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Pembrolizumab has shown improved progression-free survival versus chemotherapy in patients with newly diagnosed microsatellite instability-high or mismatch repair-deficient metastatic colorectal cancer. However, the treatment's effect on overall survival in this cohort of patients was unknown. Here, we present the final overall survival analysis of the KEYNOTE-177 study. This randomised, open-label, phase 3 study was done in 193 academic medical centres and hospitals in 23 countries. We recruited patients aged at least 18 years, with an Eastern Cooperative Oncology Group performance status of 0 or 1, and who had previously untreated microsatellite instability-high or mismatch repair-deficient metastatic colorectal cancer. Patients were randomly assigned (1:1) in blocks of four using an interactive voice response system or integrated web response system to intravenous pembrolizumab 200 mg every 3 weeks or to the investigator's choice of intravenous mFOLFOX6 (oxaliplatin 85 mg/m2 on day 1, leucovorin 400 mg/m2 on day 1, and fluorouracil 400 mg/m2 bolus on day 1 followed by a continuous infusion of 1200 mg/m2 per day for 2 days on days 1–2) or intravenous FOLFIRI (irinotecan 180 mg/m2 on day 1, leucovorin 400 mg/m2 on day 1, and fluorouracil 400 mg/m2 bolus on day 1 followed by a continuous infusion of 1200 mg/m2 per day for 2 days on days 1–2), every 2 weeks with or without intravenous bevacizumab 5 mg/kg every 2 weeks or intravenous weekly cetuximab (first dose 400 mg/m2, then 250 mg/m2 for every subsequent dose). Patients receiving chemotherapy could cross over to pembrolizumab for up to 35 treatment cycles after progression. The co-primary endpoints were overall survival and progression-free survival in the intention-to-treat population. KEYNOTE-177 is registered at ClinicalTrials.gov, NCT02563002, and is no longer enrolling patients. Between Feb 11, 2016, and Feb 19, 2018, 852 patients were screened, of whom 307 (36%) were randomly assigned to pembrolizumab (n=153) or chemotherapy (n=154). 93 (60%) patients crossed over from chemotherapy to anti-PD-1 or anti-PD-L1 therapy (56 patients to on-study pembrolizumab and 37 patients to off-study therapy). At final analysis (median follow-up of 44·5 months [IQR 39·7–49·8]), median overall survival was not reached (NR; 95% CI 49·2–NR) with pembrolizumab vs 36·7 months (27·6–NR) with chemotherapy (hazard ratio [HR] 0·74; 95% CI 0·53–1·03; p=0·036). Superiority of pembrolizumab versus chemotherapy for overall survival was not demonstrated because the prespecified α of 0·025 needed for statistical significance was not achieved. At this updated analysis, median progression-free survival was 16·5 months (95% CI 5·4–38·1) with pembrolizumab versus 8·2 months (6·1–10·2) with chemotherapy (HR 0·59, 95% CI 0·45–0·79). Treatment-related adverse events of grade 3 or worse occurred in 33 (22%) of 153 patients in the pembrolizumab group versus 95 (66%) of 143 patients in the chemotherapy group. Common adverse events of grade 3 or worse that were attributed to pembrolizumab were increased alanine aminotransferase, colitis, diarrhoea, and fatigue in three (2%) patients each, and those attributed to chemotherapy were decreased neutrophil count (in 24 [17%] patients), neutropenia (22 [15%]), diarrhoea (14 [10%]), and fatigue (13 [9%]). Serious adverse events attributed to study treatment occurred in 25 (16%) patients in the pembrolizumab group and in 41 (29%) patients in the chemotherapy group. No deaths attributed to pembrolizumab occurred; one death due to intestinal perforation was attributed to chemotherapy. In this updated analysis, although pembrolizumab continued to show durable antitumour activity and fewer treatment-related adverse events compared with chemotherapy, there was no significant difference in overall survival between the two treatment groups. These findings support pembrolizumab as an efficacious first-line therapy in patients with microsatellite instability-high or mismatch repair-deficient metastatic colorectal cancer. MSD.

Time-sensitive networking (TSN) technologies have garnered attention for supporting time-sensitive communication services, with recent interest extending to the wireless domain. However, adapting TSN to wireless areas faces challenges due to the competitive channel utilization in IEEE 802.11, necessitating exclusive channels for low-latency services. Additionally, traditional TSN scheduling algorithms may cause significant transmission delays due to dynamic wireless characteristics, which must be addressed. This paper proposes a wireless TSN model of IEEE 802.11 networks for the exclusive channel access and a novel time-sensitive traffic scheduler, named the wireless intelligent scheduler (WISE), based on deep reinforcement learning. We designed a deep reinforcement learning (DRL) framework to learn the repetitive transmission patterns of time-sensitive traffic and address potential latency issues from changing wireless conditions. Within this framework, we identified the most suitable DRL model, presenting the WISE algorithm with the best performance. Experimental results indicate that the proposed mechanisms meet up to 99.9% under the various wireless communication scenarios. In addition, they show that the processing delay is successfully limited within the specific time requirements and the scalability of TSN streams is guaranteed by the proposed mechanisms.

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device’s signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.

Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT.

Hardware bottlenecks can throttle smart device (SD) performance when executing computation-intensive and delay-sensitive applications. Hence, task offloading can be used to transfer computation-intensive tasks to an external server or processor in Mobile Edge Computing. However, in this approach, the offloaded task can be useless when a process is significantly delayed or a deadline has expired. Due to the uncertain task processing via offloading, it is challenging for each SD to determine its offloading decision (whether to local or remote and drop). This study proposes a deep-reinforcement-learning-based offloading scheduler (DRL-OS) that considers the energy balance in selecting the method for performing a task, such as local computing, offloading, or dropping. The proposed DRL-OS is based on the double dueling deep Q-network (D3QN) and selects an appropriate action by learning the task size, deadline, queue, and residual battery charge. The average battery level, drop rate, and average latency of the DRL-OS were measured in simulations to analyze the scheduler performance. The DRL-OS exhibits a lower average battery level (up to 54%) and lower drop rate (up to 42.5%) than existing schemes. The scheduler also achieves a lower average latency of 0.01 to >0.25 s, despite subtle case-wise differences in the average latency.

Disaster management systems require accurate disaster monitoring and prediction services to reduce damages caused by natural disasters. Digital twins of natural environments can provide the services for the systems with physics-based and data-driven disaster models. However, the digital twins might generate erroneous disaster prediction due to the impracticability of defining high-fidelity physics-based models for complex natural disaster behavior and the dependency of data-driven models on the training dataset. This causes disaster management systems to inappropriately use disaster response resources, including medical personnel, rescue equipment and relief supplies, to ensure that it may increase the damages from the natural disasters. This study proposes a digital twin architecture to provide accurate disaster prediction services with a similarity-based hybrid modeling scheme. The hybrid modeling scheme creates a hybrid disaster model that compensates for the errors of physics-based prediction results with a data-driven error correction model to enhance the prediction accuracy. The similarity-based hybrid modeling scheme reduces errors from the data dependency of the hybrid model by constructing a training dataset using similarity assessments between the target disaster and the historical disasters. Evaluations in wildfire scenarios show that the digital twin decreases prediction errors by approximately 50% compared with those of the existing schemes.

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

Purpose Acrylonitrile butadiene styrene is an important material in 3D printing due to its strength, durability, heat resistance and cost-effectiveness. These properties make it suitable for various applications, from functional prototypes to end-use products. This study aims to model and predict the mechanical properties of acrylonitrile butadiene styrene parts produced using the fused deposition modeling process. Design/methodology/approach The experiment was carefully designed to determine the optimal print parameters, including layer thickness, nozzle temperature and infill density. Tensile tests were performed on all printed samples following industry standards to gauge the mechanical properties such as elastic modulus, ultimate tensile strength, yield strength and breakpoint. Taguchi optimization and variable analysis were used to explore the relationship between mechanical properties and print parameters. Furthermore, an artificial neural network (ANN) regression model was implemented to predict mechanical properties based on varying print conditions. Findings The results demonstrated that layer thickness has the most significant influence on mechanical properties when compared to other print conditions. The optimization approaches indicated a clear relationship between the selected print parameters and the material’s mechanical response. For acrylonitrile butadiene styrene material, the optimal print settings were determined to be a 0.25 mm layer thickness, a 270 °C nozzle temperature and a 30 % infill density. Moreover, the ANN model notably excelled in predicting the yield strength of the material with greater accuracy than other mechanical properties. Originality/value Comparing the accuracy and capabilities of the Taguchi and ANN models in analyzing mechanical properties, it was found that both models closely matched the experimental data. However, the ANN model showed superior accuracy in predicting tensile outcomes. In conclusion, while the ANN model offers higher predictive accuracy for tensile results, both Taguchi and ANN methods are effective in modeling the mechanical properties of 3D-printed acrylonitrile butadiene styrene materials.

Moutan Cortex (MC), the dried root bark of Paeonia suffruticosa, is used in traditional Chinese and Korean medicine to treat enteritis for its anti-inflammatory properties. This study compared the pharmacokinetic (PK) profiles of paeonol and paeoniflorin in normal and dinitrobenzene sulfonic acid (DNBS)-induced colitis rats, and to determine how repeated low-dose MC [MC(L), 0.5 g/kg] or high-dose MC [MC(H), 2.5 g/kg] alters PK and disease severity. Using ultra-performance liquid chromatography-tandem mass spectrometry, we found that DNBS modestly increased paeonol AUClast (NC: 247.8 ± 63.7 vs DNBS: 337.0 ± 120.8 hr*ng/mL) and decreased paeoniflorin (NC: 474.1 ± 11.7 vs DNBS: 463.7 ± 106.8 hr*ng/mL) compared to controls (ns). After repeated dosing, the maximum plasma concentration (Cmax) of paeonol was higher in the MC(H) than that in the MC(L) group (MC(L): 63.81 ± 29.74 vs MC(H): 4221.5 ± 1579.2 ng/mL, p < 0.05). Paeoniflorin Cmax in the MC(H) group was also higher than MC(L) group (MC(L): 60.5 ± 15.3 vs MC(H): 164.7 ± 74.7 ng/mL, p < 0.05). Repeated MC(H) treatments improved body weight loss and disease activity index. Western blots indicated that the expression of intestinal epithelial integrity-related proteins in the MC(H) group was comparable to that in the control. Inflammation did not influence paeonol and paeoniflorin PK significantly, whereas MC(H) group markedly increased systemic exposure, especially of paeonol, and demonstrated symptom relief. Appropriate dose adjustments are necessary to ensure safe and effective therapy because PK changes can lead to increased systemic exposure and affect treatment outcomes.

As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using high-fidelity disaster models and real-time observational data with distributed computing schemes. However, the previous schemes take little account of the correlations between environmental data of disasters, such as landscapes and weather. This causes inaccurate computing load predictions resulting in unbalanced load partitioning, which increases the prediction service times of the disaster management agencies. In this paper, we propose a novel distributed computing framework to accelerate the prediction services through semantic analyses of correlations between the environmental data. The framework combines the data into disaster semantic data to represent the initial disaster states, such as the sizes of wildfire burn scars and fuel models. With the semantic data, the framework predicts computing loads using the convolutional neural network-based algorithm, partitions the simulation model into balanced sub-models, and allocates the sub-models into distributed computing nodes. As a result, the proposal shows up to 38.5% of the prediction time decreases, compared to the previous schemes.