Our paper draws attention to a neglected channel of corruption—the bribe payments by state-owned enterprises. This is an important phenomenon as bribe payments by SOEs fruitlessly waste national resources, compromising public welfare and national prosperity. Using a large dataset of 30,249 firms from 50 countries, we show that, in general, SOEs are less likely to pay bribes for achieving organizational objectives owing to their political connectivity. However, in deteriorated institutional environments, SOEs may be subjected to potential managerial rent-seeking behaviors, which (...) disproportionately increase SOE bribe propensity relative to privately owned enterprises. Specifically, our findings highlight the importance of fostering democracy and rule of law, reducing prevalence of corruption and shortening power distance in reducing the incidence of SOE bribery. (shrink)

This study examines the degree of state ownership on corporate bribery. Integrating the theories of state ownership and corporate corruption, we propose that state ownership influences bribery propensity and bribery intensity in different ways; it lowers a firm’s tendency to pay bribes but increases the relative amount of bribery payment. Building on the control rights/bargaining hypotheses, we demonstrate that state ownership shields firms from bribery demands by reducing administrative hurdles that include bureaucratic requirements of obtaining licenses or settling taxes in (...) business operations. However, state ownership elevates the level of bribes by weakening their capital mobility. Using a sample of 23,018 firms from 54 countries covering 2006 to 2013, we find evidence to support our hypotheses. This article contributes to corruption research by drawing attention to an important channel of corruption and by highlighting the importance of considering not only the propensity but also the intensity of bribe payments. (shrink)

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram and functional near-infrared spectroscopy are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance (...) brain–computer interface performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go. (shrink)

The process of transition in rural economy has been observed with the deepest and fastest structural transformation from farm to non-farm sector in Pakistan. The structure and composition of labour market is also undergoing such considerable changes due to increasing share of the non-farm sector. Given this insight, the study assesses the change in rural economy particularly relying on population and migration trend and employment transition. The descriptive analysis of the study reveals that thereis declining trend in migration from rural (...) to urban areas with a considerable transition in employment from farm to non-farm sectors. The factor analysis points out the fact that push incentives of the farm sector are stronger than the pull incentives of the nonfarm sector for the movement of labour away from the farm sector in Pakistan. Hence, an immense focus is required for the farm sector in the lieu of employment opportunity while creating more diversification in non-farm activities in rural areas. Thus, there is also a dire need to enhance occupational activities such as agro-based processing, new farming practices, livestock, small scale manufacturing, retail trade, construction etc. to enhance the earnings of labour in the rural economy. (shrink)

This abstract presents a novel brain-computer interface (BCI) framework to control a prosthetic leg, for the rehabilitation of patients suffering from locomotive disorders, using functional near-infrared spectroscopy (fNIRS). fNIRS signals corresponding to walking intention and rest are used to initiate and stop the gait cycle and a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control torques of hip and knee joints for minimization of position error. The brain signals of walking intention and rest tasks (...) are acquired from primary motor cortex in the left hemisphere for nine subjects. After acquiring brain signals, in order to remove motion artifacts and physiological noises, the performance of six different filters i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass and finite impulse response, is evaluated. Afterwards, six different features are extracted from oxygenated hemoglobin signals and their different combinations were used for classification. The classification performance of five different classifiers i.e. artificial neural network, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes and support vector machine (SVM) is used. The classification accuracies obtained from SVM using hrf filter are significantly higher (p < 0.005) than the other combinations of classifier and filters. These accuracies are 77.5%, 72.5%, 68.3%, 74.2%, 73.3%, 80.8%, 65%, 76.7%, and 86.7% for all nine subjects. The control commands generated using classifier initiate and stop the gait cycle of the prosthetic leg whose knee and hip torques are controlled using PD-CTC to minimize position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower limb amputation of paralyzed patients. (shrink)

Sufism : various dimensions -- Spiritual traditions in India -- Rabindranath Tagore and spiritualism -- Music, poetry, and spiritual traditions -- Educational perspectives -- Epitomes of Indian culture.

This paper explores the relationship between export growth and economic growth in the case of Pakistan by employing time series data for the period 1971- 2013. This study has incorporated variables like GDP exports, imports and Foreign Direct Investment. We have applied ARDL to co-integration and Error Correction Model. The study provides the evidence of stationary time series variables, the existence of the long - run relationship between them, and the result of ECM revealed short rum equilibrium adjustment. Pakistan has (...) many options for enhancing the export of the country. There is a dire need to minimize trade barriers and restrictions such as import and export quotas. Government of Pakistan had introduced Structural Reforms for liberalization, privatization and de-regulation which will actually shifted the trend of trade at a significant level in the end of 1980s. Low levels of interest rate can help exportable industries in which investments are needed to promote and enhance the exports. Stable exchange rate is the first and the best policy option for increasing the export and managing the imports. There is a cause and effect relationship between exchange rate and FDI. Pakistan has to immediately find the policies and processes that support logistics and facilitates trade. (shrink)

A passive brain–computer interface based upon functional near-infrared spectroscopy brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation state was continuously measured. Vector phase analysis (...) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features along with six VPA features were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects’ data. Trajectory slopes of CORE vector magnitude and angle: m and m are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification. (shrink)

The concept of noman in Hagen encompasses a local theory of consciousness, agency, and morality. Interview materials are given to illustrate notions of how the noman works. The Hageners recognize a kind of duality between mind and body but no fundamental split between them. Their theory of consciousness is also a theory of morality and ethics, as well as a recognition of creative agency in life. Key words: Mount Hagen, person, gender, body/mind.