Uniform Method Research Articles

Relation between Urine Cytological Findings and Renal Function in Patients with Kidney Stones in Taif, Saudi Arabia

Background and Objectives: Urine serves as a vital diagnostic fluid, and urine cytology analysis plays a crucial role in identifying urinary system illnesses such as bladder cancer and kidney stones. The Paris System for Reporting Urinary Cytology establishes a uniform method for diagnosing urinary tract cancer. This study aimed to provide valuable insights that can inform diagnostic strategies related to kidney stones and ultimately improve patient outcomes via the early detection of the cellular changes associated with kidney stones and their relation to kidney function tests. Materials and Methods: A comparative study was conducted and comprised two groups: group 1, consisting of 50 patients diagnosed with kidney stones, and group 2, comprising 50 patients diagnosed with other kidney diseases. Renal function tests and urinalysis (via the PAP staining of urine cellular deposits to detect nuclear changes) were performed, and the results were analyzed. Results: There was a statistically significant increase in urinary red blood cells, white blood cells, and nuclear reactive atypical changes in urinary sediments of kidney stone patients compared to the patients without stones, while there was a decrease in the estimated glomerular filtration rate (eGFR). eGFR showed a 96.7% specificity in detecting cases with nuclear reactive atypia. Conclusions: eGFR emerges as a reliable diagnostic marker for the comprehensive assessment of kidney stones, particularly when associated with nuclear atypia. The significant correlation between the indicators of chronic kidney disease, such as decreased eGFR, and the presence of kidney stones emphasizes the urgent need for efficient diagnostic practices.

Study on preparation and properties of GA/h-BN modified road petroleum asphalt based on ball milling method

ABSTRACT Graphene (GA), a two-dimensional nanomaterial, is noted for its exceptional mechanical, thermal and electrical properties. The integration of GA with road materials can potentially develop high-performance composite road materials. However, its application in road engineering is limited by complex preparation processes and high costs. This paper presents a GA/h-BN composite material developed through mechanical stripping using flake graphite and hexagonal boron nitride (h-BN). A mathematical model was created employing uniform design methods and optimised through numerical analysis to identify the optimal process parameters. Microscopic techniques, including X-ray diffraction and atomic force microscopy, confirmed that the GA/h-BN product consists of approximately three layers, forming a ‘graphene-hexagonal boron nitride-graphene’ nanolayer structure. The composite was then used to modify matrix asphalt, with performance tests determining the optimal dosage. The results indicated that adding 0.3% GA/h-BN significantly improved the asphalt's high- and low-temperature performance, fatigue resistance and cracking resistance. Moreover, the evaluation of the physical properties of matrix asphalt modified solely with h-BN revealed that, despite h-BN comprising 70% of the mass fraction in the GA/h-BN modifier, it exerted a minimal impact on the performance of the asphalt. This study introduces a novel source of raw materials for the application of GA in road engineering.

Effectiveness and acceptability of different lifestyle interventions for women with polycystic ovary syndrome: protocol for a systematic review and network meta-analysis.

Women with polycystic ovary syndrome (PCOS) experience various metabolic, endocrine, reproductive and psychosocial manifestations. Lifestyle modification is crucial for the management of PCOS to reduce long-term complications. Nonetheless, the efficacy and acceptability of lifestyle interventions differs, and there are no uniform methods of clinical application. Hence, a systematic review and network meta-analysis (NMA) are needed to explore the efficacy and acceptability of lifestyle interventions to inform clinical practice. Ten databases (Cochrane Gynaecology and Fertility Specialised Register, Cochrane Register of Studies Online, PubMed, EMBASE (Excerpta Medica Database), PsycINFO, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Chinese National Knowledge Infrastructure, WanFang, VIP, and Sinomed) and four clinical trial registry platforms will be searched to identify literature published in English or Chinese reporting results of randomised clinical trials conducted to evaluate the effects of lifestyle interventions for women with PCOS. The reference lists of the included studies will be manually searched. Primary outcomes will include biochemical and clinical hyperandrogenism, recruitment and retention rates. Secondary outcomes will encompass menstrual regularity, ovulation, anthropometry and quality of life. Literature selection and extraction of data will be performed independently by at least two researchers. An NMA random-effects model will be implemented for amalgamating evidence. All treatments will be ranked based on the value of p. OpenBUGS will be used for Bayesian modelling, with output verifications generated in Stata and R. The quality of evidence supporting network estimates of major outcomes will also be appraised using the Grading of Recommendations Assessment, Development, and Evaluation framework. Ethical approval is not required for this review as no data will be collected from human participants. Results will be presented in a peer-reviewed publication. CRD42024499819.

An Effective and Controllable Platform with Ag-Stepped Nanocone Arrays Serving as Stable SERS Substrate

Facile, controllable and efficient SERS substrate preparation has always been the focus project in the SERS field, especially the solid-state nanoarray substrate. Herein, a simple and convenient method for preparing a series of homogeneous Ag-stepped nanocone arrays (Ag-SNCAs) using polymer nanocompression printing is presented. By analyzing the 1162[Formula: see text]cm[Formula: see text] characteristic peak of crystal violet (CV), the relative standard deviation (RSD) of the Raman intensity was only 4.71%. The detection limit of malachite green (MG) reached 0.001[Formula: see text]ppm, and it also has good SERS detection results for drug molecules such as melamine, methadone, morphine, methamphetamine and thiabendazole. This work provides a large area uniform and controllable preparation method of sharp cone nanostructure array, which has important application in plasmonic photocatalysis and deep light field regulations.

Distinct cellular uptake patterns of two anticancer unsymmetrical bisacridines and their metabolic transformation in tumor cells

Unsymmetrical bisacridines (UAs) represent a novel class of anticancer agents. Their high cytotoxicity towards multiple human cancer cell lines and inhibition of human tumor xenograft growth in nude mice signal their potential for cancer treatment. Therefore, the mechanism of their strong biological activity is broadly investigated. Here, we explore the efflux and metabolism of UAs, as both strongly contribute to the development of drug resistance in cancer cells. We tested two highly cytotoxic UAs, C-2028 and C-2045, as well as their glucuronic acid and glutathione conjugates in human cancer cell lines (HepG2 and LS174T). As a point of reference for cell-based systems, we examined the rate of UA metabolic conversion in cell-free systems. A multiple reaction monitoring (MRM)-mass spectrometry (MS) method was developed in the present study for analysis of UAs and their metabolic conversion in complex biological matrices. Individual analytes were identified by several features: their retention time, mass-to-charge ratio and unique fragmentation pattern. The rate of UA uptake and metabolic transformation was monitored for 24 h in cell extracts and cell culture medium. Both UAs were rapidly internalized by cells. However, C-2028 was gradually accumulated, while C-2045 was eventually released from cells during treatment. UAs demonstrated limited metabolic conversion in cells. The glucuronic acid conjugate was excreted, whereas the glutathione conjugate was deposited in cancer cells. Our results obtained from cell-free and cell-based systems, using a uniform MRM-MS method, will provide valuable insight into the mechanism of UA biological activity in diverse biological models.

Multi-Objective Combinatorial Optimization Algorithm Based on Asynchronous Advantage Actor–Critic and Graph Transformer Networks

Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs, such as model non-convergence, lengthy training periods, and insufficient diversity of solutions, this study introduces a novel multi-objective combinatorial optimization algorithm based on DRL. The proposed algorithm employs a uniform weight decomposition method to simplify complex multi-objective scenarios into single-objective problems and uses asynchronous advantage actor–critic (A3C) instead of conventional REINFORCE methods for model training. This approach effectively reduces variance and prevents the entrapment in local optima. Furthermore, the algorithm incorporates an architecture based on graph transformer networks (GTNs), which extends to edge feature representations, thus accurately capturing the topological features of graph structures and the latent inter-node relationships. By integrating a weight vector layer at the encoding stage, the algorithm can flexibly manage issues involving arbitrary weights. Experimental evaluations on the bi-objective traveling salesman problem demonstrate that this algorithm significantly outperforms recent similar efforts in terms of training efficiency and solution diversity.

A physics-driven sensor placement optimization methodology for temperature field reconstruction

Perceiving the global field from sparse sensors has been a grand challenge in the monitoring, analysis, and design of physical systems. In this context, sensor placement optimization is a crucial issue. Most existing works require large and sufficient data to construct data-based criteria, which are intractable in data-free scenarios without numerical and experimental data. To this end, we propose a novel physics-driven sensor placement optimization (PSPO) method for temperature field reconstruction using a physics-based criterion to optimize sensor locations. In our methodological framework, we firstly derive the theoretical upper and lower bounds of the reconstruction error under noise scenarios by analyzing the optimal solution, proving that error bounds correlate with the condition number determined by sensor locations. Furthermore, the condition number, as the physics-based criterion, is used to optimize sensor locations by the genetic algorithm. Finally, the best sensors are validated by reconstruction models, including non-invasive end-to-end models, non-invasive reduced-order models, and physics-informed models. Experimental results, both on a numerical and an application case, demonstrate that the PSPO method significantly outperforms random and uniform selection methods, improving the reconstruction accuracy by nearly an order of magnitude. Moreover, the PSPO method can achieve comparable reconstruction accuracy to the existing data-driven placement optimization methods.

EHR Interoperability Challenges Leveraging HL7 FHIR for Seamless Data Exchange in Healthcare

Electronic health records, often known as EHRs, have revolutionised the healthcare industry by making patient information more easily accessible and more efficient on the whole. There is still a huge obstacle to overcome in order to achieve seamless data transmission across different electronic health record systems. variances in data formats, terminologies, and standards often give rise to interoperability concerns. These variances impede the smooth flow of information across various systems and have the potential to cause problems. The Fast Healthcare Interoperability Resources (FHIR) standard, which is based on Health Level Seven (HL7), has emerged as a potentially useful option to overcome these difficulties. Through the provision of a uniform and standardized method for the sharing of healthcare data, FHIR is intended to facilitate the interchange of data in a manner that is both more efficient and trustworthy. The issues that are involved with electronic health record (EHR) interoperability are investigated in this study, as is the manner in which HL7 FHIR may ease the interchange of data in healthcare settings in a smooth manner. It starts out by providing an overview of the basic problems that exist in electronic health record (EHR) interoperability. These problems include data fragmentation, inconsistent data formats, and the absence of standardized protocols. HL7 FHIR is then presented in the article, along with a description of its fundamental principles. These concepts include the emphasis placed on interoperability, the simplicity of implementation, and the support for contemporary web technologies.

A METHOD OF SIMULATING SEAT LOAD FOR NUMERICAL ANALYSIS OF WOOD CHAIR STRUCTURE

This study aimed to investigate the characteristic values of the human-seat interface in a normal sitting posture, and to numerically mode the load on the chair seat for the structural design of chairs. The stress distributions and the characteristic values of seat were measured under normal sitting posture by using a human body pressure distribution measurement system considering the effects of gender and body mass index (BMI). The stress distribution on the seat was then numerically modeled using three modeling methods. The observed results and the numerical analysisresults were compared. The results showed that an inverted U-shaped pressure distribution was observed in normal sitting posture. The stress was concentrated on the ischial tuberosity with a maximum value of 0.066 MPa. The ratio of the load on the seat to the gravity of the human body weight was about 65.3%. The numerical model established using the body pressure mapping method was superior to those of the uniform load method and the standard loading pad method in terms of stress distribution, maximum stress, and contact area.

A constructive approach to strategy game map generation

Designing strategy games poses unique challenges due to the complicated nature of the genre where balancing game elements is a major concern. Maps in strategy games commonly contain elements such as resources to gather and buildings with different capabilities, playing a crucial role in the game life cycle. Resource and base placements must be done carefully. It is difficult to design maps manually and even harder to design multiple variations. This study presents a method for procedurally creating strategy game maps. A strategy game was developed and used as a test-bed for the study. A constructive approach is followed in the main steps of the algorithm design and a novel procedural noise function is used together with a uniform stratified sampling method to create and distribute the resources. Then eligible base locations are detected and scored according to a fitness function that defines how favorable a location is. These scores are used to place bases appropriately. The algorithm is evaluated using simulations and a small-scale user study is conducted at the end. The proposed method is capable of creating playable, diverse, and fair maps and is fast enough to work at runtime.

Interval Constrained Multi-Objective Optimization Scheduling Method for Island-Integrated Energy Systems Based on Meta-Learning and Enhanced Proximal Policy Optimization

Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable energies. We introduce an innovative algorithm for Interval Constrained Multi-objective Optimization Problems (ICMOPs), which incorporates meta-learning and an improved Proximal Policy Optimization with Clipped Objective (PPO-CLIP) approach. This algorithm fills a notable gap in the application of DRL to complex ICMOPs within the field. Initially, the multi-objective problem is decomposed into several single-objective problems using a uniform weight decomposition method. A meta-model trained via meta-learning enables fine-tuning to adapt solutions for subsidiary problems once the initial training is complete. Additionally, we enhance the PPO-CLIP framework with a novel strategy that integrates probability shifts and Generalized Advantage Estimation (GAE). In the final stage of scheduling plan selection, a technique for identifying interval turning points is employed to choose the optimal plan from the Pareto solution set. The results demonstrate that the method not only secures excellent scheduling solutions in complex environments through its robust generalization capabilities but also shows significant improvements over interval-constrained multi-objective evolutionary algorithms, such as IP-MOEA, ICMOABC, and IMOMA-II, across multiple multi-objective evaluation metrics including hypervolume (HV), runtime, and uncertainty.

Detection of uniformity and spatial correlation of subgrade compaction quality based on intelligent compaction technology

Intelligent compaction uniformity and spatial correlation detection of subgrade is an important part of intelligent compaction technology. In this paper, a subgrade compaction uniformity detection method is established based on the univariate statistical normal distribution 3σ criterion, and further a subgrade compaction quality spatial correlation detection method is established by the multivariate statistical spatial semi-variance function, and finally the established subgrade compaction uniformity and spatial correlation methods are verified by field tests. The subgrade compaction uniformity and spatial correlation are detected based on field tests. The results obtained in this paper show that the 3σ criterion based on normal distribution can be used for the detection of intelligent compaction uniformity of subgrade, and the field test results are verified by this method, and all the intelligent compaction uniformity of subgrade meets the requirements; considering the construction efficiency and the influence between rollers, it is recommended that the rolling speed should be in the interval range of 2–3 m/s, and the construction spacing of rollers is maintained at 10 m and above.

Universal proof theory: Semi-analytic rules and Craig interpolation

We provide a general and syntactically defined family of sequent calculi, called semi-analytic, to formalize the informal notion of a “nice” sequent calculus. We show that any sufficiently strong (multimodal) substructural logic with a semi-analytic sequent calculus enjoys the Craig Interpolation Property, CIP. As a positive application, our theorem provides a uniform and modular method to prove the CIP for several multimodal substructural logics, including many fragments and variants of linear logic. More interestingly, on the negative side, it employs the lack of the CIP in almost all substructural, superintuitionistic and modal logics to provide a formal proof for the well-known intuition that almost all logics do not have a “nice” sequent calculus. More precisely, we show that many substructural logics including UL−, MTL, R, Łn (for n⩾3), Gn (for n⩾4), and almost all extensions of IMTL, Ł, BL, RMe, IPC, S4, and Grz (except for at most 1, 1, 3, 8, 7, 37, and 6 of them, respectively) do not have a semi-analytic calculus.

Standardized Sperm Cryofreezing Technique for Caspian Sea Sturgeons (Huso huso, Acipenser persicus, Acipenser stellatus, Acipenser nudiventris)

Sturgeons, considered `living fossils,` face extinction due to various factors, according to the International Union for Conservation of Nature's Red List. The present study describes a uniform sperm cryopreservation method for the endangered Caspian Sea sturgeon species, which are beluga (Huso huso), Persian (Acipenser persicus), stellate (Acipenser stellatus), and ship (Acipenser nudiventris) sturgeons for both artificial reproduction and ex-situ conservation attempts. For this aim, sperm samples were diluted 1:1 in extender composed of 118 mM Tris buffer (pH=8) and 23.4 mM sucrose-based extender containing 15% DMSO and equilibrated for one hour at 4°C. Then, sperm samples were placed into 0.50-ml straws and multi-stage cooling was applied to freeze with the help of a programmable freezer with an accuracy of ±0.1C/min, and finally stored in liquid nitrogen (-196C) tank. Thawing was performed at 40°C water baths for 25 s. The results indicated that storage for two years resulted in a 24.7% fertilization rate for the stellate sturgeon, while the Persian sturgeon had a remarkable rate of 58.2% after five years storage.

Volumetric Analyses of Dysmorphic Maxillofacial Structures Using 3D Surface-Based Approaches: A Scoping Review.

Background/Objectives: Three-dimensional (3D) analysis of maxillofacial structures in dysmorphic patients offers clinical advantages over 2D analysis due to its high accuracy and precision in measuring many morphological parameters. Currently, no reliable gold standard exists for calculating 3D volumetric measurements of maxillofacial structures when captured by 3D surface imaging techniques. The aim of this scoping review is to provide an overview of the scientific literature related to 3D surface imaging methods used for volumetric analysis of the dysmorphic maxillofacial structures of patients affected by CL/P or other syndromes and to provide an update on the existing protocols, methods, and, when available, reference data. Methods: A total of 17 papers selected according to strict inclusion and exclusion criteria were reviewed for the qualitative analysis out of more than 4500 articles published between 2002 and 2024 that were retrieved from the main electronic scientific databases according to the PRISMA-ScR guidelines. A qualitative synthesis of the protocols used for the selection of the anatomical areas of interest and details on the methods used for the calculation of their volume was completed. Results: The results suggest a great degree of heterogeneity between the reviewed studies in all the aspects analysed (patient population, anatomical structure, area selection, and volume calculation), which prevents any chance of direct comparison between the reported volumetric data. Conclusions: Our qualitative analysis revealed dissimilarities in the procedures specified in the studies, highlighting the need to develop uniform methods and protocols and the need for comparative studies to verify the validity of methods in order to achieve high levels of scientific evidence, homogeneity of volumetric data, and clinical consensus on the methods to use for 3D volumetric surface-based analysis.

Low complexity and finer granularity quantization scheme for perturbation-based fiber nonlinearity compensation in coherent optical communication systems

In the efficient implementation of perturbation theory-based nonlinearity compensation method for reliable fiber-optic coherent communication systems, different quantization schemes have been proposed to reduce the required computational and implementation complexity. For example, the k-means clustering algorithm focused on reducing the number of perturbation coefficients to alleviate the burden of calculating the perturbation terms. In this paper, from the bit-level architectures of multipliers, we propose a multi-segment mixed-word-length quantization scheme that assigns different word lengths for the quantized perturbative coefficients to save computational resources, where Bayesian optimization is employed to derive the optimal hyperparameters involved in this scheme. The proposed scheme was evaluated by numerical simulations of single-channel and multi-channel optical fiber communication systems with dual-polarization 16-QAM modulation. The comprehensive numerical simulation results show that our proposed scheme demonstrates a complexity reduction of more than 65% without performance degradation compared with the traditional uniform quantization method with fixed-word-length, and was robust to the fiber length and baud-rate. We also carried out an experiment of single-carrier transmission over 18 × 100 km standard single-mode fiber with 20 Gbaud single-polarization 16-QAM signal. Similar to the numerical results, the hardware complexity can be reduced by 70.7% with negligible performance deterioration over the uniform quantization scheme. The proposed scheme shows great potential in real-world applications.

A new corrective method for the Tanzer’s group IIB constricted ear: Helical expansion using V-Y advancement flap combined with concha cartilage graft

BackgroundThe complexity of the constricted ear's shape renders a uniform surgical method unattainable, thus posing an ongoing challenge in its correction. The Tanzer’s group IIB constricted ear is characterized by a prominent downward folding, an underdeveloped antihelix, and the absence of sacpha. The present study utilized a V-Y advancement flap combined with concha cartilage for the repair of Tanzer’s group IIB constricted ear. MethodFrom September 2016 to September 2022, a total of 16 patients diagnosed with type IIB ear constriction were enrolled in this retrospective study. The correction procedure for constricted ear involved the utilization of a V-Y advancement flap combined with concha cartilage graft. The auricle shape data of the patients, as well as their VAS satisfaction score and auricle aesthetic score, were examined both preoperatively and 12 months postoperatively. ResultThe mean duration of follow-up in this study was 18 months. The postoperative measurements of ear length, ear width, bilateral differences in ear length, and bilateral differences in ear width exhibited significant improvement compared to preoperative values. The mean preoperative AOS score was 1.12±0.34, while the mean postoperative AOS score increased to 3.81±0.40. The preoperative VAS satisfaction score was recorded as 2.31±0.70, while the postoperative VAS score significantly increased to 8.00±0.89. The follow-up period did not present any cases of flap necrosis, hematoma, infection, or wound dehiscence. ConclusionThe combination of V-Y advancement flap and concha cartilage transplantation for the correction of Tanzer’s group IIB constricted ear can achieve a natural and aesthetically pleasing auricle shape, resulting in high patient satisfaction.

Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets.

Correlation Between Planned and Executed Bone Cuts Using Robotics in Total Knee Arthroplasty: A Prospective Study of 500 Patients.

This study evaluated the precision of robotic-arm-assisted total knee arthroplasty (RATKA) in performing bone resection, predicting component size, managing soft tissue tension, and determining postoperative range of motion (ROM). A total of 500 participants were enrolled in this prospective cohort research. The procedures were conducted at a single facility, with a uniform method and implant design. The Cuvis system, a fully automated robot, was utilized for the study. The precise removal of bone at both the tibial and femur sites, the positioning of the implant, and the release of soft tissue were documented and then compared to the preoperative plan. The distal (medial and lateral) femoral cuts had a mean absolute deviation from the plan of 0.23mm, while the posterior (medial and lateral) femoral cuts had a mean absolute difference of 1mm and 1.4mm, respectively. The absolute discrepancies in the medial and lateral tibial cuts are 0.93mm and -0.06mm, respectively. Out of 1000 bone resections, 980 (98%) were within < 1mm from the preoperative plan. The predictions for the sizes of the tibial and femoral components had accuracies of 100% and 98.9%, respectively. These findings collectively underscore the effectiveness of the fully automated Cuvis robotic system in achieving consistent and accurate results in bone resections and implant sizing, highlighting its potential as a valuable tool in orthopedic surgery.

Applying feature-similarity-metrics for long-tailed problem of phytoplankton microscopic images classification

The distribution of phytoplankton in natural water bodies holds significant importance for aquatic for maintaining healthy aquatic environments. While deep learning has become a popular research field of automating phytoplankton identification, its performance is diminished by the uneven species distribution in nature world, which biases classification models towards more advantaged species. Addressing this long-tailed problem, the paper proposed a novel deep learning idea termed feature-similarity-metrics species which utilizes the principle of decreasing intra-class differences and increasing the inter-class differences. A method called feature-center-constraint is introduced to implement the idea. Initially, a center is presupposed for each category, and these centers are distributed evenly and sparsely within the feature space using a uniform distribution initialization method. During model training, the Euclidean distance is used to measure the similarity between sample features and corresponding center. This approach enhanced the model's ability to predict disadvantaged phytoplankton species without compromising the classification accuracy of advantaged ones. Using a phytoplankton dataset of 26,564 images from 68 genera, collected from Chaohu Lake, this method demonstrates superior performance compared to existing techniques. Specially, it achieves a 5.69 % increase in micro-average F1 score and an 11.85 % rise in macro-average F1 score over general method, while F1 score is widely recognized as a reliable metric for measuring classification models' performance. These enhancements not only increase the accuracy of automated phytoplankton identification but also facilitate more effective monitoring of biodiversity and ecological health in aquatic systems. This makes the research highly beneficial for practical applications in environmental science, offering rapid and accurate insights into phytoplankton community structures, for ecological assessments.