Antibiotic resistance and nanotechnology: A narrative review.

The rise of antibiotic resistance poses a significant threat to public health worldwide, leading researchers to explore novel solutions to combat this growing problem. Nanotechnology, which involves manipulating materials at the nanoscale, has emerged as a promising avenue for developing novel strategies to combat antibiotic resistance. This cutting-edge technology has gained momentum in the medical field by offering a new approach to combating infectious diseases. Nanomaterial-based therapies hold significant potential in treating difficult bacterial infections by circumventing established drug resistance mechanisms. Moreover, their small size and unique physical properties enable them to effectively target biofilms, which are commonly linked to resistance development. By leveraging these advantages, nanomaterials present a viable solution to enhance the effectiveness of existing antibiotics or even create entirely new antibacterial mechanisms. This review article explores the current landscape of antibiotic resistance and underscores the pivotal role that nanotechnology plays in augmenting the efficacy of traditional antibiotics. Furthermore, it addresses the challenges and opportunities within the realm of nanotechnology for combating antibiotic resistance, while also outlining future research directions in this critical area. Overall, this comprehensive review articulates the potential of nanotechnology in addressing the urgent public health concern of antibiotic resistance, highlighting its transformative capabilities in healthcare.

Satellite-derived bathymetry integrating spatial and spectral information of multispectral images.

As a significant and cost-effective method of obtaining shallow seabed topography, satellite derived bathymetry (SDB) can acquire a wide range of shallow sea depth by integrating a small quantity of in-situ water depth data. This method is a beneficial addition to traditional bathymetric topography. The seafloor's spatial heterogeneity leads to inaccuracies in bathymetric inversion, which reduces bathymetric accuracy. By utilizing multispectral data with multidimensional features, an SDB approach incorporating spectral and spatial information of multispectral images is proposed in this study. In order to effectively increase the accuracy of bathymetry inversion throughout the entire area, first the random forest with spatial coordinates is established to control bathymetry spatial variation on a large scale. Next, the Kriging algorithm is used to interpolate bathymetry residuals, and the interpolation results are used to adjust bathymetry spatial variation on a small scale. The data from three shallow water sites are experimentally processed to validate the method. Compared with other established bathymetric inversion techniques, the experimental results show that the method effectively reduces the error in bathymetry estimation caused by spatial heterogeneity of the seabed, producing high-precision inversion bathymetry with a root mean square error of 0.78 to 1.36 meters.

A Heuristic-Based Adaptive Iterated Greedy Algorithm for Lot-Streaming Hybrid Flow Shop Scheduling Problem with Consistent and Intermingled Sub-Lots.

Owing to the different quantities and processing times of sub-lots, intermingling sub-lots with each other, instead of fixing the production sequence of sub-lots of a lot as in the existing studies, is a more practical approach to lot-streaming flow shops. Hence, a lot-streaming hybrid flow shop scheduling problem with consistent and intermingled sub-lots (LHFSP-CIS) was studied. A mixed integer linear programming (MILP) model was established, and a heuristic-based adaptive iterated greedy algorithm (HAIG) with three modifications was designed to solve the problem. Specifically, a two-layer encoding method was proposed to decouple the sub-lot-based connection. Two heuristics were embedded in the decoding process to reduce the manufacturing cycle. Based on this, a heuristic-based initialization is proposed to improve the performance of the initial solution; an adaptive local search with four specific neighborhoods and an adaptive strategy has been structured to improve the exploration and exploitation ability. Besides, an acceptance criterion of inferior solutions has been improved to promote global optimization ability. The experiment and the non-parametric Kruskal-Wallis test (p = 0) showed the significant advantages of HAIG in effectiveness and robustness compared with five state-of-the-art algorithms. An industrial case study verifies that intermingling sub-lots is an effective technique to enhance the utilization ratio of machines and shorten the manufacturing cycle.

ICESat-2 laser data denoising algorithm based on a back propagation neural network.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon data is the emerging satellite-based LiDAR data, widely used in surveying and mapping due to its small photometric spot and high density. Since ICESat-2 data collect weak signals, it is difficult to denoise in shallow sea island areas, and the quality of the denoising method will directly affect the precision of bathymetry. This paper proposes a back propagation (BP) neural network-based denoising algorithm for the data characteristics of shallow island reef areas. First, a horizontal elliptical search area is constructed for the photons in the dataset. Suitable feature values are selected in the search area to train the BP neural network. Finally, data with a geographic location far apart, including daily and nightly data, are selected respectively for experiments to test the generality of the network. By comparing the results with the confidence labels provided in the official documents of the ATL03 dataset, the DBSCAN algorithm, and the manual visual interpretation, it is proved that the denoising algorithm proposed in this paper has a better processing effect in shallow island areas.

Island feature classification for single-wavelength airborne lidar bathymetry based on full-waveform parameters.

Because it is lightweight, low cost, and has high sampling density, single-wavelength airborne lidar bathymetry (ALB) is an ideal choice for shallow water measurements. However, due to severe waveform mixing, waveform classification has become the key difficulty in the research of single-wavelength ALB signal detection. Generally, the interaction between a laser and a water column leads to energy attenuation, pulse delay, or broadening of the water waveform, which has a discernible difference between terrestrial laser echo. This work attempts to focus on the morphology features in different waveforms to classify isolated, supersaturated, land, and water waveforms, and obtain a water-land division. The generalized Gaussian model optimized by the Levenberg-Marquardt algorithm (LM-GGM) is driven to extract 38-dimensional waveform parameters, covering different echo signals and their relationships. Ten-dimensional dominant features are selected from the feature matrix based on the random forest feature selection (RFFS) model, and input to the random forest classification model. Experiments show that the overall classification accuracy of the waveform is 97%.

Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty.

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers' expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

Measurement of Atmospheric CO2 Column Concentrations Based on Open-Path TDLAS.

Monitoring of CO2 column concentrations is valuable for atmospheric research. A mobile open-path system was developed based on tunable diode laser absorption spectroscopy (TDLAS) to measure atmospheric CO2 column concentrations. A laser beam was emitted downward from a distributed feedback diode laser at 2 µm and then reflected by the retroreflector array on the ground. We measured the CO2 column concentrations over the 20 and 110 m long vertical path. Several single-point sensors were distributed at different heights to provide comparative measurements for the open-path TDLAS system. The results showed that the minimum detection limit of system was 0.52 ppm. Some similarities were observed in trends from the open-path TDLAS system and these sensors, but the average of these sensors was more consistent with the open-path TDLAS system values than the single-point measurement. These field measurements demonstrate the feasibility of open-path TDLAS for measuring the CO2 column concentration and monitoring carbon emission over large areas.

Reinforcement Learning-Based Multiobjective Evolutionary Algorithm for Mixed-Model Multimanned Assembly Line Balancing Under Uncertain Demand.

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimanned assembly line balancing under uncertain demand, and presents a new robust mixed-integer linear programming model to minimize the production and penalty costs simultaneously. In addition, a reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed to tackle the problem. The algorithm includes a priority-based solution representation and a new task-worker-sequence decoding that considers robustness processing and idle time reductions. Five crossover and three mutation operators are proposed. The Q -learning-based strategy determines the crossover and mutation operator at each iteration to effectively obtain Pareto sets of solutions. Finally, a time-based probability-adaptive strategy is designed to effectively coordinate the crossover and mutation operators. The experimental study, based on 269 benchmark instances, demonstrates that the proposal outperforms 11 competitive MOEAs and a previous single-objective approach to the problem. The managerial insights from the results as well as the limitations of the algorithm are also highlighted.