Comprehensive Information Research Articles

Quantum-Inspired Fusion for Open-Domain Question Answering

Open-domain question-answering systems need models capable of referencing multiple passages simultaneously to generate accurate answers. The Rational Fusion-in-Decoder (RFiD) model focuses on differentiating between causal relationships and spurious features by utilizing the encoders of the Fusion-in-Decoder model. However, RFiD reliance on partial token information limits its ability to determine whether the corresponding passage is a rationale for the question, potentially leading to inappropriate answers. To address this issue, we propose a Quantum-Inspired Fusion-in-Decoder (QFiD) model. Our approach introduces a Quantum Fusion Module (QFM) that maps single-dimensional into multi-dimensional hidden states, enabling the model to capture more comprehensive token information. Then, the classical mixture method from quantum information theory is used to fuse all information. Based on the fused information, the model can accurately predict the relationship between the question and passage. Experimental results on two prominent ODQA datasets, Natural Questions and TriviaQA, demonstrate that QFiD outperforms the strong baselines in automatic evaluations.

A weakly supervised model for incomplete multimodal MRI synthesis with tumor-aware approach.

Magnetic resonance images (MRIs) are a valuable tool in the study of brain tumors, and multimodal sequences provide unique insights into different aspects of brain tumors. However, in clinical practice, missing modalities are often encountered due to various factors. This makes it difficult to obtain comprehensive and reliable information related to brain tumors. The purpose of this work is to develop an algorithm for the synthesis of missing MRI modality with high precision, and to center on generating accurate tumor-related information to offer more data for clinical diagnosis. A novel weakly supervised MRI synthesis model named TAM-DAM-GAN has been proposed, which integrates tumor-aware and detail adjustment mechanisms to enhance the quality of tumor generation. The tumor-aware mechanism leverages weak label information to guide the network to classify images based on crucial information in local structures, thereby compelling the generative network to identify and highlight the learning of local tumor regions. The detail adjustment mechanism utilizes a discriminator to create attention maps at the pixel level in real-time. These maps are then used to modify the loss weight, which in turn adjusts the details that are generated. Generation quality of four tasks (T1-to-T2, T2-to-T1, T1-to-FLAIR, and FLAIR-to-T1) was evaluated. Experiments on the BRATS2015 dataset show that the proposed approach is superior in both qualitative and quantitative measures. Taking FLAIR-to-T1 as an example, TAM-DAM-GAN improves PSNR of tumor region from 18.556 to 20.576 compared to baseline. Also, using real FLAIR data with generated T1 data boosts tumor segmentation accuracy by 10% compared to using only real FLAIR data. This finding will be conducive to enhancing the accuracy of cross-modality synthesis in incomplete multimodal MRI, especially for tumor regions, thereby providing more dependable and comprehensive data for clinical diagnosis and scientific research.

Deformation Monitoring and Analysis of Baige Landslide (China) Based on the Fusion Monitoring of Multi-Orbit Time-Series InSAR Technology.

Due to the limitations inherent in SAR satellite imaging modes, utilizing time-series InSAR technology to process single-orbit satellite image data typically only yields one-dimensional deformation information along the LOS direction. This constraint impedes a comprehensive representation of the true surface deformation of landslides. Consequently, in this paper, after the SBAS-InSAR and PS-InSAR processing of the 30-view ascending and 30-view descending orbit images of the Sentinel-1A satellite, based on the imaging geometric relationship of the SAR satellite, we propose a novel computational method of fusing ascending and descending orbital LOS-direction time-series deformation to extract the landslide's downslope direction deformation of landslides. By applying this method to Baige landslide monitoring and integrating it with an improved tangential angle warning criterion, we classified the landslide's trailing edge into a high-speed, a uniform-speed, and a low-speed deformation region, with deformation magnitudes of 7~8 cm, 5~7 cm, and 3~4 cm, respectively. A comparative analysis with measured data for landslide deformation monitoring revealed that the average root mean square error between the fused landslide's downslope direction deformation and the measured data was a mere 3.62 mm. This represents a reduction of 56.9% and 57.5% in the average root mean square error compared to the single ascending and descending orbit LOS-direction time-series deformations, respectively, indicating higher monitoring accuracy. Finally, based on the analysis of landslide deformation and its inducing factors derived from the calculated time-series deformation results, it was determined that the precipitation, lithology of the strata, and ongoing geological activity are significant contributors to the sliding of the Baige land-slide. This method offers more comprehensive and accurate surface deformation information for dynamic landslide monitoring, aiding relevant departments in landslide surveillance and management, and providing technical recommendations for the fusion of multi-orbital satellite LOS-direction deformations to accurately reconstruct the true surface deformation of landslides.

Impact of the mini-map on the interpretation of spatial situations in the virtual geographical space of a video game

ABSTRACT Mini-map as a world map in miniature format is often used as the most important cartographic element to facilitate the navigation and orientation of users in virtual geographical space. It provides detailed information about the position, orientation, nearby objects or terrain around the user in a compact and comprehensive way. The authors of this article wanted to find out whether the mini-map helps users with different levels of knowledge about the game to understand and interpret spatial situations that occur when playing in the virtual geographical space. The study was conducted in the form of an offline survey that required respondents to watch five short videos depicting different spatial situations during the League of Legends game. The 60 respondents were divided into 3 different groups whose responses were analyzed using statistical tests and mini-map heatmap eye-tracking analysis. The results of the study confirm that there is a correlation between the mini-map and the correctness of the interpretation of spatial situations in the game in all groups investigated. Previous gaming experiences were found to reduce the need to use a mini-map to interpret spatial situations, proving that video games provide players with comprehensive information about various events on the user interface.

Surface soil sampling underestimates soil carbon and nitrogen storage of long-term cover cropping

Cover cropping is a promising management practice for soil health and climate change mitigation by improving soil organic carbon (SOC) and total nitrogen (TN) stocks. However, limited studies focused on deeper soil layers (>30 cm depth) where soil C is more stable than that in surface soil (≤30 cm depth). Here, deep soil sampling was conducted in a 15-year cover cropping experiment, in a horticulture-grain system on sandy loam soil. The SOC and TN stocks were expressed on an equivalent soil mass basis using a cubic spline model. Overall, long-term cover cropping had significantly greater SOC and TN stocks by 22 % (95 %CI: 5–43 %) and 26 % (95 %CI: 6–49 %), respectively in the 0–120 cm depth, compared to no cover cropping. Additionally, the mean SOC and TN sequestration rate (0–30 cm depth) was 0.53 Mg C ha−1 yr−1 and 0.06 Mg N ha−1 yr−1, respectively. However, if only 0–15 cm depth was evaluated, long-term cover cropping did not significantly affect SOC and TN stocks. These results indicated that shallow sampling (<15 cm depth) may not provide comprehensive information on the effect of long-term cover cropping on soil C and N storage. To better understand the mechanism of bulk soil C and N storage, we investigated their distribution between particulate and mineral-associated organic matter pools (POM and MAOM). We found POM pool was the main store of bulk SOC and TN stocks in surface soils while it was the MAOM pool in deeper soil layers, without soil texture change with soil depth. These findings indicated that soil C and N sources for bulk SOC and TN accrual differed in surface and deeper soils. Our study demonstrated that long-term cover cropping can facilitate SOC accumulation in the soil below 15 cm deep, which calls into question carbon capture protocols that focus on shallow soil depths.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Fusing remote and social sensing data for flood impact mapping

AbstractThe absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end‐to‐end system, that ingests data from multiple nontraditional data sources such as remote sensing, social sensing, and geospatial data. We employ state‐of‐the‐art natural language processing and computer vision models to identify flood exposure, ground‐level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real‐world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through various statistical analyses using official ground‐truth data, showcasing its strong performance and explanatory power of integrating multiple data sources. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard‐hit districts and enhancing disaster response.

ScTML: a pan-cancer single-cell landscape of multiple mutation types.

Investigating mutations, including single nucleotide variations (SNVs), gene fusions, alternative splicing and copy number variations (CNVs), is fundamental to cancer study. Recent computational methods and biological research have demonstrated the reliability and biological significance of detecting mutations from single-cell transcriptomic data. However, there is a lack of a single-cell-level database containing comprehensive mutation information in all types of cancer. Establishing a single-cell mutation landscape from the huge emerging single-cell transcriptomic data can provide a critical resource for elucidating the mechanisms of tumorigenesis and evolution. Here, we developed scTML (http://sctml.xglab.tech/), the first database offering a pan-cancer single-cell landscape of multiple mutation types. It includes SNVs, insertions/deletions, gene fusions, alternative splicing and CNVs, along with gene expression, cell states and other phenotype information. The data are from 74 datasets with 2 582 633 cells, including 35 full-length (Smart-seq2) transcriptomic single-cell datasets (all publicly available data with raw sequencing files), 23 datasets from 10X technology and 16 spatial transcriptomic datasets. scTML enables users to interactively explore multiple mutation landscapes across tumors or cell types, analyze single-cell-level mutation-phenotype associations and detect cell subclusters of interest. scTML is an important resource that will significantly advance deciphering intra-tumor and inter-tumor heterogeneity, and how mutations shape cell phenotypes.

Does the development of strategic readiness represent a response to the relationship of strategic competencies and the healthcare information system? Evidence from private hospitals in Jordan

This study introduces a model designed to improve the strategic readiness of private hospitals in Amman by incorporating strategic competencies as an independent variable and using a healthcare information system as a mediator. Targeting private hospitals with over 140 beds, the research included a population of 3263 employees across various managerial levels. Data collection methods involved interviews and electronic questionnaires, resulting in a sample size of 344. Statistical analyses comprised exploratory and confirmatory factor analysis, structural equation modeling, and hypothesis testing with SMART PLS 3.3.3 software. The results indicated medium levels of both strategic competencies and healthcare information systems, while strategic readiness was found to be low. Nevertheless, the proposed model showed a direct positive effect of strategic competencies on strategic readiness, with the healthcare information system acting as a significant partial mediator. Evaluation metrics included the arithmetic mean, standard deviation, and path analysis. This model surpasses traditional methods by effectively linking strategic competencies and information systems to enhance strategic readiness, providing a strong framework for improving hospital responses to crises and dynamic changes. The study suggests focusing on enhancing and developing strategic competencies and integrating a comprehensive healthcare information system to optimize hospital operations and increase readiness.

TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching.

This study tackles image matching in difficult scenarios, such as scenes with significant variations or limited texture, with a strong emphasis on computational efficiency. Previous studies have attempted to address this challenge by encoding global scene contexts using Transformers. However, these approaches have high computational costs and may not capture sufficient high-level contextual information, such as spatial structures or semantic shapes. To overcome these limitations, we propose a novel image-matching method that leverages a topic-modeling strategy to capture high-level contexts in images. Our method represents each image as a multinomial distribution over topics, where each topic represents semantic structures. By incorporating these topics, we can effectively capture comprehensive context information and obtain discriminative and high-quality features. Notably, our coarse-level matching network enhances efficiency by employing attention layers only to fixed-sized topics and small-sized features. Finally, we design a dynamic feature refinement network for precise results at a finer matching stage. Through extensive experiments, we have demonstrated the superiority of our method in challenging scenarios. Specifically, our method ranks in the top 9% in the Image Matching Challenge 2023 without using ensemble techniques. Additionally, we achieve an approximately 50% reduction in computational costs compared to other Transformer-based methods. Code is available at https://github.com/TruongKhang/TopicFM.

Multiwavelength Surface-Enhanced Raman Scattering Fingerprints of Human Urine for Cancer Diagnosis.

Label-free surface-enhanced Raman spectroscopy (SERS) is capable of capturing rich compositional information from complex biosamples by providing vibrational spectra that are crucial for biosample identification. However, increasing complexity and subtle variations in biological media can diminish the discrimination accuracy of traditional SERS excited by a single laser wavelength. Herein, we introduce a multiwavelength SERS approach combined with machine learning (ML)-based classification to improve the discrimination accuracy of human urine specimens for bladder cancer (BCa) diagnosis. This strategy leverages the excitation-wavelength-dependent SERS spectral profiles of complex matrices, which are mainly attributed to wavelength-related vibrational changes in individual analytes and differences in the variation ratios of SERS intensity across different wavelengths among various analytes. By capturing SERS fingerprints under multiple excitation wavelengths, we can acquire more comprehensive and unique chemical information on complex samples. Further experimental examinations with clinical urine specimens, supported by ML algorithms, demonstrate the effectiveness of this multiwavelength strategy and improve the diagnostic accuracy of BCa and staging of its invasion with SERS spectra from increasing numbers of wavelengths. The multiwavelength SERS holds promise as a convenient, cost-effective, and broadly applicable technique for the precise identification of complex matrices and diagnosis of diseases based on body fluids.

HO-1: An emerging target in fibrosis.

Fibrosis, an aberrant reparative response to tissue injury, involves a disruption in the equilibrium between the synthesis and degradation of the extracellular matrix, leading to its excessive accumulation within normal tissues, and culminating in organ dysfunction. Manifesting in the terminal stages of nearly all chronic ailments, fibrosis carries a high mortality rate and poses a significant threat to human health. Heme oxygenase-1 (HO-1) emerges as an endogenous protective agent, mitigating tissue damage through its antioxidant, anti-inflammatory, and antiapoptotic properties. Numerous studies have corroborated HO-1's potential as a therapeutic target in anti-fibrosis treatment. This review delves into the structural and functional attributes, and the upstream and downstream pathways of HO-1. Additionally, the regulatory networks and mechanisms of HO-1 in cells associated with fibrosis are elucidated. The role of HO-1 in various fibrosis-related diseases is also explored. Collectively, this comprehensive information serves as a foundation for future research and augments the viability of HO-1 as a therapeutic target for fibrosis.

The utility of ChatGPT in gender-affirming mastectomy education

IntroductionThe integration of AI such as ChatGPT in medicine has been showing promise in enhancing patient education. Gender-affirming mastectomy (GAM) is a surgical procedure designed to help individuals transition to their self-identified gender, playing a crucial role in mitigating psychological distress for many transmasculine and non-binary (TNB) patients. With increased demand and attention towards GAM, plastic and reconstructive surgeons may rely on AI-driven chatbots as an accessible, accurate, and patient-driven model for relevant details on this procedure. Specific Aim(s)This study aimed to assess the quality and readability of information provided by ChatGPT in response to frequently asked questions (FAQs) related to GAM. MethodsInspired by online forums and physician websites, 10 frequently asked questions (FAQs) about pre- and postoperative topics were submitted to ChatGPT and assessed using validated readability score measures and expert interpretation. ResultsThe average readability score was 16.0 ± 1, indicating a college or graduate reading level. Mean accuracy, comprehensiveness, and danger scores were 8.8 ± 0.5, 7.8 ± 0.7, and 2.2 ± 0.4, respectively. Although physicians appreciated ChatGPT's tone, patient autonomy, and advice to seek professional medical and mental help, they also cited instance of generic information, misinformation, support of debated techniques, and pathologization of gender dysphoria. ConclusionEven with its promise in providing accurate and comprehensive information on GAM, ChatGPT’s current limitations suggest caution as a supplementary tool to physician consultation.

Bearing fault diagnosis based on novel hierarchical multiscale dispersion entropy in corresponding color block images

Abstract The rolling bearing is a critical component of mechanical equipment, and its failure can lead to serious consequences. In order to effectively extract fault features of rolling bearings and improve fault diagnosis performance, a fault diagnosis framework based on hierarchical multiscale dispersion entropy (HMDE) and improved histogram of oriented gradient (HOG) is proposed by combining entropy method with image recognition method. Firstly, the original vibration signal is subjected to moving average filtering to eliminate sudden noise and outliers. Then, HMDE is used for the extraction of fault features. HMDE can evaluate the complexity of the signal at different levels and scales, thereby extracting more comprehensive information. Based on HMDE, entropy color block (ECB) images are generated and the improved HOG of the images are extracted. Finally, Knearest neighbor (KNN) is used to classify the improved HOG features, completing the recognition of different working states of rolling bearings. The validity and robustness of the proposed fault diagnosis framework are proved by the verification experiments on the public bearing datasets in Case Western Reserve University and Southeast University.&amp;#xD;

Clinical Meaningfulness of an Algorithm-Based Service for Analyzing Treatment Response in Patients with Metastatic Cancer Using FDG PET/CT.

Background/Objectives: Determining how a patient with metastatic cancer is responding to therapy can be difficult for medical oncologists, especially with text-only radiology reports. In this investigation, we assess the clinical usefulness of a new algorithm-based analysis that provides spatial location and quantification for each detected lesion region of interest (ROI) and compare it to information included in radiology reports in the United States. Methods: Treatment response radiology reports for FDG PET/CT scans were retrospectively gathered from 228 patients with metastatic cancers. Each radiology report was assessed for the presence of both qualitative and quantitative information. A subset of patients (N = 103) was further analyzed using an algorithm-based service that provides the clinician with comprehensive quantitative information, including change over time, of all detected ROI with visualization of anatomical location. For each patient, three medical oncologists from different practices independently rated the usefulness of the additional analysis overall and in four subcategories. Results: In the 228 radiology reports, quantitative information of size and uptake was provided for at least one lesion at one time point in 78% (size) and 95% (uptake) of patients. This information was reported for both analyzed time points (current scan and previous comparator) in 52% (size) and 66% (uptake) of patients. Only 7% of reports quantified the total number of lesions, and none of the reports quantified changes in all lesions for patients with more than a few lesions. In the assessment of the augmentative algorithm-based analysis, the majority of oncologists rated it as overall useful for 98% of patients (101/103). Within specific categories of use, the majority of oncologists voted to use it for making decisions regarding systemic therapy in 97% of patients, for targeted therapy decisions in 72% of patients, for spatial location information in 96% of patients, and for patient education purposes in 93% of patients. Conclusions: For patients with metastatic cancer, the algorithm-based analysis of all ROI would allow oncologists to better understand treatment response and support their work to more precisely optimize the patient's therapy.

Female directors on the sensitivity of investment to growth opportunities

AbstractThis paper shows that female directors decrease the sensitivity of investment to the market misvaluation of growth opportunities. When the market misvaluation is high, such as during the COVID period, the sensitivity of investment to overall market valuation exhibits a significant drop. On the other hand, female directors increase the sensitivity of investment to alternative measures of growth not associated with market assessment. Firms with female directors achieve higher investment efficiency. Further analyses indicate that this contribution is mainly channelled through the board's advising function. Our main findings are robust to a battery of alternative explanations and robustness checks. Overall, this paper provides novel evidence that female directors contribute to capital investment decisions through their comprehensive information processing strategy.

Informatization of the processes of administration of the quality of medical services in surgical hospitals

Among the various forms of quality control of the provision of medical care, self-control and internal control of the quality of medical services are the most effective and significant. Currently, there is no comprehensive information technology and algorithm for recording data on the provision of medical services and determining their quality. The goal is to develop and analyze the effectiveness of business processes and an algorithm for quality control of the performance of medical services in a surgical health care institution by establishing their daily accounting in the information system, control of indicators characterizing the safety and quality of their performance, as well as an opportunity to assess the dynamics changes in indicators for making management decisions. In the study, an analysis of literary sources was carried out, business processes were formulated, and on the materials of a retrospective analysis algorithms for the use of information technologies were developed based on the methods of generalization, comparison, content analysis, system-analytical and logical analysis, a graphic analysis was performed to increase the clarity of the presentation of the material. The research was approved by the Biomedical Ethics Committee. For the first time, as a result of the work of the internal quality commission, daily and summarized forms of reports on the work of the medical center, which have five sections are obtained. The statistical indicators of the operation of the surgical unit for any period are fixed, formed in an automated mode and analyzed. Clinical data characterizing all prescribed procedures applied are subject to analysis, and the results provide an opportunity to isolate from the records the disorders and complications reflected in the reports. The quality control of the use of antibiotics, the frequency of their use for preventive and therapeutic purposes is also carried out. The section that contains multivariate epidemiological control of the work of the treatment facility, as well as the section that shows the quality of medical record keeping and patient care is important. Together with the inpatient chart, e-mails of medical appointments are analyzed, patient falls are recorded, appointments and monitoring of patient care, pain assessment after surgery, allergic reactions are recorded, as well as a retrospective audit of the charts regarding the justification of the appointment of antimicrobial therapy. Control of patient safety and the quality of the provided medical services requires constant verification of their implementation and paying attention to be up to standards, the occurrence of deviations and complications. The application of a specially developed algorithm based on information technologies, aimed at daily accounting of medical services and obtaining data on deviations in their application, makes it possible to make urgent management decisions and influence the improvement of the qualifications of personnel who provide such services.

MULTI-AGENCY DEVELOPMENT OF A NEW INFORMATION RESOURCE FOR PATIENTS WITH BRAIN METASTASES

Abstract AIMS Based in a large neuro-oncology centre which provides specialist care for hundreds of patients with BM per year, the authors identified a need for relevant, non-disease specific, technically accurate and holistically focussed patient information material. This project aimed to develop a comprehensive and patient-focussed information resource for patients with BM. METHOD A scoping exercise of twenty UK-based cancer charity websites was carried out. Generic cancer charity sites and sites of charities supporting the four main primary cancer groups which metastasize to the brain were targeted. Information pertaining to BM was collated and reviewed by a multidisciplinary group to assess for scope and quality of information provided. Particular attention was paid to identifying information which was technically accurate and specifically addressed the holistic needs of this patient group. RESULTS Information found was primary disease-specific, technically inaccurate and did not acknowledge the holistic needs of the patient. This exercise highlighted the need for comprehensive and relevant patient-focussed information material. A working group consisting of health professionals and BrainsTrust charity staff was formed to create an effective patient information resource. Existing BrainsTrust information templates were used to structure the new resource. Evidence-based clinical information was provided by the healthcare professionals involved and holistic information was created collaboratively with charity input. A patient reviewer was enlisted to provide feedback on draft versions. CONCLUSION This project resulted in production of a patient-focussed and comprehensive information resource on BM. Patient and healthcare professional feedback to date has been positive.

Investigation of oceanic wave solutions to a modified (2+1)-dimensional coupled nonlinear Schrödinger system

This paper explores the oceanic wave characteristics exhibited by a modified integrable generalized [Formula: see text]-dimensional nonlinear Schrödinger system of equations through variable coefficients. Two newly modified methods, specifically the improved nonlinear Ricatti equation method and the improved sub-equation method, have been proposed to investigate the aforementioned nonlinear system. Through the utilization of these methods, we successfully obtain traveling and solitary waves solutions for this nonlinear system. We emphasize several constraint conditions that serve to guarantee the existence of these solutions. More comprehensive information about the physical dynamical representation of some of the solutions presented is illustrated through graphical depictions. The Mathematica software package is employed to produce both three-dimensional and their corresponding contour plots, thereby improving the visualization and comprehension of the solutions. This paper illustrates that the two proposed approaches provide straightforward and efficient means of acquiring various types of soliton, rational, trigonometric, hyperbolic and exponential solutions. Moreover, they present a more potent mathematical tool for addressing a variety of other nonlinear partial differential equations that hold significance in the field of applied science and engineering.

Acceptability and use of HIV self-testing among young people in sub-Saharan Africa: a mixed methods systematic review

BackgroundYoung people (YP) are disproportionately affected by the HIV pandemic in sub-Saharan Africa (SSA), but testing rates remain low despite global targets of testing 95% of people with HIV infection by 2030. HIV self-testing (HIVST) has been recently introduced to reach high-risk population groups such as these. Thus, synthesis of emerging evidence on the acceptability and use of HIVST among YP in SSA is needed so that comprehensive information can be generated to inform policy and practice.MethodsWe employed a mixed methods systematic review of quantitative and qualitative literature reporting on HIVST among YP involving any design and published in English by 31st of October 2023. The review synthesized quantitative evidence on acceptability and use of HIVST, and qualitative evidence on perspectives of YP about HIVST. We searched databases of published articles (e.g. MEDLINE, CINAHL) and Gray literature sources (e.g. Google, Google Scholar). The concepts for the search included self-testing, HIV/AIDS, and countries in SSA. Two authors independently screened, retrieved full-text, and assessed quality of the studies.ResultsA total of 4150 studies were retrieved and 32 studies were finally included in the review. Acceptability of HIVST computed from a single item asking YP on their preference or willingness or demand for HIVST was moderate (34–67%) to high (≥ 67%) among YP in SSA. Nine of the fourteen studies that reported on acceptability found high acceptability of HIVST. Use of HIVST ranged from 0.8 to 100% while in most studies the use rate was below 50%. Key barriers to HIVST use were coping with a positive test in the absence of counselling and support, physical discomfort, and cost of kits. Perceived enablers included perceptions of HIVST as promoting personal empowerment and autonomy; privacy and confidentiality; and convenience in location, time, and skill.ConclusionsHIVST was highly accepted but not well utilized among YP in SSA. YP showed diversified needs with mixed preferences for location, and modalities of service provision. Overall, the review identified heterogeneous evidence in terms of methods, population, outcome measures, and results. The review was registered in the International Prospective Registry of Systematic Reviews (PROSPERO: ID = CRD42021278919).