Search Results (78)

Introduction: Advances in pediatric oncology have transformed cancer into a condition with chronic and long-term developmental consequences. While survival rates have improved significantly, the literature on psychosocial outcomes remains fragmented and inconsistent, with a notable lack of person-centered analyses that account for the heterogeneity of adaptive trajectories. Current evidence fails to explain why survivors with similar clinical profiles exhibit divergent psychological phenotypes, particularly regarding the late effects of multimodal treatments. The aim of this study was to identify heterogeneous psychosocial profiles among adolescent cancer survivors and to examine their associations with treatment complexity and quality of life. Materials and Methods: This cross-sectional study included 165 adolescents aged 12–18 years (mean age: 14.64 years) who were in clinical remission following oncological treatment. Standardized assessment tools were used: the Children’s Depression Inventory 2 (CDI-2™) to measure depressive symptoms, the KIDSCREEN-10 index to assess health-related quality of life (HRQoL), and a scale evaluating satisfaction across 14 life domains. Adaptive profiles were identified using a Two-Stage Cluster Procedure, and risk factors were examined using multinomial logistic regression. Results: Four clusters were identified in the study population: a depressive–dysphoric profile, an anhedonic-withdrawn profile, a highly adaptive profile, and a mixed (struggling) profile. Treatment complexity was identified as a significant independent predictor of membership in the high-distress (depressive) cluster. While each additional therapeutic modality beyond standard chemotherapy was associated with a markedly increased risk (OR = 8.91; p < 0.001), the relatively wide confidence interval (95% CI: 3.27–24.31) suggests that the exact magnitude of this effect should be interpreted with caution. The high lower bound of the interval (3.27), however, strongly supports the directional association of cumulative iatrogenic burden with psychological adaptation. Subjective quality of life functioned as a protective factor against depressive symptoms (OR = 0.57); however, paradoxically, higher self-reported quality of life increased the likelihood of classification into the anhedonic group (OR = 1.81). This divergence between high self-reported HRQoL and social withdrawal potentially suggests a ‘well-being paradox’. It is hypothesized that standard HRQoL instruments may primarily capture physical remission and relief from acute somatic symptoms, potentially masking underlying social–emotional deficits. This suggests that HRQoL scores in survivors should be interpreted with caution and complemented by specific affective screenings. Conclusions: The absence of a uniform pattern of psychological response to cancer among adolescent survivors supports the validity of a patient-centered approach. The burden associated with intensive multimodal treatment significantly increases the likelihood of full-syndrome depression during adolescence. Moreover, the identification of a cluster suggestive of anhedonic and socially withdrawn features highlights the limitations of standard screening tools focused solely on the detection of overt sadness. This heterogeneity underscores the need for personalized psycho-oncological care and the implementation of intensified monitoring for patients at high medical risk. Full article

The transition from Industry 4.0 to Industry 5.0 represents a shift toward human-centric work systems that prioritize employee well-being and meaningful human–technology collaboration. Research examining employee well-being, job satisfaction, and organizational performance in Industry 5.0 contexts remains conceptually fragmented and methodologically heterogeneous, limiting cumulative theoretical development. This study addresses how fragmented insights on employee well-being, job satisfaction, and organizational performance can be conceptually integrated through a human-centric operational excellence perspective. Accordingly, an integrative review was conducted using PRISMA 2020-guided screening and reporting procedures, resulting in a final sample of 84 peer-reviewed studies published between 2015 and 2025. The literature was analyzed through inductive thematic synthesis to identify recurring patterns, tensions, and conceptual configurations within digitally mediated work environments. The findings indicate that employee well-being and job satisfaction Industry 5.0 contexts are multidimensional, dynamic, and frequently paradoxical: digital technologies simultaneously function as enablers of autonomy, meaningful work, and cognitive support while also generating technostress, algorithmic control, and cognitive overload. Relationships between well-being, satisfaction, and performance appear non-linear and context-dependent, with high performance sometimes coexisting with employee strain. In this sense, this study contributes to the Industry 5.0 literature by advancing human-centric operational excellence (HCOE) as an interpretive lens for reconciling human–technology tensions without presuming linear causal relationships. Full article

This study examines educator staffing instability in New Mexico by analyzing certified staffing rosters from the New Mexico Public Education Department (2014–2019) alongside a statewide Teacher Working Conditions Survey (N = 4481). The goal was to identify which working conditions districts can influence and to highlight practical strategies for improving teacher retention. Headcount and vacancy analyses show that instability persisted even during periods of workforce growth: vacancies remained high despite increases in educator numbers, reflecting replacement churn and role-specific shortages rather than an overall teacher supply deficit. Vacancy patterns also fluctuated year to year, indicating a labor market responsive to shocks rather than moving toward stability. Turnover estimates further show that educator loss is structural and cumulative across districts, not episodic. The survey findings indicate that job satisfaction varies by grade band, while years of experience do not, suggesting turnover risk is driven more by organizational context than career stage. District-level regression models support this: compensation, leadership instability, student behavior and discipline conditions, and class size predict both annual and long-term turnover. Time for planning, preparation, and collaboration uniquely predicts long-term retention, while administrative discipline support is more strongly associated with annual exits. Overall, the findings highlight retention—not supply—as the central challenge. Full article

Background: Breast augmentation is traditionally performed under general anesthesia, but tumescent local anesthesia (TLA) offers advantages in terms of rapid recovery and reduced risks. This study presents the largest European series on the use of TLA for breast augmentation, analyzing the cumulative results of 16 years of experience. Methods: A multicenter retrospective analysis was conducted on 1644 consecutive patients (982 subglandular and 662 subpectoral) between 2008 and 2024. All procedures were performed under TLA with conscious sedation without the use of general anesthesia. The tumescent solution consisted of 25 mL of 2% lidocaine, 8 mEq of sodium bicarbonate, and 1 mL of epinephrine (1 mg/1 mL) in 1000 mL of 0.9% saline solution. Infiltration protocols differed between groups: the subglandular approach utilized a single-plane technique (mean 589 mL per breast), whereas the subpectoral approach required a two-stage process (pre-fascial and retromuscular) with a higher mean volume (770 mL per breast). Intraoperative parameters, complication rates, and patient-reported outcomes (BREAST-Q) were analyzed. Statistical comparisons between the two surgical planes were performed using Independent Samples T-tests. Results: The procedure was successfully completed under TLA in 100% of cases, with no conversions to GA. The subpectoral approach was associated with significantly higher mean operating times (141 ± 11.2 min vs. 90.3 ± 11 min; p < 0.001) and TLA solution volumes (770 ± 16.1 mL vs. 589 ± 53.6 mL; p < 0.001). The overall major complication rate was 4.74%, with a significantly higher incidence of hematoma in the subpectoral group compared to the subglandular group (3.51% vs. 1.83%; p = 0.015). Regarding severe capsular contracture (Baker III–IV), although a slightly higher incidence was observed in the subpectoral cohort compared to the subglandular group (2.11% vs. 1.22%), this difference was not statistically significant (p = 0.155). Patient satisfaction via Breast-Q was high, with dissatisfaction exclusively linked to implant dislocation. Conclusions: This 16-year cumulative analysis validates TLA as a safe, effective, and reproducible alternative to general anesthesia for both subglandular and subpectoral breast augmentation. While the subpectoral plane entails longer surgical times and a slightly higher risk of minor complications, the TLA protocol ensures excellent pharmacological safety and rapid functional recovery, supporting its use in modern outpatient surgical settings. Full article

Although organisational climate is increasingly examined, explicit modelling of demographic interaction effects remains comparatively underrepresented. A search strategy was conducted (25 September 2025), and 358 records were identified and filtered in the Scopus and Covidence databases; subsequently, 60 peer-reviewed articles met the inclusion criteria following PRISMA-guided screening. R-project, reference to VOSviewer, and Biblioshiny were used to perform the bibliometric mapping to demonstrate three (3) large thematic clusters: (1) conceptual models with a focus on the Job Demands–Resources (JD–R) framework; (2) growing cross-sector and post-COVID literature; and (3) small but growing incorporation of interactive demographic variables (age, gender, tenure) other than control-variable treatment. The results show that organisational climate is always placed at the forefront as an important predictor of satisfaction, but intersectional demographic modelling is underdeveloped and geographically biased to Western and Asian factors. Yet improvements have been made in theoretical integration; however, a lack of constructs, methodological conservatism, and geographic skewness limit theoretical cumulation and practical translation. The proposed multi-factor model is conceptually derived from bibliometric patterns and requires empirical validation using CFA, SEM, and multilevel modelling. However, organisations should integrate satisfaction policies that reflect diverse demographic and contextual realities, rather than adopting a general approach. The study advances the model of employee satisfaction research by offering practical evidence and a theoretical framework to support the sustainability of industrial and organisational psychology. Full article

Background/Objectives: Chronic inflammation is a fundamental biological process underlying aging and frailty. We recently demonstrated that an anti-inflammatory diet, assessed using the Dietary Inflammatory Index (DII), was associated with serum high-sensitivity C-reactive protein levels and frailty incidence among community-dwelling older adults. The present study aimed to co-produce behavior change intervention promoting an anti-inflammatory diet by participatory action research with older adults. Particularly, increasing intake of dietary fiber was targeted as it represents a nutrient with the highest anti-inflammatory potential within the DII framework. Methods: Participants were community-dwelling older adults engaged in frailty checkup activity. Six co-production workshops were conducted between May 2022 and February 2023, integrating semi-structured group work and scientific evidence. Participant satisfaction was assessed after each session. Changes in dietary behavior were evaluated using DII score and dietary intake assessed by the Brief Self-Administered Diet History Questionnaire (BDHQ). Results: A cumulative total of 66 participants was involved (mean age, 73.7 ± 4.8 years; 80.0% women). When compared before and after co-production workshops, total DII scores and DII scores calculated by anti-inflammatory nutrients significantly decreased (p = 0.031 and p = 0.020, respectively). Dietary fiber intake also significantly increased following the workshop (p = 0.044). Among dietary fiber-rich food groups, mushroom consumption showed a particularly significant increase (p = 0.048). Conclusions: Co-production workshops integrating group work and scientific evidence were effective in promoting behavioral changes toward an anti-inflammatory diet among community-dwelling older adults. This developed intervention may represent a feasible and practical dietary strategy for frailty prevention in community settings. Full article

Background/Aim: Cardiovascular disease (CVD) represents a relevant global public health challenge with dyslipidemia as a major modifiable cardiovascular risk factor (CVRF). Recent advances have introduced injectable lipid-lowering therapies (LLT). Their clinical effectiveness in real-world practice seems to depend not only on pharmacological efficacy but also on patients’ acceptance, adherence, and persistence, influenced directly by perceived barriers and facilitators. The main objective of this scoping review is to map the barriers and facilitators related to the use of novel injectable LLTs among adult patients with dyslipidemia or CVD. Methods: This review was conducted in accordance with JBI methodology and reported according to Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for scoping reviews (PRISMA-ScR); pre-registration on Open Science Framework (OSF) was performed. A search was conducted in MEDLINE from PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL) from EBSCOhost, and Google Scholar up to June 2025. Eligible studies included qualitative, quantitative, mixed-methods, and review papers involving adult patients with dyslipidemia who reported experiences, perceptions or challenges related to the use of injectable LLT in any healthcare or community setting worldwide. Two reviewers independently screened studies, selected and extracted data. Results: Out of 665 records identified, 7 studies met the inclusion criteria. Patients’ adherence to injectable LLTs is shaped by psychological fears, prior negative experiences, and perceived efficacy. Satisfaction increases when patients feel supported and informed. Convenience, self-administration, and motivational meaning facilitate persistence. Organizational support and economic accessibility further influence uptake, highlighting that adherence depends on both patient experience and structural factors. Conclusions: Patient acceptance and persistence with injectable LLT depends on a complex interplay of emotional, clinical, organizational and economic factors, beyond pharmacological efficacy alone. Fear of injections, previous statin-related experiences, administrative complexity, and high costs remain major barriers, while shared decision-making, trust in healthcare providers, perceived efficacy, regimen convenience, and supportive structures act as strong facilitators. Addressing these challenges requires multidimensional and multidisciplinary strategies for policy makers and clinical managers. Full article

Background: Treatment options for vulvar lichen sclerosus (VLS) remain limited; therefore, therapies that improve quality of life and reduce neoplastic risk are needed. Photodynamic therapy (PDT) is a potential option. This study aimed to evaluate quality of life and sexual function in patients treated according to the protocol used at our institution. Methods: Forty patients with refractory VLS underwent PDT using a 10% 5-aminolevulinic acid nanoemulsion (Ameluz^®) applied to lesions under an occlusive aluminum foil dressing. Patients received 1–6 sessions of 10 min illumination (LED: 37 J/cm², ~77 mW/cm²) at 4–6-week intervals. The Dermatology Life Quality Index (DLQI) and Female Sexual Function Index (FSFI) were used for assessment. Results: Thirty-seven participants answered DLQI, while 20 declared themselves to be sexually active and were included in the analysis. Greater number of PDT sessions was associated with a lower DLQI score (τ = −0.583; adjusted p < 0.001). The number of PDT sessions and the total FSFI score (p = 0.014), as well as desire (p = 0.016), arousal (p = 0.020), orgasm (p = 0.020), and satisfaction (p = 0.016) domains were significantly correlated. Age correlated positively with DLQI scores (p = 0.016), indicating greater disease burden in older patients. Longer disease duration was also associated with poorer quality of life (p = 0.020). Conclusions: PDT can be considered an effective treatment for patients with VLS refractory to standard topical corticosteroid and calcineurin inhibitor therapies when delivered using a refined, patient-centered protocol. This optimized approach used in our institution is based on short irradiation time and precise light delivery, providing a favorable balance between therapeutic efficacy, patient comfort, and treatment feasibility. Our findings also suggest that the cumulative number of PDT sessions is a key factor for clinical response. Further studies should address long-term outcomes. Full article

Optimizing long-term user satisfaction in sequential recommender systems is a critical challenge. Offline reinforcement learning (RL) offers a promising solution by learning recommendation policies from historical interaction logs without incurring the high costs of online exploration. However, offline RL suffers from severe distribution shift: the learned policy often overestimates the value of out-of-distribution (OOD) items, leading to unreliable recommendations and compromising user satisfaction. To address this issue, we propose a novel framework known as the Q-Learning Regularized Decision Transformer (QRDT). Built upon the Decision Transformer architecture, QRDT models recommendations as a sequence prediction task to capture complex user interest dynamics. To mitigate distribution shift, the QRDT integrates Kullback–Leibler (KL) divergence and maximum entropy regularization into the Q-value function, enabling conservative long-term value estimation while encouraging diverse exploration within the logged data distribution. Extensive experiments on four real-world Amazon e-commerce datasets (CDs, Clothing, Cellphones, and Beauty) demonstrate that the QRDT achieves competitive performance and outperforms the PGPR baseline in most scenarios. Specifically, the proposed method yields improvements of 2.99% in Hit Rate (HR), 2.19% in Normalized Discounted Cumulative Gain (NDCG), 0.94% in Recall, and 0.84% in Precision, verifying the effectiveness of our regularization approach. Full article

To address the challenges of coordinated optimization in building integrated energy systems (IES) under the dual-carbon targets—characterized by strong multi-energy coupling, significant uncertainty in renewable generation, and stringent safety constraints—a novel safe deep reinforcement learning algorithm, Safe-DDPG, is proposed. Traditional deep reinforcement learning methods often suffer from high constraint-violation risk and limited policy reliability due to coupled objectives in building IES optimization. To overcome these limitations, a dual-channel critic architecture is designed to independently evaluate and decouple economic and safety objectives. In addition, a dynamic safety–penalty mechanism based on logarithmic barrier functions is introduced, together with an adaptive exploration strategy, enabling dynamic balancing between economic cost and constraint satisfaction according to system states during training. Experimental results demonstrate that, compared with mainstream algorithms, Safe-DDPG achieves substantial improvements across multiple key performance indicators: safety violations are reduced by up to 96.7%, average daily operating costs decrease by 18.5%, and cumulative rewards increase by more than 30%. Ablation studies further confirm the effectiveness and necessity of each core component. Two DRL methods from reference papers are reproduced, and their performance is compared with the proposed method in the existing experimental results, showing that the proposed method has significant advantages in reward value and economic cost. This work provides a safe, reliable, and efficient reinforcement-learning-based approach for optimization and scheduling of building energy systems under complex operational constraints. Full article

Natural gas pipeline networks are subject to supply instability due to random fluctuations. Current forecasting methodologies often suffer from limited accuracy, inadequate uncertainty quantification, and poor integration with dynamic network evaluation mechanisms. To address these challenges, this study presents an integrated framework that bridges short-term demand forecasting with supply assurance assessment. A deep learning model that combines a graph convolutional network and a bidirectional long short-term memory network is developed to produce accurate 72 h demand forecasts. Forecasting uncertainty is quantified using the cumulative distribution function. Based on the probabilistic forecasts, a supply assurance evaluation model is constructed that accounts for the dynamic regulation capability of line pack. The comprehensive indicator system incorporates key metrics such as user satisfaction and the line pack demand−storage ratio. A case study was conducted with the proposed method based on a regional real-world pipeline network. The results demonstrate that the proposed model outperforms conventional baselines, achieving a mean absolute percentage error of less than 1%. The uncertainty quantification captures the risk probability associated with demand fluctuations. The proposed evaluation method identifies vulnerable sections and assesses supply margins under various scenarios, thus providing effective decision support for operational scheduling and supply assurance. Full article

Space–Air–Ground–Sea Integrated Networks (SAGSINs) are emerging as a key enabling architecture for broadband maritime connectivity, where heterogeneous access tiers (shore, aerial, and satellite) jointly support delay-sensitive and mission-critical uplink traffic such as alarms, telemetry, and surveillance video. However, uplink resource scheduling in maritime SAGSINs remains challenging due to time-varying channels, locally bursty traffic, and intense contention, while centralized optimization is ill-suited, as global information collection is often delayed, incomplete, and inconsistent over long-haul maritime links. Therefore, this paper investigates distributed uplink scheduling in maritime SAGSINs, where maritime nodes jointly select the access tier, spectrum slice, and transmit power under interference, spectrum, deadline, and energy constraints. Specifically, we formulate the uplink resource scheduling as a cumulative value of information (VoI) maximization problem, and develop a game-theoretic distributed multi-agent reinforcement learning algorithm, termed GTMARL. Therein, maritime nodes learn transmission policies from local observations, coordinated through congestion prices broadcast by access nodes. These prices are derived from Lagrangian relaxation and act as coordination signals that align individual decisions with global objectives. To ensure stable operation, a two-timescale mechanism is adopted, where maritime nodes make fast slot-level transmission decisions, while access nodes adapt and broadcast congestion prices on a slower timescale. Extensive experiments demonstrate that GTMARL achieves up to 90% of the idealized upper bound, significantly outperforming baselines in deadline satisfaction, throughput, delay, energy efficiency and fairness under varying traffic loads and network densities. Full article

Lexicographic preference trees (LP-Trees) provide a compact and expressive representation for modeling complex decision-making scenarios, yet measuring similarity between complete or partial structures remains a challenge. This study introduces PLPSim, a novel metric for quantifying alignment between partial lexicographic preference trees (PLP-Trees) and develops three coalition formation algorithms—HRECS1, HRECS2, and HRECS3—that leverage PLPSim to group agents with similar preferences. We further propose ContractLex and PriceLex protocols (comprising CLF, CFB, CFW, CFA, CFP) for coalition-based contract and pricing strategies, along with a new evaluation metric, F@Lex, which is designed to assess satisfaction under lexicographic preferences. To illustrate the framework, we generate a synthetic dataset (PLPGen) contextualized in a hybrid renewable energy market, where consumers’ PLP-Trees are aggregated and matched with suppliers’ tariff contracts. Experiments across 162 market scenarios, evaluated using Normalized Discounted Cumulative Gain (nDCG), Davies–Bouldin dispersion, and F@Lex, demonstrate that PLPSim-based coalitions outperform baseline approaches. The combination HRECS3 + CFP yields the highest consumer satisfaction, while HRECS3 + CFB achieves balanced satisfaction for both consumers and suppliers. While electricity tariffs and renewable energy contracts—static and dynamic—serve as the motivating example, the proposed framework generalizes to diverse multi-agent systems, offering a foundation for preference-driven coalition formation, adaptive policy design, and sustainable market optimization. Full article

Automated negotiation in multi-agent electronic commerce environments relies heavily on efficient and reliable matching mechanisms to connect negotiation participants. However, existing matching protocols often fail to ensure transaction security and user data privacy, while also lacking adaptability to dynamic negotiation contexts. To address these challenges, this study proposes a privacy-enhanced multi-agent matching optimization framework that integrates trust evaluation, privacy protection, and adaptive decision-making. First, a trust-based negotiation relationship network is constructed through complex network analysis to establish a secure and trustworthy negotiation environment. Second, a privacy-enhanced automated negotiation protocol is developed, employing the cumulative distribution function to transform sensitive data into probabilistic representations, thereby safeguarding user privacy without compromising data availability. Finally, a reinforcement learning algorithm is incorporated to optimize the matching process dynamically, using satisfaction as the reward function to achieve efficient and Pareto-optimal results. A series of experiments verify the framework’s effectiveness, demonstrating significant improvements in system robustness, adaptability, and matching accuracy. This study aims to provide a comprehensive solution that integrates trust network modeling, privacy protection, and adaptive matching optimization, serving as a valuable reference for the development of secure and intelligent automated negotiation platforms. Full article

To address the issues of imprecise user segmentation, inadequate handling of fuzzy evaluation information, and low recommendation accuracy in current electricity retail package recommendations, a novel recommendation method based on K-prototypes clustering and interval-valued intuitionistic fuzzy theory is proposed. First, a multi-dimensional user profile is constructed, incorporating five numerical tags—such as monthly average electricity consumption and monthly load factor—and two categorical tags: industry characteristics and value-added service demand. The K-prototypes algorithm is employed to cluster users, effectively resolving the profile distortion problem caused by the neglect of categorical features in traditional K-means clustering. Second, interval-valued intuitionistic fuzzy numbers are introduced to transform user linguistic evaluations into quantitative indicators. A projection measure-based model is established to objectively determine attribute weights, thereby eliminating subjective weighting bias. Finally, a comprehensive ranking of electricity retail packages is generated by integrating satisfaction levels of similar users and similar measures of new users. The recommendation performance is validated using Root Mean Square Error (RMSE), Kendall’s τ, Normalized Discounted Cumulative Gain (NDCG@5), and Discrimination Index (S). A case study involving users from a region in China demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) to 0.32, which is 31.25% lower than the next best traditional method (K-prototypes + equal weight clustering with RMSE = 0.48), accurately addresses the core demands of diverse user groups, significantly improves recommendation precision and user satisfaction, and exhibits substantial practical application value. Full article