Search Results (38,043)

The short cultivation cycle and high essential oil content of basil plants render them a valuable raw material source for diverse industries. However, large-scale production is hindered by the lack of specific protocols to assess seed vigor; thus, a consistent supply of high-quality seeds that meet consumer demands cannot be ensured. This study investigated the effectiveness of an automated system for seedling analysis as a tool for evaluating basil seed vigor and compared it to traditional tests. For this purpose, seeds from eight commercial lots were evaluated in two separate trials spaced six months apart using the following tests: germination, first germination count, saturated salt accelerated aging, primary root emergence, mean germination time, seedling emergence, seedling emergence speed index, and computerized seedling image analysis. The parameters provided by the system allowed us to clearly and objectively classify the basil seed lots based on vigor, and the results were strongly and significantly correlated with the findings of traditional vigor tests, particularly between the vigor index and seedling length. Digital analysis of four-day-old seedlings proved to be a fast and efficient technique for evaluating basil seed vigor and has the potential for use in automating the data collection and analysis process. Full article

As mining technology advances, intelligent robots in open-pit mining require precise localization and digital maps. Nonetheless, significant pitch variations, uneven highways, and rocky surfaces with minimal texture present substantial challenges to the precision of feature extraction and positioning in traditional visual SLAM systems, owing to the intricate terrain features of open-pit mines. This study proposes an improved SLAM technique that integrates visual and Inertial Measurement Unit (IMU) data to address these challenges. The method incorporates a point–line feature fusion matching strategy to enhance the quality and stability of line feature extraction. It integrates an enhanced Line Segment Detection (LSD) algorithm with short segment culling and approximate line merging techniques. The combination of IMU pre-integration and visual feature restrictions is executed inside a tightly coupled visual–inertial framework utilizing a sliding window approach for back-end optimization, enhancing system robustness and precision. Experimental results demonstrate that the suggested method improves RMSE accuracy by 36.62% and 26.88% on the MH and VR sequences of the EuRoC dataset, respectively, compared to ORB-SLAM3. The improved SLAM system significantly reduces trajectory drift in the simulated open-pit mining tests, improving localization accuracy by 40.62% and 61.32%. The results indicate that the proposed method demonstrates significance. Full article

Soil aggregate stability (SAS) is needed to evaluate the soil’s resistance to degradation and erosion, especially in semi-arid regions. Traditional laboratory methods for assessing SAS are labor-intensive and costly, limiting timely and cost-effective monitoring. Thus, we developed cost-efficient wall-to-wall spatial prediction maps for two fundamental SAS proxies [mean weight diameter (MWD) and geometric mean diameter (GMD)], across a 5000-hectare area in Southwest Iran. Machine learning algorithms coupled with environmental and soil covariates were used. Our results showed that topographic covariates were the most influential covariates in predicting these SAS proxies. Overall, our SAS maps are valuable tools for sustainable soil and natural resource management, enabling decision-making for addressing potential soil degradation and promoting sustainable land use in semi-arid regions. Full article

Background/Objectives: Populations in Mediterranean countries are abandoning the traditional Mediterranean diet (MD) and lifestyle, shifting towards unhealthier habits due to profound cultural and socioeconomic changes. The SWITCHtoHEALTHY project aims to demonstrate the effectiveness of a multi-component nutritional intervention to improve the adherence of families to the MD in three Mediterranean countries, thus prompting a dietary behavior change. Methods: A parallel, randomized, single-blinded, and controlled multicentric nutritional intervention study will be conducted over 3 months in 480 families with children and adolescents aged 3–17 years from Spain, Morocco, and Turkey. The multi-component intervention will combine digital interactive tools, hands-on educational materials, and easy-to-eat healthy snacks developed for this study. Through the developed SWITCHtoHEALTHY app, families will receive personalized weekly meal plans, which also consider what children eat at school. The engagement of all family members will be prompted by using a life simulation game. In addition, a set of activities and educational materials for adolescents based on a learning-through-playing approach will be codesigned. Innovative and sustainable plant-based snacks will be developed and introduced into the children’s dietary plan as healthy alternatives for between meals. By using a full-factorial design, families will be randomized into eight groups (one control and seven interventions) to test the independent and combined effects of each component (application and/or educational materials and/or snacks). The impact of the intervention on diet quality, economy, and the environment, as well as on classical anthropometric parameters and vital signs, will be assessed in three different visits. The COM-B behavioral model will be used to assess essential factors driving the behavior change. The main outcome will be adherence to the MD assessed through MEDAS in adults and KIDMED in children and adolescents. Conclusions: SWITCHtoHEALTHY will provide new insights into the use of sustained models for inducing dietary and lifestyle behavior changes in the family setting. It will facilitate generating, boosting, and maintaining the switch to a healthier MD dietary pattern across the Mediterranean area. Registered Trial, National Institutes of Health, ClinicalTrials.gov (NCT06057324). Full article

Like many other industries, healthcare is constantly evolving. Driven by rapid advances in technology and the ever-growing influence of the digital landscape, the art of caring for the physical body and the digital world are becoming increasingly intertwined. The field of cosmetic dermatology is no exception; in fact, it may exceedingly embody this entanglement. The marriage of innovation, social media, and telehealth consultations has changed how patients perceive and seek beauty and how they engage with dermatologists. As such, dermatologists must engage as active participants as cosmetic dermatology continues to grow in the digital age [...] Full article

Reducing inequality within and between countries and enhancing social welfare are important components of the United Nations’ sustainable development goals. Based on the experience of developed countries or regions, the digital economy and fiscal policy are effective ways of promoting inclusive regional economic growth. Fully considering the completeness and availability of the data, we select the data of China’s less developed regions from 2001 to 2011 for empirical testing. This study empirically examined the effect of the digital economy on regional sustainable development and the underlying mechanism, taking an analysis of the regulatory effect of fiscal policy into account. The results show the following: (1) The digital economy and regional sustainable development exhibit an inverted “U” curve relationship. In the early stage of development, relying on its own economies of scale, the digital economy releases the “digital dividend”, positively affecting the sustainable development of the regional economy; in the later stage, the “digital divide” has a positive impact on the development of the regional economy. The inhibitory effect of the “digital divide” is greater than the enhancement effect of the “digital dividend”, negatively affecting the sustainable development of regional economies. (2) There is regional heterogeneity in the mechanism of the effect of the digital economy on regional sustainable development. The digital economy affects the level of regional sustainable development through the two paths of “comprehensive prosperity” and “holistic well-being”. For the samples in the eastern and western regions, the digital economy plays the role of promoting and then inhibiting, while for the samples in the central region, it plays the role of inhibiting and then promoting. (3) Fiscal policy negatively regulates the effect of the digital economy on regional sustainable development, but the regulatory effect is not significant. Full article

China’s agricultural development is challenged by delays in digital transformation. Understanding the alignment and trends of digital factors with rural–industrial integration is crucial for advancing agricultural digitalization. This study analyzes data from several Chinese provinces (2013–2021) using the entropy-weighted TOPSIS method to assess digital factors and rural–industrial integration levels. A coupling coordination model evaluates their interplay over time, and a center of gravity analysis along with the Dagum Gini system investigates their spatial distribution and divergence. The results show that from 2013 to 2021, digital factors and rural–industrial integration exhibited fluctuating trends. Provinces with higher levels were predominantly in the eastern region, which also had advanced digital transformation. There was a high level of interaction between these elements, especially in the eastern region. By 2021, the coupling index ranged from 0.9 to 1 across all provinces. Coordination generally remained low, with the eastern region showing medium-level high coordination, the central region low-level high coordination, and the western region high-level moderate coordination. Spatial analysis indicates a southwestward trend in the development of digital factors and rural–industrial integration. The epicenter is shifting towards the southern coastal regions, with regional disparities widening. Inter-regional disparities are diminishing annually, while intra-regional disparities are increasing, highlighting significant coupling and coordination effects. To enhance rural–industrial integration, digital strategies that meet the unique needs of rural industries are essential, fostering innovation and leveraging technological advancements effectively. Full article

Foodborne illnesses and mortalities persist as a significant global health issue. The World Health Organization estimates that one out of every ten individuals becomes ill following the consumption of contaminated food. However, in the age of digitalization and technological progress, more and more data and data evaluation technologies are available to counteract this problem. A specific challenge in this context is the efficient and beneficial utilization of the continuously increasing volume of data. In pursuit of optimal data utilization, the objective of the present study was to develop a Multi-Criteria Decision Analysis (MCDA)-based assessment scheme to be prospectively implemented into an overall artificial intelligence (AI)-supported database for the autonomous risk categorization of food incident reports. Such additional evaluations might help to identify certain novel or emerging risks by allocating a level of risk prioritization. Ideally, such indications are obtained earlier than an official notification, and therefore, this method can be considered preventive, as the risk is already identified. Our results showed that this approach enables the efficient and time-saving preliminary risk categorization of incident reports, allowing for the rapid identification of relevant reports related to predefined subject areas or inquiries that require further examination. The manual test runs demonstrated practicality, enabling the implementation of the evaluation scheme in AI-supported databases for the autonomous assessment of incident reports. Moreover, it has become evident that increasing the amount of information and evaluation criteria provided to AI notably enhances the precision of risk assessments for individual incident notifications. This will remain an ongoing challenge for the utilization and processing of food safety data in the future. Full article

Seafood preservation techniques have evolved from ancient methods to modern innovations like canning, freezing, and surimi production. Canning in the 19th century introduced airtight containers, while commercial freezing technologies like flash freezing extended shelf life. Surimi pastes in the 20th century led to affordable imitation seafood products. Emerging technologies continue to enhance seafood preservation methods. Moreover, the integration of digital technology, automation, and data sharing, known as Industry 4.0, is transforming various industries. This integration encompasses blockchain technology, automation, robotics, and big data analytics, aiming to enhance production, sustainability, traceability, and efficiency in fish processing. With a focus on the seafood market dynamics affecting these advances, this research was conducted with the aim to understand how technical breakthroughs in the seafood business are dispersed and implemented across different nations. We aim to determine the correspondence between the technological sophistication of machinery in seafood processing companies and map it across different countries across the globe to obtain an understanding of the generation of technology used in prominence. Variations in adoption rates and technological trends reflect regional market dynamics. The Seafood Expo ASIA 2023 study looked at the use of Industry 4.0 technologies, operational procedures, and technology adoption in the global seafood processing industry. Notably, countries like Norway, the Republic of Korea, Spain, Turkey, and the Netherlands have rapidly embraced Industry 4.0 technologies. The market factors driving these technological advancements across different countries include rising consumer demand for sustainable seafood, economic incentives, and global competition. A correspondence analysis was employed to analyze the correspondence between countries and the level of technological sophistication in the machinery used. We successfully mapped the level of technology utilized in machinery across global seafood processing companies, providing insights into the technological advancements shaping the industry. Full article

Background: The increasing integration of AI in chest X-ray evaluation holds promise for enhancing diagnostic accuracy and optimizing clinical workflows. However, understanding its performance in real-world clinical settings is essential. Objectives: In this study, we evaluated the sensitivity (Se) and specificity (Sp) of an AI-based software (Arterys MICA v29.4.0) alongside a radiology resident in interpreting chest X-rays referred from the emergency department (ED), using a senior radiologist’s assessment as the gold standard (GS). We assessed the concordance between the AI system and the resident, noted the frequency of doubtful cases for each category, identified how many were considered positive by the GS, and assessed variables that AI was not trained to detect. Methods: We conducted a retrospective observational study analyzing chest X-rays from a sample of 784 patients referred from the ED at our hospital. The AI system was trained to detect five categorical variables—pulmonary nodule, pulmonary opacity, pleural effusion, pneumothorax, and fracture—and assign each a confidence label (“positive”, “doubtful”, or “negative”). Results: Sensitivity in detecting fractures and pneumothorax was high (100%) for both AI and the resident, moderate for pulmonary opacity (AI = 76%, resident = 71%), and acceptable for pleural effusion (AI = 60%, resident = 67%), with negative predictive values (NPV) above 95% and areas under the curve (AUC) exceeding 0.8. The resident showed moderate sensitivity (75%) for pulmonary nodules, while AI’s sensitivity was low (33%). AI assigned a “doubtful” label to some diagnoses, most of which were deemed negative by the GS; the resident expressed doubt less frequently. The Kappa coefficient between the resident and AI was fair (0.3) across most categories, except for pleural effusion, where concordance was moderate (0.5). Our study highlighted additional findings not detected by AI, including 16% prevalence of mediastinal abnormalities, 20% surgical materials, and 20% other pulmonary findings. Conclusions: Although AI demonstrated utility in identifying most primary findings—except for pulmonary nodules—its high NPV suggests it may be valuable for screening. Further training of the AI software and broadening its scope to identify additional findings could enhance its detection capabilities and increase its applicability in clinical practice. Full article

Objective: This study aimed to establish a correlation between fecal calprotectin levels (FC) and intestinal inflammation in patients with spondyloarthritis without inflammatory bowel disease. Methods: A total of 180 SpA patients were included in the study of them 20.6% required Digital chromoendoscopy (DCE). FC, C-reactive protein (CRP), HLA-B*27 and clinical indices were assessed. Results: Positive fecal calprotectin (PFC) and high fecal calprotectin (HFC) levels were observed in 27.0% and 16.0% of patients, respectively. HFC correlated with a Bath Ankylosing Spondylitis Functional Index (BASFI) score > 4.0 (p = 0.036) and a Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score > 4.0 (p = 0.047). Loss of vascular pattern in the ileum (LVPI) was observed in approximately 70.0% of patients (p = 0.005), which was associated with PFC and abdominal bloating (p = 0.020). LVPI was also linked to microscopic inflammation (p = 0.012) and PFC with abdominal pain (p = 0.007). HFC was significantly associated with alterations in the ileal mucosa (p = 0.009) and LVPI (p = 0.001). Additionally, HFC and diarrhea were associated with LVPI in 27.3% of patients (p = 0.037) and with erosions in the ileum (p = 0.031). Chronic ileal inflammation correlated with HFC (p = 0.015), ASDAS-CRP > 2.1 (p = 0.09), LVPI (p = 0.001), and villous atrophy (p = 0.014). Factorial analysis of mixed data (FAMD) identified significant associations between micro/macroscopic changes in chronic inflammation and HFC (CC = 0.837); increased levels of CRP and microscopic acute inflammation (CC = 0.792); and clinical activity scores of ASDAS-CRP and BASDAI (CC = 0.914). Conlusions: FC levels were significantly elevated in patients with SpA, particularly those with LVPI, suggesting their potential as a valuable biomarker for managing SpA when joint manifestations coincide with ileal villous atrophy. This indicates a shared immune pathway linked to chronic gut damage. Full article

The digital economy enhances economic efficiency and improves economic structure, driving economic growth through transformations in efficiency, momentum, and quality. It has become a new driving force for advancing common prosperity. This study uses SDM, SDID, and SPSTR models to explore the impact of digital economy on common prosperity, which constructs the index system to evaluate the common prosperity from process index and outcome index. According to the panel data of 76 cities in the Yellow River Basin from 2011 to 2021, and the findings are as follows: (1) The digital economy exhibits a development pattern characterized by high activity downstream and lower activity upstream, and the development trend is stable. The development pattern of common prosperity has changed from sporadic distribution to regional agglomeration, and the level of common prosperity in most cities has improved. (2) The digital economy has a significant positive spatial effect on common prosperity. And the findings are robust after introducing the “Big Data” exogenous policy impact, dynamic SDM model, and other methods. Moreover, spatial heterogeneity exists. The promotion effect in the upper and lower reaches is stronger, while the middle reaches are weakly affected by the digital economy. (3) The spatial spillover effect of the digital economy on common prosperity has a boundary, and the positive spillover reaches a maximum value at 600–650 KM. (4) Nonlinear analysis confirms that the digital economy provides momentum for common prosperity industrial structure optimization that can effectively stimulate the “endogenous” growth mechanism, strengthen the marginal increasing effect of the digital economy driving common prosperity and enhance the effect of “making a bigger pie”. The digital economy makes effective use of digital resources and technologies, promotes the equalization of public services, exerts a positive impact on the realization of common prosperity, and consolidates the effect of “dividing a better cake”. Full article

This paper proposes DigitalUpSkilling, a novel IoT- and AI-based framework for improving and personalising the training of workers who are involved in physical-labour-intensive jobs. DigitalUpSkilling uses wearable IoT sensors to observe how individuals perform work activities. Such sensor observations are continuously processed to synthesise an avatar-like kinematic model for each worker who is being trained, referred to as the worker’s digital twins. The framework incorporates novel work activity recognition using generative adversarial network (GAN) and machine learning (ML) models for recognising the types and sequences of work activities by analysing an individual’s kinematic model. Finally, the development of skill proficiency ML is proposed to evaluate each trainee’s proficiency in work activities and the overall task. To illustrate DigitalUpSkilling from wearable IoT-sensor-driven kinematic models to GAN-ML models for work activity recognition and skill proficiency assessment, the paper presents a comprehensive study on how specific meat processing activities in a real-world work environment can be recognised and assessed. In the study, DigitalUpSkilling achieved 99% accuracy in recognising specific work activities performed by meat workers. The study also presents an evaluation of the proficiency of workers by comparing kinematic data from trainees performing work activities. The proposed DigitalUpSkilling framework lays the foundation for next-generation digital personalised training. Full article

The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment. Full article

Introduction: This pilot two-arm, parallel group, randomized clinical trial aimed to compare the efficacy of tooth movement and patient comfort during orthodontic leveling and alignment with the BRIUS™ lingual system (BR) versus labial full fixed appliances (LFFAs). Methods: Patients in the permanent dentition with mild to moderate crowding were recruited at the University at Buffalo and randomly assigned into the BR group (seven patients) or LFFAs group (six patients). The patients’ dental arches were scanned before bonding (T1) and after 18 weeks (T2). Digital dental model superimpositions were performed to three-dimensionally evaluate tooth movement. Changes between T1 and T2 were measured in the x, y, and z planes. Little’s Irregularity Index (LII) was also assessed at T1 and T2. An electronic questionnaire was completed daily for 7 days after T1 to evaluate the patients’ comfort levels. Results: After 18 weeks, similar displacements were observed for all teeth in both groups except for the lower left second premolar (LL5) in the x-axis which showed greater displacement in the BR group (p = 0.016). Groups showed similar changes in LII. Discomfort during the first week after bonding was greater on the tongue in the BR group and on the lips and cheeks in the LFFAs group. Tongue discomfort caused by the BR lasted for around 3 days. Conclusion: The BR and LFFAs showed similar teeth displacements and therefore were equally effective at leveling and aligning teeth. LFFAs caused cheek- and lip-borne discomfort while the BR caused tongue-borne discomfort during the first week after bonding. Larger studies with longer follow-ups are needed to obtain more definitive results. Full article