Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT-Enabled Integrated Safety Systems for Mitigating Human Hazards in Pneumatic-Operated Buses

The safety of public transportation is of utmost importance to ensure the well-being of passengers and drivers. In recent years, there has been an increasing trend towards the use of pneumatic-operated buses due to their energy efficiency and low carbon footprint. However, these buses pose a significant risk to human safety in the event of an emergency such as a fire or accident. This research work proposes an IoT-enabled integrated safety system for mitigating human hazards in pneumatic-operated buses. The system utilizes temperature, smoke, and vibration sensors to detect potential emergency situations and trigger the opening of the bus door. The integration of these sensors allows for an immediate response to emergency situations, and the automatic opening of the door ensures quick and safe evacuation of passengers. The system is designed to be user-friendly and intuitive, with a simple and easy-to-understand interface for the bus driver. The sensors are placed strategically throughout the bus, ensuring maximum coverage and accuracy in detecting emergency situations. The temperature sensor detects changes in temperature within the bus, which could be an indicator of a fire. The smoke detector senses the presence of smoke, and the vibration sensor detects any sudden impacts or jolts that could indicate an accident. The proposed system has been tested using a pneumatic-operated bus in a controlled environment. The results of the testing show that the system is effective in detecting emergency situations and triggering the automatic opening of the bus door. The system can significantly reduce the risks to human safety in emergencies, providing a fast and reliable response. In conclusion, the IoT-enabled integrated safety system proposed in this research provides an innovative solution for mitigating human hazards in pneumatic-operated buses. By using temperature, smoke, and vibration sensors, the system can quickly detect emergency situations and ensure the safety of passengers and drivers by automatically opening the bus door. The proposed system has the potential to improve the safety and security of public transportation and can be implemented in pneumatic-operated buses worldwide.
Dairy Programs to Reduce Manure-Related Environmental Problems: The Case of the Louisiana Milkshed

Decreasing the amount of dairy manure by lowering the population of dairy cows is one route for coping with the problems associated with dairy manure. In Louisiana’s major milkshed, a recent survey of dairy gave insights into rules that would both reduce levels of dairy manure and minimize conflicts with efficiency and equity. Payments to dairy producers for undertaking voluntary practices that either reduce cow numbers or mitigate environmental damages associated with dairy manure were more acceptable than mandatory programs. This study uses the “cheap talk” method to elicit values for willingness to accept (WTA) payment to participate in the milk diversion program (MDP) or the dairy termination program (DTP). The study compares the survey respondents’ participation and amounts received in the MDP twenty years ago to hypothetical WTA payments if the programs were to be instituted today. Logistic and tobit regression models were used to identify the factors affecting dairy program participation and levels and reported bid values submitted by producers. Results indicated that past participation is the key variable in explaining future participation in a voluntary milk reduction program. Bootstrap results confirm that most of the estimated parameters fall within the range of the bootstrap confidence interval.
Analysis of Robust Predictive Current Control in PMSM Drive Systems for EVs

The control strategy of Permanent Magnet Synchronous Motors (PMSMs) plays a vital role in determining the driving performance of electric vehicles. Predictive Current Control (PCC) is an advanced control scheme widely employed in PMSM drive systems, known for its fast dynamic response, high precision, and flexible control capabilities. This paper focuses on the challenges associated with parameter mismatch in traditional PCC schemes, including high parameter dependence, limited robustness, and suboptimal control performance. Specifically, it examines the issues related to model predictive current control (MPCC) and deadbeat predictive control (DBPC). The paper reviews various optimization approaches to address these challenges and provides an overview of robust control methods such as disturbance observation and parameter identification. Additionally, the impact of these control methods on the robustness of predictive control is analyzed. Finally, the paper discusses future development trends in robust predictive control.
Model Predictive Torque Control of NPC Three-Level Inverter for Induction Motor Based on Generalized Two-Vectors Considering Neutral-Point Potential Balance

In order to maintain the neutral point potential balance and enhance the output current quality of the Neutral Point Clamped (NPC) three-level inverter, a novel approach called Neutral Point Balance based on Generalized Two-Vector Model Predictive Torque Control (NPB-GTV-MPTC) is proposed in this paper. The NPB-GTV-MPTC method evaluates vector combinations and their durations using a predefined cost function, aiming to achieve global minimization of torque error and optimal current quality. It effectively addresses the issue of neutral point potential imbalance in the NPC three-level topology. The effectiveness of the proposed NPB-GTV-MPTC is verified through a comparison of the inverter's output current quality, the motor's electromagnetic torque ripple, and motor speed response speed. The results demonstrate that NPB-GTV-MPTC outperforms the conventional GTV-MPTC approach, achieving superior performance in the considered aspects.
Review of Advances in Cooling Schemes for Yokeless and Segmented Armature (YASA) Axial Flux Motors

This technical review evaluates advanced cooling techniques for the yokeless and segmented armature (YASA) axial flux motors. Due to high torque density and efficiency advantage, YASA motors are getting more and more attention; however, their thermal management still remains a challenge. Therefore, this paper highlights the latest developments in cooling technology for YASA motors.
Analyzing the Impacts of Gasoline Price Change on Nationwide Trip Demand and Drivers’ Behavior Using Regression Discontinuity Design

13 Gasoline is one of the essential commodities in today's societies and its price can affect different aspects 14 of citizens' lives. This article studies the impacts of the sudden increase in gasoline price on the volume of 15 intercity traffic at national and provincial scales, along with the changes in driving speed distribution and 16 the percentage of speeding violations. The studied period includes four months in two consecutive years. 17 Data was collected from more than 2400 loop detectors placed on the roads of 31 provinces of Iran. This 18 data is used for implementing Regression Discontinuity Design (RDD) on traffic volume and speeding 19 violation percentage. Statistical measures of speed distribution are also used to examine changes in 20 driving speed. According to the literature, the optimal driving speed for fuel consumption is about 50 to 21 70 km/h. This study showed a 7.4% increase in the probability of driving in the 50 to 70 km/h range after 22 the price increase. Also, according to research results, the volume of intercity traffic and the percentage of 23 speeding violations has decreased due to the rise in gasoline price.
Human-in-the-loop mixup

Synthetic data is proliferating and powering many advances in machine learning. However, it is not always clear if synthetic labels are perceptually sensible to humans. The web provides us with a platform to take a step towards addressing this question from a human-centric perspective, through online elicitation. We design a series of elicitation interfaces, which we release as HILL MixE Suite , and recruit 159 participants, to provide perceptual judgments over the kinds of synthetic data constructed during mixup training: a powerful regularizer shown to improve model robustness, generalization, and calibration. We find that human perception does not consistently align with the labels traditionally used for synthetic points and begin to demonstrate the applicability of these findings to potentially increase the reliability of down-stream models. We release all elicited judgments in a new data hub we call H-Mix .
Magmatic source and metamorphic grade of metavolcanic rocks from the Granjeno Schist: was northeastern Mexico a part of Pangaea?

The geochemistry of the metavolcanic rocks from the Granjeno Schist in northeastern Mexico indicates an origin in different tectonic environments: mid‐ocean ridge and ocean island. High ratios of Hf/Th and Th/Nb (4.4–14 and 0.08–0.15), low ratios of LaN/YbN and LaN/SmN (0.74–1.7 and 0.60–1.4) and depleted LREE patterns in metabasalt display mid‐ocean ridge characteristics. In contrast, the pattern of trace‐element ratios and REEs in metabasalt and metapillow lava 60 km to the west indicates a magma source with ocean‐island basalt characteristics. Both areas were metamorphosed during the Late Carboniferous (300 ± 4 Ma). Estimated metamorphic conditions deduced from white mica and chlorite compositions, distinguish greenschist facies (350 °C and 4 kbar) for the mid‐ocean ridge basalt, and prehnite–pumpellyite facies (250 °C and 2.5 kbar) for the ocean‐island‐type basalt. This metamorphism took place at an active continental margin during Pennsylvanian time. Our new tectonic model, which differs from earlier models, suggests that the origin of the Granjeno Schist is related to a subduction zone located at the western margin of Pangaea, active after Laurentia–Gondwana collision. Copyright © 2015 John Wiley & Sons, Ltd.
Deep Learning-Aided NOMA Codebook Design with Improved Performance

Nonorthogonal multiple access (NOMA) techniques have drawn significant interest in recent years as the demand for high data rate and spectral efficiency increases. Among the NOMA designs, sparse code multiple access (SCMA) has been shown to achieve outstanding performance. How-ever, the SCMA performance highly depends on codebook construction, and it is difficult to construct codebooks that are optimal for different application scenarios in a hand-crafted manner. To address this issue, some solutions aided by deep learning were proposed to automate the codebook design. One of the solutions is to construct the codebook and decoder with the autoencoder structure. This paper further investigates this structure for codebook construction by proposing ideas to improve the performance of the constructed codebook and reduce the learning complexity. In addition to improving the performance by modifying different aspects of the training setting, we also show that the deep neural network (DNN) based encoder can be simplified to only one linear layer without sacrificing the performance. This result suggests that the DNN structure does not well exploit the potential of a general (possibly nonlinear) encoder, and a better structure may be needed to suit the NOMA application.
Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia (SCAP) in children: a nomogram prediction model

Background: Early prediction of poor outcomes (PO) is of great signi�cance to the improvement of the longterm prognosis of children with a diagnosis of severe community-acquired pneumonia (SCAP). The study aimed to explore the risk factors for PO of SCAP in children. We further aimed to develop and validate a PO-predictive nomogram model for SCAP in children. Methods: A population-based, retrospective case-control study was conducted in children with a diagnosis of SCAP who were hospitalized in our hospital from August 1, 2018 to October 31, 2021. Based on the occurrence of poor outcomes (PO), children were divided into PO and the non-PO groups. The multivariate logistic regression model was used to construct the nomogram model. Results: A total of 300 children with a diagnosis of SCAP were included, and 56 children had PO. The results of multivariate logistic regression analysis revealed that the possible independent risk factors for PO were comorbidity and invasive mechanical ventilation (IMV). In internal validation, the model displayed good discrimination with a C-index of 0.866 (95% CI: 0.772~0.960) and high quality of calibration plots in the nomogram model was noted. Conclusions: The nomogram model presented good discrimination and calibration in estimating the risk of PO among pediatric patients with a diagnosis of SCAP.
Evaluation of Reconfigurable Isolated Bidirectional DC/DC Converters for an EV Charging system with Bipolar DC-link

In this paper, four 10 kW reconfigurable, isolated, and bidirectional DC/DC converter topologies, supplied by +750/0/-750V bipolar DC-link, are evaluated based on efficiency, loss distribution, thermal considerations, volt-ampere ratings of SiC MOSFETs, and transformer losses for an electric vehicle (EV) charging system. Taking into account the merits and demerits of the evaluation outcomes, the full-bridge based modular dual active bridge converter topology is shown to be the optimal choice for the considered EV charging system.
Analysis of English Education Multimedia System Based on Artiﬁcial Intelligence

In order to solve the problem of English teaching among different schools in the same school, a cognitive model based on English teaching management is proposed. On the basis of completing the design of English teaching storing module and system server, the hardware design of the system has been completed; Complete hardware design; complete. It’s done. On the basis of the system running environment design and database design, the software design of the system is realized. The results show that when the number of multimedia courseware in English teaching is more than 300, the time for traditional leaders to acquire multimedia information in teaching materials gradually decreases. The time for traditional management to acquire multimedia information in teaching courseware has been shortened. The techniques based on multimedia information management have reduced the time required to acquire the English multimedia teaching, with a time limit of at least 17.5 s. English users spend more time looking for English multimedia information using techniques based on teaching English multimedia information management has been reduced by 36.3 s, make it suitable for sharing English multimedia information. The English teaching management proposed in this article has advantages in English teaching.
Multi-Layer Feature Fusion-based Deep Multi-layer Depth Separable Convolution Neural Network for Alzheimer's Disease Detection

Alzheimer's disease (AD) is a severe degenerative neurological disorder that can cause heart and respiratory dysfunction. Thus, early detection of such disease is highly required. Using MRI images, a number of deep models have been created to predict AD as artificial intelligence (AI) has advanced. However, these models suffer from limited representation in extracting fine-grained features from MRI images thereby performance is declined. The proposed model overcomes such limitation and presents a methodology for AD detection by proposing a novel multi-layer feature fusion-based deep multi-layer depth-wise separable convolution neural network (CNN). The proposed model enhances the quality of features by fusing multi-layer features. These features range from representations of low-level features to features at the object level. The fused multiscale features are used for predicting AD disease. Publicly available dataset is used to validate the model's performance. With an accuracy of 95.16 percent, the suggested model performs better than the current state of the art.
Evaluación de la efectividad de Bacillus sphaericus contra larvas de Anopheles aquasalis Curry (Diptera: Culicidae) en criaderos naturales del estado Sucre, Venezuela

Anopheles aquasalis Curry is the main vector of malaria in northeastern Venezuela. A study of its biological control was carried out in the Sucre State. The aim of this study was to evaluate the field effectivity and persistence of a granular formulation of Bacillus sphaericus against larval populations of An. aquasalis. The main breeding-sites of An. aquasalis are the semi-permanent freshwater wetlands with herbaceous emergent vegetation (Eleocharis mutata). In field trials, two comercial concentrations (2 and 3 g/mÂ²) of a granular formulation (VectolexÂ® CG 7.5%) of Bacillus sphaericus were evaluated in the typical breeding habitat of An. aquasalis. The granular formulation provided very good larval control. The two dosages had the same effectivity against larvae in breedingsites exposed to sunlight radiation, with acid pH and herbaceous emergent vegetation, producing a larval reduction greater than 91% during 7 days post-treatment (2 g/mÂ² and 3 g/mÂ²), equal to 94 % during 14 days (3 g/mÂ²) and equal to or greater than 66% during 21 days post-treatment (2 g/mÂ² and 3 g/mÂ²).
A Survey of IoT and Machine Learning Based Monitoring of the Growth of Crops Using Blockchain Technology

The world is rapidly changing as a result of technological advancement. New techniques and frameworks are being created over the globe in many application fields. In recent years, there has been a lot of interest in using machine learning and Internet of Things (IoT) technologies in agricultural practices. Examining the state of research and recent developments in crop monitoring, particularly in the context of IoT and machine learning, is the goal of this review. The evaluation covers a range of topics, including the different IoT device and sensor types used, the machine learning techniques used for data analysis, and the use of blockchain technology for improved data security and traceability. This review paper investigates how IoT and machine learning can be used to track agricultural growth, with a focus on how blockchain technology can be used. The advancement in technology can reduce the burden of feeding such a huge population around the world. In this paper, monitoring using machine learning and IoT for the growth of crops using Blockchain Technology is being reviewed This review outlines the advantages and difficulties related to the IoT and machine learning-based agricultural monitoring systems through a thorough analysis of the available literature. It highlights how real-time data collecting on weather conditions, soil moisture, and other pertinent characteristics that affect crop growth is made possible by IoT devices, including sensors and actuators. Additionally, it investigates the use of machine learning algorithms for data analysis, crop growth pattern prediction, and resource allocation optimization for better agricultural practices The integration of blockchain technology is also highlighted in this assessment as a viable way to improve data security and transparency in the context of crop monitoring. It looks at the potential of blockchain to provide end-to-end traceability in the agricultural supply chain, ensure data integrity, foster stakeholder confidence, enable smart contracts for automated transactions, and more. The results of this review add to a thorough understanding of the advantages and difficulties of implementing IoT- and machine learning-based systems for crop monitoring. The knowledge acquired from this analysis will help direct future research and development projects that will use new technologies for productive and sustainable agricultural practices. The promise of combining IoT, machine learning, and blockchain technology for agricultural growth monitoring is highlighted by this review's findings. Developing agricultural practices and promoting the cause of global food security, lays the groundwork for future research and innovation in this field. After review, it is found that models previously developed need further optimization. These models can be trained faster but their evaluation speed is slow. The problem of overfitting and underfitting needs to be tackled.
Measurement of nitrous oxide emissions from fertilized grassland using closed chambers

The aims of this study were to use closed chambers to improve estimates of N 2 O-N losses from intensively managed grassland on poorly drained soils and to provide measurements for comparison with fluxes determined simultaneously using micrometeorological methods. A 10-ha field on clay soil in central Scotland received 185 kg NH 4 NO 3 -N ha −1 on April 3, 1992. Twenty-four closed chambers were installed, six in a 2-3-ha area grazed by cattle the previous summer, the remainder in an ungrazed area. Fluxes were measured regularly for 3 weeks. Nitrous oxide accumulation in the chambers was determined by gas chromatography. No flux was detected before fertilization. After fertilization, fluxes from the ungrazed and grazed areas were 153±9 and 551±101 g N 2 O-N ha −1 d −1 , respectively (means and standard errors of all measurements)
Evaluation of a branch target address cache

Branches interrupt the sequential flow of instructions and introduce pipeline bubbles. Branch penalty can be a significant component of effective cpi (cycles per instruction) in multiple instruction issue processors. Two key issues need to be resolved to alleviate this problem: a branch resolution scheme to decide the direction and target of a branch early in the pipeline, thus allowing target instruction fetch to start, and mechanisms to minimize the impact of unpredictable branches. We propose a technique of caching branch target addresses for our fully predicated processor architecture, that would allow the branch decision to be made in the fetch stage of the pipeline. We discuss the impact of different branch target caching policies and cache sizes on the efficiency of branch target address cache. Impact of register-relative branches which may have variable target addresses is considered and a solution is suggested. <<ETX>>
Targeted indoor insecticide and malaria control in the western highlands Kenya

The study investigated the use of targeted indoor residual spraying (IRS) as a single malaria intervention in the western highlands, Kenya. Houses were randomly selected in study sites and IRS targeted 30% of houses at focal sites ‘hotspots’ at valley bottoms. Indoor resting densities of adult Anopheles gambiae s.s were monitored biweekly by pyrethrum spray capture in sprayed and control houses. Microscopic examination of blood smears were used to confirm malaria infection and records on malaria cases from health centers were analyzed and used to determine changes in malaria prevalence. The indoor A. gambiae s.s declined after IRS. Low vector densities were also recorded in the control houses with no malaria cases. Malaria cases reported at health centers dramatically declined after the targeted IRS. Low coverage targeted IRS was effective as a single intervention strategy as it led to decline in annual disease prevalence from 12 to 1% in the study sites. The effectiveness of targeted IRS appeared to be dependent on anthropophily of the local vector, its susceptibility to the insecticide and seasonal nature of malaria transmission.
Opportunity recognition and perception of uncertainty among entrepreneurs in Christchurch: an exploratory study

The entrepreneur is required (Keh et al, 2002) to recognise and act upon an opportunity in a context of both risk (eventualities that can be predicted together with an assessment of likelihood of occurrence) and uncertainty : the unpredictable or, more precisely, those feelings which arise when we are faced with the unpredictable, whether stemming from the complex, ambiguous, rapidly changing business environment (Ansoff, 1979) or arising from within the business venture (Pearson, 1991).