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Featured Application: Synchromodal optimisation; decision support systems; dynamical transport optimisation. Intermodal freight transport (IFT) requires a large number of optimisation measures to ensure its attractiveness. This involves numerous control decisions on different time scales, making integrated optimisation with traditional methods almost unfeasible. Recently, a new trend in optimisation science has emerged: the application of Deep Learning (DL) to combinatorial problems. Neural combinatorial optimisation (NCO) enables real-time decision-making under uncertainties by considering rich context information—a crucial factor for seamless synchronisation, optimisation, and, consequently, for the competitiveness of IFT. The objective of this study is twofold. First, we systematically analyse and identify the key actors, operations, and optimisation problems in IFT and categorise them into six major classes. Second, we collect and structure the key methodological components of the NCO framework, including DL models, training algorithms, design strategies, and review the current State of the Art with a focus on NCO and hybrid DL models. Through this synthesis, we integrate the latest research efforts from three closely related fields: optimisation, transport planning, and NCO. Finally, we critically discuss and outline methodological design patterns and derive potential opportunities and obstacles for learning-based frameworks for integrated optimisation problems. Together, these efforts aim to enable a better integration of advanced DL techniques into transport logistics. We hope that this will help researchers and practitioners in related fields to expand their intuition and foster the development of intelligent decision-making systems and algorithms for tomorrow's transport systems. [ABSTRACT FROM AUTHOR]

This review focuses on the pivotal role of radiotracers in breast cancer imaging, emphasizing their importance in accurate detection, staging, and treatment monitoring. Radiotracers, labeled with radioactive isotopes, are integral to various nuclear imaging techniques, including positron emission tomography (PET) and positron emission mammography (PEM). The most widely used radiotracer in breast cancer imaging is 18F-fluorodeoxyglucose (18F-FDG), which highlights areas of increased glucose metabolism, a hallmark of many cancer cells. This allows for the identification of primary tumors and metastatic sites and the assessment of tumor response to therapy. In addition to 18F-FDG, this review will explore newer radiotracers targeting specific receptors, such as estrogen receptors or HER2, which offer more personalized imaging options. These tracers provide valuable insights into the molecular characteristics of tumors, aiding in tailored treatment strategies. By integrating radiotracers into breast cancer management, clinicians can enhance early disease detection, monitor therapeutic efficacy, and guide interventions, ultimately improving patient outcomes. Ongoing research aimed at developing more specific and sensitive tracers will also be highlighted, underscoring their potential to advance precision medicine in breast cancer care. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive evaluation of catalytic ozonation as an effective strategy for indigo dye bleaching, particularly examining the performance of four carbon-based catalysts, activated carbon (AC), multi-walled carbon nanotubes (MWCNT), graphitic carbon nitride (g-C3N4), and thermally etched nanosheets (C3N4-TE). The study investigates the efficiency of catalytic ozonation in degrading Potassium indigotrisulfonate (ITS) dye within the constraints of short contact times, aiming to simulate real-world industrial wastewater treatment conditions. The results reveal that all catalysts demonstrated remarkable decolorization efficiency, with over 99% of indigo dye removed within just 120 s of mixing time. Besides, the study delves into the mechanisms underlying catalytic ozonation reactions, elucidating the intricate interactions between the catalysts, ozone, and indigo dye molecules with the processes being influenced by factors such as PZC, pKa, and pH. Furthermore, experiments were conducted to analyze the adsorption characteristics of indigo dye on the surfaces of the materials and its impact on the catalytic ozonation process. MWCNT demonstrated the highest adsorption efficiency, effectively removing 43.4% of the indigo dye color over 60 s. Although the efficiency achieved with C3N4-TE was 21.4%, which is approximately half of that achieved with MWCNT and less than half of that with AC, it is noteworthy given the significantly lower surface area of C3N4-TE. [ABSTRACT FROM AUTHOR]

Simple Summary: Resistance to standard therapies represents a major challenge in melanoma treatment. This is mainly related to the high heterogeneity of this malignancy due to the presence of different types of cells in the tumor mass. Cancer stem cells (CSCs) are a small subset of cells endowed with self-renewal and tumorigenic capacity and the ability to escape anticancer therapies. Melanoma CSCs can be identified by the expression of biomarkers and intracellular pathways deeply involved in their malignant phenotype. Dissecting the molecular mechanisms underlying CSC biological functions might pave the way for novel therapeutic approaches. Herein, we highlight the intricate bidirectional communication between melanoma CSCs and their surrounding cells in the tumor microenvironment and discuss how this interplay might impact the processes of drug resistance and tumor relapse. We also address the potential benefits of CSC-targeted strategies, in combination with conventional drugs, for improving melanoma treatment strategies. Cutaneous melanoma still represents a significant health burden worldwide, being responsible for the majority of skin cancer deaths. Key advances in therapeutic strategies have significantly improved patient outcomes; however, most patients experience drug resistance and tumor relapse. Cancer stem cells (CSCs) are a small subpopulation of cells in different tumors, including melanoma, endowed with distinctive capacities of self-renewal and differentiation into bulk tumor cells. Melanoma CSCs are characterized by the expression of specific biomarkers and intracellular pathways; moreover, they play a pivotal role in tumor onset, progression and drug resistance. In recent years, great efforts have been made to dissect the molecular mechanisms underlying the protumor activities of melanoma CSCs to provide the basis for novel CSC-targeted therapies. Herein, we highlight the intricate crosstalk between melanoma CSCs and bystander cells in the tumor microenvironment (TME), including immune cells, endothelial cells and cancer-associated fibroblasts (CAFs), and its role in melanoma progression. Specifically, we discuss the peculiar capacities of melanoma CSCs to escape the host immune surveillance, to recruit immunosuppressive cells and to educate immune cells toward an immunosuppressive and protumor phenotype. We also address currently investigated CSC-targeted strategies that could pave the way for new promising therapeutic approaches for melanoma care. [ABSTRACT FROM AUTHOR]

Sodium alginate (SA)–purple sweet potato peel extracts (PPE) from industrial waste indicator films were developed at different drying temperatures (25, 30, 35, 40, 45, 50, and 55 °C). The effects of drying temperatures on the film's structural, physical, and sensitive properties were investigated. On the structural properties, scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction indicated that compactness, intermolecular interactions, and crystallinity of indicator films were improved at a lower drying temperature. On the physical properties, with the drying temperature increasing, elongation at the break increased significantly (p < 0.05); ΔE and water-vapor permeability decreased significantly (p < 0.05); and thickness and tensile strength initially increased significantly (90.46 → 98.46, 62.99 → 95.73) and subsequently decreased significantly (98.46 → 71.93, 95.73 → 55.44) (p < 0.05), with the maximum values obtained at 30 °C. On sensitivity, the corresponding colors of the films became lighter as the drying temperature increased, and the films exhibited relatively excellent pH and NH3 sensitivity, with easily discernible color changes at lower temperatures. The results of this paper revealed that the overall film characteristics are improved at lower drying temperatures, which will provide valuable references for selecting the drying temperature for preparing indicator films as a guide for industrialized production. [ABSTRACT FROM AUTHOR]

Background: Body dissatisfaction (BD) has been consistently linked to adverse consequences on mental health and overall well-being, and is recognized as a significant contributing factor in the initiation and persistence of eating disorders (EDs). Empirical evidence has demonstrated that an elevated body mass index (BMI) and media influence and pressure about a thin ideal heighten the risk of subsequent BD. Moreover, suggestibility, a propensity to accept and act upon messages without critical evaluation, has been shown to be positively associated with greater susceptibility to the influence of sociocultural messages that endorse the thin ideal. This study aimed to assess whether suggestibility moderates the association between BMI and BD in women. Methods: A total of 117 women completed assessments using the Eating Disorder Inventory-3 (EDI-3) BD subscale and the Suggestibility Inventory, which encompasses a general suggestibility index and a subscale that evaluates susceptibility to influence by others. We conducted moderation analyses employing the PROCESS macro, with BMI as the central predictor, BD as the outcome variable, and suggestibility and its subscale as moderators. Results: The findings revealed statistically significant positive moderating interactions for both the general suggestibility index and susceptibility to influence by others. Specifically, women who exhibited high levels of suggestibility and susceptibility to influence by others demonstrated a more pronounced increase in BD as their BMI increased. Conclusions: These outcomes are in line with the sociocultural model of EDs, suggesting that greater susceptibility to external influences amplifies the impact of societal pressures to conform to thin ideals. [ABSTRACT FROM AUTHOR]

Background: Perinatal hypoxia may result in coagulation dysfunction. Diminished blood flow or oxygen to the fetus/neonate during the perinatal period can cause bone marrow and liver function impairment, leading to thrombocytopenia, impaired synthesis of clotting and fibrinolytic factors, and increased destruction of platelets in the small blood vessels. The goal of the present study was to evaluate the hemostatic status of newborns with perinatal hypoxia via the non-activated thromboelastometry (NATEM) assay in cord blood samples. Methods: 134 hypoxic neonates born in our maternity unit over a 1.5-year period were enrolled in this observational cohort study, and 189 healthy neonates served as the control group. Participation in the study was voluntary and parents signed informed consent prior to recruitment. Demographic and clinical data were recorded on admission, and the NATEM method was performed on cord blood samples. The following NATEM values were evaluated: clotting time (CT), alpha angle (α-angle), clot formation time (CFT), clot amplitude at 5 and 10 min. (A5, A10), maximum clot firmness (MCF), clot lysis index at 60 min. after CT (LI60), and maximum clot elasticity (MCE). Statistical analysis was conducted utilizing the SAS for Windows 9.4 software platform. Results: Neonates with perinatal hypoxia exhibited decreased fibrinolytic potential in comparison to healthy neonates, as indicated by increased LI60, and this difference was statistically significant (LΙ60: 94 (92–96) Vs 93 (91–95), p value = 0.0001). There were no statistically significant differences noted among the remaining NATEM variables. Conclusion: Our findings indicate decreased fibrinolytic potential in hypoxic neonates in comparison to healthy neonates, suggesting that NATEM could serve as an effective tool for promptly identifying hemostasis dysfunction in this group of neonates. [ABSTRACT FROM AUTHOR]

While comprehensive knowledge networks can be instrumental in finding solutions to complex problems or supporting the development of detailed simulation models, their large number of nodes and edges can become a hindrance. When the representation of a network becomes opaque, they stop fulfilling their role as a shared representation of a system between participants and modelers; hence, participants are less engaged in the model-building process. Combating the information overload created by large conceptual models is not merely a matter of changing formats: shifting from an unwieldy diagram to enormous amounts of text does not promote engagement. Rather, we posit that participants need an environment that provides details on demand and where interactions with a model rely primarily on a familiar format (i.e., text). In this study, we developed a visual analytics environment where linked visualizations allow participants to interact with large conceptual models, as shown in a case study with hundreds of nodes and almost a thousand relationships. Our environment leverages several advances in generative AI to automatically transform (i) a conceptual model into detailed paragraphs, (ii) detailed text into an executive summary of a model, (iii) prompts about the model into a safe version that avoids sensitive topics, and (iv) a description of the model into a complementary illustration. By releasing our work open source along with a video of our case study, we encourage other modelers to use this approach with their participants. Their feedback and future usability studies are key to respond to the needs of participants by improving our environment given individual preferences, models, and application domains. [ABSTRACT FROM AUTHOR]

This case study explores the potential of integrating attentional bias modification training (ABMT) with mirror exposure therapy (MET), utilizing virtual reality and eye-tracking, for a 14-year-old girl diagnosed with anorexia nervosa (AN). The ABMT-MET intervention was used alongside a standard treatment program called Home Treatment (HoT), which combines cognitive behavioral therapy with family-based therapy. Though the patient began HoT with a 3-week inpatient phase, the ABMT-MET intervention specifically took place during the subsequent Home Treatment sessions. The experimental treatment, comprising five consecutive weekly sessions, was bookended by pre- and post-assessment sessions and included a six-month follow-up. During the sessions, the patient engaged in systematic and hierarchical exposure to a virtual representation of her silhouette, with gradual adjustments made to the avatar's body mass index (BMI) toward a healthier range. ABMT sessions, conducted before each MET session, aimed to redistribute the patient's focus evenly across her body, successfully neutralizing her initial attentional bias toward non-weight-related body parts. The patient demonstrated consistent decreases in anxiety and fear of weight gain, effectively progressing through the BMI hierarchy in the virtual setting. Post-treatment assessments indicated significant enhancements in body dissatisfaction, drive for thinness, body-checking behaviors, and body appreciation, with these gains preserved at the six-month follow-up, although the attentional bias returned to pre-treatment levels. Though the single-case design limits definitive conclusions, these findings suggest ABMT-MET may be a promising adjunct therapy for AN, requiring further research for confirmation. [ABSTRACT FROM AUTHOR]

Hypothyroidism is a frequently diagnosed endocrine disorder. Common signs and symptoms include fatigue, cold intolerance, hoarseness, dry skin, constipation, a slow relaxation phase of deep tendon reflexes, and bradycardia. However, some patients may exhibit atypical signs and symptoms, which can result in diagnostic confusion. Pituitary hyperplasia resulting from longstanding primary hypothyroidism was first described by Niepce in 1851. It is usually asymptomatic, but sometimes, in addition to symptoms of overt hypothyroidism, patients may complain of headaches, hypopituitarism, visual field impairment, and hyperprolactinemia. Furthermore, on imaging, pituitary hyperplasia can be mistaken for a pituitary adenoma. Distinguishing between the two is crucial, as their management differs; the former often responds to thyroid hormone replacement therapy, while the latter might need treatment with surgery and/or radiotherapy. Here we describe a patient who developed pituitary hyperplasia in the setting of longstanding uncompensated primary hypothyroidism due to a lack of compliance with levothyroxine replacement therapy. We also review the clinical, laboratory, and radiologic findings of the case reports available in the literature up to now in order to improve the knowledge and the care of the disease. [ABSTRACT FROM AUTHOR]

Why do customers incorporate concerns about social and environmental issues into the decision-making process? How ethical are food choices in the modern world? Answers to these questions have often revolved around how informed consumers might be and whether they have the appropriate skills to act on concerns they might have. Today, ethical food consumption is a growing market where consumers' behavior shifts from the rational manner focusing on the products price and attributes to the food ethics associated with environment, social welfare, public health, and morality. Using data selected from a purposive sample of 20 consumers, this study employed a qualitative research procedure to explore the main dimensions that influence the decision-making process and eating preferences in the post-COVID 19 era and within an economically turbulent environment. The main results showed that health protection, sustainability, and social wefare constitute the main axes of ethical food consumption. Participants were found to be more individualists than altruists since the "personal health" dimension was the most prevalent. Future research should extend these findings and explore variations in the ethical consumption factors among various consumer segments. [ABSTRACT FROM AUTHOR]

Caffeine (1,3,7-trimethylxanthine) is a widely consumed bioactive substance worldwide. Our recent study showed that a reduction in both reproduction and yolk protein production (vitellogenesis) caused by caffeine intake were improved by vitamin B12 supplementation, which is an essential co-factor in methionine metabolism. In the current study, we investigated the role of methionine in the reproduction of caffeine-ingested animals (CIAs). We assessed the effect of methionine metabolism on CIAs and found that caffeine intake decreased both methionine levels and essential enzymes related to the methionine cycle. Furthermore, we found that the caffeine-induced impairment of methionine metabolism decreased vitellogenesis and increased germ cell apoptosis in an LIN-35/RB-dependent manner. Interestingly, the increased germ cell apoptosis was restored to normal levels by methionine supplementation in CIAs. These results indicate that methionine supplementation plays a beneficial role in germ cell health and offspring development by regulating vitellogenesis. [ABSTRACT FROM AUTHOR]

The ability to efficiently separate CO2 from other light gases using membrane technology has received a great deal of attention due to its importance in applications such as improving the efficiency of natural gas and reducing greenhouse gas emissions. A wide range of materials has been employed for the fabrication of membranes. This paper highlights the work carried out to develop novel advanced membranes with improved separation performance. We integrated a polymerizable and amino acid ionic liquid (AAIL) with zeolite to fabricate mixed matrix membranes (MMMs). The MMMs were prepared with (vinylbenzyl)trimethylammonium chloride [VBTMA][Cl] and (vinylbenzyl)trimethylammonium glycine [VBTMA][Gly] as the polymeric support with 5 wt% zeolite particles, and varying concentrations of 1-butyl-3-methylimidazolium glycine, [BMIM][Gly] (5–20 wt%) blended together. The membranes were fabricated through photopolymerization. The extent of polymerization was confirmed using FTIR. FESEM confirmed the membranes formed are dense in structure. The thermal properties of the membranes were measured using TGA and DSC. CO2 and CH4 permeation was studied at room temperature and with a feed side pressure of 2 bar. [VBTMA][Gly]-based membranes recorded higher CO2 permeability and CO2/CH4 selectivity compared to [VBTMA][Cl]-based membranes due to the facilitated transport of CO2. The best performing membrane Gly-Gly-20 recorded permeance of 4.17 GPU and ideal selectivity of 5.49. [ABSTRACT FROM AUTHOR]

Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling techniques are sought. Co-simulation and closure modeling are two prevalent composable modeling techniques that divide a multi-scale/multi-domain system into sub-systems, use smaller component models to capture each sub-system, and coordinate data transfer between component models. While the two techniques have similar goals, differences in their methods lead to differences in the complexity and computational efficiency of a simulation model built using one technique or the other. This paper presents a probabilistic analysis of the complexity and computational efficiency of these two composable modeling techniques for multi-scale/multi-domain complex system modeling and simulation applications. The aim is twofold: to promote awareness of these two composable modeling approaches and to facilitate complex system model design by identifying circumstances that are amenable to either approach. [ABSTRACT FROM AUTHOR]

Paratemporal methods based on tree expansion have proven to be effective in efficiently generating the trajectories of stochastic systems. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this paper, we tackle this scalability problem by developing a systems theory-based framework covering both conventional and proposed tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving the desired accuracy. An example is discussed to illustrate the tree expansion framework in which a discrete event system specification (DEVS) Markov stochastic model takes the form of a tree isomorphic to a free monoid over the branching alphabet. We derive the computation times for baseline, non-merging, and merging tree expansion algorithms to compute the distribution of output values at any given depth. The results show the remarkable reduction from exponential to polynomial dependence on depth effectuated by node merging. We relate these results to the similarly reduced computation time of binomial coefficients underlying Pascal's triangle. Finally, we discuss the application of tree expansion to estimating temporal distributions in stochastic simulations involving serial and parallel compositions with potential real-world use cases. [ABSTRACT FROM AUTHOR]

Industry 4.0 enables the modernisation of machines and opens up the digitalisation of processes in the manufacturing industry. As a result, these machines are ready for predictive maintenance as part of Industry 4.0 services. The benefit of predictive maintenance is that it can significantly extend the life of machines. The integration of predictive maintenance into existing production environments faces challenges in terms of data understanding and data preparation for machines and legacy systems. Current AI frameworks lack adequate support for the ongoing task of data integration. In this context, adequate support means that the data analyst does not need to know the technical background of the pilot's data sources in terms of data formats and schemas. It should be possible to perform data analyses without knowing the characteristics of the pilot's specific data sources. The aim is to achieve a seamless integration of data as information for predictive maintenance. For this purpose, the developed data-sharing infrastructure enables automatic data acquisition and data integration for AI frameworks using interoperability methods. The evaluation, based on two pilot projects, shows that the step of data understanding and data preparation for predictive maintenance is simplified and that the solution is applicable for new pilot projects. [ABSTRACT FROM AUTHOR]

With the booming development of door-to-door housekeeping service, the platform faces the problem of order assignment. Improving the matching mechanism between orders and housekeepers based on a dynamic programming (DP) algorithm can not only achieve flexible order allocation but can also improve the service efficiency and service quality. In this paper, a single objective nonlinear programming model is established, which takes the maximum total weight value as the objective function to study the order allocation problem under offline and online conditions. Under the offline condition, the number of housekeepers is taken as the decision variable. The status of order and housekeeper, order time, and action trajectory are taken as constraints. For online assignment, the order backlog status is treated as the decision variable. The reliability of the model was verified using real data from 20 groups of housekeepers and 50 groups of orders. Finally, the effect of order backlog on online allocation is discussed and the optimal threshold and maximum weight are found. The online order assignment model is compared with the nearest distance assignment model. The results show that the online assignment model with a total weighted score of 1045.14 is better than the nearest distance assignment model with a score of 810.25. [ABSTRACT FROM AUTHOR]

Diagnosis of primary hyperparathyroidism (PHP) is based on blood assessments in terms of synchronous high calcium and PTH (parathormone), but further management, particularly parathyroid surgery that provides the disease cure in 95–99% of cases, requires an adequate localisation of the parathyroid tumour/tumours as the originating source, with ultrasound and 99m-Technetium (99m-Tc) sestamibi scintigraphy being the most widely used. We aimed to introduce an adult female case diagnosed with PHP displaying unexpected intra-operatory findings (ectopic thyroid tissue) in relation to concordant pre-operatory imaging modalities (ultrasound + dual-phase 99m-Tc pertechnetate and sestamibi scintigraphy + computed tomography) that indicated bilateral inferior parathyroid tumours. A sudden drop in PTH following the removal of the first tumour was the clue for performing an extemporaneous exam for the second mass that turned out to be non-malignant ectopic thyroid tissue. We overviewed some major aspects starting from this case in point: the potential pitfalls of pre-operatory imaging in PHP; the concordance/discordance of pre-parathyroidectomy localisation modalities; the need of using an additional intra-operatory procedure; and the clues of providing a distinction between pathological parathyroids and thyroid tissue. This was a case of adult PHP, whereas triple localisation methods were used before parathyroidectomy, showing concordant results; however, the second parathyroid adenoma was a false positive image and an ectopic thyroid tissue was confirmed. The pre-operatory index of suspicion was non-existent in this patient. Hybrid imaging modalities are most probably required if both thyroid and parathyroid anomalies are suspected, but, essentially, awareness of the potential pitfalls is mandatory from the endocrine and surgical perspectives. Current gaps in imaging knowledge to guide us in this area are expected to be solved by the significant progress in functional imaging modalities. However, the act of surgery, including the decision of a PTH assay or extemporaneous exam (as seen in our case), represents the key to a successful removal procedure. Moreover, many parathyroid surgeons may currently perform 4-gland exploration routinely, precisely to avoid the shortcomings of preoperative localisation. [ABSTRACT FROM AUTHOR]

This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field's impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka's law is reflected in the author's contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford's Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape. [ABSTRACT FROM AUTHOR]