Mean Maximum Research Articles

A general multi-source ensemble transfer learning framework for health prognostic of lithium-ion batteries

This paper proposes a novel multi-source domain adaptation method for the health prediction in the absence of target batteries labels. First, a computationally efficient and effective source domain selection method is proposed. The proposed source domain selection method selects source domains with excellent transferability based on Euclidean distance and Sample Entropy thresholds, thereby improving the performance of multi-source domain adaptation. Second, improved maximum mean discrepancy is proposed to guide the representation alignment. Compared with the original maximum mean discrepancy, it matches the source data with the target data in the order of aging, thus preserving the time order of the distribution. Third, we develop a multi-source domain adaptation network that contains a domain-invariant feature generator and several domain-specific feature generators and estimators. Two out of 12 datasets are used as target LIBs to evaluate the performance of the proposed method. The mean absolute error of the proposed method on these two datasets is only 0.671% and 0.868%, respectively. While the maximum error of MDA-IM-GC on both datasets does not exceed 5.2%. The experiment results indicate that the proposed network provides impressive accuracy with only 1/10 of unlabeled target historical data accessible and outperforms the single-source domain adaptation approach.

A Semi-Supervised Lie Detection Algorithm Based on Integrating Multiple Speech Emotional Features

When people tell lies, they often exhibit tension and emotional fluctuations, reflecting a complex psychological state. However, the scarcity of labeled data in datasets and the complexity of deception information pose significant challenges in extracting effective lie features, which severely restrict the accuracy of lie detection systems. To address this, this paper proposes a semi-supervised lie detection algorithm based on integrating multiple speech emotional features. Firstly, Long Short-Term Memory (LSTM) and Auto Encoder (AE) network process log Mel spectrogram features and acoustic statistical features, respectively, to capture the contextual links between similar features. Secondly, the joint attention model is used to learn the complementary relationship among different features to obtain feature representations with richer details. Lastly, the model combines the unsupervised loss Local Maximum Mean Discrepancy (LMMD) and supervised loss Jefferys multi-loss optimization to enhance the classification performance. Experimental results show that the algorithm proposed in this paper achieves better performance.

A novel fault early warning method for centrifugal blowers based on stacked denoising autoencoder and transfer learning

Centrifugal blowers are easy to get faults due to the harsh working environment, and appropriate fault early warning is of great significance for predictive maintenance. Traditional fault early warning methods have poor resistance and feature learning ability in dealing with multivariate data with noise, and cannot achieve domain adaptation in different working environments. Aimed at solving these problems, this paper proposes a novel fault early warning method for centrifugal blowers based on stacked denoising autoencoder with sliding window (SW-SDAE) and transfer learning. The developed SW-SDAE model can effectively learn representative degradation features and temporal dependence from multivariate time-series data with noise. The reconstruction errors of SW-SDAE are used to construct the health indicators, which accurately characterizes the health status of the centrifugal blower. Meanwhile, transfer learning is employed to solve the problem of domain adaptation for different working environments. The established source domain warning model is successfully transferred to the target domain by minimizing the maximum mean discrepancy. When the health indicator exceeds the warning threshold, a fault early warning is performed. Experimental results demonstrate that the developed SW-SDAE warning model integrating transfer learning significantly resists the interference of noise and improves the domain adaptability for different working conditions. The proposed method achieves fault early warning 5.67 h without false alarms before failure and shows superior warning performance compared with traditional warning methods.

Climatic Suitability for Outdoor Tourism in Romania’s Big Cities

This research aims to assess the climatic temporal suitability over the year and identify the appropriate season for open-air tourism in ten Romanian cities. It was evaluated using the Enhanced Tourism Climatic Index on a temporal scale of one day and then aggregated to 10 days over 61 years (1961–2021). Daily mean and maximum temperature, mean and minimum relative humidity, wind speed, precipitation, and sunshine hours were employed in the investigation. The Mann–Kendall test and Sen’s slope were used for trend detection in the frequency, season duration, and first/last suitable day during the year for outdoor tourism. Acceptable or better weather conditions usually begin in the last part of April and end in mid-October, with Good or better conditions lasting between 260 and 310 days/year. The trend shows a shift of Good conditions earlier in the year (0.3–9.0 days/decade), resulting in a longer season duration (0.8–13.0 days/decade) for open-air activities. The trend is statistically significant mainly for the extra-Carpathian regions. Big differences in open-air events number during the climatically suitable season have been identified among the cities considered (2–19 events/year). This study is useful for better planning open-air events and activities for tourism and recreation.

Pengaruh Return on Asset, Net Profit Margin, Firm Size dan Debt To Equity Ratio Terhadap Harga Saham pada Perusahaan Subsektor Farmasi yang Terdaftar di Bursa Efek Indonesia (BEI) Periode Tahun 2015-2022

Finding and elucidating the relationship between stock prices and return on asset, net profit margin, firm size, and debt to equity ratio is the goal of this study. Secondary data from www.idx.co.id are the ones utilized. Twenty-five pharmaceutical sub-sector businesses listed on the Indonesia Stock Exchange for the Financial Report Period of 2016–2022 comprise the study population, and the sample size comprises forty financial reports from these companies. Using a purposive sampling strategy, the Sampling Technique employs nonprobability sampling. Utilizing both a descriptive and verification technique, the study methodology is quantitative. Maximum Value, Minimum Value, Mean, and Standard Deviation are the components of descriptive statistical analysis. Validation The tests used in statistical analysis include the determination coefficient test, the product moment correlation coefficient test, the multiple linear regression test, and the classical assumption test. Partially (t-test) and fully (f-test) concurrent hypothesis testing is done. The following conclusions were drawn from the partial hypothesis testing (t-test) results: (1) Return on Asset has a positive and substantial effect on stock price; (2) Net Profit Margin has no effect on stock price; and (3) Firm Size has no effect on stock price”. (4) The debt to equity ratio is unaffected by the price of stocks. Return on Asset, Net Profit Margin, Firm Size, and Debt to Equity Ratio were found to have no simultaneous effects on Stock Price based on the findings of the simultaneous hypothesis testing (f-test).

Tracking palaeotemperatures in Coniacian–Maastrichtian seas

In this study the stable isotopes of belemnites, are presented from the Coniacian–Maastrichtian interval (∼76–66 Ma) derived from the chalks of Yorkshire and Norfolk, UK, deposited on the western North Atlantic shelf. Cathodoluminescence and elemental geochemistry of the belemnites reveals that most of the rostra were well preserved. If interpreted in terms of temperature, our oxygen isotope record reveals that during the Coniacian (at ∼43 °N) the climate was relatively warm, with maximum mean temperatures of ∼26 °C, followed by cooling to <∼21 °C during the Campanian and Maastrichtian. This overall stratigraphic trend is similar to other records, suggesting that the cooling pattern was not a regional trend and, therefore, driven predominantly by global mechanisms. Within our belemnite data, we also observe a decline in δ13C at the Campanian- Maastrichtian boundary, again consistent with other records. This trend has been interpreted as a result of an increased ratio of organic to inorganic carbon introduced into the oceans, driven by increased weathering and reworking of organic-rich sediments exposed on continental shelves during a sea-level fall. The latter related to a build-up of polar ice. Although our oxygen isotope data point to a cooling this was not necessarily linked to polar ice formation.

Optimizing the Morphology and Solidification Behavior of Fe-Rich Phases in Eutectic Al-Si-Based Alloys with Different Fe Contents by Adding Mn Elements.

A high Fe content easily produces Fe-rich phases with a harmful morphology, resulting in a huge detrimental effect on the properties and recycling ability of Al-Si alloys. Therefore, finding ways to effectively transform Fe-rich phases to form a beneficial phase or shape is of great significance. Accordingly, Al-Si-based alloys with Fe contents ranging from 0.1 wt.% to 2.0 wt.% were modified by different Mn additions. Moreover, experiments combined with simulations were utilized to comprehensively analyze the mechanism of Mn on the morphology and microstructural evolution of Fe-rich phases from different perspectives. The current findings determine that adding different Fe contents changes the phase-transition reactions in alloys. Without Mn, and by increasing the Fe content from 0.1 wt.% to 2.0 wt.%, the Fe-rich phases gradually convert from a skeleton-shaped α-Al8Fe2Si (<0.25 wt.%) to β-Al9Fe2Si2 with a fibrous (0.5 wt.%), needle-like (1.0 wt.%) and plate-like shape without curvatures (2.0 wt.%). The maximum length and mean aspect ratio increase from 12.01 μm to 655.66 μm and from 1.96 to 84.05, while the mean curvature decreases from 8.66 × 10-2 μm-1 to 8.25 × 10-4 μm-1. The addition of 0.35 wt.% Mn promotes a new Chinese-character and petal-shaped α-Al15(FeMn)3Si2, with an atomic ratio of Fe and Mn of 1:1 when the Fe content is lower than 0.5 wt.%, while it transforms to β-Al15(FeMn)3Si2 with an atomic ratio of 5:1, presenting as a refined plate-like shape with a certain curvature, as the Fe content increases to 2.0 wt.%. Mn alters the phase reactions and increases the threshold of the Fe content required for β-Al15(FeMn)3Si2, limiting the formation and growth of them simultaneously in time and space. The enrichment of Mn atoms and solute diffusion at the growth front of β-Al15(FeMn)3Si2, as well as the strong atomic-binding ability, can deflect the growth direction of β-Al15(FeMn)3Si2 for it to have a certain curvature. Additionally, the enriched Mn atoms easily form α-Al15(FeMn)3Si2 and cause the long β-Al15(FeMn)3Si2 to be broken and refined to further reduce the damages caused to the alloy's performance. Ultimately, the maximum length and mean aspect ratio can be effectively reduced to 46.2% and 42.0%, respectively, while the mean curvature can be noticeably increased by 3.27 times with the addition of Mn.

A Cross-Working Condition-Bearing Diagnosis Method Based on Image Fusion and a Residual Network Incorporating the Kolmogorov–Arnold Representation Theorem

With the optimization and advancement of industrial production and manufacturing, the application scenarios of bearings have become increasingly diverse and highly coupled. This complexity poses significant challenges for the extraction of bearing fault features, consequently affecting the accuracy of cross-condition fault diagnosis methods. To improve the extraction and recognition of fault features and enhance the diagnostic accuracy of models across different conditions, this paper proposes a cross-condition bearing diagnosis method. This method, named MCR-KAResNet-TLDAF, is based on image fusion and a residual network that incorporates the Kolmogorov–Arnold representation theorem. Firstly, the one-dimensional vibration signals of the bearing are processed using Markov transition field (MTF), continuous wavelet transform (CWT), and recurrence plot (RP) methods, converting the resulting images to grayscale. These grayscale images are then multiplied by corresponding coefficients and fed into the R, G, and B channels for image fusion. Subsequently, fault features are extracted using a residual network enhanced by the Kolmogorov–Arnold representation theorem. Additionally, a domain adaptation algorithm combining multiple kernel maximum mean discrepancy (MK-MMD ) and conditional domain adversarial network with entropy conditioning (CDAN+E ) is employed to align the source and target domains, thereby enhancing the model’s cross-condition diagnostic accuracy. The proposed method was experimentally validated on the Case Western Reserve University (CWRU) dataset and the Jiangnan University (JUN) dataset, which include the 6205-2RS JEM SKF, N205, and NU205 bearing models. The method achieved accuracy rates of 99.36% and 99.889% on the two datasets, respectively. Comparative experiments from various perspectives further confirm the superiority and effectiveness of the proposed model.

Numerical optimization of drying of white button mushroom (Agaricus bisporus) employing microwave and fluidized bed drying for preparing value added product

The microwave-assisted fluidized bed two-stage novel, energy-efficient drying method was used for Agaricus bisporus drying which can be further utilised for mushroom protein powder, and soup. The effect of microwave heating time (MHT), fluidized bed drying temperature (FBDT), and slice thickness (ST) on rehydration ratio, total colour difference (TCD), and overall acceptability of white button mushroom (WBM) powder was investigated. The optimized values, i.e., 1 min (MHT), 60 °C (FBDT), and 2 mm (ST), were obtained during optimization using design expert software (ver. 13.0.1). The result revealed that MHT, FBDT, and ST affect the responses significantly. TCD was found to be increased while overall acceptability decreased as MHT and FBDT increased. Rehydration is an important criterion for using dry products in curry or soup since they may absorb water. The optimized WBM (O-WBM) powder was used to prepare the soup mix. Out of three formulations (F1, F2, and F3), F3 had a 9 % O-WBM powder score with a maximum mean value for colour, taste, flavor, consistency, and overall acceptability, followed by F2 with a 7 % O-WBM powder. The soup’s composition includes functional ingredients such as garlic, shallots, and milk powder, making it healthier.

Adversarial enhancement generation method for side-scan sonar images based on DDPM–YOLO

As marine exploration advances, efficient and accurate seabed target identification becomes increasingly critical. However, traditional methods and current technologies face challenges such as scarce samples and complex imaging conditions when dealing with side-scan sonar images. Given the scarcity of sample augmentation methods for side-scan sonar, this paper iteratively trains the Denoising Diffusion Probabilistic Models(DDPM), integrating the DDPM diffusion model and the downstream You Only Look Once(YOLO) retrieval task into a mutually reinforcing framework, proposing an adversarial enhancement generation method based on the DDPM and YOLO detection models. Experiments demonstrate that the DDPM model generated through this adversarial enhancement generation method can improve the accuracy of downstream YOLO target detection tasks by 7%. The images generated by this model also perform optimally on the Fréchet Inception Distance(FID), Maximum Mean Discrepancy(MMD), and Learned Perceptual Image Patch Similarity(LPIPS) metrics, thereby proving that our method can enhance the quality of generated images from the side-scan sonar diffusion model and offer a new avenue for improving the construction of underwater target detection models.

Application of Submucosal Trans-Septal Suturing Technique in a Septal Extension Graft With Porous High-Density Polyethylene and Concurrent Nasal Lateral Osteotomy.

Recently, a submucosal trans-septal suturing (STSS) technique was introduced to obviate the dead space of septum after septal extension graft (SEG) with porous high-density polyethylene (pHDPE). To investigate STSS technique after SEG with pHDPE and concurrent nasal lateral osteotomy (LO). A retrospective study was conducted in 53 patients who underwent a STSS technique after SEG with pHDPE and concurrent LO. The postoperative discomfort (nasal pain and obstruction), the width of the nasal bony base, Nasal Obstruction Symptom Evaluation (NOSE), Rhinoplasty Outcomes Evaluation (ROE), and complications were recorded and assessed. The mean surgical duration of STSS was 862.53±227.73 seconds. The maximal mean score of postoperative nasal pain and nasal obstruction was 2.132±0.921 and 1.868±0.8995, respectively. The values on the width of the nasal bony base and ROE were significantly improved after surgery. There was no significant difference in NOSE values preoperatively versus 6 months postoperatively. An infection was found in 1 patient, a recurrent minor bleeding of septum in another patient, and a symptomatic nasal obstruction in other 2 patients. There was no major bleeding, hematoma, foreign body sensation, septal perforation, and other infection. STSS can eliminate the postoperative dead space of nasal septum with low discomfort and complications, and help to avoid a lateral displacement of osteotomized nasal bony segment (ONBS) in patients undergoing SEG with pHDPE and concurrent LO.

MIMO Detection using Optimized Neural Network model for 5G Communication

Multiple Input Multiple Output (MIMO) Wireless communication uses several antennas in the transmitter and receiver end. Multiple antennas at each end of the communication circuit work together to reduce errors, increase data speed, and improve radio transmission efficiency. MIMO techniques, which increases the number of antennas at the base station, will be used in 5G. Interference between users, which reduces a wireless link's spectral efficiency (SE), poses a major challenge during uplink communications with mm-wave systems. A linear MIMO detector such as maximum ratio (MR) or minimum mean square error (MMSE) cannot resolve this phenomenon. Therefore, this paper proposes a MIMO detection method based on an optimised extreme learning machine neural network (OELMNN). The Adaptive Rat Optimization algorithm is used to optimise the weight and bias values of ELMNN to improve its performance. The proposed scheme's performance is measured in terms of SNR, MSE, BER, and spectral efficiency.

Black carbon and particulate matter concentrations amid central Chile's extreme wildfires

The increase in the frequency and severity of global wildfires has been largely influenced by climate change and land use changes. From February 2 to 6, 2024, central Chile experienced its most devastating wildland-urban interface wildfire in history, severely impacting the Valparaíso region. This catastrophic event, which led to extensive forest destruction, the loss of thousands of homes, and over a hundred human fatalities, directly impacted the area surrounding the campus of Federico Santa María Technical University. In that period, an air quality monitoring campaign was set up on the campus to measure black carbon (BC) and particulate matter (PM) during the wildfire season. The monitoring station was located directly within the smoke plume, allowing for the collection of unprecedented air quality data. Extremely high concentrations of BC at 880 nm were reported during the wildfires, with a daily mean (±σ) of 14.83 ± 19.52 μg m−3. Peak concentrations measured at 880 nm and 375 nm reached 812.89 μg m−3 and 1561.24 μg m−3, respectively. The maximum daily mean BC concentrations at these wavelengths were 55 and 99 times higher, respectively, compared to the pre-event period. The mean Ångström absorbing coefficient during the event was 1.66, indicating biomass burning as the primary BC source, while the maximum BC/PM2.5 ratio (at 375 nm) reached 57 %. From February 2 to 5, 2024, PM concentrations exceeded the Chilean air quality standard by 82 % and 198 % for coarse and fine particles, respectively. These levels are 4.7 and 6.0 times higher than the World Health Organization's recommendations. These elevated concentrations persisted for up to three days after the fire was extinguished.This study provides unique evidence of the rapid deterioration of regional air quality during a wildfire event using in situ measurements, serving as a stark reminder of the far-reaching consequences of a warming climate.

Intelligent fault diagnosis of multi-source cross-machine bearings based on center-weighted optimal transport and class-level alignment domain adaptation

Abstract Most of the current domain adaptation research primarily focuses on the single-source or multi-source domain transfer constructed under different working conditions of the same machine. However, when faced with cross-machine tasks with significant domain discrepancies, forcing the direct feature alignment between source and target domain samples may lead to negative transfer, thereby reducing the model’s diagnostic performance. To overcome the above limitations, this paper proposes a multi-source deep transfer model based on center-weighted optimal transport (CWOT) and class-level alignment domain adaptation. Firstly, to enhance the representation capability of deep features, a multi-structure feature representation network is constructed to enrich the information capacity embedded within the deep features, thereby achieving better domain adaptation capabilities. Then, the local maximum mean discrepancy is introduced to fully exploit fine-grained information and discriminative features among different source domains, minimizing the distribution differences among the source domains to the greatest extent, thus capturing reliable and highly generalized multi-source domain invariant features. On this basis, a CWOT strategy is designed, which comprehensively considers the transport cost of intra-domain uncertainty and inter-domain correlation among samples, establishing a more effective transport between source and target domains, alleviating the problem of sample negative transfer, and improving the model’s cross-machine diagnostic performance. Finally, instance studies are conducted through multiple cross-machine transfer diagnostic tasks, demonstrating that the proposed method outperforms existing domain adaptation methods in terms of diagnostic accuracy and fault transfer capability. This research provides a reliable fault diagnosis method for detecting the health status of rotating machinery equipment, promoting the application of domain adaptation technology in practical industry.

Subject-independent auditory spatial attention detection based on brain topology modeling and feature distribution alignment

Auditory spatial attention detection (ASAD) seeks to determine which speaker in a surround sound field a listener is focusing on based on the one’s brain biosignals. Although existing studies have achieved ASAD from a single-trial electroencephalogram (EEG), the huge inter-subject variability makes them generally perform poorly in cross-subject scenarios. Besides, most ASAD methods do not take full advantage of topological relationships between EEG channels, which are crucial for high-quality ASAD. Recently, some advanced studies have introduced graph-based brain topology modeling into ASAD, but how to calculate edge weights in a graph to better capture actual brain connectivity is worthy of further investigation. To address these issues, we propose a new ASAD method in this paper. First, we model a multi-channel EEG segment as a graph, where differential entropy serves as the node feature, and a static adjacency matrix is generated based on inter-channel mutual information to quantify brain functional connectivity. Then, different subjects’ EEG graphs are encoded into a shared embedding space through a total variation graph neural network. Meanwhile, feature distribution alignment based on multi-kernel maximum mean discrepancy is adopted to learn subject-invariant patterns. Note that we align EEG embeddings of different subjects to reference distributions rather than align them to each other for the purpose of privacy preservation. A series of experiments on open datasets demonstrate that the proposed model outperforms state-of-the-art ASAD models in cross-subject scenarios with relatively low computational complexity, and feature distribution alignment improves the generalizability of the proposed model to a new subject.

Enhanced aerobic granular sludge by thermally-treated dredged sediment in wastewater treatment under low superficial gas velocity

The positive contributions of carriers to aerobic granulation have been wildly appreciated. In this study, as a way resource utilization, the dredged sediment was thermally-treated to prepared as carriers to promote aerobic granular sludge (AGS) formation and stability. The system was started under low superficial gas velocity (SGV, 0.6 cm/s)for a lower energy consumption. Two sequencing batch reactors (SBR) labeled R1 (no added carriers) and R2 (carriers added), were used in the experiment. R2 had excellent performance of granulation time (shortened nearly 43%). The maximum mean particle size at the maturity stage of AGS in R2 (0.545 mm) was larger compared to R1 (0.296 mm). The sludge settling performance in R2 was better. The reactors exhibited high chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) removal rates. The total phosphorus (TP) removal rate in R2 was higher than R1 (almost 15% higher) on stage II (93-175d). R2 had a higher microbial abundance and dominant bacteria content. The relative abundance of dominant species was mainly affected by the carrier. However, the enrichment of dominant microorganisms and the evolution of subdominant species were more influenced by the increase of SGV. The results indicated that the addition of carriers induced the secretion of extracellular polymeric substances (EPS) by microorganisms and accelerated the rapid formation of initial microbial aggregates. This work provided a low-cost method and condition to enhance aerobic granulation, which may be helpful in optimizing wastewater treatment processes.

Multi-source subdomain negative transfer suppression and multiple pseudo-labels guidance alignment: A method for fault diagnosis under cross-working conditions

Extensive researches have been conducted on transfer learning based fault diagnosis. However, negative information transfer may arise due to significant differences in the subdomain distribution across multiple source domains (MSDs). Most existing methods focus solely on the impact of subdomains from a single source domain (SSD) on the target domain (TD). Therefore, this paper proposed a novel multi-stage alignment multi-source subdomain adaptation (MAMSA) method. The global feature extractor is designed to extract domain-invariant features. Three domain-specific feature extractors capture high-level fault features from different domains with a customized adaptation strategy, which combines adversarial learning and distribution alignment based on multiple pseudo-label-guided local maximum mean discrepancy (MP-LMMD) to learn subdomain-invariant features. MP-LMMD utilizes pseudo-labels generated from all classifiers in the TD to guide the alignment of subdomains, suppressing negative transfer from the source domains (SDs). The experimental results indicate that the MAMSA method has excellent capabilities to suppress negative transfer, and the diagnostic performance can be greatly promoted with MAMSA under cross-working conditions.

A novel hybrid electrochemical equivalent circuit model for online battery management systems

Accurate battery modeling and parameter identification play pivotal roles in ensuring safety and reliability across the entire battery life cycle. Equivalent circuit models (ECM) are convenient but do not represent physical characteristics well; in contrast, electrochemical models with strong physical meaning are hard to parameterizing in an online setting. To address these challenges, this paper introduces a novel hybrid electrochemical Equivalent Circuit Model (eECM), which integrates electrochemical processes into an ECM, representing slow-dynamic internal processes with a simplified representation of solid- and liquid-phase diffusion; fast-dynamics are represented by ECM terms. The model is supported by an Adaptive Extended Kalman Filter (AEKF) to manage battery state changes and mitigate noise. To enhance parameter identification, a Fisher information matrix-enhanced Variable Forgetting Factor Recursive Least Squares (Fisher-VFFRLS) approach is employed, guided by the Cramér–Rao bound for identifying the most sensitive data points directly from the discharge cycle. Electrochemical parameters are determined via post-charging rest via a Genetic Algorithm (GA). The proposed methodology is validated on three dynamic cycles—DST, US06, and FUDS-demonstrates the effectiveness of the proposed eECM and parameter identification strategy, with maximum Root Mean Square Error (RMSE) for terminal voltage and State of Charge (SoC) estimation below 0.0076 and 0.0122, respectively.

Prognostic value of 18F-FDG PET/CT in postoperative recurrence of retroperitoneal liposarcoma: a single-center retrospective study.

Recurrence is the leading cause of tumor-related death in retroperitoneal liposarcoma (RPLPS). Variant subtypes of RPLPS determine different recurrence 18F]-fluoro-2-deoxy-D-glucose (18F-FDG) PET/computed tomography (PET/CT). This study analyzed the characteristics of different histologic subtypes of 18F-FDG PET/CT and their associations with recurrence and prognosis. Clinical-pathological information, 18F-FDG PET/CT data, recurrence, and progression-free survivals (PFS) of 83 patients with RPLPS were collected. Maximum and peak standardized uptake values (SUVmax and SUVpeak, respectively) and mean CT value (CTmean) of tumors were measured and correlated with histologic subtype. The predictability of SUVmax, SUVpeak, and CTmean for the histologic subtype was evaluated using receiver operating characteristics (ROC) max and SUVpeak for recurrence. Kaplan-Meier analysis was performed to max and SUVpeak were risk factors for recurrence. Studied patients with different types of liposarcomas. Dedifferentiated liposarcomas (DDLPS) had higher SUVmax and SUVpeak than well-differentiated (WDLPS) and myxoid/round cell (MLPS) types. WDLPS had lower CTmean values compared to MLPS and DDLPS. Using ROC curves, determined cut-off values for SUVmax (5.1) to differentiate DDLPS, SUVpeak (3.0) for WDLPS, and CTmean (12.3 Hu) for WDLPS. These cut-offs were found to be best for predicting recurrence. Kaplan-Meier analysis showed that histologic subtype, SUVmax, and SUVpeak were all linked to recurrence-free survival. The use of SUV and CT features on 18F-FDG PET/CT imaging may increase confidence in subtype diagnosis. Patients with SUVmax > 5.1 or SUVpeak > 3.0 suggest a poor prognosis.

Comparison of Anorectal Function and Quality of Life Measures in Patients Following Intersphincteric Resection and Lower Anterior Resection.

This study explored postoperative outcomes for patients with lower rectal cancer who underwent low anterior resection (LAR) or intersphincteric resection (ISR). A total of 49 patients (33 LAR, 16 ISR) were followed using anorectal manometry and quality of life (QOL) questionnaires over a year, pre- and post surgery. The primary aim of this study is to clarify differences in anal manometry, sphincter function, fecal incontinence, and QOL between the two surgical arms. The secondary aim was to identify indicators suitable for assessing relationships between anorectal manometry measurements, fecal incontinence, and QOL. Anorectal manometry elements (AMEs), such as atmospheric maximum mean squeeze pressure (aMSP), maximum tolerable volume (MTV), and incremental maximum mean squeeze pressure (iMSP), showed no significant differences during the observation period. However, maximum resting pressure (MRP), high-pressure zone length (HPZ), and threshold volume (TV) were significantly worse in the ISR group. Fecal incontinence, measured by Wexner and Kirwan scores, was significantly better in the LAR group. We observed no differences in SF36 between the two groups. Multi-correlation analysis revealed positive and negative correlations among these factors, with inverse correlations between anorectal manometry measurements and incontinence assessments decreasing post-surgery. We found no correlation between SF36 and anorectal manometry at any time. The findings indicate that surgical technique affects postoperative anal function, fecal incontinence, and SF36. However, combined assessment methods should be used with caution when deriving relationships between anal function and SF36.