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This study aims to analyze the role of green transformational leadership and human resource management in organizational performance with the mediating effect of green innovation. The study also proposes the moderating role of commitment to management change. The study used a quantitative research method with a random sampling technique and surveyed 235 managers from the Cement industry across Pakistan. The statistical techniques used to assess the study's hypotheses were partial least squares structural equation modeling (PLS-SEM) and SPSS. The findings show that green transformational leadership and HRM positively impact organizational performance, with green innovation partially mediating effect. Furthermore, affective commitment to management change strongly moderates the relationship between green innovation and organizational performance. In addition, the proposed relationship is based on the resource-based view (RBV) theory. Therefore, Managers and executives should adopt green principles and practices and build an environmentally friendly culture that encourages employees to engage in green behaviors and activities to improve organizational performance. To successfully implement green practices in a developing country like Pakistan, the Cement sector must understand how the cement industry improves its performance and whether employees demonstrate green innovation.

Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques are unable to fully comprehend complex activity patterns over different periods, emphasizing the need for enhanced architectures to further increase accuracy by learning spatiotemporal dependencies in the data individually. Therefore, in this work, we develop an attention-enhanced dual-stream network (PAR-Net) for physical activity recognition with the ability to extract both spatial and temporal features simultaneously. The PAR-Net integrates convolutional neural networks (CNNs) and echo state networks (ESNs), followed by a self-attention mechanism for optimal feature selection. The dual-stream feature extraction mechanism enables the PAR-Net to learn spatiotemporal dependencies from actual data. Furthermore, the incorporation of a self-attention mechanism makes a substantial contribution by facilitating targeted attention on significant features, hence enhancing the identification of nuanced activity patterns. The PAR-Net was evaluated on two benchmark physical activity recognition datasets and achieved higher performance by surpassing the baselines comparatively. Additionally, a thorough ablation study was conducted to determine the best optimal model for human physical activity recognition.

The recent advancements in the field of Virtual Reality (VR) and Augmented Reality (AR) have a substantial impact on modern day technology by digitizing each and everything related to human life and open the doors to the next generation Software Technology (Soft Tech). VR and AR technology provide astonishing immersive contents with the help of high quality stitched panoramic contents and 360° imagery that widely used in the education, gaming, entertainment, and production sector. The immersive quality of VR and AR contents are greatly dependent on the perceptual quality of panoramic or 360° images, in fact a minor visual distortion can significantly degrade the overall quality. Thus, to ensure the quality of constructed panoramic contents for VR and AR applications, numerous Stitched Image Quality Assessment (SIQA) methods have been proposed to assess the quality of panoramic contents before using in VR and AR. In this survey, we provide a detailed overview of the SIQA literature and exclusively focus on objective SIQA methods presented till date. For better understanding, the objective SIQA methods are classified into two classes namely Full-Reference SIQA and No-Reference SIQA approaches. Each class is further categorized into traditional and deep learning-based methods and examined their performance for SIQA task. Further, we shortlist the publicly available benchmark SIQA datasets and evaluation metrices used for quality assessment of panoramic contents. In last, we highlight the current challenges in this area based on the existing SIQA methods and suggest future research directions that need to be target for further improvement in SIQA domain.

Conventional RSA algorithm, being a basis for several proposed cryptosystems, has remarkable security laps with respect to confidentiality and integrity over the internet which can be compromised by state-of-the-art attacks, especially, for different types of data generation, transmission, and analysis by IoT applications. This security threat hindrance is considered to be a hard problem to solve on classical computers. However, bringing quantum mechanics into account, the concept no longer holds true. So, this calls out for the modification of the conventional pre-quantum RSA algorithm into a secure post-quantum cryptographic-based RSA technique. In this research, we propose a post-quantum lattice-based RSA (LB-RSA) for IoT-based cloud applications to secure the shared data and information. The proposed work is validated by implementing it in 60-dimensions. The key size is about 1.152×105-bits and generation time is 0.8 hours. Furthermore, it has been tested with AVISPA, which confirms security in the presence of an intruder. Moreover, the proposed LB-RSA technique is compared with the existing state-of-the-art techniques. The empirical results advocate that the proposed lattice-based variant is not only safe but beats counterparts in terms of secured data sharing.

This article proposes and compares the performance of three flexible and bandwidth efficient transceivers. The terminology of Over-Complete Mapping (OCM) is introduced in the first two schemes. All of the schemes, namely Non-Convergent Serial Concatenated OCM Coding (NCSCOC), Convergent Serial Concatenated OCM Coding (CSCOC) and Self-Concatenated Convolutional Coding (SECCC); are simulated for the Rayleigh channel and employing iterative decoding to attain the refined output stream for feeding the video decoder. Specifically, the iterative decoding is beneficial in acquiring the convergence of EXtrinsic Information Transfer (EXIT) curves by repeatedly sharing of the mutual information. The difference between the NCSCOC and CSCOC schemes lies in the inner and outer rates. This change reflects an improvement in the Bit-Error Rate (BER) and improved EXIT convergence of CSCOC scheme, with reference to NCSCOC scheme. Results show that NCSCOC scheme never reaches the point of perfect convergence despite iterative decoding. However, CSCOC and SECCC schemes succeed in securing perfect convergence. Investigating the BER curves, it is deducible that SECCC is the most desirable transceiver system, having the least BER. Furthermore, it is plausible that the overall performance of SECCC is much better than the preceding schemes. Explicitly, our experimental results show that the proposed CSCOC scheme outperforms its identical code rate counterpart NCSCOC scheme by about 3 dB at the Peak Signal-to-Noise Ratio (PSNR) degradation point of 1 dB. Additionally, an Eb/N0 gain of 20 dB is attained using SECCC scheme relative to identical code rate NCSCOC benchmarked scheme.

Machine learning in association with remote sensing has assisted agricultural specialists in monitoring, classification and yield estimation of crops. Tobacco is a major taxable crop of Pakistan, however the existing traditional methods for its monitoring and yield estimation are not only expensive and time consuming but also have limitations in terms of accuracy of collected data by a large number of diverse human surveyors. Due to the existence of such loopholes in the employed mechanism for tobacco crop monitoring and yield estimation, its illicit growth and distribution is on the rise. In this paper we have established a sophisticated machine learning mechanism for tobacco crop estimation using temporally stacked sentinel-2 satellite's data of Pakistan. Instead of the conventional approach of using single remotely sensed imagery for the target crop classification, we propose a machine learning based classification algorithm while keeping in view the phonological cycle of the target tobacco crop. Using the proposed mechanism, the temporal variations within the tobacco crop and its association with the variations of other vegetation is considered to improve the classification performance of the employed machine learning algorithm. Furthermore, the impact of stacking the vegetation indices derived from near infrared and vegetation red edge bands of sentinel-2 with the original sentinel-2 datasets, including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Index 45 (NDI45), on the classification performance of the machine learning mechanism is investigated. Ground Truth data for training of our Artificial Neural Networks classifier, was obtained using indigenously developed survey application “GEOSurvey”. Experiments were conducted using our proposed mechanism while considering various input data setups - including single date imagery, temporally stacked datasets based on phonological cycle of tobacco crop and different combinations of NDVI and NDI45 stacking. Our proposed experimental setup consisting of temporally stacked imagery along with NDVI stacking results in the best classification performance of 95.81% with reference to the single date imagery stacked with NDVI and NDI45, with performance gain of 07.32%.

The WSNs (Wireless Sensor Networks) lead to great opportunities to explore it scientifically. In this network different numbers of SN (Sensor Nodes) are deployed in a specific area to gather information. The UWSNs (Underwater Wireless Sensor Networks) is a highly distributed network of sensor nodes deployed underwater to gather environmental information. Hence, acquirement of real-time data at enhanced data rate and to reduce power consumption is a key concern while designing routing protocol for UWSNs. In this paper, a cooperation based solution is suggested. The solution proposed here uses the DF (Decode and Forward) strategy for relying the information from the source to the destination using a relay node. The signals coming towards the destination are weighted and combined on the basis of their SNRC (Signal to Noise Ratio Combing). The simulation results verify enhancement in different factors, required for evaluation of a UWSN. After implementation of the proposed solution the stability of the network is increased which maximize the PDR (Packet Delivery Ratio). In our proposed solution the transmission is based on channel estimation, an estimate is made for higher reliable channel, which reduces retransmission of packets. Hence, sink receive the packets with lesser delay and as a result E2E (End-to-End) delay is decreased. Data is forwarded using data forwarding by neighbor nodes. It improves average energy consumption of the system. Hence the overall performance and lifetime of a UWSN is increased.

Particle swarm optimization (PSO) is a swarm-based optimization technique capable of solving different categories of optimization problems. Nevertheless, PSO has a serious exploration issue that makes it a difficult choice for multi-objectives constrained optimization problems (MCOP). At the same time, Multi-Protocol Label Switched (MPLS) and its extended version Generalized MPLS, has become an emerging network technology for modern and diverse applications. Therefore, as per MPLS and Generalized MPLS MCOP needs, it is important to find the Pareto based optimal solutions that guarantee the optimal resource utilization without compromising the quality of services (QoS) within the networks. The paper proposes a novel version of PSO, which includes a modified version of the Elitist learning Strategy (ELS) in PSO that not only solves the existing exploration problem in PSO, but also produces optimal solutions with efficient convergence rates for different MPLS/ GMPLS network scales. The proposed approach has also been applied with two objective functions; the resource provisioning and the traffic load balancing costs. Our simulations and comparative study showed improved results of the proposed algorithm over the well-known optimization algorithms such as standard PSO, Adaptive PSO, Bat and Dolphin algorithm.

Natural gas is an important and fast-growing energy resource in the world and its purification is important in order to reduce environmental hazards and to meet the required quality standards set down by notable pipeline transmission, as well as distribution companies. Therefore, membrane technology has received great attention as it is considered an attractive option for the purification of natural gas in order to remove impurities such as carbon dioxide (CO2) and hydrogen sulphide (H2S) to meet the usage and transportation requirements. It is also recognized as an appealing alternative to other natural gas purification technologies such as adsorption and cryogenic processes due to its low cost, low energy requirement, easy membrane fabrication process and less requirement for supervision. During the past few decades, membrane-based gas separation technology employing hollow fibers (HF) has emerged as a leading technology and underwent rapid growth. Moreover, hollow fiber (HF) membranes have many advantages including high specific surface area, fewer requirements for maintenance and pre-treatment. However, applications of hollow fiber membranes are sometimes restricted by problems related to their low tensile strength as they are likely to get damaged in high-pressure applications. In this context, braid reinforced hollow fiber membranes offer a solution to this problem and can enhance the mechanical strength and lifespan of hollow fiber membranes. The present review includes a discussion about different materials used to fabricate gas separation membranes such as inorganic, organic and mixed matrix membranes (MMM). This review also includes a discussion about braid reinforced hollow fiber (BRHF) membranes and their ability to be used in natural gas purification as they can tackle high feed pressure and aggressive feeds without getting damaged or broken. A BRHF membrane possesses high tensile strength as compared to a self-supported membrane and if there is good interfacial bonding between the braid and the separation layer, high tensile strength, i.e., upto 170Mpa can be achieved, and due to these factors, it is expected that BRHF membranes could give promising results when used for the purification of natural gas.

Zeolitic imidazole framework 8 (ZIF-8) nanoparticles were successfully synthesized in an aqueous solution at the ambient condition with a relatively low molar ratio of zinc salt and an organic ligand, Zn+2/Hmim (1: 8). ZIF-8 has remarkable thermal and chemical stability, tunable microporous structure, and a great potential for absorption, adsorption, and separation. Various physicochemical characterization techniques like X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), attenuated total reflected infrared spectroscopy (ATR-IR), thermogravimetric analysis (TGA), and surface area with pore textural properties by micromeritics gas adsorption equipment were performed to investigate the effect of base type additive triethylamine (TEA) on the morphology, crystallinity, yield, particle and crystal size, thermal stability and microporosity of ZIF-8 nanoparticles. The total quantity of basic sites and carbon dioxide (CO2) desorption aptitude was also calculated using CO2 temperature-programmed desorption (CO2-TPD) system. The pure ZIF-8 nanoparticles of 177 nm were formed at TEA/total mole ratio of 0.002. Furthermore, the size of ZIF-8 nanoparticles was decreased to 77 nm with increasing TEA/total mole ratio up to 0.004. The structures, particle sizes and textural properties of ammonia modified ZIF-8 particle can easily be tailored by the amount of aqueous ammonium hydroxide solution. The smallest ZIF-8 nanoparticles obtained were 75 nm after ammonia modification which shows excellent thermal stability and improved microporosity. The ZIF-8 basicity and uptakes of CO2 improved with TEA and ammonia modification which followed the order: A25ml–Z4 > Z4 > Z3 > Z5 > Z2 > A50ml–Z4. The proposed economical and efficient synthesis method has great potential for large-scale production of ZIF-8. Keywords: Synthesis, Modification, Low molar ratio, High basicity, Desorption capacity

In indoor environments, accurate location or positioning becomes an essential requirement, driven by the need for autonomous moving devices, or to identify the position of people in large spaces. Single technology schemes which use WiFi and Bluetooth are affected by fading effects as well as by signal noise, providing inaccuracies in location estimation. Hybrid locating or positioning schemes have been used in indoor situations and scenarios in order to improve the location accuracy. Hence, this paper proposes a hybrid scheme (technique) to implement fingerprint-based indoor positioning or localization, which uses the Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points as well as Wireless Sensor Networks (WSNs) technologies. Our approach consists of performing a virtual tessellation of the indoor surface, with a set of square tiles encompassing the whole area. The model uses an Artificial Neural Network (ANN) approach for position estimate, in which related RSS is associated to a 1 m × 1 m tile. The ANN was trained to match the RSS signal strength to the corresponding tile. Experimental results indicate that the average distance error, based on tile identification accuracy, is 0.625 m from tile-to-tile, showing a remarkable improvement compared to previous approaches.

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications.

In the recent past, a significant increase has been observed in the use of underwater wireless sensor networks for aquatic applications. However, underwater wireless sensor networks face several challenges including large propagation delays, high mobility, limited bandwidth, three-dimensional deployments, expensive manufacturing, and energy constraints. It is crucial for underwater wireless sensor networks to mitigate all these limitations primarily caused by the harsh underwater environment. To address some of the pertinent challenges, adaptive hop-by-hop cone vector-based forwarding routing protocol is proposed in this article which is based on the adaptive hop-by-hop vector-based forwarding. The novelty of adaptive hop-by-hop cone vector-based forwarding includes increasing the transmission reliability in sparse sensor regions by changing the base angle of the cone according to the network structure. The number of duplicate packets and end-to-end delay is also reduced because of the reduced base angle and a smart selection criterion for the potential forwarder node. The proposed routing protocol adaptively tunes the height and opening of the cone based on the network structure to effectively improve the performance of the network. Conclusively, this approach significantly reduces energy tax, end-to-end delay, and packet delivery ratio.

Hollow fiber membrane (HFM) technology has received significant attention due to its broad range separation and purification applications in the industry. In the current study, we applied bibliometric analysis to evaluate the global research trends on key applications of HFMs by evaluating the global publication outputs. Results obtained from 5626 published articles (1970–2020) from the Scopus database were further manipulated using VOSviewer software through cartography analysis. The study emphasizes the performance of most influential annual publications covering mainstream journals, leading countries, institutions, leading authors and author’s keywords, as well as future research trends. The study found that 62% of the global HFM publications were contributed by China, USA, Singapore, Japan and Malaysia, followed by 77 other countries. This study will stimulate the researchers by showing the future-minded research directions when they select new research areas, particularly in those related to water treatment, biomedical and gas separation applications of HFM.

2016 and 2017 were marked by strong El Niño and weak La Niña events, respectively, in the tropical East Pacific Ocean. The strong El Niño and weak La Niña events in the Pacific significantly impacted the sea surface temperature (SST) in the tropical Indian Ocean (TIO) and were followed by extreme negative and weak positive Indian Ocean Dipole (IOD) phases in 2016 and 2017, which triggered floods in the Indian subcontinent and drought conditions in East Africa. The IOD is an irregular and periodic oscillation in the Indian Ocean, which has attracted much attention in the last two decades due to its impact on the climate in surrounding landmasses. Much work has been done in the past to investigate global climate change and its impact on the evolution of IOD. The dynamic behind it, however, is still not well understood. The present study, using various satellite datasets, examined and analyzed the dynamics behind these events and their impacts on SST variability in the TIO. For this study, the monthly mean SST data was provided by NOAA Optimum Interpolation Sea Surface Temperature (OISST). SST anomalies were measured on the basis of 30-year mean daily climatology (1981–2010). It was determined that the eastern and western poles of the TIO play quite different roles during the sequence of negative and positive IOD phases. The analysis of air-sea interactions and the relationship between wind and SST suggested that SST is primarily controlled by wind force in the West pole. On the other hand, the high SST that occurred during the negative IOD phase induced local convection and westerly wind anomalies via the Bjerknes feedback mechanism. The strong convection, which was confined to the (warm) eastern equatorial Indian Ocean was accompanied by east–west SST anomalies that drove a series of downwelling Kelvin waves that deepened the thermocline in the east. Another notable feature of this study was its observation of weak upwelling along the Omani–Arabian coast, which warmed the SST by 1 °C in the summer of 2017 (as compared to 2016). This warming led to increased precipitation in the Bay of Bengal (BoB) region during the summer of 2017. The results of the present work will be important for the study of monsoons and may be useful in predicting both droughts and floods in landmasses in the vicinity of the Indian Ocean, especially in the Indian subcontinent and East African regions.

Underwater wireless communication has gained a great deal of attention in the last couple of decades because of its applications in the military, industrial, and monitoring sectors. Despite the extreme physical and MAC layer difficulties, acoustics are used for various applications among the various modes of underwater communication technologies used. While significant research efforts have been made to address these issues, the bottleneck remains in achieving high bandwidth, high throughputs, and data rate. Researchers have begun to look into full duplex (FD) implementation to improve bandwidth efficiency and increase data rate and throughput. Users can send and receive data simultaneously over the FD links, maximizing bandwidth utilization and increasing throughput. As a result, we thoroughly reviewed various FD physical layered UWAC systems and MAC layered protocols for underwater communication. The various problems that the aforementioned systems and protocols have faced, as well as the solutions suggested in previous works to solve each problem, are also highlighted. Various metrics are used to compare the performance of various physical layered FD systems and FD MAC protocols. We also explore some of the open research questions in these FD-physical layered and MAC layered protocols, as well as future research directions. Based on ample information, we suggest a cross-layered architecture based on various IBFD-SI cancellations, DA-CSMA, and FD-MAC protocols. This review provides a broad view of the current FD physical and MAC layered protocols based on acoustic communication, as well as recommendations.

Due to large spectral efficiency and low power consumption, the Massive Multiple-Input-Multiple-Output (MIMO) became a promising technology for the 5G system. However, pilot contamination (PC) limits the performance of massive MIMO systems. Therefore, two pilot scheduling schemes (i.e., Fractional Pilot Reuse (FPR) and asynchronous fractional pilot scheduling scheme (AFPS)) are proposed, which significantly mitigated the PC in the uplink time division duplex (TDD) massive MIMO system. In the FPR scheme, all the users are distributed into the central cell and edge cell users depending upon their signal to interference plus noise ratio (SINR). Further, the capacity of central and edge users is derived in terms of sum-rate, and the ideal number of the pilot is calculated which significantly maximized the sum rate. In the proposed AFPS scheme, the users are grouped into central users and edge users depending upon the interference they receive. The central users are assigned the same set of pilots because these users are less affected by interference, while the edge users are assigned the orthogonal pilots because these users are severely affected by interference. Consequently, the pilot overhead is reduced and inter-cell interference (ICI) is minimized. Further, results verify that the proposed schemes outperform the previous proposed traditional schemes, in terms of improved sum rates.

A structural health monitoring (SHM) system is an approach for identifying the damages caused to various kinds of structures using different system functions and providing the necessary feedback about structure’s conditions. As civil structures are the backbone of our society, to determine its daily operations is a very important issue. The performance measurement of those structures is manual whereas a computer-based monitoring system could automatically assess the structural damages and identify its exact location. Recently, wireless sensor networks (WSNs) have attracted a great deal of attention for remote sensing applications due to flexibility to measure of various activity of large scale network. Since technology is advancing day by day, the overall cost of a monitoring system is also decreased. However, the major challenging fact of a WSNs is to provide scalability for covering a large area. The main question is arisen how much capable have of a monitoring system to turn off unnecessary nodes to save energy while there are no events detected. To support the scalability required of an existing network and save the node energy for future use, we propose a topology maintenance protocol integrated with construction to address the issue of a node’s energy consumption by placing it optimally and extending the monitoring system’s lifetime. As per the authors’ acknowledgement that, a little attention has been paid to developing such a hybrid approach. To mitigate node energy consumption issue with large scale support, an Internet of Things (IoT)-based maintenance approach is the best candidate for obtaining better system lifetime responses. Therefore, the main goal of this work is to develop an ‘on-the-fly’-based topology maintenance monitoring system, which can maintain a network’s infrastructure while gathering a node’s information to switch its state regularly when the present network is no longer optimal.

There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.

The traditional free-space and half-space analysis method ignore the reflection of the upper and lower boundaries of shallow sea and are not suitable for analyzing shallow sea problems especially at high frequency. Hence, a method combining ray theory and wave superposition theory is proposed in this paper to predict the high-frequency radiated acoustic field in shallow water. The proposed method takes into account the effect of channel boundaries on the acoustic field and has good adaptability to complex channel environments and accuracy of the calculated acoustic field.

The theme of the study was to estimate the elasticity of aggregated and selected disaggregated import demand function with relative prices and income for Pakistan from 1982 to 2011 on annual data. The most effective estimation tool was used (Auto-regressive Distributed Lag ARDL) by employing the Bounds Test method and Error Correction Modeling (ECM) techniques. The Un-restricted Error Correction Model (UECM) ARDL to calculate the long run elasticity of import while the short term dynamics is estimated through restricted ECM. In this research, aggregated import demand and disaggregated import demand commodities groups were made. Petroleum, Chemical, Manufacturing Goods and Machinery and Transport Groups are response variables whereas Gross Domestic Product (GDP) and Relative Prices variables are treated as explanatory variables. The disaggregated analysis of above mentioned various commodities groups are conducted in order to identify the fundamental drivers of import demand and to design fiscal and economic policy accordingly. The results indicated that at aggregated level, import demand with respective relative prices is elastic whereas it is inelastic with income in the long run. At disaggregated level, import demand with relative prices of machinery and transport group is elastic only while it is inelastic with income level in all selected disaggregated imports demand in long run. Furthermore, all import demand models are statistically significant having expected association and are stable in long run. In sort run aggregate import demand, petroleum and chemical groups are statistically significant while manufacturing goods, machinery and transport groups are insignificant. Furthermore all alternate hypotheses were accepted.