Search Results (997)

Container liner shipping companies operate within a complex environment where they must balance profitability and service reliability. Meanwhile, evolving regulatory policies, such as surcharges imposed on ships of a particular origin or type on specific trade lanes, introduce new operational challenges. This study addresses the heterogeneous ship routing and demand acceptance problem, aiming to maximize two conflicting objectives: weekly profit and total transport volume. We formulate the problem as a bi-objective mixed-integer programming model and prove that the ship chartering constraint matrix is totally unimodular, enabling the reformulation of the model into a partially relaxed MIP that preserves optimality while improving computational efficiency. We further analyze key mathematical properties showing that the Pareto frontier consists of a finite union of continuous, piecewise linear segments but is generally non-convex with discontinuities. A case study based on a realistic liner shipping network confirms the model’s effectiveness in capturing the trade-off between profit and transport volume. Sensitivity analyses show that increasing freight rates enables higher profits without large losses in volume. Notably, this paper provides a practical risk management framework for shipping companies to enhance their adaptability under shifting regulatory landscapes. Full article

Background/Objectives: Dental practitioners widely use dental implants to treat traumatic cases. Titanium implants are currently the most popular choice among dental practitioners and surgeons. The discovery of newer polymeric materials is also influencing the interest of dental professionals in alternative options. A comparative study between existing titanium implants and newer polymeric materials can enhance professionals’ ability to select the most suitable implant for a patient’s treatment. This study aimed to investigate material property advantages of high-performance thermoplastic biopolymers such as PEEK and PEKK, as compared to the time-tested titanium implants, and to find the most suitable and economically fit implant material. Methods: Three distinct implant material properties were assigned—PEEK, PEKK, and commercially pure titanium (CP Ti-55)—to dental implants measuring 5.5 mm by 9 mm, along with two distinct titanium (TI6AL4V) abutments. Twelve three-dimensional (3D) models of bone blocks, representing the mandibular right molar area with Osseo-integrated implants were created. The implant, abutment, and screw were assumed to be linear; elastic, isotropic, and orthotropic properties were attributed to the cancellous and cortical bone. Twelve model sets underwent a three-dimensional finite element analysis to evaluate von Mises stress and total deformation under 250 N vertical and oblique (30 degree) loads on the top surface of each abutment. Results: The study revealed that the time-tested titanium implant outperforms PEEK and PEKK in terms of marginal bone preservation, while PEEK outperforms PEKK. Conclusions: This study will assist dental practitioners in selecting implants from a variety of available materials and will aid researchers in their future research. Full article

Pupillometry is commonly used to evaluate cognitive effort, attention, and facial expression response, offering valuable insights into human performance. The combination of eye tracking and facial expression data under the iMotions platform provides great opportunities for multimodal research. However, there is a lack of standardized pipelines for managing pupillometry data on a multimodal platform. Preprocessing pupil data in multimodal platforms poses challenges like timestamp misalignment, missing data, and inconsistencies across multiple data sources. To address these challenges, the authors introduced a systematic preprocessing pipeline for pupil diameter measurements collected using iMotions 10 (version 10.1.38911.4) during an endoscopy simulation task. The pipeline involves artifact removal, outlier detection using advanced methods such as the Median Absolute Deviation (MAD) and Moving Average (MA) algorithm filtering, interpolation of missing data using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and mean pupil diameter calculation through linear regression, as well as normalization of mean pupil diameter and integration of the pupil diameter dataset with facial expression data. By following these steps, the pipeline enhances data quality, reduces noise, and facilitates the seamless integration of pupillometry other multimodal datasets. In conclusion, this pipeline provides a detailed and organized preprocessing method that improves data reliability while preserving important information for further analysis. Full article

Introduction: Congestive heart failure (CHF), a complex clinical syndrome characterized by the heart’s inability to pump blood effectively due to structural or functional impairments, is a growing public health concern, with profound implications for patients’ physical and emotional well-being. In India, the burden of CHF is rising due to aging demographics and increasing prevalence of lifestyle-related risk factors. Among the subtypes of CHF, heart failure with preserved ejection fraction (HFpEF), i.e., heart failure with left ventricular ejection fraction of ≥50% with evidence of spontaneous or provokable increased left ventricular filling pressure, and heart failure with reduced ejection fraction (HFrEF), i.e., heart failure with left ventricular ejection fraction of 40% or less and is accompanied by progressive left ventricular dilatation and adverse cardiac remodeling, may present differing impacts on health-related quality of life (HRQoL), i.e., an individual’s or a group’s perceived physical and mental health over time, yet comparative data remains limited. This study assesses HRQoL among CHF patients using the Minnesota Living with Heart Failure Questionnaire (MLHFQ), one of the most widely used health-related quality of life questionnaires for patients with heart failure based on physical and emotional dimensions and identifies sociodemographic and clinical variables influencing these outcomes. Methods: A cross-sectional analytical study was conducted among 233 CHF patients receiving inpatient and outpatient care at the Department of Cardiology at a quaternary care teaching hospital in coastal Karnataka in India. Participants were enrolled using convenience sampling. HRQoL was evaluated through the MLHFQ, while sociodemographic and clinical characteristics were recorded via a structured proforma. Statistical analyses included descriptive measures, independent t-test, Spearman’s correlation and stepwise multivariable linear regression to identify associations and predictors. Results: The mean HRQoL score was 56.5 ± 6.05, reflecting a moderate to high symptom burden. Patients with HFpEF reported significantly worse HRQoL (mean score: 61.4 ± 3.94) than those with HFrEF (52.9 ± 4.64; p < 0.001, Cohen’s d = 1.95). A significant positive correlation was observed between HRQoL scores and age (r = 0.428; p < 0.001), indicating that older individuals experienced a higher burden of symptoms. HRQoL also varied significantly across NYHA functional classes (χ² = 69.9, p < 0.001, ε² = 0.301) and employment groups (χ² = 17.0, p < 0.001), with further differences noted by education level, gender and marital status (p < 0.05). Multivariable linear regression identified age (B = 0.311, p < 0.001) and gender (B = –4.591, p < 0.001) as significant predictors of poorer HRQoL. Discussion: The findings indicate that patients with HFpEF experience significantly poorer HRQoL than those with HFrEF. Older adults and female patients reported greater symptom burden, underscoring the importance of demographic-sensitive care approaches. These results highlight the need for routine integration of HRQoL assessment into clinical practice and the development of comprehensive, personalized interventions addressing both physical and emotional health dimensions, especially for vulnerable subgroups. Conclusions: CHF patients, especially those with HFpEF, face reduced HRQoL. Key factors include age, gender, education, employment, marital status, and NYHA class, underscoring the need for patient-centered care. Full article

Accurate and efficient cloud detection is critical for maintaining the usability of optical remote sensing imagery, particularly in large-scale Earth observation systems. In this study, we propose FEMNet, a lightweight dual-branch network that combines state space modeling with convolutional encoding for multi-class cloud segmentation. The Mamba-based encoder captures long-range semantic dependencies with linear complexity, while a parallel CNN path preserves spatial detail. To address the semantic inconsistency across feature hierarchies and limited context perception in decoding, we introduce the following two targeted modules: a cross-stage semantic enhancement (CSSE) block that adaptively aligns low- and high-level features, and a multi-scale context aggregation (MSCA) block that integrates contextual cues at multiple resolutions. Extensive experiments on five benchmark datasets demonstrate that FEMNet achieves state-of-the-art performance across both binary and multi-class settings, while requiring only 4.4M parameters and 1.3G multiply–accumulate operations. These results highlight FEMNet’s suitability for resource-efficient deployment in real-world remote sensing applications. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

Serious games (SGs) have been widely employed in the digital preservation and transmission of architectural heritage. However, the key determinants and underlying mechanisms driving users’ continuance intentions toward ancient-architecture cultural heritage serious games (CH-SGs) have not been thoroughly investigated. Accordingly, a conceptual model grounded in the stimulus–organism–response (S–O–R) framework was developed to elucidate the affective and behavioral effects experienced by CH-SG users. Partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANNs) were employed to capture both the linear and nonlinear relationships among model constructs. By integrating sufficiency logic (PLS-SEM) and necessity logic (necessary condition analysis, NCA), “must-have” and “should-have” factors were identified. Empirical results indicate that cultural authenticity, knowledge acquisition, perceived enjoyment, and design aesthetics each exert a positive influence—of varying magnitude—on perceived value, cultural identification, and perceived pleasure, thereby shaping users’ continuance intentions. Moreover, cultural authenticity and perceived enjoyment were found to be necessary and sufficient conditions, respectively, for enhancing perceived pleasure and perceived value, which in turn indirectly bolster CH-SG users’ sustained use intentions. By creating an immersive, narratively rich, and engaging cognitive experience, CH-SGs set against ancient architectural backdrops not only stimulate users’ willingness to visit and protect heritage sites but also provide designers and developers with critical insights for optimizing future CH-SG design, development, and dissemination. Full article

Solving stiff partial differential equations with neural networks remains challenging due to the presence of multiple time scales and numerical instabilities that arise during training. This paper addresses these limitations by embedding the mathematical structure of implicit–explicit time integration schemes directly into neural network architectures. The proposed approach preserves the operator splitting decomposition that separates stiff linear terms from non-stiff nonlinear terms, inheriting the stability properties established for these numerical methods. We evaluate the methodology on Allen–Cahn equation dynamics, where interface evolution exhibits the multi-scale behavior characteristic of stiff systems. The structure-preserving architecture achieves improvements in solution accuracy and long-term stability compared to conventional physics-informed approaches, while maintaining proper energy dissipation throughout the evolution. Full article

Background/Objectives: Total knee arthroplasty (TKA) is commonly associated with postoperative muscle atrophy and weakness, while traditional rehabilitation is often limited by pain and patient compliance. Passive blood flow restriction (pBFR) training may offer a safe, low-threshold method to attenuate muscle loss in this early phase. This pilot study examined the feasibility, safety, and early effects of pBFR initiated during hospitalization on muscle mass, swelling, and functional recovery after TKA. Methods: In a prospective, single-blinded trial, 26 patients undergoing primary or aseptic revision TKA were randomized to either a control group (CON: sham BFR at 20 mmHg) or intervention group (INT: pBFR at 80% limb occlusion pressure). Both groups received 50 min daily in-hospital rehabilitation sessions for five consecutive days. Outcomes, including lean muscle mass (DXA), thigh/knee circumference, 6 min walk test (6 MWT), handgrip strength, and patient-reported outcomes, were assessed preoperatively and at discharge, six weeks, and three months postoperatively. Linear mixed models with Bonferroni correction were applied. Results: The INT group showed significant preservation of thigh circumference (p = 0.002), reduced knee swelling (p < 0.001), and maintenance of lean muscle mass (p < 0.01), compared with CON, which exhibited significant declines. Functional performance improved faster in INT (e.g., 6 MWT increase at T3: +23.7%, p < 0.001; CON: −7.2%, n.s.). Quality of life improved in both groups, with greater gains in INT (p < 0.05). No adverse events were reported. Conclusions: Initiating pBFR training on the first postoperative day is feasible, safe, and effective in preserving muscle mass and reducing swelling after TKA. These findings extend prior BFR research by demonstrating its applicability in older, surgical populations. Further research is warranted to evaluate its integration with standard rehabilitation programs and long-term functional benefits. Full article

The tensile strength of the umbilical cord (UC) is influenced by its composition—including collagen, elastin, and hyaluronan—contributing to its unique biomechanical properties. This experimental in vitro study aimed to evaluate the UC’s mechanical behavior under varying strain rates and to characterize its viscoelastic response. Twenty-nine UC specimens, each 40 mm in length, were subjected to uniaxial tensile testing and randomly assigned to three traction speed groups: Group A (n = 10) at 8 mm/min, Group B (n = 7) at 12 mm/min, and Group C (n = 12) at 16 mm/min. Four different parameters were analyzed: the ultimate tensile strength and its corresponding elongation, the elastic modulus defined as the slope of the linear initial portion of the stress–strain plot, and the elongation at the end of the test (at break). While elongation and elongation at break did not differ significantly between groups (one-way ANOVA), Group C showed a significantly higher ultimate tensile strength (p = 0.047). A linear relationship was observed between test speed and stiffness (elastic modulus), with the following regression equation: y = 0.3078e^4.425x. These findings confirm that the UC exhibits nonlinear viscoelastic properties and strain-rate-dependent stiffening, resembling non-Newtonian behavior. This novel insight may have clinical relevance during operative deliveries, where traction speed is often overlooked but may play a role in preserving cord integrity and improving neonatal outcomes. Full article

Federated learning (FL) struggles under non-IID client data when local models drift toward conflicting optima, impairing global convergence and performance. We introduce entropy-regularized federated optimization (ERFO), a lightweight client-side modification that augments each local objective with a Shannon entropy penalty on the per-parameter update distribution. ERFO requires no additional communication, adds a single-scalar hyperparameter $\lambda$, and integrates seamlessly into any FedAvg-style training loop. We derive a closed-form gradient for the entropy regularizer and provide convergence guarantees: under $\mu$-strong convexity and L-smoothness, ERFO achieves the same $O(1/T)$ (or linear) rates as FedAvg (with only $O\left(\lambda \right)$ bias for fixed $\lambda$ and exact convergence when ${\lambda }_t\to 0$); in the non-convex case, we prove stationary-point convergence at $O(1/T)$. Empirically, on five-client non-IID splits of the UNSW-NB15 intrusion-detection dataset, ERFO yields a +1.6 pp gain in accuracy and +0.008 in macro-F1 over FedAvg with markedly smoother dynamics. On a three-of-five split of PneumoniaMNIST, a fixed $\lambda$ matches or exceeds FedAvg, FedProx, and SCAFFOLD—achieving 90.3% accuracy and 0.878 macro-F1—while preserving rapid, stable learning. ERFO’s gradient-only design is model-agnostic, making it broadly applicable across tasks. Full article

Urogenital schistosomiasis, caused by Schistosoma haematobium and transmitted by Bulinus snails, affects approximately 190 million individuals globally and remains a major public health concern. Effective surveillance of snail vectors is critical for disease control, but traditional identification methods are time-intensive and require specialized expertise. Environmental DNA (eDNA) detection using qPCR has emerged as a promising alternative for large-scale vector surveillance. To prevent eDNA degradation, benzalkonium chloride (BAC) has been proposed as a preservative, though its efficacy with schistosomiasis snail vectors has not been evaluated. This study tested the impact of BAC (0.01%) on the stability of Bulinus truncatus eDNA under simulated field conditions. Water samples from aquaria with varying snail densities (0.5–30 snails/L) were stored up to 42 days with BAC. eDNA detection via qPCR and multivariable linear mixed regression analysis revealed that BAC enhanced eDNA stability. eDNA was detectable up to 42 days in samples with ≥1 snail/L and up to 35 days at 0.5 snails/L. Additionally, a positive correlation between snail density and eDNA concentration was observed. These findings support the development of robust eDNA sampling protocols for field surveillance, enabling effective monitoring in remote areas and potentially distinguishing between low- and high-risk schistosomiasis transmission zones. Full article

The design of emulsions at the nanoscale is a significant application of nanotechnology. For spherical droplets and a given volume of dispersed phase, the nanometre size of droplets inversely increases the total area, $A={\displaystyle \frac{3V}{r}}$, allowing greater contact with organic and inorganic materials during application. In topical applications, not only is cell contact increased, but also permeability in the cell membrane. Nanoemulsions typically achieve kinetic stability rather than thermodynamic stability, so their commercial application requires reasonable resistance to flocculation and coalescence, which can be affected by temperature changes. Therefore, their thermoresponsive characterisation becomes relevant. In this work, we analyse this response in an O/W nanoemulsion of Palmarosa for antibacterial purposes that has already shown stability for one year at controlled room temperature. We now study hysteresis processes and the behaviour of the statistical distribution in droplet size by Dynamic Light Scattering, obtaining remarkable stability under temperature changes up to 50 °C. This includes a maintained chemical composition observed using Fourier Transform Infrared Spectroscopy and the preservation of antibacterial properties analysed through optical density tests on cultures and the Spread-Plate technique for bacteria colony counting. We obtain practically closed hysteresis curves for some tracers of droplet size distributions through controlled thermal cycles between 10 °C and 50 °C, exhibiting a non-linear behaviour in their distribution. In general, the results show notable physical, chemical, and antibacterial stability, suitable for commercial applications. Full article

Background/Objectives: Schoolteachers play a central role in shaping their students’ beliefs and attitudes towards oral health. Our aim was to investigate the oral and periodontal health knowledge of schoolteachers in Riyadh and factors affecting this knowledge. Methods: Government schoolteachers from representative areas of Riyadh (n = 895) responded to a structured questionnaire. Descriptive statistics, t-tests, one-way analysis of variance, and multiple linear regression (p ≤ 0.05). Results: Teachers demonstrated good basic oral/periodontal health knowledge (mean score = 60.21 ± 10.68). Most knew that toothbrushing is necessary to preserve dental (78.66%) and periodontal (57.88%) health; that gingival bleeding (74.41%), swelling (64.25%), and abscess formation (52.96%) are signs of periodontal disease; about 63% identified dental biofilm as an etiologic factor, and 58% knew that periodontitis may cause gingival recession and influence systemic health (74.07%). However, only 38% knew that dental flossing is necessary to preserve periodontal health, and 66.03% believed that gingival health can be restored with a special toothpaste. Teachers who were female, older in age, worked in north Riyadh, and taught the intermediate stage demonstrated statistically significantly better knowledge than the other categories. Conclusions: The studied sample of schoolteachers possesses acceptable basic oral health knowledge but has inadequate knowledge of periodontal health. Factors influencing teachers’ knowledge were age, gender, region of work, and teaching stage. Full article

Deep learning has advanced medical image segmentation, yet existing methods struggle with complex anatomical structures. Mainstream models, such as CNN, Transformer, and hybrid architectures, face challenges including insufficient information representation and redundant complexity, which limit their clinical deployment. Developing efficient and lightweight networks is crucial for accurate lesion localization and optimized clinical workflows. We propose the VML-UNet, a lightweight segmentation network with core innovations including the CPMamba module and the multi-scale local supervision module (MLSM). The CPMamba module integrates the visual state space (VSS) block and a channel prior attention mechanism to enable efficient modeling of spatial relationships with linear computational complexity through dynamic channel-space weight allocation, while preserving channel feature integrity. The MLSM enhances local feature perception and reduces the inference burden. Comparative experiments were conducted on three public datasets, including ISIC2017, ISIC2018, and PH2, with ablation experiments performed on ISIC2017. VML-UNet achieves 0.53 M parameters, 2.18 MB memory usage, and 1.24 GFLOPs time complexity, with its performance on the datasets outperforming comparative networks, validating its effectiveness. This study provides valuable references for developing lightweight, high-performance skin lesion segmentation networks, advancing the field of skin lesion segmentation. Full article