Miracle-dz/UniFAR-FTUs huggingface dataset:
Sialolith of Unusual Size and Shape in the Anterior Segment of the Submandibular Duct

Sialolithiasis is a common condition characterized by the formation of salivary stones or calculi, also known as sialoliths, within a salivary gland or its duct. Usually, sialolithiasis presents as salivary gland swelling with intermittent pain around mealtime. All salivary glands can develop sialolithiasis, yet it occurs most commonly in the submandibular gland. In this report, we describe an unusual large sialolith measuring 1.7 cm and uncommonly resembling a canine tooth. The sialolith was removed surgically via intraoral approach, and the salivary secretion was restored.
The Significance of Lumbar Probing Combined with Continuous Irrigation and Undercutting Posterior Vertebral Body for Highly Upward Migrated Disc Herniation in ACDF: Case Reports and technical notes

The purpose of this report was to present successful cases and technical notes of the patients with up migrated cervical disc herniation to the upper level who were successfully treated using anterior cervical discectomy and fusion (ACDF), describing the evaluation of treatment outcomes and perioperative complications. The cases of two patients who had ACDF in symptomatic up migrated cervical disc herniation to the upper level in February 2021 and November 2021 were reviewed. Two patients presented with a six-week history of posterior neck pain and radiating pain. Preoperative magnetic resonance imaging (MRI) confirmed a diagnosis of up migrated cervical disc extrusion. The patients were admitted to Daegu Wooridul Spine Hospital in Daegu, Korea. ACDF was performed under general anesthesia. Treatment outcomes were exam-ined by comparing pre and postoperative Numeric Rating Scale (NRS), and MRI. Treatment outcomes were favorable: posterior neck pain and radiating pain showed a significant reduction in NRS. Postoperative MRI showed that the up migrated discs were successfully removed in both cases. Neither patient developed perioperative complications. Anterior cervical discectomy can be feasible in patients with symptomatic up migrated cervical disc herniation to the upper level.
A Pilot Study: Using the Health Belief Model to Understand Cannabidiol Use in One Amish Community

Amish use of complementary and alternative medicine is common, but little is known about their opinion of cannabidiol. Hemp legalization has popularized cannabidiol, making it available to Amish whose traditions limit access to scientific information. The purpose of this pilot study is to gain an understanding of cannabidiol use in one Old Order Amish community. A researcher-developed questionnaire was mailed to 60 members of one Amish district. The Medicinal Cannabidiol Survey for an Amish Population was adapted from the Medicinal Cannabis Survey for General Practitioners to the specification of cannabidiol, Amish cultural relevance, and the six theoretical constructs of the health belief model. A content validity survey was completed with an overall item-level content validity index score of . 92. Most respondents reported hearing discussions in the Amish community regarding cannabidiol [81.8% (n = 9)]. Slightly more agreement was found within the 10 benefit construct statements (40%) compared to six barrier statements (36.5%). Low agreement was observed among all nine questionnaire statements addressing Amish perceived threat to using cannabidiol. The barrier statement that cannabidiol cost prohibits its use had the highest agreement [90.9% (n = 10)] among all statements, which may be a stronger barrier when considering Amish culture. Higher agreement among the benefit construct compared to lower agreement among statements surrounding Amish perceived threat of cannabidiol suggests the likelihood of their cannabidiol use. A major limitation of this study was the sample size; therefore, the tool should be used in a large Amish sample to explore the findings of this pilot study.
Analysis of the Challenges of Artificial Intelligence of Things (AIoT) for the Smart Supply Chain (Case Study: FMCG Industries)

In today’s competitive world, supply chain management is one of the fundamental issues facing businesses that affects all an organization’s activities to produce products and provide services needed by customers. The technological revolution in supply chain logistics is experiencing a significant wave of new innovations and challenges. Despite the current fast digital technologies, customers expect the ordering and delivery process to be faster, and as a result, this has made it easier and more efficient for organizations looking to implement new technologies. “Artificial Intelligence of Things (AIoT)”, which means using the Internet of Things to perform intelligent tasks with the help of artificial intelligence integration, is one of these expected innovations that can turn a complex supply chain into an integrated process. AIoT innovations such as data sensors and RFID (radio detection technology), with the power of artificial intelligence analysis, provide information to implement features such as tracking and instant alerts to improve decision making. Such data can become vital information to help improve operations and tasks. However, the same evolving technology with the presence of the Internet and the huge amount of data can pose many challenges for the supply chain and the factors involved. In this study, by conducting a literature review and interviewing experts active in FMCG industries as an available case study, the most important challenges facing the AIoT-powered supply chain were extracted. By examining these challenges using nonlinear quantitative analysis, the importance of these challenges was examined and their causal relationships were identified. The results showed that cybersecurity and a lack of proper infrastructure are the most important challenges facing the AIoT-based supply chain.
Multidomain Feature Fusion Network for Fault Diagnosis of Rolling Machinery

Mechanical vibration constitutes a valuable cue for performing fault diagnosis as it is directly related to the transient regime of rolling machinery. This study establishes a multidomain feature fusion network (MFFN) to extract and fuse multidomain features through a novel multistream architecture. Three primary features are simultaneously extracted from the time, frequency, and time-frequency domains. Then, highly representative features are extracted via three convolutional branches in one- or two-dimensional spaces. A novel squeeze-connection-excitation (SCE) module is proposed to adaptively fuse features in the three domains. The advantage offered by the proposed method is that it can leverage cues from the raw vibration signal, resulting in accurate fault diagnosis. Experimental results comprehensively demonstrate and analyze the high accuracy and generalization achieved by this MFFN-based fault diagnosis method.
Implicit and explicit emotion regulation in adolescents with dispositional optimism

The development of adaptive emotion regulation (ER) plays a pivotal role in adolescent mental health and socio-emotional adaptation. Dispositional optimism, as an important protective factor for adolescent adjustment, may affect adolescent ER and subsequently influence adaptive outcomes. In this review, the changes and challenges, the role of ER in socio-emotional adjustment, and the developmental characteristics of implicit and explicit ER during adolescence are described. Subsequently, by employing the top-down model of personality, coping, and emotion, how dispositional optimism may affect psychological adjustment from the perspective of ER is analyzed. Furthermore, how the differences in adolescents’ dispositional optimism may be reflected by the differences in implicit ER is discussed. Finally, recommendations for future research are outlined.
Lean Six Sigma As An Improvement Tool In Academia

Six Sigma is a quality management philosophy, problem solving approach, and statistical measure that helps organizations improve processes, reduce variation, and eliminate waste. It has been used successfully in the automotive, chemical, healthcare, high-tech, financial and retail industries. The Industrial Engineering and Management Systems Department at the University of Central Florida has incorporated service experiential learning opportunities into the curriculum within a Total Quality Improvement course. This course teaches the Six Sigma body of knowledge together with Lean Principles and provides hands-on Six Sigma project experience. This paper describes how a team of faculty and students used the Lean Six Sigma Technology to document and improve the pre-application process used by the College of Engineering and Computer Science at the University of Central Florida.
Simulation and Optimization of a Planar-Type Micro-Hotplate with Si3N4-SiO2 Transverse Composite Dielectric Layer and Annular Heater

Micro-hotplates (MHPs) have become widely used basic structures in many micro sensors and actuators. Based on the analysis of the general heat transfer model, we propose a new MHP design based on a transversal composite dielectric layer, consisting of different heat transfer materials. Two general proven materials with different thermal conductivity, Si3N4 and SiO2, are chosen to form the composite dielectric layer. An annular heater is designed with a plurality of concentric rings connected with each other. The relationship between MHP performance and its geometrical parameters, including temperature distribution and uniformity, thermal deformation, and power dissipation, has been fully investigated using COMSOL simulation. The results demonstrate that the new planar MHP of 2 μm thick with a Si3N4-SiO2 composite dielectric layer and annular heater can reach 300 °C at a power of 35.2 mW with a mechanical deformation of 0.132 μm, at a large heating area of about 0.5 mm2. The introduction of the composite dielectric layer effectively reduces the lateral heat conduction loss and alleviates the mechanical deformation of the planar MHP compared with a single SiO2 dielectric layer or Si3N4 dielectric layer.
Rolling Stock Planning at DSB S-tog - Processes, Cost Structures and Requirements: Technical Report

A central issue for operators of suburban passenger train transport systems is providing sufﬁcient number of seats for the passengers while at the same time minimising operating costs. The process of providing this is called rolling stock planning. This technical report documents the terminology, the processes, the cost structures and the requirements for rolling stock planning at DSB S-tog, the suburban passenger train operator of the City of Copenhagen. The focus of the technical report is directed at practical train operator oriented issues. The technical report is thought to serve as a basis for investigating better methods to perform the rolling stock planning (to be the topic of later papers). This technical report is produced as a part of the current industrial Ph. D. project to improve the rolling stock planning process of DSB S-tog.
Spatiotemporal Distribution of Drought and Humidity in China Based on the Pedj Drought Index (PDI)

Drought is one of the most devastating natural disasters, especially in China. In drought assessment, the PDI has high robustness, is easier to obtain than indices such as SPEI and PDSI, and is more advantageous in regions with sparse stations. The present study employs the annual PDI with the precipitation and temperature data from 830 meteorological stations to systematically study the interannual variation characteristics of drought and humidity in China during 1970–2019. The results showed the following: (1) 26.6% of the total statistics from meteorological stations showed significant (p < 0.05) increases in annual PDI values throughout China during 1970–2019. (2) Air temperature plays a more remarkable role than precipitation in assessing the drying trend with PDI throughout China. (3) About 71% of stations experienced more drought events (PDI > 1) than humidity events (PDI < −1), 14% of stations experienced more humidity events than drought events. (5) All stations experienced drought events (PDI > 1) with a frequency range from 6% to 32% and humidity events (PDI < −1) with a frequency range from 8% to 36%. Most of the stations experienced extreme drought events (PDI > 3) with a frequency range from 2% to 10%, while only 177 stations experienced extreme humidity events (PDI < −3) with a frequency range from 2% to 4%. (6) More than 67% of stations experienced drought conditions during both periods of 1972–1974 and 2000–2002 and even exceeded 80% in the three years 1972, 2000, and 2001. Both periods of 1976–1983 and 1985–1999 can be considered to be a humidity period throughout China. In conclusion, the PDI successfully expresses the interannual variation characteristics of drought and humidity events throughout China previously captured by other prominent, recommended drought indices.
Contraceptive practices and contraceptive counselling in high-performance Portuguese athletes

We present an observational, prospective, descriptive study of answers collected through an online self-assessment questionnaire in High Performance Portuguese Athletes aged 18 years old or over. The main objective was to evaluate contraceptive practices, menstrual patterns and contraceptive counselling in these athletes. Overall, 115 women aged between 18 and 39 years were studied, from 18 different sports. In our sample, most athletes used some type of contraception. Most believed that they had a better performance after menstruation and that starting contraception did not interfere with their performance. However, a high number of athletes believes that contraceptives are contraindicated for sports.
XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System

This work presents XBeats, a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection. The platform encompasses a data acquisition ECG patch with 12 leads to collect heart signals, perform on-chip processing, and transmit the data to healthcare providers in real-time for further analysis. The ECG patch provides a dynamically configurable selection of the active ECG leads that could be transmitted to the backend monitoring system. The selection ranges from a single ECG lead to a complete 12-lead ECG testing configuration. XBeats implements a lightweight binary classifier for early anomaly detection to reduce the time to action should abnormal heart conditions occur. This initial detection phase is performed on the edge (i.e., the device paired with the patch) and alerts can be configured to notify designated healthcare providers. Further deep analysis can be performed on the full fidelity 12-lead data sent to the backend. A fully functional prototype of the XBeats has been implemented to demonstrate the feasibly and usability of the proposed system. Performance evaluation shows that XBeats can achieve up to 95.30% detection accuracy for abnormal conditions, while maintaining a high data acquisition rate of up to 441 samples per second. Moreover, the analytical results of the energy consumption profile show that the ECG patch provides up to 37 h of continuous 12-lead ECG streaming.
Biomechanical Effects of Plastic Heel Cup on Plantar Fasciitis Patients Evaluated by Ultrasound Shear Wave Elastography

The plastic heel cup has been adopted to treat plantar heel problems for years. However, its mechanisms and biomechanical effects are yet to be fully understood. The purpose of this study was to investigate the effects of the plastic heel cup on the microchamber and macrochamber layers of the heel pad by comparing the stiffness (in terms of the shear wave speed) and thickness of these two layers with and without a plastic heel cup during static standing. Fifteen patients with unilateral plantar fasciitis were recruited. The shear wave speed and thickness of the microchamber and microchamber layers of each symptomatic heel pad during standing measured by ultrasound shear wave elastography were compared between conditions with and without a plastic heel cup. It was found that a plastic heel cup reduced the shear wave speed of the microchamber layer to 55.5% and increased its thickness to 137.5% compared with the condition without a plastic heel cup. For the microchamber layer, the shear wave speed was reduced to 89.7%, and thickness was increased to 113.6% compared with the condition without a plastic heel cup. The findings demonstrate that a plastic heel cup can help to reduce the stiffness and increase the thickness for both layers of the heel pad during standing, suggesting that the mechanism of a plastic heel cup, and its resulting biomechanical effect, is to reduce the internal stress of the heel pad by increasing its thickness through confinement.
Evaluation by simulation to optimize information systems personalization quality in logistics

Personalized Information Systems (PIS), related to different fields (travelling and mobility, production, logistics…), represent an object of many research and development perspectives. Using PIS, it becomes possible to supply the user only with the pertinent information that suits his/her preferences. In deed, thanks to personalization, user may feel that PIS is developed for him/her. This system adaptation becomes a necessity for the user’s satisfaction. In this context, many studies were oriented toward the user modeling, the design methods of PIS and the personalization algorithms, etc., but, the evaluation of these systems is neglected. Difficulties concerning context-centred evaluation appear. This article is focussed on the evaluation of the personalized information systems in order to optimize the personalization quality according to several criteria. For such systems, it is important to envisage new adapted evaluation methods. An evaluation method using simulation of a model of PIS, called MetSim (Method evaluation per Simulation) is proposed. MetSim is also based on Case Based Reasoning system to identify problems. This evaluation approach has been validated by applying it to assess PIS in logistics field.
An Efficient and Explainable Ensemble Learning Model for Asphalt Pavement Condition Prediction Based on LTPP Dataset

Accurate prediction of asphalt pavement condition is important to guide pavement maintenance practices. The existing models for pavement condition predictions are predominantly based on linear regressions or simple machine learning techniques. However, additional work on these models is needed to improve their basic assumptions, training efficiency, and interpretability. To this end, a new modeling approach is proposed in this manuscript, which includes a ThunderGBM-based ensemble learning model, coupled with the Shapley Additive Explanation (SHAP) method, to predict the International Roughness Index (IRI) of asphalt pavements. The SHAP method was applied to interpret the underlying influencing factors and their interactions. Twenty features were initially identified as the model inputs, and 2,699 observations were extracted from the Long-Term Pavement Performance (LTPP) database. Three benchmark models, namely the Mechanistic-Empirical Pavement Design Guide (MEPDG) model, the ANN model and the RF model, were used for comparison. The results showed that the developed model achieved a satisfactory result with a R-squared ( $\mathbf {R}^{2}$ ) value of 0.88 and Root Mean Square Error (RMSE) of 0.08, both better than three benchmark models. It ran 86 times and 2.3 times faster than the ANN and RF model, respectively. Feature interpretation was performed to identify the top influencing factors of IRI. The 20-feature model was further simplified based on the analysis result. The simplified model only required six features to efficiently and effectively predict IRI using the proposed ThunderGBM-based approach, which can reduce the workload in data collection and management for pavement engineers.
An Efficient Secure Cryptography Scheme for New ML-based RPL Routing Protocol in Mobile IoT Environment

Internet of Things (IoT) offers reliable and seamless communication for the heterogeneous dynamic lowpower and lossy network (LLNs). To perform effective routing in IoT communication, LLN Routing Protocol (RPL) is developed for the tiny nodes to establish connection by using deflaut objective functions: OF0, MRHOF, for which resources are constraints like battery power, computation capacity, memory communication link impacts on varying traffic scenarios in terms of QoS metrics like packet delivery ratio, delay, secure communication channel. At present, conventional Internet of Things (IoT) are having secure communication channels issue for transmission of data between nodes. To withstand those issues, it is necessary to balance resource constraints of nodes in the network. In this paper, we developed a security algorithm for IoT networks with RPL routing. Initially, the constructed network in corporates optimizationbased deep learning (reinforcement learning) for route establishment in IoT. Upon the establishment of the route, the ClonQlearn based security algorithm is implemented for improving security which is based onaECC scheme for encryption and decryption of data. The proposed security technique incorporates reinforcement learning-based ClonQlearnintegrated with ECC (ClonQlearn+ECC) for random key generation. The proposed ClonQlearn+ECCexhibits secure data transmission with improved network performance when compared with the earlier works in simulation. The performance of network expressed that the proposed ClonQlearn+ECC increased the PDR of approximately 8% - 10%, throughput of 7% - 13%, end-to-end delay of 5% - 10% and power consumption variation of 3% - 7%.