Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of Competitive Advantage and Competitive Strategy Model of Sports Enterprises Based on Multicase Study and In-Depth Learning

Summary. With the support of national policies and the holding of the 2022 Beijing Winter Olympic Games, China’s sports market presents an unprecedented prosperity, but it also faces many challenges. The market environment in which the sports industry is located is full of uncertainty and complexity. Creative products, competitive sports enterprises, and clear business models are urgently needed. Research purpose. To explore the future development mode and the prospect of China’s sports undertakings and enterprises through the analysis and research on the competitive advantage of China’s sports industry. In this context, since 2016, this study has started with the micromarket players. Research methods. Using the case study method highly praised by the international economic management academic circles, combined with in-depth learning, this study has conducted a systematic and comprehensive study on a total of 50 Chinese sports enterprises by mining and analyzing the unstructured text, video, and other data obtained from public materials and interviews. This paper deeply analyzes the competitive strategy of China's sports enterprises, creates and maintains competitive advantages in the sports industry, and studies the model construction of case studies and analysis. Conclusion. The study found that the product and business model, the leadership of managers, the innovation ability of teams, and the external market environment are the core elements for sports enterprises to formulate competitive strategies and create and maintain competitive advantages. The organic combination of the four eventually forms a competitive advantage that is difficult to replicate and finally forms a value chain through the implementation of competitive strategies so as to realize the value promotion and sustainable development of enterprises.
Altered TDP-43 Structure and Function: Key Insights into Aberrant RNA, Mitochondrial, and Cellular and Systemic Metabolism in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neuromuscular disorder with no cure available and limited treatment options. ALS is a highly heterogeneous disease, whereby patients present with vastly different phenotypes. Despite this heterogeneity, over 97% of patients will exhibit pathological TAR-DNA binding protein-43 (TDP-43) cytoplasmic inclusions. TDP-43 is a ubiquitously expressed RNA binding protein with the capacity to bind over 6000 RNA and DNA targets—particularly those involved in RNA, mitochondrial, and lipid metabolism. Here, we review the unique structure and function of TDP-43 and its role in affecting the aforementioned metabolic processes in ALS. Considering evidence published specifically in TDP-43-relevant in vitro, in vivo, and ex vivo models we posit that TDP-43 acts in a positive feedback loop with mRNA transcription/translation, stress granules, cytoplasmic aggregates, and mitochondrial proteins causing a relentless cycle of disease-like pathology eventuating in neuronal toxicity. Given its undeniable presence in ALS pathology, TDP-43 presents as a promising target for mechanistic disease modelling and future therapeutic investigations.
Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

Recently, developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives. It results in illegal access to users’ private data and compromises it. Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data. Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity. This article focuses on the design of biogeography based optimization with deep learning for Phishing Email detection and classification (BBODL-PEDC) model. The major intention of the BBODLPEDC model is to distinguish emails between legitimate and phishing. The BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning, tokenization, and stop word elimination. Besides, TF-IDF model is applied for the extraction of useful feature vectors. Moreover, optimal deep belief network (DBN) model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning process. The performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several dimensions. Extensive comparative studies reported the superior outcomes of the BBODLPEDC model over the recent approaches.
EXOSAT observations of variability in active galactic nuclei

Les caracteristiques des spectres de puissance de 6 noyaux actifs de galaxies, observes dans la gamme RX moyenne (2-10 keV) du satellite EXOSAT, sont analysees en terme de pente spectrale et de variabilite. Des modeles statistiques sont utilises pour lisser les donnees recueillies parce qu'aucune echelle de variabilite caracteristique n'a pu etre mise en evidence et qu'en fait l'emission semble aleatoire
Effective potential and polaronic mass shift in a trapped dynamical impurity–Luttinger liquid system

Recent experiments with cold atoms on the impurity motion in one-dimensional liquids of interacting bosons have revealed an interesting interplay between the polaronic impurity mass shift and the renormalization of the optical potential. We show that the influence of the external trap on the Bose gas leads to a steeper effective potential for the impurity. We propose a framework in which this potential renormalization and the mass shift can be quantitatively understood by combining a semi-classical theory of density wave excitations in the Luttinger liquid with the non-equilibrium formalism of a quantum Brownian particle. The obtained theoretical results reproduce well recent experimental data.
Noise-coupled /spl Delta//spl Sigma/ ADC's

The first-order noise coupling scheme proposed earlier is generalised to the realisation of higher-order enhancement. It is also extended to single-stage DeltaSigma loops, and to split structures with self-enhancement. The advantages and limitations of these new DeltaSigma architectures are compared with those of conventional single-stage and cascade DeltaSigma structures. As demonstrated by an example, they exhibit improved stability and robustness under practical fabrication conditions
Molecular Insight into the Self-Assembly Process of Cellulose Iβ Microfibril

The self-assembly process of β-D-glucose oligomers on the surface of cellulose Iβ microfibril involves crystallization, and this process is analyzed herein, in terms of the length and flexibility of the oligomer chain, by means of molecular dynamics (MD) simulations. The characterization of this process involves the structural relaxation of the oligomer, the recognition of the cellulose I microfibril, and the formation of several hydrogen bonds (HBs). This process is monitored on the basis of the changes in non-bonded energies and the interaction with hydrophilic and hydrophobic crystal faces. The oligomer length is considered a parameter for capturing insight into the energy landscape and its stability in the bound form with the cellulose I microfibril. We notice that the oligomer–microfibril complexes are more stable by increasing the number of hydrogen bond interactions, which is consistent with a gain in electrostatic energy. Our studies highlight the interaction with hydrophilic crystal planes on the microfibril and the acceptor role of the flexible oligomers in HB formation. In addition, we study by MD simulation the interaction between a protofibril and the cellulose I microfibril in solution. In this case, the main interaction consists of the formation of hydrogen bonds between hydrophilic faces, and those HBs involve donor groups in the protofibril.
Regularized Estimation of High-dimensional Covariance Matrices.

Many signal processing methods are fundamentally related to the estimation of covariance matrices. In cases where there are a large number of covariates the dimension of covariance matrices is much larger than the number of available data samples. This is especially true in applications where data acquisition is constrained by limited resources such as time, energy, storage and bandwidth. This dissertation attempts to develop necessary components for covariance estimation in the high-dimensional setting. The dissertation makes contributions in two main areas of covariance estimation: (1) high dimensional shrinkage regularized covariance estimation and (2) recursive online complexity regularized estimation with applications of anomaly detection, graph tracking, and compressive sensing. New shrinkage covariance estimation methods are proposed that significantly outperform previous approaches in terms of mean squared error. Two multivariate data scenarios are considered: (1) independently Gaussian distributed data; and (2) heavy tailed elliptically contoured data. For the former scenario we improve on the Ledoit-Wolf (LW) shrinkage estimator using the principle of Rao-Blackwell conditioning and iterative approximation of the clairvoyant estimator. In the latter scenario, we apply a variance normalizing transformation and propose an iterative robust LW shrinkage estimator that is distribution-free within the elliptical family. The proposed robustified estimator is implemented via fixed point iterations with provable convergence and unique limit. A recursive online covariance estimator is proposed for tracking changes in an underlying time-varying graphical model. Covariance estimation is decomposed into multiple decoupled adaptive regression problems. A recursive recursive group lasso is derived using a homotopy approach that generalizes online lasso methods to group sparse system identification. By reducing the memory of the objective function this leads to a group lasso regularized LMS that provably dominates standard LMS. Finally, we introduce a state-of-the-art sampling system, the Modulated Wideband Converter (MWC) which is based on recently developed analog compressive sensing theory. By inferring the block-sparse structures of the high-dimensional covariance matrix from a set of random projections, the MWC is capable of achieving sub-Nyquist sampling for multiband signals with arbitrary carrier frequency over a wide bandwidth.
Rolnictwo ekologiczne w Polsce jako źródło dóbr publicznych na obszarach szczególnie predestynowanych do jego rozwoju

Cel opracowania to ustalenie stanu rozwoju rolnictwa ekologicznego w Polsce, w tym na obszarach (w gminach) o trudnych i szczególnie trudnych warunkach do gospodarowania. Istotne jest tu również ustalenie cech środowiskowych i organizacyjnych rolnictwa z gmin o dużym nasyceniu produkcją ekologiczną. W tym celu wykorzystano dane publikowane przez Inspekcję Jakości Handlowej Artykułów Rolno-Spożywczych (IJHARS) za lata 2004–2020 oraz dane jednostkowe z gospodarstw, a także w ujęciu gmin otrzymane z Agencji Restrukturyzacji i Modernizacji Rolnictwa (ARiMR) i wygenerowane na podstawie wniosków gospodarstw rolnych ubiegających się o płatności ekologiczne w ramach Wspólnej Polityki Rolnej (WPR) za lata 2010–2021. W pierwszej części artykułu podkreślono rolę rolnictwa ekologicznego jako dostawcy dóbr publicznych w kontekście nowej ekonomii instytucjonalnej oraz podkreślono wpływ instytucji na trwałość, ciągłość i stabilność jego funkcjonowania. W drugiej części scharakteryzowano stan rozwoju rolnictwa ekologicznego w Polsce w latach 2004–2021. Natomiast w trzeciej przedstawiono sytuację w gminach szczególnie predestynowanych do rozwoju tego rodzaju rolnictwa w latach 2010–2021. W czwartej, ostatniej, wynikowej części wskazano zaś cechy środowiskowe i organizacyjne rolnictwa w 2021 r. w gminach o różnym nasyceniu produkcją ekologiczną wspartą w ramach WPR 2014–2020. Ustalono, że w latach 2010–2021 w gminach o trudnych i szczególnie trudnych warunkach gospodarowania ulokowano od 74,5 do 76,8% ogólnej powierzchni użytków rolnych (UR) z produkcją ekologiczną wspartą w ramach WPR. Ważną cechą gospodarstw ekologicznych z tych gmin była znacząca jak na przeciętne warunki krajowe ich średnia powierzchnia UR oraz często brak produkcji zwierzęcej lub jej prowadzenie poza zasadami rolnictwa ekologicznego.
Activated Carbon-Loaded Titanium Dioxide Nanoparticles and Their Photocatalytic and Antibacterial Investigations

Activated carbon doping TiO2 nanoparticles were synthesised by zapota leaf extract using the co-precipitation method. The bio-constituents of plant compounds were used in the reactions of stabilization and reductions. The carbon loading on the TiO2 nanoparticles was characterised by XRD, FTIR, UV-DRS, SEM with EDX, and TEM analysis. The loading of activated carbon onto the TiO2 nanoparticles decreased the crystallite size and optical bandgap, and their doping improved the surface structure of AC/TiO2 nanoparticles. Mesoporous/microporous instability was remodified from the activated carbon, which was visualised using SEM and TEM analysis, respectively. The photocatalytic dye degradation of Rh-B dye was degraded in TiO2 and AC/TiO2 nanoparticles under visible light irradiation. The degradation efficiencies of TiO2 and AC/TiO2 nanoparticles were 73% and 91%, respectively. The bacterial abilities of TiO2 and AC/TiO2 nanoparticles were examined by E. coli and S. aureus. The water reclamation efficiency and bactericidal effect of TiO2 and AC/TiO2 nanoparticles were examined via catalytic dye degradation and bacterial efficiency of activated carbon-doped titanium dioxide nanoparticles.
Hypospadias: A Comprehensive Review Including Its Embryology, Etiology and Surgical Techniques

Hypospadias is among the most prevalent urogenital malformations in male newborns. It is characterized by the displacement of the urethral meatus to the ventral side of the penis, an aberrant ventral curve of the penis referred to as "chordee," and an abnormally arranged foreskin with a "hood" found dorsally and lacking foreskin ventrally. Patients may have an extra genitourinary abnormality based on the area of the lesion. In around 70% of cases, the urethral meatus is positioned distally to the shaft, representing a milder form of the disease. The remaining 30% of cases are located proximally, are more complicated, and require further evaluation. Although the origin of hypospadias is mostly obscure, several suggestions exist about genetic susceptibility and hormonal factors. The objective of hypospadias restoration is to restore aesthetic and functional regularity, and surgery is currently advised at a young age, mostly between six and 18 months. At any age, hypospadias can be repaired with an equivalent risk of complications, functional outcomes, and aesthetic outcomes. However, the best age of treatment is still undetermined. Even though the long-term effects on appearance and sexual function are usually good, males may be less likely to make the first move after rectification. Also, people who have hypospadias treated are twice as likely to have problems with their lower urinary tract. These problems can last for years after the initial repair.
Optimization Method of High-Speed Train Composite Material Workshop Planning and Scheduling

With the continuous speed increase of high-speed trains, higher performance requirements are put forward for the braking technology and braking device. In order to improve productivity and overall competitiveness, manufacturing companies are actively researching new manufacturing technologies and production methods, and shop floor scheduling is one of the core components of this problem. This paper mainly studies the high-speed train composite workshop planning and scheduling optimization method. This experiment adopts B/S-based architecture mode. The prototype system is developed with Microsoft’s integrated development platform Visual Studio 2013, and Microsoft’s SQL Server 2008 is used as the background database management system. The experiment mainly uses the white box test method; the test content mainly includes module interface test, module local data structure test, and module boundary test. The interface parameters of each module are checked, and the boundary values of some functions are also analyzed and tested. According to the results, the planning management personnel revise the priority order again until all the molds meet the requirements of delivery and constraints. If the scheduled results do not meet the requirements, the methods such as those compressing the lead time, those urging the casting to be in place in advance, single process outsourcing, and overtime shall be considered. In this paper, a layered coding strategy is adopted. The first layer of coding represents the batch processing sequence. The second layer of coding determines to which process the corresponding batch belongs to. Each layer of coding is divided into different machine segments to represent the batch processing sequence on different machines. When the production process needs to switch orders, it can know which equipment parameters need to be adjusted in advance, which can effectively avoid the wrong operation caused by temporary adjustment of production parameters, reduce the order switching time, and improve the utilization rate of the production line. The data show that, compared with the artificial experience method and the priority rule method, the order production cycle after genetic optimization is reduced by 7.34% and 8.98%, respectively. The results show that the workshop scheduling optimization can help enterprises save stamping scheduling time, reduce production costs, and improve the rationality of scheduling.
Monolithic integration of GaInAsP/InP collimating GRIN lens with tapered waveguide active region

A GaInAsP/InP collimation GRIN lens monolithically integrated with a tapered waveguide laser was realized, and a circular beam divergence (/spl theta//sub p/=7/spl deg/, /spl theta//sub /spl perp//=7.5/spl deg/) was obtained with an output width of 15 /spl mu/m and a core thickness of the GRIN lens of about 8 /spl mu/m.
Modulus-based matrix splitting iteration methods with new splitting scheme for horizontal implicit complementarity problems

In this paper, we propose the horizontal implicit complementarity problems, which cover many complementarity problems that have wide applications. After reformulating the horizontal implicit complementarity problem as an implicit fixed-point equation, the modulus-based matrix splitting iteration methods are applied to solve the problems. Besides, this paper introduces a new kind of matrix splitting, called modulus-based Hermitian and skew-Hermitian matrix relaxation splitting. The corresponding modulus-based matrix splitting iteration method is shown superior to the nonsmooth Newton's method and the existing matrix splitting schemes by some numerical experiments. The convergence analysis is given and validates that our methods are more practical in applications.
The feeding behavior of dairy cattle under tropical heat stress conditions at smallholder urban farming

Abstract. Despal, Faresty C, Zahera R, Toharmat R. 2022. Title The feeding behavior of dairy cattle under tropical heat stress conditions at smallholder urban farming. Biodiversitas 23: 3771-3777. Dairy cattle (Bos taurus) feeding behavior influences feed intake and affect animal welfare and disease management. This study observed dairy cattle feeding behaviors under tropical smallholder urban farming environments. Thirty-six cattle at the Kebon Pedes area of Bogor City were observed during 12 h feeding (6 am-6 pm). These traits included eating, ruminating, resting duration, and frequencies, measured using a continuous method. Temperature, humidity, temperature-humidity index (THI), feed and nutrient intake, milk production, and components were measured. The results showed that the cattle were in mild to severe heat stress. The average cow daily eating, ruminating, and resting frequencies were 6.61, 7.61, and 22.33 times, while the average eating, ruminating, and resting durations per cow per 12 h were 129.64, 105.67, and 484.69 minutes, respectively. The dry matter intake was 11.17 kg with a roughage to concentrate ratio of 0.73:1. The protein, ash, ether extract, and crude fiber intake were 10.25, 6.62, 3.67, and 22.08% DM, respectively. The average milk productions and fat contents were 11.01 L/head/d and 5.44%. The results showed that eating and ruminating levels correlated positively with feed, and CF intakes while resting correlated negatively. It was concluded that the cattle were in heat-stressed (THI of >78) condition, reduced DM intake, and deficient in energy and CP, which led to lower milk production but higher milk components, specifically milk fat. It is recommended to improve forage quality that promotes eating and ruminating and reduces resting durations, improving milk production.
Nonlinear dynamic transfer functions for certain retinal neuronal systems

The applicability of the white-noise method to the identification of a nonlinear system is investigated. Subsequently, the method is applied to certain vertebrate retinal neuronal systems and nonlinear, dynamic transfer functions are derived which describe quantitatively the information transformations starting with the light-pattern stimulus and culminating in the ganglion response which constitutes the visually-derived input to the brain. The retina of the catfish, Ictalurus punctatus, is used for the experiments. The Wiener formulation of the white-noise theory is shown to be impractical and difficult to apply to a physical system. A different formulation based on crosscorrelation techniques is shown to be applicable to a wide range of physical systems provided certain considerations are taken into account. These considerations include the time-invariancy of the system, an optimum choice of the white-noise input bandwidth, nonlinearities that allow a representation in terms of a small number of characterizing kernels, the memory of the system and the temporal length of the characterizing experiment. Error analysis of the kernel estimates is made taking into account various sources of error such as noise at the input and output, bandwidth of white-noise input and the truncation of the gaussian by the apparatus. Nonlinear transfer functions are obtained, as sets of kernels, for several neuronal systems: Light → Receptors, Light → Horizontal, Horizontal → Ganglion, Light → Ganglion and Light → ERG. The derived models can predict, with reasonable accuracy, the system response to any input. Comparison of model and physical system performance showed close agreement for a great number of tests, the most stringent of which is comparison of their responses to a white-noise input. Other tests include step and sine responses and power spectra. Many functional traits are revealed by these models. Some are: (a) the receptor and horizontal cell systems are nearly linear (small signal) with certain "small" nonlinearities, and become faster (latency-wise and frequency-response-wise) at higher intensity levels, (b) all ganglion systems are nonlinear (half-wave rectification), (c) the receptive field center to ganglion system is slower (latency-wise and frequency-response-wise) than the periphery to ganglion system, (d) the lateral (eccentric) ganglion systems are just as fast (latency and frequency response) as the concentric ones, (e) (bipolar response) = (input from receptors) - (input from horizontal cell), (f) receptive field center and periphery exert an antagonistic influence on the ganglion response, (g) implications about the origin of ERG, and many others. An analytical solution is obtained for the spatial distribution of potential in the S-space, which fits very well experimental data. Different synaptic mechanisms of excitation for the external and internal horizontal cells are implied.