RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of playtesting agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline.Comment: 14 pages, 6 figures, 6 tables, 2 algorithm

Pneumomediastinum and pneumothorax after orthognathic surgery -A case report-

The occurrences of pneumomediastinum and pneumothorax after oral and/or maxillofacial surgery are rare, but both are potentially life-threatening complications. Most of the cases that present pneumomediastinum and pneumothorax in the oral and/or maxillofacial surgery result from air dissecting down the fascial planes of the neck. We report a case of a 23-year-old male patient who underwent bilateral sagittal split ramus osteotomy under general anesthesia and developed pneumomediastinum and pneumothorax without any traumatic introduction of air through the cervical fascia three days postoperatively. The possible causes and its prevention are discussed with a review of the relevant literature

Agreement and Reliability Analysis of Machine Learning Scaling and Wireless Monitoring in the Assessment of Acute Proximal Weakness by Experts and Non-Experts: A Feasibility Study

Assessing the symptoms of proximal weakness caused by neurological deficits requires the knowledge and experience of neurologists. Recent advances in machine learning and the Internet of Things have resulted in the development of automated systems that emulate physicians’ assessments. The application of those systems requires not only accuracy in the classification but also reliability regardless of users’ proficiency in the real environment for the clinical point-of-care and the personalized health management. This study provides an agreement and reliability analysis of using a machine learning-based scaling of Medical Research Council (MRC) proximal scores to evaluate proximal weakness by experts and non-experts. The system trains an ensemble learning model using the signals from sensors attached to the limbs of patients in a neurological intensive care unit. For the agreement analysis, we investigated the percent agreement of MRC proximal scores and Bland-Altman plots of kinematic features between the expert- and non-expert scaling. We also analyzed the intra-class correlation coefficients (ICCs) of kinematic features and Krippendorff’s alpha of the observers’ scaling for the reliability analysis. The mean percent agreement between the expert- and the non-expert scaling was 0.542 for manual scaling and 0.708 for autonomous scaling. The ICCs of kinematic features measured using sensors ranged from 0.742 to 0.850, whereas the Krippendorff’s alpha of manual scaling for the three observers was 0.275. The autonomous assessment system can be utilized by the caregivers, paramedics, or other observers during an emergency to evaluate acute stroke patients

Association between Obesity Indexes and Thyroid Cancer Risk in Korean Women: Nested Case–Control Study

Objective: This study aimed to identify the association between various obesity indexes, including waist circumference (WC), waist–hip ratio (WHR), waist–height ratio (WHTR), and BMI, and their combinations with body mass index (BMI) and thyroid cancer risk. Methods: Of the 65,639 participants who completed a follow-up survey of the Health Examinee Study (HEXA), a prospective cohort of the Korean Genome and Epidemiology Study, 412 female incident thyroid cancer cases, and 1648 birth year- and enrollment year-matched female controls were included. Multiple conditional logistic regression was used to estimate the association between obesity indexes and thyroid cancer risk. Results: The risk of developing thyroid cancer was increased by 1.37-fold (95% confidence interval (CI) = 1.03–1.81) higher in the obese BMI group (≥25.0 Kg/m2) compared to that in the normal BMI group (<23.0 Kg/m2). Obesity in terms of WC (≥85.0 cm) and WHTR (≥0.5) was associated with an increased risk of thyroid cancer (OR 1.55, 95% CI = 1.16–2.07; OR 1.37, 95% CI = 1.07–1.75, respectively). However, increased WHR levels did not show any significant association. Women with both obese levels of BMI (≥25.0 Kg/m2) and other obesity indexes (WC ≥ 85.0 cm, WHR ≥ 0.85, or WHTR ≥ 0.5) showed an increased risk of thyroid cancer with OR of 1.63 (95% CI = 1.14–2.31), 1.49 (95% CI = 1.05–2.12), and 1.42 (95% CI = 1.04–1.94), compared to those with normal levels of BMI and each obesity index. Conclusion: These results provide evidence of the contribution of both total and central adiposity across the lifespan of thyroid cancer incidence. Risk factor modifications must be considered to explain the current thyroid cancer epidemic

An Energy-Efficient Multi-Level Sleep Strategy for Periodic Uplink Transmission in Industrial Private 5G Networks

This paper proposes an energy-efficient multi-level sleep mode control for periodic transmission (MSC-PUT) in private fifth-generation (5G) networks. In general, private 5G networks meet IIoT requirements but face rising energy consumption due to dense base station (BS) deployment, particularly impacting operating expenses (OPEX). An approach of BS sleep mode has been studied to reduce energy consumption, but there has been insufficient consideration for the periodic uplink transmission of industrial Internet of Things (IIoT) devices. Additionally, 5G New Reno’s synchronization signal interval limits the effectiveness of the deepest sleep mode in reducing BS energy consumption. By addressing this issue, the aim of this paper is to propose an energy-efficient multi-level sleep mode control for periodic uplink transmission to improve the energy efficiency of BSs. In advance, we develop an energy-efficient model that considers the trade-off between throughput impairment caused by increased latency and energy saving by sleep mode operation for IIoT’s periodic uplink transmission. Then, we propose an approach based on proximal policy optimization (PPO) to determine the deep sleep mode of BSs, considering throughput impairment and energy efficiency. Our simulation results verify the proposed MSC-PUT algorithm’s effectiveness in terms of throughput, energy saving, and energy efficiency. Specifically, we verify that our proposed MSC-PUT enhances energy efficiency by nearly 27.5% when compared to conventional multi-level sleep operation and consumes less energy at 75.21% of the energy consumed by the conventional method while incurring a throughput impairment of nearly 4.2%. Numerical results show that the proposed algorithm can significantly reduce the energy consumption of BSs accounting for periodic uplink transmission of IIoT devices

Application of an Internet of Medical Things (IoMT) to Communications in a Hospital Environment

IoT technology is used in various industries, including the manufacturing, energy, finance, education, transportation, smart home, and medical fields. In the medical field, IoT applications can provide high-quality medical services through the efficient management of patients and mobile assets in hospitals. In this paper, we introduce an IoT system to the medical field using Sigfox, a low-power communication network for indoor location monitoring used as a hospital network. A proof-of-concept (PoC) was implemented to evaluate the effectiveness of medical device and patient safety management. Specific requirements should be considered when applying the IoMT system in a hospital environment. In this study, the location and temperature of various targets sending signals to the monitoring system using three different networks (Sigfox, Hospital and Non-Hospital) were collected and compared with true data, the average accuracy of which were 69.2%, 72.5%, and 83.3%, respectively. This paper shows the significance in the application of an IoMT using the Sigfox network in a hospital setting in Korea compared with existing hospital networks

Ag(I) ions working as a hole-transfer mediator in photoelectrocatalytic water oxidation on WO3 film

Ag(I) is commonly employed as an electron scavenger to promote water oxidation. In addition to its straightforward role as an electron acceptor, Ag(I) can also capture holes to generate the high-valent silver species. Herein, we demonstrate photoelectrocatalytic (PEC) water oxidation and concurrent dioxygen evolution by the silver redox cycle where Ag(I) acts as a hole-transfer mediator. Ag(I) enhances the PEC performance of WO3 electrodes at 1.23 V vs. RHE with increasing O-2 evolution, while forming Ag(II) complexes ((AgNO3+)-N-II). Upon turning off both light and potential bias, the photocurrent immediately drops to zero, whereas O-2 evolution continues over similar to 10 h with gradual bleaching of the colored complexes. This phenomenon is observed neither in the Ag(I)-free PEC reactions nor in the photocatalytic (i.e., bias-free) reactions with Ag(I). This study finds that the role of Ag(I) is not limited as an electron scavenger and calls for more thorough studies on the effect of Ag(I).11Ysciescopu