Accurate Information Research Articles

Classification of human walking context using a single-point accelerometer

Real-world walking data offers rich insights into a person’s mobility. Yet, daily life variations can alter these patterns, making the data challenging to interpret. As such, it is essential to integrate context for the extraction of meaningful information from real-world movement data. In this work, we leveraged the relationship between the characteristics of a walking bout and context to build a classification algorithm to distinguish between indoor and outdoor walks. We used data from 20 participants wearing an accelerometer on the thigh over a week. Their walking bouts were isolated and labeled using GPS and self-reporting data. We trained and validated two machine learning models, random forest and ensemble Support Vector Machine, using a leave-one-participant-out validation scheme on 15 subjects. The 5 remaining subjects were used as a testing set to choose a final model. The chosen model achieved an accuracy of 0.941, an F1-score of 0.963, and an AUROC of 0.931. This validated model was then used to label the walks from a different dataset with 15 participants wearing the same accelerometer. Finally, we characterized the differences between indoor and outdoor walks using the ensemble of the data. We found that participants walked significantly faster, longer, and more continuously when walking outdoors compared to indoors. These results demonstrate how movement data alone can be used to obtain accurate information on important contextual factors. These factors can then be leveraged to enhance our understanding and interpretation of real-world movement data, providing deeper insights into a person’s health.

(298) AUA vs AI: An Inquiry into Testosterone Guidelines

Abstract Introduction ChatGPT is an artificial intelligence with an accessible user interface that allows for conversational input. It uses deep learning, a subset of machine learning based on artificial neural networks, to “speak” to the user. Patients and clinicians alike have been querying ChatGPT with medical questions, and recent publications have shown that the validity of responses vary. Limited information is available pertaining to ChatGPT’s capacity to accurately discuss testosterone management and deficiency. It is imperative for the medical community to be aware of potentially accurate or inaccurate information sources. This project is designed to assess ChatGPT’s clinical competency compared to the American Urologic Association (“AUA”) guidelines regarding the evaluation and management of testosterone deficiency. Objective To assess the competency of ChatGPT to answer general and clinical questions related to testosterone deficiency and replacement compared to the AUA guidelines. Methods The AUA guidelines for evaluation and management of testosterone deficiency include 31 main points. Each point was prompted to ChatGPT in the form of a question, accompanied by requests for validation of information, providing further information, or clinical next-step recommendations. The answers were then sorted into 3 categories all relative to the AUA guidelines, being accurate and complete, (AC), accurate but incomplete (AI), and incorrect or misleading (IM). Results Of the 31 guideline-based questions queried, 22/31 (71%) were AC, 4/31 (13%) were AI, and 5/31 (16%) were IM. The highest number of AI answers pertained to treatment related queries. The highest number of IM answers pertained to counseling, particularly on commonly contested topics, such as the relationship between testosterone and prostate cancer or cardiovascular health. Conclusions When queried about the evaluation and management of testosterone deficiency, ChatGPT offered complete and accurate answers in 71% of cases. It had increased accuracy of even up to 100% when answering regarding firmly established or more binary topics, such as diagnosis requirements or recommended adjunct testing. As such, patients and providers may consider using ChatGPT as an ancillary resource for uncomplicated or firmly established testosterone related queries. However, it did much more poorly when discussing more contested topics such as testosterone’s relationship to prostate cancer, or when approaching elements of medicine that require a more personalized human touch, such as counseling. While ChatGPT may be helpful in the base or binary matters, interpersonal and patient specific matters are best handled by experts in the field. Disclosure No.

An modified intelligent real-time crack detection method for bridge based on improved target detection algorithm and transfer learning

The combination of UAV camera and intelligent algorithm is a promising method for non-contact bridge crack detection. In this paper, an inspection tool based on UAV Image Acquisition Technology (UAVIAT) and Improved Intelligent Target Detection Technology (IITDT) called Improved Intelligent Real-Time Crack Detection Method for Bridges (IIRTCDMB) is proposed for efficient crack detection. The contributions of this paper are (1) The Squeeze-Excitement (SE) attention module is integrated into the target detection algorithm - You Only Look Once version 7 (YOLOv7) model to improve the learning ability of the feature channel. A Focal-efficient intersection over union (Focal-EIoU) loss function is also introduced to improve the regression accuracy of the model. As a result, a new crack image detection algorithm, YOLOv7-CD, is proposed. (2) A training process based on two-stage transfer learning (TSTL) is established, and hyper-parameter optimization of YOLOv7-CD is carried out. The feasibility and excellent performance of the proposed method are verified by applying it on the Cuntan Yangtze River Bridge. The results show that the average precision (AP) of the YOLOv7-CD model is improved by 3.19% compared with the original YOLOv7 model. After TSTL and hyperparameter optimization, the AP of the YOLOv7-CD model for bridge crack detection reaches 98.01%, which is higher than that of the popular target detection models. The IIRTCDMB proposed in this paper can acquire bridge surface images more safely and efficiently, and provide inspectors with more accurate structural crack information with lower computational and hardware requirements, which can provide technical support for the assessment of structural safety conditions and the formulation of maintenance programs.

(376) High Capability of ChatGPT to Answer Common Patient Questions on Vasectomy Reviewed by Sexual Medicine Specialists

Abstract Introduction Novel technologies utilizing artificial intelligence (AI) have shown their potential to reduce provider workload and improve task efficiency in certain clinical settings. ChatGPT is an AI-powered chatbot which utilizes natural language processing (NLP) technology to provide human-like responses to open-ended questions posed by users. Recent studies have shown ChatGPT’s ability to provide diabetes counseling in lieu of trained educators, and effectively communicate technical research papers in layman’s terms to patients. Vasectomy is a common urologic clinic procedure that can be a source of anxiety for patients, many of whom desire the opportunity for further pre-operative counseling. Objective The objective of this preliminary study was to evaluate the ability of ChatGPT to respond to common patient questions on vasectomy encountered by sexual medicine specialists. Methods We composed a list of 20 questions commonly asked by patients pertaining to vasectomy. The questions were submitted to ChatGPT Plus with the prompt: “Answer the following questions in 50 words or less”. 3 fellowship-trained sexual medicine specialists then rated the accuracy of ChatGPT’s responses on a scale from 1-5 (1 representing a completely inaccurate response and 5 representing a response in line with what they would recommend to patients). Scores were tabulated and summarized (Table 1). Descriptive statistics were performed. Weighted Cohen’s kappa was calculated to assess for interrater reliability. Results Median score across all 20 responses from 3 raters was 4.1 (IQR 1.3). The most common rating was 5, indicating a high level of agreement between ChatGPT’s responses and those of sexual medicine specialists. The lowest score assigned was 2 (indicating poor accuracy), and was received by 6 responses. Weighted Cohen’s kappa was 0.49, implying moderate agreement amongst raters. Overall, ChatGPT was able to provide relatively accurate and concise responses to these common questions. Conclusions Our results demonstrate the capability of using ChatGPT as a potentially reliable source of information for patients desiring vasectomy counseling. Given the high clinical volume faced by many vasectomy providers, ChatGPT could possibly improve patient satisfaction and reduce provider burden. Further collection of real-world data is needed to better assess the accuracy and reliability of ChatGPT’s responses. Sexual medicine specialists should be aware of the strengths and limitations of emerging AI technologies as a medium for providing accurate health information to patients. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Fellow, Posterity Health, Coloplast, Hims, Genentech.

(021) ChatGPT's Ability to Assess Quality and Readability of Online Medical Information

Abstract Introduction Health literacy plays a crucial role in enabling patients to understand and effectively use medical information. As technology rapidly advances, the significance of health literacy becomes even more pronounced, particularly in comprehending complex medical information. Artificial Intelligence (AI) platforms have garnered significant attention for their remarkable ability to generate automated responses to a wide range of prompts. However, their capacity to assess the quality and readability of provided text remains uncertain. Given the growing prominence of AI web assistant tools, we hypothesized that integrating these tools into patients' web searches could enhance the retrieval of accurate medical information. Objective To evaluate the proficiency of Conversational Generative Pre-Trained Transformer (ChatGPT) in assessing readability, and utilizing the DISCERN tool to assess quality of online content regarding shock wave therapy for erectile dysfunction. Methods Websites were generated using a Google search of “shock wave therapy for erectile dysfunction” with location filters disabled. Readability was analyzed using the Readable software (Readable.com, Horsham, United Kingdom). Quality was assessed independently by three reviewers using the DISCERN tool. The same plain text files collected were inputted into ChatGPT to determine whether it produced comparable metrics for readability and quality. Results The study results revealed a notable disparity between ChatGPT's readability assessment and that obtained from a reliable tool, Readable.com (p<0.05). This indicates a lack of alignment between ChatGPT's algorithm and that of established tools, such as Readable.com. Similarly, the DISCERN score generated by ChatGPT differed significantly from the scores generated manually by human evaluators (p<0.05), suggesting that ChatGPT may not be capable of accurately identifying poor-quality information sources regarding shock wave therapy as a treatment for erectile dysfunction. Conclusions ChatGPT’s evaluation of the quality and readability of online text regarding shockwave therapy for erectile dysfunction differs from that of human raters and trusted tools. ChatGPT's current capabilities were not sufficient for reliably assessing the quality and readability of textual content. Further research is needed to elucidate the role of AI in the objective evaluation of online medical content in other fields. Continued development in AI and incorporation of tools such as DISCERN into AI software may enhance the way patients navigate the web in search of high-quality medical content in the future. Disclosure No.

(297) Assessment of the Accuracy of Chatgpt on Testosterone Management Uncovers the Limits of Generative Artificial Intelligence

Abstract Introduction Testosterone has become an increasing cultural touchstone. It is estimated that one third of men on testosterone replacement are not testosterone deficient and that another large percentage of men who would qualify for treatment do not receive it. This is likely partially due to the significant amount misinformation and disinformation available online. Recently there has been an explosion of interest in generative artificial intelligence (AI). Given the sensitive nature of questions regarding sexual health, many men may see generative AI as an anonymous, confidential, and accurate source of information. Specifically, OpenAI’s ChatGPT is the most used generative AI that offers an easy to navigate conversation-based resource where patients may inquire about many topics including sexual health concerns. This study aims to validate ChatGPT responses to questions about testosterone and evaluate the references used for its claims. Objective To evaluate the accuracy of ChatGPT responses to common questions about testosterone therapy and the sources substantiating claims. Methods Questions regarding testosterone function and therapy were created based on American Urological Association (AUA) Care Foundation brochures for patient education on testosterone. ChatGPT was prompted to respond to these questions during a single interactive session with the ending phrase, “include references,” to evaluate the sources of responses. Responses were then separated into individual statements made by the AI and their associated references. Each response was evaluated for accuracy by matching the AI generated statements to AUA guidelines and published AUA resources. References were evaluated on the accuracy of the authors, article, journal, and date of publication based on the PubMed index, Google Scholar, and the cited journal’s website. Results A total of 9 questions were posed to the generative AI. The interactive session with ChatGPT yielded 53 separate statements backed by 21 references. The majority of queries yielded true statements by matched criteria to AUA guidelines (50/53, 94.3%). The references provided by the generative AI were drawn primarily from peer-reviewed journals (15/21, 71.4%); hospital websites (3/21, 14.3%), unknown sources (2/21, 9.5%) and AUA guidelines (1/21, 4.8%). The mean impact factor for the cited peer reviewed journals was 27.2 (SD 60.5). A majority of references were found to be legitimate sources (13/21, 61.9%). However, 4 (19.0%) included citation errors and 4 (19.0%) references were found to be fabricated by the AI. The question yielding the most fabricated references (3/4, 75%) was “How do I boost my testosterone levels naturally?” Conclusions We found that ChatGPT offers a reliable conversation-based resource where patients may receive accurate information in response to their testosterone related questions. However, when queried about topics with fewer references, the large language model fabricated sources with real reputable authors and real journals. This is highly concerning as the general public would not be able to differentiate between real and fake information. Patients should be counseled with caution, and guardrails should be put in place for the safety of patients using large language models for medical information. Disclosure No.

A flood knowledge-constrained large language model interactable with GIS: enhancing public risk perception of floods

Public’s rational flood mitigation behaviors depend on accurate perception of flood risks. The use of natural language for flood risk perception is an effective approach, and it is critical to ensure the accuracy and comprehensibility of the flood information provided by the system in natural language dialogues. This study presents a framework for large language model (LLM) that is constrained by flood knowledge and can interact with geographic information system (GIS), aimed at enhancing the public’s perception of flood risks. We tested the performance of LLM within this framework and the results demonstrate that LLM can generate accurate information about floods under the constraints of entities and relationships in the knowledge graph, and interact with GIS to produce personalized knowledge through real-time coding. Furthermore, we conducted flood risk perception experiments on users with different cognitive levels. The results indicate that using natural language dialogue can narrow the differences brought about by cognitive levels, allowing the public to equally access knowledge related to flood events.

Post-graduation career pathways: a nationwide survey among dental students in Germany.

Oral and maxillofacial surgery (OMFS) has to compete with other specialties for the best candidates. With the upcoming change of generations (Z and Alpha) and the movement toward gender parity of dentistry, understanding changing preferences and misconceptions is essential. An online survey was conducted by the German-Association-of-Oral-and-Maxillofacial-Surgery (DGMKG) across German dental schools. The survey collected demographic data, academic background, and career aspirations, with a focus on OMFS. The dental student survey results were compared to a survey given to OMFS Specialists. 637 dental students, mainly female (70%), from 30 German universities participated. 27% had defined career aspirations post-graduation, with self-employment and academia being popular choices. 67% were unsure. Specializations leaned towards restorative dentistry (41%), orthodontics (36%), and prosthodontics (31%). While 73% showed interest in surgical practices, 20% were attracted in specializing in OMFS. Of those averse to OMFS, 78% cited long training duration as the deterrent, 12% were put off by perceived unattractive working hours. Other reasons included negative undergraduate experiences, scarcity of part-time positions, and perceived inadequate earnings. Accurate data is crucial for career decisions. OMFS societies must proactively share accurate information and guide students. OMFS offers family-friendly hours, and while its training might be longer than dental specialties, it is on par with other surgical professions. Dental students consistently regard OMFS as commendable career path. To guarantee sustained OMFS expertise, it is imperative to nurture this interest through dedicated academic mentorship and innovative education, thereby solidifying their professional direction.

Evaluating AI in medicine: a comparative analysis of expert and ChatGPT responses to colorectal cancer questions

Colorectal cancer (CRC) is a global health challenge, and patient education plays a crucial role in its early detection and treatment. Despite progress in AI technology, as exemplified by transformer-like models such as ChatGPT, there remains a lack of in-depth understanding of their efficacy for medical purposes. We aimed to assess the proficiency of ChatGPT in the field of popular science, specifically in answering questions related to CRC diagnosis and treatment, using the book “Colorectal Cancer: Your Questions Answered” as a reference. In general, 131 valid questions from the book were manually input into ChatGPT. Responses were evaluated by clinical physicians in the relevant fields based on comprehensiveness and accuracy of information, and scores were standardized for comparison. Not surprisingly, ChatGPT showed high reproducibility in its responses, with high uniformity in comprehensiveness, accuracy, and final scores. However, the mean scores of ChatGPT’s responses were significantly lower than the benchmarks, indicating it has not reached an expert level of competence in CRC. While it could provide accurate information, it lacked in comprehensiveness. Notably, ChatGPT performed well in domains of radiation therapy, interventional therapy, stoma care, venous care, and pain control, almost rivaling the benchmarks, but fell short in basic information, surgery, and internal medicine domains. While ChatGPT demonstrated promise in specific domains, its general efficiency in providing CRC information falls short of expert standards, indicating the need for further advancements and improvements in AI technology for patient education in healthcare.

Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

Information about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests sustainably, combating desertification, halting and reversing land degradation, and halting biodiversity loss. However, traditional methods employed to identify and map coniferous forests are costly and labor-intensive, particularly in dealing with large-scale regions. Consequently, a methodological framework is proposed to identify coniferous forests in northwestern Liaoning, China, in which there are semi-arid and barren environment areas. This framework leverages a multi-classifier fusion algorithm that combines deep learning (U2-Net and Resnet-50) and shallow learning (support vector machines and random forests) methods deployed in the Google Earth Engine. Freely available remote sensing images are integrated from multiple sources, including Gaofen-1 and Sentinel-1, to enhance the accuracy and reliability of the results. The overall accuracy of the coniferous forest identification results reached 97.6%, highlighting the effectiveness of the proposed methodology. Further calculations were conducted to determine the area of coniferous forests in each administrative region of northwestern Liaoning. It was found that the total area of coniferous forests in the study area is about 6013.67 km2, accounting for 9.59% of northwestern Liaoning. The proposed framework has the potential to offer timely and accurate information on coniferous forests and holds promise for informed decision making and the sustainable development of ecological environment.

Beyond Belief Correction: Effects of the Truth Sandwich on Perceptions of Fact-checkers and Verification Intentions

ABSTRACT As mis- and disinformation may threaten democracy by fueling misperceptions, it is important to assess the effectiveness of journalistic interventions combating false information. This study aims to better understand how fact-checks relate to various outcomes relevant to audiences’ resilience to false information. We randomly exposed 752 Dutch participants to fact-checks of a disputed health-related claim vs. no fact-check. The fact-checks either followed a classic format which repeated the false claim or followed a truth sandwich format wrapping the false claim in accurate information. While the truth sandwich was not effective in correcting false beliefs, it had indirect benefits. First, those who saw a truth sandwich perceived the intentions of fact-checkers more positively thinking that their intention was to inform rather than to manipulate or spread lies. Second, those who saw a truth sandwich showed the least resistance to reading subsequent fact-checks. For journalism practice this implies that different fact-check formats can be strategically employed to achieve desired outcomes. A more classic fact-check format might be preferable if the primary aim is to correct false beliefs, while the truth sandwich may be employed to reach more indirect and long-term aims like rebuilding confidence in fact-checkers or stimulating future verification behaviors.

Report of diplodia stem rot disease in SoE-Mollo-Citrus in Timor Tengah Selatan Regency, East Nusa Tenggara

SoE-Mollo-Citrus is prominent in East Nusa Tenggara. However, a consortium of pathogens infects citrus plants, causing symptoms of broken stems with peeled bark associated with or without gum production. These symptoms could be caused by Lasiodiplodia sp. or Phytophthora sp. Considering the importance of SoE-Mollo-Citrus for society and the Government, actual and accurate information is profound regarding the presence of primary diseases that threaten it. This research aimed to determine disease intensity in central SoE-Mollo-Citrus production villages and to identify pathogens isolated from stem rot symptoms. Stratified purposive sampling method was applied to select seven Districts and ten Villages having large SoE-Mollo-Citrus populations. In each village, one orchard was randomly chosen for observation. SoE-Mollo-Citrus showing symptom of wet and dry diplodia were present in every observation village. The incidence and severity of the disease ranged from 30% in Mnelalete village to 75% in Oelbubuk village and between 21% in Tubuhue village to 51% in Oelbubuk village, respectively. Seventy percent of the collected isolates were pathogenic. Morphological and molecular identification using Primers ITS2 and ITS5 confirmed that the stem rot pathogen was Lasiodiplodia theobromae Pat. Syn. Botryodiplodia theobromae. Good agricultural practices are mandatory so this disease does not harm citrus production.

The Role of BIM in Managing Risks in Sustainability of Bridge Projects: A Systematic Review with Meta-Analysis

With the significant and rapid growth observed in bridge projects worldwide, the associated environmental, economic, and social concerns are on the rise. A systematic review of bridge sustainability with meta-analysis according to the PRISMA guidelines was performed, aiming to improve understanding of the importance of using building information modeling (BIM) in bridge projects by investigating the role of proper implementation of this technology to avoid and mitigate risks and improve sustainability. The relevant international literature was collected and scrutinized. The findings demonstrated that the accurate implementation of BIM significantly enhances the efficient management of risks in bridge projects. Consequently, this has a positive effect on improving the three essential (environmental, economic, and social) aspects of sustainability. The impact mentioned is especially apparent in enhancing the management of information throughout the entire lifespan of a bridge. This, in turn, facilitates precise decision-making during the design phase, aligns with assessments of environmental impact, enables real-time monitoring during execution, effectively manages the maintenance of the structure, facilitates efficient allocation and utilization of resources, and improves design practices by providing designers with accurate information. Delving into the nuances of this review has shed light on the transformative potential of BIM in shaping sustainable bridge projects, laying the groundwork for future advancements in this critical field.

PENERAPAN MEDIA AUDIO VISUAL PADA MATERI PENJUMLAHAN KELAS 2 DI SDN 3 WONOBOYO

Mathematics as a field of study taught in formal education institutions is an important part of efforts to improve the quality of education. Media is a tool for conveying internal information about the learning process, use of media, learning objectives that are expected to be more easily achieved compared to what was planned to be achieved previously. Audiovisual media is media that contains sound components and audio images. The type of research used is a qualitative research method. Here the researcher used a direct survey to make observations by interviewing one of the teachers by asking several questions regarding video-based audio-visual media for teaching mathematics for class 2 addition material at SDN 3 Wonoboyo so that the researcher could immediately get precise and accurate information. results. answers and results. By applying audio-visual media to class 2 addition material at SDN 3 Wonoboyo, it is able to increase students' enthusiasm for learning mathematics. Apart from that, the application of audio-visual media in addition to material can improve student learning outcomes.

Herbal Components for the Treatment of Various Kidney Disorders

Abstract:: Acute kidney injury is described as a loss of renal excretory function that occurs suddenly. AKI is one of several ailments grouped together as acute kidney diseases and disorders (AKD), in which progressive degradation of kidney function or persistent renal dysfunction is associated with irreversible loss of kidney cells and nephrons, potentially leading to chronic kidney disease (CKD). The prevalence and incidence of KDs are assessed in light of population disparities in the prevalence of risk factors such as diabetes, hypertension, and obesity. However, the presence of these associated illnesses does not fully explain the increased rate of progression of chronic renal disease in high-risk patients (CKD). The development of renal disease is accompanied by a decline in renal function, which eventually leads to renal failure. The current status of diagnostic testing does not allow for accurate early disease diagnosis, prognosis, or therapy efficacy monitoring. Despite significant advances in care and understanding of the causes of acute renal failure (ARF), many aspects of ARF remain controversial, confusing, and uncertain. Accurate information on the disease's prevalence, genesis, and clinical manifestations is required to successfully advocate for enough funding and resources to manage the disease. Herbal components for the treatment of a variety of kidney disorders, and the page includes a list of plants that are good for kidney treatment. This review focuses on several herbal products used to treat renal problems. It is clear from this study that medicinal plants play an important role in the fight against many different diseases. Several different plants and plant extracts have been shown to have beneficial effects in treating renal disease. These medications have great promise for use in treating kidney damage because of their nephroprotective, cytoprotective, immunomodulatory, antioxidant, anti-inflammatory, and reducing effects on oxidative stress, renal hypertension, and blood toxins such as urea, creatinine, and others. Therefore, India's traditional medicine offers a wide variety of medications that can be utilized in renal illnesses due to their effectiveness in both treating the ailment and restoring normal kidney function. Therefore, it is suggested that additional research be conducted to learn many aspects and mechanisms of medications.

A Discussion of Implausible Total Solar-Irradiance Variations Since 1700

The Sun plays a role in influencing Earth’s climate, making it important to have accurate information about variations in the Sun’s radiative output. Models are used to recover total solar-irradiance (TSI) variations in the past when direct space-based measurements are not available. One of the most cryptic such TSI reconstructions is the one by Hoyt and Schatten (J. Geophys. Res. 98, 18, 1993, HS93). The rather vague description of the model methodology, the arbitrary selection of solar indices it employs, and the short overlap between the HS93 series and directly measured TSI values has hindered any evaluation of the performance of this model to this day. Here, we aim at rectifying this by updating the HS93 model with new input data. In this way we are also contributing in the discussion on the possible long-term changes in solar irradiance.We find that the analysis by HS93 included a number of erroneous processing steps that led to an artificial increasing trend towards the end of the reconstructed TSI series as well as shifting the peak of the TSI in the mid-twentieth century back in time by about 11 years. Furthermore, by using direct measurements of the TSI we determined that the free parameter of the model, the magnitude of variations (here defined as percentage variations of the difference between the maximum to minimum values), is optimal when it is minimised (being ≤0.05%). This is in stark contrast to the high magnitude of variations, of 0.25%, that was imposed by HS93. However, our result is consistent with more recent estimates, such as those from the Spectral And Total Irradiance REconstruction (SATIRE) model and Naval Research Laboratory TSI (NRLTSI), which were used by the Intergovernmental Panel on Climate Change (IPCC). Overall, we find that the previously reported agreement of the HS93 TSI series to temperature on Earth was purely due to improper analysis and artefacts of the processing.

Dynamic correlations: exact and approximate methods for mutual information.

Proteins are dynamic entities that undergo conformational changes critical for their functions. Understanding the communication pathways and information transfer within proteins is crucial for elucidating allosteric interactions in their mechanisms. This study utilizes mutual information (MI) analysis to probe dynamic allostery. Using two cases, Ubiquitin and PLpro, we have evaluated the accuracy and limitations of different approximations including the exact anisotropic and isotropic models, multivariate Gaussian model, isotropic Gaussian model, and the Gaussian Network Model (GNM) in revealing allosteric interactions. Our findings emphasize the required trajectory length for capturing accurate mutual information profiles. Long molecular dynamics trajectories, 1 ms for Ubiquitin and 100 µs for PLpro are used as benchmarks, assuming they represent the ground truth. Trajectory lengths of approximately 5 µs for Ubiquitin and 1 µs for PLpro marked the onset of convergence, while the multivariate Gaussian model accurately captured mutual information with trajectories of 5 ns for Ubiquitin and 350 ns for PLpro. However, the isotropic Gaussian model is less successful in representing the anisotropic nature of protein dynamics, particularly in the case of PLpro, highlighting its limitations. The GNM, however, provides reasonable approximations of long-range information exchange as a minimalist network model based on a single crystal structure. Overall, the optimum trajectory lengths for effective Gaussian approximations of long-time dynamic behavior depend on the inherent dynamics within the protein's topology. The GNM, by showcasing dynamics across relatively diverse time scales, can be used either as a standalone method or to gauge the adequacy of MD simulation lengths. Mutual information codes are available at https://github.com/kemaldemirtas/prc-MI.git.

A segmentation network for farmland ridge based on encoder-decoder architecture in combined with strip pooling module and ASPP.

In order to effectively support wheat breeding, farmland ridge segmentation can be used to visualize the size and spacing of a wheat field. At the same time, accurate ridge information collecting can deliver useful data support for farmland management. However, in the farming ridge segmentation scenarios based on remote sensing photos, the commonly used semantic segmentation methods tend to overlook the ridge edges and ridge strip features, which impair the segmentation effect. In order to efficiently collect ridge information, this paper proposes a segmentation method based on encoder-decoder of network with strip pooling module and ASPP module. First, in order to extract context information for multi-scale features, ASPP module are integrated in the deepest feature map. Second, the remote dependence of the ridge features is improved in both horizontal and vertical directions by using the strip pooling module. The final segmentation map is generated by fusing the boundary features and semantic features using an encoder and decoder architecture. As a result, the accuracy of the proposed method in the validation set is 98.0% and mIoU is 94.6%. The results of the experiments demonstrate that the method suggested in this paper can precisely segment the ridge information, as well as its value in obtaining data on the distribution of farmland and its potential for practical application.

Examining the Authenticity of Autistic Portrayals in US Adult and Children's Television Shows Using Medical and Social Models of Disability.

Television programs have introduced viewers to characters on the autism spectrum (e.g., Sesame Street, The Good Doctor), impacting audiences' knowledge and attitudes. Thus, it is essential that character representations convey accurate health information. This study explores how autistic portrayals across six adult and children's television programs align with the medical (e.g., American Psychiatric Association in Diagnostic and statistical manual of mental disorders, 5th ed., text rev., 2022, https://doi.org/10.1176/appi.books.9780890425787 ) and social models of disability. A content analysis methodology was used to investigate defining characteristics of autism (i.e., medical model) and how characters navigated their environment (i.e., social model) in 252 scenes (across six characters in 22 episodes). Measures included the frequency with which autism characteristics (e.g., social-communicative difficulties, restrictive repetitive behaviors) were present across autistic portrayals and the valence (e.g., positive, negative, neutral) with which characters interacted with their environment (e.g., character and neurodiversity affirming valence). Findings indicate that (a) television portrayals depict social-communication difficulties significantly more than behavioral characteristics, (b) children's programming portrays autistic characters with significantly more positive personal attributes (i.e., character valence) than adult programs, and (c) the majority of programs portray characters navigating autism in positive or neutral ways (i.e., neurodiversity affirming valence). Results offer stakeholders (e.g., writers, advocates, neurodiverse community) insight into how autistic characters are portrayed on television, adding to a growing body of literature examining how such representations impact public knowledge, attitudes, and behaviors towards individuals on the autism spectrum. Collectively, such studies highlight how changes in diagnostic criteria, legal protections, and social inclusivity are presented to viewers, who are seeking entertainment but gaining public health information.

Improved thermal network model for hot spot location in traction transformer windings

Obtaining accurate location information of the hot spot in advance is a prerequisite for locating the hot spot. To tackle this problem, we propose an improved thermal network model, which provides a quantitative method for placing fiber optic sensors at hot-spot locations. The initial step is to construct an enhanced thermal network model that takes into account the thermal resistance of the boundary layer using heat transfer theory. Then, we compare this model with Computational Fluid Dynamics (CFD) in example calculations to validate its effectiveness and precision. The results show that our proposed model can accurately and rapidly calculate temperature distribution within winding areas, enabling the identification of hot spots. This information is reliable for installing monitoring equipment to measure transformer temperatures.