This paper relies on 1 prerequisite mathematical and architectural foundations.
Background: Wastewater collection, treatment, discharge, additionally as reusing the treated wastewater in urban are critical factors for maintaining public health preventing contamination of water resources. This study aimed to investigate the performance of enhanced modified Ludzack Ettinger process-oxic settling anaerobic (MLE-OSA) process for treating real municipal wastewater in Sari wastewater Treatment, Sari City, Iran. Methods: To combine the OSA process technique with the MLE system, the Sludge Holding Tanks (SHT) were implemented in the return sludge line of designed pilot studies which comprised 1) MLE, which was regarded as the control system similar; 2) MLE-OSA4 with a 70-L SHT operated at 4-h Hydraulic Retention Time (HRT), and 3) MLEOSA6 with a 107-L SHT operated at 6-h HRT. To start the process, the overflow effluent of the primary settling tank of the Sari wastewater treatment plant was used. After 45-60 days, the reactors reached a steady state. The parameters of Dissolved Oxygen (DO), Oxidation-Reduction Potential (ORP), temperature, pH, Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5), Total Suspended Solids (TSS), and nutrients were analyzed. Results: The results revealed that the average TSS, COD, BOD, phosphorus, nitrite, nitrate, and total nitrogen in MLEOSA4 effluent decreased 20.1, 14.6, 2.97, 10, 51.1, 19.3, and 20.7%, respectively compared to MLE. Similarly, in MLE-OSA6, the values decreased 34.2, 16.1, 14.6, 16.7, 69.6, 31.3, and 25.8%, respectively compared to the control process. Conclusion: Therefore, using natural wastewater, the enhanced MLE-OSA process showed better performance in removing the study parameters and better quality for the effluent. Furthermore, the quality of effluent is following the Iran Department of Environment (IDE).
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
Background: Wastewater collection, treatment, discharge, additionally as reusing the treated wastewater in urban are critical factors for maintaining public health preventing contamination of water resources.
Methods: To combine the OSA process technique with the MLE system, the Sludge Holding Tanks (SHT) were implemented in the return sludge line of designed pilot studies which comprised 1) MLE, which was regarded as the control system similar; 2) MLE-OSA4 with a 70-L SHT operated at 4-h Hydraulic Retention Time (HRT), and 3) MLEOSA6 with a 107-L SHT operated at 6-h HRT.
Methods: To combine the OSA process technique with the MLE system, the Sludge Holding Tanks (SHT) were implemented in the return sludge line of designed pilot studies which comprised 1) MLE, which was regarded as the control system similar; 2) MLE-OSA4 with a 70-L SHT operated at 4-h Hydraulic Retention Time (HRT), and 3) MLEOSA6 with a 107-L SHT operated at 6-h HRT.
Trade-offs between peak accuracy and memory overhead remain under investigation across diverse hardware environments.
We demonstrate that training LLMs with RL does not improve performance equally across a dataset. RL shows large improvements on easy problems that an LLM is already good at solving, but small improvements on hard problems. We call this the Matthew Effect in RL for LLMs, after the phenomenon of cumulative advantage from economics and network science summarized as "the rich get richer". The naive explanation is that hard problems require more compute to find a solution. We argue that modern RL methods are exacerbating the issue by wasting too much compute on easy problems and instead should dynamically reallocate how they use compute. We introduce Never Give Up (NGU), a simple adaptive sampling method that keeps generating samples for a problem until one is correct. By leveraging asynchronous RL, this naturally uses fewer samples to filter out easy problems and allocates more compute to solving harder problems. We investigate the design choices that affect NGU, such as off-policy robustness, and develop a set of best practices. On the math benchmark Deepscaler, NGU improves performance per compute, especially on harder problems. On a recent coding task, Manufactoria, standard GRPO with a per-test reward fails to fully solve problems that have a range of easy and difficult tests. NGU iteratively improves, solving harder and harder tests, until it learns to fully solve coding problems.
Background: This study aimed to investigate the concentrations of heavy metals bound to airborne Particulate Matter (PM) in high-traffic districts of Tehran, Iran, and to determine the carcinogenic risk and Hazard Quotient (HQ) of these metals using a descriptive-applied method. Methods: Six indoor/outdoor stations were established in three high-traffic districts of Tehran. Each station was sampled with six replicates in winter 2018 (36 samples in total). After extracting the metals from fiberglass filters by acid digestion based on the ASTM (the American Society for Testing and Materials method), the concentrations of heavy metals were determined by an Inductively Coupled Plasma (ICP-OES) device. The human health risk was evaluated according to the US EPA (Environmental Protection Agency) standard method. The obtained data were analyzed by the Spearman correlation and Multivariate Analysis of Variance (MANOVA) in SPSS. Results: Districts 2, 3, and then 15 were the most high-traffic areas of Tehran in descending order. Average heavy metal concentrations were detected in order of Al>Fe>Pb>Mn>Cu>Zn>Cr>As>Ni>Cd. Also, the heavy metals concentrations were significantly different between indoor and outdoor environments. The correlations between heavy metal concentrations, carcinogenic risk, and HQ were significant in all three districts (P<0.05). Mean carcinogenic risk variables, HQ levels, and heavy metal concentrations in all three regions were in order of districts 15>2>3 and outdoors>indoors. Conclusion: Based on the results, serious measures are recommended to control traffic congestion in Tehran to prevent cancer risk and other health hazards caused by heavy metal bonded TSP (total suspended particulate matter).
