Miracle-dz/UniFAR-FTUs huggingface dataset:
Spine Surgery Uses of Artificial Learning and Machine Learning: A LDH Treatment

The study evaluates the efficacy of various conventional techniques and ML (machine learning) models in predicting patients' 1-year follow-up outcomes based on preoperative factors. The study used the DaneSpine to identify sufferers who met the inclusion criteria and underwent (LDH) Lumbar Disc Herniation surgery. The model's initial training consisted of 16 distinct features, such as presurgical and demographic measures based on patient self-reports. The criteria for inclusion in this study encompassed sufferers who underwent LDH (Lumbar Disc Herniation) surgical treatment, recognized through the DaneSpine (Danish national registry for spine surgery). The patients were divided into groups based on whether they achieved the least clinically significant variation for EuroQol, VAS Back, Oswestry Disability Index (ODI), VAS (Visual Analog Scale) Leg, and their capacity to resume work duties after a one-year follow-up period. A random splitting method was used to create three subsets from the data, comprising testing, validation, and training sets with a ratio of 15%, 35%, and 50%, respectively. To compare the performance of various models like decision trees, deep learning, random forest, support vector machines, and boosted tree models were trained, while LR and MARS models were employed. Model fitness was evaluated by examining the performance and AUC for the duration of validation. The study generated seven models, with classification errors ranging from a minimum of 1% to a maximum of 4% standard deviation over the validation folds. Both deep learning and MARS (Multivariate Adaptive Regression Splines) models consistently performed well. The study developed two conventional and five ML (Machine Learning) predictive models to predict improvement in patients with LDH (Lumbar Disc Herniation) at the 1-year follow-up. The results indicate that building an ensemble of models requires minimal effort and is an initial basis for additional model selection and optimization.
Voltage Current Cooperative Control for DC Microgrid Under Input Unknown

For DC microgrids (MGs) with multi-distributed generations (DGs), complex communication and physical networks among DGs result in input unknown. This paper studies the cooperative control problem of current sharing and voltage recovery for DC MGs with multi-DGs under input unknown. Different from most existing design methods of state observers, an unknown input observer is developed via applying augmented system to approximate the unmeasured state and disturbance information with high accuracy. The proposed cooperative control protocol is easy for implementation. It is also theoretically shown that all signals are bounded in the closed-loop system and the voltage recovery and current sharing can be achieved. The effectiveness of our present control method is verified via the simulation results.
Model Building and Current Sharing Analysis for IPOP Converter System Based on Two-Stage LLC Resonant Converter

The two-stage LLC resonant converter has been widely used in wide voltage matching occasions. However, the errors of resonant parameters can cause considerable power imbalance between cascading modules in the input-parallel output-parallel (IPOP) converter system. In this paper, a parallel current sharing model considering parasitic resistances is established and the steady-state operating points of the parallel modules is solved. Further, the mechanism of resonant elements on the current sharing characteristic is revealed. Finally, a prototype including two 1-kW 28-V boost + LLC resonant converters is built to verify the theoretical analysis.
Passivity-Based Design of 6k±1-Order Harmonic Repetitive Controller for LCL-Type Grid-Connected Inverters

This paper proposes a passivity-based design method of 6k±1-order harmonic repetitive control (6k±1 RC) for LCL-type grid-connected inverters (GCIs). According to the proposed design method, the output admittance of the 6k±1 RC-RC-controlled inverters is tuned to be passive at all frequencies so that it can be plug-and-play connected to the grid regardless of the grid impedance variations. Meanwhile, compared with the traditional repetitive control (CRC), 6k±1 RC has a faster error convergence rate and can produce low THD and high tracking accuracy even in distorted grid conditions. Experimental results verify the effectiveness of the proposed method.
Microstrip DC-Block on Textiles Using Self-Adhesive Copper Tape

The realization of the DC-block in the microstrip technique on textiles (felt) is given. A vertical coupling of self-adhesive copper strips without widening of the 50 ohm line was used. Dielectric tapes or just glue of self-conducting copper tapes were used as a dielectric between the joined tapes.
Learning Adaptive Cruise Control for Autonomous Vehicles Using End-to-End Deep Reinforcement Learning

The most challenging task for autonomous vehicles (AVs) is to share the road with human-driven vehicles (HDVs), since the driving behaviors of HDVs are unknown to the AVs. And AVs are supposed to make optimal decisions in real time based on onboard sensors only. To achieve this goal, we model the problem as a Partially Observable Markov Decision Process (POMDP) and propose an end-to-end decision-making framework for AVs based on a deep reinforcement learning (DRL) algorithm in combination with classical control methods to allow vehicles to achieve an adaptive cruise control. To reduce the gap in the Sim2real problem, a high-fidelity simulator is developed using ROS-Gazebo, which allows for a realistic multi-vehicle simulation with various sensors. The raw data obtained from these sensors is the input of a LSTM neural network, which could be mapped directly to the low-level commands. Then, an adaptive driving policy will be learned automatically from the virtual environment through self-play training mode. Finally, the trained model is tested in both virtual platform and corresponding real-world scenario, which validates the effectiveness and feasibility of the proposed decision-making framework.
NeuralGiga: Neural Giga-Image Representation with Anti-Aliasing and Continuous Viewing

A gigapixel image consists of billions of pixels with color information to record fine details of the scene, leading to tremendous data overload for storage and display. Recent advances of gigapixel imaging still suffer from large storage size, I/O overhead or spatial aliasing for achieving real-time rendering especially during zoom-in or zoom-out. To fill this gap, in this paper, we propose NeuralGiga, a novel neural representation of gigapixel images with an effective neural rendering scheme. NeuralGiga implicitly encodes the entire image into a light-weight network which maps pixel coordinates into RGB values with efficient storage overload. In our novel neural rendering network, to enable high-quality giga-image regression with anti-aliasing and continuous viewing effect, we introduce a Spectrum Multi-Layer Perceptron (MLP) design and a Gaussian-based Integrated Random Fourier Feature Mapping (GIRFFM) scheme. Extensive experiments on various scenarios illustrate the effectiveness of our approach to achieve high-quality neural giga-image representation for both storage and display.
Neural Networks-Based Adaptive Control for Linear Motors with Cogging Force Compensation

This paper proposed a neural networks-based adaptive control scheme for linear motors considering cogging force compensation. The cogging force is modeled and compensated for improving the tracking performance. An indirect parameter adaptive strategy is proposed to address the problem of parametric uncertainties. Compared with the direct adaptive strategy, this strategy can promote the parameter estimations to converge to the true values. In addition, radial basis neural networks are designed to estimate remaining system uncertainties, including unmodeled dynamics, model errors, and external disturbances. Comparative experiments are conducted on an iron-core permanent magnet linear synchronous motor platform. The experimental results show that the proposed control scheme can achieve excellent control performance.
Leadership Approaches During Digital Transformations in Small and Medium Enterprises

— Organizational leaders have increasingly turned to enterprise resource planning (ERP) applications, also known as decision-support systems, to make their firms’ operational, tactical, and strategic processes more efficient and effective in the changing global marketplace. High failure rates in ERP systems implementations make these projects risky, however. Most prior research on critical success factors for conventional ERP implementation has been on large enterprises, resulting in a gap in knowledge on these factors in the small and medium enterprises that constitute the majority of U.S. employer firms. A qualitative modified Delphi study with an expert panel of U.S. manufacturing consultants and three iterative rounds of data collection and analysis revealed consensus on 8 critical success factors in ERP implementations, with the highest agreement on top management support and commitment, enterprise resource planning fit with the organization, quality management, and a small internal team of the best employees. In addition to furthering knowledge in the fields of leadership and enterprise applications, the study expands enterprise resource planning experts’ and scholars’ understanding of strategies to improve project success and the triple bottom line for any size enterprise in the manufacturing industry. Practitioners in the ERP industry can also apply approaches outlined during ERP implementations to mitigate risk during these engagements. Implications for positive social change include additional job opportunities and higher wages through increased efficiencies in ERP applications.
Mostly Painless Scientific Computing With Rust

Scientific software is required to be fast, painless to change, and easy to deploy. Historically, compiled languages such as C/C++ and Fortran have been preferred when writing software with the highest performance requirements. However these languages are complex, and the resulting software is challenging to maintain and deploy across platforms. We present our recent software projects written in Rust, a fast-growing, ergonomic, systems-level programming language with a toolchain designed for high-performance and simple cross platform builds. We illustrate the current state of the scientific computing ecosystem in Rust, through our experience developing high-performance MPI-distributed software for computational physics problems. CCSConcepts
Evaluation the Quality of Administrative System and Health Care Provided to the Patients among Hospital Managers

: Administration in hospital is the art and science of guidance, leadership, and control of the efforts of a group of individuals towards some common goal. A common goal is quality patient care. The hospital administrator has a great responsibility to understand and appreciate the aspects of the patient care. The purpose of the study was to examine the relationship between the quality of administrative system and health care provided to patients among hospital managers. Design: A descriptive comparative research design was utilized in the current study. Setting: This study was conducted at inpatient and critical care units at Menoufia University Hospitals. Methods: A simple random sample for each category of study participants was selected. They were 57 Nurses' managers, 55 physicians' managers, 33 administrative managers, and 75 patients. Two instruments were used for data gathering. The first instrument was managers’ observational checklist. The second instrument was patients structured interview questionnaire. Results: The main findings of this study revealed that the administrative system total quality in Menoufia University Hospital among managers was high (70.3%). Majority of patients denoted that the quality of health services provided to them were moderate quality. Conclusion: More than two thirds of patients' perceived that the quality of services was moderate. There was high significant difference between patients' perception and all hospital managers and the total quality score of administrative system. It was recommended that in-service educational programs based on management educational needs for novice hospital managers must be developed to maintain the quality of administrative tasks. Further researches studies are required to pay more attention toward comparing between in-patient departments regard quality of structure of administrative system.
Defining an absolute reference frame for plate motions on Earth from minimizing subduction zone trench migration

Since the advent of plate tectonics different reference frames have been proposed to describe the ``absolute' motion of plates. The most prominent ones include the hot spot reference frame and the no-net-rotation reference frame. The difference in absolute plate motion between different reference frames can be substantial (up to 4 cm/yr). These differences are truly significant, considering that the velocity of the fast plates is only 6-12 cm/yr in such reference frames. It is thus of primary importance to extract which of these reference frames is most likely to describe the absolute motion of plates. Here we propose that the best candidate representing an absolute reference frame is the one, which minimizes the cumulative amount of trench migration for all subduction zones. From the absolute reference frames proposed thus far, it is found that the cumulative amount of trench migration is lowest in the Indo-Atlantic hot spot reference frame. This implies that the Indo-Atlantic hot spot reference frame is currently the best approximation of an absolute reference frame on Earth.
FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC OXYGENATION PATTERNS / SAMANTA ROSATI;GABRIELLA BALESTRA;FILIPPO MOLINARI.-In: JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY

FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC OXYGENATION PATTERNS / SAMANTA ROSATI;GABRIELLA BALESTRA;FILIPPO MOLINARI. In: JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY. ISSN 0219-5194. ELETTRONICO. 12:4(2012), pp. 1240013-1-1240013-14. Original FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC
The changing water cycle: climatic and socioeconomic drivers of water‐related changes in the Andes of Peru

Water resources in high mountains play a fundamental role for societies and ecosystems both locally and downstream. Impacts of global change, including climate change, glacier shrinkage, and socioeconomic forces related to demographics, agroindustrial development, and hydroelectricity generation; pose new hydrological risks for human livelihoods. However, these hydroclimatic and socioeconomic drivers of water resource change are often poorly quantified and interconnected, while data scarcity poses challenges in these regions. Here we review the state of knowledge for two major catchments in the Peruvian Andes, which hold the largest tropical glacier mass worldwide: the Santa River (Cordillera Blanca) and Vilcanota River (Cordillera Vilcanota). Our integrative review of water resource change and comparative discharge analysis of two gauging stations in the Santa and Vilcanota River catchments show that the future provision of water resources is a concern to regional societies and must be factored more carefully into water management policies. In this context, observed hydroclimatic and socioeconomic changes represent important drivers of water availability, allocation, and conflicts over water resources. The legal framework and decentralized institutional architecture in Peru could potentially provide a basis for participatory integrative water management; however, unequal power relations, institutional fragility and increasing competition over water resources hamper these efforts. We identify several research gaps, including the need for more in situ data, cultural analyses, and a risk‐based framework that combines climate‐related hazards with human and natural vulnerabilities. Finally, this review suggests that future adaptation plans for water management should better link science, society, and policy. WIREs Water 2015, 2:715–733. doi: 10.1002/wat2.1105
Determining the correlations between faculty of sport sciences students’ unemployment anxiety and their levels of hopelessness

This study to determine the correlations between sport sciences students’ anxiety about unemployment and their levels of hopelessness. The study group was composed of 258 students in total 167 of whom were male and 91 of whom were female who attended the sport sciences faculty of Adıyaman University in 2021-2022 year. “Unemployment Anxiety Scale” and “Beck Hopelessness Scale” were used to collect research data. According to the results obtained, while the unemployment anxiety levels of the students showed a significant difference in the demographic variable according to their grade point averages (p<0.05), there was no difference in Beck hopelessness levels according to their age and general grade averages (p>0.05). As a result of the correlation analysis, a positive, moderate and significant relationship was found between unemployment anxiety and hopelessness in general (p<0.05). As a result, it has been found that all sub-factors of unemployment anxiety have an increasing effect, except for the contraction in employment and the decrease in the workforce. Therefore, it can be said that students' grade point averages and ages are significant variables in unemployment anxiety and/or hopelessness
Aromatic annular carbon allotropes: cumulenic cyclo[10]carbon and Peierls-transition-intermediate cyclo[14]carbon

: Cyclo[ n ]carbon allotropes built from sp-hybridized carbon atoms have aroused substantial interests, but until now even their structures (i.e., polyynic or cumulenic) and stabilities (i.e., cyclic or linear) are still under debate. Due to their high reactivity, it is rather difficult to synthesize in condensed phase, which hampers further real-space characterizations. Recently, isolated cyclo[18]carbon (C 18 ) and cyclo[16]carbon (C 16 ) molecules were generated on the surface by atom manipulation, which allowed to identify the polyynic structure of C 18 and C 16 . More interestingly, for the smaller cyclo[ n ]carbon, questions arise: is it still stable in cyclic form? If so, what is the structure? Here, we successfully produce two aromatic cyclocarbons, that is, cyclo[10]carbon (C 10 ) and cyclo[14]carbon (C 14 ), via tip-induced dehalogenation and followed by retro-Bergman reaction of fully chlorinated naphthalene (C 10 Cl 8 ) and anthracene (C 14 Cl 10 ) molecules, on a bilayer NaCl/Au(111) surface at 4.7 kelvin. The cumulenic structure of C 10 and Peierls-transition intermediate of C 14 , different from the polyynic structure of C 18 and C 16 , are revealed by bond-resolved atomic force microscopy. Our results demonstrate a universal synthetic strategy to generate aromatic cyclo[ n ]carbons on the surface, opening an avenue for characterizing a series of annular carbon allotropes for the structure and stability.
A Meta-Analysis of Emotional Intelligence and Work Attitudes 1

Our meta-analysis of emotional intelligence (EI) demonstrates that: First, all three types of EI are significantly related with job satisfaction (ability EI: ? ̂? = .08; self-report EI: ? ̂? = .32; and mixed EI: ? ̂? = .39). Second, both self-report EI and mixed EI exhibit modest yet statistically significant incremental validity (ΔR = .03 for self-report EI and ΔR = .06 for mixed EI) and large relative importance (31.3% for self-report EI and 42.8% for mixed EI) in the presence of cognitive ability and personality when predicting job satisfaction. Third, we found mixed support for the moderator effects (i.e., emotional labor demand of jobs) for the relationship between EI and job satisfaction. Fourth, the relationships between all three types of EI and job satisfaction are mediated by state affect and job performance. Fifth, EI significantly relates to organizational commitment (self-report EI: ? ̂? = .43; mixed EI: ? ̂? = .43) and turnover intentions (self-report EI: ? ̂? = -.33). Sixth, after controls, both self-report EI and mixed EI demonstrate incremental validity and relative importance (46.9% for self-report EI; 44.2% for mixed EI) in predicting organizational commitment. Seventh, self-report EI demonstrates incremental validity and relative importance (60.9%) in predicting turnover intentions. EMOTIONAL INTELLIGENCE AND WORK ATTITUDES 2 Practitioner Points  Employees with higher emotional intelligence have higher job satisfaction, higher organizational commitment, and lower turnover intentions.  Adding emotional intelligence measures to the set of personality and cognitive measures currently being used can improve the ability to assess employee job satisfaction, organizational commitment, and turnover intentions.  Emotional intelligence improves job satisfaction by helping employees reduce negative feelings and by increasing positive feelings.  Emotional intelligence improves job satisfaction by improving job performance.  To produce productive and satisfied workers, organizations should incorporate emotional intelligence in employee recruitment, training, and development programs. EMOTIONAL INTELLIGENCE AND WORK ATTITUDES 3 A Meta-Analysis of Emotional Intelligence and Work Attitudes
Advances in Mobile Mapping Technology: ISPRS Series, volume 4

Mobile mapping refers to a means of collecting geospatial data using mapping sensors mounted on a mobile platform. Its development was primarily driven by the advances in digital imaging and direct-georeferencing technologies. With the escalating use of telecommunication networks and the increasing availability of low-cost and portable sensors, mobile mapping has become more dynamic, and even pervasive. This book addresses a wide variety of research issues in the mobile mapping community, ranging from system development to sensor integration, imaging algorithms and mobile GIS applications. It provides researchers and practitioners a good overall view of what is being developed in this field.
Evidence that seismic anisotropy captures upstream palaeo 1 ice fabric, with implications on present day deformation at 2 Whillans Ice Stream, Antarctica 3

: Understanding deformation and slip at ice streams, which are responsible for 90% of Antarctic ice loss, is vital for accurately modelling large-scale ice flow. Ice preferred crystal orientation fabric (COF) has a first-order effect on ice stream deformation. For the first time, we use shear-wave splitting (SWS) measurements of basal icequakes at Whillans Ice Stream (WIS), Antarctica, to determine a shear-wave anisotropy with an average delay time of 7 ms and fast S-wave polarisation (φ) of 29. 3º. φ is expected to align perpendicular to ice flow, whereas our observation is oblique to the current ice flow direction (~280º). Our results suggest that ice at WIS preserves upstream fabric caused by palaeo-deformation developed over at least the past 450 years, implying that changes in the shape of WIS occurs on timescales shorter than COF re-equilibration. The “palaeo-fabric” can somewhat control present-day ice flow, which we suggest may somewhat counteract the long-term slowdown at WIS. Our findings suggest that seismic anisotropy can provide information on past ice sheet dynamics
First results of X-SAR interferometry

Repeat-pass interferometry data have been acquired during the first and second SIR-C/X-SAR missions in April and October 1994. This paper presents the first results from X-SAR interferometry on four different sites. The temporal separations were one day and six months. At two sites the coherence requirements were met, resulting in high quality interferograms. A digital elevation model has been derived. The limitations of the X-SAR interferometry are discussed.