The Golden Touch by Light: A Finely Engineered Luminogen Empowering High Photoactivatable and Photodynamic Efficiency for Cancer Phototheranostics

Photoactivatable agent is a powerful tool in biomedicine studies due to high-precision spatiotemporal control of light. However, those previously reported agents generally suffer from short wavelength, fluorescence self-quenching effect, and the lack of photosensitizing property, which severely restrict their practical applications. To address these issues, molecular engineering of 1,4-dihydropyridine derivatives is conducted to obtain an optimized agent, namely TPA-DHPy-Py, which exhibits low oxidation potential, high photoactivation efficiency, and excellent type I/II combined photodynamic activity. Concurrently, its photoactivated counterpart is featured by aggregation-induced near-infrared emission and remarkable reactive oxygen species (ROS) production efficiency. Upon photoactivation, TPA-DHPy-Py is capable of precisely identifying cancer cells from co-culturing cancer cells and normal cells without the assistance of any extra targeting units, and in situ monitoring lipid droplets and endoplasmic reticulum alteration under ROS stress, as well as achieving fluorescent visualization of tumor in vivo with supremely high imaging contrast. Furthermore, the unprecedented performance on photodynamic cancer therapy is demonstrated by the significant inhibition of tumor growth. Therefore, the photoactivatable TPA-DHPy-Py with dual-organelle-targeted and excellent photodynamic activity associated with self-monitoring ability is highly promising for cancer theranostics in clinical trials.     

A NIR-II AIEgen-Based Supramolecular Nanodot for Peroxynitrite-Potentiated Mild-Temperature Photothermal Therapy of Hepatocellular Carcinoma

Compared to conventional photothermal therapy (PTT) which requires hyperthermia higher than 50 °C, mild-temperature PTT is a more promising antitumor strategy with much lower phototoxicity to neighboring normal tissues. However, the therapeutic efficacy of mild-temperature PTT is always restricted by the thermoresistance of cancer cells. To address this issue, a supramolecular drug nanocarrier is fabricated to co-deliver nitric oxide (NO) and photothermal agent DCTBT with NIR-II aggregation-induced emission (AIE) characteristic for mild-temperature PTT. NO can be effectively released from the nanocarriers in intracellular reductive environment and DCTBT is capable of simultaneously producing reactive oxygen species (ROS) and hyperthermia upon 808 nm laser irradiation. The generated ROS can further react with NO to produce peroxynitrite (ONOOˉ) bearing strong oxidization and nitration capability. ONOOˉ can inhibit the expression of heat shock proteins (HSP) to reduce the thermoresistance of cancer cells, which is necessary to achieve excellent therapeutic efficacy of DCTBT-based PTT at mild temperature (<50 °C). The antitumor performance of ONOOˉ-potentiated mild-temperature PTT is validated on subcutaneous and orthotopic hepatocellular carcinoma (HCC) models. This research puts forward an innovative strategy to overcome thermoresistance for mild-temperature PTT, which provides new inspirations to explore ONOOˉ-sensitized tumor therapy strategies.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Investigations on mode-division multiplexed free-space optical transmission for inter-satellite communication link

Wireless Networks - Inter-satellite data transmission links are very crucial for providing global inter-connectivity. We report designing and investigations on high date rate inter-satellite...     

Evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm for data clustering

Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi-objective data clustering problems. To address these issues, an evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm (ES-NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high-computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination-dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets. The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES-NMPBFO were designed, and all three forms of ES-NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.     

Optimal imperfect preventive maintenance policy for equipment leased during successive periods

In this paper, we consider randomly failing equipment leased several times during their life cycle with a given warranty period. A mathematical model is developed to determine the optimal efficiency levels of preventive maintenance (PM) to be performed on the equipment between successive lease periods, maximising the expected total profit of the lessor over the equipment life cycle. The model considers the expected leasing revenue as well as the equipment acquisition cost and the average PM and repair costs. PM actions allow reducing the age of the equipment to a certain extent with a corresponding cost depending on the PM level adopted. The efficiency of the PM is determinant of the expected revenue during the next lease period. Given a set of K possible PM levels and the number of lease periods n over the equipment life cycle, K^{n?1 PM} strategies are possible. A genetic algorithm is proposed in order to obtain nearly optimal policies in situations where the number of possibilities K^n?1 is very high. Obtained numerical results are discussed. Small- and big-size instances of the problem are considered in the case of a service company in the oil and gas industry specialised in leasing specific equipment such as separators, to oil companies for production activities with a limited duration of several months like well testing or short production tests.     

Effect of Initial Annealing Temperature on Microstructural Development and Microhardness in High‐Purity Copper Processed by High‐Pressure Torsion

The effect of the initial annealing temperature on the evolution of microstructure and microhardness in high purity OFHC Cu is investigated after processing by HPT. Disks of Cu are annealed for 1 h at two different annealing temperatures, 400 and 800 °C, and then processed by HPT at room temperature under a pressure of 6.0 GPa for 1/4, 1/2, 1, 5, and 10 turns. Samples are stored for 6 months after HPT processing to examine the self‐annealing effects. Electron backscattered diffraction (EBSD) measurements are recorded for each disk at three positions: center, mid‐radius, and near edge. Microhardness measurements are also recorded along the diameters of each disk. Both alloys show rapid hardening and then strain softening in the very early stages of straining due to self‐annealing with a clear delay in the onset of softening in the alloy initially annealed at 800 °C. This delay is due to the relatively larger initial grain size compared to the alloy initially annealed at 400 °C. The final microstructures consist of homogeneous fine grains having average sizes of ≈0.28 and ≈0.34 µm for the alloys initially annealed at 400 and 800 °C, respectively. A new model is proposed to describe the behavior of the hardness evolution by HPT in high purity OFHC Cu.     

Modulating the Surface State of SiC to Control Carrier Transport in Graphene/SiC

Silicon carbide (SiC) with epitaxial graphene (EG/SiC) shows a great potential in the applications of electronic and photoelectric devices. The performance of devices is primarily dependent on the interfacial heterojunction between graphene and SiC. Here, the band structure of the EG/SiC heterojunction is experimentally investigated by Kelvin probe force microscopy. The dependence of the barrier height at the EG/SiC heterojunction to the initial surface state of SiC is revealed. Both the barrier height and band bending tendency of the heterojunction can be modulated by controlling the surface state of SiC, leading to the tuned carrier transport behavior at the EG/SiC interface. The barrier height at the EG/SiC(000‐1) interface is almost ten times that of the EG/SiC(0001) interface. As a result, the amount of carrier transport at the EG/SiC(000‐1) interface is about ten times that of the EG/SiC(0001) interface. These results offer insights into the carrier transport behavior at the EG/SiC heterojunction by controlling the initial surface state of SiC, and this strategy can be extended in all devices with graphene as the top layer.     

Discrimination between accelerated life models via Approximate Bayesian Computation

Accelerated life testing (ALT) is widely used in high-reliability product estimation to get relevant information about an item's performance and its failure mechanisms. To analyse the observed ALT data, reliability practitioners need to select a suitable accelerated life model based on the nature of the stress and the physics involved. A statistical model consists of (i) a lifetime distribution that represents the scatter in product life and (ii) a relationship between life and stress. In practice, several accelerated life models could be used for the same failure mode and the choice of the best model is far from trivial. For this reason, an efficient selection procedure to discriminate between a set of competing accelerated life models is of great importance for practitioners. In this paper, accelerated life model selection is approached by using the Approximate Bayesian Computation (ABC) method and a likelihood-based approach for comparison purposes. To demonstrate the efficiency of the ABC method in calibrating and selecting accelerated life model, an extensive Monte Carlo simulation study is carried out using different distances to measure the discrepancy between the empirical and simulated times of failure data. Then, the ABC algorithm is applied to real accelerated fatigue life data in order to select the most likely model among five plausible models. It has been demonstrated that the ABC method outperforms the likelihood-based approach in terms of reliability predictions mainly at lower percentiles particularly useful in reliability engineering and risk assessment applications. Moreover, it has shown that ABC could mitigate the effects of model misspecification through an appropriate choice of the distance function.