Understanding stakeholder perceptions of waste management performance at Universitas Pertamina: An exploratory factor and cluster analysis approach

Predicting the settlement behavior of jet-grout column groups in reclaimed coastal areas remains a significant geotechnical challenge, as conventional models do not capture the complex interaction between isolated stiff columns and the compliance of the composite system under wide-area loading. This study presents a field-calibrated analytical approach that reconciles single-column mechanics with full-scale group performance at a port terminal founded on highly compressible, liquefaction-prone marine backfill improved by 800 mm jet-grout columns. An extensive field-testing program—including cone penetration tests (CPTs), single-column load tests (SCLTs), and surface loading tests (SLTs)—was conducted. SCLT results revealed an elastic modulus exceeding 10 GPa, and CPT data confirmed up to a 250% increase in inter-column soil tip resistance. However, SLTs under an 85 kPa operational load yielded a back-calculated system stiffness of approximately 105 MPa, which is drastically lower than the theoretical unit cell prediction of 933 MPa. This empirical relation demonstrates that unit cell models fundamentally overestimate jet-grout group stiffness. Rather than proposing a site-specific static mobilization factor (β ≈ 0.11), this study introduces a novel, adaptive methodology. By systematically integrating single-column rigidity, group interaction, and stress transfer mechanics into untreated soil, this framework establishes a robust paradigm for accurately predicting composite stiffness and settlements across diverse geotechnical conditions.

Background: Understanding determinants of quality of life (QoL) in older adults is crucial in aging societies. Intrinsic capacity (IC), combining physical and mental capacities, may influence QoL changes, but evidence on specific IC domains and QoL is limited. This study examines associations between IC domains and two-year QoL changes in older Europeans, focusing on sex and regional differences. This study integrates factor and network analytical approaches to examine IC as a multidimensional system.Methods: Data from 11,493 adults aged ≥50 from 13 European countries in SHARE Waves 5 and 6 (2013–2015) were analyzed. IC was operationalized across five domains: mobility, cognition, psychological well-being, sensory function, and vitality. Exploratory factor analysis validated IC’s multidimensional structure. Network analysis assessed domain interrelations and links to QoL (CASP-12). Sex and regional differences were explored via stratified analyses and ANOVA.Results: IC domains formed a coherent multidimensional construct. Psychological well-being and mobility showed the strongest associations with QoL. Depressive symptoms and fatigue correlated negatively with CASP-12 (r = −0.284 and −0.324, p < 0.001). Cognitive and mobility domains had weaker but significant links. Over two years, modest IC declines paralleled QoL changes. Women and individuals in Southern and Eastern Europe exhibited greater IC deficits and lower QoL.Conclusions: Intrinsic capacity significantly influences short-term QoL changes in older Europeans. Psychological and mobility domains are key targets for interventions. Addressing sex and regional disparities in IC may improve well-being and reduce inequalities in aging populations.

The cooperative multiagent reinforcement learning (MARL) has been widely used in many practical applications. Despite its success, a fundamental issue arises in MARL that agents face the dilemma of whether to select the best action to maximize rewards or to acquire more information collectively by exploring the novel states/actions due to partial observability. To solve this issue, existing methods merge exploration and exploitation methods. However, these methods are always suboptimal and may lead to failure in finishing tasks. In this article, we theoretically prove the existence of a latent state that can guarantee the optimal individual and global policies. Moreover, we prove that such a latent state can be approximately obtained by local observations. Based on the analysis, we propose a method named unified MARL (UMARL), which is a weighted value function factorization approach unifying exploitation and exploration in one framework. Specifically, we design the agent representation network (ARN) and individual weighting networks (IWNs) to learn agents' unified representations and weights of credit. Moreover, a latent state regularizer (LSR) is designed to encourage agents' representations to approximate the latent state. Extensive experiments show that UMARL can achieve superior performance compared with 12 state-of-the-art methods on $m$ -step matrix game, level-based foraging (LBF), StarCraft II, and Google research football (GRF). The source code is available at: https://github.com/CrazyBayes/UMARL.

This piece is a personal and evidence-informed perspective on the current status and future direction of addiction psychiatry in the UK, based on my 30 years as a clinical academic addiction psychiatrist. Alcohol and drug addictions are associated with immense burdens to the individual and society, and this is likely to endure. Challenges facing addiction services to overcome include under-investment, changes in workforce composition with less psychiatric input, improving integration with mental and physical health services as well as stigma. Supporting those with addiction requires a multidisciplinary approach and consideration of neurobiological, psychological and social factors. The progress and limitations in treatment development are reviewed, noting that psychological therapies remain the mainstay, while relatively few new pharmacological treatments have become available in recent decades. Emerging approaches, including novel pharmacological targets, psychedelics and neuromodulation show promise but require larger, better-designed trials, particularly in clinically representative populations with polydrug use and comorbid mental illness. Neuroimaging is highlighted as a key tool for understanding mechanisms, identifying biomarkers, and accelerating treatment development. To drive innovation and ultimately improve outcomes for people affected by addiction senior strategic leadership, robust research infrastructure, global collaboration, reduced stigma, and a research-capable workforce is required.

To investigate the association between daily toothbrushing and hand washing practices among Brazilian adolescent students. This cross-sectional study analysed data from the 2019 Brazilian National School-Based Health Survey. The sample comprised 120,054 schoolchildren aged 13-17 who completed an electronic questionnaire at school. The outcome variable was 'low toothbrushing frequency' (< twice/day). The independent variables were hand washing frequency before eating, after using the toilet, and with soap. The covariates were the adolescents' sociodemographic characteristics: sex, age, colour/race and their mother's level of education. Data analysis included Chi-square tests with Rao-Scott correction and logistic regression for complex samples. After adjustments, adolescents who rarely/sometimes and those who never washed their hands before eating were more likely to have a low brushing frequency (< twice/day) than those who most of the time/always washed their hands before eating. Similar results were found for hand washing after using the toilet and for washing with soap. Associations were found between oral and hand hygiene practices. Regardless of their sociodemographic characteristics, adolescents who reported infrequent hand washing were more likely to brush their teeth less frequently. These findings reinforce the importance of adopting a common risk factors approach to oral and general health in school health programmes.

This study is devoted to the analysis of symbolic 2-plithogenic limits and the development of systematic procedures for their computation. In particular, the plithogenic factorization approach and the plithogenic rationalization approach are examined as effective techniques for evaluating such limits. The accuracy of the obtained expressions is further confirmed through the application of L’Hôpital’s rule. Additionally, several special stats of symbolic 2-plithogenic limits, including trigonometric forms, are established and analyzed. The theoretical findings are supported and clarified by solving carefully selected numerical examples.

• QCorroFFS-III: A plug-in for fitness for service Level 3 assessment of pipes. • Handles pipe geometry, corrosion mapping, and meshing automatically. • Uses Mapped Field for precise corrosion modeling in shell elements. • Speeds up Level 3 fitness for service assessment and lowers cost. Pipeline integrity management is critical to ensuring the safe, reliable, and cost-effective operation of industrial systems. Operating under high pressures and in corrosive environments, pipelines are highly susceptible to mechanical damage and corrosion. Inadequate fitness-for-service (FFS) assessments and irregular inspections can result in severe consequences, including system failures, costly repairs, unplanned shutdowns, and even the replacement of critical assets. To address these challenges, this study introduces an innovative plug-in named QCorroFFS-III, developed for Level 3 FFS assessments of corroded pipelines using the Remaining Strength Factor (RSF) approach. The plug-in features advanced capabilities, including automated pipeline geometry generation, material selection based on the limit load method, implementation of corrosion maps using the Mapped Field technique, application of internal, external, and thrust pressures, as well as automated meshing. By streamlining complex analytical procedures, this tool significantly reduces the time and cost associated with FFS evaluations while enhancing technical accuracy. This article elaborates on the theoretical foundations underlying the design of the QCorroFFS-III plug-in and demonstrates its performance and effectiveness through several practical examples. The plug-in enables engineers to proactively and efficiently manage the pipeline lifecycle, ensuring production sustainability and economically safeguarding physical assets.

Introduction: Periodontal disease (PD) shares risk factors with NCDs. CRFA can enable integrated prevention but faces implementation barriers. Methods: Mixed-methods: KAP survey among 440 medical/dental practitioners and interviews; hospital-based cross-sectional analytical study with 600 participants categorized into NCD+PD, NCD only, PD only. Results: High awareness but limited CRFA implementation due to time/resources and lack of integrated systems. Shared predictors: age &gt;50, education, obesity, HbA1c ≥6.5%, triglycerides &gt;150 mg/dL. Poor oral hygiene predicted PD; family history and dyslipidaemia predicted NCDs. Conclusions: CRFA integration into NCD programs is recommended, with interdisciplinary training, infrastructure investment, and policy support to improve population health.

&lt;div&gt;Electric vehicle chassis integration control aims to improve vehicle handling and comfort. Previous studies encountered significant practical limitations, such as computational overhead in real-time execution scenarios. Designing effective and efficient algorithms for actuator coordination remains challenging. This article presents a synergetic controller for chassis coordination, combining fuzzy logic and stability region theory. First, the controller targets are the yaw rate and side slip angle, which are obtained from a highly accurate multi-body dynamic model. In addition, based on the generated fuzzy rules, the system calculates the required additional yaw moments for each actuator and optimizes their output. Then, the designed controller can distribute control effort optimally in real-time between braking and rear-wheel steering based on the stability status of the vehicle. Furthermore, a stability factor approach is used to formulate a dynamic safety strategy executed by the chassis. It helps to create the safety boundary of the vehicle and avoid excessive force and angle of execution. Finally, real-vehicle tests are conducted, and the experimental results and real-vehicle tests demonstrate significant improvements: steering wheel angle reduction by 10%, enhanced yaw stability (9% higher safety threshold) for the slalom test, and better elk testing performance (&amp;gt;2%). The proposed method offers practical, real-world applicability and provides valuable insights and a reference for yaw control research in the automotive industry.&lt;/div&gt;

Subject of study. The subject of the study is the theoretical and methodological approaches to assessing the economic sustainability of enterprises under conditions of an unstable external environment and intensification of crisis phenomena. Economic sustainability is considered as a complex multidimensional economic category that reflects the ability of an enterprise to maintain functional equilibrium, ensure continuity of economic activity, and adapt to changes in the internal and external environment. The aim of the study. The aim of the study is to substantiate theoretical and methodological foundations for assessing the economic sustainability of enterprises under crisis conditions and to justify the feasibility of applying a comprehensive assessment approach. The study focuses on revising traditional sustainability assessment methods, which are mainly based on financial indicators and do not fully reflect the systemic nature of enterprise functioning. Research methods. The methodological basis of the research includes general scientific and special methods of economic analysis, namely analysis and synthesis, comparison, systematization, abstraction, logical generalization, and structural analysis. These methods were used to analyze existing approaches to assessing economic sustainability and to identify their advantages and limitations in the context of crisis transformations. Results of work. Existing methods of assessing the economic sustainability of enterprises, including coefficient-based, integral, resource-based, factor, and expert approaches, are analyzed. Their strengths and limitations under crisis conditions are identified. The necessity of forming a comprehensive assessment approach that integrates financial, production, resource, managerial, and adaptive indicators of enterprise activity is substantiated. A structured system of indicators for assessing economic sustainability is proposed, which combines quantitative and qualitative characteristics of enterprise functioning and takes into account the impact of external destabilizing factors. Scope of application of results. The feasibility of applying comprehensive assessment as a tool for diagnosing the state of enterprises and forming well-grounded managerial decisions under crisis conditions is substantiated. The practical significance of the results lies in the possibility of using the proposed theoretical and methodological provisions to improve enterprise management systems, enhance adaptability, and ensure long-term economic sustainability.

This study explores the implementation and impact of a focused clinical daily safety huddle (FCDSH) as a non-technical intervention aimed at enhancing patient safety, reducing hospital-acquired pressure ulcers (HAPUs), and improving interprofessional collaboration. The project employed Kotter's eight-step change model to guide the implementation process and used Plan-Do-Study-Act (PDSA) cycles for iterative development. Stakeholder engagement, coaching leadership methodology, and a robust data collection process, including run charts, supported the intervention. The results demonstrated a significant increase in the number of days between reported pressure ulcers, with the ward achieving 142 days without an incident post intervention compared with a baseline average of 18.2 days. The findings highlight the effectiveness of FCDSHs in improving communication, fostering teamwork, and reducing preventable patient harm. This service evaluation contributes to the growing evidence base for human factors approaches and non-technical skills in clinical practice.

Mercury (Hg) emissions from open biomass burning (OBB) represent a significant component of global atmospheric Hg cycling. Previous estimates have relied on the emission factor (EF) approach, which carries substantial uncertainties due to its inherent limitations. Here, we developed a mass-balance model to re-estimate global Hg emissions from OBB during 2010-2019, enabling quantification of emissions from individual vegetation tissues. Our results indicate that global annual Hg emissions average 280 Mg yr-1 (ranging 93-803 Mg yr-1), including 129 Mg yr-1 from vegetation-derived combustion, 81 Mg yr-1 from litterfall combustion, and 70 Mg yr-1 from peatland fires. Emissions exhibit strong latitudinal and longitudinal variability, with hotspots in central Africa, the Indo-China Peninsula, and boreal Asia. By category, the largest contributors are Africa (49%) among continents, savannas (29%) among biomes, and leaves (58%) among vegetation-derived emissions. Seasonal peaks occur in January, March, and August, while total annual emissions remain relatively stable over the decade, despite extreme anomalies such as the 2015 Indonesian fires. This approach reveals distinct emission sources and high spatial heterogeneity, providing a more accurate and nuanced assessment of Hg emissions from global OBB.

This paper investigates a LPV-based output voltage mitigation method for the capacitive wireless power transfer (CPT) system employing a series Buck converter under varying mutual capacitance conditions. A linear parameter varying (LPV) model is utilized to represent the dynamics of the Buck converter under input voltage fluctuations. Based on the estimated model, a robust controller is designed using the normalized coprime factorization (NCF) approach to ensure system stability and performance. Experimental results demonstrate the effectiveness of the proposed method. The system achieves rapid voltage regulation and maintains stable output voltage under various disturbance frequencies and load conditions. These findings validate the robustness and adaptability of the proposed control strategy, offering a reliable solution for enhancing the performance of CPT systems.

Youth Alcohol, Tobacco, and Other Drug (ATOD) use rates in a municipal youth and family master plan.

Corrigendum to “Dealing with high dimensional multi-view data: A comprehensive review of non-negative matrix factorization approaches in data mining and machine learning” [Computer Science Review 58 (2025) 100788

The principal of generic product development is to match the critical quality attributes. Most of the time during complex product development, life cycle management; biowaiver, pre- and post-change approvals the significant efforts are made by scientist to match the drug release profile. In order to get the vivo bioequivalence testing waived based on in vitro performance of drug product; the dissolution testing is mostly act as a surrogate or performance indicator. Hence, assessment of similarity or equivalence of release profile is most critical aspect with respect to regulatory decision making. Available guideline defines the methodologies and acceptance criteria for same based on data structure e.g., application of mathematical and statistical model like similarity factor (F2), bootstrapped F2, model independent and model dependent approach etc. However, during regulatory review lot of discrepancies usually raise by regulators with respect to similarity demonstration like selection of proper methodology, define suitable acceptance criteria in case of high variability. Current article emphases on the visions behind regulatory expectations, with respect to dissolution profile comparison and highlights the prerequisites and answer the common question like how to choose the correct methodology, what are the limitations, way forward and regulatory expectations and alternative methodologies in order to evaluate the dissolution data statistically to make wise decision on in vitro equivalence. Overall, various approaches are available for dissolution similarity analysis. However, the intension of statistical comparability should be like; neither force the dissimilar product to pass the criteria, nor fail the product which are similar. This comprehensive review will enhance the overall understanding and help the formulation and biopharmaceutics scientists; how to ensure regulatory compliance during similarity evaluation.

Value factorization methods have become a standard tool for cooperative multi-agent reinforcement learning (MARL) in the centralized-training, decentralized-execution (CTDE) setting. QMIX (a monotonic mixing network for value factorization), in particular, constrains the joint action–value function to be a monotonic mixing of per-agent utilities, which guarantees consistency with individual greedy policies but can severely limit expressiveness on tasks with non-monotonic agent interactions. This work revisits this design choice and proposes Relaxed Monotonic QMIX (R-QMIX), a simple regularized variant of QMIX that encourages but does not strictly enforce the monotonicity constraint. R-QMIX removes the sign constraints on the mixing network weights and introduces a differentiable penalty on negative partial derivatives of the joint value with respect to each agent’s utility. This preserves the computational benefits of value factorization while allowing the joint value to deviate from strict monotonicity when beneficial. R-QMIX is implemented in a standard PyMARL (an open-source MARL codebase) and evaluated on the StarCraft Multi-Agent Challenge (SMAC). On a simple map (3m), R-QMIX matches the asymptotic performance of QMIX while learning substantially faster. On more challenging maps (MMM2, 6h vs. 8z, and 27m vs. 30m), R-QMIX significantly improves both sample efficiency and final win rate (WR), for example increasing the final-quarter mean win rate from 42.3% to 97.1% on MMM2, from 0.0% to 57.5% on 6h vs. 8z, and from 58.0% to 96.6% on 27m vs. 30m. These results suggest that soft monotonicity regularization is a practical way to bridge the gap between strictly monotonic value factorization and fully unconstrained joint value functions. A further comparison against QTRAN (Q-value transformation), a more expressive value factorization method, shows that R-QMIX achieves higher and more reliably convergent win rates on the challenging SMAC maps considered.

Efficient formation of user and/or item groups is crucial for maximising individual user satisfaction across various recommendation scenarios. Most of the existing models employ explicit clustering techniques to construct such groups. Although these methods provide a straightforward way of group formation, they fail to adapt to the dynamic nature of real-world groups. As a result, these systems often produce suboptimal recommendations, particularly in contexts where group composition and item relevance are constantly evolving. To address this, we propose a novel Deep Dynamic Group Learning model (DDGLM) that dynamically learns latent group structures for both users and items within a unified neural architecture. Unlike conventional matrix factorization approaches, the model introduces probabilistic soft group assignments using a temperature-scaled softmax over user- and item-specific logits. These group probabilities are projected through linear transformation layers to obtain group-aware representations of users and items. The model supports a wide range of recommendation settings by learning both personalized and group-level representations without relying on static group definitions. It handles both scalar and ordinal prediction tasks through mean squared error and smooth hinge loss, respectively. Experimental results show that the proposed approach effectively captures latent group dynamics and consistently outperforms traditional group-aware baselines across multiple recommendation scenarios.

We investigate the semileptonic decays Λ b → Λ c ℓ − ν ¯ ℓ (with ℓ = e , μ , τ ) within the framework of perturbative QCD. The six independent Λ b → Λ c transition form factors are first calculated in the low- q 2 region using the k T factorization approach. These are then extrapolated to the full physical q 2 range via the model-independent z expansion, incorporating recent lattice QCD results at high q 2 . Based on the obtained form factors, we compute the branching fractions of Λ b → Λ c ℓ − ν ¯ ℓ decays. Our prediction for the lepton flavor universality ratio, R Λ c = 0.2 9 − 0.11 + 0.12 , is slightly larger than the latest experimental measurement. In addition, we analyze several angular observables, including forward-backward asymmetries, lepton-side convexity parameters, and polarization asymmetries. These results offer valuable theoretical input for current and future experimental investigations of semileptonic heavy-to-heavy baryon transitions.