Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Parking System Approaches and Positioning Technologies: A Survey

In the realm of the smart city concept, every aspect of urban life is set to become intelligent, spanning across buildings, manufacturing, healthcare, agriculture, and transportation. A crucial component of the transportation system within the smart city framework is the implementation of smart parking systems. This particular aspect has garnered considerable attention from researchers,industries,and all stakeholders involved in shaping and developing the smart city vision. Currently, a diverse array of smart parking solutions is being introduced to the market by solution providers, employing various innovative approaches.Guidance is a key component in smart parking solution, we distinguish two guidance systems: External guidance and Internal guidance. External guidance help car drivers to reach a parking zone while Internal guidance gives instructions for drivers to find parking spot inside the parking space. The primary objective of this paper is to explore the smart parking domain from three distinct perspectives: i) an overview of the latest existing approaches to parking management, ii) external positioning and guidance methods for smart parking, and iii) an in-depth survey of the most widely utilized internal positioning technologies. The state of the art related to guidance in smart parking address internal and external guidance separately. we introduce in this paper the basics of our future work in which we will combine external and internal parking guidance to provide an end-to-end guidance service for smart parking users.
Immunity Study: Port Impedance Measurement of PMU and PCI Testing Under EMP

With the increased requirements of real-time grid monitoring, disturbance location, and situation awareness, Phasor Measurement Units (PMUs) have become more critical for the Wide Area Measurement System (WAMS). However, the vulnerability of PMUs has not been well studied, especially under electromagnetic pulse (EMP) scenarios. The stable operation of the power system will be affected directly once EMP damages them. Therefore, studying their immunity to EMP events is urgent and necessary. In this article, the effective impedance measurement scheme and pulsed current injection (PCI) testing are proposed for the port impedance measurement and immunity levels of PMUs. The equivalent non-uniform transmission line model is established to eliminate the impact of the fixture in the de-embedding process. Then, the circuit of the pulsed current generator is set to generate a damping sinusoid, and the double exponential wave is applied to the port. Finally, using measured impedance as a generator load, the voltage and current responses of different ports are calculated in the PCI testing simulation. Results reveal the characteristics of port impedance, waveforms of voltage and current, and distribution of accumulative energy. The relation between port impedance and the waveforms is discussed.
Estimating the Influence of SDN Controller Intervention on Smart Grid Services

The integration of Software Defined Networking (SDN) to manage Smart Grid Services (SGSs) has raised concerns about its impact on the end-to-end response times in the system. This paper investigates the Quality of Service (QoS) of SGSs in SDN environments using Constraint Satisfaction Problem (CSP) and Network Calculus (NC) techniques. Our studies show that the implementation of SDN contributes to an increase in end-to-end delay times and negatively affects the QoS of SGSs. These results provide insights for designing more efficient and reliable energy systems with SDN environments.
SDN Controllers and ML-Based Anomaly Detection in Embedded Systems: A Comparative Analysis

Software-defined networking (SDN) is a novel networking paradigm that decouples the control plane from the data plane, thereby offering enhanced network programmability and flexibility. However, the introduction of SDN has brought forth unique security challenges. This study presents a comprehensive comparative analysis that focuses on the classification of network traffic data derived from a realistic network implementation. The dataset acquired via the REST API from three SDN controllers encompasses diverse network features. The main aim of this study is to employ machine learning algorithms, specifically Decision Tree (DT), Support Vector Machines (SVMs), and Neural Networks (NN), to perform classification tasks on network traffic data. Subsequently, the performance of these algorithms was evaluated in real time using collected network data. The results revealed notable accuracy rates for DT and SVMs, while NN showed improved performance compared with recent studies. Nevertheless, the reaction time for detecting anomalous behaviors is notably compromised across the OpenDaylight (ODL) controller. These findings underscore the significance of comprehending SDN controller performance across various attack and target scenarios when deploying machine learning algorithms for traffic data classification in embedded system networks.
A Journey to Enhance Tabular Data FAIRness : From Annotation to Repair and Augmentation

The widespread availability of tabular datasets on the internet has facilitated the adoption of technologies for information retrieval and prediction of missing or obscured data. However, a significant proportion of these datasets suffer from poor quality issues, such as misspelled or absent values, incomplete metadata. Furthermore, the manual collection and integration of data from various sources heavily rely on expert users’ knowledge, making the process laborious and time-consuming. Hence, there is a pressing need to streamline the collection and linking of data from multiple sources, ensuring that datasets are readily prepared for the intended analysis. This article introduces an approach that addresses these challenges by offering several key functionalities. Firstly, it facilitates data annotation to address concerns related to misspelled and incomplete metadata. Secondly, it enables data repair to handle missing values within the dataset. Lastly, it provides data augmentation capabilities, allowing the dynamic addition of meaningful columns and their corresponding cell values. The effectiveness of this approach has been evaluated using benchmark datasets with promising results in terms of evaluation metrics.
SCIARA: cellular automata lava flow modelling and applications in hazard prediction and mitigation

Abstract The use of thematic volcanic hazard maps is essential for policy managers and administrators in land use planning and to determine the best form of action during emergencies. In particular, hazard maps are a key tool in emergency management and are used to describe the threat expected at a certain location in the event of future eruptions. We applied the latest version of the SCIARA lava flow cellular automata model using parallel computing through general purpose graphics processing units technology to derive lava flow hazard maps for Mt Etna, Sicily. The methodology relies on an accurate analysis of the past behaviour of the volcano and is appropriate for land use planning and civil defence applications.
High-Performance Computing on Power System Transient Stability Analysis: A Review

The transient stability analysis (TSA) is a major numerical function in Energy Management System for large-scale power transmission system planning and evaluation. Ideally, the trajectory of system dynamics, such as bus voltage magnitude and generator phase angle, can be predicted to forecast issues and disturbances based on a time-domain solution of differential and algebraic equations (DAEs). However, the rise of system scales and complex machine models requires advanced computing techniques to achieve even faster than real-time (FTRT) criteria. For accelerating the execution, High-Performance Computing (HPC) on traditional CPU-based supercomputing clusters has been widely investigated for decades. Recently, as heterogeneous computing was introduced to power system domain areas, general-purpose computing devices such as graphics processing units (GPU) have been deployed to enhance computational performance and program portability further. This paper reviews the trials within the last 15 years on developing parallel TSA applications regarding mathematical formulations, programming approaches, and performance. We also address the future of TSA computation and its potential to improve the efficiency of FTRT executions.
Deep Learning-Based Golf Swing Sequence Analysis

Golf is widely recognized as one of the most popular sports globally. However, one drawback of playing golf is the relatively high cost of equipment and coaching. While numerous training programs are available to assist players in their practice, there is currently no swing analysis program developed by Thai professionals. In this project, advanced deep learning models were employed: SwingNet, capable of predicting the sequence of eight golf swing events in videos and determining the confidence level of each swing, and MoveN et, designed to identify joint positions on the body and represent them as skeletons. These models were integrated into a customized template-matching algorithm that utilized angle-based measurements to analyze the sequence of golf swings. This analysis assessed the similarity score, represented as a percentage, between two individuals for each golf swing event. Furthermore, various techniques were implemented to enhance the efficiency of SwingN et. Through performance evaluation, it was observed that the efficiency of SwingN et surpassed by one percent compared to the pre-trained model.
Caries-risk Assessment and Management for Infants, Children, and Adolescents.

This best practice reviews caries-risk assessment and patient care pathways for pediatric patients. Presented caries-related topics include caries-risk assessment, active surveillance, caries prevention, sealants, fluoride, diet, radiology, and nonrestorative treatment. Caries-risk assessment forms are organized by age: 0-5 years and ≥ 6 years old, incorporating three factor categories (social/behavioral/medical, clinical, and protective factors) and disease indicators appropriate for the patient age. Each factor category lists specific conditions to be graded “Yes” if applicable, with the answers tallied to render a caries-risk assessment score of high, moderate, or low. The care management pathway presents clinical care options beyond surgical or restorative choices and promotes individualized treatment regimens dependent on patient age, compliance with preventive strategies, and other appropriate strategies. Caries management forms also are organized by age: 0-5 years and ≥ 6 years old, addressing risk categories of high, moderate, and low, based on treatment categories of diagnostics, preventive interventions (fluoride, diet counseling, sealants), and restorative care. Caries-risk assessment and clinical management pathways allow for customized periodicity, diagnostic, preventive, and restorative care for infants, children, adolescents, and individuals with special needs. This document was developed through a collaborative effort of the American Academy of Pediatric Dentistry Councils on Clinical Affairs and Scientific Affairs to offer updated information and recommendations regarding assessment of caries-risk and risk-based management protocols.
Predicted increased distribution of non-native red drum in China’s coastal waters under climate change

Climate change and species invasions are among the most serious threats to global biodiversity, and climate change will further greatly alter the distribution of invasive species. The red drum Sciaenops ocellatus (Linnaeus, 1766) has established non-native populations in many parts of the world, leading to negative effects on local ecosystems
Failure of Proximal Humeral Fractures Osteosynthesis - Radiological Evaluation of Predictive Factors

The surgical treatment of proximal humeral fractures (PHF) remains a source of debate and research. Open reduction and internal fixation (ORIF) has become more popular with the advent of locking plates. However, it is associated with a significant rate of failure. The purpose of this study is to describe several radiological factors that can be predictive of failure in PHF osteosynthesis. A revision of all records of PHF treated with ORIF between January 2005 and December 2017 was performed and specific variables were measured in pre, post and two months radiographs. A total of 91 shoulders were analyzed. Osteosynthesis failure (dismantling) was observed in 42% of patients and it was associated with comminuted fractures (p=0.001). The use of locked plates (p=0.001) and calcar reconstruction (p=0.001) were related to a lower risk for dismantling. Cut - out was observed in 20% of patients and it was associated with tip to the apex distance (p=0.004). Higher cervicodiaphyseal angles were related to lower risk for dismantling and cut - out. Four radiological factors of PHF can be used to predict the final radiological outcome of PHF osteosynthesis: comminuted fracture pattern, the use of locked plates, calcar reconstruction, tip to the apex distance and the cervicodiaphyseal angles.
Jurnal Sylva Lestari

The present study investigated the leaf flushing, leaf shedding, flowering, and fruiting cycles in Tectona philippinensis Benth. & Hook.f . , shedding light on its adaptation mechanisms for reproductive success and survival. A plotless inventory of tree individuals was undertaken from pre-determined observation points from January 2022 to February 2023 in Lobo, Batangas. All 430 sampled trees exhibited leaf shedding and leaf flushing, while flowering and fruiting events were observed in only 201 and 133 trees, respectively. Major leaf flushing of trees was observed in mid-October, and they generally shed in summer (peak: March). Trees bloomed in early May and reached its peak in June. Fruiting occurred in the wet season (early July to September). There was a general pattern of leaf shedding, flushing, and varying flowering and fruiting cycles, indicating adaptability to environmental changes. The species also exhibited a significant time lag (7–8
Al-Qadisiyah Journal of Pure Science Al-Qadisiyah Journal of Pure Science

The current study was designed for two groups, each group contain (20) nonsmokers healthy persons males, first group was get (more than 7 hours sleep / day) , while the second group did not get enough sleep hours (less than 7 hours sleep / day). The results showed a significant increase in the level of body mass index (BMI) , the activity of glutamic pyruvic transaminase enzyme (GPT), glutamic oxaloacetic transaminase enzyme (GOT) and concentration of total serum bilirubin (TSB) at probability level (P≤0.049 , P≤0.047 , P≤0.032 , P≤0.035) Respectively in Persons with second group compared to Persons with first group, as well as the study showed a positive correlation between the studied parameters , with increase of all parameters in the second group with each other as a result of the same reason.
A novel action of insulin sensitizing drug as a potential promotor of preovulatory follicles, ovulation rate and proli�cacy in sheep: the �rst exploratory study

Impact of insulin-sensitizing drug metformin on preovulatory follicle (POF) turnover, ovulation rate, and proli�cacy was investigated in forty-six cyclic Malpura ewes. Following estrus synchronization, the ewes were equally divided into two groups (n = 23). Treatment group (MET) received a daily oral dose of metformin at the rate of 500 mg/animal for around 12 wk, spanning �ve estrous cycles, as against untreated control (CON). All the ewes were bred with proven sires at the end of treatment. Ultrasonographic ovarian scans were carried out on every estrus and D 9 of each cycle to evaluate the number and diameter of POFs and corpora lutea (CL), respectively. A comprehensive assessment was conducted on circulating hormones including, estradiol, progesterone, androstenedione, and insulin as well as metabolic indicators like glucose, and lipid pro�le parameters. By the end of medication, the treatment showed a stimulatory effect on follicular turnover with a 53.2% (P < 0.001) rise in the number of POFs. It further elevated the ovulation rate by 67.4% (P < 0.01), with a higher proportion ( χ 2df1 = 10.7, P < 0.001) of ewes in MET group having multiple ovulations compared to the CON (82.6 vs. 30.4%, respectively). The proportion of ewes delivering multiple lambs was 2.9-fold higher in MET group than the CON. The plasma estradiol, insulin, glucose, total cholesterol, and LDL-cholesterol concentrations were lower (P < 0.05) in the MET ewes than in the CON. The �ndings of the present study indicate that metformin can augment POF numbers, ovulation rate, and proli�cacy in ewe concurrent with reduced plasma estradiol, insulin, glucose and cholesterols in MET ewes.
When Is Four Far More Than Three?

What is the relationship between children’s first number words and number concepts? We used training tasks to explore children’s interpretation of number words as they acquired the words’ meanings. Children who had mastered the meanings of only the first two or three number words were systematically provided with varied input on the next word-to-quantity mapping, and their extension of the newly trained word was assessed across a variety of test items. Children who had already mastered number words to three generalized training on four to new objects and nouns, such that their representation of the newly learned number was approximate. In contrast, children who had mastered only one and two learned to apply three reliably within a single count-noun context (e.g., three dogs), but did not generalize training to new objects labeled with different nouns (e.g., three cows). Both findings suggest that children fail to map newly learned words in their counting routine to the fully abstract concepts of natural numbers.
Recent developments in curvelet-based seismic processing

Recent developments in curvelet-based seismic processing Felix J. Herrmann (Seismic Laboratory for Imaging and Modeling Department of Earth and Ocean Sciences The University of British Columbia) SUMMARY____________________________________________________________ In this paper we present recent developments in nonlinear curvelet-based sparsity-promoting formulations of problems in the seismic data processing flow. We present our latest work on a parallel curvelet transform and recent work on a curvelet-regularized formulation for the focal transform the prediction of multiples and the computation of the inverse data space. We show that the curvelet’s wavefront detection capability and invariance under wave propagation lead to a formulation of these problems
21 st Century Pathology

Imaging and microscopy have played a very important role in biological research. In this commentary, we have provided a summary of the development of different imaging modalities and quantitative techniques as an introduction. We have briefly described the technique Statistical Parametrization of Cell Cytoskeleton (SPOCC) and evaluated it against similar techniques available. We have also discussed the advantages, short comings and future prospective of SPOCC both technically and biologically.
The Electrooculogram and a New Blink Detection Algorithm The Electrooculogram and a New Blink Detection Algorithm

Accurate and efficient real-time cognitive workload assessment has many important applications, and physiological monitoring has proven quite helpful with this assessment. One such physiological signal, the electrooculogram (EOG), can provide blink rate and blink duration measures. In a recent study, we developed and validated a robust blink detection algorithm based on the vertical EOG (VEOG). This algorithm does not require baseline data and is adaptive in the sense that it works for a wide variety of individuals without any experimenter adjustments. The performance of the algorithm is quantified using truth data based on video recordings. The algorithm produced blink rate and blink duration data for participants in a simulated remotely piloted aircraft experiment. Although this paper focuses on the blink detection algorithm, some results from the study will be included. Specifically, it was found that participants blinked fewer times and with a shorter duration in the more difficult experimental conditions.
Impact of Deforestation on Environment and achieving Sustainable Development

The aim of paper has been originated from the role of people in preservation of forest in and around our place by effective implementation policies and environmental laws existing in India The environment laws existing in India made illegal destruction of forest or deforestation a rigorous punishable offense that has to be kept in mind by every individual for ecological integrity and protection of Nature within the sustainable development. The key problem of contemporary environmental laws is their lack of effectiveness. Clear challenges exist to achieve the goals and objectives of protection of forest and stopping of deforestation so as to preserve the eco-friendly climate which gave rise to the modern and updated laws for which primary focus is on the forests and wild life protection to a greater extent which can become extinguish (or) a thing of past in near future
An Enhanced Multiple Classfier System for Predicting Students’ Academic Outcome

Data-driven decision support approaches have been increasingly employed in recent years to unveil purposeful task-oriented patterns from accumulated students’ academic records. Educational data mining and Knowledge Discovery in Database (KDD) provide a viable solution to decipher implicit knowledge through predictive modelling. Most approaches used the induction of the individual classification models also known as the single classifiers and few efforts focuses on the ensemble methods; which are unstable, overfits and susceptible to skewed data or a poorly performing classifier. To overcome this work examines the existing multiple classification algorithms, Pandey and Taruna approach and proposes an enhanced MCS that uses bagging strategy to reduce variance and perhaps improves accuracy of the resulting model to improve the accuracy. The approach is based on the definition of a model that integrates several classification techniques to predict academic performance of undergraduate students. Data were collected, cleaned, preprocessed and integrated using MS (excel and access) and Weka software. Experiments were carried out using WEKA and EMCS improved using python library. The result shows that the proposed (EMCS) has an increase of 1.03% and 0.63% of accuracy over the Bagging MCS and Pandy& Taruna respectively