Design and analysis of a solar water pumping for a fish farm in Pakistan
Bibliographic record
Abstract
Aquaculture is a multibillion-dollar industry growing worldwide, especially in developing countries. This thesis focuses on a comprehensive study of off-grid fish farming in rural areas of Pakistan. A suitable site is selected for a fish farm. The solar PV system was designed and optimized for this fish farm on their annual load requirements, which are 100% renewable. Results demonstrated that the designed Solar PV system fulfilled the fish farm load smoothly and sufficiently throughout the year. Initial cost and maintenance are also estimated using HOMER Pro. Data were collected from the site survey and found there was not any system for water pumping system operations. The water pump operated manually through labor based on the visual determination of water level in ponds, Which increased production cost, electricity consumption, and wastewater. For this problem, Proposed a water pumping system automation and control using TinkerCAD. As a result, the system worked efficiently using an ultrasonic water level sensor and a low-cost motor with a microcontroller. The designed system works automatically when the water level drops to the threshold and stops. The major part of this thesis is designing and implementing an IoT-based real-time health monitoring system for the fish farm. Microcontroller Arduino Uno and Wi-Fi module ESP8266 used for the proposed system and designed a system to monitor the most critical metrics of the fish farm using an ultrasonic sensor temperature sensor, pH sensor, and dissolved oxygen sensor. ThingSpeak Cloud platform is used for data storage and display. Aquafarmers can access the fish farm health monitoring system through the web interface and phone App.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".