Update Stage Research Articles

Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm

Research on multitask scheduling systems in factory environments is a popular topic in the field of intelligent manufacturing. Existing research mainly focuses on the optimization of automated guided vehicle (AGV) path planning and scheduling, emphasizing on the minimization of conflicts and deadlocks, multi-objective task scheduling, and metaheuristic algorithm optimization, while ignoring path stability and real-time path planning in dynamic environments. Therefore, this paper aims to address these issues to better handle dynamic changes in actual operating environments. This paper establishes a mathematical model with the optimization objective of minimizing the overall running time of material distribution tasks and proposes an improved ant colony algorithm to optimize the model. First, the concept of prior time is introduced to improve the traditional ant colony algorithm. The path of the ongoing task is introduced with a time calculation, and the occupancy time window of each grid point on the path is calculated. Based on this, the initial pheromone distribution on subsequent paths is altered dynamically, which accelerates the convergence of the ants to a collision-free path. Second, in the pheromone update stage, the method of calculating the pheromone increment in the traditional ant colony algorithm is modified. The original distance influence factor is changed to a time influence factor, which ensures that all tasks still have the minimum running time when calculating a collision-free path. Finally, through 30 sets of simulation experiments on material distribution tasks, it is shown that the proposed algorithm shortens the total running time by 15.14%, 12.87%, and 10.59% compared to two ant colony algorithms and one strategic multi-AGV scheduling algorithm, respectively, thus verifying the effectiveness of the proposed method.

Dynamic path planning for spacecraft rendezvous and approach based on hybrid honey badger algorithm

In order to solve the 3D path planning of the close-range guidance phase of spacecraft in space rendezvous and approaching, and improve its optimization performance in dynamic environments, a 3D dynamic path planning method based on hybrid honey badger algorithm and cubic spline interpolation is proposed. Firstly, a hybrid honey badger algorithm is presented for dynamic path planning: the chaotic reverse learning is used to initialize the population, so as to improve the global search ability; the transfer operators are introduced in the mode transition stage to balance exploration and exploitation capabilities; the sine and cosine operators are integrated during the stage of location update to improve the quality of the optimal solution; an adaptive disturbance coefficient is introduced into the mining mode to accelerate the convergence. Secondly, the cubic spline interpolation algorithm is combined to optimize the path curve and reduce the turning angle of the curve. Finally, the comparative simulation experiments verify that the proposed method can plan a smooth path without collision. The planned path length is relatively shortest, the path turning angle and the algorithm running time are relatively smallest.

Deadline and Period Assignment for Guaranteeing Timely Response of the Cyber-Physical System

Cyber-physical systems (CPSs) need to respond to each change of each monitored object in time. The entire response process can be divided into two stages: the update stage and the control stage. Tasks in CPSs can thus be divided into two kinds: update tasks and control tasks. Assigning deadlines and periods for tasks to ensure a timely response to each change of each monitored object is an important problem in CPS research. Existing methods can ensure that all changes of all objects can receive timely responses if tasks are schedulable. However, these methods have not made efforts to ensure the schedulability of tasks. Therefore, some tasks that can actually receive services cannot be serviced under these methods. In this article, we study the problem of assigning deadlines and periods for tasks while ensuring timely responses and maximizing the schedulability of tasks. Specifically, we find that the delayed deadline assignment for update tasks is the main factor that causes the low scheduling capability of existing methods. A new deadline and period assignment method is proposed based on an optimized deadline calculation scheme and an advanced deadline determination mechanism. Theoretical analysis proves the correctness and superiority of the proposed method. Experimental results show that the new method can improve 30.02% acceptance ratio and save 98.48% runtime on average, as compared to the state-of-the-art deadline and period assignment method.

Does change in endometrial carcinoma FIGO staging (2018 to 2023) matters?

Abstract Introduction/Objective Endometrial Carcinoma (EC) is the sixth most common diagnosed cancer in women. Cancer related to corpus uteri are often diagnosed after menopause. TNM classification and FIGO staging system is used for staging of EC. College of American Pathologists (CAP) has been using FIGO staging system 2018 until new FIGO staging 2023 was published on June 20th 2023. We aim to identify if the new 2023 FIGO staging will change the previously assigned staging as per 2018 FIGO staging system of the endometrial carcinoma and if this change will help the physician to reevaluate the patient management. Methods/Case Report This is a retrospective cohort study. Total 205 cases were recruited from June 2022 till October 2023 at Danbury Hospital. All the patients of 18 years and above were selected for the study and were treated with hysterectomy for endometrial carcinoma and were reported to have FIGO staging as per 2018 CAP cancer protocol. Sixty three (63) participants were selected for the study. Exclusion criteria includes the cases diagnosed as endometrial carcinoma on biopsy, curettage, and patients who did not follow up for treatment. The data search was done on variables required to evaluate the FIGO staging for 2018 and 2023. Results (if a Case Study enter NA) Out of 63 patients, 55 cases were reported as per FIGO stage 2018 and eight cases were evaluated on TNM classification only. The staging did not change for forty nine (49/63) cases. However, six cases (6/63) had a change in staging based on the criteria out of which four cases (4/6) had an upstage and two cases (2/6) had a downstage. Conclusion Staging has a critical role in management of the patient. The updated FIGO staging includes multiple variables (including molecular) to improve the patient care who are diagnosed with endometrial carcinoma. Identifying the appropriate updated staging can be beneficial for oncologist to decide the treatment recommendation in the future.

Dictionary Learning Method Based on K-Sparse Approximation and Orthogonal Procrustes Analysis for Reconstruction in Bioluminescence Tomography.

Bioluminescence tomography (BLT) is one kind of noninvasive optical molecular imaging technology, widely used to study molecular activities and disease progression inside live animals. By combining the optical propagation model and inversion algorithm, BLT enables three-dimensional imaging and quantitative analysis of light sources within organisms. However, challenges like light scattering and absorption in tissues, and the complexity of biological structures, significantly impact the accuracy of BLT reconstructions. Here, we propose a dictionary learning method based on K-sparse approximation and Orthogonal Procrustes analysis (KSAOPA). KSAOPA uses an iterative alternating optimization strategy, enhancing solution sparsity with k-coefficients Lipschitzian mappings for sparsity(K-LIMAPS) in the sparse coding stage, and reducing errors with Orthogonal Procrustes analysis in the dictionary update stage, leading to stable and precise reconstructions. We assessed the method performance through simulations and in vivo experiments, which showed that KSAOPA excels in localization accuracy, morphological recovery, and in vivo applicability compared to other methods.

Evaluating the updated LATE-NC staging criteria using data from NACC.

Limbic-predominant age-related TAR DNA-binding protein of 43kDa encephalopathy neuropathologic change (LATE-NC) staging criteria were updated in 2023. We evaluated this updated staging using National Alzheimer's Coordinating Center data. We examined associations of LATE-NC stages with cognition and other neuropathologic changes (NCs), and with cognition while accounting for other NCs, using multilevel regression models. Of 1352 participants, 502 (37%) had LATE-NC (23% stage 1a, 6% stage 1b, 58% stage 2, 13% stage 3). LATE-NC stages were associated with cognition, hippocampal sclerosis of aging (HS-A), Alzheimer's disease NC (ADNC), Lewy bodies (LBs), and hippocampal atrophy. While stage 1b was associated with cognition and HS-A consistent with other stages, it was not associated with ADNC or LBs. All LATE-NC stages remained significantly associated with worse cognition when accounting for other NCs. The updated LATE-NC staging criteria capture variations in early TDP-43 pathology spread which are consequential for cognition and associations with other NCs. We applied the updated limbic-predominant age-related TAR DNA-binding protein of 43kDa encephalopathy neuropathologic change (LATE-NC) staging criteria to data from the National Alzheimer's Coordinating Center. LATE-NC stage 1b was identified in 22% of participants with stage 1. In contrast to other LATE-NC stages, stage 1b was not associated with Alzheimer's disease neuropathologic change (ADNC) or Lewy bodies. Stages 1a and 1b were significantly associated with dementia and memory impairment. Stages 1b+ were more strongly tied to dementia than all other neuropathologic changes except high likelihood ADNC.

Clinical profile and outcomes of patients with endometrial cancer from Northwest India: A retrospective study

ABSTRACT Background: Endometrial cancer is a significant health concern, but there is a lack of region-specific data on its clinical profile and outcomes in Northwest India. Recognizing the importance of early diagnosis and the evolving landscape of precision oncology, the study incorporated risk stratification based on established guidelines and updated staging. Objectives: The primary objective of the study was to determine the clinicopathological profile of patients with endometrial cancer. The secondary objectives were to estimate the survival outcomes, disease-free survival (DFS), and overall survival (OS) based on various staging protocols. Materials and Methods: We included patients with biopsy-confirmed carcinoma of the endometrium, managed at Mohan Dai Oswal Hospital, Ludhiana, Punjab from January 1, 2017 to August 31, 2022. Retrospective data were collected from electronic medical records and patient outpatient department files after ethics committee approval. The data were stratified according to the 2014 consensus guidelines of the European Society of Medical Oncology, the European Society of Gynecological Oncology, and the European Society for Radiotherapy and Oncology as well as the 2023 staging guidelines from the international federation of gynecology and obstetrics for analysis. Survival was calculated from histopathology confirmation to progression, death, or last follow-up. Statistical analysis was performed using Statistical Package for the Social Sciences (SPSS) v22, with Kaplan-Meier for survival estimates, Cox regression for survival associations, and log-rank tests for group comparisons. Results: We included a total of 111 patients. The median age of the subjects was 64 years (range 37-88). Surgery was the primary treatment in 109 (98.7%). The 5-year OS and DFS rates were 85.6% and 75.7%, respectively, with high-risk patients showing worse outcomes (3-year OS: 75.2%; 4-year OS: 37.6%) compared to low/intermediate-risk patients (5-year OS: 100%, P < 0.001). Conclusion: This study highlights significant survival variations based on risk stratification in endometrial cancer, underscoring the need for tailored management approaches.

Managing Mandibular Osteoradionecrosis.

Mandibular osteoradionecrosis (MORN) is a morbid complication of head and neck radiation therapy. Recent advances in surgical and medical therapies underscore the need for a shift in traditional treatment paradigms and a disease grading system that can guide appropriate management. Pubmed/MEDLINE. We conducted a detailed review of publications related to MORN, specifically focusing on its staging and management techniques. Articles meeting inclusion criteria were synthesized into a final narrative review. There has been a paradigm shift away from hyperbaric oxygen therapy in the management of MORN. Growing evidence for the efficacy of pentoxifylline and tocopherol in early-stage disease and novel surgical techniques to manage moderate and late-stage disease warrant an updated staging stratification which is proposed. This review summarizes the clinical efficacy of established and novel therapeutic modalities currently available in treating MORN, emphasizing the significant advances achieved over the last decade. It introduces a contemporary staging and treatment algorithm which incorporates traditional, evidence-supported surgical and medical management with effective early intervention strategies.

A cooperative timestamp-free clock synchronization scheme based on fast unscented Kalman filtering for time-sensitive networking

Clock synchronization, built on the classical two-way message exchange scheme, is the key prerequisite for the normal operation of time-sensitive networking (TSN). In practical TSN, the imperfect oscillator caused by environmental changes leads to clock parameters drift. Moreover, synchronization errors accumulate in multi-hop networks, making it difficult for nodes at the edge of the network to achieve precise synchronization performance. Additionally, in some industrial and vehicular scenarios, the energy consumption and complexity of clock synchronization are important factors that need to be considered. To address these problems, this paper proposes a cooperative synchronization clock offset and clock skew joint tracking algorithm based on fast Unscented Kalman filter (FUKF). To further reduce the computation and energy consumption caused by clock synchronization, we introduce randomized singular value decomposition and timestamp-free exchange. The former uses small sub-matrices approximations to replace extremely high-dimensional matrices, reducing computational time in the update stage of the UKF. The latter reduces energy consumption by setting response intervals at the receiving end, eliminating the need for timestamp exchange during the synchronization process. Therefore, this algorithm can achieve long-term synchronization without requiring excessive computational and communication overhead. The results show that the proposed method, while maintaining accuracy unchanged, reduced the running time by 20% to 90% as the number of observations increased, thus verifying the effectiveness of the algorithm.

A Multi-strategy Improved Grasshopper Optimization Algorithm for Solving Global Optimization and Engineering Problems

This paper presents a multi-strategy improved grasshopper optimization algorithm (MSIGOA), which aims to address the shortcomings of the grasshopper optimization algorithm (GOA), including its slow convergence, vulnerability to trapping into local optima, and low accuracy. Firstly, to improve the uniformity of the population distribution in the search space, the MSIGOA uses circle mapping for the population initialization. A nonlinear decreasing coefficient is utilized instead of an original linear decreasing coefficient to improve the local exploitation and global exploration capabilities. Then, the modified golden sine mechanism is added during the position update stage to change the single position update mode of GOA and enhance the local exploitation capability. The greedy strategy is added to greedily select the new and old positions of the individual to retain a better position and increase the speed of convergence. Finally, the quasi-reflection-based learning mechanism is utilized to construct new populations to improve population multiplicity and the capability to escape from the local optima. This paper verifies the efficacy of MSIGOA by comparing it with other advanced algorithms on six engineering design problems, CEC2017 test functions, and 12 classical benchmark functions. The experimental results show that MSIGOA performs better than the original GOA and other compared algorithms and has stronger comprehensive optimization capabilities.

Timeliness and Completeness of Endometrial Cancer Pathology Reports Before and After the FIGO 2023 Endometrial Cancer Staging Guidelines Update

Introduction: Uterine cancer is the most common gynecologic malignancy among women in the United States with an estimated 66,200 new cases and 13,030 deaths in 2023.1 These cancers are divided into endometrial adenocarcinomas (EAC) and uterine sarcomas. In 2013, the Cancer Genome Atlas (TCGA) proposed four new molecular classifications based on genome and exome sequencing of EACs including POLE-mutant, mismatch repair deficiency (MMRd), p53 abnormal, and no specific molecular profile (NSMP).4 In June of 2023, the International Federation of Gynecology and Obstetrics (FIGO) published an updated staging system for uterine cancer to incorporate these molecular indices along with the traditional anatomical distribution of disease and histology used in older FIGO staging guidelines.5 The primary objective of this project was to analyze the timeliness and completeness of pathology reports and IHC staining of patients who underwent hysterectomies for uterine cancer at Detroit Medical Center from October 2022 – December 2023. Methods: Patients were identified from the list of patients presented at the Karmanos Cancer Insitute’s (KCI) Gynecologic Oncology Tumor Board. Given the June publication timing of the FIGO 2023 staging guidelines, this served as the intervention for the study. The pre-intervention group consists of surgical cases from October 2022 through June 2023, and the post-intervention group consists of cases from July 2023-December 2023. Differences in time from surgery to pathology report, surgery to IHC result, pathology report to IHC result as well as completeness of IHC results were compared pre- and post-intervention. Because neither group was normally distributed, a MannWhitney-U test was used to calculate P-value. IHC result completeness pre- and post-intervention was calculated using a Fisher exact test. Results: A total of 106 cases were included, of which 67 were preintervention (63%) and 39 were postintervention. The median number of days from surgery to final pathology report was statistically significant (p=0.003) between the groups. The median number of days from pathology report to IHC result was not a statistically significant difference (p=0.19). The median number of days from surgery to completed IHC also not statistically significant. In terms of IHC staining, there was a statistically significant difference in the number of specimens pre- and post-intervention for p53 (p=0.004) and HER2 (p=0.04). There was no change in the MMR and MSI testing between the two groups. Conclusions: Following the implementation of the FIGO 2023 staging guidelines in June of 2023, there was a significant decrease in the time from surgery to finalized pathology report and a significant change in the completeness of staining for p53 and HER2 on hysterectomy specimens. By finalizing the results of both pathology and IHC for endometrial cancer specimens in a timelier manner, patients can be informed of their diagnosis sooner. Additionally, with a complete pathologic and molecular diagnosis, our gynecologic oncologists can also more efficiently and effectively communicate within a multidisciplinary team of providers including radiation oncology and genetic counseling colleagues to formulate a treatment plan.

Updated cutaneous T-cell lymphoma TNMB staging criteria fail to identify patients with Sézary syndrome with low blood burden

Comparison of the 2007 EORTC/ISCL and the 2022 EORTC/ISCL/USCLC blood staging guidelines for cutaneous T-cell lymphoma at a single institution reveals the newer guidelines fail to detect a subset of Sézary syndrome patients with low blood burden.

Updated endometrial cancer FIGO staging: the role of MRI in determining newly included histopathological criteria.

Endometrial cancer (EC) is among the prevalent malignancies in gynecology, showing an increasing occurrence and mortality rate. The updated 2023 FIGO staging integrates both histopathological and molecular analyses, which significantly impact the prognosis and treatment approaches. This research aims to examine the effectiveness of MRI in identifying essential histopathological tumor features, including histological subtype, grade, and lymphovascular space invasion. A total of 106 patients diagnosed with EC from February 2018 to December 2023 underwent preoperative pelvic MRI. Surgical procedures followed ESMO guidelines, with histopathological assessments using FIGO 2009 criteria. Two radiologists independently evaluated MRI images, measuring maximum tumor size, minimum tumor ADC value (using a free-hand ROI technique), and ADC tumor/myometrium ratio. MRI findings were compared with histopathological data. Peritoneal implant presence and tumor size exhibited significant differences between endometrioid adenocarcinoma (EAC) and non-endometrioid endometrial carcinoma (NEEC), with p values of < 0.001 and 0.003, respectively. Significant differences in age, tumor size, ADC tumor, and ADC tumor/myometrium between low-grade and high-grade tumors were observed, with p values of < 0.001, 0.004, 0.006, and 0.011, respectively. Increased tumor size, reduced ADC tumor, ADC tumor/myometrium, and pelvic peritoneal implant presence were significantly associated with LVSI, with p values of < 0.001, 0.001, 0.002, and 0.001, respectively. The AUC values for tumor size, ADC tumor, and ADC tumor/myometrium were 0.842, 0.781 and 0.747, respectively, in distinguishing between low and high-grade endometrial tumors. Similarly, obtained AUC values for predicting LVSI were 0.836, 0.719, and 0.696, respectively. Our study emphasizes MRI's role in predicting tumor characteristics such as histological subtype, grade, and LVSI based on updated FIGO criteria. By highlighting the potential of MRI, this research contributes to our comprehension of improving diagnostic and clinical management for EC. Further multicenter studies are warranted to validate these findings and establish MRI's role in EC management.

Accuracy of Salivary Circulating Tumor Human Papillomavirus DNA in Detecting Oropharyngeal Cancer

Circulating tumor human papillomavirus DNA (ctHPV DNA) has shown potential as a biomarker capable of improving outcomes in patients with HPV-related oropharyngeal (OP) cancer. It can be isolated from plasma or saliva, with the latter offering reduced invasiveness and theoretic reduction of lead time. To perform a systematic review and meta-analysis on the accuracy of salivary ctHPV DNA for detecting HPV-associated OP cancer. Cochrane Library, Embase, PubMed, and Web of Science databases were searched from inception through October 2023. All patients who underwent salivary ctHPV DNA testing at presentation for possible or diagnosed HPV-related OP cancer were included. Non-English and review publications were excluded. Two authors independently voted on article inclusion with a third resolving conflicting votes. Following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, multiple authors independently abstracted data and assessed bias of included articles. Bivariate random-effects meta-analysis was performed with I2 to assess for study heterogeneity. Sensitivities, specificities, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnostic odds ratios (DOR) with 95% CIs alongside area under the curve (AUC) of a summary receiver operating characteristic (SROC) curve were calculated. The initial analysis took place throughout December 2023. Of 440 initially identified articles, 6 met inclusion criteria and demonstrated moderate heterogeneity (I2 = 36%) with low risk of bias and low applicability concerns. Overall, 263 total patients were included with a median (range) age of 58 (39-86) years, and 228 (87%) were male patients. Per updated prognostic staging criteria, localized tumors (ie, stages 1 or 2) comprised most cancers at 139 (77%), whereas advanced ones (ie, stages 3 or 4) comprised the remaining 41 (23%). Pooled sensitivity, specificity, PLR, NLR, and DOR values were 64% (95% CI, 36%-85%), 89% (95% CI, 46%-99%), 11.70 (95% CI, 0.37-77.00), 1.21 (95% CI, 0.08-7.00), and 139.00 (95% CI, 0.05-837.00), respectively. The AUC of the SROC curve was 0.80. This study supports salivary ctHPV DNA as an acceptably specific test in detecting HPV-associated OP cancer that would benefit from testing in clinical trials prior to real-time implementation.

DEMFFA: a multi-strategy modified Fennec Fox algorithm with mixed improved differential evolutionary variation strategies

The Fennec Fox algorithm (FFA) is a new meta-heuristic algorithm that is primarily inspired by the Fennec fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. The “No free lunch” theorem shows that an algorithm has different effects in the face of different problems, such as: when solving high-dimensional or more complex applications, there are challenges such as easily falling into local optimal and slow convergence speed. To solve this problem with FFA, in this paper, an improved Fenna fox algorithm DEMFFA is proposed by adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, and differential evolution mutation strategies. Firstly, a sin chaotic mapping strategy is added in the initialization stage to make the population distribution more uniform, thus speeding up the algorithm convergence speed. Secondly, in order to expedite the convergence speed of the algorithm, adjustments are made to the factors of the formula whose position is updated in the first stage, resulting in faster convergence. Finally, in order to prevent the algorithm from getting into the local optimal too early and expand the search space of the population, the Cauchy operator mutation strategy and differential evolution mutation strategy are added after the first and second stages of the original algorithm update. In order to verify the performance of the proposed DEMFFA, qualitative analysis is carried out on different test sets, and the proposed algorithm is tested with the original FFA, other classical algorithms, improved algorithms, and newly proposed algorithms on three different test sets. And we also carried out a qualitative analysis of the CEC2020. In addition, DEMFFA is applied to 10 practical engineering design problems and a complex 24-bar truss topology optimization problem, and the results show that the DEMFFA algorithm has the potential to solve complex problems.

Abstract PO3-03-09: Clinical Significance of Grade in Triple Negative Breast Cancer

Abstract Triple negative breast cancer (TNBC) is a heterogeneous cancer type that lacks receptors for estrogen (ER), progesterone (PR), and human epidermal growth factor receptor-2 proteins (HER-2). An important prognostic factor for breast cancer patients is the tumour grade, which is the degree of cell proliferation or differentiation of the tumour cells from normal cells. In our initial study involving 305 TNBC patients from 2004-2017 at Windsor Regional Hospital Cancer Centre, we found a statistically significant difference between grade 2 and grade 3 patients, with grade 2 predicting significantly inferior progression-free and overall survival. In our initial study, the overall survival rates for grade 1, 2, and 3 tumours were 90.12%, 64.4%, and 77.2% respectively (p = 0.019) with relapse rates of 70%, 55.6%, and 75.6% respectively (p = 0.04). Our current study is an attempt to validate these initial findings by expanding our data set to include data from the London Health Sciences Centre. A literature review on TNBC and grade was conducted, followed by a retrospective chart review for 305 TNBC patients from the Windsor Regional Hospital and 515 TNBC patients from the London Health Sciences Centre. The parameters for data collection included patient demographics, tumour demographics, therapies, and patient outcomes. We calculated all patient stages using both American Joint Committee on Cancer 7th edition (AJCC7) and the updated AJCC8 staging systems. Initial review of the data supports our earlier findings with a relapse rate of 12.5% in patients with grade 3 tumours and 17% in patients with grade 1 and grade 2 tumours. The completed data set will be analyzed and presented at the conference. Validating our previous findings of significantly inferior patient outcomes for grade 2 compared to grade 3 patients may alter the staging system used for TNBC patients. Citation Format: Mah-noor Malik, Neya Ramanan, Sarang Upneja, Muriel Brackstone, Lisa Porter, Bre-Anne Fifield, Caroline Hamm. Clinical Significance of Grade in Triple Negative Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-03-09.

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.

New staging of endometrial carcinoma - FIGO 2023.

To review the changes in the new version of the FIGO 2023 staging system for endometrial cancer. The new FIGO 2023 endometrial cancer staging system provides key updates for the diagnosis and treatment of endometrial cancer. An important step in diagnosis is molecular classification, which allows more accurate risk stratification for recurrence and the identification of targeted therapies. The new staging system, based on the recommendations of the international societies ESGO, ESTRO and ESP, incorporates not only the description of the pathological and anatomical extent of the disease, but also the histopathological characteristics of the tumour, including the histological type and the presence of lymphovascular space invasion. In addition, the staging system uses molecular testing to classify endometrial cancers into four prognostic groups: POLEmut, MMRd, NSMP and p53abn. Each group has its own specific characteristics and prognosis. The most significant changes have occurred in stages I and II, in which the sub-staging better reflects the biological behaviour of the tumour. This update increases the accuracy of prognosis and improves individualized treatment options for patients with endometrial cancer. The updated FIGO staging of endometrial cancer for 2023 incorporates different histologic types, tumour features, and molecular classifications to better reflect the current improved understanding of the complex nature of several endometrial cancer types and their underlying bio logic behaviour. The aim of the new endometrial cancer staging system is to better define stages with similar prognosis, allowing for more precise indication of individualised adjuvant radiation or systemic treatment, including the use of immunotherapy.

A novel distributed bearing‐only target tracking algorithm for underwater sensor networks with resource constraints

AbstractUnderwater sensor networks hold immense potential for advancing the field of underwater target tracking, yet they encounter significant resource constraints stemming from energy storage and communication methods. In order to balance tracking accuracy and energy consumption, the authors present a distributed bearing‐only target tracking algorithm that can be used in underwater sensor networks with resource constraints. Anchored in the diffusion cubature information filter framework, this algorithm achieves fusion for non‐linear bearing measurements and state estimation. During the incremental update stage, individual nodes leverage the Posterior Cramer‐Rao Lower Bound as a metric for tracking performance. Subsequently, a strategy for selecting neighbouring nodes is introduced, ensuring tracking accuracy while efficiently kerbing energy consumption. In the diffusion update stage, a multi‐threshold event triggering mechanism is employed to partially diffuse the intermediate estimation. Additionally, an adaptive convex combination weight is proposed for cases involving partial diffusion. Through theoretical analysis, the asymptotic unbiasedness and convergence of the algorithm have been proven. Through Monte Carlo simulation experiments, the authors verify that the algorithm is superior to existing algorithms. Furthermore, the algorithm significantly reduces energy consumption in information interaction, minimising tracking accuracy loss.

Mayfly Optimization for Real-World Problems: A Review of Applications and Challenges

The Mayfly Optimization Algorithm (MO), inspired by the swarming and mating behavior of adult mayflies, has emerged as a powerful tool for tackling optimization problems, particularly those seeking minimum values. This review paper delves into the core principles of MO, exploring its stages of swarming, velocity update, movement, mating, and selection. We analyze the strengths of MO, including its ability to balance exploration and exploitation during the search process, leading to well-converged solutions. Additionally, the paper examines recent advancements in MO that address potential limitations. We discuss how incorporating techniques like greedy selection and auto-termination can enhance the convergence speed and efficiency of the algorithm. Furthermore, the review explores various applications of MO across diverse fields, highlighting its potential for solving real-world minimization problems. Finally, we identify and discuss ongoing research directions, including hybridization with other algorithms and exploration of advanced termination strategies. This review paper aims to provide a comprehensive understanding of the Mayfly Optimization Algorithm, its capabilities, and promising areas for future development. Index Terms— Mayfly optimization, particle swarm optimization, swarm intelligence, nature inspired algorithm.