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Simultaneous planning of liner ship speed optimization, scheduling and fleet deployment with container transhipment
No full textOwing to substantial growth in global waterborne trade volumes and changes in climate, shipping companies must enhance operational and energy efficiency. A multi-objective mixed-integer nonlinear programming (MINLP) model is proposed to optimize service schedules, fleet, vessel speed, and cargo flow, including transhipment operations. Innovative features of this research reside in the multi-objective model formulation that integrates these complex and crucial operational decisions of the maritime industry. This MINLP model presents a trade-off between economic and environmental aspects considering shipping time and shipping cost as the two conflicting objectives. Two evolutionary algorithms, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Online Clustering-based Evolutionary Algorithm (OCEA), are applied to attain the near-optimal solution. The results indicate that the proposed model can contribute to saving fuel costs, reducing emissions and finding trade-offs between shipping cost and time. Furthermore, the study reflects how shipping companies can use this model to make data-driven decisions in their operations
Primary Mediastinal Germ Cell Tumors: A Real-World Analysis of Clinical Characteristics, Treatment, and Survival Outcomes From Two Tertiary Cancer Centers in India
No full textPurpose
Mediastinal germ cell tumors (GCTs) are rare malignancies, predominantly affecting young males, with limited real-world data on treatment outcomes in India. This study aimed to evaluate the clinical characteristics, treatment patterns, and survival outcomes of patients with mediastinal GCTs, emphasizing the effectiveness of first-line chemotherapy regimens.
Methods
A retrospective analysis was conducted on 81 patients diagnosed with mediastinal GCTs at two tertiary cancer centers in India from 2005 to 2023. Data on demographics, histological subtypes, presenting symptoms, treatment regimens, and outcomes were collected. Kaplan-Meier analysis was used to calculate progression-free survival (PFS) and overall survival (OS).
Results
The median age at diagnosis was 26 years, with a male predominance (96.3%). Common histological subtypes included seminoma (34.3%) and yolk sac tumor (31.3%). First-line chemotherapy comprised bleomycin, etoposide, and cisplatin (BEP; 60.5%) and etoposide, ifosfamide, and cisplatin (VIP; 27.8%). Response rates included complete response (25.3%) and partial response (54.4%). After a median follow-up of 15 months, the median PFS and OS were not reached, with 2-year PFS and OS rates of 92.7% and 93.3%, respectively. Seminomas demonstrated better PFS compared with nonseminomatous GCTs (P = .004). Severe toxicities were observed in 48.1% of patients, with febrile neutropenia being the most common.
Conclusion
This study highlights the effectiveness of BEP and VIP chemotherapy in achieving high response rates and favorable survival outcomes in mediastinal GCTs. Early diagnosis, appropriate histological classification, and aggressive multimodal treatment strategies are essential for improving long-term outcomes in this predominantly young patient population. Further research is warranted to validate these findings and optimize therapeutic approaches
Prevalence of Germline Variants in Advanced Renal Cell Carcinoma in North India
No full textPurpose
Genetic predisposition plays an important role in the pathogenesis of renal cell carcinoma. The prevalence of pathogenic/likely pathogenic (P/LP) in patients with renal cell cancer (RCC) is highly variable, and close to 40% of these can be missed with the current testing guidelines.
Methods
This is a prospective study of all patients with metastatic RCC unselected for high-risk features, registered at our center between September 2023 and August 2024. Baseline clinicopathologic details were collected, and germline whole-exome sequencing was done on blood samples. Our aim was to determine the frequency of germline mutations in an unselected cohort of patients with metastatic RCC. Germline P/LP variants were visualized using cBioPortal, and chi-square and Mann-Whitney U tests were used to identify differences in patients with/without these variants.
Results
Out of 140 participants, P/LP variants in cancer-predisposition genes were detected in 20%, and 4.2% were in RCC-associated genes. Fumarate hydratase was the most common RCC-associated variant (2.8%), while WT1, BRCA1, BRIP1, and ATM (1.4% each) were the commonest non–RCC-associated variants. RCC-associated genes were more frequent in non–clear cell histology (P = .02); there was no difference in cancer predisposition genes on the basis of age, histology, or sex.
Conclusion
Patients with advanced RCC have a high prevalence of germline variants in both RCC-associated and non-RCC cancer-specific genes irrespective of the high-risk genetic features, signifying the importance of a baseline genetic evaluation in all patients with advanced RCC as it has implications for family screening and, in future, selection of therapy
Caregiver Burden and Quality of Life Among Parents of Children With Cancer: A Cross-Sectional Study
No full textBackground
Parents caring for children with cancer face substantial physical, emotional, social, and financial challenges, especially in low- and middle-income countries like India.
Objective
In this study, we aimed to assess caregiver burden and quality of life among parents of children with cancer in the Indian context, and to describe the socio-cultural and economic factors influencing these outcomes.
Methods
In this cross-sectional study, 200 primary caregivers of paediatric oncology patients were assessed using the Zarit Burden Interview (ZBI), WHOQoL-BREF, and the Multidimensional Scale of Perceived Social Support (MSPSS). Socio-demographic and clinical data were collected through interviews and review of medical records.
Results
The median ZBI score was 66 (IQR 23.5), with 66% of caregivers experiencing severe burden. Caregiver burden was higher among mothers, caregivers with lower education, those unemployed, and those living in nuclear families. Burden negatively correlated with duration of illness (r = −0.75, P < 0.05) and quality of life across all domains. Perceived social support was low, particularly among caregivers living alone or in nuclear families.
Conclusions
High caregiver burden and impaired quality of life were observed among caregivers in a resource-limited setting. Strengthening social support and implementing family-centred interventions may help reduce the burden and improve outcomes
Tiger-man appearance in a case of DLBCL
No full textPrimary cutaneous Epstein–Barr virus‐positive diffuse large B‐cell lymphoma (PC‐EBV‐DLBCL) is a rare and aggressive neoplasm with a poorer prognosis than other cutaneous lymphoma. Here, we present the 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) scan findings of a 39-year-old male with erythematous ulcerative nodules, which showed a gamut of cutaneous lesions consistent with EBV-associated PC‐DLBCL. We highlight the utility of 18F-FDG PET/CT in evaluating the extent of disease, involvement of extranodal sites, and response to treatment in PC‐EBV‐DLBCL.
A 39-year-old male presented with erythematous ulcerative nodules which were gradually enlarging reddish and painless cutaneous-subcutaneous masses surrounded with some erythematous patches and ulcers on his back for 1 year. Clinical examination revealed impaired general condition and multiple large ulcerations, the largest measuring 15 cm × 10 cm on the back with no evidence of mucosal lesions or peripheral lymphadenopathies. Further, his blood investigations showed elevated lactate dehydrogenase levels (405 U/L). Histopathological examination of the tissue from the skin lesion revealed atypical lymphoid cells. 18F-FDG PET/CT was done to rule out systemic involvement [Figure 1]
Thermal and pressure-dependent lattice dynamics of TlBiSe<sub>2</sub> and its chromium-doped variants
No full textTopological insulator (TI) materials, which are conductive at the surface but insulate in bulk, have drawn significant attention in the past decade due to their fascinating properties and potential applications in spintronics, quantum computing, and topological superconductivity. Among three-dimensional TIs, thallium (Tl)-based III-V-VI2 chalcogenides stand out due to their simple electronic band structure near the Fermi level. The study of lattice dynamic properties is crucial for the practical application of any material. In this work, we report the synthesis and lattice dynamics of TlBiSe2 and Cr-doped TlBiSe2. The long- and short-range ordering of the materials was investigated upon Cr doping by powder X-ray diffraction (XRD), X-ray absorption spectra (XAS), and Raman scattering. Temperature-dependent XRD (123-500 K) and Raman scattering (100-500 K), as well as pressure-dependent XRD up to 15 GPa, were carried out to understand lattice dynamics. Both pristine TlBiSe2 and Cr0.06TlBi0.94Se2 show structural stability across the entire temperature and pressure ranges. However, long-range ordering in Cr0.02TlBi0.98Se2 changes above 300 K. Additionally, Cr0.02TlBi0.98Se2 undergoes a monoclinic phase transition at a lower pressure (∼5.0 GPa) compared to that of pristine TlBiSe2 (∼6.8 GPa). This anomalous behavior regarding local structural distortion in Se atoms upon Cr doping at the Bi atomic site is understood
Accelerating the stabilized column generation using machine learning
No full textColumn Generation (CG) is a well-established methodology for tackling large-scale real-world optimization problems. Nevertheless, as problem sizes increase, challenges like long-tail effects and degeneracy become more prevalent. Various strategies for stabilizing dual variables have demonstrated their effectiveness in mitigating these challenges. Generally, numerical tests are employed to identify the best parameter values for stabilized CG using different configurations for the same problem. This study introduces an innovative approach using machine learning (ML) to predict the best algorithm configuration, eliminating the need for extensive numerical experimentation. The core objective of this study is to predict optimal dual variables to generate improved bounds in the Restricted Master Problem of stabilized CG. By and large, this comprehensive approach represents a robust and flexible framework, optimizing algorithm configurations and expediting the convergence of the CG model. Extensive computational experiments confirm the efficacy of our ML-based approach in accurately predicting optimal dual variables and outperforming conventional methods. The practical utility is exemplified in optimizing workforce scheduling, demonstrating significant reductions in computational time across problem instances. This real-world application highlights the remarkable benefits of the smart approach in enhancing the efficiency and effectiveness of CG-based optimization solution
Weighted Deformable Network for Efficient Segmentation of Lung Tumors in CT
No full textThe computerized delineation and prognosis of lung cancer is typically based on Computed Tomography (CT) image analysis, whereby the region of interest (ROI) is accurately demarcated and classified. Deep learning in computer vision provides a different perspective to image segmentation. Due to the increasing number of cases of lung cancer and the availability of large volumes of CT scans every day, the need for automated handling becomes imperative. This requires efficient delineation and diagnosis through the design of new techniques for improved accuracy. In this article, we introduce the novel Weighted Deformable U-Net (WDU-Net) for efficient delineation of the tumor region. It incorporates the Deformable Convolution (DC) that can model arbitrary geometric shapes of region of interests. This is enhanced by the Weight Generation (WG) module to suppress unimportant features while highlighting relevant ones. A new Focal Asymmetric Similarity (FAS) loss function helps handle class imbalance. Ablation studies and comparison with state-of-the-art models help establish the effectiveness of WDU-Net with ensemble learning, tested on five publicly available lung cancer datasets. Best results were obtained on the LIDC-IDRI lung tumor test dataset, with an average Dice score of 0.9137, the Hausdorff Distance 95% (HD95) of 5.3852, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.9449
Agreement tests on graphs and hypergraphs
No full textAgreement tests are a generalization of low degree tests that capture a local-to-global phenomenon, which forms the combinatorial backbone of most probabilistically checkable proof (PCP) constructions. In an agreement test, a function is given by an ensemble of local restrictions. The agreement test checks that the restrictions agree when they overlap, and the main question is whether average agreement of the local pieces implies that there exists a global function that agrees with most local restrictions. There are very few structures that support agreement tests, essentially either coming from algebraic low degree tests or from direct product tests (and recently also from high-dimensional expanders). In this work, we prove a new agreement theorem which extends direct product tests to higher dimensions, analogously to how low degree tests extend linearity testing. As a corollary of our main theorem, it follows that an ensemble of small graphs on overlapping sets of vertices can be glued together to one global graph assuming they agree with each other on average. We prove the agreement theorem by (re)proving the agreement theorem for dimension 1, and then generalizing it to higher dimensions (with the dimension 1 case being the direct product test and dimension 2 being the graph case). A key technical step in our proof is the reverse union bound, which allows us to treat dependent events as if they are disjoint, and may be of independent interest. An added benefit of the reverse union bound is that it can be used to show that the “majority decoded” function also serves as a global function that explains the local consistency of the agreement theorem, a fact that was not known even in the direct product setting (dimension 1) prior to our work. Beyond the motivation to understand fundamental local-to-global structures, our main theorem allows us to lift structure theorems from the standard uniform distribution μ1/2 to the p-biased distribution μp. As a simple demonstration of this paradigm, we show how the low degree testing result of Alon et al. and Bhattacharyya et al., originally proved for μ1/2, can be extended to the p-biased hypercube μp, even for very small subconstant p
Ultrasonic imaging technique for NDE: arbitrary virtual array source aperture with using sign coherence factor
No full textIn this paper, we propose the ultrasound imaging method, arbitrary virtual array sources aperture (AVASA), using signal sign coherence (SC) information to inspect thick, highly attenuating structural components and enhance image resolution. The AVASA-SC employs phased array (PA) parallel transmission to focus beamforming at multiple virtual sources, improve the signal-to-noise ratio (SNR) of received A-scan signals, and record the reflected signals with all the array elements. The high-resolution imaging is reconstructed on the reception by an AVASA beamformer that virtually focuses on each point in the inspection region through the coherence summing of the signal sign bit, reducing image processing time. AVASA effectively images thicker structures by focusing the ultrasound beam at virtual sources through exciting parallel transmission. However, in AVASA, the SNR of deeper reflectors can be reduced due to signal amplitude-based image reconstruction. Therefore, AVASA-SC uses the instantaneous signal sign bit of the AVASA beamforming aperture data to create imaging. To compare AVASA-SC’s defect SNR and imaging resolution for deeper-located defects, two test samples (one with known defects, one with unknown) were scanned using AVASA and full matrix capture-total focusing method (FMC-TFM) techniques. AVASA-SC significantly improves image resolutions, enabling enhanced defect characterization