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Originating from the desire to improve sustainability, producing fuels and chemicals from the conversion of biomass and waste plastic has become an important research topic in the twenty-first century. Although biomass is natural and plastic synthetic, the chemical nature of the two are not as distinct as they first appear. They share substantial structural similarities in terms of their polymeric nature and the types of bonds linking their monomeric units, resulting in close relationships between the two materials and their conversions. Previously, their transformations were mostly studied and reviewed separately in the literature. Here, we summarize the catalytic conversion of biomass and waste plastics, with a focus on bond activation chemistry and catalyst design. By tracking the historical and more recent developments, it becomes clear that biomass and plastic have not only evolved their unique conversion pathways but have also started to cross paths with each other, with each influencing the landscape of the other. As a result, this Review on the catalytic conversion of biomass and waste plastic in a unified angle offers improved insights into existing technologies, and more importantly, may enable new opportunities for future advances. Biomass and plastic share structural similarities in their composition and types of bond linkage between their monomeric units. Reviewing their catalytic conversion technologies in a unified angle provides new insights and opportunities for future advances.

Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as “IF” and “THEN”, and operators, such as “>“ and “<“, to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall.

Internet-of-Things (IoT) is a technology that is extensively being used in various fields. Companies like Samsung, LG, and Apple are launching home appliances that use IoT as a part of their smart home business. Currently, Intelligent Things which combine artificial intelligence (AI) and IoT are being developed. Most of these devices are configured to collect and respond to human behavior (motion, voice, etc.) through built-in sensors. If IoT devices do not ensure high security, personal information could be leaked. This paper describes the IoT security threats that can cause information leakage from a hierarchical viewpoint of cyberspace. In addition, because these smart home-based IoT devices are closely related to human life, considering social damage is a problem. To overcome this, we propose a framework to measure the risk of IoT devices based on security scenarios that can occur in a smart home.

Metal promotion is the most widely adopted strategy for enhancing the hydrogenation functionality of an oxide catalyst. Typically, metal nanoparticles or dopants are located directly on the catalyst surface to create interfacial synergy with active sites on the oxide, but the enhancement effect may be compromised by insufficient hydrogen delivery to these sites. Here, we introduce a strategy to promote a ZnZrO x methanol synthesis catalyst by incorporating hydrogen activation and delivery functions through optimized integration of ZnZrO x and Pd supported on carbon nanotube (Pd/CNT). The CNT in the Pd/CNT + ZnZrO x system delivers hydrogen activated on Pd to a broad area on the ZnZrO x surface, with an enhancement factor of 10 compared to the conventional Pd-promoted ZnZrO x catalyst, which only transfers hydrogen to Pd-adjacent sites. In CO 2 hydrogenation to methanol, Pd/CNT + ZnZrO x exhibits drastically boosted activity—the highest among reported ZnZrO x -based catalysts—and excellent stability over 600 h on stream test, showing potential for practical implementation. Boosting activity of oxide catalysts is a long-lasting challenge to developing efficient catalysts for industrially important reactions such as CO 2 -to-methanol. Here, the authors report a strategy for enhancing the activity of a ZnZrO x methanol synthesis catalyst via engineered nanoscale H supply.

Cybersecurity in Industrial Internet of Things (IIoT) has become critical as smart cities are becoming increasingly linked to industrial control systems (ICSs) used in critical infrastructure. Consequently, data-driven security systems for analyzing massive amounts of data generated by smart cities have become essential. A representative method for analyzing large-scale data is the game bot detection approach used in massively multiplayer online role-playing games. We reviewed the literature on bot detection methods to extend the anomaly detection approaches used in bot detection schemes to IIoT fields. Finally, we proposed a process wherein the data envelopment analysis (DEA) model was applied to identify features for efficiently detecting anomalous behavior in smart cities. Experimental results using random forest show that our extracted features based on a game bot can achieve an average F1-score of 0.99903 using 10-fold validation. We confirmed the applicability of the analyzed game-industry methodology to other fields and trained a random forest on the high-efficiency features identified by applying a DEA, obtaining an F1-score of 0.997 using the validation set approach. In this study, an anomaly detection method for analyzing massive smart city data based on a game industry methodology was presented and applied to the ICS dataset.

Radiocontrast media is a major cause of nephrotoxic acute kidney injury(AKI). Contrast-enhanced CT(CE-CT) is commonly performed in emergency departments(ED). Predicting individualized risks of contrast-associated AKI(CA-AKI) in ED patients is challenging due to a narrow time window and rapid patient turnover. We aimed to develop machine-learning(ML) models to predict CA-AKI in ED patients. Adult ED patients who underwent CE-CT between 2016 and 2020 at an academic, tertiary, referral hospital were included. Demographic, clinical, and laboratory data were collected from electronic medical records. Five ML models based on logistic regression; random forest; extreme gradient boosting; light gradient boosting; and multilayer perceptron were developed, using 42 features. Among 22,984 ED patients who underwent CE-CT; 1,862(8.1%) developed CA-AKI. The LGB model performed the best (AUROC = 0.731). Its top 10 features, in order of importance for predicting CA-AKI, were baseline serum creatinine; systolic blood pressure; serum albumin; estimated glomerular filtration rate; blood urea nitrogen; body weight; serum uric acid; hemoglobin; triglyceride; and body temperature. Given the difficulty of predicting risk of CA-AKI in ED, this model can help clinicians with early recognition of AKI and nephroprotective point-of-care interventions.

Background Negative fluid balance during continuous renal replacement therapy (CRRT) is associated with improved clinical outcomes. The effects of negative fluid balance in patients with heterogeneous characteristics undergoing CRRT remain unknown. Methods This retrospective study included 2658 adult patients who received CRRT for ≥ 4 days between 2009 and 2020. Patients were divided according to disease category. Fluid balance was assessed using percent change in body weight (PCB) or cumulative input/output (I/O). Primary outcome was 90‐day mortality. Results More positive PCB results in the fluid overload phase and fewer negative PCB results in the fluid removal phase were associated with mortality. A less negative PCB in the fluid removal phase was associated with mortality in medical patients (odds ratio [OR]: 1.052; 95% confidence interval [CI]: 1.023–1.082, and p = 0.001 in the medical patients and OR: 1.064; 95% CI: 1.011–1.120, and p = 0.017 in cardiovascular medical patients) but not in surgical patients. In patients with discrepancies between PCB and cumulative I/O, PCB was associated with mortality, which was consistent with the overall pattern; however, cumulative I/O was not associated with mortality. Conclusions Early negative fluid balance is associated with better survival in medical patients but not in surgical patients, who may require more careful fluid balance.

During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Government agencies and local authorities have placed strict regulations regarding the location and amount allowed for gambling. These efforts are made to prevent gambling addictions and monitor fraudulent activities. The revenue earned from gambling provides a considerable amount of tax revenue. The inception of internet gambling have allowed professional gamblers to par take in unlawful acts. However, the lack of studies on the technical inspections and systems to prohibit unlawful internet gambling has caused incidents such as the Walkerhill Hotel incident in 2016, where fraudsters placed bets abnormally by modifying an Internet of Things (IoT)-based application called “MyCard”. This paper investigates the logic used by smartphone IoT applications to validate the location of users and then confirm continuous threats. Hence, our research analyzed transactions made on applications that operated using location authentication through IoT devices. Drawing on gambling transaction data from the Korea Racing Authority, this research used time series machine learning algorithms to identify anomalous activities and transactions. In our research, we propose a method to detect and prevent these anomalies by conducting a comparative analysis of the results of existing anomaly detection techniques and novel techniques.

The open-source structure and ease of development in the Android platform are exploited by attackers to develop malicious programs, greatly increasing malicious Android apps aimed at committing financial fraud. This study proposes a machine learning (ML) model based on static analysis to detect malware. We validated the significance of private datasets collected from Bank A, comprising 183,938,730 and 11,986 samples of benign and malicious apps, respectively. Undersampling was performed to adjust the proportion of benign applications in the training data because the data on benign and malicious apps were unbalanced. Moreover, 92 datasets were compiled through daily training to evaluate the proposed approach, with benign app data updated over 70 days (D-70 to D-1) and malware app data cumulatively aggregated to address the imbalance. Five ML algorithms were used to evaluate the proposed approach, and the optimal hyperparameter values for each algorithm were obtained using a grid search method. We then evaluated the models using common evaluation metrics, such as accuracy, precision, recall, F1-Score, etc. The LightGBM model was selected for its superior performance, achieving high accuracy and effectiveness. The optimal decision threshold for determining whether an application was malicious was 0.5. Following re-evaluation, the LightGBM model obtained accuracy and F1-Score values of 99.99% and 97.04%, respectively, highlighting the potential of using the proposed model for real-world financial fraud detection.