This article investigates the nexus between bank-specific risks and the financial stability of the banks for a panel data set of 15 scheduled banks in Pakistan over a 12-year period from 2009 to 2020. Using the fixed-effect model, the study result shows that bank-specific risks, i.e., credit risk and liquidity risk are detrimental to bank stability, whereas funding risk has no significant impact on bank stability. Besides these, bank size has also a negative impact on bank stability, whereas the return (...) on assets revealed a positive influence. To ensure stability, bank management should establish policies that confirm secure loan granting and timely reimbursement from customers to minimize the credit risk. Besides this, management should keenly observe the liquidity position and should also effectively mobilize the customer deposits to attain financial stability. (shrink)

Qism 1. al-Turāth al-falsafī al-Yūnānī ḥattá Aflāṭūn.

With the increasing growth of online shopping, businesses are intertwining to establish new shopping antecedents. Customer experience has steadily become the most important source of retailers’ long-term competitive advantage via difference. To preserve long-term and sustained consumer loyalty, retailers must continually improve the customer experiences. This study presents a framework for online retailing in a digital environment called the Online Customer Experience-Engagement Context model in the presence of value co-creation. Data was gathered from 189 people who purchased products online. For (...) additional analysis, testing the hypothesis, and model construction, SPSS 26 and Smart-PLS were used. The data was then evaluated further using descriptive statistics, path analysis, measurement, and structural model. The findings show that the online customer experience substantially influenced customer engagement, which improved customer loyalty. The VCC strengthened the link between online customer experience and customer engagement. It suggests that including consumers in co-creating a delightful online customer experience from time to time may be a valuable strategy for online retailers to increase customer engagement and loyalty. To create an overarching outcome, information integration theory, multi-attribute utility theory, and the attitude-behavior-context theory theories are converged to explain the proposed model in the study. (shrink)

Accurate and reliable prediction of Perfobond Rib Shear Strength Connector is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression, artificial neural network, and M5 tree model. In the (...) second scenario, a comparable AI model hybridized with genetic algorithm as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient and lowest value root mean square error. However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model. (shrink)

Objective To find whether Western guidelines on breaking bad news in a nonemergency department are appropriate for an emergency department of a non-Western country; according to patients’ preferences. Method We designed a 19 items questionnaire of Likert-type scale and interviewed 156 patients in the emergency department of a referral hospital in Iran. Results The patients’ preferences in 9 out of 19 statements were similar to the guidelines. “Using the maternal language” received the strongest agreement. The strongest disagreement was on “encouraging (...) the patients to talk after receiving bad news.” The summative scores of subsection indicated strong agreement for cultural issues, followed by communication skills, breaking bad news session, and privacy. Conclusion The patients’ preferences were not completely consistent with the guidelines. We could not determine if it was a situational or cultural issue. However, it is reasonable to design a new guideline for breaking bad news, considering these factors. (shrink)

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT (...) systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%. (shrink)

On linguistic studies of Malay language; collection of articles.

Purpose Human genomic research demands very large pools of data to generate meaningful inference. Yet, the sharing of one’s genetic data for research is a voluntary act. The collection of data sufficient to fuel rapid advancement is contingent on individuals’ willingness to share. Privacy risks associated with sharing this unique and intensely personal data are significant. Genetic data are an unambiguous identifier. Public linkage of donor to their genetic data could reveal predisposition to diseases, behaviors, paternity, heredity, intelligence, etc. The (...) purpose of this paper is to understand individuals’ willingness to volunteer their private information in this high-risk/high-reward context. Design/methodology/approach The authors collect survey data from 273 respondents and use structural equation modeling techniques to analyze responses. Findings The authors find statistical support for our theorization. They find that while heightened awareness of the benefits and risks of sharing correlates with increased privacy concerns, the net impact is an increase in intention to share. Social implications The findings suggest that prescriptive awareness might be an effective tool with which policy-makers can gain the sufficient voluntary participation from individuals necessary to drive large-scale medical research. Originality/value This study contributes a theoretically and empirically informed model which demonstrates the impact of awareness and privacy concern on individuals’ willingness to share their genetic data for large-scale HGR. It helps inform a rising class of data sufficiency problems related to large-scale medical research. (shrink)

Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence (...) of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system. (shrink)

The aim of present paper is to introduce the concept of ‘expressive perception’ in Ernst Cassirer’s philosophical mythology. Having Cited Dorothy Emmet’s methodological objection, the author, by recalling Kantian aspect of Cassirer’s thought and referring to the concept of ‘expressive perception’, would make an attempt to reply on his part: according to Cassirer, this level of perceptive experience is the origin of the mythical form of thinking as a whole and, at the same time, is the original and irreducible altogether. (...) Thus, taking ‘mythical thought’ as an independent form of thinking, beside the ‘critical-empirical’ form of thinking, is, for one thing, completely cogent; for another, due to its dependency upon expressive form of perception, mythical thought would never be quite omitted, rather, keep living latently beneath the skin of mental life. In addition, author explains how much Cassirer owes, regarding to the concept of ‘expressive form of perception’, to Max Scheler. Avoiding to elaborate on Scheler’s detailed argument, Cassirer relies strongly on the consequences Scheler draws from his considerations in his work ‘Wesen und Formen der Sympathie’. Restating Scheler’s argument briefly should help in understanding Cassirer’s idea. (shrink)

BackgroundNurses provide the majority of health-care services and face numerous health challenges during an epidemic. During the COVID-19 epidemic, nurses are subjected to physical, mental, and social disorders that impair their quality of life and hardiness. Therefore, it is important to be aware of the situation of nurses. The current study aimed to compare the compassion satisfaction, compassion fatigue and hardiness among nurses before and during the COVID-19 outbreak.Materials and MethodsThis cross-sectional study included 508 clinical nurses from one public hospital (...) in southern Iran. The subjects were recruited using census sampling methods in 2019–2020. Sampling was performed before and during the COVID-19 with a 1-year interval. Although, the study setting was the same before and during the COVID-19, questionnaires were completed by different nurses before and during the COVID-19. Demographic questionnaire, professional quality of life questionnaire and Occupational Hardiness Questionnaire were used to collect data.ResultsThe scores of compassion satisfaction, compassion fatigue and hardiness did not differ significantly during the COVID-19 compared with before the COVID-19. Before COVID-19, hardiness and work experience predicted 11% of the variance of compassion satisfaction, whereas during COVID-19, hardiness and gender predicted 26% of the variance of compassion satisfaction. Before COVID-19, hardiness and work experience predicted 3% of the variance of compassion fatigue, whereas during COVID-19, hardiness, type of employment and gender predicted 6% of the variance of compassion fatigue.ConclusionThe current study found that compassion satisfaction, compassion fatigue and hardiness did not change during the COVID-19 outbreak compared with before the COVID-19 outbreak. However, during the COVID-19, the hardiness was a significant predictor of compassion satisfaction and compassion fatigue. The study results showed that it was possible to increase the compassion satisfaction and reduce the compassion fatigue by strengthening the hardiness of nurses. However, these results need to be considered in future studies, especially in crises such as COVID-19 disease. (shrink)

Logical thinking; criticism and interpretation; Arab philosophers.

jild-i 1. Az ʻAbd al-Ḥamīd Kātib (59 Sh.) tā Abū al-Ḥasan Masʻūdī (336 Sh.) -- jild-i 2. Az Abū al-Ḥasan ʻĀmirī (291 Sh.) tā Abū al-Faz̤l Bayhaqī (465 Sh.) -- jild-i 3. Az Nāṣir Khusraw (383 Sh.) tā Sadīd al-Dīn ʻAwfī (616 Sh.) -- jild-i 4. Az Najm al-Dīn Rāzī (553 Sh.) tā Fakhr al-Muḥaqqiqīn (748 Sh.) -- jild-i 5. Az ʻUbayd Zākānī (670 Sh.) tā Jalāl al-Dīn Sayūṭī (884 Sh.) -- jild-i 6. Az Rūzbihān Khunjī (825 Sh.) tā (...) Shaykh Ḥurr ʻĀmilī (1071 Sh.) -- jild-i 7. Az ʻAlāmah Majlisī (1006 Sh.) tā Muḥammad Bāqir Shafaq (1223 Sh.) -- jild-i 8. Az Muḥammad Ṣādiq Marvazī (1147 Sh.) tā Mīrzā Fatḥʻalī Ākhūndzādah (1289 Sh.) -- jild-i 9. Az Mīrzā-yi Shīrāzī Avval (1194 Sh.) tā Ākhūnd Khurāsānī (1290 Sh.) -- jild-i 10. Az ʻAbd Allāh Māzandarānī (1219 Sh.) tā Saʻd Zaghlūl (1306 Sh.) -- jild-i 11. Az ʻImād al-ʻUlamāʼ Khalkhālī (1237 Sh.) tā ʻAbd al-ʻAzīz S̲aʻālibī (1323 Sh.) -- jild-i 12. Az Āyat Allāh Burūjirdī (1254 Sh.) tā Muḥammad Amīn Rasūlʹzādah (1333 Sh.) -- jild-i 13. Az ʻAbd al-Karīm Zanjānī (1263 Sh.) tā Amīn al-Ḥusaynī (1353 Sh.) -- jild-i 14. Az Jamālzādah (1274 Sh.) tā Mahdī Bāzargān (1373) -- jild-i 15. Az Muḥammad Ḥamīd Allāh (1286 Sh.) tā Muṭahharī (1358 Sh.) -- jild-i 16. Az Farīdūn Ādamīyat (1299 Sh.) tā Muṣṭafá Khumaynī (1356 Sh.) -- jild-i 17. Az Samīr Amīn (1310 Sh.) tā Ḥasan Ḥanafī -- jild-i 18. Az Sayyid Muḥammad Bāqir Ṣadr (1314 Sh.) tā Rāshid al-Ghanūshī -- jild-i 19. Az ʻAlī Awmlīl (1321 Sh.) tā Ṭāriq Ramaz̤ān. (shrink)

Business Ethics, the Environment &Responsibility, Volume 31, Issue 4, Page 1157-1173, October 2022.

Monetary intelligence theory asserts that individuals apply their money attitude to frame critical concerns in the context and strategically select certain options to achieve financial goals and ultimate happiness. This study explores the dark side of monetary Intelligence and behavioral economics—dishonesty. Dishonesty, a risky prospect, involves cost–benefit analysis of self-interest. We frame good or bad barrels in the environmental context as a proxy of high or low probability of getting caught for dishonesty, respectively. We theorize: The magnitude and intensity of (...) the relationship between love of money and dishonest prospect may reveal how individuals frame dishonesty in the context of two levels of subjective norm—perceived corporate ethical values at the micro-level and Corruption Perceptions Index at the macro-level, collected from multiple sources. Based on 6382 managers in 31 geopolitical entities across six continents, our cross-level three-way interaction effect illustrates: As expected, managers in good barrels, mixed barrels, and bad barrels display low, medium, and high magnitude of dishonesty, respectively. With high CEV, the intensity is the same across cultures. With low CEV, the intensity of dishonesty is the highest in high CPI entities —the Enron Effect, but the lowest in low CPI entities. CPI has a strong impact on the magnitude of dishonesty, whereas CEV has a strong impact on the intensity of dishonesty. We demonstrate dishonesty in light of monetary values and two frames of social norm, revealing critical implications to the field of behavioral economics and business ethics. (shrink)

Monetary Intelligence theory asserts that individuals apply their money attitude to frame critical concerns in the context and strategically select certain options to achieve financial goals and ultimate happiness. This study explores the bright side of Monetary Intelligence and behavioral economics, frames money attitude in the context of pay and life satisfaction, and controls money at the macro-level and micro-level. We theorize: Managers with low love of money motive but high stewardship behavior will have high subjective well-being: pay satisfaction and (...) quality of life. Data collected from 6586 managers in 32 cultures across six continents support our theory. Interestingly, GDP per capita is related to life satisfaction, but not to pay satisfaction. Individual income is related to both life and pay satisfaction. Neither GDP nor income is related to Happiness. Our theoretical model across three GDP groups offers new discoveries: In high GDP entities, “high income” not only reduces aspirations—“Rich, Motivator, and Power,” but also promotes stewardship behavior—“Budget, Give/Donate, and Contribute” and appreciation of “Achievement.” After controlling income, we demonstrate the bright side of Monetary Intelligence: Low love of money motive but high stewardship behavior define Monetary Intelligence. “Good apples enjoy good quality of life in good barrels.” This notion adds another explanation to managers’ low magnitude of dishonesty in entities with high Corruption Perceptions Index. In low GDP entities, high income is related to poor Budgeting skills and escalated Happiness. These managers experience equal satisfaction with pay and life. We add a new vocabulary to the conversation of monetary intelligence, income, GDP, happiness, subjective well-being, good and bad apples and barrels, corruption, and behavioral ethics. (shrink)

Corruption involves greed, money, and risky decision-making. We explore the love of money, pay satisfaction, probability of risk, and dishonesty across cultures. Avaricious monetary aspiration breeds unethicality. Prospect theory frames decisions in the gains-losses domain and high-low probability. Pay dissatisfaction (in the losses domain) incites dishonesty in the name of justice at the individual level. The Corruption Perceptions Index, CPI, signals a high-low probability of getting caught for dishonesty at the country level. We theorize that decision-makers adopt avaricious love-of-money aspiration (...) as a lens and frame dishonesty in the gains-losses domain (pay satisfaction-dissatisfaction, Level 1) and high-low probability (CPI, Level 2) to maximize expected utility and ultimate serenity. We challenge the myth: Pay satisfaction mitigates dishonesty across nations consistently. Based on 6500 managers in 32 countries, our cross-level three-dimensional visualization offers the following discoveries. Under high aspiration conditions, pay dissatisfaction excites the highest- (third-highest) avaricious justice-seeking dishonesty in high (medium) CPI nations, supporting the certainty effect. However, pay satisfaction provokes the second-highest avaricious opportunity-seizing dishonesty in low CPI entities, sustaining the possibility effect—maximizing expected utility. Under low aspiration conditions, high pay satisfaction consistently leads to low dishonesty, demonstrating risk aversion—achieving ultimate serenity. We expand prospect theory from a micro and individual-level theory to a cross-level theory of monetary wisdom across 32 nations. We enhance the S-shaped Curve to three 3-D corruption surfaces across three levels of the global economic pyramid, providing novel insights into behavioral economics, business ethics, the environment, and responsibility. (shrink)