M. A. Shah

In many developing countries of the world including Pakistan motorcycle steeling is one of the main issue that needs to be dealt with an iron hand as it leads to economic losses as well as crime increase in the society, according to the DOWN REPORT there were 28609 motorcycle stolen in 2019 and this number has been increased to 34908 in 2020. Now due to the increasing motorcycles stealing crime what measures can we take to reduce the number of motorcycle theft the rule of technology is enormous in this regards specially information technology (IT) , information technology can play an important role in the security of motorcycles from being theft by the thieves, Information technology provide us the GSM based control system which can be very help full to construct a security system for motorcycle in order to make it antitheft.

Knee osteoarthritis (KOA) is a frequently arising orthopaedic condition that impacts a larger number of the population worldwide, causing significant pain and disability. Early detection of KOA is critical for timely intervention and improving patient outcomes. In this research, we develop a KOA prediction model using X-ray images and the Kellgren-Lawrence (KL) scale to predict the presence of KOA. Medically, photos in the form of knee X-ray images were collected and labelled by radiologists based on the KL scale, which ranges from 0 (no KOA) to 4 (severe KOA). Convolutional neural network (CNN) modelling is done to interpret X-ray pictures and forecast the KL score. This developed model attains a great degree of accuracy in predicting the KL score, with an overall accuracy of 81%. The model also demonstrates high sensitivity and specificity in detecting different levels of KOA. We conducted an evaluation of the model utilizing performance indicators like accuracy, model loss, F1 score, recall and precision. The study results suggest that the developed KOA prediction model can accurately predict the presence of KOA on the KL scale using X-ray images. The model has the potential to assist healthcare providers in early detection of KOA. This project demonstrates the potential of deep learning algorithms, such as CNN, to develop KOA prediction models using X-ray images and the KL scale.

P akistan has diverse agro-ecological regions com- prising both plains and mountains of Baluchistan, Punjab, Sindh, Khyber Pakhtunkhwa (KP), Azad Jammu and Kashmir and Gilgit-Baltistan.These regions include high precipitous valleys and irrigated plains.The environmental issues such as temperature fruitfulness status, soil distinctiveness and rainfall play a vital role in the varietal recital.Wheat is successfully grown in Khyber Pakhtunkhwa.It occupies 40% of the total cropped area and 60% of the area grown to cereals in the province.In 2013-14 the total area devoted to wheat cultivation in KP was 0.72 million ha, which produced 1.15 million tonnes of wheat with an average yield of 1590 kg/ha (GoP, 2015).Improvement in wheat production is essential for future purposes because of ever increasing population pressure.We need to develop such varieties that fulfil our nutritional requirements.Breeding objectives for bread wheat include suitability of different maturity groups for early, mild, and late planting and also special importance are given on high yield, resistance to diseases, resistance to lodging, wide adaptation, better milling, baking and nutritional qualities.Heritability and genetic advance are important to plant Abstract | Heritability plays a pivotal role in selection.Breeders could predict the performance of crop plants by knowing the magnitude of heritability.An experiment was conducted using a set of spring wheat genotypes to estimate heritability for various plant traits during crop season 2014-15 at the University of Agriculture, Peshawar.The randomized complete block design with three replications was used in the experiment.Data were recorded on yield and some other important plant traits like days to heading (days), days to maturity (days), flag leaf area (cm 2 ), spike length (cm), grains spike -1 (no.), grains weight spike -1 (g), biological yield (kg ha -1 ) and grain yield (kg ha -1 ).Mean performance of genotypes showed that all the spring wheat genotypes performed well for all the traits.Spring wheat cultivar Tatara showed best performance for maturity, similarly, genotype SM12 performed well for grains spike -1 and grain weight spike -1 , whereas, genotype SM4 performed well for grain yield.Relatively high heritability with low genetic advance was estimated for days to maturity, while moderate to high heritability coupled with genetic advance was recorded for other yield related traits.Correlation analysis showed that grain yield had strong positive phenotypic and genetic correlation with heading, maturity, flag leaf area and biological yield.Spring wheat genotype SM12 had best performance for grains spike -1 and grain weight spike -1 should be further tested at different location of Khyber Pakhtunkhwa for possible release as a new cultivar.

The CMS experiment, located at the Large Hadron Collider (LHC) in CERN, has a redundant muon system composed by three different gaseous detector technologies: Cathode Strip Chambers (in the forward regions), Drift Tubes (in the central region), and Resistive Plate Chambers (both its central and forward regions). All three are used for muon reconstruction and triggering. The CMS RPC system confers robustness and redundancy to the muon trigger. The RPC system operation in the challenging background and pileup conditions of the LHC environment is presented. The RPC system provides information to all muon track finders and thus contributing to both muon trigger and reconstruction. The summary of the detector performance results obtained with proton-proton collision at √s = 13 TeV during 2016 and 2017 data taking have been presented. The stability of the system is presented in terms of efficiency and cluster size vs time and increasing instantaneous luminosity. Data-driven predictions about the expected performance during High Luminosity LHC (HL-LHC) stage have been reported.

The CMS experiment recorded 177.75 /fb of proton-proton collision data during the RUN-1 and RUN-2 data taking period. Successful data taking at increasing instantaneous luminosities with the evolving detector configuration was a big achievement of the collaboration. The CMS RPC system provided redundant information for the robust muon triggering, reconstruction, and identification. To ensure stable data taking, the CMS RPC collaboration has performed detector operation, calibration, and performance studies. Various software and related tools are developed and maintained accordingly. In this paper, the overall performance of the CMS RPC system and experiences of the data taking during the RUN-2 period are summarised.

This study addresses the central question that how can a precise measurement of the gluon Parton Distribution Function (PDF) be attained, thereby enhancing our understanding of the intricate quark-gluon composition within nucleons? Through a dual approach involving Quantum Chromodynamics (QCD) and high-energy particle collisions, the research combines computational tools and experimental techniques, focusing on protons and neutrons. Scattering experiments and advanced detectors are utilized to probe nucleon structure. Theoretical frameworks like QCD equations and the Momentum Sum Rule investigate Parton Distribution Functions at specific momentum fractions and energy scales. The primary goal is to achieve an accurate measurement of the gluon PDF, crucial for unraveling strong force complexities and internal nucleon dynamics. The study's theoretical plots and equations illustrate hypothetical behaviors of gluon distribution, quark distribution, and splitting functions, contributing to a deeper understanding of fundamental particles.

This study addresses the central question that how can a precise measurement of the gluon Parton Distribution Function (PDF) be attained, thereby enhancing our understanding of the intricate quark-gluon composition within nucleons? Through a dual approach involving Quantum Chromodynamics (QCD) and high-energy particle collisions, the research combines computational tools and experimental techniques, focusing on protons and neutrons. Scattering experiments and advanced detectors are utilized to probe nucleon structure. Theoretical frameworks like QCD equations and the Momentum Sum Rule investigate Parton Distribution Functions at specific momentum fractions and energy scales. The primary goal is to achieve an accurate measurement of the gluon PDF, crucial for unraveling strong force complexities and internal nucleon dynamics. The study's theoretical plots and equations illustrate hypothetical behaviors of gluon distribution, quark distribution, and splitting functions, contributing to a deeper understanding of fundamental particles.

The CMS experiment, located at the CERN Large Hadron Collider, has a redundant muon system composed by three different detector technologies: Cathode Strip Chambers (in the forward regions), Drift Tubes (in the central region) and Resistive Plate Chambers (both its central and forward regions). All three are used for muon reconstruction and triggering. During the first long shutdown (LS1) of the LHC (2013-2014) the CMS muon system has been upgraded with 144 newly installed RPCs on the forth forward stations. The new chambers ensure and enhance the muon trigger efficiency in the high luminosity conditions of the LHC Run2. The chambers have been successfully installed and commissioned. The system has been run successfully and experimental data has been collected and analyzed. The performance results of the newly installed RPCs will be presented.

The Resistive Plate Chambers detector system at the CMS experiment at the LHC provides robustness and redundancy to the muon trigger. A total of 1056 double-gap chambers cover the pseudo-rapidity region < 1.6. The main detector parameters and environmental conditions are constantly and closely monitored to achieve operational stability and high quality data in the harsh conditions of the second run period of the LHC with center-of-mass energy of 13 TeV. First results of overall detector stability with 2015 data and comparisons with data from the LHC RUN-1 period at 8 TeV are presented.

The CMS experiment, located at the CERN Large Hadron Collider, has a redundant muon system composed by three different detector technologies: Cathode Strip Chambers (in the forward regions), Drift Tubes (in the central region) and Resistive Plate Chambers (both its central and forward regions). All three are used for muon reconstruction and triggering. During the first long shutdown (LS1) of the LHC (2013-2014) the CMS muon system has been upgraded with 144 newly installed RPCs on the forth forward stations. The new chambers ensure and enhance the muon trigger efficiency in the high luminosity conditions of the LHC Run2. The chambers have been successfully installed and commissioned. The system has been run successfully and experimental data has been collected and analyzed. The performance results of the newly installed RPCs will be presented.

The CMS experiment at the CERN Large Hadron Collider (LHC) has a redundant muon system composed by three different detector technologies: Cathode Strip Chambers (CSC, in the endcap regions), Drift Tubes (DT, in the barrel region), and Resistive Plate Chambers (RPC, both in the barrel and endcap).The RPC are designed mainly as a trigger detector but they contribute also to the muon reconstruction.Thus the monitoring and the analysis of the system performance are necessary and essential for the final data quality.The main detector characteristics and the hit efficiency and cluster size are presented in the paper.The stability of the system in the conditions of high instantaneous luminosity and high number of pile up (PU) events are presented in a view of history monitoring and stable trend.