Search Results (10,635)

Background/Objectives: Evidence-based nursing practice (EBNP) is essential in high-acuity settings such as intensive care units (ICUs) and emergency departments (EDs), where nurses are frequently required to make time-critical, high-stakes clinical decisions that directly influence patient safety and outcomes. Despite its recognized importance, the implementation of EBNP remains inconsistent, with frontline nurses often facing barriers to accessing and applying current evidence. Methods: This descriptive, cross-sectional study systematically mapped and prioritized clinical questions generated by ICU and ED nurses at a tertiary hospital in South Korea. Using open-ended questionnaires, 204 clinical questions were collected from 112 nurses. Each question was coded and classified according to the Nursing Interventions Classification (NIC) taxonomy (8th edition) through a structured cross-mapping methodology. Inter-rater reliability was assessed using Cohen’s kappa coefficient. Results: The majority of clinical questions (56.9%) were mapped to the Physiological: Complex domain, with infection control, ventilator management, and tissue perfusion management identified as the most frequent areas of inquiry. Patient safety was the second most common domain (21.6%). Notably, no clinical questions were mapped to the Family or Community domains, highlighting a gap in holistic and transitional care considerations. The mapping process demonstrated high inter-rater reliability (κ = 0.85, 95% CI: 0.80–0.89). Conclusions: Frontline nurses in high-acuity environments predominantly seek evidence related to complex physiological interventions and patient safety, while holistic and community-oriented care remain underrepresented in clinical inquiry. Utilizing the NIC taxonomy for systematic mapping establishes a reliable framework to identify evidence gaps and support targeted interventions in nursing practice. Regular protocol evaluation, alignment of continuing education with empirically identified priorities, and the integration of concise evidence summaries into clinical workflows are recommended to enhance EBNP implementation. Future research should expand to multicenter and interdisciplinary settings, incorporate advanced technologies such as artificial intelligence for automated mapping, and assess the long-term impact of evidence-based interventions on patient outcomes. Full article

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

We investigate the statistical entropy of black holes within the framework of the generalized uncertainty principle (GUP) by employing effective metrics that incorporate leading-order and all-order quantum gravitational corrections. We construct three distinct effective metrics induced by the GUP, which are derived from the GUP-corrected temperature, entropy, and all-order GUP corrections, and analyze their impact on black hole entropy using ’t Hooft’s brick wall method. Our results show that, despite the differences in the effective metrics and the corresponding ultraviolet cutoffs, the statistical entropy consistently satisfies the Bekenstein–Hawking area law when expressed in terms of an invariant (coordinate-independent) distance near the horizon. Furthermore, we demonstrate that the GUP naturally regularizes the ultraviolet divergence in the density of states, eliminating the need for artificial cutoffs and yielding finite entropy even when counting quantum states only in the vicinity of the event horizon. These findings highlight the universality and robustness of the area law under GUP modifications and provide new insights into the interplay between quantum gravity effects and black hole thermodynamics. Full article

Background/Objectives: Infertility, defined as the failure to achieve pregnancy after one year of regular unprotected intercourse, represents a significant global health challenge, with male factors contributing to approximately 50% of cases. In this epidemiological context, both primary male infertility (the inability to conceive a first child) and secondary male infertility (which occurs when a man who has already fathered a child faces difficulty conceiving again) remain poorly understood at the genetic level. This study explored the role of single-nucleotide polymorphisms (SNPs) in mitochondrial genes (MT-ND3, MT-ND4L, and MT-ND4) in primary and secondary male infertility. Methods: This study analyzed the genotype distributions of SNPs in 68 infertile males (49 with primary infertility and 19 with secondary infertility) using Sanger sequencing. Results: Key findings revealed that studied SNPs were significantly associated with infertility type. Specifically, rs2857285 (T>C,G) in the ND4 gene showed a significant correlation (p = 0.023) with the TT genotype, which is prominent in primary infertility. Another SNP, rs28358279 (T>A,C) in the ND4L gene, also demonstrated a significant correlation (p = 0.046) with the TT genotype, being more common in primary infertility. In addition, rs869096886 (A>G) in the ND4 gene had a borderline correlation (p = 0.051), indicating a possible association between this SNP and reproductive duration. Conclusions: This study emphasizes the potential relevance of mitochondrial malfunction in male infertility, specifically the effects of studied SNPs on sperm survival and function over time. These findings suggest that certain mitochondrial SNPs might be potential biomarkers for infertility risk. Larger studies are needed to confirm these associations and examine the functional effects of these SNPs. Combining genetic analysis with environmental and lifestyle factors could enhance our understanding of male infertility and improve diagnostic and therapeutic strategies. Full article

The abandoned goaf resulting from coal resource integration in China poses a significant threat to coal mine safety. The transient electromagnetic method (TEM) has emerged as a crucial technology for detecting goafs in coal mines due to its adaptable equipment and efficient implementation. In recent years, small-loop TEM has demonstrated high resolution and adaptability in challenging terrains with vegetation, such as coal mine ponding areas, karst regions, and reservoir seepage scenarios. By considering the sedimentary characteristics of coal seams and addressing the resistivity changes encountered in single-point inversion, a joint optimization inversion process incorporating lateral weighting factors and vertical roughness constraints has been developed to enhance the connectivity between adjacent survey points and improve the continuity of inversion outcomes. Through an OCCAM inversion approach, the regularization factor is dynamically determined by evaluating the norms of the data objective function and model objective function in each iteration, thereby reducing the reliance of inversion results on the initial model. Using the Xiaolong Coal Mine as a geological context, the impact of lateral and vertical weighting factors on the inversion outcomes of high- and low-resistivity structural models is examined through a control variable method. The analysis reveals that optimal inversion results are achieved with a combination of a lateral weighting factor of 0.5 and a vertical weighting factor of 0.1, ensuring both result continuity and accurate depiction of vertical and lateral electrical interfaces. The practical application of this approach validates its effectiveness, offering theoretical support and technical assurance for old goaf detection in coal mines, thereby holding significant engineering value. Full article

Background: Schizophrenia affects patients’ organizational thinking, as well as their ability to identify problems. The main objective of this study was to explore healthcare consultants’ application of AI mind maps to educate patients with schizophrenia regarding their perceptions of family function, social support, quality of life, and loneliness, and to help these patients think more organizationally and understand problems more effectively. Methods: The study used a survey research design and purposive sampling method to recruit 66 participants with schizophrenia who attended the psychiatric outpatient clinic of a hospital in central Taiwan. They needed to be literate, able to respond to the topic, and over 18 years old (inclusive), and they attended individual and group health education using AI mind maps over a 3-month period during regular outpatient clinic visits. Results: The study results show that patients’ family function directly affects their quality of life (p < 0.05) and loneliness (p < 0.05), satisfaction with social support affects quality of life and loneliness directly (p < 0.05), and satisfaction with social support is a mediating factor between family function and quality of life (p < 0.05), as well as a mediating factor between family function and loneliness (p < 0.05). Conclusions: Therefore, this study confirms the need to provide holistic, integrated mental health social care support for patients with schizophrenia, showing that healthcare consultants can apply AI mind maps to empower patients with schizophrenia to think more effectively about how to mobilize their social supports. Full article

Solving competitive facility location problems can optimize market share or operational efficiency in environments where multiple firms compete for customer attention. In such contexts, facility attractiveness is shaped not only by geographic proximity but also by customer preference characteristics. This study presents a novel heuristic framework that integrates multi-view K-means clustering with customer behavior modeling reinforced by a co-regularization mechanism to align clustering results across heterogeneous data views. By jointly exploiting spatial and behavioral information, the framework clusters customers and facilities into meaningful market segments. Within each segment, a bilevel optimization model is applied to represent the sequential decision-making of competing entities—where a leader first selects facility locations, followed by a reactive follower. An empirical evaluation on a real-world dataset from San Francisco demonstrates that the proposed approach, using optimal co-regularization parameters, achieves a total runtime of approximately 4.00 s—representing a 99.34% reduction compared to the full CFLBP-CB model (608.58 s) and a 99.32% reduction compared to a genetic algorithm (585.20 s). Concurrently, it yields an overall profit of 16,104.17, which is an approximate 0.72% increase over the Direct CFLBP-CB profit of 15,988.27 and is only 0.21% lower than the genetic algorithm’s highest profit of 16,137.75. Moreover, comparative analysis reveals that the proposed multi-view clustering with co-regularization outperforms all single-view baselines, including K-means, spectral, and hierarchical methods. This superiority is evidenced by an approximate 5.21% increase in overall profit and a simultaneous reduction in optimization time, thereby demonstrating its effectiveness in capturing complementary spatial and behavioral structures for competitive facility location. Notably, the proposed two-stage approach achieves high-quality solutions with significantly shorter computation times, making it suitable for large-scale or time-sensitive competitive facility planning tasks. Full article

Specific Emitter Identification (SEI) is a specialized technique for identifying different emitters by analyzing the unique characteristics embedded in received signals, known as Radio Frequency Fingerprints (RFFs), and SEI plays a crucial role in civilian applications. Recently, various SEI methods based on deep learning have been proposed. However, in real-world scenarios, the scarcity of accurately labeled data poses a significant challenge to these methods, which typically rely on large-scale supervised training. To address this issue, we propose a novel SEI framework based on self-supervised contrastive learning. Our approach comprises two stages: an unsupervised pretraining phase that uses contrastive loss to learn discriminative RFF representations from unlabeled data, and a supervised fine-tuning stage regularized through virtual adversarial training (VAT) to improve generalization under limited labels. This framework enables effective feature learning while mitigating overfitting. To validate the effectiveness of the proposed method, we collected real-world satellite navigation signals using a 40-meter antenna and conducted extensive experiments. The results demonstrate that our approach achieves outstanding SEI performance, significantly outperforming several mainstream SEI methods, thereby highlighting the practical potential of contrastive self-supervised learning in satellite transmitter identification. Full article

Introduction: Living with coeliac disease (CD) requires lifelong adherence to a strict gluten-free diet (GFD). This study assessed GFD adherence, symptom burden, autoimmune comorbidities, and dietetic support among Hellenic CD patients. Methods: A cross-sectional survey was completed by 272 adults with CD. Adherence was measured using the Hellenic version of the Celiac Dietary Adherence Test (H-CDAT). Results: The mean H-CDAT score was 13.5 ± 3.5. Good adherence was observed in 44.9% of participants, while 14.3% showed poor adherence. Symptom burden was high: 39.3% reported partial symptom resolution and 3.7% had ongoing symptoms. Among patients, 25.0% had multiple autoimmune conditions, ranging from two to four. Dietetic support was limited: 61.5% were not referred to a dietitian at diagnosis, and 75.4% had no regular follow-up. Higher H-CDAT scores, indicating poorer adherence, were significantly associated with younger age (p = 0.014), earlier diagnosis (p = 0.01), and ongoing symptoms (p < 0.01). Age at diagnosis was also positively associated with autoimmune comorbidity count. Conclusions: These findings highlight the need for earlier diagnosis, improved access to structured dietetic support, and individualized care to optimize GFD adherence and improve outcomes in patients with CD. Full article

Background/Objectives: Neuromodulation is under investigation as a possibly effective add-on therapy in Alzheimer’s disease (AD). While transcranial pulse stimulation (TPS) has shown positive short-term effects, long-term effects have not yet been fully explored. This study aims to evaluate the long-term feasibility, safety, and potential cognitive benefits of TPS over one year in patients with Alzheimer’s disease, focusing on domains such as memory, speech, orientation, visuo-construction, and depressive symptoms. Methods: We analyzed preliminary data from the first ten out of thirty-five patients enrolled in a prospective TPS study who completed one year of follow-up and were included in a dedicated long-term database. The protocol consisted of six initial TPS sessions over two weeks, followed by monthly booster sessions delivering 6000 pulses each for twelve months. Patients underwent regular neuropsychological assessments using the Alzheimer Disease Assessment Scale (ADAS), Mini-Mental Status Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Beck Depression Inventory (BDI-II). All adverse events (AEs) were documented and monitored throughout the study. Results: Adverse events occurred in less than 1% of stimulation sessions and mainly included mild focal pain or transient unpleasant sensations, as well as some systemic behavioral or vigilance changes, particularly in patients with underlying medical conditions, with some potentially related to the device’s stimulation as adverse device reactions (ADRs). Cognitive test results showed significant improvement after the initial stimulation cycle (ADAS total improved significantly after the first stimulation cycle (M_pre = 28.44, M_post = 18.56; p = 0.001, d = 0.80, 95% CI (0.36, 1.25)), with stable scores across all domains over one year. Improvements were most notable in memory, speech, and mood. Conclusions: TPS appears to be a generally safe and feasible add-on treatment for AD, although careful patient selection and monitoring are advised. While a considerable number of participants were lost to follow-up for various reasons, adverse events and lack of treatment effect were unlikely primary causes. A multifocal stimulation approach (F-TOP2) is proposed to enhance effects across more cognitive domains. Full article

Control charts (CCs) are one of the most important techniques in statistical process control (SPC) used to monitor the behavior of critical variables. SPC is based on the averages of the samples taken. In this way, not every measurement is observed, and errors in measurements or out-of-control behaviors that are not shown graphically can be hidden. This research proposes a novel segmented control chart (SCC) that considers each measurement of the samples, expressed in matrix form. The vision system technique is used to segment measurements by shading and segmenting into binary values based on the control limits of SPC. Once the matrix is segmented, the seven main features of the matrix are extracted using the translation-, scale-, and rotation-invariant Hu moments of the segmented matrices. Finally, a grouping is made to classify the samples in clear and simple language as excellent, good, or regular using the k-means algorithm. The results visually display the total pattern behavior of the samples and their interpretation when they are classified intelligently. The proposal can be replicated in any production sector and strengthen the control of the sampling process. Full article

The utilisation of edible insects for human nutrition is a long-standing practice in many parts of the globe, and is being gradually introduced into countries without an entomophagic tradition as well. These unconventional sources of protein of animal origin have arisen as a sustainable alternative to other animal protein sources, such as meat. This review intends to present the compilation of data in the scientific literature on the chemical composition and nutritional value of the bee brood of A. mellifera species and subspecies as edible foods. For this, a comprehensive search of the scientific literature was carried out using the databases ScienceDirect, Scopus, Pub-Med, BOn, and SciELO. Appropriate keywords were used for the search to reach the research works that addressed the topics of the review. The results showed that bee brood has considerable quantities of protein, fat and carbohydrates. The most abundant amino acids are leucine and lysine (these two being essential amino acids) and aspartic acid, glutamic acid, and proline (these three being non-essential amino acids). As for the fatty acids, bee broods contain approximately equal fractions of saturated and monounsaturated fatty acids, while the polyunsaturated fatty acids are negligible. The dietary minerals present in higher quantities are potassium, phosphorus, and magnesium, and the most abundant vitamins are vitamin C and niacin; choline is also present, although it is not a true vitamin. Although bee brood from A. mellifera has potential for human consumption as a nutrient-rich food, there are still many aspects that need to be further studied in the future, such as safety and hazards linked to possible regular consumption. Full article

The crystal structures of the 2-amino-5-halogenopyridines (halogen = Cl (1), Br (2)) and 2,3-diamino-5-halogenopyridines (halogen = Cl (3), Br (4)) were compared with respect to their intermolecular interactions. An ab-initio-based method for evaluating the interaction energies between molecules was employed to estimate the driving forces of crystal formation. As a result, regularities in crystal structure organization were identified. For compounds 1 and 2, a dimeric building unit is formed by two N–H…N_pyr hydrogen bonds. These dimers are further connected to neighboring units by C–H…π, C–H…N, N…X (X = Cl, Br), and non-specific interactions. The aforementioned intermolecular interactions give rise to layered structures that are similar but not isotypical. No significant contributions from π–π or N–H…N(H₂) interactions are observed in 1 and 2. The structures of 3 and 4 are isotypical and crystallize in the non-centrosymmetric space group P2₁2₁2₁. The most important intermolecular interactions are N–H…N_pyr, N–H…N(H₂), and stacking interactions. These interactions lead to identical columnar-layered structures in both 3 and 4. No significant contributions from halogen bonds of the type N…X (X = Cl, Br) are found in 3 and 4. Full article

Background: The consumption of ultra-processed foods (UPFs) represents an important public health challenge, especially among education workers, whose intense routine can negatively impact eating habits. This study aimed to analyze the factors associated with the regular consumption of UPF among employees of the Federal Network of Professional, Scientific and Technological Education (RFEPCT) in Brazil. Methods: This was a cross-sectional study, with a quantitative approach, carried out with 1563 education workers. Validated instruments on eating habits (PeNSE), mental health (DASS-21) and quality of life (WHOQOL-bref) were used. The regular consumption of UPF was defined as intake on ≥5 days in the last seven days. The association between the regular consumption of UPF and sociodemographic, occupational, behavioral, mental health and quality of life variables was assessed by Poisson regression with robust variance, generating adjusted prevalence ratios (PRadj) and respective 95% confidence intervals. Results: The regular consumption of UPF was associated mainly with female gender, a lower age group, Southeast and Midwest regions, dissatisfaction with sleep and the body, physical inactivity and poor sleep quality. In addition, the findings suggested a significant relationship between the worst stress scores and soft drinks (PRadj: 2.11; CI: 1.43–3.13), anxiety and soft drinks (PRadj: 1.83; CI: 1.24–2.70) and depression and industrialized/ultra-processed salty foods (PRadj: 2.43; CI: 1.82–3.26). The same was observed in the scores for the worst perception of quality of life, where there was a prevalence of up to 2.32 in the psychological domain and the consumption of industrialized/ultra-processed salty foods. Conclusions: The findings indicate that multiple interrelated factors—individual, psychosocial and occupational—are associated with the consumption of UPF among education workers. These results reinforce the importance of institutional policies that integrate actions to promote dietary health, mental health care and improved working conditions in the education sector. Full article

Background: Type 2 diabetes (T2D) is a growing public health concern, particularly in low- and middle-income countries. Although prediabetes is a major risk factor for T2D, it remains largely underdiagnosed and untreated. Structured lifestyle interventions have proven effective in preventing diabetes, but their feasibility within the Brazilian public health system remains unclear. Methods: This multicenter pilot randomized controlled trial assessed the feasibility of a culturally adapted lifestyle intervention (PROVEN-DIA) across the five regions of Brazil. A total of 220 adults at high risk for T2D were randomized to an intervention group or a control group (usual care) and followed for three months. Both groups received similar educational content on healthy eating and physical activity, but the intervention group participated in a structured and personalized lifestyle program with regular follow-up sessions. The primary outcome was adherence to dietary recommendations, assessed using the BALANCE Index—a validated dietary score (range: 0–40) based on the Brazilian Cardioprotective Diet that classifies foods into color-coded groups according to nutritional quality—along with engagement in moderate-to-vigorous physical activity (MVPA). Secondary outcomes included diet quality (DQIR), anthropometric and metabolic parameters. Results: Feasibility was demonstrated by a 93.2% retention rate (n = 205). There was no significant difference in the primary outcome (simultaneous improvement in diet and MVPA). However, the PROVEN-DIA group exhibited significantly greater improvements in diet quality, with a 2.8-point increase in the BALANCE Index (vs. 0.5 in the control, p = 0.03), and a significant improvement in the DQIR (p < 0.001). No significant differences between groups were observed in MVPA, HbA1C, glycaemia, or body weight. Conclusions: The PROVEN-DIA intervention proved feasible within the Brazilian public health context, resulting in significant improvements in dietary quality among individuals at high risk for T2D. A larger trial with longer follow-up is warranted to evaluate its effectiveness in preventing the progression to diabetes. However, to enhance physical activity outcomes, specific adaptations and targeted strategies may be required to better support participant engagement in exercise. Full article