Search Results (1,448)

Citrus production in Mexico relies predominantly on manual labor and traditional harvesting tools, which are often associated with physical overload, awkward postures, and reduced productivity. This study presents an exploratory, perception-based field evaluation of the BLIMPER, an early-stage ergonomic harvesting prototype designed for selective Persian lime collection. A total of 93 citrus harvesters participated through snowball sampling. A structured 33-item questionnaire was administered, covering five perception dimensions and open-ended comments. The instrument was expert-validated and demonstrated good internal consistency (Cronbach’s α = 0.85). Data analysis included descriptive statistics, Welch’s t-test for gender-based comparisons, and Hedges’ g to estimate the magnitude of the difference between groups. A modified Kano model was applied to classify perceived tool attributes and identify priorities for design refinement. The results indicated that 83–85% of respondents valued material strength, 64–70% approved of the unloading system, and 67–75% perceived reduced fatigue in the shoulders and lower back. The findings should be interpreted as an initial ergonomic validation based on user perceptions under real working conditions, rather than as evidence of readiness for large-scale deployment. The BLIMPER prototype shows potential to improve comfort and posture, while highlighting design aspects—weight distribution, mobility, and material selection—that require further optimization overall. Full article

In the information society, open data is an important resource for creating economic value. This study conducts a systematic review, following the PRISMA methodology, of articles published between 2000 and 2025 in Scopus and Web of Science that include the terms Open Data and Spain (in Spanish or English) in their title and/or abstract, with the aim of assessing how Law 37/2007 on the reuse of public sector information has influenced the publications analyzed. After identifying 240 articles in Scopus and 109 in Web of Science and applying the exclusion criteria, we observe that 37 studies use the Diamond Open-Access publishing route. The results are organized into four categories corresponding to the research questions, which represent a meaningful theoretical contribution and enhance current knowledge on open data research in Spain. The identification of obstacles to the effective use of open data—such as the lack of standardization, poor information quality, and the vague definition of reuse conditions—entails practical implications of significant value for managers of open data portals seeking to improve their initiatives. Full article

This work presents a reinforcement learning (RL) framework integrated into GMV’s GSharp^® precise point positioning (PPP) algorithm to optimize GNSS measurement processing. Initially developed for multipath mitigation, the RL agent has evolved into a decision-making tool that evaluates the usefulness of GNSS observations to enhance positioning accuracy. The model processes GNSS data epoch by epoch using features such as pseudoranges, signal-to-noise ratios, elevation angles, and residuals. Based on these inputs, the agent decides whether each measurement should be included in the positioning solution. A custom reward function encourages decisions that reduce positioning error while maintaining solution stability. The system was trained on over 50 h of GNSS raw data collected in diverse environments, including urban canyons, suburban areas, and open spaces, promoting generalization across real-world conditions. Preliminary validation shows that the RL-enhanced PPP algorithm achieves accuracy improvements over the baseline GSharp^® solution in several challenging scenarios. These results suggest that RL can support GNSS data processing by adaptively managing the quality and relevance of observations, potentially enabling more robust and precise positioning in complex environments. Full article

Against the backdrop of the digital economy reshaping the global competitive landscape and the urgent demand for sustainable development, how data factors drive startups to break through resource constraints, achieve a niche leap, and realize long-term sustainable growth has become a critical issue of common concern in academia and policy circles. Drawing on resource orchestration theory and the dynamic capability view, this study constructs a theoretical framework of “Data Factor Allocation → Digital Capability Integration → Niche Leap → Sustainable Growth” and conducts an empirical test, using 412 technology-based startups as samples. The findings are as follows: (1) Data factor allocation (encompassing scenario-based access, lightweight tool penetration, and ecological sharing) exerts a significant inverted U-shaped relationship impact on both digital capability integration and the startup niche leap (range of quadratic term coefficients for core dimensions: −0.165~−0.203, p < 0.01), with turning points between 3.41 and 3.72 on a 5-point scale. Excessive data investment may trigger risks of capability hollowing and niche lock-in, hindering sustainable growth. (2) Digital capability integration (including technology application, resource coordination, and dynamic adaptation capabilities) plays a non-linear mediating role, with mediation proportions ranging from 18.7% to 32.4%. Among them, the technology application capability exhibits the highest transmission efficiency between lightweight tool penetration and the niche leap (32.4%), thereby promoting sustainable value creation. (3) The moderating effect of data governance maturity is heterogeneous: governance adaptability significantly strengthens the mediating path of the technology application capability (β = 0.187, p < 0.01) and security compliance enhances the transmission efficiency of the resource coordination capability (β = 0.165, p < 0.01), while the moderating effect of open sharing is insignificant. These findings provide a dynamic framework for the non-linear and sustainable leap of startups by integrating two core theories. They offer a decision-making basis for enterprises to optimize data allocation strategies (e.g., controlling allocation thresholds to avoid resource waste) and for governments to improve governance policies (e.g., data vouchers, trusted data spaces), thereby facilitating the implementation of the “Data Factor × Innovation and Entrepreneurship × Sustainable Development” initiative and promoting the sustainable growth of the digital economy ecosystem. Full article

(1) Background: Malnutrition increases the risk of complications, prolongs the period of hospitalization, worsens the results of treatment, and increases the costs of hospital stay. Patients’ lack of knowledge on how to cope with it may increase the occurrence of these unfavorable consequences. The aim of this study was to assess hospitalized patients’ awareness of foods for special medical purposes (FSMP) and to determine the perception of the dietitian’s role in the hospital treatment process. (2) Methods: The survey was conducted among patients hospitalized in one of the hospitals in the Małopolska region. The sample consisted of 100 respondents. Participation in the research was anonymous and voluntary. The author’s survey contained 14 closed- and open-ended questions. The answers were single or multiple choice. A knowledge test was used to determine the level of awareness among respondents. The maximum score was 8. Appropriately selected tests were applied to the collected data, such as Spearman’s correlation, Shapiro–Wilk’s normality test, and Levene’s and Mann–Witney’s tests. The level of statistical significance was assumed to be p ≤ 0.05. (3) Results: Respondents were most familiar with the term Nutridrink (68%). Only 66% declared they knew what foods for special medical purposes were used for. Most were unfamiliar with the concept of immunomodulatory ingredients. Statistically significant correlation was found between age and knowledge. Older patients achieved lower scores (rho = −0.32, p = 0.001). No statistical significance was found between sexes or comorbidities and knowledge on the discussed topic. A dietitian was pointed out as the expert in selecting FSMP (78.6%). The findings indicate that that 87% of respondents believe that FSMP consumption may be beneficial for nutritional status. (4) Conclusions: The results indicate limited knowledge among hospitalized patients about foods for special medical purposes. The role of dietitians in the treatment process is highly valued by respondents. The study results suggest that educational initiatives in hospitals may be relevant to increasing patient awareness. Potentially, such initiatives could increase the effectiveness of nutritional therapy and preventive measures aimed at improving patient nutritional status. Full article

In recent years, Latin America has experienced a growing incorporation of Artificial Intelligence (AI) into business and organizational environments, driven by digital transformation, data availability, and competitive pressures. Across multiple sectors, AI-based tools are increasingly used to support complex decision-making processes, raising both opportunities and challenges related to efficiency, ethics, and organizational readiness. Within this context, this systematic review examines the scientific evidence on the implementation of AI in business decision-making in Latin America. Following PRISMA 2020 guidelines, a systematic search was conducted in the Scopus database for articles published between 2021 and 2025. The search strategy combined Boolean operators related to AI and decision-making. Inclusion criteria comprised original, open-access research articles conducted in Latin American countries and published in Spanish or Portuguese. After screening for temporality, geographic focus, language, document type, accessibility, duplication, and relevance, 27 studies were selected from an initial pool of 276,302 records. The studies originated mainly from Peru, Colombia, Chile, and Ecuador. The findings show that AI is applied across sectors such as industry, agriculture, finance, education, and public services, primarily to enhance predictive capacity, automate processes, and support data-driven decisions. While AI adoption improves efficiency, cost reduction, and strategic innovation, its effectiveness depends on staff training, ethical governance, and strategic alignment. Persistent challenges include resistance to change, data quality limitations, and concerns regarding transparency and algorithmic bias. Overall, AI emerges as a transformative but context-dependent tool for business decision-making in Latin America. Full article

The integration of machine learning (ML) algorithms in oncology creates a new path for prognosis, early diagnosis, prevention, and treatment customization. However, large-scale clinical implementation is difficult due to the lack of standardized assessments and the variation in reported performance. A systematic review of the most recent research on ML applications in oncology (1 January 2020–31 December 2025) was conducted. The databases employed are Web of Science, Scopus, and PubMed. Filters applied for open-access articles that were simultaneously indexed and had numerical data in the abstract. From an initial of 13,292 articles, successive selection according to the PRISMA diagram resulted in a final set of 1364 studies. These were analyzed from four perspectives: the types of cancer investigated, the characteristics of the datasets (reproducibility and generalizability), the ML models used, and the performance achieved (accuracy, precision, recall, F1-score, and AUC). There is high interest in breast cancer (350 articles), colorectal cancer (337 articles), and lung cancer (151 articles), with frequent use of the databases The Cancer Genome Atlas (133 studies), Gene Expression Omnibus (94 studies), and Surveillance, Epidemiology, and End Results (72 studies). The Random Forest model proved to be predominant due to its tolerance for incomplete data. Reported performance varies considerably between cancer types and even within the same type. This analysis demonstrates the potential of ML methods for deciphering genomic alterations and supports the development of integrated personalized medicine approaches in oncology. Full article

Coniferous forests account for 78% of the western US forests and store a substantial amount of carbon. Wildfires significantly alter vegetation structure and associated forest carbon stocks. This study evaluates postfire biomass recovery trajectories (1984–2017) and total biomass accumulation in conifer forests that historically experienced low-severity, high-frequency fire regimes in the western US using recently launched Global Ecosystem Dynamic Investigations (GEDI) mission lidar data. All three ecoregions studied, including the Pacific Northwest (PNW), Southern Rockies (SR), and Northern Rockies (NR), show site-specific biomass recovery trajectories shaped by fire severity. The recovery trajectories were characterized by an initial decline and a variable gain with time since fire across the three ecoregions. Regions with low burn severity recovered to the unburned background state within the first three decades, while regions with higher burn severity only recovered in the Northern Rockies after five decades without fire. Moderate- and high-severity burned areas in both SR and PNW exhibited slow declines or sustained low biomass periods following fires, implying potential ecosystem transformation or an arrested state of lower biomass. Time since fire and fire severity were identified as the most significant drivers of postfire biomass recovery, likely because they reflect both reduced seed availability and the process of seedling establishment and regeneration. In addition, distance to unburned area, drought (measured using the Standardized Precipitation Evapotranspiration Index (SPEI)), elevation, and fire size were important drivers of biomass recovery. Our results demonstrate that all three ecoregions experienced a loss of overall biomass (15–23% (+/−40%)), with the largest losses occurring in the areas with high-severity burns (59% (+/−23%)) in the Southern Rockies compared to unburned forests within the first three decades. This study thus confirms GEDI’s ability to assess disturbance-driven vegetation biomass dynamics and provides an open-science methodology that could be utilized for other regions. In conclusion, our study indicates that an increase in fire severity within low-severity, high-frequency fire regimes, beyond historically observed levels, results in greater carbon losses. It is therefore important to consider the effects of increases in fire severity on vegetation recovery trajectories to infer the future carbon potential in these ecosystems. Full article

The paper addresses the limitations of fragmented and delayed hydrological information systems in supporting timely disaster risk mitigation. The paper introduces the RoWaterAPI, a framework that integrates near real-time hydrological measurements with geospatial analytics to improve awareness during flood-related events. The methodology utilizes open-source technologies, including Django, Kafka, and PostGIS, to support scalable data ingestion and hazard mapping. Initial baseline evaluation under a simulated bursty workload indicates an end-to-end latency of ≈1–3 s and a peak throughput of ≈6000–8500 messages/s. This performance supports real-time alerts for data variations, bridging advanced geoprocessing with user-centered design for public and institutional stakeholders. Ultimately, RoWaterAPI provides a transferable implementation model that can be adapted to any national context facing similar constraints in data fragmentation and operational accessibility. Full article

Thoracic aortic pathology involving the aortic arch is most commonly treated with open total arch replacement. However, open surgery is still associated with significant risk of mortality and morbidity, particularly in the elderly, patients with high-risk comorbidities, and those with previous cardiac surgery. Multiple endovascular approaches to enable zone 0 arch reconstruction have been developed, including custom-made, physician-modified, and off-the-shelf fenestrated/branched endografts. The initial experiences of this approach have been plagued by high incidence of stroke; although improvements have been made over the past decade, it remains suboptimal. Several factors contribute to this stagnation, including limited descriptive studies with small sample sizes, heterogeneous patient populations, varied techniques, and lack of data granularity and standardization. These limitations reduce the ability to analyze factors that could improve patient selection, device design, and procedural techniques. In addition, consistent follow-ups have not been reported, and the long-term outcome of these interventions are unknown. To address these issues, a randomized controlled trial of open versus endovascular arch repair or multicenter registry with standardized data reporting, follow-up protocol, and sufficient sample size would be needed. High-quality data will help identify patient clinical or anatomical features as well as procedural factors that can improve outcomes. Full article

Recent implementations with novel target drugs of the hypomethylating agent/venetoclax doublet challenge our treatment approach in acute myeloid leukemia patients ineligible for intensive chemotherapy. Given the doublets’ efficacy, associations of agents based on the disease’s biology to the doublet backbone are leading to novel triplet (or more) combinations. In the present review mainly FLT3, IDH and KMT2A are discussed as possible targets in this context. These triplets do not only have efficacy in relapsed/refractory patients but also in treatment-naïve patients. Results from concluded and ongoing clinical trials, as well as real-world experiences, report high efficacies competing with intensive chemotherapy. For instance, the azacytidine/venetoclax/gilteritinib triplet as first-line is reported to induce a complete remission rate with and without incomplete recovery (CR/CRi) of 96%, with 90% of responders achieving minimal residual negativity. Once a stable CR was obtained, 47% of patients who were initially considered too frail for intensive chemotherapy were able to undergo allogeneic stem cell transplantation. However, there are still open questions and challenges regarding toxicity, post-remission therapy, and overall treatment duration. The present review will not only present the specific potency of these arising triplets, but also discuss their challenges and limitations, based on currently available data. Besides regimens containing approved inhibitors, triplets with next-generation inhibitors, including completely orally administered triplet regimens, are also summarized. Their promising results are leading to advanced phase clinical studies by international consortia and collaborative groups, aiming to further refine their clinical management. Full article

Objectives: This study aims to assess the prevalence of complications and failures associated with impacted canine eruption in a specialized referral center, with the goal of identifying factors that contribute to these outcomes. Methods: This retrospective cohort study included cases of impacted canines treated at the School of Dental Medicine between 2010 and 2020. Clinical and radiographic data were collected and evaluated for failures and complications by two independent clinicians (MK, MG). In addition, specialists in oral and maxillofacial surgery and orthodontics (YM, TS, NS) independently assessed all complications and failures. Results: Among the 214 impacted maxillary canines included, 23 (10.7%) failed to erupt following initial surgical–orthodontic treatment and required re-intervention. Eruption difficulty was attributed to orthodontic factors in 43.5% of cases, surgical factors in 13.0%, and combined factors in the remainder. Following a second procedure, 15 canines erupted successfully, while 8 did not, resulting in an overall failure rate of 3.7%. Treatment failure was significantly associated with both anatomical and procedural factors. Canines with centrally positioned crowns exhibited a significantly higher failure rate than those with buccal or palatal positions (χ² test, p = 0.025). Failure was also more common when the canine root apex was located in close proximity to a cortical plate. Lateral incisor root resorption was significantly associated with treatment complications (p = 0.030). In the multivariable logistic regression analysis, root resorption remained an independent predictor of treatment failure, increasing the odds of failure approximately fourfold (OR = 0.255, CI = 0.077–0.843, p = 0.025). Timing and surgical technique were also significantly associated with treatment outcome. Surgical exposure performed shortly after diagnosis was linked to an increased risk of treatment complications (p = 0.006). Closed surgical exposure demonstrated a significantly higher failure rate compared with open exposure (Pearson exact test, p = 0.009). Although open exposure was associated with a greater likelihood of successful eruption, it was also significantly associated with increased gingival morbidity (Fisher’s test, p = 0.030). Conclusions: Failure of impacted maxillary canine eruption following combined surgical–orthodontic treatment is uncommon but is significantly associated with distinct anatomical and procedural risk factors. Central crown position, cortical plate involvement, lateral incisor root resorption, early surgical exposure, and the use of closed exposure techniques all increase the likelihood of treatment failure and complications. Although open exposure enhances the probability of successful eruption, it may also negatively affect gingival outcomes, underscoring the need for individualized, multidisciplinary treatment planning. Full article

This study presents the results of electrochemical impedance spectroscopy (EIS) conducted on a commercial 38120S cylindrical LiFePO₄ cell with a nominal capacity of 10 Ah. Measurements were performed at various states of charge, and the parameters of an equivalent circuit model were subsequently identified. The model consists of an inductance, ohmic resistance, two resistor–constant phase element (R-CPE) pairs, and a CPE representing diffusion effects. To ensure high measurement repeatability and minimize data dispersion, a surrogate model was used for equipment calibration, and a custom fixture was designed to maintain consistent cell positioning. Each measurement session was preceded by an open-circuit voltage reading and a relaxation period of approximately 24 h to ensure steady-state conditions. Furthermore, impedance spectra were analyzed over a range of frequencies by simulating model responses using various CPE constants. These simulations were visualized in LTspice and discussed, providing practical insights into the initialization of equivalent circuit model identification algorithms. Full article

This study investigates the development and impact of a teaching–learning sequence (TLS) designed for biology students with the aim of enhancing their understanding of key aspects of the nature of science (NOS) and the nature of scientific inquiry (NOSI). The TLS was developed within a design-based research (DBR) framework and centers on Griffith’s pivotal historical experiment to provide contextual depth and integrate both epistemic and non-epistemic dimensions of science. Instruction was based on explicit and reflective inquiry involving progressive scaffolding of students from structured towards more open investigative activities. An initial implementation with nine students, drawing on data from questionnaires and interviews, revealed their prior views regarding several NOS and NOSI aspects. Following the TLS, students demonstrated a more sophisticated understanding of the role of research questions in guiding experimental design, as well as a richer conception of scientific hypotheses. They also internalized the experimental logic underlying Griffith’s work and recognized the importance of creativity and imagination in scientific practice. The study discusses contextual limitations and highlights the potential of TLSs to provide robust instructional contexts, making NOS and NOSI aspects meaningful and accessible to students through historical experiments. Full article

Background: The Camellia genus is widely recognized for its remarkable diversity in floral morphology and coloration, with Camellia petelotii (Merr.) Sealy being particularly notable for its rare golden-yellow flowers, which possess exceptional ornamental value. Despite its horticultural significance, the molecular mechanisms governing its flowering process remain poorly elucidated, presenting a substantial barrier to effective conservation and breeding initiatives. Methods: To address this knowledge gap, we conducted a comprehensive transcriptomic analysis, focusing on three distinct developmental stages of C. petelotii floral organs: the alabastrum stage (S1), the half-opened flower stage (S2), and the full bloom stage (S3). These samples were subjected to high-throughput sequencing using the Illumina platform. Following rigorous quality control and alignment with the reference genome, we performed transcript assembly and integrated comprehensive gene annotation data with quantitative gene expression profiles. Results: Our analysis identified 18,732 differentially expressed genes (DEGs) showing significant expression changes across developmental stages. Notably, we identified 134 DEGs as potential flowering-related genes, which were functionally associated with key pathways involved in floral regulation, including plant hormone signal transduction (e.g., AUX/IAA, ARF, SAUR, GH3, JAR4, GID1 and SOC1), starch (SS, SUS, BAM) and sucrose metabolism (HK, FrK, and GH32), circadian rhythm regulation (e.g., PIF3, ELF3, LHY, and PRR), and the Autonomous pathway. Building upon these findings, we have proposed a comprehensive model illustrating the regulatory network underlying flowering transition in C. petelotii. The reliability of the transcriptomic data was demonstrated through the validation of 11 genes using quantitative real-time PCR (qRT-PCR). Conclusions: These insights not only enhance our understanding of the molecular basis of flowering in this species but also provide a valuable theoretical framework for future genetic improvement and breeding programs of C. petelotii. Full article