Optimal Decision Research Articles

Nomogram for predicting the early death of patients with stage IV ovarian cancer: a retrospective analysis of the SEER database.

Ovarian cancer is a major health problem for women all over the world and tends to progress to advanced stages. Therefore, it is important to predict the early survival of patients with advanced ovarian cancer. The purpose of this study is to assist clinicians in predicting the short-term prognosis of patients with stage IV ovarian cancer in order to make optimal medical decisions. A retrospective analysis was conducted on data from the Surveillance, Epidemiology, and End Results database, involving 3,077 patients with stage IV ovarian cancer. Univariate and multivariate logistic regression analyses were performed to identify risk factors. Using R software, relevant predictive models were constructed. The calibration, discrimination, and clinical utility of these models were assessed in a validation cohort. A nomogram model was developed utilizing four independent risk factors to predict the probability of early death in patients with stage IV ovarian cancer. The model exhibited satisfactory discrimination in both the training cohort (area under the receiver operating characteristic curve =0.816) and the validation cohort (area under the receiver operating characteristic curve =0.827). The calibration curve demonstrated a high level of predictive accuracy for the model. Furthermore, the decision curve analysis indicated that the nomogram holds clinical utility and offers a net benefit to patients within certain limitations. The predictive effectiveness of the nomogram was verified by the Kaplan-Meier survival curve. We have successfully developed a nomogram and risk classification system to accurately predict the probability of early death in patients with stage IV ovarian cancer.

Integrating predictive analytics into strategic decision-making: A model for boosting profitability and longevity in small businesses across the United States

The integration of predictive analytics into strategic decision-making has emerged as a transformative approach for small businesses in the United States, offering enhanced pathways to profitability and longevity. This study proposes a conceptual model that explores how predictive analytics can empower small businesses to make data-driven decisions, optimize resources, and mitigate risks. By leveraging advanced machine learning algorithms, data mining, and statistical techniques, small businesses can identify trends, predict customer behavior, and uncover growth opportunities. The proposed model focuses on integrating predictive analytics across key operational areas: financial planning, marketing, inventory management, and customer relationship management. In financial planning, predictive analytics enables more accurate forecasting of revenues and expenses, supporting budget optimization and investment decisions. In marketing, businesses can analyze consumer data to design targeted campaigns, improving conversion rates and customer retention. Inventory management benefits from predictive insights by anticipating demand patterns, reducing overstocking or stockouts. Moreover, customer relationship management is enhanced through personalized recommendations and proactive engagement strategies. This research emphasizes the accessibility of predictive analytics tools, particularly through cloud-based platforms, enabling small businesses to adopt these technologies without incurring substantial costs. Additionally, the study highlights challenges such as data quality, privacy concerns, and the need for technical expertise, proposing strategies to overcome these barriers. Case studies of small businesses that have successfully implemented predictive analytics demonstrate significant outcomes, including increased operational efficiency, reduced costs, and sustained competitive advantage. The findings underscore the importance of fostering a data-driven culture and investing in training to enhance employees’ analytical capabilities. Policymakers and industry stakeholders are encouraged to support initiatives that promote the adoption of predictive analytics in small businesses through funding, education, and technical assistance programs.

Attitudinal Measures for Socio-environmental Governance in Public Organisations of the Amazon Region

This study involves attitudinal measures by public agents committed to the environment, society and governance. It seeks to build idealists in socio-environmental management in the Amazon, promoting the induction of behaviours that lack a logical methodology to support solutions in a strategy for sustainability. The question to be raised here is: What attitudinal measures could be adopted by public agents in responsible socio-environmental governance? To answer this question, the general objective is to investigate the attitudinal measures of public officials in the face of their commitment to socio-environmental management in organisations. Thus, the content analysis method and related procedures were applied through bibliographic and documentary surveys made available on platforms such as ANPAD’s Spell, Google Scholar, Scopus and Web of Science, using keywords and Boolean criteria. Habermas’ Theory of Communicative Action was used in the analysis. The results made it possible to carry out a critical study that is valid in empirical experiments, which makes it possible to outline measures for the development of socio-environmental actions; it considered participatory and collaborative management models, environmental diagnosis in organisations; it offers the reader a SWOT framework in the face of a middle-up-down structure, through which knowledge creation is induced; it focuses on the institutional tactical level, aligning knowledge management processes involving leadership, coordination, control and measurement; it outlines the establishment of sustainability indicators, making it possible to measure socio-environmental performance in public organisations. This is an academic contribution to those interested in promoting socio-environmental management in the public sector, pointing to innovative practices as valid models to be considered in optimal decision-making.

JUVENILE VICTIMS OF CRIMES AGAINST SEXUAL INVIOLABILITY COMMITTED IN THE FAMILY (VICTIMIZATION CHARACTERISTICS OF PERSONALITY AND THEIR USE IN THE INVESTIGATION)

The article examines the content of the age structure of criminal victimization of minors victims of crimes against sexual integrity in the family . The necessity of identifying the causes and characteristics of juvenile victims of crimes in the categories of cases under consideration is argued in order to solve certain tactical tasks and select special tactical techniques when conducting investigative actions .Based on scientific knowledge in the field of criminology and victimology, the author conducts a comprehensive analysis of the psychological characteristics of victim behavior of juvenile victims, considering victimological characteristics in accordance with their age parameters, in relation to the mechanism of the crime, in the interaction of the dyad “juvenile victim - criminal” .The research uses general scientific methods of dialectical epistemology in combination with private scientific methods: comparative method and method of multifactor analysis, a systematic, integrated and differentiated approach is carried out . The empirical basis of the research is the materials of investigative and judicial practice, as well as the author’s own experience of investigative actions .As a result of the research work, the author comes to the conclusion that the identification and differentiation of the age characteristics of juvenile victims, the typification of victim behavior and victim potential of families of the so-called risk group is of applied importance: it allows making optimal tactical decisions during the investigation of crimes against the sexual integrity of minors committed in the family, and is also used for the development of measures to prevent victimization in children and adolescents .

Blood-glucose regulator design for diabetics based on LQIR-driven Sliding-Mode-Controller with self-adaptive reaching law.

Type I Diabetes is an endocrine disorder that prevents the pancreas from regulating the blood glucose (BG) levels in a patient's body. The ubiquitous Linear-Quadratic-Integral-Regulator (LQIR) is an optimal glycemic regulation strategy; however, it is not resilient enough to withstand measurement noise and meal disruptions. The Sliding-Mode-Controller (SMC) yields robust BG regulation effort at the expense of a discontinuous insulin infusion rate that perturbs the BG concentrations. Hence, the novel contribution of this article is the formulation of a hybridized LQIR-driven SMC strategy that retrieves the benefits of the aforesaid control schemes while avoiding their inherent problems. The proposed control approach is realized by linearly combining a glycemic LQIR law with an innovative sign function sliding mode reaching law that is driven by a customized LQIR-driven sliding surface. The hybridized control scheme generates optimal control decisions yielded by the LQIR while mimicking the robustness characteristic of SMC against bounded exogenous disturbances. Additionally, the SMC reaching law in the proposed control scheme is augmented with a nonlinear adaptation mechanism that flexibly modulates the control activity to effectively compensate for the external perturbations while minimizing the chattering content. The controller parameters are numerically optimized offline. The efficacy of the prescribed hybrid control law is analyzed via customized MATLAB simulations that normalize the patient's BG level to 80 mg/dL, under measurement noise and meal disruptions, from an initial hyperglycemic state. The results justify the improved BG regulation accuracy and disturbance-rejection capability of the proposed control procedure.

Economic analysis of parking, vehicle charging and vehicle-to-grid services in the era of electric vehicles

With the advances in electrical technologies (especially the vehicle-to-grid or V2G technologies), electric vehicles (EVs) now can be used as power storage. The latent power storage capacity in EVs can provide additional flexibility to the power system, and thus helps enhance the overall efficiency, stability and reliability of the power grid. With the V2G facility in place, EV users can choose to share their vehicles to the power grid as temporary storage while the vehicle is being parked or charged (termed as ‘V2G parking or charging service’). This study investigates the pricing and capacity decisions of parking, charging and V2G operators, subject to the EV users’ choice equilibrium. An EV user who demands parking or charging can choose the (conventional) dedicated parking or charging slot (managed by the parking or charging operator) or the slot of V2G facility such that his/her vehicle can be used by the power grid as temporary storage while being parked or charged (managed by the V2G operator). We formulate and analyze the EV user choice equilibrium subject to parking, charging and V2G service provision, and then investigate parking, charging and V2G operators’ optimal service fare and capacity decisions in different market regimes, where the operators may compete or cooperate with each other (e.g., charging and V2G facilities might be operated jointly). The main findings are as follows. (i) Introducing the V2G-based parking/charging service might earn a positive profit for the V2G operator and also benefit customers who request for parking or charging, but the parking and charging operators will suffer a loss. (ii) The competition between operators tends to reduce the service fares, while cooperation tends to increase the fares and yield more profits for the operators. (iii) The optimal capacity of parking, charging, or V2G facilities should be set to balance the marginal capacity acquisition cost and the marginal facility searching time cost. (iv) When V2G operator cooperates with parking/charging operator, if the additional gains of parking/charging operator through cooperation are smaller than that of V2G operator, the optimal service fare of parking/charging should be smaller, and thus will benefit the parkers/chargers (after V2G service is introduced). (v) The collaboration between parking (or charging) and V2G operators might also benefit the charging (or parking) operator. Overall, this study enhances the understanding in relation to parking and charging operators’ reactions to emerging V2G-based parking and charging services, and provides insights regarding how the V2G service should be planned and optimized.

An automatic IoT application placement using an improved equilibrium optimizer algorithm in fog computing

PurposeThe rapid proliferation of Internet of Things (IoT) devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet. Fog computing, which brings computation resources closer to IoT devices, has emerged as a promising solution. An automatic service placement framework is needed to use fog computing resources efficiently.Design/methodology/approachIn this study, first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers, facilitating automatic application deployment. Next, an enhanced version of the equilibrium optimizer (EO) algorithm, inspired by physics, is designed for service placement in fog computing environments.FindingsSimulations reveal that the proposed approach surpasses existing methods, achieving a 99% success rate compared to the closest alternative’s 93%. The algorithm also significantly reduces waiting and planning times for service placement, proving its efficiency and effectiveness in optimizing IoT service deployment in fog computing.Research limitations/implicationsOne of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads. Although improved EO offers improvements in placement efficiency, it may not be fully optimized for highly fluctuating environments. Another important limitation is the uncertainty in node resources. Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes. This uncertainty can affect the algorithm’s ability to consistently make optimal decisions for IoT service placement.Practical implicationsFrom a practical perspective, the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems. Organizations that rely on IoT networks, particularly those with critical real-time requirements, can benefit from reduced service placement times and lower failure rates. This can lead to better resource utilization, reduced operational costs and improved overall performance of IoT systems. The commercial impact is evident in industries such as smart cities, healthcare, where fast data processing is crucial.Social implicationsOur proposed framework has important implications for real-world IoT applications, particularly in areas requiring low latency processing, such as healthcare, smart cities. By reducing service delays and optimizing resource allocation, the framework can significantly improve the quality and reliability of services. Additionally, improved resource management leads to cost savings and better system efficiency, making the technology accessible to a wider range of applications.Originality/valueExisting resource placement strategies have shown inadequate performance, highlighting the need for more advanced algorithms. This study introduces a three-layer automatic framework for enhancing the application deployment of a fog system beside a novel improved EO algorithm to offer a robust solution for assigning IoT applications to fog nodes.

IMPACT OF ARTIFICIAL INTELLIGENCE ON CLINICAL DECISION SUPPORT SYSTEMS IN HOSPITAL SETTINGS

BackgroundArtificial Intelligence (AI) in clinical decision support systems (CDSS) has become an essential tool in improving patient outcomes, enhancing diagnostic accuracy, and assisting in treatment planning. However, healthcare professionals’ perceptions and accessibility to AI-powered CDSS vary across different roles, impacting their effectiveness. Understanding how clinicians, physicians, and nurses interact with AI can provide valuable insights for improving AI integration in hospitals. ObjectiveTo evaluate the perception, accessibility, and practical use of AI-powered CDSS among clinicians, physicians, and nurses in a hospital setting, focusing on how these factors influence the overall adoption and effectiveness of AI. MethodsA cross-sectional survey was conducted in a hospital setting in Sheikhupura, Pakistan, over three months (Feb 2024 to Apr 2024). A total of 54 participants were divided into three groups: clinicians (n=18), physicians (n=18), and nurses (n=18). Data was collected using a structured questionnaire assessing perception (positive/negative), accessibility (easy/complex), and beneficiaries of AI (yes/no). Descriptive statistics, chi-square tests, and ANOVA were used for data analysis, performed using SPSS version 25. ResultsClinicians showed the highest positive perception of AI at 83.3% (15/18), compared to 77.8% (14/18) for physicians and 55.6% (10/18) for nurses. AI accessibility was reported as easy by 66.7% (12/18) of clinicians, 55.6% (10/18) of physicians, and 44.4% (8/18) of nurses. Beneficiaries of AI were 72.2% (13/18) of clinicians, 66.7% (12/18) of physicians, and 50% (9/18) of nurses. Statistically significant differences were observed among the groups (p < 0.05). ConclusionThe study demonstrated that while clinicians and physicians generally have a positive perception and easier access to AI, nurses experience more challenges in both areas. Targeted interventions, including training and support, are essential to improving AI accessibility and perception across all healthcare professional groups for optimal clinical decision-making.

A systematic and optimal design method for replacing steel backrest in automotive seat with carbon fiber-reinforced polymer composites

Carbon fiber-reinforced polymer composites (CFRPC) are extensively utilized in the lightweight design of automobiles due to their superior material properties. This study focuses on the automotive seat backrest as the subject of research. To address the challenge of achieving both lightweight and performance optimization in CFRPC, subsequent to replacing the steel backrest in automotive seat, a systematic optimization design method is proposed. Firstly, the experimentally validated finite element analysis model of a steel seat, subjected to four typical working conditions, is redesigned for the CFRPC backrest in automotive seat. Building upon this foundation, the optimization design of the CFRPC backrest is segmented into two phases: optimization of layup size and optimization of layup sequence. During the optimization of layup size, a sophisticated optimization strategy is employed. This strategy combines many-objective optimization and multi-criteria decision-making. It integrates the modified extensible lattice sequence experimental design, the hybrid surrogate model utilizes the global response search method in conjunction with weighted prediction error lowering, the non-dominated sorting genetic algorithm-III, the improved double combination weighting model of game theory, and the improved VIsekriterijumsko KOmpromisno Rangiranje method, which incorporates Euclidean distance and gray relational analysis (IVIKOR-ED&GRA). This approach is utilized to ascertain the optimal count and thickness of the number of layers required for each specific angle. The simulation results demonstrate that the optimized CFRPC backrest, featuring refined layup dimensions, not only fulfills regulatory requirements but also enhances a majority of performance metrics, accompanied by a substantial weight reduction of 38.66%. Ultimately, the OptiStruct optimization technique of layup sequence is employed to further enhance the performance of the CFRPC backrest seat. This is achieved by modifying the layup stacking sequence, while maintaining the original material’s weight and the number of layups for each angle unchanged.

Two-stage optimization strategy for the active distribution network considering source-load uncertainty

This study aims to advance the development of the active distribution network (ADN) by optimizing resource allocation across different stages to enhance overall system performance and economic benefits. First, an ADN optimization model is constructed based on a two-stage robust optimization approach. The first stage focuses on determining optimal decision variables within the uncertainty set, while the second stage adjusts control variables based on the initial stage decisions. This model effectively addresses source-load uncertainties while preserving the flexibility and adaptability of decision-making solutions. Additionally, this study explores uncertainty models that incorporate correlation factors. The IEEE33-node model is employed to validate the effectiveness and superiority of the proposed optimization strategy through numerical simulations. Simulation results demonstrate that Model 3 comprehensively accounts for photovoltaic and wind turbine generator planning by optimizing their capacity configurations, leading to a 23% increase in distributed generation (DG) penetration. During high-load periods (e.g., 13:00 and 16:00), DG output reaches 47% and 50% of the demand load, underscoring the critical role of DG in supporting the power grid during peak hours. Overall, the proposed two-stage optimization strategy considers source-load uncertainties, significantly reducing economic costs, enhancing DG output, and improving overall system performance. In scenarios with correlated uncertainties, the optimized results exhibit greater accuracy and reliability, providing robust support for the planning and operation of practical distribution networks.

BIM-GIS Integration for Interactive, Open and Low-Cost 3D Land Use Registration and Urban Neighbourhood Management

This paper is part of an on-going research study on developing a low-cost and approachable methodology for f-f-p, transparent and future inclusive 3D land use management for an urban neighbourhood by constructing a homogenous, open, cloud-based and free-to-use virtual BIM-GIS geospatial infrastructure. The proposed platform is structured for registering, classifying and visualizing volumetric land use prisms, distributing conceptual and geometric information, conducting statistical analyses and data exchange aiming for optimal decision making, urban neighbourhood management and land use tracking. The proposal aspires to aid the successful and timely achievement of the UN Sustainable Agenda 2030, in particular SDGs 1,9 and 11. The methodology includes: 1) the creation of approximate BIMs by utilizing open and available data and platforms, 2) modelling of land uses as 3D volumetric prisms, 3) registering semantic and geometrical land use information and 4) crafting statistical graphs, emphasising on data compilation and BIM-GIS standards implementation.

Management of Ganglioneuroma and Ganglioneuroblastoma Intermixed: A United Kingdom Children's Cancer and Leukaemia Group (UK CCLG) Nationwide Study Report.

Ganglioneuroblastoma intermixed (GNBi) and ganglioneuroma (GN) represent benign variants of peripheral neuroblastic tumours. While historically surgical resection was recommended, watchful active observation has become the accepted management for a subset of patients. To review clinical features, biology, natural history and management of a retrospective UK CCLG study cohort of GN and GNBi, and compare outcomes of patients treated with surgical resection or watchful active observation. Retrospective review of histologically confirmed non-metastatic GN and GNBi diagnosed between 1990 and 2020. A total of 259 patients were identified (163 GN, 93 GNBi, median age 62months). In all 201 (78%) had initial surgery and 58 (22%) were observed. Overall survival was 98%. Twenty-one of 58 observed (36%) required subsequent surgery due to progressive tumour growth (52%). Gross total resection (GTR) was achieved in 79% of patients with a 19% complication rate. GTR was obtained in a similar proportion of patients having initial surgery (65%) and delayed surgery (76%). Patients obtaining GTR were more likely to have complete symptom(s) control (43% vs. 24%). In 45 cases (39%), surgical pathology was different from pathology at biopsy, and in 14 (12%) the pathology changed from GN/GNBi to neuroblastoma/GNB nodular. Watchful active observation can be a safe approach, with surgical resection reserved for symptomatic patients. However, a small proportion of patients in the observation group had their diagnosis revised to malignant at surgery. Careful assessment of surgical risk and expected benefits, after considering an initial period of observation, will allow clinicians to make optimal decisions for patients and their families.

Study on Emergency Decision-Making of Mine External Fires Based on Deduction of Precursory Scenarios

External mine fires are known for their unpredictability, rapid spread, and difficulty in terms of extinguishment, often resulting in severe casualties and property damage when not managed swiftly. This study examines the progression of coal mine fire incidents through scenario deduction and presents an emergency decision-making model based on precursor scenario analysis. We classify precursor elements according to the causes of coal mine fires, organizing scenario elements into states, precursors, and emergency activities using knowledge meta-theory. A dynamic Bayesian network forms the core of the decision-making model, enabling calculation of scenario node probabilities and the development of expert-driven response strategies for critical scenarios. Additionally, we design a comprehensive evaluation index system, utilizing multi-attribute decision-making to establish decision matrices and attribute weights. An improved entropy-weighting TOPSIS method is used to select the optimal emergency decision scheme. The model’s effectiveness is demonstrated through a case study of the “9–27” fire incident at the Chongqing Songzao Coal Mine, where findings affirm the model’s practicality and accuracy in supporting timely, effective emergency responses to external coal mine fires.

A review of optimization and decision models of prescribed burning for wildfire management.

Prescribed burning is an essential forest management tool that requires strategic planning to effectively address its multidimensional impacts, particularly given the influence of global climate change on fire behavior. Despite the inherent complexity in planning prescribed burns, limited efforts have been made to comprehensively identify the critical elements necessary for formulating effective models. In this work, we present a systematic review of the literature on optimization and decision models for prescribed burning, analyzing 471 academic papers published in the last 25 years. Our study identifies four main types of models: spatial-allocation, spatial-extent, temporal-only, and spatial-temporal. We observe a growing number of studies on modeling prescribed burning, primarily due to the expansion in spatial-allocation and spatial-temporal models. There is also an increase in complexity as the models consider more elements affecting prescribed burning effectiveness. We identify the essential components for optimization models, including stakeholders, decision variables, objectives, and influential factors, to enhance model practicality. The review also examines solution techniques, such as integer programming in spatial allocation, stochastic dynamic programming in probabilistic models, and multiobjective programming in balancing trade-offs. These techniques' strengths and limitations are discussed to help researchers adapt methods to specific challenges in prescribed burning optimization. In addition, we investigate general assumptions in the models and challenges in relaxation to enhance practicality. Lastly, we propose future research to develop more comprehensive models incorporating dynamic fire behaviors, stakeholder preferences, and long-term impacts. Enhancing these models' accuracy and applicability will enable decision-makers to better manage wildfire treatmentoutcomes.

Sustainable Operation Mode Choices for Second-Hand Inspection Platforms

The sale of second-hand goods has formed a complete industrial chain, and second-hand product testing is a crucial part of it. Second-hand inspection platforms (SIPs) have achieved remarkable commercial success by providing inspection services that alleviate consumers’ quality concerns. Different SIPs typically adopt various operation modes, such as consignment, resale, or hybrid modes. Appropriate operation modes not only benefit SIPs in maintaining profitability but also contribute to the sustainable development of the sharing economy. In order to realize the sustainable operation of second-hand inspection platforms, we construct a platform-dominated Stackelberg model to explore the motivations behind SIPs’ choices of different operation modes and investigate the impacts of changes in the inspection service level on the platform’s optimal decisions and market performance. System data analysis results show that the cost of guarantee significantly influences SIPs’ choices of operation modes, specifically; SIPs are inclined to adopt consignment mode or resale mode when the cost of guarantee is relatively high or low, respectively, and choose hybrid mode when the cost of guarantee is moderate. Furthermore, in the presence of inter-channel competition, if the inspection failure loss is relatively high, SIPs may lower the prices of used products as the inspection service level increases. Additionally, although inspection service can disclose the true quality of used products, a higher inspection service level may attract more low-quality sellers into the market when the inspection failure loss is substantial. Finally, under the resale mode, consumer surplus and social welfare will decrease with the inspection service level. Conversely, under the consignment or hybrid mode, both consumer surplus and social welfare will increase with the inspection service level when the inspection failure loss is relatively low.

Relationship between [18F]FDG PET/CT findings and claudin 18.2 expression in metastatic gastric cancer.

Given that claudin 18.2 (CLDN18.2) is a cell surface protein specifically expressed by gastric cancer cells, anti-CLDN18.2 antibodies have demonstrated significant antitumor effects in patients with advanced gastric adenocarcinoma. The correlation of [18F]FDG PET/CT with CLDN18.2 expression remains unexplored. This study aimed to investigate whether CLDN18.2 expression was associated with [18F]FDG uptake and whether [18F]FDG PET/CT can be used to predict the CLDN18.2 status of gastric cancer. A retrospective analysis of [18F]FDG PET/CT images from 163 patients diagnosed with metastatic gastric cancer was conducted, and the expression of CLDN18.2 was assessed immunohistochemically. SUVmax, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated in 3D mode using vendor-provided software. The relationship between PET metabolic parameters and CLDN18.2 status was analyzed. CLDN18.2-negative tumors showed a higher median SUVmax of 13.2 (1.8-46.7) compared to CLDN18.2-positive tumors at 7.55 (2.3-34.8), with a significant difference (p < 0.001). The median TLG was significantly higher in CLDN18.2-negative tumors (231.6) than in CLDN18.2-positive ones (81.14), indicating greater metabolic activity (p = 0.001). Multivariate analysis suggested that SUVmax remained significantly correlated with the status of CLDN18.2 (p = 0.01). CLDN18.2 expression was predicted with an accuracy of 69.9% when the SUVmax value of 10.9 was used as a cutoff point for analysis. Relatively reduced [18F]FDG uptake in metastatic gastric cancers correlates with positive CLDN18.2 expression compared to those with negative CLDN18.2 expression. [18F]FDG PET/CT may be useful for predicting the CLDN18.2 status of gastric cancer and thus aid in optimal treatment decisions. Question The study resolves the clinical issue of determining the correlation between [18F]FDG PET/CT imaging and claudin 18.2 expression in metastatic gastric cancer. Findings Claudin 18.2-positive metastatic gastric cancers exhibit relatively lower [18F]FDG uptake than negative ones. The SUVmax of 10.9 moderately predicts claudin 18.2 expression. Clinical relevance [18F]FDG PET/CT imaging could be a noninvasive way to predict claudin 18.2 status in metastatic gastric cancer, helping to improve personalized treatment plans.

Optimal pricing policy for green products under supply disruption

In recent years, the frequency and extent of supply disruptions have caused serious challenges for supply chains worldwide. To address the resulting supply–demand mismatches, supply chain partners regularly have employed various pricing strategies. First, this study focuses on presenting an optimal pricing model and analysis for a retailer in a monopoly market under supply disruption, which impacts the offerings of two substitutable products with different degrees of greenness. Next, the study presents and analyzes optimal pricing decisions for a retailer in a duopoly market, where two competing retailers have similar product offerings. Results for the monopoly market indicate that as the disruption duration increases, to compensate for the increased waiting time, the price of the greener product should decrease, while the price of the less green product may either increase or decrease depending on the consumer’s sensitivity to price and wait times. In addition, the price difference between the products grows with the disparity in their degrees of greenness. In a duopoly market, the non-disrupted retailer can capitalize on their competitors’ supply issues by adjusting their pricing strategies accordingly. Sensitivity analyses conducted under both monopoly and duopoly markets reveal the impacts of critical factors individually and in combinations. These findings provide actionable insights for retailers to strategically manage product pricing and demand in the face of green supply chain disruptions.

Costly exploration produces stereotypes with dimensions of warmth and competence.

Traditional explanations for stereotypes assume that they result from deficits in humans (ingroup-favoring motives, cognitive biases) or their environments (majority advantages, real group differences). An alternative explanation recently proposed that stereotypes can emerge when exploration is costly. Even optimal decision makers in an ideal environment can inadvertently form incorrect impressions from arbitrary encounters. However, all these existing theories essentially describe shortcuts that fail to explain the multidimensionality of stereotypes. Stereotypes of social groups have a canonical multidimensional structure, organized along dimensions of warmth and competence. We show that these dimensions and the associated stereotypes can result from feature-based exploration: When individuals make self-interested decisions based on past experiences in an environment where exploring new options carries an implicit cost and when these options share similar attributes, they are more likely to separate groups along multiple dimensions. We formalize this theory via the contextual multiarmed bandit problem, use the resulting model to generate testable predictions, and evaluate those predictions against human behavior. We evaluate this process in incentivized decisions involving as many as 20 real jobs and successfully recover the classic dimensions of warmth and competence. Further experiments show that intervening on the cost of exploration effectively mitigates bias, further demonstrating that exploration cost per se is the operating variable. Future diversity interventions may consider how to reduce exploration cost, in ways that parallel our manipulations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks

The use of Reinforcement Learning (RL) techniques has been widely addressed in the literature to cope with capacity sharing in 5G Radio Access Network (RAN) slicing. These algorithms consider a training process to learn an optimal capacity sharing decision-making policy, which is later applied to the RAN environment during the inference stage. When relevant changes occur in the RAN, such as the deployment of new cells in the network, RL-based capacity sharing solutions require a re-training process to update the optimal decision-making policy, which may require long training times. To accelerate this process, this paper proposes a novel Transfer Learning (TL) approach for RL-based capacity sharing solutions in multi-cell scenarios that is implementable following the Open-RAN (O-RAN) architecture and exploits the availability of computing resources at the edge for conducting the training/inference processes. The proposed approach allows transferring the weights of the previously learned policy to learn the new policy to be used after the addition of new cells. The performance assessment of the TL solution highlights its capability to reduce the training process duration of the policies when adding new cells. Considering that the roll-out of 5G networks will continue for several years, TL can contribute to enhancing the practicality and feasibility of applying RL-based solutions for capacity sharing.

Long-term outcomes of additional surgery versus surveillance-only clinical decision for early colorectal cancer patients after non-curative endoscopic resection: a meta-analysis

ObjectivesThe clinical decision-making regarding post hoc management of early colorectal cancer (CRC) patients who have undergone non-curative endoscopic resection (ER) remains a subject of debate. This systematic review and meta-analysis aims to compare the clinical outcomes between patients undergoing additional surgery and those receiving surveillance only.MethodsA comprehensive literature search was conducted across three major medical databases: PubMed, Embase, and the Cochrane Library. STATA software was utilized for pooling analysis. The methodological quality of the included studies was assessed using the Newcastle–Ottawa Quality Scale.ResultsA total of 15 eligible studies encompassing 3,508 early CRC patients were included in this meta-analysis (additional surgery group: 1,974 cases; surveillance-only group: 1,533 cases). All included studies demonstrated good methodological quality, with Newcastle–Ottawa scores no less than 6. The results of the meta-analysis indicated that compared to the surveillance-only group, patients in the additional surgery group exhibited significantly improved overall survival (OR = 2.95, 95% CI: 2.05–4.24, P < 0.05), enhanced recurrence-free survival (OR = 2.53, 95% CI = 1.38–4.62, P < 0.05), a reduced recurrence rate (OR = 1.96, 95% CI = 1.22–3.13, P < 0.05), and a lower local recurrence rate (OR = 2.35, 95% CI = 1.12–4.95, P < 0.05). No significant sources of heterogeneity were identified among the studies analyzed; publication bias was also deemed acceptable across these investigations. Furthermore, we performed subgroup analyses based on inclusion criteria and age stratification which revealed notable differences in effect sizes between groups (JSCCR subgroup: OR = 2.09; 95% CI = 1.32–3.30 versus Non-JSCCR subgroup: OR = 1 .54; 95% CI = 0.89 -2.65, indicating negative results). Pooling analysis showed no significant difference between subgroups when stratified by age using a cutoff value of 65 years old.ConclusionsCompared to patients who underwent surveillance only, those receiving additional surgical treatment demonstrated superior outcomes in terms of overall survival, recurrence-free survival, recurrence rates, and control of local recurrences. This suggests that such an approach may represent a more optimal clinical decision for early-stage colorectal cancer (CRC) patients who have received non-curative endoscopic resection (ER). Furthermore, this study indicates that the inclusion criteria significantly influence the reported outcomes. Notably, age did not affect the recurrence rate. Overall, this is the first meta-analysis aimed at exploring and clarifying this ongoing controversy.