Running Time Research Articles

Analysis and prediction of energy consumption in office buildings with variable refrigerant flow systems: A case study

Accurately predicting building air-conditioning energy consumption is particularly important for energy management. However, for small datasets, there is a lack of simple and effective prediction methods applicable to multiple buildings. In order to fully explore the energy consumption mode of Variable Refrigerant Flow (VRF) air conditioning system and improve the accuracy of the model. This study uses the weather data of an outdoor meteorological data collection system and the air conditioning system and energy consumption data of an office building in Jinan. Initial analysis revealed that occupant behavior and daily variations in outdoor air temperatures significantly impact the energy consumption of air conditioning systems. Then, by applying the random forest algorithm and Pearson correlation analysis, the study identified daily average outdoor air temperature and VRF indoor unit running time as critical features for prediction. Subsequently, three machine learning models—Multiple Linear Regression (MLR), Back Propagation Neural Network (BPNN), and Long Short-Term Memory (LSTM) neural network—were developed and assessed using a 4:1 training-to-testing set ratio. Comparative analysis revealed that all models performed robustly, with the MLR model exhibited superior predictive accuracy (R2 (Coefficient of Determination) = 0.98, MAE (Mean Absolute Error) = 0.005 kWh/(m2·day), and RMSE (Root Mean Square Error) = 0.007 kWh/(m2·day) for the testing set) and simplicity. Furthermore, the effectiveness of the MLR model in handling small sample sizes was confirmed through cross-validation of data from two additional office buildings, highlighting its suitability for predicting energy consumption of VRF air conditioning systems in office buildings.

Liquid chromatography-mass spectrometric method for the simultaneous analysis of branched-chain amino acids and their ketoacids from dried blood spot as secondary analytes for the detection of maple syrup urine disease

BackgroundMaple syrup urine disease (MSUD) is an aminoacidopathy caused by a defective branched-chain alpha-ketoacid dehydrogenase complex, leading to the accumulation of branched-chain amino acids (BCAAs) and their respective keto acids (BCKAs). A comprehensive test was developed to measure BCAAs and BCKAs using LC-MS from dried blood spot (DBS) samples for the diagnosis and prevention of MSUD in newborns and infants. MethodsAnalytes were extracted from DBS using a methanol:0.1 % v/v formic acid solution (75:25) containing internal standards and analyzed on a Luna PFP column (150 mm × 4.6 mm, 3 µm) at a flow rate of 0.3 mL/min. The method was validated for linearity, accuracy, precision, recovery, carry-over, matrix effect, hematocrit, blood volume, and punch position effects. Biomarker stability in the matrix and stock solution was assessed. Correlation with the plasma method was determined using Pearson’s correlation coefficient and Bland-Altman analysis. The method established reference ranges for the Udupi district population in South India. ResultsThe method demonstrated linearity (r2 > 0.99), with a lower limit of detection at 2 µM (BCAA) and 1 µM (BCKA), and acceptable recovery of QC samples. Hematocrit, blood volume, punch position, and storage condition effects were within acceptable limits. Correlation and Bland-Altman analysis showed strong interconvertibility between plasma and DBS assays. Reference ranges for leucine, isoleucine, valine, KIC, KIV, and KMV were established. ConclusionThe developed DBS method, requiring no derivatization and involving simple sample preparation with short run times, is a cost-effective and reliable approach for the confirmatory diagnosis of MSUD.

Covert Communications in a Hybrid DF/AF Relay System.

In this paper, we study covert communications in a hybrid decode-and-forward (DF)/ amplify-and-forward (AF) relay system. The considered relay in normal operation forwards messages from a source node to a destination node in either DF or AF mode on request. Meanwhile, the source and destination nodes also attempt to secretly exchange covert messages such as confidential or sensitive information and avoid detection by the covert message detector embedded on the relay. We first establish an optimal DF/AF mode selection criterion to maximize the covert rate based on the analyses of delay-aware achievable covert rates of individual DF and AF modes. To further reduce the time complexity, we propose a low-complexity selection criterion as well for practical use. The numerical results demonstrate the covert rate gain as high as 50% and running time gain as high as 20% for particular system parameters, which verify the effectiveness of the proposed criteria.

Effects of XX catalysts on quantum annealing spectra with perturbative crossings

In adiabatic quantum computation the required run-time to reach a given ground-state fidelity is dictated by the size of the minimum gap that appears between the ground and first-excited state in the annealing spectrum. In general the presence of avoided level crossings demands an exponential increase in the annealing time with the system size which has consequences both for the efficiency of the algorithm and the required qubit coherence times. One promising avenue being explored to produce more favorable gap scaling is the introduction of nonstoquastic XX couplings in the form of a catalyst—of particular interest are catalysts which utilize accessible information about the optimization problem in their construction. Here we show extreme sensitivity of the effect of an XX catalyst to subtle changes in the encoding of the optimization problem. In particular, we observe that a targeted catalyst containing a single coupling can significantly reduce the gap closing with system size at an avoided level crossing. For slightly different encodings of the same problems however, these same catalysts result in closing gaps in the annealing spectrum. To understand the origin of these closing gaps, we study how the evolution of the ground-state vector is altered by the presence of the catalyst and find that the negative components of the ground-state vector are key to understanding the response of the gap spectrum. We also consider how and when these closing gaps could be utilized in diabatic quantum annealing protocols—a promising alternative to adiabatic quantum annealing in which transitions to higher energy levels are exploited to reduce the run time of the algorithm. Published by the American Physical Society 2024

Fast Context-Aware Analysis of Genome Annotation Colocalization.

An annotation is a set of genomic intervals sharing a particular function or property. Examples include genes or their exons, sequence repeats, regions with a particular epigenetic state, and copy number variants. A common task is to compare two annotations to determine if one is enriched or depleted in the regions covered by the other. We study the problem of assigning statistical significance to such a comparison based on a null model representing random unrelated annotations. To incorporate more background information into such analyses, we propose a new null model based on a Markov chain that differentiates among several genomic contexts. These contexts can capture various confounding factors, such as GC content or assembly gaps. We then develop a new algorithm for estimating p-values by computing the exact expectation and variance of the test statistic and then estimating the p-value using a normal approximation. Compared to the previous algorithm by Gafurov et al., the new algorithm provides three advances: (1) the running time is improved from quadratic to linear or quasi-linear, (2) the algorithm can handle two different test statistics, and (3) the algorithm can handle both simple and context-dependent Markov chain null models. We demonstrate the efficiency and accuracy of our algorithm on synthetic and real data sets, including the recent human telomere-to-telomere assembly. In particular, our algorithm computed p-values for 450 pairs of human genome annotations using 24 threads in under three hours. Moreover, the use of genomic contexts to correct for GC bias resulted in the reversal of some previously published findings.

Unleashing the Potential of Different Method of Paddy Straw Substrate Bag for Enhanced Yield and Biological Efficiency of Milky Mushroom

Mushrooms, particularly Calocybe indica, are highly regarded for their distinctive flavour and numerous health benefits. This study investigates various method of paddy straw substrate bags: rolled paddy straw, intact paddy straw, and chopped paddy straw to optimize the cultivation of C. indica. Employing a Completely Randomized Design (CRD), we analysed growth parameters, yield, and biological efficiency of mushrooms cultivated on these substrates. The results indicated that chopped paddy straw was the most effective substrate, significantly reducing the time for spawn run to 14.20 days, pinhead formation to 11.80 days, and sporophore maturation to 10.20 days. This method yielded the highest overall production at 664.50 g, along with a biological efficiency of 66.45%. In contrast, intact paddy straw exhibited moderate growth performance, while rolled paddy straw resulted in the lowest yield at 451.30 g and took the longest time for all growth stages, which may be attributed to less compactness in the substrate beds. The number of sporophores produced also varied, with chopped paddy straw yielding an average of 10.6 sporophores, compared to 8.6 from intact paddy straw and 7.2 from rolled paddy straw. Although stipe length and pileus diameter showed non-significant differences among treatments, they did not contribute meaningfully to yield or biological efficiency. Overall, the findings highlight that chopped paddy straw is the most suitable substrate preparation method for cultivating C. indica, enhancing both yield and efficiency in production. This research contributes valuable insights for mushroom cultivation practices, particularly in tropical regions where high temperatures prevail.

A rapid and efficient approach for quantifying metformin in human plasma using Ultra-High performance liquid chromatography with UV-Detection: Relevance in Non-Diabetes clinical studies

Metformin, a first-line drug to treat type-2 diabetes, has gained attention in various non-diabetes studies, such as anti-aging, reduced cancer incidence and mortality, and reduced symptomatic viral infection due to its immunomodulatory properties. This study aims to develop and validate a simple and cost-effective ultra-high-performance liquid chromatography method with UV detection (uHPLC-UV) for quantifying metformin in human plasma amidst operational challenges and restricted accessibility to mass spectrometry. Plasma samples were protein precipitated with acetonitrile, and metformin was separated from endogenous plasma components using a Waters Acquity UPLC BEH Amide column, 1.8 µm, (2.1 x 100 mm) with gradient elution using 0.1 M potassium phosphate adjusted with phosphoric acid to pH 5.8, and acetonitrile as mobile phases and detected at 240 nm. Method validation was performed according to U.S. FDA guidelines. Total run time achieved was 3 min and linearity was demonstrated over the range of 0.1–40 mg/L based on linear regression using 1/x weighting. The lowest quantitation limit of 0.1 mg/L was achieved. Excellent recoveries (90.3–98.4 %) and negligible matrix factors (94.6–108.1 %) were observed in the sample preparation, negating the need for using any internal standard. Finally, the developed assay was utilised to measure metformin plasma levels from healthy volunteers recruited in a randomised, double-blind, placebo-controlled study investigating potential immunomodulatory effects of metformin influence on yellow fever viremia.

Tachis: Higher-Order Separation Logic with Credits for Expected Costs

We present Tachis, a higher-order separation logic to reason about the expected cost of probabilistic programs. Inspired by the uses of time credits for reasoning about the running time of deterministic programs, we introduce a novel notion of probabilistic cost credit. Probabilistic cost credits are a separation logic resource that can be used to pay for the cost of operations in programs, and that can be distributed across all possible branches of sampling instructions according to their weight, thus enabling us to reason about expected cost. The representation of cost credits as separation logic resources gives Tachis a great deal of flexibility and expressivity. In particular, it permits reasoning about amortized expected cost by storing excess credits as potential into data structures to pay for future operations. Tachis further supports a range of cost models, including running time and entropy usage. We showcase the versatility of this approach by applying our techniques to prove upper bounds on the expected cost of a variety of probabilistic algorithms and data structures, including randomized quicksort, hash tables, and meldable heaps. All of our results have been mechanized using Coq, Iris, and the Coquelicot real analysis library.

Quantitation of the DNA-dependent protein kinase inhibitor peposertib (M3814) and metabolite in human plasma by LC-MS/MS.

The DNA-dependent protein kinase (DNA-PK) is an abundant nuclear protein that mediates DNA double-strand break repair by nonhomologous end joining (NHEJ). As such, DNA-PK is critical for V(D)J recombination in lymphocytes and for survival in cells exposed to ionizing radiation and clastogens. Peposertib (M3814) is a small molecule DNA-PK inhibitor currently in preclinical and clinical development for cancer treatment. We have developed a high-performance liquid chromatography-mass spectrometry method for quantitating peposertib and its metabolite in 0.1 mL human plasma. After MTBE liquid-liquid extraction, chromatographic separation was achieved with a Phenomenex Synergi polar reverse phase (4μm, 2× 50 mm) column and a gradient of 0.1% formic acid in acetonitrile and water over an 8min run time. Mass spectrometric detection was performed on an ABI SCIEX 4000 with electrospray, positive-mode ionization. The assay was linear from 10 to 3000 ng/mL for peposertib and 1-300 ng/mL for the metabolite and proved to be both accurate (97.3%-103.7%) and precise (<8.9%CV) fulfilling criteria from the Food and Drug Administration (FDA) guidance on bioanalytical method validation. This liquid chromatography-tandem mass spectroscopy (LC-MS/MS) assay will support several ongoing clinical studies by defining peposertib pharmacokinetics.

Weighted Context-Free-Language Ordered Binary Decision Diagrams

This paper presents a new data structure, called Weighted Context-Free-Language Ordered BDDs (WCFLOBDDs), which are a hierarchically structured decision diagram, akin to Weighted BDDs (WBDDs) enhanced with a procedure-call mechanism. For some functions, WCFLOBDDs are exponentially more succinct than WBDDs. They are potentially beneficial for representing functions of type B n → D , when a function’s image V ⊆ D has many different values. We apply WCFLOBDDs in quantum-circuit simulation, and find that they perform better than WBDDs on certain benchmarks. With a 15-minute timeout, the number of qubits that can be handled by WCFLOBDDs is 1-64× that of WBDDs(and 1-128× that of CFLOBDDs, which are an unweighted version of WCFLOBDDs). These results support the conclusion that for this application—from the standpoint of problem size, measured as the number of qubits—WCFLOBDDs provide the best of both worlds: performance roughly matches whichever of WBDDs and CFLOBDDs is better.(From the standpoint of running time, the results are more nuanced.)

An Efficient Approach for Detection of Compromised SDN Switches and Restoration of Network Flow

In Software Defined Networking (SDN) the data plane is separated from the controller plane to achieve better functionality than the traditional networking. Although this approach poses a lot of security vulnerabilities due to its centralized approach. One significant issue is compromised SDN switches because the switches are dumb in SDN architecture and in absence of any intelligence it can be a easy target to the attackers. If one or more switches are attacked and compromised by the attackers, then the whole network might be down or defunct. Therefore, in this work we have devised a strategy to successfully detect the compromised SDN switches, isolate them and then reconstruct the whole network flow again by bypassing the compromised switches. In our proposed approach of detection, we have used two controllers, one as primary and another as secondary which is used to run and validate our algorithm in the detection process. Flow reconstruction is the next job of the secondary controller which after execution is conveyed to the primary controller. A two-controller strategy has been used to balance the additional load of detection and reconstruction activity from the master controller and thus achieved a balanced outcome in terms of running time and CPU utilization. All the propositions are validated by experimental analysis of the results and compared with existing state of the art to satisfy our claim.

QuAC: Quick Attribute-Centric Type Inference for Python

Python’s dynamic typing facilitates rapid prototyping and underlies its popularity in many domains. However, dynamic typing reduces the power of many static checking and bug-finding tools. Python type annotations can make these tools more useful. Type inference tools aim to reduce developers’ burden of adding them. However, existing type inference tools struggle to support dynamic features, infer correct types (especially container type parameters and non-builtin types), and run in reasonable time. Inspired by Python’s duck typing, where the attributes accessed on Python expressions characterize their implicit interfaces, we propose QuAC (Quick Attribute-Centric Type Inference for Python). At its core, QuAC collects attribute sets for Python expressions and leverages information retrieval techniques to predict classes from these attribute sets. It also recursively predicts container type parameters. We evaluate QuAC’s performance on popular Python projects. Compared to state-of-the-art non-LLM baselines, QuAC predicts types with high accuracy complementary to those predicted by the baselines while not sacrificing coverage. It also demonstrates clear advantages in predicting container type parameters and non-builtin types and reduces run times. Furthermore, QuAC is nearly two orders of magnitude faster than an LLM-based method while covering nearly half of its errorless non-trivial type predictions. It is also significantly more consistent at predicting container type parameters and non-builtin types than the LLM-based method, regardless of whether the project has ground-truth type annotations.

Special Soundness Revisited

We generalize and abstract the problem of extracting a witness from a prover of a special sound protocol into a combinatorial problem induced by a sequence of matroids and a predicate, and present a parametrized algorithm for solving this problem. The parametrization provides a tight tradeoff between the running time and the extraction error of the algorithm, which allows optimizing the parameters to minimize: the soundness error for interactive proofs, or the extraction time for proofs of knowledge. In contrast to previous work we bound the distribution of the running time and not only the expected running time. Tail bounds give a tighter analysis when applied recursively and a concentrated running time.

Special Soundness in the Random Oracle Model

We generalize the optimal knowledge extractor for constant-round special sound protocols presented by Wikström (2018) to a knowledge extractor for the corresponding non-interactive Fiat-Shamir proofs in the random oracle model and give an exact analysis of the extraction error and running time. Relative the interactive case the extraction error and the running time are both asymptotically increased by a multiplicative factor equal to the number of oracle queries made by the prover. Through carefully chosen notation, novel concepts, and a technical lemma, we effectively recast the extraction problem of the notoriously complex non-interactive case to the interactive case. Thus, our approach may be of independent interest.

Computing Orientations from the Endomorphism Ring of Supersingular Curves and Applications

This work introduces several algorithms related to the computation of orientations in endomorphism rings of supersingular elliptic curves. This problem is at the heart of several results regarding the security of oriented-curves in isogeny-based cryptography. Under the Deuring correspondence, it can be expressed purely in terms of quaternion and boils down to representing integers by ternary quadratic forms. Our main contribution is to show that there exist efficient algorithms to solve this problem for quadratic orders of discriminant n up to O ( p 4 / 3 ) . Our approach improves upon previous results by increasing this bound from O ( p ) to O ( p 4 / 3 ) and removing some heuristics. We introduce several variants of our new algorithm and provide a careful analysis of their asymptotic running time (without heuristic when it is possible). The best proven asymptotic complexity of one of our variants is O ( n 3 / 4 / p ) in average. The best heuristic variant has a complexity of O ( p 1 / 3 ) for big enough n . We then introduce several results regarding the computation of ideals between oriented orders. The first application of this is a simplification of the known reduction from vectorization to computing the endomorphism ring, removing the assumption on the factorization of the discriminant. As a second application, we relate the problem of computing fixed-degree isogenies between supersingular curves to the problem of computing orientations in endomorphism rings, and we show that for a large range of degree d , our new algorithms improve on the state-of-the-art, and in important special cases, the range of degree d for which there exist a polynomial-time algorithm is increased. In the most special case we consider, when both curves are oriented by a small degree endomorphism, we show heuristically that our techniques allow the computation of isogenies of any degree, assuming they exist.

RP-HPLC Method for Simultaneous Quantification of Withanolides in &lt;i&gt;Withania somnifera&lt;/i&gt;

Background: Withanolides are the primary bioactive components of Withania somnifera (L.) Dunal. Among them, withanolide A, and 12-deoxy-withastramonolide are the herb’s most physiologically active ingredients. The steroidal lactones are isomers, which makes them difficult to separate. Aim: In the current research, a simple, precise, accurate, and decent RP-HPLC method has been developed, optimised and validated for simultaneous quantification and separation. Methods: Resolution was conducted on Gemini, Phenominex C18 (250mm X 4.6mm, 5 μ) column at 40oC utilising binary mobile phases of acetonitrile and ammonium acetate (10 mM) in gradient mode with 1.0 mL/min flow rate and observed at 230 nm. The total run time was twenty-five minutes. The procedure complied with ICH guidelines, covering the limit of detection, specificity, linearity, quantification, precision, accuracy, and robustness. Results: The standard curves of both relevant analytes displayed a linear pattern with regression values &gt; 0.999 in the 1–10 μg/mL range. For both herbal markers, 1 μg/mL was the lowest limit of quantification. The method’s accuracy was 100.38-100.93 % and 99.13-100.75 % for with withanolide A, and 12-deoxy-withastramonolide respectively. The precision of within and between days was found in the tolerable limit of &gt;2% for both herbal markers. Conclusion: The suggested method worked well for simultaneously analyzing the extracted samples and identifying withanolides.

LiveLattice: Real-time visualisation of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), move the sample substrate at an oblique angle relative to the detection objective's optical axis. Data from such tilted-sample-scan LSFMs require subsequent deskewing and rotation for proper visualisation and analysis. Such data preprocessing operations currently demand substantial memory allocation and pose significant computational challenges for large 4D dataset. The consequence is prolonged data preprocessing time compared to data acquisition time, which limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a memory-efficient transformation algorithm for deskewing and rotating the raw dataset, significantly reducing memory usage and the run time by more than 10-fold for large image stacks. Benchmarked against the conventional method and existing software, our approach demonstrates linear runtime compared to the cubic and quadratic runtime of the other approaches. Preprocessing a raw 3D volume of 2 GB (512 × 1536 × 600 pixels) can be accomplished in 3 s using a GPU with 24 GB of memory on a single workstation. Applied to 4D LLSM datasets of human hepatocytes, lung organoid tissue and brain organoid tissue, our method provided rapid and accurate preprocessing within seconds. Importantly, such preprocessing speeds now allow visualisation of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data preprocessing, visualisation, and analysis on standard workstations, thereby revolutionising biological imaging applications for LLSM and similar microscopes.

Intravenous enoxaparin guided by anti-Xa in venovenous extracorporeal membrane oxygenation: A retrospective, single-center study.

Unfractionated heparin is used as the most common anticoagulation for venovenous extracorporeal membrane oxygenation (VV ECMO) patients. However, it is accompanied by frequent bleeding and thrombotic complications. The aim of the study was to demonstrate the feasibility of Enoxaparin anticoagulation for VV ECMO patients. This study is a retrospective analysis of VV ECMO patients on continuous intravenous Enoxaparin anticoagulation. The primary outcome was the incidence of bleeding, thrombotic, and neurological complications during ECMO support. The secondary outcome was an analysis of secondary and primary hemostasis profiles. Data from 38 patients were analyzed in this study. The incidence of bleeding complications was 5.3%, for thrombotic complications it was 2.6% and for neurological (bleeding/ischemic events) complications it was 10.5%. The targeted anti-Xa activity of 0.4-0.6 IU/mL was achieved and maintained during whole ECMO period in 28 patients (73.8%), not affecting the hemocoagulation profile represented by APTT-r 1.15 ± 0.2, TT 18.67 ± 3.35 s, PT/INR 1.21 ± 0.19, fibrinogen 5.39 ± 1.49 g/L, antithrombin, and platelet count. Primary hemostasis pathology was diagnosed in all patients by PFA 200 tests Col/EPI 279 ± 38 s and Col/ADP 249 ± 66 s. The running time of ECMO was 7.8 ± 3.4 days. Enoxaparin anticoagulation appears to be feasible for VV ECMO patients without an increase in adverse events. Further larger-sampled and comparative studies are needed in the future to support our findings.

Design of virtual interactive educational games based on human perception

With the development of human body perception technology and virtual interactive experience, this technology has also been applied more in the field of driver education. However, the technology is not able to achieve 100 % real simulation in the process of simulating driving movement. In order to solve this problem, the study proposes to improve it by using the body sensing control wash-out filtering algorithm, and at the same time, further optimize it by genetic algorithm, and then carry out a study on the virtual interactive driving education game based on the improved technology. The proposed improved algorithm’s performance comparison results validated that it achieved the highest accuracy rate of 96 %, a final accuracy rate of 92 %, a final loss value of 1.99 %, a running time of 6.5 s, and a mean squared error of 3.8 %, all of which surpass the compared algorithm. After conducting experiments on the actual effectiveness of the designed driving education game, the maximum deviation between the simulated X-direction displacement curve of the game and the actual value was found to be 0.06 m, which outperformed the comparative experimental group. Additionally, both the angular displacement curve and perceived acceleration were the most accurate. In summary, the proposed virtual interactive driving education game based on human perception can more accurately reproduce the real scene during the driving process, providing users with better learning conditions.

Automatic visual recognition, detection and classification of weeds in cotton fields based on machine vision

Crops and weeds are involved in a continuous competition for equal resources, which may result in a potential decrease in crop yields by up to 31% and an increase in the costs of agricultural inputs by up to 22% of cultivation. Weeds further impact crop production, and their detection is crucial for effective crop management. In this research, we targeted common weeds of cotton field, specifically i) Digitaria sanguinalis (L.) Scop, ii) Amaranthus retroflexus L., iii) Acalypha australis, L., iv) Cephalanoplos segetum, and v) Chenopodium album L. Additionally, image processing techniques such as grayscale conversion, binarization, and Gaussian and morphological filters were also utilized. These methods are based on machine vision and facilitate rapid and straightforward weed detection by segmenting, scrutinizing, and comparing input images. The plant height and area were obtained during cotton planting within 32 days and fitted to develop the growth law concerning planting days for achieving the function of distinguishing cotton from weeds. We conducted recognition experiments by dividing images into four quadrants and categorizing weeds as either inter-row or intra-row. Meanwhile, the inter-row planting information was used to identify weeds, and the leaf pixel area and circularity were used as the identification methods for intra-row weeds, which reduced the algorithm's running time and improved real-time performance. The experimental results indicated that the inter-row weed recognition rate was 89.4%, with an average processing time of 102ms. Whereas in the case of intra-row weeds, the recognition rate was measured at 84.6%, and the overall recognition rate for cotton was 85.0%, with a mean time consumption of 437ms. Furthermore, the present research underscores recent advancements such as machine vision and high-resolution imaging, which have significantly improved the accuracy of automated weed identification in cotton fields while acknowledging ongoing challenges and outlining future opportunities. By Integrating state-of-the-art technology with sustainable agricultural practices, implementing an intelligent system offers a viable approach toward efficient and environmentally friendly weed management in modern agriculture.