With the widespread application of deep learning technology in various fields, power load forecasting, as an important link in power system operation and planning, has also ushered in new opportunities and challenges. Traditional forecasting methods perform poorly when faced with the high uncertainty and complexity of power loads. In view of this, this paper proposes a power load forecasting model PSO-BiTC based on deep learning and particle swarm optimization. This model combines a temporal convolutional network (TCN) and a bidirectional long short-term memory network (BiLSTM), using TCN to process long sequence data and capture features and patterns in time series, while using BiLSTM to capture long-term and short-term dependencies. In addition, the particle swarm optimization algorithm (PSO) is used to optimize model parameters to improve the model\'s predictive performance and generalization ability. Experimental results show that the PSO-BiTC model performs well in power load forecasting. Compared with traditional methods, this model reduces the MAE (Mean Absolute Error) to 20.18, 17.57, 18.61 and 16.7 on four extensive data sets, respectively. It has been proven that it achieves the best performance in various indicators, with a low number of parameters and training time. This research is of great significance for improving the operating efficiency of the power system, optimizing resource allocation, and promoting carbon emission reduction goals in the urban building sector.

In this dual-center study, we assessed the BioHermes A1C EXP M13 system for point-of-care (POC) HbA1c testing against two NGSP-certified HPLC instruments, the Bio-Rad D100 and Tosoh G8. Analyzing 605 samples, we evaluated the A1C EXP\'s reproducibility, sensitivity, specificity and impact of anemia on HbA1c measurements. The device showed excellent reproducibility with CVs under 2.4% and high sensitivity and specificity for diabetes diagnosis-98.1% and 96.8% against D100, and 97.1% and 96.7% against G8. Passing-Bablok regression confirmed a close correlation between A1C EXP and the HPLC instruments, with equations y = 0.10625 + 0.9688x (D100) and y = 0.0000 + 0.1000x (G8), and Bland-Altman plots indicated mean relative differences of -1.4% (D100) and -0.4% (G8). However, in anemic samples, A1C EXP showed a negative bias compared to HPLC devices, suggesting that anemia may affect the accuracy of HbA1c results. The study indicates that A1C EXP is a reliable POC alternative to laboratory assays, albeit with considerations for anemic patients.

The rapid development of deep learning in medical imaging has significantly enhanced the capabilities of artificial intelligence while simultaneously introducing challenges, including the need for vast amounts of training data and the labor-intensive tasks of labeling and segmentation. Generative adversarial networks (GANs) have emerged as a solution, offering synthetic image generation for data augmentation and streamlining medical image processing tasks through models such as cGAN, CycleGAN, and StyleGAN. These innovations not only improve the efficiency of image augmentation, reconstruction, and segmentation, but also pave the way for unsupervised anomaly detection, markedly reducing the reliance on labeled datasets. Our investigation into GANs in medical imaging addresses their varied architectures, the considerations for selecting appropriate GAN models, and the nuances of model training and performance evaluation. This paper aims to provide radiologists who are new to GAN technology with a thorough understanding, guiding them through the practical application and evaluation of GANs in brain imaging with two illustrative examples using CycleGAN and pixel2style2pixel (pSp)-combined StyleGAN. It offers a comprehensive exploration of the transformative potential of GANs in medical imaging research. Ultimately, this paper strives to equip radiologists with the knowledge to effectively utilize GANs, encouraging further research and application within the field.

OBJECTIVE: The aim of this study is to investigate the opinions and perspectives of The Novel Integrated Toolkit for Enhanced Prehospital Life Support and Triage in Challenging and Large Emergencies (NIGHTINGALE) end-users and tool developers regarding Key Performance Indicators (KPIs) and benchmarks that assess the prehospital response to Mass Casualty Incidents (MCIs) enhanced by the NIT-MR.
METHODS: A qualitative study employing focus group discussions was conducted to collect opinions and perspectives of end-users and tool developers regarding KPIs and benchmarks in MCI response using the NIT-MR. The criteria considered for the selection and distribution of participants within the groups was the nature of their involvement within the NIGHTINGALE project and their familiarity with the tools to be discussed.
RESULTS: Thirty-one participants from different countries were included. Four themes emerged during data analysis which are: definition/explanation is the personal understanding of participants of the term KPI, process of KPI development and relationship with User Requirements is the decision process for assigning KPIs to user requirements, benchmarking is the mental process of associating a benchmark to a KPI or for developing a benchmark, and technical/medical gap is the gap of understanding between each sides\' fields.
CONCLUSIONS: This study emphasized the need for a structured approach to using KPIs and bridging the gap between technological and medical worlds, taking the NIGHTINGALE project, funded by the European Union, which aims to develop a technological toolkit for first responders in mass casualty incidents as an example. These insights are crucial for enhancing disaster response.

OBJECTIVE: This study aimed to comprehensively evaluate Mexico\'s health system performance from 1990 to 2019 utilising the Health Access and Quality Index (HAQI) as a primary indicator.
METHODS: A retrospective ecological analysis was performed using data from the Global Burden of Diseases, Injuries and Risk Factors Study (GBD) study and the National Population Council (CONAPO).
METHODS: HAQI values for 1990, 2000, 2010, 2015, and 2019 were examined for each state in Mexico and three age groups (young, working, and post-working). Additionally, the marginalisation index was employed to assess inequalities in the HAQI distribution across states. The concentration index of the HAQI for each year was estimated, and the efficiency of states in producing the HAQI was evaluated using a data envelopment approach.
RESULTS: Through the analysis of national and subnational data, results indicated an overall improvement in healthcare access and quality during the study period. Although differences in the HAQI value related to state marginalisation decreased from 1990 to 2015, by 2019, the inequality had returned to a level comparable to 2000. Efficiency in producing health (HAQI values) exhibited substantial heterogeneity and fluctuations in the ranking order over time. States such as Nuevo León consistently performed well, while others, such as Guerrero, Chihuahua, Mexico City, and Puebla, consistently underperformed.
CONCLUSIONS: The findings from this study emphasise the necessity for nuanced strategies to address healthcare disparities and enhance the overall system performance. The study provides valuable insights for ongoing discussions about the future of Mexico\'s healthcare system, aiming to inform evidence-based policy decisions and improve the nationwide delivery of healthcare services.

OBJECTIVE: To establish and validate a set of best nursing practice indicators for medical groups in Chinese general hospitals based on value-based healthcare (VBHC) theory.
METHODS: A modified Delphi method.
METHODS: This study engaged experienced clinical nurses from public hospitals and multidisciplinary experts in operations management. Through a literature review and structured brainstorming, a comprehensive framework for assessing best nursing practices in Chinese general hospital medical groups was developed. A modified Delphi method was then employed to establish an indicator framework, followed by the Combined Empowerment-MABAC method to weight and validate the indicators using empirical data collected between June 2023 and October 2023. The CREDES checklist guided the reporting of this study.
RESULTS: Sixteen experts, each with at least 10 years of experience in nurse management from nine healthcare organizations, participated in two rounds of consultation. The experts\' responses and suggestions for rewording, deleting and adding items were incorporated into each round. Of the 34 proposed indicators, 25 were approved, covering healthcare service capacity, efficiency, quality and safety, patient experience and cost.
CONCLUSIONS: The Delphi survey reached a consensus on necessary actions to improve nursing performance. The developed indicator system provides a foundational framework for standardizing the monitoring of care quality and performance assessment in Chinese clinical healthcare groups, with broad applicability.
CONCLUSIONS: This study provides a reliable basis for developing a nursing performance assessment database, offering crucial insights for measuring quality of care and improving patient value.
UNASSIGNED: No Patient or Public Contribution.

The FinRay soft gripper achieves passive enveloping grasping through its functional flexible structure, adapting to the contact configuration of the object to be grasped. However, variations in beam position and thickness lead to different behaviors, making it important to research the relationship between structure and force. Conventional research using FEM simulations has tested various virtual FinRay models but replicating phenomena such as buckling and slipping has been challenging. While hardware-based methods that involve installing sensors on the gripper and the object to analyze their states have been attempted, no studies have focused on the tangential contact force related to slipping. Therefore, we developed a 16-way object contact force measurement device incorporating two-axis force sensors into each of the 16 segmented objects and compared the normal and tangential components of the enveloping grasping force of the FinRay soft gripper under two types of contact friction conditions. In the first experiment, the proposed device was compared with a device containing a six-axis force sensor in one segmented object, confirming that the proposed device has no issues with measurement performance. In the second experiment, comparisons of the proposed device were made under various conditions: two contact friction states, three object contact positions, and two object motion states. The results demonstrated that the proposed device could decompose and analyze the grasping force into its normal and tangential components for each segmented object. Moreover, low friction conditions result in a wide contact area with lower tangential frictional force and a uniform normal pushing force, achieving effective enveloping grasping.

This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer.

UNASSIGNED: Our research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning.
UNASSIGNED: Our model was trained and evaluated using several deep learning classifiers on the LUNA-16 dataset, achieving superior performance in terms of the Probabilistic Rand Index (PRI), Variation of Information (VOI), Region of Interest (ROI), Dice Coecient, and Global Consistency Error (GCE).
UNASSIGNED: The evaluation demonstrated a high accuracy of 91.76% for parameter estimation, confirming the effectiveness of the proposed approach.
UNASSIGNED: Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. We proposed a novel segmentation model to identify lung disease from CT scans to achieve this. We proposed a learning architecture that combines U-Net with a Two-parameter logistic distribution for accurate image segmentation; this hybrid model is called U-Net++, leveraging Contrast Limited Adaptive Histogram Equalization (CLAHE) on a 5,000 set of CT scan images.

Waste disposal systems are crucial components of environmental management, and focusing on this sector can contribute to the development of various other sectors and improve social welfare. Urban waste is no longer solely an environmental issue; it now plays a significant role in the economy, energy, and value creation, with waste disposal centers (WDCs) being a key manifestation. The purpose of this study is to measure the performance of WDCs in the state of Nuevo León, Mexico, with the aim of developing environmental, social, and governance (ESG) strategies to strengthen and prepare the WDCs for the industrial developments in this state. By identifying environmental variables and undesirable factors, the efficiency and managerial capacity of 32 WDCs were assessed. The analysis revealed that 9 out of the 32 WDCs are technically efficient, while the remaining 23 require significant improvements. Using the Data Envelopment Analysis (DEA) technique, an average efficiency score of 0.91 was found, with a standard deviation of 0.08. The managerial capacity analysis indicated that the highest-ranked WDC achieved an efficiency score of 1, whereas the lowest-ranked WDC scored 0.67. Finally, an operational map of development strategies was developed using the Interpretive Structural Modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) approach. The results indicate that four phases of development should be followed for real development and maturity of development in these WDCs, including Groundwork, Structuring, Development and Growth, and Smart Maturity.