Miracle-dz/UniFAR-FTUs huggingface dataset:
High prevalence of lumbar disc herniation among patients referring to the largest MRI center of hormozgan province-Iran

Lumbar Disc Herniation (LDH) occurs due to different reasons and much money allocated to its consequences. Prevalence of LDH in different parts of the world is due to and affected by local specialties such as financial, cultural, behavioral and occupational features, as well as, life style. The present study intends to evaluate prevalence of LDH in Bandar Abbas- Iran, correlation factors. Methods: in this study evaluated patients who referred to the Shahid Mohammadi with complain of backache. Age, sex, stature, weigh, job and living area were recorded medical history containing weight loss; history of fever, incontinency, trauma, systemic disease, child birth, section operation, anesthetics usage and positive family history were recorded. With report of MRI, presence or absence of LDH was considered. Results: in contrast 65.7% reports positive LDH. There were significant relationship between LDH and occupation, living area and high BMI (p<0.05). The most severe type of LDH reported bulging, the radiation of pain stated in thigh and hip mostly (65.9 %). Conclusion: LDH is a multi factorial disorder which could be affected by different variables such as demographic and social specialties. The present study emphasized on the prevalence of LDH in a particular population, and revealed specific risk factor of LDH due to the population differences which cannot be considered in other studies. It finally suggested that LDH is not affected by a fixed set of risk factors worldwide and more study is required to determine the LDH pattern and risk factors in each community. ( S. Parhiz, M. Afkhami Ardekani, M. Salimi, M. Salami, K. Bahmanzade, S.R. Mirsoleymani, High prevalence of lumbar disc herniation among patients referring to the largest MRI center of hormozgan province-Iran. Life Sci J 2013; 10(7s): 316-318)(ISSN:1097-8135). http://www.lifesciencesite. com.
Shape Optimization of Single-Curvature Arch Dam Based on Sequential Kriging-Genetic Algorithm

Shape optimization of single-curvature arch dams using the finite element method (FEM) is often computationally expensive. To reduce the computational burden, this study introduces a new optimization method, combining a genetic algorithm with a sequential Kriging surrogate model (GA-SKSM), for determining the optimal shape of a single-curvature arch dam. At the start of genetic optimization, a KSM was constructed using a small sample set. In each iteration of optimization, the minimizing predictor criterion and low confidence bound criterion were used to collect samples from the domain of interest and accumulate them into a small sample set to update the KSM until the optimization process converged. A practical problem involving the optimization of a single-curvature arch dam was solved using the introduced GA-SKSM, and the performance of the method was compared with that of GA-KSM and GA-FEM methods. The results revealed that the GA-SKSM method required only 5.40% and 12.40% of the number of simulations required by the GA-FEM and GA-KSM methods, respectively. The GA-SKSM method can significantly improve computational efficiency and can serve as a reference for effective optimization of the design of single-curvature arch dams.
Bandwidth‐switchable cellular CMOS RF receiver front‐end for intra‐band carrier aggregation

A bandwidth‐switchable cellular complementary metal‐oxide‐semiconductor radio frequency receiver front‐end design employing a current‐reusing technique is presented in a 65‐nm RF CMOS technology. The proposed single‐ended receiver front‐end can fully support both narrowband and wideband multiple‐channel RF signals for up to three intraband carrier aggregations with high performance and low direct current consumption. To achieve different bandwidth‐handling capability, a narrowband‐ and a wideband‐switchable low noise amplifier is provided to realize appropriate matching conditions and transconductance, while maintaining high performance in multiple component carrier (CC) modes. The design operates at frequency bands of 700‐2700 MHz with 1.2 V. The design achieves conversion gains of more than 40 and 46 dB and noise figures (NFs) of less than 4.9 and 3.5 dB in wideband and narrowband modes, respectively. NF degradation in multiple CC modes is less than 0.5 dB for both bandwidth conditions.
Implementation of a Wellness program to address health disparities in a vulnerable population

Purpose: The purpose of this project was to implement a Wellness program to address the health disparities in a vulnerable population. The population targeted was a local population that was served at a mental health facility for socioeconomically disadvantaged. The program focused on improving knowledge of cardiovascular health, empowerment and communication skills. Significance: The significance of the project is the improvement in ones wellness by provision of education, empowerment and communication skills. The attaining of these skills can translate into other areas of wellness and improved health outcomes. Populations at risk due to socioeconomic and racial differences can benefit from programs targeted to their specific needs. Methods: The methodology utilizing the theoretical framework of self-efficacy included the assessment of needs, the obtainment of stakeholder support from the organization, the implementation of the educational program and awareness campaign followed by evaluation of participants. Project Outcomes: Participants benefited from cardiovascular screening, educational counseling and skills regarding cardiovascular health, communication and selfefficacy. High risk real time identification of hypertensive individuals occurred. Faculty and staff improved their knowledge of subject area and supported the program by embracing the content alongside the patient participants. Clinical Relevance: The project can act as a template for replication in other facilities or to focus on other commonly seen health issues. Sustainability based on easy replication and low cost and applicability to high risk vulnerable populations provides significant value in today’s health care climate.
A Wireless Dimmable Lighting System Using Variable-Power Variable-Frequency Control

This article proposes and implements a wireless dimmable lighting system using variable-power variable-frequency (VPVF) control, which can eliminate the use of secondary converters via wireless power transfer (WPT). The proposed scheme integrates the secondary T-LCL circuit into a sealed antiparallel light-emitting diode (LED) lamp, which takes the key merits of safety, flexibility, maintenance free, and convenience for manufacturing. Moreover, it can effectively widen the LED dimming range and reduce the starting voltage requirements, which inherently improve the output fluctuation insensitivity and voltage deviation tolerance. In contrast to phase shift control, a WPT-inherent VPVF control is proposed to realize effective reductions in both the switching frequency and switching losses, while maintaining the regulation accuracy and output fluctuation suppression by up to 48.45% as compared with burst firing control. The system electrical efficiency can reach 88.08% with 20-cm transfer distance and it is improved by 3.35% during power control. Both theoretical analysis and practical experimentation are given to verify the feasibility of proposed VPVF-modulated wireless dimmable lighting system.
A Comparison of Filtering Approaches Using Low-Speed DACs for Hardware-in-the-Loop Implemented in FPGAs

The use of Hardware-in-the-Loop (HIL) systems implemented in Field Programmable Gate Arrays (FPGAs) is constantly increasing because of its advantages compared to traditional simulation techniques. This increase in usage has caused new challenges related to the improvement of their performance and features like the number of output channels, while the price of HIL systems is diminishing. At present, the use of low-speed Digital-to-Analog Converters (DACs) is starting to be a commercial possibility because of two reasons. One is their lower price and the other is their lower pin count, which determines the number and price of the FPGAs that are necessary to handle those DACs. This paper compares four filtering approaches for providing suitable data to low-speed DACs, which help to filter high-speed input signals, discarding the need of using expensive high-speed DACS, and therefore decreasing the total cost of HIL implementations. Results show that the selection of the appropriate filter should be based on the type of the input waveform and the relative importance of the dynamics versus the area.
A Review On Smart Deep Learning For Recommendation System

: Deep Learning is one of the next promising enormous things in Recommendation Systems innovation. Deep neural system has been succeeded in solving recent complex problems in Artificial Intelligence, image processing and natural language processing. It is accepted that the examination on the subject of deep learning will further advance the theme. Application zones are movie recommendation, music suggestion, motion picture proposal, book proposal and news proposal. Here we are interested to improve the quality of recommendations using various recent techniques of deep learning. The point of the examination is to support the use of deep learning procedures in Recommender Systems. Here it is proposed two different representational learning models. Here training data is processed and techniques designed to discover user-item relationships to produce most relevant ratings. Complex interaction between user and item is discovered using current and historical ratings. These hidden characteristics are obtained using neural networks and deep learning.
Cluster Analysis of Distribution Grids in Baden-Württemberg

With the growing share of renewable energies in the electricity supply, transmission and distribution grids have to be adapted. A profound understanding of the structural characteristics of distribution grids is essential to define suitable strategies for grid expansion. Many countries have a large number of distribution system operators (DSOs) whose standards vary widely, which contributes to coordination problems during peak load hours. This study contributes to targeted distribution grid development by classifying DSOs according to their remuneration requirement. To examine the amendment potential, structural and grid development data from 109 distribution grids in South-Western Germany, are collected, referring to publications of the respective DSOs. The resulting data base is assessed statistically to identify clusters of DSOs according to the fit of demographic requirements and grid-construction status and thus identify development needs to enable a broader use of regenerative energy resources. Three alternative algorithms are explored to manage this task. The study finds the novel Gauss-Newton algorithm optimal to analyse the fit of grid conditions to regional requirements and successfully identifies grids with remuneration needs. It is superior to the so far used K-Means algorithm. The method developed here is transferable to other areas for grid analysis and targeted, cost-efficient development.
Hybrid Ensemble Deep Learning for Deterministic and Probabilistic Low-Voltage Load Forecasting

Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks.
Effect of Channel Diameter on the Combustion and Thermal Behavior of a Hydrogen/Air Premixed Flame in a Swirl Micro-Combustor

Improving the flame stability and thermal behavior of the micro-combustor (MC) are major challenges in microscale combustion. In this paper, the micro combustions of an H2/air premixed flame in a swirl MC with various channel diameters (Din = 2, 3, 4 mm) were analyzed based on an established three-dimensional numerical model. The effects of hydrogen mass flow rate, thermal conductivity of walls, and the preferential transport of species were investigated. The results indicated that the flame type was characterized by the presence of two recirculation zones. The flame was anchored by the recirculation zones, and the anchoring location of the flame root was the starting position of the recirculation zones. The recirculation zones had a larger distribution of local equivalence ratio, especially in the proximity of the flame root, indicating the formation of a radical pool. The combustion efficiency increased with an increasing Din due to the longer residence time of the reactants. Furthermore, the MC with Din = 2 mm obtained the highest outer wall temperature distribution. However, the MC with Din = 4 mm had a better uniformity of outer wall temperature and large emitter efficiency due to the larger radiation surface. An increase in thermal conductivity boosts the thermal performance of combustion efficiency, emitter efficiency, and wall temperature uniformity. But there is a critical point of thermal conductivity that can increase the thermal performance. The above results can offer us significant guidance for designing MC with high thermal performance.
Top Management Team Shared Leadership, Market-Oriented Culture, Innovation Capability, and Firm Performance

What drives performance of small- and medium-sized enterprises remains largely unanswered and this article is an attempt in that direction to fill in the gap and help evolve the body of knowledge. The article is designed to produce theoretical insights on how top management team (TMT) sharing leadership, market culture, and firm innovation capability, relates to firm performance. Drawing on the resource-based and the dynamic capabilities-based view, we propose that firm innovation capability mediates between the linkages of shared leadership and market-oriented culture with firm performance. In this article, we performe structural equation modeling on survey data collected from 336 small- and medium-sized enterprises in the United Arab Emirates to examine the proposed hypothesized model of the study. The results reveal that both shared leadership and market-oriented culture have positive effects on firm innovation capability. This article suggests that market-oriented culture mediates the relationships of TMT-shared leadership and firm innovation capability. Similarly, firm innovation capability mediates the influence of market-oriented culture and firm performance, and the influence of TMT-shared leadership and firm performance. This article contributes to advance theory and practices. This article also makes sound theoretical and practical contributions to the usage of the resource-based and the dynamic capabilities view in the domain of the small- and medium-sized enterprises.
Oil Price and Stock Prices of EU Financial Companies: Evidence from Panel Data Modeling

Crude oil is an indispensable resource for the world economy and European Union (EU) countries are strongly dependent on oil imports. In a framework defined by generally positive correlations between oil and stock prices, the paper investigates the relationship between financial companies’ stock prices and crude oil price using a sample of major financial companies headquartered in the EU. The link between stock prices and oil price risk is modelled using a set of macroeconomic variables that includes local stock market indices, the EUR/USD exchange rate, the oil imports dependency, inflation rate, and global volatility indices. We employ panel data as the base econometric model and an ARDL extension that is more appropriated for our research objectives. Our findings show that the EU financial sector is pervasively exposed to oil price changes over the long-run and this exposure is a component of financial companies’ exposure to real economy risk factors, which points towards the key role of the financial sector in the EU economy in transmitting systemic shocks. At the same time, we detect signs of a different behavior of market investors over the short-versus the long-run concerning the valuation of financial companies’ stock prices in relation to oil price and other macroeconomic variables, which raises distressing challenges for financial authorities.
State-of-Charge Observer Design for Batteries With Online Model Parameter Identification: A Robust Approach

The state-of-charge (SOC) indicates a lithium-ion battery's remaining capacity, and an accurate SOC estimation plays a crucial role in the battery's operation optimization and lifetime extension. This article studies a robust model-based SOC estimation strategy for batteries. Based on a battery equivalent circuit model, a robust recursive-least-squares algorithm is utilized for the model parameters online extraction, which avoids unnecessary experiments prior to SOC estimation for parameter identification. Compared with the conventional recursive least squares, it can effectively guarantee the parameter identification performance in spite of outliers in battery measurement signals. Then, a robust observer with the estimated model parameters is designed for the battery's SOC estimation, which can suppress the disturbance caused by unknown model errors. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the designed SOC observer combined with robust recursive least-squares-based model identification.
A New Geometric Vector Optimization of Predictive Direct Power Control

This article proposes a new vector optimization method suitable for predictive direct power control (P-DPC). Based on the geometric property of parallelograms and diamonds, a simple generic equation is established to form a set of evenly-distributed vectors in the converter voltage space through an iteration process. The optimum vector is selected from the vector set via the detection of the optimum sector and vector sequence number using a simple algebraic function rather than solving complex differential equations common in multivector P-DPC. The application time of the selected vector is optimized to further improve the control performance. It is demonstrated that the proposed method significantly reduces the computation burden, whilst achieving a remarkable steady-state and transient response, especially at the low sampling frequency. A set of simulation studies is conducted to compare the proposed method with its popular counterparts. In addition, experimental results are provided to verify the effectiveness of the new method.
Performance Evaluation of Group Sparse Reconstruction and Total Variation Minimization for Target Imaging in Stratified Subsurface Media

Sparse reconstruction methods have been successfully applied for efficient radar imaging of targets embedded in stratified dielectric subsurface media. Recently, a total variation minimization (TVM) based approach was shown to provide superior image reconstruction performance over standard L1-norm minimization-based method, especially in case of non-point-like targets. Alternatively, group sparse reconstruction (GSR) schemes can also be employed to account for embedded target extent. In this paper, we provide qualitative and quantitative performance evaluations of TVM and GSR schemes for efficient and reliable target imaging in stratified subsurface media. Using numerical electromagnetic data of targets buried in the ground, we demonstrate that GSR and TVM provide comparable reconstruction performance qualitatively, with GSR exhibiting a slight superiority over TVM quantitatively, albeit at the expense of less flexibility in regularization parameters.
Accurate Tracking of Manoeuvring Target using Scale Estimation and Detection

Camera zoom operation and fast approaching/receding target causes scaling of acquired target in video frames. Fast moving target manifests in large inter-frame motion. In general, non-uniform background degrades performance of tracking algorithms. Fast Fourier transform (FFT)-based Correlation algorithms improve tracking in this scenario, but their applications is limited to small inter-frame motion. Increasing search region has implication on execution speed of the algorithms. Rapid target scaling, non-uniform background and large inter-frame motion of target hinder accurate and long term visual tracking. These challenges have been addressed for extended target tracking by augmenting fast discriminative scale space tracking (fDSST) algorithm with probable target location prediction and target detection. Localisation of fast motion has been achieved by applying fused outputs of Kalman filter and quadratic regression based prediction before applying fDSST. It has helped in accurate localisation of fast motion without increasing search region. In each frame, target location and size have been estimated using fDSST and further refined by target detection near this location. Smoothing and limiting of trajectory and size of detected target has enhanced tracking performance. Experimental results show considerable improvement of precision, success rate and centre location error tracking performance against state-of-the-art trackers in stringent conditions.
Inherently Accurate Attenuation-Based Digital Calibration of ADC

This paper presents inherently accurate digital calibration of ADC non-linearity, which requires neither absolute accuracy of a signal for calibration nor any modification of ADC core, hence enabling the use of existing ADC IP cores. It only relies on constant attenuation ratio provided by a passive resistive attenuator and hence it is inherently accurate as exemplified by extensive simulations as well as comparison with conventional method. A prototype $0.35\mu \text{m}$ -CMOS cyclic ADC with the on-chip signal generator for calibration has achieved the noise-limited 71.2-dB SNDR after the calibration under the sampling rate of 300 kS/s with 1.0-mW power and 0.087-mm2 chip area. The proposed calibration can be applied to the pipelined and cyclic ADCs with finite-gain non-linear op-amps as well as the SAR ADC.