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23 pages, 1562 KiB  
Article
SCNOC-Agentic: A Network Operation and Control Agentic for Satellite Communication Systems
by Wenyu Sun, Chenhua Sun, Yasheng Zhang, Zhan Yin and Zhifeng Kang
Electronics 2025, 14(16), 3320; https://doi.org/10.3390/electronics14163320 (registering DOI) - 20 Aug 2025
Abstract
Large language models (LLMs) have demonstrated powerful capability to solve practical problems through complex step-by-step reasoning. Specifically designed LLMs have begun to be integrated into terrestrial communication networks. However, relevant research in the field of satellite communications remains exceedingly rare. To address this [...] Read more.
Large language models (LLMs) have demonstrated powerful capability to solve practical problems through complex step-by-step reasoning. Specifically designed LLMs have begun to be integrated into terrestrial communication networks. However, relevant research in the field of satellite communications remains exceedingly rare. To address this gap, we introduce SCNOC-Agentic, a novel architecture especially designed to integrate the management and control of satellite communication systems in LLMs. SCNOC-Agentic incorporates four components tailored to the characteristics of satellite communications: intent refinement, multi-agent workflow, personalized long-term memory, and graph-based retrieval. Furthermore, we define four typical real-world scenarios that can be effectively addressed by integrating with LLMs: network task planning, carrier and cell optimization, fault analysis of satellites, and satellite management and control. Utilizing the SCNOC-Agentic framework, a series of open-source LLMs have achieved outstanding performance on the four tasks under various baselines, including zero-shot CoT, CoT-5, and self-consistency. For example, qwen2.5-70B with SCNOC-Agentic significantly improves the parameter generation accuracy in the network task planning task from 15.6% to 32.2%, while llama-3.3-70B increases from 16.2% to 29.0%. In addition, ablation studies were conducted to validate the importance of each proposed component within the SCNOC-Agentic framework. Full article
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19 pages, 577 KiB  
Article
When Expertise Goes Undercover: Exploring the Impact of Perceived Overqualification on Knowledge Hiding and the Mediating Role of Future Work Self-Salience
by Xiaoyun Ren, Di Wu, Qian Zhang and Haitianyu Lin
Behav. Sci. 2025, 15(8), 1134; https://doi.org/10.3390/bs15081134 - 20 Aug 2025
Abstract
Grounded in the person–environment fit theory and an identity-based perspective, this study investigated the relationship between perceived overqualification and knowledge hiding, focusing on the mediating role of future work self-salience and the moderating role of the growth mindset. We suggest that perceived overqualification [...] Read more.
Grounded in the person–environment fit theory and an identity-based perspective, this study investigated the relationship between perceived overqualification and knowledge hiding, focusing on the mediating role of future work self-salience and the moderating role of the growth mindset. We suggest that perceived overqualification as a person–job misfit would negatively impact employees’ salient hoped-for work identities, representing a low level of future work self-salience. The diminished salience of a future work self leads employees to hide their knowledge. Furthermore, the growth mindset exacerbates the negative impact of perceived overqualification. We conducted a three-wave survey with 482 employees from knowledge-intensive industries. The results revealed that perceived overqualification boosted knowledge hiding by decreasing employees’ future work self-salience. The growth mindset enhanced the negative relationship between perceived overqualification and future work self-salience. Thus, the indirect effect of perceived overqualification on knowledge hiding via future work self-salience was more significant for those with a stronger growth mindset. Our findings contribute to the literature on person–job fit and knowledge behavior while providing practical insights for managing and guiding talented employees in knowledge management. Full article
(This article belongs to the Section Organizational Behaviors)
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22 pages, 3681 KiB  
Article
Mapping Emerging Scientific Trends in Chronic Skin Disorders Using Machine Learning-Based Bibliometrics
by Nicoleta Cirstea, Andrei-Flavius Radu, Delia Mirela Tit, Ada Radu, Gabriela S. Bungau, Laura Maria Endres and Paul Andrei Negru
Bioengineering 2025, 12(8), 890; https://doi.org/10.3390/bioengineering12080890 (registering DOI) - 20 Aug 2025
Abstract
Chronic dermatologic diseases are characterized by pathophysiologic complexity and the existence of many unmet patient management needs that can contribute to treatment failure, with poor adherence being a major issue. This study aims to identify key topics in this field, using the Web [...] Read more.
Chronic dermatologic diseases are characterized by pathophysiologic complexity and the existence of many unmet patient management needs that can contribute to treatment failure, with poor adherence being a major issue. This study aims to identify key topics in this field, using the Web of Science database. To perform this analysis, tools such as VOSviewer, Bibliometrix, and Excel were used. A Python script leveraging machine learning algorithms was developed to standardize terminology. The initial search yielded 35,373 documents, which were then refined to 12,952 publications spanning 1975 to 2024 through parameter optimization. The study found an increasing interest in this research domain, with a notable surge in 2019. The analysis identified the United States, Germany, and England as the most prolific countries in terms of scientific output. Canada ranked sixth in total document production, but its documents received the highest average citations, reflecting a significant impact. Normalization analysis revealed Italy as the most specialized country in chronic skin disease research relative to total national research output. Trend analysis revealed an evolution in research topics, particularly after 2020, with a growing focus on personalized treatment methods and long-term treatment outcomes. The study highlighted international collaboration, especially among countries with cultural or regional connections, such as those within the European Union. It underscores the growing need for continuous updates and the increasing global focus on chronic skin diseases, highlighting the critical role of staying current with emerging trends to drive advancements in treatment and patient care. Full article
(This article belongs to the Section Biosignal Processing)
19 pages, 972 KiB  
Article
Baseline Hemostatic Biomarker Assessment Identifies Breast Cancer Patients at High Risk for Venous Thromboembolism During Chemotherapy
by Marina Marchetti, Patricia Gomez-Rosas, Laura Russo, Carmen Julia Tartari, Silvia Bolognini, Chiara Ticozzi, Debora Romeo, Francesca Schieppati, Luca Barcella, Roberta Sarmiento, Giovanna Masci, Giampietro Gasparini, Filippo De Braud, Carlo Tondini, Armando Santoro, Fausto Petrelli, Francesco Giuliani, Andrea D’Alessio, Roberto Labianca and Anna Falanga
Cancers 2025, 17(16), 2712; https://doi.org/10.3390/cancers17162712 - 20 Aug 2025
Abstract
(1) Background: The presence of metastatic disease significantly increases the risk of venous thromboembolism (VTE) in breast cancer, particularly during chemotherapy. Although not categorized as a highly thrombogenic malignancy, the elevated global prevalence of this cancer places a substantial number of patients at [...] Read more.
(1) Background: The presence of metastatic disease significantly increases the risk of venous thromboembolism (VTE) in breast cancer, particularly during chemotherapy. Although not categorized as a highly thrombogenic malignancy, the elevated global prevalence of this cancer places a substantial number of patients at risk of thrombosis, which cannot yet be accurately predicted by validated risk assessment models (RAMs), highlighting the need for a dedicated model. (2) Aim: This study aims to develop a RAM for VTE in newly diagnosed metastatic breast cancer patients enrolled in a prospective, observational, and multicenter study. (3) Methods: A cohort of 189 patients beginning antitumor therapy were enrolled and prospectively monitored for VTE and mortality. Blood samples collected at enrollment were tested for D-dimer, fibrinogen, FVIII, prothrombin fragment 1 + 2 (F1 + 2), and thrombin generation (TG). Competing risk analyses were performed to identify significant predictors. (4) Results: Within one year, the cumulative incidences of VTE and mortality were 7.0% and 12%, respectively. Univariable analysis identified high Ki-67, D-dimer, FVIII, fibrinogen, and TG levels, along with low hemoglobin levels, as independent predictors of VTE. Only Ki-67, fibrinogen, FVIII, and hemoglobin were retained as significant predictors in multivariable analysis. These variables were further examined by multiple linear regression, which revealed Ki-67 and fibrinogen as the most significant parameters. A continuous RAM was then developed based on Ki-67 and fibrinogen (c-statistics 0.78), categorizing patients into low-risk and high-risk groups for VTE (2% vs. 13%; SHR 3.6, p = 0.018). This stratification could not be achieved using currently validated models for VTE risk. (5) Conclusions: We developed an accurate RAM for VTE that enables the identification of metastatic breast cancer patients at high risk for VTE, which supports clinicians in personalized thromboprophylaxis strategies if externally validated. Full article
(This article belongs to the Section Cancer Epidemiology and Prevention)
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18 pages, 956 KiB  
Article
An Explainable Radiomics-Based Classification Model for Sarcoma Diagnosis
by Simona Correra, Arnar Evgení Gunnarsson, Marco Recenti, Francesco Mercaldo, Vittoria Nardone, Antonella Santone, Halldór Jónsson and Paolo Gargiulo
Diagnostics 2025, 15(16), 2098; https://doi.org/10.3390/diagnostics15162098 - 20 Aug 2025
Abstract
Objective: This study introduces an explainable, radiomics-based machine learning framework for the automated classification of sarcoma tumors using MRI. The approach aims to empower clinicians, reducing dependence on subjective image interpretation. Methods: A total of 186 MRI scans from 86 patients [...] Read more.
Objective: This study introduces an explainable, radiomics-based machine learning framework for the automated classification of sarcoma tumors using MRI. The approach aims to empower clinicians, reducing dependence on subjective image interpretation. Methods: A total of 186 MRI scans from 86 patients diagnosed with bone and soft tissue sarcoma were manually segmented to isolate tumor regions and corresponding healthy tissue. From these segmentations, 851 handcrafted radiomic features were extracted, including wavelet-transformed descriptors. A Random Forest classifier was trained to distinguish between tumor and healthy tissue, with hyperparameter tuning performed through nested cross-validation. To ensure transparency and interpretability, model behavior was explored through Feature Importance analysis and Local Interpretable Model-agnostic Explanations (LIME). Results: The model achieved an F1-score of 0.742, with an accuracy of 0.724 on the test set. LIME analysis revealed that texture and wavelet-based features were the most influential in driving the model’s predictions. Conclusions: By enabling accurate and interpretable classification of sarcomas in MRI, the proposed method provides a non-invasive approach to tumor classification, supporting an earlier, more personalized and precision-driven diagnosis. This study highlights the potential of explainable AI to assist in more secure clinical decision-making. Full article
(This article belongs to the Special Issue New Trends in Musculoskeletal Imaging)
20 pages, 426 KiB  
Article
Exploring the Role of Teacher Self-Efficacy and Personal Environmental Practices in Integrating Sustainability into Teaching: A Network Analysis of German Teachers
by Martin Daumiller, Melanie V. Keller and Markus Dresel
Sustainability 2025, 17(16), 7533; https://doi.org/10.3390/su17167533 (registering DOI) - 20 Aug 2025
Abstract
Integrating sustainability into school curricula is increasingly important, with teachers seen as key “change agents”. However, many lack specific preparation for Education for Sustainable Development (ESD), and there is considerable variability in how explicitly or implicitly they address these topics in their teaching. [...] Read more.
Integrating sustainability into school curricula is increasingly important, with teachers seen as key “change agents”. However, many lack specific preparation for Education for Sustainable Development (ESD), and there is considerable variability in how explicitly or implicitly they address these topics in their teaching. The purpose of this study was to investigate interpersonal and contextual factors related to ESD implementation, including self- and action-efficacy, personal attitudes, eco-anxiety, private engagement and knowledge, alongside perceived student interest and pressure, and school awareness. A total of 419 teachers from various German primary and secondary schools (M = 45 years, SD = 10.9; 68% female; teaching experience: M = 16 years, SD = 9.9) completed a cross-sectional online survey and knowledge test. Findings showed significant variation in how often teachers included sustainability in their teaching, unrelated to gender, school type, or training. Network analyses revealed that self-efficacy and private engagement—rather than teachers’ knowledge—were central predictors of ESD integration. Notably, private engagement emerged as a key bridge in the network, while high self-efficacy was closely tied to frequent classroom implementation. These results suggest that fostering teachers’ personal commitment and confidence may be more effective than focusing solely on knowledge to promote sustainability education. Full article
(This article belongs to the Special Issue Towards Sustainable Futures: Innovations in Education)
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19 pages, 511 KiB  
Article
Between Love and Exhaustion: A Qualitative Study of Greek Parents’ Lived Experiences Supporting Adult Children with Substance Use Disorders
by Panagiota Tragantzopoulou and Eleni Rizou
Nurs. Rep. 2025, 15(8), 306; https://doi.org/10.3390/nursrep15080306 - 20 Aug 2025
Abstract
Background/Objectives: Parents of individuals with substance use disorders (SUDs) often carry significant emotional and relational burdens, yet their voices remain underrepresented in addiction research. This study explores how Greek parents navigate the long-term challenges of caring for adult children with SUDs, with a [...] Read more.
Background/Objectives: Parents of individuals with substance use disorders (SUDs) often carry significant emotional and relational burdens, yet their voices remain underrepresented in addiction research. This study explores how Greek parents navigate the long-term challenges of caring for adult children with SUDs, with a focus on emotional strain, caregiving identity, and culturally embedded coping strategies within a collectivist context. Methods: Eight Greek parents (six mothers and two fathers, aged 47–60) participated in in-depth, semi-structured interviews. Conversations were conducted either in person or via video call, depending on participant preference and geographical constraints. Data were analyzed using Interpretative Phenomenological Analysis (IPA) to explore lived experience and the meaning-making processes shaping parental coping over time. Results: Four overarching themes were identified as follows: (1) Living in Vigilance, reflecting constant hyper-alertness, emotional exhaustion, and social withdrawal rooted in trauma; (2) Shifting Parental Identity, capturing the evolution of parents into caregivers, advocates, and informal caseworkers amid systemic neglect; (3) Struggling Within Systems, highlighting exclusion, blame, and fragmentation in institutional care—with moments of empathy holding outsized emotional weight; and (4) Coping as Cultural Duty, showing how caregiving was sustained through values of sacrifice, loyalty, and protective silence, even at great personal cost. Conclusions: Greek parents supporting adult children with SUDs face a complex interplay of trauma, cultural obligation, and institutional strain. Their coping is shaped by deeply held familial values rather than access to effective support. The findings call for culturally attuned, family-inclusive interventions and further research into long-term caregiving across diverse contexts. Full article
18 pages, 902 KiB  
Article
Immune Modulation Through KIR–HLA Interactions Influences Cetuximab Efficacy in Colorectal Cancer
by María Gómez-Aguilera, Bárbara Manzanares-Martín, Arancha Cebrián-Aranda, Antonio Rodríguez-Ariza, Rafael González-Fernández, Laura del Puerto-Nevado, Jesús García-Foncillas and Enrique Arandaa
Int. J. Mol. Sci. 2025, 26(16), 8062; https://doi.org/10.3390/ijms26168062 (registering DOI) - 20 Aug 2025
Abstract
Colorectal cancer (CRC) remains a major cause of cancer-related mortality. Cetuximab improves survival by combining EGFR inhibition with immune activation. This study evaluated the influence of killer cell immunoglobulin-like receptor (KIR)-mediated immune responses on cetuximab efficacy in 124 metastatic CRC patients: 55 with [...] Read more.
Colorectal cancer (CRC) remains a major cause of cancer-related mortality. Cetuximab improves survival by combining EGFR inhibition with immune activation. This study evaluated the influence of killer cell immunoglobulin-like receptor (KIR)-mediated immune responses on cetuximab efficacy in 124 metastatic CRC patients: 55 with wild-type (WT) KRAS and 69 with KRAS mutations. Peripheral blood was genotyped for 19 KIR genes and relevant HLA alleles, focusing on key KIR–HLA interactions (2DL1–C2, 3DL1–Bw4, 3DS1–Bw4). KRAS-WT patients showed better outcomes, receiving more treatment cycles (median: 17 vs. 4) and showing slower disease progression (60% vs. 92.8% at 12 months). WT patients had higher frequencies of inhibitory KIRs and the Bw4 allele, with KIR3DS1–Bw4 heterozygosity linked to longer survival (p = 0.013). In KRAS-mutant patients, heterozygous KIR genotypes (AB) and mixed A/B semi-haplotypes were associated with improved survival (p = 0.002). Multivariate analysis confirmed KIR3DS1–Bw4 as a favorable factor in WT patients and AB genotypes as beneficial in KRAS-mutants. In conclusion, KIR–HLA interactions significantly impact cetuximab efficacy in metastatic CRC, with distinct immunogenetic profiles in WT and KRAS-mutant patients. These results highlight the potential of KIR–HLA profiling to guide personalized treatment strategies. Full article
(This article belongs to the Section Molecular Immunology)
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14 pages, 3644 KiB  
Systematic Review
Artificial Intelligence Models for Predicting Outcomes in Spinal Metastasis: A Systematic Review and Meta-Analysis
by Vivek Sanker, Prachi Dawer, Alexander Thaller, Zhikai Li, Philip Heesen, Srinath Hariharan, Emil O. R. Nordin, Maria Jose Cavagnaro, John Ratliff and Atman Desai
J. Clin. Med. 2025, 14(16), 5885; https://doi.org/10.3390/jcm14165885 - 20 Aug 2025
Abstract
Background: Spinal metastases can cause significant impairment of neurological function and quality of life. Hence, personalized clinical decision-making based on prognosis and likely outcome is desirable. The effectiveness of AI in predicting complications and treatment outcomes for patients with spinal metastases is assessed. [...] Read more.
Background: Spinal metastases can cause significant impairment of neurological function and quality of life. Hence, personalized clinical decision-making based on prognosis and likely outcome is desirable. The effectiveness of AI in predicting complications and treatment outcomes for patients with spinal metastases is assessed. Methods: A thorough search was carried out through the PubMed, Scopus, Web of Science, Embase, and Cochrane databases up until 27 January 2025. Included were studies that used AI-based models to predict outcomes for adult patients with spinal metastases. Three reviewers independently extracted the data, and screening was conducted in accordance with PRISMA principles. AUC results were pooled using a random-effects model, and the PROBAST program was used to evaluate the study’s quality. Results: Included were 47 articles totaling 25,790 patients. For training, internal validation, and external validation, the weighted average AUCs were 0.762, 0.876, and 0.810, respectively. The Skeletal Oncology Research Group machine learning algorithms (SORG-MLAs) were the ones externally validated the most, continuously producing AUCs > 0.84 for 90-day and 1-year mortality. Models based on radiomics showed promise in preoperative planning, especially for outcomes of radiation and concealed blood loss. Most research concentrated on breast, lung, and prostate malignancies, which limited its applicability to less common tumors. Conclusions: AI models have shown reasonable accuracy in predicting mortality, ambulatory status, blood loss, and surgical complications in patients with spinal metastases. Wider implementation necessitates additional validation, data standardization, and ethical and regulatory framework evaluation. Future work should concentrate on creating multimodal, hybrid models and assessing their practical applications. Full article
(This article belongs to the Special Issue Recent Advances in Spine Tumor Diagnosis and Treatment)
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17 pages, 879 KiB  
Article
Investigation of Optic Nerve Sheath Diameter, Intraocular Pressure, and Dry Eye in Patients with Borderline Personality Disorder: The Role of Childhood Trauma
by Tunahan Sun, Demet Dursun Çakar, Caner Yeşiloğlu, Mehmet Emin Demirkol, Lut Tamam, Kerim Uğur and Hatice Polat
J. Clin. Med. 2025, 14(16), 5886; https://doi.org/10.3390/jcm14165886 - 20 Aug 2025
Abstract
Background/Objectives: Borderline personality disorder (BPD) is a psychiatric disorder characterized by emotional instability, impulsive behavior, and impaired interpersonal relationships. It is associated with a high prevalence of childhood trauma and neurobiological changes. This study aimed to compare ophthalmologic parameters, namely, optic nerve [...] Read more.
Background/Objectives: Borderline personality disorder (BPD) is a psychiatric disorder characterized by emotional instability, impulsive behavior, and impaired interpersonal relationships. It is associated with a high prevalence of childhood trauma and neurobiological changes. This study aimed to compare ophthalmologic parameters, namely, optic nerve sheath diameter, intraocular pressure, and dry eye, in patients with BPD with healthy controls and to investigate the relations between these parameters and childhood trauma. Methods: This study included 51 female patients with BPD between the ages of 18 and 35 years, who were not using psychotropic medication, and 51 healthy controls matched for age and educational level. Optic nerve sheath diameter, intraocular pressure, and tear break-up time were measured, and trauma history was evaluated using the Childhood Trauma Questionnaire-Short Form. Independent t-test and Pearson correlation analysis were used in statistical analyses. Results: Patients with BPD were found to have significantly higher mean optic nerve sheath diameter scores (left: 3.94 ± 0.43, right: 3.97 ± 0.47) compared with healthy controls (left: 3.76 ± 0.44, right: 3.78 ± 0.45) (p < 0.05). The groups showed no significant difference in intraocular pressure and dry eye parameters (p > 0.05). A significant positive correlation was noted between emotional abuse scores and the optic nerve sheath diameter of the left eye in patients with BPD (p < 0.05; r = 0.364). Conclusions: An increased optic nerve sheath diameter may be a potential peripheral biomarker reflecting chronic stress or changes in intracranial physiology in patients with BPD. This increase is particularly associated with a history of emotional abuse. Ophthalmological parameters may contribute to understanding the neurobiological basis of BPD and serve as peripheral biomarkers or indicators of neurobiological changes. Full article
32 pages, 1947 KiB  
Article
Privacy Protection in AI Transformation Environments: Focusing on Integrated Log System and AHP Scenario Prioritization
by Dong-Sung Lim and Sang-Joon Lee
Sensors 2025, 25(16), 5181; https://doi.org/10.3390/s25165181 - 20 Aug 2025
Abstract
Recent advancements in emerging technologies such as IoT and AI have driven digital innovation, while also accelerating the sophistication of cyberattacks and expanding the attack surface. In particular, inter-state cyber warfare, sophisticated ransomware threats, and insider-led personal data breaches have emerged as significant [...] Read more.
Recent advancements in emerging technologies such as IoT and AI have driven digital innovation, while also accelerating the sophistication of cyberattacks and expanding the attack surface. In particular, inter-state cyber warfare, sophisticated ransomware threats, and insider-led personal data breaches have emerged as significant new security risks. In response, this study proposes a Privacy-Aware Integrated Log System model developed to mitigate diverse security threats. By analyzing logs generated from personal information processing systems and security systems, integrated scenarios were derived. These scenarios are designed to defend against various threats, including insider attempts to leak personal data and the evasion of security systems, enabling scenario-based contextual analysis that goes beyond simple event-driven detection. Furthermore, the Analytic Hierarchy Process (AHP) was applied to quantitatively assess the relative importance of each scenario, demonstrating the model’s practical applicability. This approach supports early identification and effective response to personal data breaches, particularly when time and resources are limited by focusing on the top-ranked scenarios based on relative importance. Therefore, this study is significant in that it goes beyond fragmented log analysis to establish a privacy-oriented integrated log system from a holistic perspective, and it further validates its operational efficiency in field applications by conducting an AHP-based relative importance evaluation. Full article
54 pages, 3153 KiB  
Review
Beyond GLP-1 Agonists: An Adaptive Ketogenic–Mediterranean Protocol to Counter Metabolic Adaptation in Obesity Management
by Cayetano García-Gorrita, Nadia San Onofre, Juan F. Merino-Torres and Jose M. Soriano
Nutrients 2025, 17(16), 2699; https://doi.org/10.3390/nu17162699 - 20 Aug 2025
Abstract
Background/Objectives: Long-term obesity management consistently fails due to two major barriers: poor adherence, exacerbated by ultra-processed foods with addictive potential, and post-weight loss metabolic adaptation that reduces energy expenditure by approximately 500 kcal/day. Current paradigms—static diets and GLP-1 receptor agonists—address these barriers only [...] Read more.
Background/Objectives: Long-term obesity management consistently fails due to two major barriers: poor adherence, exacerbated by ultra-processed foods with addictive potential, and post-weight loss metabolic adaptation that reduces energy expenditure by approximately 500 kcal/day. Current paradigms—static diets and GLP-1 receptor agonists—address these barriers only partially. The objectives of this thesis-driven review are: (1) to conduct a focused evidence-mapping of Ketogenic–Mediterranean Diet (KMD) protocols; (2) to analyze why existing protocols have not explicitly countered metabolic adaptation; and (3) to present the Adaptive Ketogenic–Mediterranean Protocol (AKMP). Methods: Hybrid methodology—an argumentative narrative review anchored by a structured evidence-mapping search (PRISMA-style flow for transparency). Results: We identified 29 studies implementing KMD protocols with significant weight loss and superior adherence. However, none of the published protocols explicitly implement anti-adaptive strategies, despite an estimated ketogenic metabolic advantage (≈100–300 kcal/day), context-dependent and more consistently observed in longer trials and during weight-maintenance settings. Conclusions: Unlike GLP-1 receptor agonists—which primarily suppress appetite, require ongoing pharmacotherapy, and do not directly mitigate the decline in energy expenditure—the AKMP couples a Mediterranean foundation for adherence with a ketogenic metabolic advantage and a biomarker-guided adjustment system explicitly designed to counter metabolic adaptation, aiming to improve the durability of weight loss and patient self-management. As a theoretical construct, the AKMP requires confirmation in prospective, controlled studies; accordingly, we outline a pragmatic 24-week pilot design in “Pragmatic Pilot Trial to Validate the AKMP–Incretin Sequencing”. Full article
(This article belongs to the Special Issue The Ketogenic Diet: Biochemical Mechanisms and Clinical Applications)
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29 pages, 1068 KiB  
Article
Order Allocation Strategy Optimization in a Goods-to-Person Robotic Mobile Fulfillment System with Multiple Picking Stations
by Junpeng Zhao and Chu Zhang
Appl. Sci. 2025, 15(16), 9173; https://doi.org/10.3390/app15169173 (registering DOI) - 20 Aug 2025
Abstract
The order picking process in Goods-to-Person (G2P) systems involves a set of interdependent yet often separately addressed decisions, such as order allocation, sequencing, and rack handling. This study focuses on the joint optimization of order allocation, order sequencing, rack selection, and rack sequencing [...] Read more.
The order picking process in Goods-to-Person (G2P) systems involves a set of interdependent yet often separately addressed decisions, such as order allocation, sequencing, and rack handling. This study focuses on the joint optimization of order allocation, order sequencing, rack selection, and rack sequencing in a G2P robotic mobile fulfillment system with multiple picking stations. To model this complex problem, we develop a mathematical formulation and propose a two-phase heuristic algorithm that combines simulated annealing, genetic algorithms, and beam search for efficient solution. In addition, we explore and compare two order allocation strategies—order similarity and order association—across a range of operational scenarios. Extensive computational experiments and sensitivity analyses demonstrate the effectiveness of the proposed approach and provide insights into how strategic order allocation can significantly improve picking efficiency. Computational experiments on small-scale instances show that our algorithm achieves near-optimal solutions with up to 93.3% reduction in computation time compared to exact optimization for small cases. In large-scale scenarios, the order similarity strategy reduces rack movements by up to 44.8% and the order association strategy by up to 33.5% relative to a first-come, first-served baseline. Sensitivity analysis reveals that the association strategy performs best with fewer picking stations and lower rack capacity, whereas the similarity strategy is superior in systems with more stations or higher rack capacity. The findings offer practical guidance for the design and operation of intelligent warehousing systems. Full article
(This article belongs to the Section Applied Industrial Technologies)
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13 pages, 1264 KiB  
Article
Decision Tree Modeling to Predict Myopia Progression in Children Treated with Atropine: Toward Precision Ophthalmology
by Jun-Wei Chen, Chi-Jie Lu, Chieh-Han Yu, Tzu-Chi Liu and Tzu-En Wu
Diagnostics 2025, 15(16), 2096; https://doi.org/10.3390/diagnostics15162096 - 20 Aug 2025
Abstract
Background/Objectives: Myopia is a growing global health concern, especially among school-aged children in East Asia. Topical atropine is a key treatment for pediatric myopia control, but individual responses vary, with some children showing rapid progression despite higher doses. This retrospective observational study aims [...] Read more.
Background/Objectives: Myopia is a growing global health concern, especially among school-aged children in East Asia. Topical atropine is a key treatment for pediatric myopia control, but individual responses vary, with some children showing rapid progression despite higher doses. This retrospective observational study aims to develop an interpretable machine learning model to predict individualized treatment responses and support personalized clinical decisions, based on data collected over a 3-year period without a control group. Methods: A total of 1545 pediatric eyes treated with topical atropine for myopia control at a single tertiary medical center are analyzed. Classification and regression tree (CART) is constructed to predict changes in spherical equivalent (SE) and identify influencing risk factors. These factors are mainly received treatments for myopia including atropine dosage records, treatment duration, and ophthalmic examinations. Furthermore, decision rules that closely resemble the clinical diagnosis process are provided to assist clinicians with more interpretable insights into personalized treatment decisions. The performance of CART is evaluated by comparing with the benchmark model of least absolute shrinkage and selection operator regression (Lasso) to confirm the practicality of CART usage. Results: Both the CART and Lasso models demonstrated comparable predictive performance. The CART model identified baseline SE as the primary determinant of myopia progression. Children with a baseline SE more negative than −3.125 D exhibited greater myopic progression, particularly those with prolonged treatment duration and higher cumulative atropine dosage. Conclusions: Baseline SE has been identified as the key factor affecting SE difference. The generated decision rules from CART demonstrate the use of explainable machine learning in precision myopia management. Full article
22 pages, 1706 KiB  
Review
Integrating Precision Medicine and Digital Health in Personalized Weight Management: The Central Role of Nutrition
by Xiaoguang Liu, Miaomiao Xu, Huiguo Wang and Lin Zhu
Nutrients 2025, 17(16), 2695; https://doi.org/10.3390/nu17162695 - 20 Aug 2025
Abstract
Obesity is a global health challenge marked by substantial inter-individual differences in responses to dietary and lifestyle interventions. Traditional weight loss strategies often overlook critical biological variations in genetics, metabolic profiles, and gut microbiota composition, contributing to poor adherence and variable outcomes. Our [...] Read more.
Obesity is a global health challenge marked by substantial inter-individual differences in responses to dietary and lifestyle interventions. Traditional weight loss strategies often overlook critical biological variations in genetics, metabolic profiles, and gut microbiota composition, contributing to poor adherence and variable outcomes. Our primary aim is to identify key biological and behavioral effectors relevant to precision medicine for weight control, with a particular focus on nutrition, while also discussing their current and potential integration into digital health platforms. Thus, this review aligns more closely with the identification of influential factors within precision medicine (e.g., genetic, metabolic, and microbiome factors) but also explores how these factors are currently integrated into digital health tools. We synthesize recent advances in nutrigenomics, nutritional metabolomics, and microbiome-informed nutrition, highlighting how tailored dietary strategies—such as high-protein, low-glycemic, polyphenol-enriched, and fiber-based diets—can be aligned with specific genetic variants (e.g., FTO and MC4R), metabolic phenotypes (e.g., insulin resistance), and gut microbiota profiles (e.g., Akkermansia muciniphila abundance, SCFA production). In parallel, digital health tools—including mobile health applications, wearable devices, and AI-supported platforms—enhance self-monitoring, adherence, and dynamic feedback in real-world settings. Mechanistic pathways such as gut–brain axis regulation, microbial fermentation, gene–diet interactions, and anti-inflammatory responses are explored to explain inter-individual differences in dietary outcomes. However, challenges such as cost, accessibility, and patient motivation remain and should be addressed to ensure the effective implementation of these integrated strategies in real-world settings. Collectively, these insights underscore the pivotal role of precision nutrition as a cornerstone for personalized, scalable, and sustainable obesity interventions. Full article
(This article belongs to the Section Nutrition and Public Health)
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