| 1. |
- Asim, Muhammad, et al.
(författare)
-
Techno-economic assessment of energy and environmental impact of waste-to-energy electricity generation
- 2023
-
Ingår i: Energy Reports. - : Elsevier. - 2352-4847. ; 9:Suppl 1, s. 1087-1097
-
Tidskriftsartikel (refereegranskat)abstract
- This study explored cumulative 127.5MW waste to energy (WtE) potential in five populous cities of Pakistan based on local waste characterization profiles and global standards. The 50MW WtE plant in Lahore using National electricity regulator codes and practices resulted in an attractive Levelized cost of electricity (LCOE) of US¢ 7.86/kWh over 25 years with a $151.5 million investment cost. The net savings to Lahore Waste Management Company can be $103.4 and $137.7 million respectively with and without tipping fees on account of waste disposal cost, bricks revenue using bottom ash, and waste fee. The project developers can get net savings of $16.9 and $51.5 million respectively with and without tipping fees other than LCOE. Furthermore, the greenhouse gas emissions of 216.6 million tons of CO2eq can be saved throughout plant life against 279 GWh/year energy generation, in terms of grid emission factor and current methane release into the atmosphere from the dumping site.
|
|
| 2. |
- Sarfraz, Yasir, et al.
(författare)
-
Application of statistical and machine learning techniques for landslide susceptibility mapping in the Himalayan road corridors
- 2022
-
Ingår i: Open Geosciences. - : De Gruyter Open Ltd. - 2391-5447. ; 14:1, s. 1606-1635
-
Tidskriftsartikel (refereegranskat)abstract
- Landslides are frequent geological hazards, mainly in the rainy season along road corridors worldwide. In the present study, we have comparatively analyzed landslide susceptibility by employing integrated geospatial approaches, i.e., data-driven, knowledge-driven, andmachine learning (ML), along themain road corridors of the Muzaffarabad district. The landslide inventory of three road corridors is developed to evaluate landslide susceptibility, and eleven landslide causative factors (LCFs) were analyzed. After statistical significance analysis, these eleven LCFs generated susceptibility models using WoE, AHP, LR, and RF. Distance from roads, landcover, lithological units, and slopes are considered more influential LCFs. The performancematrix of different LSMs is evaluated through the area under the curve (AUC-ROC), overall accuracy, Kappa index, F1 score, Mean Absolute Error, and Root Mean Square Error. The AUC-ROC for WoE, AHP, LR, and RF techniques along Neelumroad is 0.86, 0.82, 0.91, and 0.97, respectively, along Jhelum Valley road is 0.83, 0.81, 0.93, and 0.95, respectively, while along Kohala road is 0.89, 0.88, 0.89, and 0.92, respectively. The produced LSMs through ML (i.e., RF and LR) showed better prediction accuracies than WoE and AHP along these three road corridors. The LSMs are categorized into very high, high, moderate, and low susceptible zones along these roads. The LSM generated through hybrid models can facilitate the concerned local agencies to implement landslide mitigation policies for the landslideprone zones along road corridors.
|
|
| 3. |
- Shahzad, ., et al.
(författare)
-
RS-RLNC : A Reinforcement Learning-Based Selective Random Linear Network Coding Framework for Tactile Internet
- 2023
-
Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 11, s. 141277-141288
-
Tidskriftsartikel (refereegranskat)abstract
- Tactile Internet (TI) has very stringent networking requirements and the transport layer plays a crucial role in meeting these requirements. However, the transport layer has several inherent limitations (e.g., bufferbloat, incast issue, and head of line blocking) due to which the performance of the current transport layer solutions is not optimal. We advocate replacing the “store-and-forward” strategy in transport layer solutions with the “compute-and-forward” strategy. One way to implement the “compute-and-forward” strategy is random linear network coding (RLNC). This paper proposes a learning-based RLNC framework called RS-RLNC that utilizes network and receiver feedback to optimally select between block-RLNC and sliding-RLNC to improve overall network performance. We present a simulation-based performance evaluation of current transport layer solutions against the state-of-the-art RLNC and RS-RLNC in terms of throughput, latency, and decoding complexity. Delay is reduced by a factor of 8.5% and decoding complexity is improved up to 20% compared to the state-of-the-art. Simulation results indicate that RS-RLNC has the potential to meet the stringent requirements of TI applications. Additionally, we present three future directions outlining the evolution of RS-RLNC to enhance the transport layer for TI compatibility.
|
|
