A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing.

Semantic segmentation of Remote Sensing (RS) images involves the classification of each pixel in a satellite image into distinct and non-overlapping regions or segments. This task is crucial in various domains, including land cover classification, autonomous driving, and scene understanding. While deep learning has shown promising results, there is limited research that specifically addresses the challenge of processing fine details in RS images while also considering the high computational demands. To tackle this issue, we propose a novel approach that combines convolutional and transformer architectures. Our design incorporates convolutional layers with a low receptive field to generate fine-grained feature maps for small objects in very high-resolution images. On the other hand, transformer blocks are utilized to capture contextual information from the input. By leveraging convolution and self-attention in this manner, we reduce the need for extensive downsampling and enable the network to work with full-resolution features, which is particularly beneficial for handling small objects. Additionally, our approach eliminates the requirement for vast datasets, which is often necessary for purely transformer-based networks. In our experimental results, we demonstrate the effectiveness of our method in generating local and contextual features using convolutional and transformer layers, respectively. Our approach achieves a mean dice score of 80.41%, outperforming other well-known techniques such as UNet, Fully-Connected Network (FCN), Pyramid Scene Parsing Network (PSP Net), and the recent Convolutional vision Transformer (CvT) model, which achieved mean dice scores of 78.57%, 74.57%, 73.45%, and 62.97% respectively, under the same training conditions and using the same training dataset.

A multilevel conceptual framework on green practices: Transforming policies into actionable leadership and employee behavior.

As organizations have recognized their cause/solution relationship with the environment, increasing attention is being given to the role of employees make in achieving green organizational objectives. Even though, business sustainability initiatives are often led by leaders; employee green behavior (EGB) plays a vital role in success of such initiatives. The current paper focuses on relatively less researched topic of EGB. It uses a narrative review approach to develop a multi-level conceptual framework that draws upon the connectivity of leadership influence at firm and team levels, and how this influences individual level EGB. The paper offers a holistic approach to influencing effective green strategies in organizational contexts. By doing so, it contributes to the larger debate on different dimensions, mechanisms, and levels of environmentally responsible behavior in organizational settings and opens up new avenues for multi-level and cross-layer empirical research.

Effect of abusive supervision on organizational cynicism (Cognitive, affective, behavioral) mediating effect of playing dumb.

The purpose of the present study is to examine the link of Abusive Supervision with Organizational Cynicism i.e. Cognitive, Emotional, or Behavioral Cynicism by focusing on the mediating role of Abusive Supervisor's Knowledge Hiding behavior of Playing Dumb in Higher Education Institutions in Pakistan. Data was collected using questionnaire under the survey research design. The participants included 400 faculty and staff members from Higher Education Institutions in Pakistan. Structural Equation Modelling using SmartPLS is used to test the hypothesized relationships of Abusive Supervision and Knowledge Hiding Behavior of Abusive Supervisors with the faculty and staff's Organizational Cynicism behaviors. The results indicate that Abusive Supervision is significantly and positively related with faculty and staff's Cognitive, Emotional, and Behavioral Cynicism. This study also indicates that Knowledge Hiding behavior of Playing Dumb fully mediates the relationship between Abusive Supervision and Cognitive Cynicism and partially mediates the relationship between Abusive Supervision and Behavioral Cynicism. However, Playing Dumb as a knowledge Hiding behaviour does not impact the relationship between Abusive Supervision and Emotional Cynicism. This means that Knowledge Hiding by Playing dumb contributes to the adverse effect of Abusive Supervision, leading to increased Cognitive and Behavioral Cynicism. This study contributes to literature on Organizational Cynicism and Abusive Supervision by studying the link between them and effect of Abusive Supervisor's Knowledge Hiding behavior of Playing Dumb as a mediator. The study also indicates that Abusive Supervision characterized by Knowledge Hiding behavior of Playing Dumb is really a problem in Higher Education Institutions in Pakistan. This study holds importance for top management in Higher Education Institutions in to curb the negative effects of Abusive Supervision by developing a policy framework that ensures prevention of Organizational Cynicism in faculty and staff. Moreover, the policy guidelines should ensure that control of essential resources such as Knowledge in the hands of the Abusive leaders should not be misused, causing Organizational Cynicism and eventually leading to problems such as turnover and psychological and behavioral issues among faculty and staff in Higher Education Institutions of Pakistan.

Self-Reported Study Analyzing Physicians' Personal Compliance with Health Prevention Guidelines in a Medium-Sized Canadian Community.

There have only been limited studies that have assessed the attitude of Canadian physicians toward their own physical health. The aim of our study was to explore the self-reported health maintenance behavior and the predictors of health practices among physicians in a small-medium sized Canadian community. We used a descriptive mailed in self-report survey to contact all 649 physicians registered with the Essex County Medical Society, with a 36% response rate. Our results showed that 81% of physicians in Windsor-Essex County were satisfied with how well they care for themselves, despite reporting low levels of physical activity and a lower percentage of respondents having family physicians than the general population. Five independent factors were identified with physician self-perceived health satisfaction: Physician age of 45 to 54 (95% CI 0.17-0.92; OR 0.39), graduating from Canadian medical schools (95% CI 0.15 to 0.80; OR 0.35), having more than one co-morbidity (95% CI 0.13-0.72; OR 0.31), physicians who had a regular family doctor (95% CI 1.12-5.52; OR 2.43), and engagement in regular moderate weekly exercise (95% CI 1.05-4.94; OR 2.28). We also contrasted the preventive health screening markers of our study to compliance rates of the general population as well as the national physician study. Our results showed that screening rates among our study physician group differed markedly from the general population. For colorectal and breast cancers, physicians in our study reported screening rates of 77.8% and 37.3% respectively, compared with the general population, who's screening rates are 32.3% and 72.5%. Future studies exploring specific targeted health promotion interventions that could address these factors may be warranted in order to further improve Canadian physician health, and ultimately improve their ability to take care of their patients.

The interplay between servant leadership, psychological safety, trust in a leader and burnout: assessing causal relationships through a three-wave longitudinal study.

COVID-19 pandemic has brought unprecedented psychological challenges for frontline healthcare workers, especially nurses, causing anxiety and depression leading to burnout. The responsibility of healthcare leaders has increased manyfold to deal with such challenges. This study attempts to employ the conservation of resources theory to examine the relationship between servant leadership and nurses' burnout, with the mediating role of psychological safety and the moderating effect of trust in leader. A three-wave longitudinal design was employed for data collection from 1204 nurses from 27 hospitals in China. The partial least squares structural equation modeling technique was used for data analyses with SmartPLS version 3.2.8. The findings endorse that servant leadership at time 1 significantly reduces nurses' burnout measured at time 3 through the mediating role of psychological safety measured at time 2, and that a higher level of trust in the leader enhances the impact of servant leadership in reducing nurses' burnout.

Classification of Skin Cancer Lesions Using Explainable Deep Learning.

Skin cancer is among the most prevalent and life-threatening forms of cancer that occur worldwide. Traditional methods of skin cancer detection need an in-depth physical examination by a medical professional, which is time-consuming in some cases. Recently, computer-aided medical diagnostic systems have gained popularity due to their effectiveness and efficiency. These systems can assist dermatologists in the early detection of skin cancer, which can be lifesaving. In this paper, the pre-trained MobileNetV2 and DenseNet201 deep learning models are modified by adding additional convolution layers to effectively detect skin cancer. Specifically, for both models, the modification includes stacking three convolutional layers at the end of both the models. A thorough comparison proves that the modified models show their superiority over the original pre-trained MobileNetV2 and DenseNet201 models. The proposed method can detect both benign and malignant classes. The results indicate that the proposed Modified DenseNet201 model achieves 95.50% accuracy and state-of-the-art performance when compared with other techniques present in the literature. In addition, the sensitivity and specificity of the Modified DenseNet201 model are 93.96% and 97.03%, respectively.

Multi-method modeling to predict the onset conditions and resonance of the piezo coupled thermoacoustic engine.

This article seeks to perform a combination of methodologies to fully model and evaluate the rudimentary performance of a thermoacoustic engine integrated with a piezoelectric energy harvester (TAP). First, the root locus method was employed to determine the critical design operating values of the thermoacoustic engine. Later, a lumped parameter model was developed as a matlab Simulink program to calculate the transient temperature and pressure responses of the thermoacoustic engine. In addition, a two-element reduced model (executed on matlab) and finite element analysis tools were used to simulate and assess the performance of aluminum-piezo (lead zirconate titanate (PZT-5H) and lead manganese niobate-lead titanate (PMN-PT)) disks that are to be integrated with the thermoacoustic engine. Last but most importantly, the piezo-diaphragm and thermoacoustic engine were coupled using the electrical analogy technique through which the onset conditions and resonance frequency of the integrated TAP system were determined. We take a traveling wave thermoacoustic engine and a commercially available piezoelectric disk as a test case for the analysis. It is concluded that the outcomes from the multiple methods are in good agreement with the experimental results.

Environmentally Specific Servant Leadership and Employees' Pro-Environmental Behavior: Mediating Role of Green Self Efficacy.

INTRODUCTION: Employees' pro-environmental behavior is crucial for accomplishing organizations' green initiatives. There is a dearth of empirical research that explored the underlying mechanism of environmentally specific servant leadership (ESL) influencing employees' pro-environmental behavior (EPB). The theoretical lens of self-efficacy theory is employed to explore the influence of ESL in predicting EPB. Employees' green self-efficacy was introduced as the mediator through which ESL influences EPB. METHODOLOGY: Time-lagged data from 381 dyads of employee-supervisor from Pakistan's energy sector were collected during the months of June and July 2021 through systematic random sampling. The partial least squares structural equation modeling (PLS-SEM) technique was employed to analyze data and assess hypothesized relationships. RESULTS: The results show that all hypotheses are supported. Findings indicate that environmentally specific servant leadership has a significant direct impact on employees' pro-environmental behavior and employees' green self-efficacy partially mediates the positive influence of ESL on EPB. DISCUSSION: The study's managerial and theoretical implications are presented along with future research directions.

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques.

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.

Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset.

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.

Curbing nurses' burnout during COVID-19: The roles of servant leadership and psychological safety.

AIMS: This study examines the role of servant leadership through the mechanism of psychological safety in curbing nurses' burnout during the COVID-19 pandemic. BACKGROUND: During the COVID-19 pandemic, studies have shown an increased level of stress and burnout among health care workers, especially nurses. This study responds to the call for research to explore the mechanisms of servant leadership in predicting nurses' burnout by employing the perspective of conservation of resources theory. METHODS: Through a cross-sectional quantitative research design, data were collected in three waves from 443 nurses working in Pakistan's five public sector hospitals. Data were analysed by employing the partial least squares path modelling (PLS-PM) technique. RESULTS: Servant leadership (ß = -0.318; 95% CI = 0.225, 0.416) and psychological safety (ß = -0.342; CI = 0.143, 0.350) have an inverse relationship with nurses' burnout and explain 63.1% variance. CONCLUSIONS: Servant leadership significantly reduces nurses' burnout, and psychological safety mediates this relationship. IMPLICATIONS FOR NURSING MANAGEMENT: Human resource management policies in health care must emphasize training nursing leaders in servant leadership behaviour.

How inclusive leadership paves way for psychological well-being of employees during trauma and crisis: A three-wave longitudinal mediation study.

AIMS: Nurses are at the forefront of public health emergencies facing psychological pressures ensuing from the loss of patients and potential risk of infection while treating the infected. This study examines whether inclusive leadership has a causal relationship with psychological distress and to assess the mediation effect of psychological safety on this relationship in the long run. The hypotheses are developed and interpreted with the help of theoretical underpinnings from job demands resources theory and the theory of shattered assumptions. DESIGN: Three-wave longitudinal study. METHODS: Questionnaire was used to carry out three waves of data collection from 405 nurses employed at five hospitals in Wuhan during the COVID-19 outbreak between the months of January-April 2020. Partial least square structural equation modelling (PLS-SEM) was used to analyze data while controlling for age, gender, education, experience, and working hours. RESULTS: Results supported the hypothesized relationships where inclusive leadership indicated significant inverse causal relationship with psychological distress and a positive causal relationship with psychological safety. Mediation effect of psychological safety was found significant, while the model explained 73.9% variance in psychological distress. CONCLUSION: Inclusive leadership, through its positive and supportive characteristics, can pave way for such mechanisms that improve the psychological safety of employees in the long run and curbs psychological distress. IMPACT: This is the first longitudinal study to examine the relationship between inclusive leadership and psychological distress in health care and also examines the mediating mechanism of psychology safety. There is scarcity of empirical research on factors that determine and affect behavioural mechanism of healthcare workers during traumatic events and crisis. Clinical leaders and healthcare policy makers must invest in and promote inclusive and supportive environment characterized with open and accessible leaders at workplace to improve psychological safety; it helps reduce levels of psychological distress.

Shaping Behaviors Through Institutional Support in British Higher Educational Institutions: Focusing on Employees for Sustainable Technological Change.

Technology permeates all walks of life. It has emerged as a global facilitator to improve learning and training, alleviating the temporal and spatial limitations of traditional learning systems. It is imperative to identify enablers or inhibitors of technology adoption by employees for sustainable change in education management systems. Using the theoretical lens of organizational support theory, this paper studies effect of institutional support on education management information systems (EMIS) use along with two individual traits of self-efficacy and innovative behavior of academic employees in British higher educational institutions. Data for this cross-sectional study were collected through a questionnaire completed by 591 academic employees of 23 universities from 10 cities in the United Kingdom. Partial Least Square structural equation modeling was used to analyze data with smartPLS 3.2.9 software. Results indicate that institutional support promotes self-efficacy and innovative behavior that help develop positive employee perceptions. The model explains a 52.9% variance in intention to use. Post-hoc mediation analysis shows that innovativeness and self-efficacy mediate between institutional support and employee technology adoption behavior. As opposed to student samples in past studies on educational technology, this study adds to the literature by focusing on academic employees.

How and When Does Inclusive Leadership Curb Psychological Distress During a Crisis? Evidence From the COVID-19 Outbreak.

Traumatic events such as a pandemic shatter the assumption of the workplace as a safe place. Nurses face risks of life-threatening infection, which can create psychological distress. Quality of care for infected patients depends on mental well-being of nurses which calls for research on predictors of stress among health care workers. Responding to a call for research on the effects of leadership styles on psychological distress during traumatic events, this paper uses the theoretical lens of social exchange theory and contributes to literature on relationships between inclusive leadership, psychological distress, work engagement, and self-sacrifice. Participants of this cross sectional study included 497 registered nurses from five hospitals in Wuhan. Data were collected with temporal separation through an online questionnaire. Partial least-squares structural equation modeling was used to analyze data. Results show inclusive leadership has a significant negative relationship with psychological distress. Work engagement mediates this relationship, and nurses' self-sacrificial behavior moderates it. Findings indicate inclusive leadership style serves as a sustainable mechanism to reduce psychological distress during pandemics. It can operationalize the delivery of mental health support in real-time in work settings. Results provide empirical support for social exchange theory through high work engagement to help control psychological distress among nurses.

Caring for the caregiver during COVID-19 outbreak: Does inclusive leadership improve psychological safety and curb psychological distress? A cross-sectional study.

BACKGROUND: Public health emergencies and epidemics shatter the assumptions of the world as a safe place. Healthcare workers are at the forefront of such pressures resulting from a persistent threat to their safety and well being. It is therefore important to study such mechanisms that can influence and predict the psychological distress of nurses OBJECTIVES: While there is an increasing number of studies on positive outcomes of leadership styles, their influence on curbing unwanted adverse outcomes is scarce. This study aims to observe the influence of an inclusive leadership style on psychological distress while assessing the mediating role of psychological safety. It uses the theoretical lens of job demands-resources theory and the theory of shattered assumptions to develop and test hypotheses. DESIGN: Cross-Sectional Study with Temporal Separation SETTINGS AND PARTICIPANTS: The researchers recruited 451 on-duty registered nurses from 5 hospitals providing patient care during the highly infectious phase of COVID-19 in January 2020 in Wuhan city, the epicentre of the outbreak in China METHODS: After obtaining permission from hospital administration, data were collected through an online questionnaire survey in three stages with temporal separation to avoid common method bias. Partial least square structural equation modelling was used to analyze data. The study controlled for effects of age, gender, experience, working hours and education. RESULTS: Hypothesized relationships proved significant. Inclusive leadership has an inverse relationship with psychological distress with a strong path-coefficient. Psychological safety mediates the relationship between inclusive leadership and psychological distress while explaining 28.6% variance. Multi-group analysis results indicate no significant differences between respondents based on these control variables CONCLUSIONS: Recurring or prolonged experiences of stress and anxiety at the workplace, without a mechanism to counter such effects, can culminate into psychological distress. Inclusive leadership style can serve as such a mechanism to curb psychological distress for healthcare workers by creating a psychologically safe environment.

Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication.

Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person's body. The Wi-Fi signals received using non-wearable devices are converted into time-frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%.

How Does Servant Leadership Foster Employees' Voluntary Green Behavior? A Sequential Mediation Model.

Employees' voluntary green behavior (EVGB) is indispensable in realizing organizations' environmental sustainability objectives. Leaders can act as catalysts to shape the behavior of their employees. On EVGB, noticeably the missing link is investigating the influence of servant leadership and the mechanism through which it operates. Building upon self-determination and psychological empowerment theories, this research examined the impact of servant leadership on EVGB through the simple and sequential mediation of psychological empowerment and autonomous motivation for the environment (AME). Through systematic sampling, dyadic data were collected from 315 pairs of subordinates and supervisors working in the power sector organizations of Pakistan. Results were obtained by employing the partial least squares structural modeling (PLS-SEM) technique with Smart-PLS 3.2.8 software. Findings revealed that psychological empowerment and AME simply and sequentially mediate the influence of servant leadership on EVGB. Implications for theory and organizational practitioners are offered, accompanied by suggestions for future research.

Aspartame and Sucralose-induced Fatty Changes in Rat Liver.

OBJECTIVE: To determine the cytoplasmic fatty change of rat liver due to artificial sweeteners - aspartame and sucralose. STUDY DESIGN: Experimental study. PLACE AND DURATION OF STUDY: Department of Anatomy, King Edward Medical University, Lahore, in collaboration with the University of Veterinary and Animal Sciences Lahore, from May to October 2016. METHODOLOGY: Adult Wistar albino rats were randomly divided into five groups. Group I was control group (animals were given drinking water by oral gavage). The animals of group II and III were given low (40 mg/kg body weight) and high dose (1000 mg/kg body weight) of aspartame, respectively. Animals of group IV and V were given low (5 mg/kg body weight) and high dose (1000 mg/kg body weight) of sucralose respectively by oral gavage. Doses were given once daily, six days a week for a total duration of 8 weeks. At the end of experiment, livers of all animal groups were observed for fatty change of cytoplasm. RESULTS: Artificial sweeteners cause cytoplasmic fatty change of varying degrees. This change was more marked in high dose group of aspartame (group III) as compared to low dose group (group II). In sucralose group, this change was seen only in high dose group (group V). CONCLUSION: Both artificial sweeteners-induced fatty changes in rat liver; the effect was least pronounced with low dose of sucralose.

Is insulin resistance the cause of fibromyalgia? A preliminary report.

Fibromyalgia (FM) is one of the most frequent generalized pain disorders with poorly understood neurobiological mechanisms. This condition accounts for an enormous proportion of healthcare costs. Despite extensive research, the etiology of FM is unknown and thus, there is no disease modifying therapy available for this condition. We show that most (if not all) patients with FM belong to a distinct population that can be segregated from a control group by their glycated hemoglobin A1c (HbA1c) levels, a surrogate marker of insulin resistance (IR). This was demonstrated by analyzing the data after introducing an age stratification correction into a linear regression model. This strategy showed highly significant differences between FM patients and control subjects (p < 0.0001 and p = 0.0002, for two separate control populations, respectively). A subgroup of patients meeting criteria for pre-diabetes or diabetes (patients with HbA1c values of 5.7% or greater) who had undergone treatment with metformin showed dramatic improvements of their widespread myofascial pain, as shown by their scores using a pre and post-treatment numerical pain rating scale (NPRS) for evaluation. Although preliminary, these findings suggest a pathogenetic relationship between FM and IR, which may lead to a radical paradigm shift in the management of this disorder.

Use Of Complementary And Alternative Medicine Among Asthmatic Patients.

BACKGROUND: The prevalence of complementary and alternative medicine (CAM) use in some developed countries is on the rise as surveys conducted on large scale vouch for. This study was conducted with the aim to determine the frequency of complementary and alternative medicine (CAM) use among asthmatic patients at outpatient department of tertiary care hospitals in Peshawar, Khyber Pakhtunkhwa, Pakistan. METHODS: This cross-sectional study was conducted in outpatient department of two tertiary care hospitals. A face to face interview of 423 patients was conducted through a questionnaire. Non-probability consecutive sampling method was used to select the respondents. Stata version 12.1 (StataCorp), College Station, Texas) was used to carry out the statistical analysis. RESULTS: Overall asthmatic patients in the study were 423, in which 232 (54.85%) patients reported as using CAM. There were 177 (41.84%) males and 246 (58.16%) females. The CAM use was significantly more in older age patients, suffering from asthma for more than 5 years, severe persistent type of asthma, married, unemployed, rural and current smokers. CONCLUSIONS: The use of CAM reflects a high CAM use among asthmatic patients in Pakistan. Clinicians should be aware about their patient's use of CAM.