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The study aimed to investigate the association between incidental exposure on social media political contents and political participation. Social networking sites play an important role in political participation. While existing literature extensively explores the impact of social media on political engagement in Pakistan, there is a notable gap in understanding the influence of incidental exposure on social media on political participation. In this Research quantitative research design was used. A survey method was used to collect data from the chosen sample of 300 respondents. The data was collected online through purposive sampling. The findings of the study reveal that people mostly use YouTube, Facebook and Instagram. Furthermore, Incidental exposure on social media leads to political information acquiring and also leads people to participate in political activities. This study underscores their capacity to engage individuals previously disinterested in politics. These platforms emerge as catalysts for political involvement, particularly in the context of Pakistan

Discover the profound impact of tourist engagement, satisfaction, and destination loyalty on the thriving tourism industry. The accompanying diagram provides operational details of STT and MTEs and how they can influence these crucial factors. Sample size selected for this research was 150 respondents, data was collected through online questionnaire. System named for data analysis was structural equation model (SEM) done through Smart Pls3.Results of this study indicated that STT and MTE have significant and positive affect on Destination Image and Tourism Engagement, Tourism Satisfaction and Tourism Destination Loyalty. This recollection offers empirical evidence to demonstrate the contribution of smart tourism technology and memorable trip experiences in raising tourist happiness and loyalty to travel locations. This study's primary objective is to better understand how memorable travel experiences and smart tourism technologies might increase traveller happiness and loyalty to a location in a developing nation.

Emotion recognition (ER) technology is an integral part for developing innovative applications such as drowsiness detection and health monitoring that plays a pivotal role in contemporary society. This study delves into ER using electroencephalography (EEG), within immersive virtual reality (VR) environments. There are four main stages in our proposed methodology including data acquisition, pre-processing, feature extraction, and emotion classification. Acknowledging the limitations of existing 2D datasets, we introduce a groundbreaking 3D VR dataset to elevate the precision of emotion elicitation. Leveraging the Interaxon Muse headband for EEG recording and Oculus Quest 2 for VR stimuli, we meticulously recorded data from 40 participants, prioritizing subjects without reported mental illnesses. Pre-processing entails rigorous cleaning, uniform truncation, and the application of a Savitzky-Golay filter to the EEG data. Feature extraction encompasses a comprehensive analysis of metrics such as power spectral density, correlation, rational and divisional asymmetry, and power spectrum. To ensure the robustness of our model, we employed a 10-fold cross-validation, revealing an average validation accuracy of 85.54\%, with a noteworthy maximum accuracy of 90.20\% in the best fold. Subsequently, the trained model demonstrated a commendable test accuracy of 82.03\%, promising favorable outcomes.

Penelitian ini memiliki tujuan untuk mengetahui Pengaruh Beban Kerja, Supervisor Passive Communication Style dan Kebosanan Kerja terhadap Perilaku Cyberloafing Pada Karyawan Obyek Wisata di Desa Ponggok Kabupaten Klaten. Sampel pada penelitian ini sebanyak 39 responden dengan teknik penetapan sampel yaitu menggunakan sampel jenuh. Analisis penelitian ini menggunakan aplikasi analisis SPSS for Windows .25 dengan hasil Supervisor Passive Communication Style dan Kebosanan Kerja berpengaruh positif dan signifikan terhadap Perilaku Cyberloafing. Sedangkan Beban Kerja Memiliki pengaruh positif namun tidak signifikan terhadap perilaku Cyberloafing. Sebesar 0,319 nilai R2 yang berarti sebesar 31,9% Beban Kerja, Supervisor Passive Communication Style dan Kebosanan kerja mampu menjelaskan dan berkontribusi terhadap Perilaku cyberloafing. Hasil penelitian juga menunjukan bahwa secara simultan Beban Kerja, Supervisor Passive Communication Style dan Kebosanan Kerja berpengaruh terhadap Perilaku Cyberloafing.

This research aims to examine the influence of airline service quality and safety perception on brand loyalty. Furthermore, passenger anticipation employed as mediating variable for in-depth research. In order to achieve the goal of this research, data were gathered from a sample of 384 customers who use airline services. The convenient sampling approach was employed for data collection. The findings from the structural equation modelling revealed that brand loyalty has a considerable impact on the evaluation of airline services quality and safety perception. Additionally, the study identified the important mediating function of passenger expectation. This research assists the authorities and managers in the airline sector in formulating effective and efficient strategies to meet customer demand and cultivate brand loyalty.

The concept of mindfulness is enhanced now a days over the world. It’s significant positive effects on person’s health not only valuable solution for people facing problems, like depression and anxiety but also makes a desire for society to enhance individual’s quality in many phases of life. To fulfilment of the objective of this study convenient sampling techniques is used to collected the data from the concerned respondents. To access and measurement of the model convergent validity, and discriminant validity is used and to measure the hypothesis path analysis is used. The results of it create positive travel experience which lead to higher customer satisfaction. Moreover, due to significant effects on travel anxiety and higher satisfaction with the service, customer more attract and loyal to airline. In order to take the large range of passengers the study was conducted as a survey which was taken at the international departure gate of Allama Iqbal International Airport Lahore. Data was collected and compiled from passengers who were frequent flyers in different airlines. The study also focusses on the relationship between mindfulness and travel experience with airlines of their choice. The Service quality acting as a moderator between mindfulness mediation and its effects on travel experience to improve customer satisfaction. Mindfulness has a limited attention in business perspective but this study investigates mindfulness as a business model. Mindfulness service create a positive effect to satisfied airline passengers during different phases of flight to enhance positive travel experience. There for, the possible impact of flight anxiety is investigated for better understanding of mindfulness meditation effect with the help of airline service quality.

This study aimed to investigate factors that influence consumer purchase decision in Malaysian single-priced stores, which are also popularly known as RM2 stores. The study considered six factors that could affect purchase decision in RM2 stores, namely, corporate social responsibility (CSR), sales promotion, social media marketing, perceived value, store environment, and service quality. A quantitative research methodology was employed through a survey using questionnaires administered to 550 customers of the RM2 stores. The data gathered was then analysed through SPSS method to calculate the validity and reliability of the measurement items and to verify the impact of six independent variables on purchase decision. Based on the results, CSR, sales promotion, perceived value, and service quality had significant positive effects on purchase decision, while social media marketing and store environment proved to be insignificant. Among all the factors, perceived value had the strongest influence on purchase decision. The findings are important in assisting RM2 store retailers to understand the factors that affect consumer purchase decision which could help in improving sales. This study complements the current literature and addresses existing research gaps with regard to the linkages between factors that influence consumer purchase decision in Malaysian single-priced stores.

An accurate classification of upper limb movements using electroencephalography (EEG) signals is gaining significant importance in recent years due to the prevalence of brain-computer interfaces. The upper limbs in the human body are crucial since different skeletal segments combine to make a range of motion that helps us in our trivial daily tasks. Decoding EEG-based upper limb movements can be of great help to people with spinal cord injury (SCI) or other neuro-muscular diseases such as amyotrophic lateral sclerosis (ALS), primary lateral sclerosis, and periodic paralysis. This can manifest in a loss of sensory and motor function, which could make a person reliant on others to provide care in day-to-day activities. We can detect and classify upper limb movement activities, whether they be executed or imagined using an EEG-based brain-computer interface (BCI). Toward this goal, we focus our attention on decoding movement execution (ME) of the upper limb in this study. For this purpose, we utilize a publicly available EEG dataset that contains EEG signal recordings from fifteen subjects acquired using a 61-channel EEG device. We propose a method to classify four ME classes for different subjects using spectrograms of the EEG data through pre-trained deep learning (DL) models. Our proposed method of using EEG spectrograms for the classification of ME has shown significant results, where the highest average classification accuracy (for four ME classes) obtained is 87.36%, with one subject achieving the best classification accuracy of 97.03%.

The loss of limb motion arising from damage to the spinal cord is a disability that could effect people while performing their day-to-day activities. The restoration of limb movement would enable people with spinal cord injury to interact with their environment more naturally and this is where a brain-computer interface (BCI) system could be beneficial. The detection of limb movement imagination (MI) could be significant for such a BCI, where the detected MI can guide the computer system. Using MI detection through electroencephalography (EEG), we can recognize the imagination of movement in a user and translate this into a physical movement. In this paper, we utilize pre-trained deep learning (DL) algorithms for the classification of imagined upper limb movements. We use a publicly available EEG dataset with data representing seven classes of limb movements. We compute the spectrograms of the time series EEG signal and use them as an input to the DL model for MI classification. Our novel approach for the classification of upper limb movements using pre-trained DL algorithms and spectrograms has achieved significantly improved results for seven movement classes. When compared with the recently proposed state-of-the-art methods, our algorithm achieved a significant average accuracy of 84.9% for classifying seven movements., Comment: Submitted to ISBI 2023

This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using data from wearable and non-wearable sensors. In particular, wearable sensor-based methods commonly use data from electroencephalography, electrocardiogram, galvanic skin response, electromyography, electrodermal activity, heart rate, heart rate variability, and photoplethysmography both individually and in multimodal fusion strategies. Whereas, methods based on non-wearable sensors include strategies such as analyzing pupil dilation and speech, smartphone data, eye movement, body posture, and thermal imaging. Whenever a stressful situation is encountered by an individual, physiological, physical, or behavioral change is induced which help in coping with the challenge at hand. A wide range of studies has attempted to establish a relationship between these stressful situations and the response of human beings by using different kinds of psychological, physiological, physical, and behavioral measures. Inspired by the lack of availability of a definitive verdict about the relationship of human stress with these different kinds of markers, a detailed survey about human stress detection methods is conducted in this paper. In particular, we explore how stress detection methods can benefit from artificial intelligence utilizing relevant data from various sources. This review will prove to be a reference document that would provide guidelines for future research enabling effective detection of human stress conditions., Comment: Submitted in MDPI Sensors

Mental stress is a largely prevalent condition known to affect many people and could be a serious health concern. The quality of human life can be significantly improved if mental health is properly managed. Towards this, we propose a robust method for perceived stress classification, which is based on using multimodal data, acquired from forty subjects, including three (electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG)) physiological modalities. The data is acquired for three minutes duration in an open eyes condition. A perceived stress scale (PSS) questionnaire is used to record the stress of participants, which is then used to assign stress labels (two- and three classes). Time (four from GSR and PPG signals) and frequency (four from EEG signal) domain features are extracted. Among EEG based features, using a frequency band selection algorithm for selecting the optimum EEG frequency subband, the theta band was selected. Further, a wrapper-based method is used for optimal feature selection. Human stress level classification is performed using three different classifiers, which are fed with a fusion of the selected set of features from three modalities. A significant accuracy (95% for two classes, and 77.5% for three classes) was achieved using the multilayer perceptron classifier., Comment: Submitted in IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS

In modeling reliability data, the exponential distribution is commonly used due to its simplicity. For estimating the parameter of the exponential distribution, classical estimators including maximum likelihood estimator represent the most commonly used method and are well known to be efficient. However, the maximum likelihood estimator is highly sensitive in the presence of contamination or outliers. In this study, a robust and efficient estimator of the exponential distribution parameter was proposed based on the probability integral transform statistic. To examine the robustness of this new estimator, asymptotic variance, breakdown point, and gross error sensitivity were derived. This new estimator offers reasonable protection against outliers besides being simple to compute. Furthermore, a simulation study was conducted to compare the performance of this new estimator with the maximum likelihood estimator, weighted likelihood estimator, and M-scale estimator in the presence of outliers. Finally, a statistical analysis of three reliability data sets was conducted to demonstrate the performance of the proposed estimator.

Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory. An early detection can prevent the patient from further damage of the brain cells and hence avoid permanent memory loss. In past few years, various automatic tools and techniques have been proposed for diagnosis of AD. Several methods focus on fast, accurate and early detection of the disease to minimize the loss to patients mental health. Although machine learning and deep learning techniques have significantly improved medical imaging systems for AD by providing diagnostic performance close to human level. But the main problem faced during multi-class classification is the presence of highly correlated features in the brain structure. In this paper, we have proposed a smart and accurate way of diagnosing AD based on a two-dimensional deep convolutional neural network (2D-DCNN) using imbalanced three-dimensional MRI dataset. Experimental results on Alzheimer Disease Neuroimaging Initiative magnetic resonance imaging (MRI) dataset confirms that the proposed 2D-DCNN model is superior in terms of accuracy, efficiency, and robustness. The model classifies MRI into three categories: AD, mild cognitive impairment, and normal control: and has achieved 99.89% classification accuracy with imbalanced classes. The proposed model exhibits noticeable improvement in accuracy as compared to the state-fo-the-art methods.

This research addresses the TikTok addictions that are affecting those people who are inspired by TikTok videos and people making those videos. TikTok nowadays creating different kinds of conflicts among both of the genders. I.e. inferiority complex and superiority complex and beauty complex. The philosophy of the study was positivism. It was a deductive approach and the Connivance sampling technique is used in this study. It was quantitative research and data has been collected through surveys. .The adapted questionnaire was used to collect data from 190 university students. Statistical analysis through SPSS was performed to check the hypothesis. This research is highly beneficial to create the awareness among people that they are addicted to something very un-useful so their friends and family taking serious note on it and help them to drive them out of TikTok addiction and psychological make them ensure that it is just app use for entertainment and seek and show your talent.

In Marketing, making a choice highly depends upon the reviews and ratings. A purchase decision can easily get affected by the rating of five stars from several reviewers. A trend regarding fashion goods is increasing in Pakistan. The present research was conducted on bandwagon and its effect on the consumption of fashion goods in Sialkot. The purpose was to know how others’ perceptions influence youth in Sialkot, Pakistan. Quantitative research nature was used. While the self-administered questionnaire survey was conducted for the collection of data and distributed among sample size of 200 youth in Sialkot Pakistan. Correlation and Regression analyses were carried out in SPSS for testing hypotheses. Results indicate that bandwagon has positive and significant influence on the consumption of fashion goods. Findings will be beneficial to the fashion good companies to help them in making strategies in Sialkot Pakistan.

Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing.The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities.In this study, long-term stress is classified using baseline EEGsignal recordings. The labelling for the stress and control groupsis performed using two methods (i) the perceived stress scalescore and (ii) expert evaluation. The frequency domain featuresare extracted from five-channel EEG recordings in addition tothe frontal and temporal alpha and beta asymmetries. The alphaasymmetry is computed from four channels and used as a feature.Feature selection is also performed using a t-test to identifystatistically significant features for both stress and control groups.We found that support vector machine is best suited to classifylong-term human stress when used with alpha asymmetry asa feature. It is observed that expert evaluation based labellingmethod has improved the classification accuracy up to 85.20%.Based on these results, it is concluded that alpha asymmetry maybe used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation., Comment: Submitted to IEEE JBHI

Tactile enhanced multimedia is generated by synchronizing traditional multimedia clips, to generate hot and cold air effect, with an electric heater and a fan. This objective is to give viewers a more realistic and immersing feel of the multimedia content. The response to this enhanced multimedia content (mulsemedia) is evaluated in terms of the appreciation/emotion by using human brain signals. We observe and record electroencephalography (EEG) data using a commercially available four channel MUSE headband. A total of 21 participants voluntarily participated in this study for EEG recordings. We extract frequency domain features from five different bands of each EEG channel. Four emotions namely: happy, relaxed, sad, and angry are classified using a support vector machine in response to the tactile enhanced multimedia. An increased accuracy of 76:19% is achieved when compared to 63:41% by using the time domain features. Our results show that the selected frequency domain features could be better suited for emotion classification in mulsemedia studies., Comment: Accepted in IEEE EMBC 2019

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, feature extraction, and perceived human stress classification. The physiological data of $28$ participants are acquired in an open eye condition for a duration of three minutes. Four different features are extracted in time domain from EEG, GSR and PPG signals and classification is performed using multiple classifiers including support vector machine, the Naive Bayes, and multi-layer perceptron (MLP). The best classification accuracy of 75% is achieved by using MLP classifier. Our experimental results have shown that our proposed scheme outperforms existing perceived stress classification methods, where no stress inducers are used., Comment: Accepted for publication in EMBC 2019

Dengue Fever is a hot issue under discussion around the world, specifically in tropical and sub-tropical countries. It is an epidemiological disease which covers the massive geographical area within no time from person to person. It is difficult to take over dengue fever with limited technical resources; administrative authorities should be equipped with technical resources and infrastructure. Geographical Information System (GIS) is well-developed technology to express all sort of information in thematically maps along with the reflection of hotspot areas. In this research paper, we used Arc View as GIS tool to exemplify Male Patient data of Dengue outbreak in Punjab 2011.In Punjab14500 male patients were registered in thirty-seven epidemiological weeks during this outbreak. This research analysis discovered significant results of geographical pattern and intensity of dengue fever among males belong to all districts of Punjab. These results can assist to Health departments and decision making authorities to make plans to avoid from future outbreak and emphasis on hotspot areas with preventive measures so that human and financial losses can control up to a minimum.&nbsp

Kota Tangerang adalah kota yang dahulu memiliki julukan kota industri, kini sudah berubah dengan kota yang liveable, invetastable, visitable, dan e-city. Tentunya pemerintah terus berupaya menanggulangi setiap permasalahan yang ada di Kota Tangerang. Termasuk dari sekian banyaknya adalah permasalahan sampah dan banjir yang setiap orang berupaya untuk mengurangi tingkat kerawanan pada tiap daerahnya masing-masing. Penanggulangan yang cukup mudah adanya dengan pemilahan sampah organik dan anorganik. Dalam penyampaian kepada masyarakat, Pemerintahan Kota Tangerang menerapkan dengan berbagai poster dan selebaran. Berdasarkan adanya fenomena tersebut melalui observasi, studi pustaka, dan wawancara, d analisa jika media yang diterapkan dirasa belum cukup efektif dalam penyampaiannya. Oleh karena itu, Maka dibuatlah Video sosialisasi berupa motion graphic mengenai pimilahan sampah yang menarik dalam mensosialisasikan kepada masyarakat yang akan disosialisasikan ke berbagai media sosial dan event-event. Video sosialisasi ini di rancang sesuai dengan permintaan Humas Pemerintahan Kota Tangerang, manfaat dari video sosialisasi ini agar menjadi sarana edukasi masyarakat Kota Tangerang tentang pemilihan sampah organik dan non organik serta pemanfaatannya. Dengan Video sosialisasi mengenai pimilahan sampah meningkatkan kesadaran masyarakat mengenai pentingnya pemilahan sampah sebesar 0.3% dari penduduk Kota Tangerang dilihat dari banyaknya view pada sosial media dan respon positif masyarakat pada setiap acara event penanggulangan sampah. Kata kunci: Sosialisasi, Video, Kebersihan sampah

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an effective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Detection of brain tumor using a segmentation based approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. The automation of brain tumor segmentation remains a challenging problem mainly due to significant variations in its structure. An automated brain tumor segmentation algorithm using deep convolutional neural network (DCNN) is presented in this paper. A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images. Recent developments in deep neural networks such as drop-out, batch normalization, non-linear activation and inception module are used to build a new ILinear nexus architecture. The module overcomes the over-fitting problem arising due to scarcity of data using drop-out regularizer. Images are normalized and bias field corrected in the pre-processing step and then extracted patches are passed through a DCNN, which assigns an output label to the central pixel of each patch. Morphological operators are used for post-processing to remove small false positives around the edges. A two-phase weighted training method is introduced and evaluated using BRATS 2013 and BRATS 2015 datasets, where it improves the performance parameters of state-of-the-art techniques under similar settings., Comment: Submitted to Neurocomputing

With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the semantic gap that exists between the low level visual information captured by imaging devices and high level semantic information perceived by human. The efficacy of such systems is more crucial in terms of feature representations that can characterize the high-level information completely. In this paper, we propose a framework of deep learning for CBMIR system by using deep Convolutional Neural Network (CNN) that is trained for classification of medical images. An intermodal dataset that contains twenty four classes and five modalities is used to train the network. The learned features and the classification results are used to retrieve medical images. For retrieval, best results are achieved when class based predictions are used. An average classification accuracy of 99.77% and a mean average precision of 0.69 is achieved for retrieval task. The proposed method is best suited to retrieve multimodal medical images for different body organs., Comment: Submitted to Neurocomputing

Every year Muslims worldwide fast during the Islamic month of Ramadan. In 2010 alone, more than 1.2 billion Muslims globally, and 155 million Muslims in Indonesia, were potentially exposed to their mother's fasting. My first paper uses longitudinal data (the Indonesian Family Life Survey) to study the persistent effects of in utero exposure to Ramadan over the life cycle. The exposed children have lower birth weights, they study fewer hours during elementary school, do more child labor, score 7.8 percent lower on cognitive tests and 5.9 percent lower on math test scores. As adults, the exposed children work 4.5 percent fewer hours and are more likely to be self-employed.In my second chapter, which is joint work with Jorge Aguero, we look at the role which conflict plays in perpetuating inequalities. The identification of the effect of wars on human capital tends to focus on the population of school age children at the time of the conflict. Our paper introduces a methodology to estimate the effect of war on the stock of human capital by examining the changes in the presence of educated people after the Rwanda genocide. We find that the genocide reduced the stock of human capital in Rwanda severely. The before-and-after results show that highly educated individuals (i.e., those with primary education or more) are ``missing'' at a rate that is 19.4\% higher than the less educated. Moreover, Rwanda's average years of schooling is lower by 0.37 years. When comparisons with Uganda are made, these estimates more than double suggesting that, if anything, the previous finding were biased downwards. Interestingly, when the cross-sectional variation within Rwanda variation in intensity of genocide is exploited there is no evidence of statistically significant differences. This suggests that the losses in the stock of human capital due to the Rwandan genocide were aggregate in nature. In my third chapter, I review the most recent literature from developing countries on parental

The objective of study is to examine the impact of employee commitment on organizational performance in education sector. Data was collected through questionnaire and sample size was 100. Statistical tests Correlation and regression were used to analysis the data. The arithmetical test proves that employee commitment has positive effect on organizational performance. There are few limitations in this study. There is only one independent variable. The sample size was small. Convenience sampling technique was used for collection of data demographical scale was small.

There has been significant growth in Islamic banking sector over the last two decades and this market has become very competitive in many Islamic countries. Islamic banking products are regulated by law of Islam and regulatory authority in that particular country. Our main objective is that given the standards of Islamic financial systems, are the Islamic banks compliance with those principles. Some specific banks will be selected and so shall be the scope of standards. The solutions implemented by Islamic banks will be analyzed critically against these standards and suggestions for improvement will be presented. The secondary objective is to compare specific products offered by Islamic banks with corresponding products of conventional banks (competitors working in same country and thus regulated by same government body). Then we will present critical analysis of both products in terms of monetary value and conditions imposed by each bank.