Standard System Research Articles

Using Hybrid Coating to Fabricate Highly Stable and Expandable Transparent Liquid Marbles.

Liquid marbles (LMs) are microliter-scale droplets coated with hydrophobic solid particles. The particle size and hydrophobicity of the surface coating determine their properties, such as transparency, expandability, and resistance to evaporation and coalescence, one or more of which can be critical to their application as microreactors. This study reports the use of a mixture of two different hydrophobic powders for fabrication of LMs for colorimetric assays: trichloro(1H,1H,2H,2H-perfluorooctyl) silane-linked silica gel (modified silica gel (MSG), particle size: 40-75 μm) and hexamethyldisilazane-linked fumed silica (modified fumed silica (MFS), average aggregate length: 200-300 nm). The hybrid coating mixture (MIX) prepared by mixing these MSG and MFS powders in a ratio of 3:7 (w/w), respectively, contained particles of different sizes as well as different hydrophobicity as the silane linked to MSG is more hydrophobic than the one linked to MFS. LMs fabricated using MIX as the surface coating were characterized and compared to LMs coated with MSG or MFS alone. It was observed that MIX LMs were comparable to the MFS LMs in transparency (higher than the MSG LMs), expandability (more than 20 times their initial volume), and stability against evaporation (for more than 4 h at 78% relative humidity at 26 °C). However, in terms of resistance to coalescence, the MIX LMs showed a resistance comparable to that of MSG LMs, much higher than that of MFS LMs. Further experiments demonstrated that it is the presence of the particles of different sizes (MSG particles are ∼100 times larger than MFS) that improves the resistance to coalescence rather than the higher hydrophobicity of the MSG. Three different colorimetric assays were performed in the MIX LMs, and the results obtained were comparable in accuracy and precision to those obtained in a standard polystyrene microwell plate system. Low quantities of the analytes could be detected and quantified, as evidenced by the limit of detection (alkaline phosphatase (AP): 0.18 μg/mL; bovine serum albumin (BSA): 2.28 μg/mL; and chymotrypsin: 3.69 μM) and limit of quantification (AP: 0.59 μg/mL; BSA: 12.29 μg/mL; and chymotrypsin: 7.59 μM) values. Color intensities in LMs were quantified using a smartphone application, which provides the added benefit of an instrument-free approach. These findings highlight the potential of using LMs stabilized with mixtures of nano- and microparticles as robust, versatile microreactors for portable and sensitive colorimetric assays, paving the way for more accessible and efficient diagnostic tools.

Trends and Characteristics of the Whole-Grain Diet.

Whole-grain foods are good choices for a nutritious diet and play an important role in lowering the risk of chronic diseases. China is a large cereal-consuming country that is experiencing rapid economic growth and an increased burden of noncommunicable diseases. Studies have shown that insufficient intake of whole grains has increased the burden on public health to some extent. Therefore, we aimed to analyze the characteristics and trends of whole-grain consumption in China to contribute to the development of a healthy Chinese population. We searched for important policies, standards, and dietary recommendations related to whole grains through the official websites of the China State Council and its affiliated ministries. Official and public databases, such as the National Bureau of Statistics and the China Health and Nutrition Survey (CHNS), were utilized to obtain data on whole grains and different foods. This approach was employed to gain insight into changes in whole-grain intake levels and dietary structure. Since 2010, the development of whole grains in China has received significant attention. Supportive policies have been continuously introduced on both the supply and demand sides. Numerous whole-grain standards have been developed, and terms and definitions related to whole grains have been preliminarily defined. The consumption of whole grains has fluctuated among Chinese residents since 2000, with a downward trend from 2000 to 2009, followed by an upward trend from 2009 to 2018. However, despite this increasing trend, the whole-grain intake of 80% of the adult residents was still lower than the minimum recommended dietary intake (50[Formula: see text]g/day). Furthermore, the dietary structure of Chinese residents has undergone a significant transformation, characterized by high consumption of high-fat foods, excessive consumption of meat, low intake of dietary fiber, and insufficient consumption of whole grains. These findings highlight the need for further monitoring of whole-grain intake in China. In addition, a sound whole-grain standardization system should be established, and the availability and consumer awareness of whole grains should be improved, with the goal of increasing the intake of whole grains.

Bosonic Pauli+: Efficient Simulation of Concatenated Gottesman-Kitaev-Preskill Codes

A promising route towards fault-tolerant quantum error correction is the concatenation of a Gottesman-Kitaev-Preskill (GKP) code with a qubit code. Development of such concatenated codes requires simulation tools which realistically model noise, while being able to simulate the dynamics of many modes. However, so far, large-scale simulation tools for concatenated GKP codes have been limited to idealized noise models and GKP code implementations. Here, we introduce the Bosonic Pauli+ model (BP+), which can be simulated efficiently for a large number of modes, while capturing the rich dynamics in the bosonic multi-mode Hilbert space. We demonstrate the method by simulating a hybrid surface code, where the data qubits are finite-energy GKP qubits stabilized using the small-Big-small (sBs) protocol, and the syndrome qubits are standard two-level systems. Using BP+, we present logical error rates of such an implementation. Confidence in the accuracy of the method is gained by comparing its predictions with full time evolution simulations for several relevant quantum circuits. While developed specifically for GKP qubits stabilized using the sBs protocol, the mathematical structure of BP+ is generic and may be applicable also to the simulation of concatenations using other bosonic codes.

Overview of Terrain Traversability Evaluation for Autonomous Robots

ABSTRACTTraversability evaluation is the foundation and core of unmanned platforms for scene understanding and autonomous navigation, whose successful completion relies on the analysis of the platform's characteristics and the semantic and geometric features of the surrounding environment. This topic has been reviewed by many literatures, which are characterized by a single perspective and lack comprehensive evaluation frameworks. Thus, the concept and developmental trajectory of traversability evaluation are initially outlined in this paper, distinguishing it from other issues, while constructing an evaluation framework based on two categories: direct assessment and downstream task assessment. Subsequently, traversability evaluation methods are classified based on multiple dimensions, including sensor types, robot types, usage scenarios, and learning approaches. On the basis of the constructed evaluation framework, comparisons are made among existing algorithms in terms of performance and runtime. Subsequently, a summary is provided on commonly used features and their mainstream computation methods in terrain evaluation. Additionally, open‐source data sets in this field and projects for scene construction and algorithm validation are compiled and organized. Finally, an analysis is conducted on the development direction and trends, emphasizing the urgent need to establish standardized evaluation systems and comparison baselines. Furthermore, it is imperative that various environmental and platform information be comprehensively integrated into algorithms, while also ensuring that simulation, demonstration, and exploration are incorporated into a unified framework to enhance the robot's learning capability.

Estimation of gait parameters in healthy and hemiplegic individuals using Azure Kinect: a comparative study with the optoelectronic system

IntroductionWalking ability is essential for maintaining functional independence, but it can be impaired by conditions like hemiplegia resulting from a stroke event. In post-stroke populations, accurately assessing gait anomalies is crucial for rehabilitation to promote functional recovery, and to prevent falls or injuries.MethodsThe aim of this study is to evaluate gait-related parameters using a solution based on a single RGB-D camera, specifically Microsoft Azure Kinect DK (MAK), on a short walkway in both healthy (n= 27) and post-stroke individuals with hemiplegia (n= 20). The spatio-temporal and center of mass (CoM) parameters estimated by this approach were compared with those obtained from a gold standard motion capture (MoCap) system for instrumented 3D gait analysis.ResultsThe overall findings demonstrated high levels of accuracy (> 93%), and strong correlations (r > 0.9) between the parameters estimated by the two systems for both healthy and hemiplegic gait. In particular, some spatio-temporal parameters showed excellent agreement in both groups, while CoM displacements exhibited slightly lower correlation values in healthy individuals.DiscussionThe results of the study suggest that a solution based on a single optical sensor could serve as an effective intermediate tool for gait analysis, not only in clinical settings or controlled environments but also in those contexts where gold standard systems are not feasible.

Modification of Erroneous and Correct Digital Texts

The end-user paradox and the illusion of digital prosperity reveal the contradictory situation in which both non-professional and professional computer scientists and engineers seem satisfied with digital development but unaware of the magnitude of waste generated by end-users and their digital artifacts. To measure this waste and to reveal end-users’ problem-solving strategies, our research group set up an objective measuring system that can calculate the entropy of digital texts (EDT). To calculate EDT, a testing process of 53 participants was launched where erroneous and correct natural language digital texts were modified according to the requirements of the tasks. It was found that erroneous documents require more time and information to be modified, which implies that waste is generated by handling these documents. It was also found that when the problem-solving processes are broken down into atomic steps, EDT can reveal uncertainty and idleness, which further increases waste. The goals of the present paper are to call attention to (1) the hidden waste generated by billions of end-users and its consequences, (2) educational approaches and general ignorance which have led to these low-level results, and (3) the need to set up a standard evaluation system for further analyses.

Virtual restoration of ancient tomb murals based on hyperspectral imaging

The virtual restoration of historic murals holds immense importance in the realm of cultural heritage preservation. Currently, there are three primary technical issues. First and foremost, it is imperative to delineate the precise location where the mural necessitates restoration. Second, the original color of the mural has changed over time, resulting in a difference from its current appearance. Then, while the method utilizing convolutional neural networks is effective in restoring small defaced areas of murals, its effectiveness significantly diminishes when applied to larger areas. The primary objectives of this paper are as follows: (1) To determine the large and small areas to be restored, the authors employ hyperspectral super-pixel segmentation and support vector machine-Markov random field (SVM-MRF) classification. (2) The authors transform the hyperspectral mural images into more realistic and accurate red-green-blue (RGB) images using the Commission Internationale de l’Eclairage (CIE) standard colorimetric system. (3) The authors restored the images respectively using convolutional neural network and matching image block-based approaches depending on the size of the areas to be mended. The proposed method has enhanced the image quality assessment (IQA) in terms of both color quality and restoration effects. In contrast to the pseudo-color fusion method, the color optimization algorithm described in this research enhances the multi-scale image quality (MUSIQ) by 8.42%. The suggested technique enhances MUSIQ by 2.41% when compared to the convolutional neural network-based image inpainting algorithm.

A Novel Classificationof Nasal SillMorphologyProvides Strategies for Secondary Cleft Rhinoplasty.

Inconsistent and suboptimal outcomes in secondary cleft lip deformity repairs stem from the absence of a standardized classification system for nasolabial deformities, causing surgeons to rely heavily on their experience to determine surgical strategies. Nasal sill morphology reflects the severity of nasolabial deformities and plays a crucial role in facial aesthetics. This study introduces a novel classification for secondary cleft nasolabial deformities based on nasal sill morphology from the base view and provides tailored surgical techniques for each type. The study included 276 patients who underwent secondary cleft nasolabial correction at Peking University Third Hospital between August 2019 and July 2024. Patients were categorized according to the novel classification system and received surgeries tailored to their specific deformity type. Among the 276 patients, 13 (4.71%) were categorized as type I, 25 (9.06%) as type II, 73 (26.45%) as type III, and 165 (59.78%) as type IV. All patients achieved improved nasal sill aesthetics and enhanced nasal symmetry. This novel classification system, based on nasal sill morphology, accurately characterizes secondary nasolabial deformities and offers a structured approach for surgeons to tailor surgical strategies to each patient's deformity type. The present study tries to help establish an objective standard for secondary surgery of cleft nasolabial deformity for the first time. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Local QSAR based on quantum chemistry calculations for the stability of nitrenium ions to reduce false positive outcomes from standard QSAR systems for the mutagenicity of primary aromatic amines

BackgroundPrimary aromatic amines (PAAs) present significant challenges in the prediction of mutagenicity using current standard quantitative structure activity relationship (QSAR) systems, which are knowledge-based and statistics-based, because of their low positive prediction values (PPVs). Previous studies have suggested that PAAs are metabolized into genotoxic nitrenium ions. Moreover, ddE, a relative-energy based index derived from quantum chemistry calculations that measures the stability nitrenium ions, has been correlated with mutagenicity. This study aims to further examine the ability of the ddE-based approach in improving QSAR mutagenicity predictions for PAAs and to develop a refined method to decrease false positive predictions.ResultsInformation on 1,177 PAAs was collected, of which 420 were from public databases and 757 were from in-house databases across 16 laboratories. The total dataset included 465 Ames test-positive and 712 test-negative chemicals. For internal PAAs, detailed Ames test data were scrutinized and final decisions were made using common evaluation criteria. In this study, ddE calculations were performed using a convenient and consistent protocol. An optimal ddE cutoff value of -5 kcal/mol, combined with a molecular weight ≤ 500 and ortho substitution groups yielded well-balanced prediction scores: sensitivity of 72.0%, specificity of 75.9%, PPV of 65.6%, negative predictive value of 80.9% and a balanced accuracy of 74.0%. The PPV of the ddE-based approach was greatly reduced by the presence of two ortho substituent groups of ethyl or larger, as because almost all of them were negative in the Ames test regardless of their ddE values, probably due to steric hindrance affecting interactions between the PAA and metabolic enzymes. The great majority of the PAAs whose molecular weights were greater than 500 were also negative in Ames test, despite ddE predictions indicating positive mutagenicity.ConclusionsThis study proposes a refined approach to enhance the accuracy of QSAR mutagenicity predictions for PAAs by minimizing false positives. This integrative approach incorporating molecular weight, ortho substitution patterns, and ddE values, substantially can provide a more reliable basis for evaluating the genotoxic potential of PAAs.

Research on the Response of Urban Sustainable Development Standards to the United Nations Sustainable Development Goals Based on Knowledge Graphs

In the new era of the vigorous development of digitalization and intelligence, digital technology has widely penetrated various fields. International authoritative standardization bodies, such as the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), proposed a timely new standard concept called Standards Machine Applicable, Readable, and Transferable (SMART) in order to meet this development trend. Its core feature is that the standard can be machine-readable, usable, understandable, and resolvable without human labor so as to achieve the goals of standard formulation, promotion, publicity, and implementation more effectively. Simultaneously, China’s standardization industry is responding to the strategic deployment of “new quality productivity” by actively promoting the digital development of standards and establishing standard information databases, standard formulation management systems, etc., which provide data support and a platform basis for applying new technologies. Advanced technologies such as big data, artificial intelligence, blockchain, and knowledge graphs can be combined with standardization to improve the efficiency of standard development, application accuracy, and implementation effects. To align with these trends, this study focuses on analyzing the responses of national and international standards in the field of urban sustainable development to the United Nations Sustainable Development Goals (UN-SDGs). This study proposes an innovative approach involving the application of knowledge graph technology to the standardization of urban sustainable development and establishing a response correlation between the indicator library for cities’ sustainable development (ILCSD) and SDGs. It also provides additional functions, such as the intelligent extraction of cities’ sustainable characteristic evaluation indicators and aided decision analysis, which greatly enhance the practicability and efficiency of the ILCSD as a technical tool. Based on knowledge graphs, this study analyzes the different responses of important standards in the field of urban sustainable development to the 17 SDGs, accurately identifies weak trends and gaps in standards, and provides a basis for improving the standardization system of urban sustainable development. Simultaneously, by comparing national and international standards and technologies, this study promotes the mutual recognition of standards, which can help China’s urban sustainable development work align with international standards. In addition, the process of establishing and maintaining knowledge graphs facilitates the continuous adoption of new standards through which the indicator library is automatically updated. Finally, in this study, we propose several inspirations for the standardization of urban sustainable development in China, such as an optimization standard system of benchmarking SDGs and a localization application of the original SDG indicators.

Typical Case Studies and Classification with Evaluation of Carbon Dioxide Geological Sequestration in Saline Aquifers

To achieve carbon neutrality in China’s fossil energy sector, saline aquifer CO2 geological storage has become a critical strategy. As research into carbon reduction and storage potential evaluation advances across various geological scales, the need arises for consolidating key CO2 storage cases and establishing a standardized classification system and evaluation methodology. This paper provides a comprehensive review of notable CO2 storage projects in saline aquifers, covering aspects such as project overviews, structural and reservoir characteristics, caprock integrity, and seismic monitoring protocols. Drawing on insights from mineral and oil and gas exploration, as well as international methods, this paper outlines the stages and potential levels of saline aquifer storage in China. It proposes an evaluation framework with formulas and reference values for key coefficients. The study includes successful global projects, such as Sleipner and Snøhvit in Norway, In Salah in Algeria, and Shenhua in China’s Ordos Basin, which provide valuable insights for long-term carbon capture and storage (CCS). By examining geological characteristics, injection, and monitoring protocols in these projects, this paper analyzes how geological features impact CO2 storage outcomes. For example, the Sleipner project’s success is linked to its straightforward structure, favorable reservoir properties, and stable caprock, while Snøhvit illustrates diverse structural suitability, and In Salah demonstrates the influence of fractures on storage efficacy. CO2 storage activities are segmented into four stages—survey, investigation, exploration, and injection—and are further categorized by storage potential: geological, technical, techno-economic, and engineering capacities. This study also presents evaluation levels (prediction, control, technically recoverable, and engineering) that support effective reservoir selection, potential classification, and calculations considering factors like reservoir stability and sealing efficacy. Depending on application needs, volumetric or mechanistic methods are recommended, with precise determination of geological, displacement, and cost coefficients. For China, a dynamic evaluation mechanism characterized by multi-scale, tiered approaches and increasing precision over time is essential for robust storage potential assessment. The levels and methods outlined here serve as a scientific foundation for regional and stage-based comparisons, guiding engineering approvals and underground space management. To align with practical engineering demands, ongoing innovation through laboratory experiments, simulations, and field practice is crucial, supporting continual refinement of formulas and key parameter determinations.

Diagnosis of ring cracks on the shafting line of a turbo unit

Due to the growing number of accidents associated with damage to turbine unit shafts, there is a pressing need to diagnose the occurrence and growth of ring cracks not only during planned repair work on the turbine unit, but also directly during its operation to prevent emergency failures and costly repairs of the machine. Ring cracks that develop in the shafting do not lead to an increase in the level of vibration of the rotor or its support bearings and, therefore, cannot be detected by standard operational vibrodiagnostic systems. In this paper we propose a method that allows for the diagnosis of the occurrence and growth of ring cracks on the turbine unit shafting. The method is based on the influence of the depth of an annular crack in the shaft on the angle of its twist during machine operation. The presence of this relationship is determined through calculations based on the parameters of the experimental low-pressure turbine 2, used by the Central Boiler and Turbine Institute named after I. I. Polzunov. The practical implementation of this method is proposed to be carried out using the twist angle determination technique developed based on the discrete-phase method by means of measuring and analyzing changes in the shaft twist angle during turbine unit operation. Technically, this is achieved by employing a system consisting of inductors and sensors fixed to the stator and rotor parts of the machine. The proposed method can be used as a supplement to existing methods of operational control of the actual condition of the shafting, as well as, in the future, to assess the actual efficiency of the turbomachinery cylinders.

Effect of Standard versus Advanced Dimmable Lighting Systems on the Circadian Rhythms of Hospital Personnel

ABSTRACT The health of shift workers is seriously compromised due to circadian rhythm disruption. Not only do these shift workers lack sufficient exposure to natural light, the main circadian rhythm synchronizer, but they are also exposed to static electric lighting environments with the same luminous flux intensity throughout the entire night. Advances in lighting design make the accurate control of flux and spectra of electric lighting possible, allowing settings to be optimized for the appropriate regulation of circadian rhythms while also ensuring task performance. This research aims to quantify the effect of advanced lighting systems on the biological clock of nurses working night shifts in intensive care units, by replacing the fluorescent fixtures with dimmable luminaires. Diurnal and nocturnal levels of the main urine metabolite of melatonin, 6-sulfatoximelatonin, were assayed and a perception test of sleep quality and work performance was also carried out. The results show that the use of LED dimmable lighting results in an improvement in melatonin and cortisol levels of night shift workers, as well as improved concentration and sleep quality.

Abstract 4128519: Performance of a Popular Large Language Model in Answering Cardiovascular Related Queries: A Systematic Review and Pooled-Analysis

Background: The integration of large language models (LLMs) such as ChatGPT into healthcare can have significant implications for patient education and clinical decision-making. Aims: This systematic review and pooled analysis aim to evaluate the accuracy of ChatGPT 3.5 and 4 in answering simple queries across cardiovascular (CV) medicine disciplines. Methods: Literature searches were conducted in PubMed, Embase, and Cochrane Central in May 2024. Keywords included “ChatGPT”, “LLMs”, and “Chat-based artificial intelligence models”. Cross-sectional, peer-reviewed studies published in 2023 and 2024 investigating ChatGPT’s performance in CV medicine-related queries (Table/Figure) were extracted and included. All queries were evaluated by expert physicians in the corresponding fields within each study (and not by our readjudication), and a standardized grading system was employed for pooled analysis using an "accurate" and "inaccurate" grading scale for each answer. Results: Out of 127 identified and screened peer-reviewed studies, fourteen studies involving 542 CV-related queries were included. Pooled analysis revealed an overall accuracy of 84.5% (458/542) (95% CI [81.5, 87.6]). Stratification by model (ChatGPT-4 vs. ChatGPT-3.5) did not show a significant difference in accuracy (p=0.32). Furthermore, no significant differences in accuracies were seen between answers in 2023 and 2024 (p=0.07). The accuracies across the various topics were statistically comparable, except in the field of cardio-oncology, which showed significantly lower accuracy at 68% (p=0.02). Detailed performances per topic are included in the table and figure. Conclusion: ChatGPT demonstrated consistently high accuracy in answering CV-related queries with no significant differences across model versions or years. These results support the potential use of online-chat based LLMs as an informational tool in cardiology.

Surrogate Modeling for Solving OPF: A Review

The optimal power flow (OPF) problem, characterized by its inherent complexity and strict constraints, has traditionally been approached using analytical techniques. OPF enhances power system sustainability by minimizing operational costs, reducing emissions, and facilitating the integration of renewable energy sources through optimized resource allocation and environmentally aligned constraints. However, the evolving nature of power grids, including the integration of distributed generation (DG), increasing uncertainties, changes in topology, and load variability, demands more frequent OPF solutions from grid operators. While conventional methods remain effective, their efficiency and accuracy degrade as computational demands increase. To address these limitations, there is growing interest in the use of data-driven surrogate models. This paper presents a critical review of such models, discussing their limitations and the solutions proposed in the literature. It introduces both Analytical Surrogate Models (ASMs) and learned surrogate models (LSMs) for OPF, providing a thorough analysis of how they can be applied to solve both DC and AC OPF problems. The review also evaluates the development of LSMs for OPF, from initial implementations addressing specific aspects of the problem to more advanced approaches capable of handling topology changes and contingencies. End-to-end and hybrid LSMs are compared based on their computational efficiency, generalization capabilities, and accuracy, and detailed insights are provided. This study includes an empirical comparison of two ASMs and LSMs applied to the IEEE standard six-bus system, demonstrating the key distinctions between these models for small-scale grids and discussing the scalability of LSMs for more complex systems. This comprehensive review aims to serve as a critical resource for OPF researchers and academics, facilitating progress in energy efficiency and providing guidance on the future direction of OPF solution methodologies.

AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors

This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards.

Gender and migration: A continuum of gender-based violence echoing across the Sinai Desert and into Israel

Gender-based violence (GBV) is widespread globally and is based on social roles. These social roles represent society’s expectations of men and women carrying out stereotyped functions and behaviours. These gendered social expectations vary across culture, space and time. Drawing from an empirical study in Israel and building on the previous literature on these issues, this qualitative and interdisciplinary article identifies various forms of GBV along Eritrean women’s journey from their home country into Israel. The empirical work helps to examine the findings of previous theoretical studies. This article establishes that there is a continuum of violence for women, especially refugees and asylum seekers, rooted in the standard system of oppression – patriarchy. This has triggered the flight and trafficking of refugees from Eritrea through the Sinai Desert and into Israel. The article argues that structural and cultural violence emanating from both the hosted community and the host community play significant roles in allowing the circumstances for GBV to thrive throughout the entire refugee cycle. What can be seen is the creation of multiple layers of vulnerabilities, particularly the specific social–legal–economic marginalization of segments of the population, including Eritrean asylum-seeking women. Reacting to this continuum of violence, many participants in the study argued that it was necessary to adopt strategies to create a continuum of resilience and resistance grounded on women’s sorority, support and agency.

Evaluasi Penggunaan Sistem Akuntansi Pengeluaran Kas Pada Usaha Mikro Kecil Dan Menengah Mealsmound.Id Pati

Mealsmound.Id is one of the UMKM in Pati District, Pati Regency. Mealsmound.Id UMKM operates in the trading sector which sells a variety of contemporary food desserts. The income earned by these UMKM every month reaches around 30 million. However, this business does not yet have a good accounting system, so its financial reporting is not well structured and appears to be less effective and efficient. This research uses a case study method with a qualitative approach. This research was conducted at the Mealsmound.Id shop located in Pati District, Pati Regency. This research was conducted between May and August 2024. The results of this research state that Mealsmound.Id UMKM have not implemented a standard accounting system. UMKM Mealsmound.Id does not record frequent transactions in detail, UMKM Mealsmound.Id only records sales transactions. The process of establishing a new system at UMKM Mealsmound.Id consists of: (1) Cash In Form (2) Cash Out Form (3) Profit and Loss Report. The implementation that has been carried out is based on data obtained in June 2024, resulting in total revenue for 1 June to 25 June 2024 of IDR 13,228,500 with a cost of goods sold of IDR 32,203,500 and total costs or cash out of IDR 18,975,000.

A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography

Breast cancer is among the most prevalent cancers in the female population globally. Therefore, screening campaigns as well as approaches to identify patients at risk are particularly important for the early detection of suspect lesions. This study aims to propose a workflow for the automatic classification of patients based on one of the most relevant risk factors in breast cancer, which is represented by breast density. The proposed classification methodology takes advantage of the features automatically extracted from mammographic images, as digital mammography represents the major screening tool in women. Textural features were extracted from the breast parenchyma through a radiomics approach, and they were used to train different machine learning algorithms and neural network models to classify the breast density according to the standard Breast Imaging Reporting and Data System (BI-RADS) guidelines. Both binary and multiclass tasks have been carried out and compared in terms of performance metrics. Preliminary results show interesting classification accuracy (93.55% for the binary task and 82.14% for the multiclass task), which are promising compared to the current literature. As the proposed workflow relies on straightforward and computationally efficient algorithms, it could serve as a basis for a fast-track protocol for the screening of mammograms to reduce the radiologists’ workload.

Higher BMI Is Associated With Wound Breakdown Following Resection of Haglund Deformity.

Haglund deformity is characterized by an enlarged posterosuperior calcaneus, often with inflammation of the retrocalcaneal bursa and Achilles tendon. This study aims to determine if obesity is associated with an increased rate of complications after Haglund resection. A retrospective review was conducted on patients who underwent surgical resection for Haglund deformity from January 2015 to December 2023 at a single academic center. The patient cohort was initially stratified by body mass index (BMI) into normal (BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30). For those classified as obese, further subclassification was performed using the standard system: obesity class I (mild obesity) = BMI 30.0-34.9; obesity class II (moderate obesity) = BMI 35.0-39.9; and obesity class III (severe obesity) = BMI ≥40.0. Data on demographics, surgical techniques, and weightbearing were collected and analyzed. Postoperative complications were compared between groups. Of the 370 patients included in this study, 20 (5.4%) were classified as normal, 77 (20.8%) were overweight, and 273 (73.8%) were obese. Within the cohort of patients with obesity, 96 (35.2%) were classified as obesity class I, 96 (35.2%) as obesity class II, and 81 (29.7%) as obesity class III.The obese group had a higher proportion of females (70.0%) and Black/African American race (24.5%), and a higher prevalence of diabetes mellitus (22.0%) and American Society of Anesthesiologists scores compared with other groups. Additionally, analysis within the obesity subclassifications revealed significant differences in smoking status, with a higher proportion of nonsmokers as obesity class increased (58.3% in class I, 76.0% in class II, and 79.0% in class III; P = .01). Follow-up duration averaged 10.5 months, with wound breakdown rates significantly higher in the obese group vs the overweight or normal groups (11.0% vs 2.6% vs 0.0%, P = .02). No significant differences in wound complications or outcomes were observed between patients based on different obesity subclassifications. Our findings demonstrate that after Haglund resection, obese patients have a higher risk of complications, particularly wound breakdown. This underscores the necessity of careful patient selection and perioperative optimization.