In 2010, Pakistan experienced a massive flood that took the lives of 1985 individuals, in addition to causing huge damage to livestock, shelters, and domestic goods. Multiple local and international organizations extended support to the victims of the 2010 Pakistan flood. Beside relief support, media highly criticized their relief activities. The study was conducted in the district of Layyah, in the Punjab province of Pakistan. The study primarily aims at determining aspects of the flood relating to: ground situation and extent of damages, quality of services provided by the government and non-government organizations (NGOs). The study gathers data and analysis of data was carried out with simple statistical techniques. Ground situation in the country appeared alarming: flood affected 160,000 square kilometer of land, damaged to crop approached US$ 0ne billion, and affected around 20 million people. In the study area 40 % of livestock could not survive, 94.5 % houses were completed abolished and 38.7 % of domestic goods were heavily damaged. District government role was appreciated by 66.4 % of the respondents. Around 50 % of the respondents reported against the performance of the Provincial Disaster Management Authority and National Disaster Management Authority. 96.2 % of the respondents recognized the role of NGOs while respondents suggested working of NGOs through district governments.

Sentiment analysis seeks to reveal textual knowledge of literary documents in which people communicate their thoughts and views on shared platforms, such as social blogs. On social blogs, users detail is available as short comments. A question of sentiment analysis has been raised by information across large dimensions published on these blogs. Although, some language libraries are established to address the problem of emotional analysis but limited work is available on Roman Urdu language because most of the comments or opinions available online are published in text-free style. The present study evaluates emotions in the comments of Roman Urdu by using a machine learning technique. This analysis was done in different stages of data collection, labeling, pre-processing, and feature extraction. In the final phase, we used the pipeline method along with Multinomial Logistic Regression for the classification of the dataset into four categories (Politics, Sports, Education and Religion). The whole dataset was divided into training and test sets. We evaluated our test set and achieved results by using Precision, Recall, Accuracy, F1 Score and Confusion Matrix and found the accuracy ranging to 94%.

Wheat lies among the most prominent cereal crop of Pakistan which has a significant role in the stability of Pakistan’s economy. Certain biotic and a biotic factors including agro-climatic conditions, rainfall, lack of irrigation infrastructure and conventional agricultural methods are raising the risk of aphid attacks. The current study utilized satellite imagery for obtaining thermal datasets of complete wheat growth. Results revealed that rainfall is a significant parameter for the determination of aphid growth on wheat plant. A region receiving 0-10 mm rainfall, supported the growth of aphid. Moreover, the aphid survival was highly supported at a moderate temperature ranging between 20-25 oC with relative humidity ranging from 70-75 %. The study also revealed the production of weed in wheat crop acted as a moistrizing agent which consequently provided favorable conditions to the aphid population for growth. Inapropriate usage of fertilizers increased the nitrogen content in soil which turned to be favourable for the aphid attack. Thus, the study concludes that agro-climatic conditions must be considered before the aplication of pesticides.

Blind image deconvolution, a technique for obtaining restored image as well as the blur kernel from an inexact image. This research uses spatial characteristics to tackle the problem of blind image deconvolution. To work, the proposed method does not necessitate prior information about the blur kernel. Many applications, such as remote sensing, astronomy, and medical X-ray imaging, necessitate blind image deconvolution algorithms. This study used the maximum a posteriori (MAP) paradigm to create a new blind deblurring approach for removing blur from images. In beginning, we employed a Laplacian of Gaussian (LoG)-based image before regularising the gradients of an image. In the second phase, we used an operator known as the Iterative Shrinkage Thresholding Algorithm (ISTA) to cope with the non-convex challenge that develops during the entire deblurring procedure. Finally, we compared our method to several well-known methods in terms of quantitative and qualitative qualities, and we were able to determine which strategy was the most effective. Our findings show that the strategy we propose outperforms the others by a large margin.

The dramatic growth of mobile users, IoT-based applications, and astounding channel capacity requirements to connect trillions of devices are some huge challenges of the previous mobile generations, 5G turned up the key solution. Although the 5G MIMO can boost channel capacity and spectrum efficiency, it is very challenging to integrate multiple antennas into a mobile phone with limited space. Therefore, we presented a multi-band 10-elements array antenna operating at the LTE (long term evolution) 42, 43, and 46 frequency spectrum (sub-6 GHz band) for MIMO applications in fourth/fifth generation (4G/5G) modern mobile phones in this paper. A simple T-shaped slot antenna is designed to acquire 10-element MIMO antenna implementation in LTE 42/43 and 46 bands. The presented antenna array is integrated using a low-priced FR-4 substrate which is typically used for 5.7- 6-inch smartphones and possesses dimensions of 150mm × 80mm × 0.8mm. The simulated results show superb impedance matching and isolation between ports (> -12 dB), radiation efficiency (>70 %), and Envelope Correlation Coefficient (ECC< 0.05) over the operational frequency. Consequently, the designed MIMO antenna array is effectively favorable for the 5G MIMO smartphone to enhance data output and the spectrum efficiency.

Cognitive processes are constantly under critical relevance in the field of neuroscience. In the examination of Electroencephalogram (EEG) data, frequency bands are always significant because they determine how the brain responds to various activities and circumstances in different situations. However, it is usually accepted that each brain region is associated with a certain activity, such as auditory, visual, or cognitive tasks. Moreover, to retrieve additional information from the brain, it is often required to establish new neural connections. In this study, the researchers established a functional relationship between different EEG frequencies and the cognitive task under investigation. The frequencies of alpha, beta, and theta waves are often discussed in connection to cognitive tasks, but the frequency of the delta wave is seldom referenced throughout the cognitive process. We then focused on the frequency of delta waves in different parts of the brain, such as the occipital and front alregions

Wastewater from industrial discharged into other water bodies that pose serious risks to human health as well as the environment. The oil and ghee industries are also the main contributors to water pollution along with various other industries. The present study aimed to evaluate microbial load in waste water of oil industries in Lahore and its treatment using chemical and natural coagulants. Water samples were collected from three selected oil and ghee industries in Lahore. Physicochemical properties (Chemical oxygen demand (COD), Biological oxygen demand (BOD), and turbidity) and microbial contamination of water samples were analyzed before and after treatment. It was observed that samples treated with natural coagulants such as orange and banana peel, and date seeds showed a mild reduction in physicochemical parameters. Orange and banana peel coagulants caused a 30% reduction, while date seeds coagulants caused a 60% reduction in physicochemical parameters. A significant decrease in microbial load was noticed by using natural coagulants. However, for the chemical coagulants, it was observed that ferric chloride with alum and Ca+2 cation with bleaching powder caused an extreme reduction in physicochemical indicators and microbial load. While no significant decrease was observed in physicochemical indicators and microbial load when waste water samples were treated with Poly Aluminum chloride (PAC) and alum. It was concluded that chemical coagulants have a better ability to treat waste water as compared to natural coagulants.

The integrated practice of remote-sensing and GIS techniques provides an active tool for assessment of spatial and temporal variability of land features. Based on literature, it can be suggested that various studies over the recent years have been carried out to explore the potential of geospatial techniques and were found highly efficient to understand the interdependency of landscape changes, land surface temperature changes (LST) and creation of Urban Heat Island (UHI) in major cities around globe. The current research was conducted in Dera Ghazi Khan, Punjab- Pakistan which is located at latitude 30.04587 N and longitude 70.64029 E. The Landsat 8 TIRS and OLI images were obtained free of cost from USGS e-data portal. These images have already been rectified to WGS-1984-UTM-Zone_43N. The meteorological data file (MTL) for Dera Ghazi Khan- contains the study was acquired from Pakistan Meteorological Department. As per results vegetation cover has been decreased up to 15 % from 2001 to 2021, which was directly affecting the land surface temperature. It has been observed that LST derived from the satellite was closely matched with ground climatic data; there was a mere temperature difference of 2°C to 3°C. It is concluded that LST was negatively correlated with vegetation cover of the area under study. It is suggested to implement road map as provided in Dera Ghazi Khan Master Plan-2021 in order to have a control on unplanned landscape changes, urban evolution and rapid population growth.

Food borne illnesses are common in both developed as well as developing countries. The majority of foodborne diseases are caused by consuming contaminated meat products. This study aimed to evaluate the microbial contamination in different meat samples. Chicken (n=20), Mutton (n=20) and beef (n=20) samples were collected from 10 towns. Total viable count (TVC) and Total coliform count (TCC) in different meat samples were checked. Microscopic, macroscopic and biochemical profiling of isolates (n=108) was done. It was observed that E. coli was the more common (34%) pathogenic bacteria found in raw chicken followed by Salmonella (28%), Staphylococcus (25%), Shigella (8%), Enterobacter (2%), and Bacillus (3%). In Beef Samples E. coli (39%) was more common followed by Salmonella (30%), Staphylococcus (18%) and Enterobacter (8%), and Shigella (5%). While in Mutton Samples E. coli (32%), Salmonella (32%), Staphylococcus (12%), Shigella (12%), Enterobacter (9%), and Bacillus (3%). Antibacterial activity of natural preservatives i.e., Ginger, Garlic, and Radish, and commonly used synthetic preservatives i.e., Sodium nitrite was also checked on isolated strains. It was observed that Ginger and Garlic showed maximum antibacterial activity at the highest concentration used up to 0.8g/ml. Radish showed no antibacterial activity at any concentration. Antibacterial activity of Sodium nitrite was also higher at the maximum concentration used (0.006mM). The renowned harmful effects of Sodium nitrite, make it necessary to devise the use of natural preservatives. It was observed that ginger and garlic may serve as natural preservatives for meat preservation without any side-effect. However, more research is required for the implementation of natural preservatives for meat storage and safety.

Rice is considered as a major crop due to its demand globally. Pakistan is famous throughout the world to produce export quality rice which have healthy contribution in boosting the regional economy. Rice plant require plenty of water for its proper growth and development therefore, water conservation is significant to maintain water reserves for a sustainable future. The main objective of this study was to identify day-to-day availability of water in rice fields from Germination to Ripening (GTR) using Carnegie Ames Stanford Approach (CASA) model. CASA model incorporates real-time parameter e.g., temperature, pressure, extraterrestrial radiations, Leaf Area Index (LAI), vapor pressure and sunshine hours to compute net-shortwave radiations (Rns), net-longwave radiations (Rnl), net-radiations (Rn), actual incoming radiations (Rso), sensible heat flux (H), ground heat flux (Go) and finally the water stress (W). The averaged values of Rn, Rso, Rns, Rnl and H were computed as 206, 319, 178, 34 and 124 (wm-2) respectively for GTR. Total expected sunshine hours were 1584h but we could receive only 874 h during GTR due to “off and on” cloud activity. LAI and Go were observed in inverse relation to each other.

When studying lichens in Pakistan, we came across a crustose species with small to moderate squamulose on a thin blackish hypothallus with a dry, rough, gray-brown to the black upper surface. The standard chemical tests integrated with conventional to modern taxonomic tools were used to name the specimen. Consequently, with minor differences in the morphology, and no difference in nucleotides, the lichen species was baptized Parmeliella thriptophylla (Ach.) Müll. Arg. The descriptive taxonomy and n-ITS-based phylogeny of this species with its habitus are presented in this study. No previous record of this species, genus, or family was found in Pakistan.

Glaciers can be an important indicator of climate change. In Pakistan, glaciers are found in the Northern Part of the country. In this study, we have discussed the occurrence of surge velocity, lake formation, and outburst of Shisper Glacier, Hunza, Gilgit, and Baltistan. To conduct this study satellite remote sensing techniques have been used. For this purpose, Sentinel-2 and Landsat 8 have been downloaded for 2015-2019. While to examine the elevation difference ASTER 2000 and SRTM 2014 Digital Elevation Model (DEM) have been downloaded, along with this other remote sensing analyses were applied including temporal change, land cover, and morphometric analysis including indices Normalized Difference Snow Index (NDSI) and Normalized Difference Water Index (NDWI). It has been analyzed that Ice-Dammed Lake formed in November 2018, which is the greatest threat to the study area as it can cause glacial lake outbursts and the volume of the lake is showing a continuous increase from 2018- to 2020. The land cover change indicated that snow area has increased about 35% from 2015 to 2018 along with an increase in debris glacier by about 20%. Whereas, a decrease in vegetation was observed up to 2% in 3 years. This study concludes that snow and clear glacier are enlarged over time. Taking into account our research, our suggestion is obligatory stride should be taken to keep away from the disastrous occurrence in the Shisper glacier.

Management of land records includes actions such as registration and transfer of property ownership. For many nations, land ownership and management are important sources of income. Corrupted spans from small-scale payments to large-scale cause an abuse for government. In the literature, a number of concerns have been raised about Land Record Management. There are several problems with Land Record Management in developing nations, such as tampering with land records and no methods of retrieving a full property ownership record, operating multiple linked Land Record Management Systems independently, etc. Traditional land record management solutions do not solve these challenges. We propose a Blockchain-based Land Record Management system for Pakistan to solve these concerns. It has been decided to use the suggested system, and the specifics of its implementation are described in this thesis.

In many large organizations, there is an Enterprise Resource Planning (ERP) system that needs to be integrated with a third-party system to provide real-time services to their customers. To achieve this, these organizations’(ERP) systems should have inbound (IB) and outbound (OB) integration capability through APIs. In this paper, we have proposed and implemented a Hybrid Integration Model (HIM) in Industry 4.0 for Lean Management. This research was implemented in an Airline Travel Company (ATC) in which an In-house ERP system is real-time-integrated (In-bound) with Global Distribution System (GDS) through API’s i-e, soap &rest-based web services for online ticketing as well other Miscellaneous facilities i-e (Auto SMS Email) using Horizontal Integration Model in Industry 4.0 and also integrated (Outbound) with 3rd party General Ledger (GL) system. Integrating an ERP system with GL is an automated business process that requires implementing API’s and developing automatic routines to move the data between ERP & GL Systems as per user requirements. Our proposed model is fully automated where no human intervention is involved while transferring data from ERP to GL and following all standards recommended by industry 4.0 for Lean Management.

Nowadays the significant concern in IoT infrastructure is anomaly and attack detection from IoT devices. Due to the advanced technology, the attack issues are increasing gradually. There are many attacks like Data Type Probing, Denial of Service, Malicious Operation, Malicious Control, Spying, Scan, and Wrong Setup that cause the failure of the IoT-based system. In this paper, several machine learning model performances have been compared to effectively predict the attack and anomaly. The performance of the models is compared with evaluation matrices (Accuracy) and confusion matrix for the final version of the effective model. Most of the recent studies performed experiments on an unbalanced dataset; that is clear that the model will be biased for such a dataset, so we completed the experiments in two forms, unbalanced and balanced data samples. For the unbalanced dataset, we have achieved the highest accuracy of 98.0% with Generalized Linear Model as well as with Random Forest; Unbalanced dataset means most of the chances are that model is biased, so we have also performed the experiments with Random Under Sampling Technique (Balancing Data) and achieved the highest accuracy of 94.3% with Generalized Linear Model. The confusion matrix in this study also supports the performance of the Generalized Linear Model.

Open-source code hosted online at programming portals is present in 99% of commercial software and is common practice among developers for rapid prototyping and cost-effective development. However, research reports the presence of vulnerabilities, which result in catastrophic security compromise, and the individual, organization, and even national secrecy are all victims of this circumstance. One of the frustrating aspects of vulnerabilities is that vulnerabilities manifest themselves in hidden ways that software developers are unaware of. One of the most critical tasks in ensuring software security is vulnerability detection, which jeopardizes core security concepts like integrity, authenticity, and availability. This study aims to explore security-related vulnerabilities in programming languages such as C, C++, and Java and present the disparities between them hosted at popular code repositories. To attain this purpose, 708 programs were examined by severity-based guidelines. A total of 1371 vulnerable codes were identified, of which 327 in C, 51 in C++, and 993 in Java. Statistical analysis also indicated a substantial difference between them, as there is ample evidence that the Kruskal-Wallis H-test p-value (.000) is below the 0.05 significance level. The Mann-Whitney Test mean rank for GitHub (Mean-rank=676.05) and Rosettacode (Mean-rank=608.64) are also different. The novelty of this article is to identify security vulnerabilities and grasp the nature severity of vulnerability in popular code repositories. This study eventually manifests a guideline for choosing a secure programming language as a successful testing technique that targets vulnerabilities more liable to breaching security.

With the advancement in technology, non-linear loads, continue to increase, and the enigma of harmonics is getting more and more serious. The huge addition of electronic loads in power systems has formed the problem of harmonic generation that has resulted in many associated drawbacks. This paper aims to analyze the harmonics in advanced power electronics-based systems and describes a solution to mitigate them through two distinguished filtering techniques. A rigorous analysis is done concerning the generation of harmonic distortion through different types of loads and harmonic mitigation by employing passive and active power filters. The active power (adaptive) filtering method mitigates all sorts of undesirable frequency components using artificial intelligence (AI) based algorithms by calculating the weight of the fundamental component and generating a harmonics replica to subtract it from the original periodic wave. The simulations that were done show that the indicated techniques can mitigate undesirable harmonics and can lower the total harmonic distortion (THD) effectively according to the statutory limit of the IEEE 519-2014 standard, thus lowering the associated drawbacks of harmonic generation in advanced power electronics systems.

Urban Planning plays a crucial role in managing the systematic growth of the cities. Over time, it has been observed that institutions dealing with the planning, development & regulations are suffering from unproductivity because of numerous reasons. The objective of the study is to find the social, psychological, administrative, structural & academic factors affecting the most in the non-performance and unproductivity of the institutions dealing with the Urban Planning & Development and their respective severity so that the causes of those factors could be evaluated & so as remedial measures & reforms could be suggested. The performance evaluation is needed for the organizations working in the Urban Planning field. Otherwise, the Master plan/strategic plan/ development plan will remain merely an academic concept. It’s about giving a chance to look deep inside your organization to enhance its efficiency and effectiveness.

Modern nanotechnology is playing a vital role in our daily life by contributing in different domains such as usage of nanoparticles for target-specific drug delivery system, as these nanoparticle are being used as scratch proof coating on glass for tracking of biomolecules. Some emerging applications of nanoparticles include usage of nanoparticles for diagnostic purposes such as biomedical imaging and as green technology producing nano pesticides. The use of endophytic or plant beneficial bacteria for the production of metallic nanoparticles have shown promising results in not only controlling the pest but also contributing in enhanced developmental growth due to their small size, target specificity, and enhanced interaction with the plant in controlled environment. As for increasing environmental crisis, use of biological methods to remediate the environment is becoming a necessity. Green technology based nano-materials being used now a days in multiple fields, especially in bio-control of pests. This review is based on the microbial synthesized metallic nanoparticles, which are being used as nano pesticides (nanoparticles are pesticides).

The phenomenon of commercialization of residential properties is taking place rapidly. Although conversion of landuse from residential to commercial in built-up areas is not new in developing countries but planned housing schemes also start experiencing this land-use conversion, and now are no more predominantly residential. Furthermore, declaring a road commercial by the competent authorities, even without taking necessary improvements in infrastructure and consent of the community, further exaggerate the problem. The negative consequences of this phenomenon have a severe effects on residential areas and include environmental problems, traffic congestion, noise, and air pollution, and affect the residential character of the area in negative manner for which they were initially developed. The aim of the study is to describe briefly about remits of commercialization polices in Lahore and then to assess the functionality of commercialized residential roads through assessment criteria based upon the indicators established to assess capacity of road infrastructure before declaring it commercial. This includes road management plan, details of road network with condition of road, its width, Pedestrian and Public transport facilities, structure, including the primary, mixed-use, and secondary nodes. Perception of the users and residents regarding the change in the use of land is also weighed up. The findings of this research will draw the attention of responsible authorities to improve the design guidelines, which are essential to consider before commercializing the residential roads.

It is a challenging task to interpret sign language automatically, as it comprises high-level vision features to accurately understand and interpret the meaning of the signer or vice versa. In the current study, we automatically distinguish hand signs and classify seven basic gestures representing symbolic emotions or expressions like happy, sad, neutral, disgust, scared, anger, and surprise. Convolutional Neural Network is a famous method for classifications using vision-based deep learning; here in the current study, proposed transfer learning using a well-known architecture of VGG16 to speed up the convergence and improve accuracy by using pre-trained weights. We obtained a high accuracy of 99.98% of the proposed architecture with a minimal and low-quality data set of 455 images collected by 65 individuals for seven hand gesture classes. Further, compared the performance of VGG16 architecture with two different optimizers, SGD, and Adam, along with some more architectures of AlexNet, LeNet05, and ResNet50.

Human has aptitude to understand the knowledge from different life circumstances, experiences for acquire from these experiences and use itself for the erudition of Common Sense acquaintance for making the imperative conclusions in life. That is principal alteration between Out-dated Artificial Intelligence (AI), Expert Systems (ES) and Humans. Due to this capability, human could directly handle conclusion circumstances but in systems they have deficiency of generality, rules state of mind and Sentiments skills. Human circumstantial experiences communicate about how to live in social cultures where the common-sense knowledge is very vigorous. Subsequently Opinion Mining become very substantial method in now days. Two core categories for the system how to grow Sentimental Knowledge Acquisitions with the help of these two. Analyses around diverse methods used for Opinion Mining and forthcoming work might be ensue in approaching years for Sentimental Knowledge Acquisition.