10,327 research outputs found
Hydraulic simulations to evaluate and predict design and operation of the Chashma Right Bank Canal
Full text linkIrrigation systems / Irrigation canals / Flow control / Velocity / Canal regulation techniques / Hydraulics / Simulation models / Design / Operations / Crop-based irrigation / Distributary canals / Water delivery / Policy / Protective irrigation / Water allocation / Water requirements / Sedimentation / Water distribution / Equity / Water conveyance / Pakistan / Chashma Right Bank Canal
Entering the semi new market by use of cause related marketing (CRM) and trust factor "A case of basic health unit"
No full textMuhammad Mohsin-Ul-MulkDissertation Alpen-Adria-Universität Klagenfurt 201
Entering the semi new market by use of cause related marketing (CRM) and trust factor "A case of basic health unit"
No full textMuhammad Mohsin-Ul-MulkDissertation Alpen-Adria-Universität Klagenfurt 201
EXAMINING THE ROLE OF HAJI MUHAMMAD MOHSIN IN THE SOCIOECONOMIC LANDSCAPE OF 18TH CENTURY COLONIAL BENGAL: A COMPREHENSIVE ANALYSIS
No full textABSTRACT : 18th Century Bengal witnessed the stage from prosperity to decline , as per the statement of prominent historian Sushil Roy . This period saw the socio – economic condition of the people of Bengal under Mughal puppet emperors, next by independent Nawabs and later on by the colonial East India Company and in this socio-economic background the emergence of a great philanthropist Haji Muhammad Mohsin. In this paper the role of all these ruling classes including the role of colonial British East India Company in spreading Education and philanthropy has also been discussed critically. The role of Haji Muhammad Mohsin in this regard is highlighted and a comparative study of the Indigenous and colonial ruling class in case of spreading Education has been discussed. Attempts are also taken to find out the fact whether Haji Muhammad Mohsin was supported by the Colonial ruling class or interrupted by them in case of his continuing philanthropic activities. Apart from this , the role of contemporary wealthy persons and the role played by Haji Muhammad Mohsin’s regarding philanthropy has been discussed here. Haji Muhammad Mohsin longed for spreading Anglo – Persian Education . Secular characteristics of Haji Muhammad Mohsin and his contribution regarding the promotion of Education has been highlighted here.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV
Adaptive Holding time and Depth-Based Routing for Underwater Wireless Sensor Networks
Full text linkIn Underwater Wireless Sensor Networks (UWSNs), traditional enhancements of Depth-Based Routing (DBR) scheme rely either on increasing the network overhead or on the adoption of offline localization schemes to improve the network performance in terms of energy consumption, end-to-end delay or network throughput. Unfortunately, localization based techniques are very hard to implement in practice. In this work we show some preliminary results about the performance of a routing scheme called Adaptive Holding time and Depth-based routing (AHD) that we propose to dynamically adapt DBR configuration parameters. Specifically, we show a set of simulation experiments that suggest that networks implementing AHD show a reduced energy consumption with respect to those implementing the standard version of DBR. Simulations are performed by using our simulation library [8] of DBR [11] developed for the simulator AquaSim-Next Generation (NG) underwater simulator, which is based on Network Simulator-3 (NS-3). The characteristics of this library (detailed representation of cross-layer communications and operation modes of the modems) allows us\ud
an accurate prediction of the performance improvement of AHD with respect to standard DBR
A Systematic Literature Review on the Implementation and Challenges of Zero Trust Architecture Across Domains
No full textThe Zero Trust Architecture (ZTA) model has emerged as a foundational cybersecurity
paradigm that eliminates implicit trust and enforces continuous verification across users, de-
vices, and networks. This study presents a systematic literature review of 74 peer-reviewed
articles published between 2016 and 2025, spanning domains such as cloud computing
(24 studies), Internet of Things (11), healthcare (7), enterprise and remote work systems (6),
industrial and supply chain networks (5), mobile networks (5), artificial intelligence and
machine learning (5), blockchain (4), big data and edge computing (3), and other emerging
contexts (4). The analysis shows that authentication, authorization, and access control
are the most consistently implemented ZTA components, whereas auditing, orchestration,
and environmental perception remain underexplored. Across domains, the main chal-
lenges include scalability limitations, insufficient lightweight cryptographic solutions for
resource-constrained systems, weak orchestration mechanisms, and limited alignment with
regulatory frameworks such as GDPR and HIPAA. Cross-domain comparisons reveal that
cloud and enterprise systems demonstrate relatively mature implementations, while IoT,
blockchain, and big data deployments face persistent performance and compliance barriers.
Overall, the findings highlight both the progress and the gaps in ZTA adoption, under-
scoring the need for lightweight cryptography, context-aware trust engines, automated
orchestration, and regulatory integration. This review provides a roadmap for advancing
ZTA research and practice, offering implications for researchers, industry practitioners, and
policymakers seeking to enhance cybersecurity resilience
Pioneers of Library Movement in Pakistan
Full text linkThe paper aims to describe in brief the contribution of seven leaders of Pakistan librarianship, viz. K.B. Khalifa M. Asadullah, Prof. Dr. Abdul Moid, Dr. Abdus Subuh Qasimi, Muhammad Shafi, Fazal Elahi, Khawaja Nur Elahi and S. V. Hussain. The early library developments are given for better understanding of the role of these leaders
Deep Learning-Powered Computer Vision System for Selective Disassembly of Waste Printed Circuit Boards
No full textThe rapid growth of WPCBs poses significant environmental and resource challenges for their treatment due to their complex composition, which includes approximately 50 CRMs such as copper, beryllium and gallium. While WPCBs represent an environmental problem for their disposal, they also represent an important opportunity because their CRMs content is very high, even compared to that found in naturally occurring mines. Today, however, the recovery of high density CRMs from a circular economy perspective, through automated disassembly WPCBs have the potential because crushing and chemical treatment, is limited, for economic reasons, to a few CRMs (usually gold, copper, palladium and silver), resulting in the loss of the other CRMs.
In order to make the extraction of CRMs from WPCBs cost-effective, this research proposes a new circular economy framework, exploiting the latest machine learning and computer vision techniques, aimed at selectively disassembling (or selecting on a conveyor belt) different types of electronic components to obtain different material, each with a high concentration of specific CRMs, so as to make chemical treatment aimed at their extraction efficient. The developed AI system combines object recognition models, including the YOLO and Transformer architectures, with a recyclability evaluation framework. The system is designed to detect and classify electronic components, quantify their recyclability based on material composition, and optimize CRMs recovery. The V-PCB dataset, curated and annotated as part of this work, serves as a benchmark for training and evaluating the proposed models. Data collection involved high-resolution imaging of V-PCBs using camera modules integrated with NVIDIA Jetson Nano, ensuring scalability for real-world applications. The methodology employs iterative training and domain adaptation techniques to improve model performance. Multi-stage transfer learning strategies were used to adapt the model to varying real-world conditions, significantly improving component recognition accuracy. In addition, the recyclability assessment integrates material analysis techniques such as XRD, providing a comprehensive assessment of the CRMs recovery potential.
Experimental results demonstrate the superiority of the proposed system over traditional methods, achieving high mAP for component detection and ultimately increased high density CRMs recovery rates. The results highlight the feasibility of an automated, sustainable approach to WPCBs recycling that addresses key gaps in existing methods by focusing on component-level disassembly and reuse. This research makes a significant contribution to the field of e-waste management by providing a scalable and efficient solution for the recovery of CRMs. It aligns with circular economy principles by reducing environmental impact, minimizing waste, and promoting the reuse of functional components. The results have been disseminated at leading conferences and the proposed system is ready to transform industrial recycling workflows, making it a critical step towards sustainable electronics manufacturing
Automatic Prostate Cancer Grading Using Deep Architectures
No full textProstate cancer is the second most aggressive type of cancer among men aged over 45, and it has a major effect on people's lives. Early diagnosis and grading of prostate cancer from tissue images is necessary. Large scale inter observer reproducibility exists in grading the prostate biopsies. This leads us to move towards a computer based model that can accurately detect and grade the cancerous prostate from non-cancerous one. The paper is focused on deep learning based models to automatically grade the prostate cancer from tissue microarray images. Deep learning models directly learn the features via convolutional layers. Two datasets have been used for implementation of our proposed model, Harvard dataset and Gleason Challenge 2019. Our proposed UNET based architecture is used for training as well as validation and testing. We used four different deep learning models, VGG19, ResNet50, Mobilenetv2 and ResNext50 for our UNET based encoder. With our proposed framework, we have achieved 0.728 and 0.732 average Cohen's kappa with F1 on both datasets respectively. The results show that our proposed UNET based deep learning model shows better performance as compared to other state of the art models
Analysis and optimisations in depth-based routing for underwater sensor networks
Full text linkUnderwater Sensor Networks (UWSNs) employ sensor nodes and acoustic communication to detect physical attributes of water such as temperature, pressure, etc. Research on UWSNs has emerged thanks to their wide spectrum of applications which includes the management of the oil reservoirs and the prevention of aqueous disasters, as well as military surveillance. The dynamic conditions of water, the energy constraints and the high error probability during data transmission are prominent challenges in the design of routing protocols in UWSNs. One of the main routing schemes is Depth-based routing (DBR) that performs a specialized anycast routing to the surface sinks, based along the depth measured from pressure sensors. In this thesis, we study and optimise some routing protocols for UWSNs, specifically those based on DBR. To this aim, we designed a novel simulator for studying DBR and its enhancements. Our simulator is based on AquaSim-NG and NS-3 (Network Simulator). With respect to the state of the art, we implemented the cross-layer communication required by DBR and an accurate representation of the operational modes of acoustic modems with the associated energy consumption. We developed some analytical models for UWSNs with the aim of a) identifying the optimal transmission range for sensor nodes given the state of the system, b) finding the optimal number of hops between the source and destination under various network settings, c) evaluating the role of the depth threshold in the definition of the routing scheme. In this work, a pivotal role is played by the energy consumption and expected lifetime of the network. Finally, based on our findings, we designed the Residual energy-Depth (RD) routing protocol which improves the network lifetime
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