation. We found that MoRCN1 directly interacted with the calcineurin catalytic subunit MoCNA and affected its protein stability, which was therefore important for regulating the calcineurin pathway. Transcriptome analysis showed that MoRCN1 significantly activated 491 genes and suppressed 337 genes in response to calcium ion, partially overlapped with the MoCRZ1-bound genes. Gene Ontology and KEGG pathway analyses indicated that MoRCN1-regulated genes were enriched in stress adaptation, lipid metabolism, and secondary metabolite biosynthesis, reflecting a function of MoRCN1 in host cell adaptation. Altogether, these results suggest MoRCN1 functions as a regulator of the calcium/calcineurin signaling pathway for fungal development and infection of host cells.
Fitness Promotion in a Jump Rope-Based Homework Intervention for Middle School Students: A Randomized Controlled Trial

Physical activity (PA) homework offers a promising approach for students to be physically active after school. The current study aims to provide holistic insights into PA homework design and the effects of implementation in practice. In total, ninety-three middle school students were randomly assigned to a homework group (HG) or control group (CG). Participants in HG (n = 47) were requested to complete jump rope homework three times per week for 12 weeks, while their counterparts in CG attended one health education class every week. A homework sheet was used to provide instructions and record information for exercise behaviors during homework completion. Physical fitness tests were conducted to investigate the effects of the jump rope homework on the physical fitness of middle school students. After the intervention, participants in HG reported moderate to vigorous PA during jump rope exercise. The average duration for each practice was approximately 48 min. The returned homework sheets accounted for 86.88% of all homework assignments, indicating a good completion rate. Compared with their counterparts in CG, participants performing jump rope exercise indicated greater improvement in speed, endurance, power, and core muscular endurance. Jump rope homework strengthened physical fitness for middle school students, which provided a valuable addition to comprehensive school PA practice.
EEG based motor imagery classification using CNN

Brain-computer interfacing with non-invasive devices is a feasible solution by which severe quadriplegic patients can find an alternative neural path for expressing their actions. We have proposed a deep framework for the four-class classification of motor imagery (MI) tasks (left hand, right hand, both feet, tongue) from an available dataset of BCI competition IV-2a. We converted the electroencephalogram signals of the MI tasks from the motor-cortex area of the brain to scalograms by using the Morse wavelet. A novel deep architecture of a convolutional neural network is presented in this paper to classify these scalogram images. This analysis is carried out in the 8-30 Hz frequency band. The maximum classification accuracy reached up to 73.28%. Parameter tuning of the proposed framework is done by varying the batch size and number of epochs until an optimum value is reached. Our results show the possibility of the use of non-invasive MI to extend in medical and non-medical applications also.
Flood vulnerability assessment of the Upper Jhelum Basin using HEC-HMS model

Abstract The five watersheds of the Upper Jhelum Basin (UJB) in the Kashmir Himalaya, India has flood prone hydrographic features resulting in high-frequency of flooding in the basin. With this in mind, HEC-HMS was calibrated to estimate excess-runoff potential of five watersheds and the model results showed a good agreement with the observed streamflow. The study revealed that due to the presence of rocky-outcrops, precipitous-topography and impervious-surfaces, Vishav, Lidder and Sandran are the major contributors of the excess-runoff among the 5 watersheds as compared to Bringi and Kuthar. For the model-efficacy, the excess-runoff potential and the social-vulnerability of the region were compared to the floodwater-depth observed during the 2014-flooding event. The comparison reveals that the flood-vulnerability assessment of the UJB are well corroborated by the observed floodwater levels and the Vishav watershed is most vulnerable. The findings of this study are expected to inform flood management plans in the UJB.
Feasibility and Techno-Economic Analysis of Electric Vehicle Charging of PV/Wind/Diesel/Battery Hybrid Energy System with Different Battery Technology

Promoting the development of green technologies and replacing fossil fuel vehicles with electric ones can abate the environmental anxieties and issues associated with energy supply security. The increasing demand for electric vehicles requires an upgrade and expansion of the available charging infrastructure to accommodate the fast public adoption of this type of transportation. Ethiopia set a pro-electric cars policy and made them excise-free even before the first electric vehicle charging stations were launched by Marathon Motors Engineering in 2021. This paper presents the first ever technical, economic and environmental evaluation of electric vehicle charging stations powered by hybrid intermittent generation systems in three cities in Ethiopia. This paper tests this model using three different battery types: Lead-acid (LA), Flow-Zince-Bromine (ZnBr) and Lithium-ion (LI), used individually. Using these three battery technologies, the proposed hybrid systems are then compared in terms of system sizing, economy, technical performance and environmental stability. The results show that the feasible configuration of Solar Photovoltaic (PV)/Diesel Generator (DG)/ZnBr battery systems provide the lowest net present cost (NPC), with values of $2.97M, $2.72M and $2.85M, and cost of energy (COE), with values $0.196, $0.18 and $0.188, in Addis Ababa, Jijiga and Bahir Dar, respectively. Of all feasible systems, the Wind Turbine (WT)/PV/LI, PV/LI and WT/PV/LI configurations have the highest values of NPC and COE in Addis Ababa, Jijiga and Bahir Dar. Using this configuration, the results demonstrate that ZnBr battery is the most favorable choice because the economic parameters, including total NPC and COE, are found to be lowest.
Carcinoma arising within phyllodes tumors of the breast: clinicopathologic observation and a literature review of five cases

*Correspondence jnblk2013@163.com (Na Jiang) Abstract Objective: Cases of phyllodes tumors (PTs) of the breast with a malignant epithelial component are rare. The pathogenesis and optimal treatment of patients with carcinoma within PT are still not clear. Methods: We collected five cases of carcinoma arising within PT of the breast between 2007 and 2018, and performed morphological observations and immunohistochemistry using the EnVision method. Results: Four cases arose from benign PT, including two cases of low-grade ductal carcinoma in situ (DCIS), and two cases from classical lobular carcinoma in situ (LCIS). One case with squamous cell carcinoma (SCC) arose from malignant PT. Immunohistochemistry revealed that the LCIS and DCIS cases were diffusely positive for ER and PR, while the single case of SCC was negative. After 15–64 months of follow-up, all patients were alive. Conclusion:To date, there has been no standard treatment for this disease. Thorough sampling is needed to avoid a missed diagnosis. Sentinel lymph node biopsy should be performed if invasive cancer is present.
Automated partial discharge data evaluation during monitoring of stator winding

In this paper, the use of specific tools that enable the condition assessment of stator insulation in rotating electrical machines is described. The signal distinction between noise and PD defects is performed by means of a synchronous multi-channel evaluation technique. An automated PD defect classification system, which identifies the type of stator winding insulation defect and its location, has been developed and implemented in the continuous monitoring system described in this paper. The validation and evaluation of this system has been confirmed as successful by different case studies from real PD monitoring system installations. Słowa kluczowe: maszyny wirujące, izolacja statora, pomiar wyładowań niezupełnych, monitorowanie.
Evaluation of machine learning techniques for structural health monitoring using ultrasonic guided waves under varying temperature conditions

The implementation of machine learning methods for structural health monitoring applications has proven to be very effective, especially in detecting damage and compensating for environmental and operational conditions. The use of guided waves in this area has also shown to be a powerful tool due to their sensitivity to structural changes in the propagation medium. In this work, two strategies for detecting damage and distinguishing their positions and for dealing with temperature variations without an additional classical temperature compensation technique are investigated. For this purpose, four unsupervised dimensionality reduction learning methods were used and compared: Principal Component Analysis, Kernel Principal Component Analysis, t-distributed stochastic neighbour embedding and Autoencoder. The first strategy (score plot) consists of using the latent dimensions directly to distinguish the data points of different states of the structure, and the second (DI plot) proposes a method to use Q- and T2-statistics, which have been proposed in previous work for PCA, computed using the compressed representation of the monitoring data. To this end, monitoring data from intact and damaged states of a 500x500x2 mm plate of carbon-fibre–reinforced polymer recorded by 12 piezoelectric transducers at different temperatures are examined. As a reversible damage model, a 10 mm thick aluminium disc is placed at four different locations on the plate. The results primarily show the success of the methods used with DI plot in detecting damage regardless of varying temperature. The autoencoder in the first strategy also demonstrates promising performance in detecting and distinguishing the position of the damage, even in the presence of varying temperature conditions.
A Synchronous Magnitude Estimation with P-Wave Phases’ Detection Used in Earthquake Early Warning System

How to estimate an earthquake’s magnitude rapidly and accurately is a challenge for any earthquake early warning system. In order to reach a balance between accuracy and timeliness, a synchronous magnitude estimation method with P-wave phases’ detection is proposed. In this method, the P-wave phases are detected by the changes of the signal-to-noise ratio (SNR) of the seismic records, where the SNRs are calculated by the short-term power and long-term power ratio (STP/LTP). Meanwhile, the variations of the SNR are applied to estimate the magnitude of the earthquake. By the statistics of some earthquake cases, a synchronous magnitude estimation model of the variation of the P-wave phases’ SNR, the earthquake magnitude, and the hypocentral distance was built. Compared with some other magnitude estimation methods, the suggested method inherits the robustness of the STP/LTP method and is more accurate and rapid than the peak displacement (Pd) method.
Capability maturity model for assessing flood resilience capabilities of the built environment

Disasters often result in severe socio-economic losses, although there is significant progress in dealing with the social aspect, economic aspect is addressed relatively insufficiently. Therefore, the capabilities of organisations in managing disasters need to be improved, and there should be a way of assessing these capabilities. Hence, this study used capability maturity model (CMM) methodology to develop an assessment method that identifies the built environment flood resilience capabilities of micro, small and medium-sized businesses (MSMEs). Towards achieving the aim of the study, a conceptual model was developed using the capability areas identified from the literature. The model was then verified and refined by an expert forum. The conceptual model was further improved in case studies, applied in case organisations and after that validated; this resulted in the final model. This study identified 19 capability areas for enhancing built environment flood resilience and developed a capability maturity model that contains maturity level definitions ranging from level 1 (Ad hoc) to level 5 (Optimising). The outcome of this study provides both theoretical and practical contributions to knowledge. Among the theoretical contributions are the identified and verified built environment flood resilience capability areas for MSMEs. The capability areas can be adopted for use in subsequent studies. Also, the application of capability maturity modelling (CMM) in disaster resilience of the built environment is novel, this has expanded the boundary of CMM application. The practical contributions of the study include the identified capability areas, the capability level definitions and the capability maturity model for profiling MSMEs and benchmarking the capabilities of businesses for built environment flood resilience. The model indicates what needs to be carried out to achieve a higher capability maturity level for flood resilience.
LIVE: life-immersive virtual environment with physical interaction-aware adaptive blending

We present LIVE, a system enabling a life-immersive Mixed Reality experience. Daily MR usage is challenging in that the user's interaction state with the physical objects continuously change over time, while the immersion and the utility should be supported simultaneously in the process. As many works of blending the virtual and physical world are designed for a single interaction state, they are not enough to support life-immersive MR. We propose the initial design of LIVE that (i) selects the current user's context among the three states of interaction with physical object and (ii) applies the most suitable blending method to balance immersion and the utility.
The role of urban atmosphere for non-work activity locations

Abstract Urban atmospheres as emotionally perceived quality of a situation, have received limited attention in empirically based studies so far. This study applies the concept of urban atmosphere to explore what, whether and how atmospheric characteristics of different locations, mainly downtown areas but also neighbourhood centres and non-central areas, influence and are of importance for residents’ perceived quality of life. The results stem from 33 qualitative in-depth interviews conducted in the metropolitan areas of Oslo and Stavanger. Nine themes of atmospheres were identified in the interviews, referring to several common groups of both positive and negative characteristics.
Hybrid large‐eddy simulation with adaptive downstream anisotropic eddy viscosity model

Turbulence pervades most flows of engineering interest, and its prediction remains a challenge on both accuracy and cost. One promising predictive approach that reduces cost combines large‐eddy simulation (LES) with simulation based on Reynolds averaged Navier–Stokes equations (RANS). This study presents a method to overcome stability and accuracy issues associated with these hybrid LES‐RANS methods. The method developed involves extracting the Reynolds stresses from the upstream LES solution and then using that information to convert the downstream RANS model from a scalar eddy‐viscosity model to an anisotropic nonlinear eddy‐viscosity model. The method developed differs from the downstream tensorial eddy‐viscosity model by being independent of the coordinate system. The method developed was evaluated by computing film cooling of a flat plate with the coolant injected through one row of circular holes. Results obtained show instabilities at the LES‐to‐RANS interface to be eliminated. Also, the method developed yielded solutions that compare reasonably well with those from LES, even though a significant portion of the flow is computed by the adapted anisotropic RANS model instead of LES, which significantly reduced the number of grid points and computational time needed. Since modification of the downstream RANS model is based on information extracted from the upstream LES solution, the method developed is adaptive to the problem being studied.
A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data

Social media platforms have many users who share their thoughts and use these platforms to organize various events collectively. However, different upsetting incidents have occurred in recent years by taking advantage of social media, raising significant concerns. Therefore, considerable research has been carried out to detect any disturbing event and take appropriate measures. This review paper presents a thorough survey to acquire in-depth knowledge about the current research in this field and provide a guideline for future research. We systematically review 67 articles on event detection by sensing social media data from the last decade. We summarize their event detection techniques, tools, technologies, datasets, performance metrics, etc. The reviewed papers mainly address the detection of events, such as natural disasters, traffic, sports, real-time events, and some others. As these detected events can quickly provide an overview of the overall condition of the society, they can significantly help in scrutinizing events disrupting social security. We found that compatibility with different languages, spelling, and dialects is one of the vital challenges the event detection algorithms face. On the other hand, the event detection algorithms need to be robust to process different media, such as texts, images, videos, and locations. We outline that the event detection techniques compatible with heterogeneous data, language, and the platform are still missing. Moreover, the event and its location with a 24 × 7 real-time detection system will bolster the overall event detection performance.
A Fiber-Optic Accelerometer Based on Extrinsic Fabry-Perot Interference for Low Frequency Micro-Vibration Measurement

A fiber-optic accelerometer based on extrinsic Fabry-Perot interferometer (EFPI) is reported. The sensor adopts flywheel diaphragm structure as a sensitive element, where the mass block is bonded in the center, while the beam of the flywheel acts as a spring. The sensitive element forms single degree of freedom spring-mass-damper system. The parameters of the accelerometer are designed based on finite element simulation (FEM), and the transfer function of the sensor is established by numerical simulation. The experiments indicate that the sensor has a wide detection frequency band with the detectable ability for micro-vibration low as 0.01 Hz, and the sensitivity is 7.02 rad/g @20 Hz, and reach an average sensitivity of 6.39 rad/g in 5–20 Hz. The research shows that the accelerometer has a good development prospect in the field of low frequency micro-vibration detection.
Novel Epileptic Detection System using Portable EMG-based Assistance

The unpredictability of seizures creates stress and disability for affected people and caregivers. If a seizure can be reliably detected, then the patient's caregiver or the nursing staff can be warned. It may help prevent any injury or death. The purpose of this project is to develop a cost-effective, wearable, and portable device that can detect seizures and alert patients and their families about the detection of any motor activity for a particular seizure. Unlike other seizure detection devices which uses EEG and ECG modules which make it complicated and uncomfortable for the patients to wear, this proposed model is designed to be less complicated and as an easily manageable device with the help of EMG module and sensors to get proper and accurate readings and it facilitates continuous monitoring of epilepsy patients without interrupting daily activities.
The Sexual Dimorphism in Cerebrospinal Fluid Protein Content Does Not Affect Intrathecal IgG Synthesis in Multiple Sclerosis

(1) Background: Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS) that mainly affects young adults and females more than males. The detection of intrathecal IgG synthesis (IIS) on cerebrospinal fluid (CSF) analysis supports the diagnosis of MS. A sexual dimorphism has recently been described in CSF protein content. (2) Methods: Clinical and laboratory data from 340 MS patients (F = 231, M = 99) and 89 people with clinically isolated syndrome (CIS) (F = 57, M = 32) were retrospectively analyzed to assess the presence of variables affected by sex and age. (3) Results: In MS, the albumin quotient (QAlb), reflecting the blood–CSF barrier (BCSFB) function, was higher in males (5.6 vs. 4.34) and correlated to age with a constant difference between sexes (F = 41.71). In CIS patients, QAlb increased with age only in males (r = 0.3567). Age was positively correlated to disease duration and severity in MS (r = 0.3502, r = 0.2986, respectively). No differences emerged for quantitative and qualitative IIS determinations. (4) Discussion: Although the main difference between males and females concerns the function of BCSFB assessed by QAlb, this sexual dimorphism does not affect the determination of the IIS evaluated both by quantitative and qualitative methods.
Influence of Social Media Marketing on Customer Equity Drivers and Purchase Intention: Understanding World’s Most Valuable Brand - Amazon

Social Media is getting incredibly popular amongst brands and consumers both. This study aims to identify the constructs of perceived Social Media Marketing (SMM) activities of an e-commerce brand, Amazon and to evaluate the influence of those activities on customer equity drivers; value equity, relationship equity and brand equity and purchase intention using a PLS-SEM approach. The five constructs of perceived SMM activities are Interactivity, Informativeness, Personalization, Trendiness and Word of Mouth. The effects of SMM activities on value equity, relationship equity and brand equity were significantly positive. Out of the three customer equity drivers, only brand equity had a significant impact on the purchase intention, whereas value equity and relationship equity had no significant influence on purchase intention. The findings of this study will help the e-commerce brands to forecast the future purchasing behavior of their customers as well as manage their SMM activities.