Characteristics Of Different Types Research Articles

Characterization and Risk Factors of Folliculitis after Hair Transplantation: A Multicenter Retrospective Study.

Postoperative folliculitis is a common complication of hair transplantation (HT) requiring effective preventive interventions. This study characterized postoperative folliculitis and determined risk factors in patients underwent HT. We retrospectively reviewed 1317 patients who underwent HT and completed 9-month follow-up between January 2018 and June 2021 at four medical centers. The incidence of postoperative folliculitis and patient demographics were assessed. Logistic regression analysis was used to identify risk factors, and the characteristics of different types of folliculitis were compared. The overall incidence of postoperative folliculitis was 12.11%, and clinical characteristics varied among the different types of folliculitis. Surgery in summer (odds ratio [OR], 1.772, 95% confidence interval [CI]: 1.05-2.992), number of transplant grafts ≥4000 (OR: 4.818, 95% CI: 1.45-16.014), transplant density >45 grafts per/cm 2 (OR: 2.152, 95% CI: 1.376-3.367), and first nursing time >3 days (OR: 1.555, 95% CI: 1.088-2.223) were the main risk factors for postoperative folliculitis. Postoperative folliculitis after HT presents different characteristics. Surgical factors and postoperative nursing were demonstrated to be related to folliculitis. Therefore, we propose a preventive folliculitis model based on preoperative, intraoperative, and postoperative factors.

AUTHENTIC CHEESES: MICROBIOLOGY, STARTERS, AND TECHNOLOGICAL ASPECTS OF PRODUCTION

The work is dedicated to the varieties, microbiological peculiarities and technological aspects of production of certain types of authentic cheeses. The purpose of work is to determine the microbiological peculiarities of authentic cheeses, to analyze their varieties and microbiota, to define the biotechnological peculiarities of production of certain types of authentic cheeses. Monographic, constructive, causal analysis and logical generalization methods were used. Cheese is the most popular food product in the world; there are 5,000 varieties of cheese. Although standard cheeses produced on large-scale productions are the most popular and widespread, authentic cheeses are also popular due to their unique organoleptic properties. Many factors influence the unique characteristics of authentic cheeses, the main of which are the quality of milk, its microbiological and chemical composition, and the technological methods used in cheese-making. All authentic cheeses are characterized by the presence of lactic acid bacteria, but the use of untreated milk sometimes leads to the presence of foodborne pathogens in the final product. Certain authentic cheeses have a complex microbiota, which complicates their production and the creation of starter cultures. However, for many cheeses, especially those like feta, there are now technological approaches to production, and most research is aimed at achieving an authentic taste and improving the organoleptic properties of the final product. Analysis of the quantitative and qualitative composition of the fermenting microbiota and the technological features of different types of traditional cheeses makes it possible to determine the composition of new fermenting compositions that are inherent to a particular product and to choose ways to increase the efficiency of cheese production.

Кератопротезы в современной офтальмохирургии

The shortage of donor corneas for keratoplasty in patients with severe disorders (persistent opacity, corneal leucoma of IV–V categories, including leucoma with vascularization, after burns, multiple transplant failures, with extensive symblepharon, Stevens – Johnson syndrome, Lyell's syndrome, ocular cicatricial pemphigoid, etc) is currently compensated by keratoprostheses, which partly or completely consist of synthetic materials, show good engraftment and high vision restoration efficacy. The article is a review of main scientific papers on keratoprostheses. The authors state characteristics of different types of keratoprosthesis, indications and outcomes of their use, complications, efficacy rate and postoperative visual acuity. Each of the presented types of keratoprosthesis has its own benefits and drawbacks, so their improvement to achieve better surgical outcomes remains relevant and needs further study. Key words: ophthalmic surgery, corneal opacity, keratoplasty, multiple transplant failures, keratoprostheses

Intelligent Lithology Identification Methods for Rock Images Based on Object Detection

Lithology identification is a crucial step in geological research. In recent years, the development of artificial intelligence technologies has provided new insights into solving problems associated with subjectivity and labor intensity of traditional manual identification. However, when rocks are identified in situ, existing algorithms cannot accurately identify them if the image features of different types of rocks are similar or the rock textures are varied. In this regard, the study of lithology identification for the rock images captured from the field was carried out. First, the object detection algorithm of single shot multibox detector was improved by adding residual net and adaptive moment estimation, and a lithology identification model was constructed. Second, based on the above improved algorithm, the technologies of database and geographic information system were combined to develop an integrated identification method. Third, the proposed methods were applied to 12 types of rocks in Xingcheng area, China, for testing their validity, and feasibility in field geological surveys. Finally, the effects of learning rate and batch size on the identification were discussed, as the epoch number was increased. We found that the average accuracies of the improved single shot multibox detector and integrated method were 89.4% and 98.4%, respectively. The maximum accuracy could even reach 100%. The identification results were evaluated based on accuracy, precision, recall, F1-score, and mean average precision. It was demonstrated that the integrated method has a strong identification ability compared with other neural network methods. Generally, a small learning rate can lead to low loss and high accuracy, whereas a small batch size can lead to high loss and high accuracy. Moreover, the newly proposed methods helped to improve the lithology identification accuracy in the field and support the study of intelligent in situ identification for rock images.

3D point cloud global denoising algorithm based on different noise characteristics

When acquiring object point cloud data by three-dimensional scanning technology, noise is generated due to instrument accuracy and external factors. Existing algorithms rarely consider the characteristics of different noises and different regional noises when solving the point cloud denoising problem, this results in a limited effect on denoising. This paper presents an algorithm for denoising based on the characteristics of different types of noise and different regions in the point cloud. The algorithm includes large-scale noise removal and small-scale noise smoothing. Remove large-scale noise points by the relationship between the local point cloud and the global point cloud. For small-scale noise, the feature regions and non-feature regions are extracted according to the normal cosine information entropy. According to the characteristics of the small-scale noise in two regions, the noise distance distribution and the optimized bilateral filtering are used to deal with the small-scale noise in two regions respectively. Comparison experiments show that our algorithm can effectively remove the noise points mixed in the normal point cloud. The accuracy of large-scale noise removal reaches 99.1%. The proposed algorithm can protect the feature areas from being over-smoothed while smoothing the small-scale noise in non-featured areas.

Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis.

Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and efficient diagnostic methods to guide treatment decisions. Traditional pathology practices have limitations, including interobserver variability and time-consuming evaluations. In recent years, digital pathology tools emerged as a promising solution to enhance the diagnosis and management of renal cancer. This review aims to provide a comprehensive overview of the current state and potential of digital pathology in the context of renal cell carcinoma. Through advanced image analysis algorithms, artificial intelligence (AI) technologies facilitate quantification of cellular and molecular markers, leading to improved accuracy and reproducibility in renal cancer diagnosis. Digital pathology platforms empower remote collaboration between pathologists and help with the creation of comprehensive databases for further research and machine learning applications. The integration of digital pathology tools with other diagnostic modalities, such as radiology and genomics, enables a novel multimodal characterization of different types of renal cell carcinoma. With continuous advancements and refinement, AI technologies are expected to play an integral role in diagnostics and clinical decision-making, improving patient outcomes. In this article, we explored the digital pathology instruments available for clear cell, papillary and chromophobe renal cancers from pathologist and data analyst perspectives.

A Multiscale Neighbor-Aware Attention Network for Collaborative Filtering

Most recommender systems rely on user and item attributes or their interaction records to find similar neighbors for collaborative filtering. Existing methods focus on developing collaborative signals from only one type of neighbors and ignore the unique contributions of different types of neighbor views. This paper proposes a multiscale neighbor-aware attention network for collaborative filtering (MSNAN). First, attribute-view neighbor embedding is modeled to extract the features of different types of neighbors with co-occurrence attributes, and interaction-view neighbor embedding is leveraged to describe the fine-grained neighborhood behaviors of ratings. Then, a matched attention network is used to identify different contributions of multiscale neighbors and capture multiple types of collaborative signals for overcoming sparse recommendations. Finally, we make the rating prediction by a joint learning of multi-task loss and verify the positive effect of the proposed MSNAN on three datasets. Compared with traditional methods, the experimental results of the proposed MSNAN not only improve the accuracy in MAE and RMSE indexes, but also solve the problem of poor performance for recommendation in sparse data scenarios.

Investigating comparisons on the coal and gangue in various scenarios using multidimensional image features

Online intelligent recognition of coal and gangue is an important aspect of coal mine intelligent development. Current research focuses more on improving recognition accuracy through optimizing image processing algorithms, but there is limited research on the characteristics of coal gangue materials themselves, particularly the comparative analysis of their appearance in images under different scenarios. Therefore, this paper proposes a multi-dimensional feature-based analysis method for coal-gangue images, which can be used to evaluate the characteristics and distinguishability of coal and gangue images. Firstly, the original feature space of coal and gangue images is constructed based on 55 global image feature operators. Then, the characteristics of coal and gangue images in both the original space and the two-dimensional space after dimensionality reduction are analyzed using the similarity between the left and right sides of the split violin plot and the distance of probability distributions. At the same time, different types of features are compared and analyzed in terms of their impact on classification performance when used as inputs with various machine learning classifiers. Furthermore, by leveraging the interpretability of tree models, the contribution of different feature types to coal gangue classification tasks is analyzed. The proposed method is applied to analyze a large number of raw coal images collected from three different conditions. The experimental results demonstrate that the distinguishability of coal gangue images varies under different scenarios, and the distance between the two distributions in the reduced space effectively measures this distinguishability. In coal gangue images, the contribution of color features and texture features to the classification task is related to the type of coal, with texture features playing a more significant role in classifying coking coal compared to thermal coal. However, overall, color features dominate the classification task. These findings are further validated in experiments using machine learning classifiers, which are crucial for designing more refined classification algorithms.

Machine Learning Algorithms for Cloud Computing Security: A Review

: Cloud computing has revolutionized the way businesses and individuals access and manage IT resources. However, cloud computing also introduces new security challenges. Cloud-based systems are often more complex and distributed than traditional on-premises systems, making them more vulnerable to attack.Machine learning (ML) is a powerful tool that can be used to analyze cloud computing attacks and improve cloud security. ML algorithms can be trained on large datasets of attack data to learn the patterns and characteristics of different types of attacks. Once trained, ML models can be used to detect new attacks in real time and recommend mitigation strategies. This paper reviews the state of the art in using ML to analyze cloud computing attacks. It discusses the different ways that ML can be used to improve cloud security, including developing intrusion detection systems (IDS), anomaly detection systems, and attack forecasting systems. The paper also discusses the challenges of using ML for cloud security, such as the need for large and high-quality training data and the need to adapt ML models to new and emerging threats.The paper concludes by discussing the future of using ML for cloud security. It argues that ML is a key technology for improving cloud security in the face of increasingly sophisticated and targeted attacks.

Clinical characteristics of patients with early-onset diabetes mellitus: a single-center retrospective study

BackgroundThe prevalence of diabetes mellitus (DM) is dramatically increasing around the world, and patients are getting younger with changes in living standards and lifestyle. This study summarized and analyzed the clinical characteristics of different types of newly diagnosed diabetes mellitus patients with an onset age between 18 and 40 years to provide clinical evidence for the early diagnosis and treatment of diabetes, reduce short-term and long-term complications and offer scientific and personalized management strategies.MethodsA total of 655 patients newly diagnosed with early-onset diabetes mellitus in the Department of Endocrinology, the First Medical Center of PLA General Hospital from January 2012 to December 2022 were retrospectively enrolled in this study, with an onset age of 18–40 years. Their clinical data were collected and investigated. All patients were divided into two groups according to whether they presented with diabetic microangiopathy. Similarly, patients with early-onset type-2 diabetes were grouped in accordance with whether they had ketosis at the time of diagnosis. Binary logistic regression analysis was performed to analyze risk factors, and receiver-operating characteristic (ROC) analysis was used to explore the predictive value of significant risk factors.ResultsThe findings were as follows: (1) Of 655 enrolled patients, 477 (72.8%) were male and 178 (27.1%) were female, with a mean age of onset of was 29.73 years ± 0.24 SD. (2) The prevalence of early-onset diabetes was gradually increasing. Type-2 diabetes was the most common type of early-onset diabetes (491, 75.0%). The ages of onset of early-onset type-1 diabetes, type-2 diabetes and LADA were mainly 18–24 years, 25–40 years and 33–40 years, respectively. (3) Initial clinical manifestations of early-onset diabetes were classic diabetes symptoms (361, 55.1%), followed by elevated blood glucose detected through medical examination (207, 31.6%). (4) Binary logistic regression analysis suggested that high serum uric acid (UA), a high urinary albumin-to-creatinine ratio (UACR) and diabetic peripheral neuropathy (DPN) were risk factors for microangiopathy in early-onset diabetes patients (P < 0.05). The area under the curve (AUC) on ROC analysis of the combination of UA, UACR and DPN was 0.848, 95% CI was 0.818 ~ 0.875, sensitivity was 73.8% and specificity was 85.9%, which had higher predictive value than those of UA, UACR and DPN separately. (5) Weight loss, high glycosylated hemoglobin (HbA1c) and young onset age were risk factors for ketosis in patients with early-onset type-2 diabetes (P < 0.05).Conclusion(1) Men were more likely to have early-onset diabetes than women. (2) Early-onset diabetes patients with high serum uric acid levels, high UACRs and peripheral neuropathy were prone to microangiopathy. Comprehensive evaluation of these risk factors could have higher predictive value in the prediction, diagnosis and treatment of microvascular lesions. (3) Patients with weight loss at onset, high HbA1c and young onset age were more likely to develop ketosis. Attention should be given to the metabolic disorders of these patients.

Performance analysis of frequency-mixed PPP-RTK using low-cost GNSS chipset with different antenna configurations

Low-cost Global Navigation Satellite System (GNSS) devices offer a cost-effective alternative to traditional GNSS systems, making GNSS technology accessible to a wider range of applications. Nevertheless, low-cost GNSS devices often face the challenges in effectively capturing and tracking satellite signals, which leads to losing the observations at certain frequencies. Moreover, the observation peculiarities of low-cost devices are in contradistinction to those of traditional geodetic GNSS receivers. In this contribution, a low-cost PPP-RTK model that considers the unique characteristics of different types of measurements is developed and its performance is fully evaluated with u-blox F9P receivers equipped with three distinctive antenna configurations: vertical dipole, microstrip patch, and helix antennas. Several static and kinematic experiments in different scenarios are conducted to verify the effectiveness of the proposed method. The results indicate that the mixed-frequency PPP-RTK model outperforms the traditional dual-frequency one with higher positioning accuracy and fixing percentage. Among the three low-cost antennas tested, the vertical dipole antenna demonstrates the best performance under static conditions and shows a comparable performance as geodetic antennas with a positioning accuracy of 0.02 m, 0.01 m and 0.07 m in the east, north, and up components, respectively. Under low-speed kinematic scenarios, the helix antenna outperforms the other two with a positioning accuracy of (0.07 m, 0.07 m, 0.34 m). Furthermore, the helix antenna is also proved to be the best choice for vehicle navigation with an ambiguity fixing rate of over 95% and a positioning accuracy of (0.13 m, 0.14 m, 0.36 m).

Vertically constructed wetlands for greywater reuse: Performance analysis of plants

Vertical Flow constructed wetlands (VFCWs) are environmentally feasible engineered systems that mimic the functions of natural wetlands. They are alternative engineering systems that are economical, and simple in structure with reduced land area compared to Horizontal Flow Constructed Wetlands (HFCW). Thus provides a sustainable solution for greywater treatment to a considerable extent. However, VFCWs feasibility and plant performance were not tested in the context of Sri Lanka for the greywater treatment. Therefore, the purpose of this study is to evaluate the potential of household greywater treatment using apilot-scale VFCW and examine the performance characteristics of different types of plants. Three types of plants, the Canna plant (Canna indica), Ferns plant (Matteuccia struthiopteris), and Cattail plant (Typha latifolia) were used as emergent plants and a retention tank was constructed to retain solid particles in the greywater as primary treatment. The experiments were carried out for two months using a Completely Randomized Design (CRD) for three replicates. The quality of the influent and effluentwas testedfortnight for a number of water quality parameters. Results revealed that the removal efficiency of contaminants was increased. Cattail plants showed higher removal efficiency for dissolved oxygen (DO), chemical oxygen demand (COD), nitrates (NO31-), turbidity, and electrical conductivity. In addition, Canna plants had higher efficiencies for the removal of total dissolved solids (TDS) and phosphates (PO43-). Furthermore, Ferns plants presented higher efficiency only for removing sulphate (SO43-). Conclusively, Cattail plants presented the overall best performance in treating greywater. This can be attributed to the ability of the Cattail's dense fibrous root system to absorb more contaminants from greywater. This research also discussed the importance of microplastic analysis in greywater treatment which is a vital part of the current day research. The results of this study will be helpful to the further advanced research. Furthermore, this methodology can be implemented to other similar plants across the globe irrespective of geographical area.

OC13.06: Uteroplacental perfusion characteristics of different types of selective growth restriction assessed by three‐dimensional power Doppler ultrasound

OC13.06: Uteroplacental perfusion characteristics of different types of selective growth restriction assessed by three‐dimensional power Doppler ultrasound

Testability design of power distribution system considering noise immunity characteristics

MOSFET-based power electronics systems have high-reliability requirements, and a high-accuracy fault diagnosis method is necessary. However, imperfect conditions such as severe signal noise and poor consistency exist in actual products. Conventional fault diagnosis methods are difficult to identify faults accurately. This paper describes a simulation-based testability design method for batch products considering noise immunity. First, the components' soft and hard fault models are determined with failure mode and effect analysis (FMEA), and the circuit simulation model is constructed. Then, the fault injection simulation is performed to structure the dependency matrix. The test points scheme is selected and optimized by extracting noise immunity characteristics of different types of signals. Finally, a power distribution system is taken as a case study for the testability design method, and the effectiveness of the test points is evaluated through simulation and experiment verification.

PKAMNet: A Transmission Line Insulator Parallel- Gap Fault Detection Network Based on Prior Knowledge Transfer and Attention Mechanism

Insulator parallel gaps in a transmission line protect the insulator from arc burn and provide protection from lightning. Detection of insulator parallel-gap faults based on aerial inspection images of transmission lines may be hindered by various factors, such as complex background, scale distortion, and occlusion. In this study, we propose a prior knowledge transfer and attention mechanism network (PKAMNet) to detect faulty insulator parallel gaps. First, the capability of PKAMNet to learn the features of different types of parallel-gap faults is improved by constructing a prior knowledge transfer model based on visual saliency. Owing to the difficulty of effectively expressing the features of the fault target due to the deviation of the shooting angle and complex background of the inspection image, we enhance the feature expression ability of the fault area by embedding the coordinate attention block. Additionally, the parameters of the detection network are simplified, and the modules are optimized to improve detection efficiency. Comparison results on 3-year aerial inspection videos of 500 kV high-voltage transmission lines show that PKAMNet has a high detection accuracy and can ameliorate the phenomenon of missed and erroneous detection caused by insufficient expression of parallel-gap faults.

역사문화거리 사운드스케이프의 유형에 따른 분석

The purpose of this study is to investigate the concepts related to typology-based historic and cultural districts and soundscapes. The study is based on Chinese historic and cultural districts and is limited in four aspects: geographical, chronological, spatial, and physical. This study mainly adopts the literature research method to analyze the characteristics of historic and cultural districts, classify them according to their functions and environmental images, and analyze the characteristics of different types of districts. Then the characteristics and associated constituents of the soundscape are analyzed from the perspective of history and culture, the soundscape is classified according to the sound source, and the characteristics and associated constituents are combined to derive the characteristics that the soundscape has in terms of history and culture. Based on the results of this study, the concepts related to the characteristics, constituent elements and features of different types of historic and cultural district soundscapes can be derived to provide a basis and foundation for further research.

Complex resistivity spectrum of pollutant soils with low-concentration heavy metals

Soil contamination by heavy metals occurs globally, with varying degrees of severity, especially in agricultural fields. Investigating the frequency response characteristics of different types of heavy metal pollutants through induced polarization can provide valuable evidence for surveys based on this method. Soil specimens with varying low concentrations of copper (Cu), chromium (Cr), cadmium (Cd), and lead (Pb) heavy metals were prepared for this study, and parameters including complex resistivity, amplitude-frequency, and resistivity phase were measured. Our findings reveal the following trends: Complex resistivity decreases as heavy metal concentrations increase, demonstrating significant shifts within lower concentration ranges but presenting limitations for assessing pollution in high-concentration areas. Conversely, amplitude-frequency increases with higher heavy metal concentrations, displaying excellent performance in high-concentration scenarios. The differences in complex resistivity and amplitude-frequency among different types of heavy metal pollutants are distinct. In contrast, the absolute phase decreases with rising heavy metal concentrations. The resistivity phase spectra for various heavy metal pollutants exhibit unique patterns. For example, copper-contaminated soil exhibits phase peaks in the frequency range of 8–32 Hz, whereas chromium-contaminated soil shows phase peaks at 16–64 Hz. Cadmium-contaminated soil displays phase peaks ranging from 0.25 Hz to 2 Hz, while lead-contaminated soil exhibits phase peaks within the 0.5 Hz–4 Hz range. Leveraging the frequency range corresponding to phase peaks as an identification method for heavy metal pollution types proves effective. The frequency response characteristics of induced polarization vary significantly among different types and concentrations of heavy metal pollutants, providing important foundations for the application of induced polarization method in the field of heavy metal pollution detection.

Recent Advances in Living Algae Seeding Wound Dressing: Focusing on Diabetic Chronic Wound Healing

AbstractDiabetic chronic wounds (DCWs) are a severe complication of diabetes, and current treatments present several deficiencies, including unsatisfactory efficacy and inability to continuously supply oxygen. This review highlights the recent advances in a novel therapeutic strategy that addresses these challenges: considerable biological powerful inclusions, oxygen or hydrogen and ROS production, DCW glucose consumption, chemotaxis at the wound site, highly editable, great biological safety, low cost, and readily available. Over the past decade, this strategy has shown encouraging results in improving the healing delay induced by hyperglycemia, forming a new concept of “living material” application based on living algae. This review discusses the unique advantages of algae as a treatment strategy, the applications and characteristics of different types of living algae in wound dressings, and the challenges and improvement strategies for this innovative approach.

Optical and acoustic phonons in turbostratic and cubic boron nitride thin films on diamond substrates

We report an investigation of the bulk optical, bulk acoustic, and surface acoustic phonons in thin films of turbostratic boron nitride (t-BN) and cubic boron nitride (c-BN) grown on B-doped polycrystalline and single-crystalline diamond (001) and (111) substrates. The characteristics of different types of phonons were studied using Raman and Brillouin-Mandelstam light scattering spectroscopies. The atomic structure of the films was determined using high-resolution transmission electron microscopy (HRTEM) and correlated with the Raman and Brillouin-Mandelstam spectroscopy data. The HRTEM analysis revealed that the cubic boron nitride thin films consisted of a mixture of c-BN and t-BN phases, with c-BN being the dominant phase. It was found that while visible Raman spectroscopy provided information for characterizing the t-BN phase, it faced challenges in differentiating the c-BN phase either due to the presence of high-density defects or the overlapping of the Raman features with those from the B-doped diamond substrates. In contrast, Brillouin-Mandelstam spectroscopy clearly distinguishes the bulk longitudinal and surface acoustic phonons of the c-BN thin films grown on diamond substrates. Additionally, the angle-dependent surface Brillouin-Mandelstam scattering data show the peaks associated with the Rayleigh surface acoustic waves, which have higher phase velocities in c-BN films on diamond (111) substrates. These findings provide valuable insights into the phonon characteristics of the c-BN and diamond interfaces and have important implications for the thermal management of electronic devices based on ultra-wide-band-gap materials.

The current status and prospects of transformer in multimodality

At present, the attention mechanism represented by transformer has greatly promoted the development of natural language processing (NLP) and image processing (CV). However, in the multimodal field, the application of attention mechanism still mainly focuses on extracting the features of different types of data, and then fusing these features (such as text and image). With the increasing scale of the model and the instability of the Internet data, feature fusion has been difficult to solve the growing variety of multimodal problems for us, and the multimodal field has always lacked a model that can uniformly handle all types of data. In this paper, we first take the CV and NLP fields as examples to review various derived models of transformer. Then, based on the mechanism of word embedding and image embedding, we discuss how embedding with different granularity is handled uniformly under the attention mechanism in multimodal scenes. Further, we reveal that this mechanism will not only be limited to CV and NLP, but the real unified model will be able to handle tasks across data types through pre-training and fine tuning. Finally, on the specific implementation of the unified model, this paper lists several cases, and analyzes the valuable research directions in related fields.