Miracle-dz/UniFAR-FTUs huggingface dataset:
Spinal gout with intervertebral foramen infiltration: A rare case perfectly mimicking degenerative lumbar disc disease

Spinal gout is a relatively rare disease characterized by significant clinical symptoms. In the current study, the first case of spinal gout with tophus in the intervertebral foramen, which perfectly mimicked degenerative lumbar disc disorders, was presented. The patient was a 57-year-old man with a medical history of gout who had suffered from progressive neurological deterioration for the last 12 months. Imaging examination revealed bilateral stenosis in the L5/S1 intervertebral foramen, mimicking degenerative lumbar disc disease. Nerve root radiculography and blocking were performed and the neurological symptoms were completely relieved. Open surgery was further performed and unexpectedly, the intra-operative findings were amorphous chalky white lesions. Histopathology confirmed the diagnosis of spinal gout. After surgery, the patient was prescribed a medication and achieved complete remission of clinical symptoms. No deterioration was found at the 1-year follow-up. To the best of our knowledge, this is the first report of spinal gout tophus in intervertebral foramen in the literature. It was concluded that, although intraspinal tophaceous gout is relatively rare, orthopedic surgeons should take it into consideration as a differential diagnosis, particularly if the patient has a medical history of gout. Early diagnosis and timely medical management may possibly be able to avoid neurological compromise and the need for surgery.
A Review of Effectiveness and Efficiency Methodology of Decision Support System for Selecting Suppliers

In today's highly competitive global business environment, effective supplier selection plays a crucial role in the success and sustainability of organizations. Decision Support Systems (DSS) have emerged as powerful tools to aid in this complex process by providing valuable insights and data-driven recommendations for selecting suppliers. This research paper presents a comprehensive review of the methodologies used to assess the effectiveness and efficiency of Decision Support Systems employed in supplier selection processes. The primary objective of this study is to evaluate and compare the various methodologies applied in Decision Support Systems, focusing on their effectiveness and efficiency in assisting decision-makers during supplier selection. The research critically analyzes a range of published literature, academic papers, and case studies to highlight the strengths and limitations of different DSS methodologies in this context. The paper commences by elucidating the fundamental concepts of supplier selection and the significance of Decision Support Systems in streamlining this multifaceted process. Subsequently, a systematic review of existing literature is conducted to identify the key methodologies employed in DSS frameworks. Furthermore, the research explores the novel integration of emerging technologies, such as Artificial Intelligence, Machine Learning, and Big Data analytics, within Decision Support Systems for supplier selection. This investigation aims to discern how these advanced techniques contribute to the enhancement of decision-making precision and the overall efficiency of the selection process. By critically analyzing and comparing the effectiveness and efficiency of various Decision Support System methodologies, this research seeks to provide valuable insights for both academics and practitioners in supply chain management. The findings of this study will enable decision-makers to make informed choices regarding the adoption and customization of DSS frameworks according to their organizational context, requirements, and available resources. This research paper presents a comprehensive review of the effectiveness and efficiency of methodologies employed in Decision Support Systems for supplier selection. It not only enhances our understanding of the current state of DSS in supplier selection but also identifies potential areas for future research and development. The insights gained from this study have the potential to significantly contribute to the advancement of supplier selection processes, ultimately leading to improved operational efficiency, reduced costs, and enhanced competitive advantage for organizations across industries.
Rock Transport after an Outburst and the Fluidisation Phenomenon – Can it Occur during a Dolomite and Gas Outburst?

rock and gas outburst is a phenomenon in which fragmented rock material is transported deep into a pit. the transport of rock material by gas is a two-phase process. the article deals with the fluidisation of rock material. considerations on the fluidisation phenomenon were carried out, and experiments were performed to help clarify whether the fluidisation of dolomite is possible. In the last chapter, a discussion was carried out, and the results obtained were analysed regarding the possibility of occurrence in mine conditions.
An Efficient CSPK-FCM Explainable Artificial Intelligence Model on COVID-19 Data to Predict the Emotion Using Topic Modeling

—Incessant COVID-19 pandemic negatively impacts nations throughout the globe. It is necessary to determine how people react to public health interventions and understand their concerns. Twitter is a social media platform that has emerged as a tool for disseminating information, debating concepts, and reviewing or commenting on global issues. This study applies Explainable Artificial Intelligence (XAI) methods, like Cosine Similarity and Polynomial Kernel-centered Fuzzy C-Means (CSPK-FCM) centered topic modeling and Fuzzy Logic with Improved Long Short-Term Memory (FL-ILSTM) centered Sentiment Analysis to COVID-19 data on Twitter. The proposed model has five major steps: preprocessing, feature extraction, term weighting, topic modeling (clustering), and classification. Twitter comments relating to the COVID-19 pandemic are initially collected from publicly accessible websites. The collected data are then preprocessed to remove irrelevant information, namely, noises. The Feature Extraction phase is then performed by extracting emoticon and non-emoticon features. The extracted feature dataset is scored: the Term Frequency Inverse Document Frequency-Chi-Square (TFIDF-CHI) method is utilized for non-emoticon, and the score for the emoticon is assigned based on a few criteria. For Topic modeling, the TFIDF-CHI scores are provided to the CSPK-FCM clustering algorithm, which groups the most frequently discussed topics throughout COVID-19. FL-ILSTM executes the Sentiment analysis of clustered topics and emoticon features. It has extraordinary performance when compared to other methodologies.
HFSM bicone antenna for estimating the water saturation of soil

In this paper, a continuous ground moisture detection system is presented. Particular focus is given to a bicone antenna especially developed for the application. The measure of the quantity of water in soil and its stratification is important to estimate the risk of shallow landslide events. The antenna has been preliminarily validated with electromagnetic simulation and tested in different test sites.
Experimental Investigation of Power Signatures for Cavitation and Water Hammer in an Industrial Parallel Pumping System

Among the total energy consumption by utilities, pumping systems contribute 30%. It is evident that a tremendous energy saving potential is achievable by improving the energy efficiency and reducing faults in the pumping system. Thus, optimal operation of centrifugal pumps throughout the operating region is desired for improved energy efficiency and extended lifetime of the pumping system. The major harmful operations in centrifugal pumps include cavitation and water hammering. The pump faults are simulated in a real-time experimental setup and the operating point of the pump is estimated correspondingly. In this article, the experimental power quality and vibration measurements of cascade pumps during cavitation and water hammering is recorded for different operating conditions. The results are compared with the normal operating conditions of the pumping system for fault prediction and parameter estimation in a cascade water pumping system. Moreover, the Fast Fourier Transform (FFT) analysis comparison of normal and water hammering (faulty condition) highlights the frequency response of the pumping system. Also, the various power quality issues, i.e., voltage, current, total harmonic distortion, power factor, and active, reactive, and apparent power for a cascade multipump control is discussed in this article. The vibration, FFT, and various power quality measurements serve as input data for the classification of faulty pump operating condition in contrast with the normal operation of pumping system.
Workforce sustainability and quality of service in accredited hospitals in India: Mediating role of job engagement

The healthcare industry in India has witnessed remarkable growth and transformation in recent years, with a burgeoning demand for quality healthcare services. The sustainability of the healthcare workforce and the service quality of accredited hospitals are critical factors that significantly influence the overall healthcare system in India. This study aims to investigate the relationship between workforce sustainability, quality of healthcare service, and job engagement within the context of accredited hospitals in India. A descriptive research design is employed, and a structured questionnaire is used to gather primary data from the doctors and nurses as they have more burnout intentions at the workplace. The sample size of the study is 384 respondents: 86.2% are doctors and 13.8% are nurses. The study results reveal that unmarried doctors and nurses exhibit greater sustainability (with a score of 3.7767). Similarly, the millennial workforce demonstrates higher sustainability (scoring 3.8106) than the Gen X cohort (scoring 3.7775). Notably, doctors exhibit greater sustainability in the workplace (scoring 3.7772) when contrasted with nurses (scoring 3.6061). Job engagement varies with the annual income. The female workforce holds more favorable perceptions regarding the quality of healthcare service (scoring 3.8583). The study found that workforce sustainability, quality of healthcare service, and job engagement are positively correlated. Further, the study revealed that workforce sustainability positively and significantly impacts service quality in hospitals (22.4%), and job engagement mediates the workforce sustainability and quality of healthcare services.
Failure Mechanism Analysis of the Stochastic Galerkin Method in EMC Simulation Considering Geometric Randomness

By virtue of its high calculational accuracy and efficiency, the stochastic Galerkin method (SGM) has been successfully applied many times in electromagnetic compatibility (EMC) simulation in recent years. This paper proposes a calculating example taking geometric uncertainty factors into consideration. As is proved in the paper, there is a relatively large error when using the SGM to solve the example mentioned above. According to failure mechanism, the fundamental reason of the failure of the simulation lies in the additional error caused by using numerical integration to solve the inner product formula. Meanwhile, it is proved that no additional errors are introduced when using the stochastic collocation method (SCM), so the SCM is better than the SGM in stability. In the end, the paper revised the general selective strategy for uncertainty analysis methods, thus providing theoretical basis for their universal application in EMC field.
Combined BSA-Seq and RNA-Seq Reveal Genes Associated with the Visual Stay-Green of Maize (Zea mays L.)

Maize has become one of the most widely grown grains in the world, and the stay-green mutant allows these plants to maintain their green leaves and photosynthetic potential for longer following anthesis than in non-mutated plants. As a result, stay-green plants have a higher production rate than non-stay-green varieties due to their prolonged grain-filling period. In this study, the candidate genes related to the visual stay-green at the maturation stage of maize were investigated. The F2 population was derived from the T01 (stay-green) and the Xin3 (non-stay-green) cross. Two bulked segregant analysis pools were constructed. According to the method of combining ED (Euclidean distance), Ridit (relative to an identified distribution unit), SmoothG, and SNP algorithms, a region containing 778 genes on chromosome 9 was recognized as the candidate region associated with the visual stay-green in maize. A total of eight modules were identified using WGCNA (weighted correlation network analysis), of which green, brown, pink, and salmon modules were significantly correlated with visual stay-green. BSA, combined with the annotation function, discovered 7 potential candidate genes, while WGCNA discovered 11 stay-green potential candidate genes. The candidate range was further reduced due through association analysis of BSA-seq and RNA-seq. We identified Zm00001eb378880, Zm00001eb383680, and Zm00001eb384100 to be the most likely candidate genes. Our results provide valuable insights into this new germplasm resource with reference to increasing the yield for maize.
Unmasking the impact: unveiling the effects of 12-hour shifts on performance among South African police officers

The South African Police Service (SAPS) has implemented a shift system in which police officers are scheduled to work for four consecutive days, resulting in a total of 48 hours per week. The implementation of the 12-hour shift pattern has been found to have an adverse effect on police officers. Consequently, this appears to have a detrimental effect on police effectiveness. Studies investigating the effects of the 12-hour shift have revealed that this work schedule significantly influences the reaction time, sleep patterns, and overall health of police officers, thereby impacting their performance. This study aims to provide a qualitative analysis of the effects of the 12-hour shift on police performance at Pretoria Central (SAPS) Police Station in Gauteng Province, South Africa. Data was collected from eight (8) individuals from the SAPS, namely at Pretoria Central Police Station, using semi-structured interviews. The participants have suggested that officials in the SAPS should evaluate the existing shift structure and increase the deployment of police officers at the police station. This would help alleviate the workload of the officers and enhance service delivery.
Detection of COVID-19 epidemic outbreak using machine learning

Background The coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread. Objective In this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data. Methods We developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days. Results ML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection. Conclusion The study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.
Biocatalysts Based on Immobilized Lipases for the Production of Fatty Acid Ethyl Esters: Enhancement of Activity through Ionic Additives and Ion Exchange Supports

Ionic additives affect the structure, activity and stability of lipases, which allow for solving common application challenges, such as preventing the formation of protein aggregates or strengthening enzyme–support binding, preventing their desorption in organic media. This work aimed to design a biocatalyst, based on lipase improved by the addition of ionic additives, applicable in the production of ethyl esters of fatty acids (EE). Industrial enzymes from Thermomyces lanuginosus (TLL), Rhizomucor miehei (RML), Candida antárctica B (CALB) and Lecitase®, immobilized in commercial supports like Lewatit®, Purolite® and Q-Sepharose®, were tested. The best combination was achieved by immobilizing lipase TLL onto Q-Sepharose® as it surpassed, in terms of %EE (70.1%), the commercial biocatalyst Novozyme® 435 (52.7%) and was similar to that of Lipozyme TL IM (71.3%). Hence, the impact of ionic additives like polymers and surfactants on both free and immobilized TLL on Q-Sepharose® was assessed. It was observed that, when immobilized, in the presence of sodium dodecyl sulfate (SDS), the TLL derivative exhibited a significantly higher activity, with a 93-fold increase (1.02 IU), compared to the free enzyme under identical conditions (0.011 IU). In fatty acids ethyl esters synthesis, Q-SDS-TLL novel derivatives achieved results similar to commercial biocatalysts using up to ~82 times less enzyme (1 mg/g). This creates an opportunity to develop biocatalysts with reduced enzyme consumption, a factor often associated with higher production costs. Such advancements would ease their integration into the biodiesel industry, fostering a greener production approach compared to conventional methods.
An Evidence Collection Using Blockchain for Cybercrime Detection

Inspections into cybercrime rely heavily on the use of digital evidence because of its ability to connect individuals to specific illegal activity. During a probe into a computer crime, it is essential that the integrity, authenticity, and auditability of digital evidence be maintained at all times when it is being transferred through the chain of custody from the beginning to the finish. The digitalization of banking is paralleled by an equally digitalization of the environment for financial crime. Because laws, rules, and forensic techniques are unable to keep up with the fast development of new technologies, investigations into embezzlement schemes might benefit from the standardization of processes and recording of the related approach. The applicability and adaptability of our method may be extended to include a wide variety of fraud investigations as well as routine internal audits. We offer a working Ethereum-based solution, and we incorporate standardised forensic processes and chain of custody preservation techniques. In conclusion, we investigate the challenges surrounding the mutually beneficial link between blockchain technology and financial investigations, as well as the managerial effect and potential avenues for further study. r wicked actors. In this sense, the characteristics afforded by blockchain technology, such as immutability, verifiability, and authentication, contribute to an increase in the degree of rigor that may be achieved in financial forensics. In this article, we describe not only the current status of blockchain-based digital forensic procedures but also a taxonomy of the most popular methodologies used in financial investigations. Our solution makes it possible for consumers to trace the history of their data by making use of smart contracts (CS). In conclusion, the development of an Artificial Neural Network (ANN) for blockchain makes the collection of evidence more easier. Java, which is used for clouds and blockchains, and network simulator-3.26, which is used for software-defined networking (SDN), are both used inside a single testing environment. Response time, Evidence input time, Evidence verification time, All aspects of the suggested forensic architecture, including communication overhead, hash calculation time, key generation time, encryption time, decryption time, and overall change rate, show potential.
Nontrivial clustering of microseismicity induced by hydraulic fracturing

For induced microseismicity associated with hydraulic fracturing, the frequency‐magnitude distribution is typically characterized by a falloff with increasing magnitude that is significantly faster than for seismicity along active fault systems. This characteristic may arise from a break in scale invariance, possibly due to mechanical layering that typifies fine‐grained sedimentary rocks in many shale gas and tight oil reservoirs. The latter would imply the presence of spatiotemporal magnitude correlations. Using three microseismic catalogs for well stimulations in widely separated locations with varying hydraulic‐fracturing methods, we show that events with similar magnitudes indeed tend to cluster in space and time. In addition, we show that the interevent time distribution can be described by a universal functional form characterized by two power laws. One exponent can be related to the presence of interevent triggering as in aftershock sequences and is a consequence of the Omori‐Utsu law. The other one is a reflection of the intrinsic spatial variation in the microseismic response rates. Taken together, these features are indicative of nontrivial spatiotemporal clustering of induced microseismicity and, hence, of direct relevance for time‐dependent seismic hazard assessment.
Design and Development of a 6-DOF Parallel Mechanism Platform using Compact Size Omni-directional Robots

This paper presents a new robot design for a 6 degree of freedom (6-DOF) parallel mechanism platform that increases its workspace compared to the Stewart platform, which has a small workspace. The new design uses three fixed-length legs that connect to the platform via a revolute joint at the upper end of each leg. At the lower end of the legs, a balljoint connects to the top of three omni-directional robots, which move the lower end of the leg in a planar direction. The new motor position for the 3-wheeled omni-directional robot makes it more compact, which can further increase the platform’s workspace. Moreover, a control system is developed using inverse kinematics and encoders attached at the revolute joint for measuring leg angles. Numerical analysis shows the mechanism has a larger workspace than typical Stewart platforms, with no limitations in x and y movement and z rotation. Additionally, the paper presents the results of robot testing to follow a trajectory in x and y rotation. The testing confirms the control system’s effectiveness in controlling this type of robot mechanism.