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Background Rapid diagnostic tests (RDTs) for malaria are a vital part of global malaria control. Over the past decade, RDT prices have declined, and quality has improved. However, the relationship between price and product quality and their larger implications on the market have yet to be characterized. This analysis used purchase data from the Global Fund together with product quality data from the World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) Malaria RDT Product Testing Programme to understand three unanswered questions: (1) Has the market share by quality of RDTs in the Global Fund’s procurement orders changed over time? (2) What is the relationship between unit price and RDT quality? (3) Has the market for RDTs financed by the Global Fund become more concentrated over time? Methods Data from 10,075 procurement transactions in the Global Fund’s database, which includes year, product, volume, and price, was merged with product quality data from all eight rounds of the WHO-FIND programme, which evaluated 227 unique RDT products. To describe trends in market share by quality level of RDT, descriptive statistics were used to analyse trends in market share from 2009 to 2018. A generalized linear regression model was then applied to characterize the relationship between price and panel detection score (PDS), adjusting for order volume, year purchased, product type, and manufacturer. Third, a Herfindahl–Hirschman Index (HHI) score was calculated to characterize the degree of market concentration. Results Lower-quality RDTs have lost market share between 2009 and 2018, as have the highest-quality RDTs. No statistically significant relationship between price per test and PDS was found when adjusting for order volume, product type, and year of purchase. The HHI was 3,570, indicating a highly concentrated market. Conclusions Advancements in RDT affordability, quality, and access over the past decade risk stagnation if health of the RDT market as a whole is neglected. These results suggest that from 2009 to 2018, this market was highly concentrated and that quality was not a distinguishing feature between RDTs. This information adds to previous reports noting concerns about the long-term sustainability of this market. Further research is needed to understand the causes and implications of these trends.

Researchers have explored the application of combinatorial interaction testing (CIT) methods to construct samples to drive systematic testing of software system configurations. Applying CIT to highly-configurable software systems is complicated by the fact that, in many such systems, there are constraints between specific configuration parameters that render certain combinations invalid. Many CIT algorithms lack a mechanism to avoid these. In recent work, automated constraint solving methods have been combined with search-based CIT construction methods to address the constraint problem with promising results. However, these techniques can incur a non-trivial overhead. In this paper, we build upon our previous work to develop a family of greedy CIT sample generation algorithms that exploit calculations made by modern Boolean satisfiability (SAT) solvers to prune the search space of the CIT problem. We perform a comparative evaluation of the cost-effectiveness of these algorithms on four real-world highly-configurable software systems and on a population of synthetic examples that share the characteristics of those systems. In combination our techniques reduce the cost of CIT in the presence of constraints to 30 percent of the cost of widely-used unconstrained CIT methods without sacrificing the quality of the solutions.

Since the early 2000's, digital reading applications have enhanced the language and literacy skills of typically hearing young children; however, no digital storybook intervention currently exists to scaffold the early language and literacy skills of their peers who are deaf or hard of hearing. To address this gap, our research team developed a novel digital storybook intervention called Hear Me Read with the aim of enhancing the therapeutic, language, and literacy benefits of speech-language therapy. This prospective clinical trial (registered at clinicaltrials.gov, NCT#: 05245799) aims to determine the efficacy of adding Hear Me Read to in-person speech-language therapy for children aged three to five years who are deaf or hard of hearing. Fifty caregivers, their child, and their child's treating speech-language pathologist participate in the trial for 12 months. In the first six months, children attend standard-of-care speech-language therapy sessions. In the second six months, children continue to attend standard-of-care speech-language therapy sessions and use the Hear Me Read application, via a study supplied iPad. The primary outcome of this trial is that, compared to in-person speech-language therapy alone, in-person speech-language therapy with Hear Me Read will improve vocabulary, speech, and language outcomes in children aged three to five years who are deaf or hard of hearing. The secondary outcome is that, compared to in-person speech-language therapy alone, in-person speech-language therapy with Hear Me Read will improve literacy outcomes in children aged three to five years who are deaf or hard of hearing. The goal of this intervention is to help children who are deaf or hard of hearing achieve their vocabulary, speech, language, and literacy goals through interactive digital storybook reading.

This study aimed at developing an Online Assessment based on the Shareable Content Object Reference Model (SCORM) package. This study focused on: (1) obtaining an online assessment design based on SCORM package for the subject of Medical Instrumentation and Electronics based on needs analysis; (2) examining the functionality of the developed online assessment and (3) analyzing the usability of the developed online assessment. This software development process used the ADDIE development model. The testing stage of this study was conducted to verify and to validate the software. The software verification process was performed with functionality testing by media and material experts, and usability testing by users. The results indicated that: (1) it was obtained the design of an online assessment based on SCORM package for the subject of Medical Instrumentation and Electronics, including an online assessment in Besmart packed with SCORM Packages in the form of quiz integration (multiple-choice, short answer, true or false, drag and drop questions); (2) the functionality testing by material experts with a score of 3.88 and a media expert with a score of 4.16 suggested that the developed online assessment was feasible in the aspect of functionality; (3) usability testing by users achieved the score of 3.88 indicating that the developed online assessment was feasible in the aspect of usability.

Diagnostics are crucial in mitigating the effect of disease outbreaks. Because diagnostic development and validation are time consuming, they should be carried out in anticipation of epidemics rather than in response to them. The diagnostic response to the 2014–15 Ebola epidemic, although ultimately effective, was slow and expensive. If a focused mechanism had existed with the technical and financial resources to drive its development ahead of the outbreak, point-of-care Ebola tests supporting a less costly and more mobile response could have been available early on in the diagnosis process. A new partnering model could drive rapid development of tests and surveillance strategies for novel pathogens that emerge in future outbreaks. We look at lessons learned from the Ebola outbreak and propose specific solutions to improve the speed of new assay development and ensure their effective deployment.

In many crops worldwide, including hazelnuts, the majority of stages in production and delivery to end-users are conducted either manually or with machine equipment lacking the advancements brought by technology. Non-destructive, fast, and reliable methods, particularly deep learning algorithms, have emerged as prominent techniques for determining product quality and classification in fruits, vegetables, and cereal products in recent years. This study aims to classify hazelnuts using deep learning algorithms, thereby minimizing the labor, time, and cost expended during the sorting process. Hazelnut images were obtained from Giresun, Ordu, and Van hazelnut varieties. The dataset consists of 1165 images of Giresun, 1324 images of Ordu, and 1138 images of Van hazelnut varieties. The classification was performed using deep learning models such as InceptionV3 and ResNet50. To combine the classification capabilities of the models, an InceptionV3 + ResNet50 data fusion model was created using the data fusion method. In addition, feature reduction processes were conducted by adding a convolutional layer to the data fusion model to decrease the number of features. The classification was conducted using a total of 3627 images, resulting in a 100% classification accuracy. Furthermore, the classification times of all models were analyzed. Based on these analyses, the 1024 reduced features data fusion model with 100% classification accuracy exhibited the shortest classification time. This model was selected, and a mobile application was developed for easy on-field hazelnut classification. The hazelnut classification performed using deep learning algorithms in the application will facilitate the work of both non-experts and professionals in industrial and personal domains. Through these methods, patents for products and devices developed for use in different industries can be obtained, thereby increasing the economic value added of our country.

Globally, blood donation has been disturbed due to the pandemic. Consequently, the optimization of preoperative blood preparation should be a point of concern. Machine learning (ML) is one of the modern approaches that have been applied by physicians to help decision-making. The main objective of this study was to identify the cost differences of the ML-based strategy compared with other strategies in preoperative blood products preparation. A secondary objective was to compare the effectiveness indexes of blood products preparation among strategies. The study utilized a retrospective cohort design conducted on brain tumor patients who had undergone surgery between January 2014 and December 2021. Overall data were divided into two cohorts. The first cohort was used for the development and deployment of the ML-based web application, while validation, comparison of the effectiveness indexes, and economic evaluation were performed using the second cohort. Therefore, the effectiveness indexes of blood preparation and cost difference were compared among the ML-based strategy, clinical trial-based strategy, and routine-based strategy. Over a 2-year period, the crossmatch to transfusion (C/T) ratio, transfusion probability (Tp), and transfusion index (Ti) of the ML-based strategy were 1.10, 57.0%, and 1.62, respectively, while the routine-based strategy had a C/T ratio of 4.67%, Tp of 27.9%%, and Ti of 0.79. The overall costs of blood products preparation among the ML-based strategy, clinical trial-based strategy, and routine-based strategy were 30, 061.56 , 57,313.92 , and 136,292.94 , respectively. From the cost difference between the ML-based strategy and routine-based strategy, we observed cost savings of 92,519.97(67.88%) for the 2-year period. The ML-based strategy is one of the most effective strategies to balance the unnecessary workloads at blood banks and reduce the cost of unnecessary blood products preparation from low C/T ratio as well as high Tp and Ti. Further studies should be performed to confirm the generalizability and applicability of the ML-based strategy.

Background A large number of variants have been employed in various medical applications, such as providing medication instructions, disease susceptibility testing, paternity testing, and tumour diagnosis. A high multiplicity PCR will outperform other technologies because of its lower cost, reaction time and sample consumption. To conduct a multiplex PCR with higher than 100 plex multiplicity, primers need to be carefully designed to avoid the formation of secondary structures and nonspecific amplification between primers, templates and products. Thus, a user-friendly, highly automated and highly user-defined web-based multiplex PCR primer design software is needed to minimize the work of primer design and experimental verification. Results Ultiplex was developed as a free online multiplex primer design tool with a user-friendly web-based interface ( http://ultiplex.igenebook.cn ). To evaluate the performance of Ultiplex, 294 out of 295 (99.7%) target primers were successfully designed. A total of 275 targets produced qualified primers after primer filtration, and 271 of those targets were successfully clustered into one compatible PCR group and could be covered by 108 primers. The designed primer group stably detected the rs28934573(C > T) mutation at lower than a 0.25% mutation rate in a series of samples with different ratios of HCT-15 and HaCaT cell line DNA. Conclusion Ultiplex is a web-based multiplex PCR primer tool that has several functions, including batch design and compatibility checking for the exclusion of mutual secondary structures and mutual false alignments across the whole genome. It offers flexible arguments for users to define their own references, primer Tm values, product lengths, plex numbers and tag oligos. With its user-friendly reports and web-based interface, Ultiplex will provide assistance for biological applications and research involving genomic variants.

The three-parameter Poisson Inverse Weibull (PIW) distribution offers enhanced flexibility for modeling system failure times. This study introduces the signed log-likelihood ratio test (SLRT) for hypothesis testing of the scale parameter ( ω ) in the PIW distribution and compares its performance with the test based on the asymptotic normality of maximum likelihood estimators (ANMLE). Simulation studies show that the SLRT consistently maintains type I error rates within the acceptable range of 0.04 to 0.06 at a significance level of 0.05, satisfying Cochran’s criterion across various sample sizes and parameter configurations. In contrast, the ANMLE method tends to be conservative, often underestimating the nominal significance level. In terms of empirical power, the SLRT outperforms the ANMLE, particularly in small-sample scenarios ( n = 10, 15), and maintains superior power across all tested configurations. For example, when testing H 0 : ω = 0.25 against H 1 : ω = 0.5 with β = 0.5 , λ = 1 , and n  = 10, the SLRT achieves a power of 0.6621, compared to 0.4181 for the ANMLE, demonstrating the SLRT’s robustness and reliability in limited-data. Moreover, the ANMLE generally exhibits low power in most cases, indicating reduced sensitivity to detecting true effects in small samples. However, with medium and large sample sizes ( n = 30, 50, 80 and 100), the power of the ANMLE begins to approach that of the SLRT. Despite this, the ANMLE never outperforms the SLRT, highlighting a fundamental limitation of this method. Additionally, varying the shape parameter β while fixing λ = 1 showed a negligible impact on power, further confirming the robustness of the SLRT. Sensitivity analyses also validate the reliability of the SLRT under extreme values of ω and across different sample sizes. To support practical application, the PIW4LIFETIME web application (accessible at https://jularatchumnaul.shinyapps.io/PIW4LIFETIME/ ) was developed to enable users to assess whether data fit the PIW distribution, estimate model parameters using maximum likelihood, and perform two-sided test for the scale parameter using SLRT. The performance of the proposed method and the PIW4LIFETIME web application was demonstrated through a real-world example.

The increasingly serious problem of consumers returning goods on e-commerce platforms has brought high costs to the Internet economy, carbon pollution to the environment, and waste of social resources. E-commerce platforms can provide useful information to assist consumers to make rational decisions, but they are often filled with useless, repetitive, and even false excessive information, which will lead to information overload and impulsive decision-making of consumers. Most of the previous literature focuses on reverse logistics, return policy, and consumer behavior tendency, etc. From the perspective of consumers’ perception of information displayed on e-commerce platforms, there are few research endeavors on the formation mechanism of perceived information overload on consumers’ return intention. Taking perceived information overload as an independent variable and consumers’ perceived environmental effectiveness as a moderation variable, this study constructs a chain mediation model that affects consumers’ online return intention. Based on the analysis of the mediating effects of impulsive buying behavior and cognitive dissonance, this study explored the moderating mechanism of consumers’ perceived environmental effectiveness on the chain mediation model. The results show that perceived information overload has a positive influence on online return intention through impulsive buying behavior, and perceived information overload has a positive influence on online return intention through cognitive dissonance. Perceived information overload also positively affects cognitive dissonance through impulsive buying behavior and thus has a significant positive chain mediating effect on consumers’ online return intention. More importantly, this research shows that consumers’ perceived environmental effectiveness can significantly moderate the chain mediation path by reducing the positive effect of the cognitive dissonance on online return intention. On this basis, this study put forward the corresponding managerial implications from the perspectives of consumers and e-commerce platforms.