Efficient magnetic solid-phase extraction, UPLC-MS/MS detection, and consumption assessment of five trace psychoactive substances.

Wastewater-based epidemiology (WBE) has become an objective and updated surveillance strategy for monitoring and estimating consumption trends of psychoactive substances (PSs) in the population. Firstly, magnetic shrimp shell biochar-based adsorbent (DZMBC) was synthesized and employed for extraction trace PSs from municipal wastewater. Proper pyrolysis temperature and increased KOH activator content favored the pore structure and surface area, thus facilitating extraction.

How hydrogen bonding and π-π interactions synergistically facilitate mephedrone adsorption by bio-sorbent: An in-depth microscopic scale interpretation.

Mephedrone (4-methylmethcathinone, MEPH) exhibited severe ecologic hazards and health detriments. A novel deep eutectic solvent functionalized magnetic ZIF-8/hierarchical porous carbon (DMZH) with excellent selectivity, interference resistance and recyclability, was developed for the rapid adsorption of MEPH. Initially, potential adsorption sites of MEPH were predicted.

Signal Property Information-Based Target Detection with Dual-Output Neural Network in Complex Environments.

The performance of traditional model-based constant false-alarm ratio (CFAR) detection algorithms can suffer in complex environments, particularly in scenarios involving multiple targets (MT) and clutter edges (CE) due to an imprecise estimation of background noise power level. Furthermore, the fixed threshold mechanism that is commonly used in the single-input single-output neural network can result in performance degradation due to changes in the scene. To overcome these challenges and limitations, this paper proposes a novel approach, a single-input dual-output network detector (SIDOND) using data-driven deep neural networks (DNN).

Deep insight into selective adsorption behavior and mechanism of novel deep eutectic solvent functionalized bio-sorbent towards methcathinone: Experiments and DFT calculation.

This work designed and synthesized novelly selective, highly efficient and friendly environmental biochar nanomaterial (ZMBC@ChCl-EG) by screening suitable deep eutectic solvent (DES) as the functional monomer via Density Functional Theory (DFT). The prepared ZMBC@ChCl-EG achieved the highly efficient adsorption of methcathinone (MC) and exhibited excellent selectivity as well as good reusability. Selectivity analysis concluded that the distribution coefficient value (K) of ZMBC@ChCl-EG towards MC was 3.

Enhanced adsorption of 3,4-methylenedioxymethamphetamine by magnetic graphene oxide-polydopamine nanohybrid modified zeolitic imidazolate framework-67 and its micro-mechanism: Experiments and calculations.

Exploring the structure-dependent adsorption mechanism of contaminants in wastewater is beneficial to high-efficiency adsorbents design and environmental remediation. In this study, emerging porous material of zeolitic imidazolate framework-67 (ZIF-67) has been modified by the magnetic graphene oxide-polydopamine nanohybrid (mGOP) to obtain three-dimensional ZIF-67/mGOP through an in-situ growth strategy, which was applied to adsorb 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy") in wastewater. A combination of characterizations, experiments (pH, humic acid and ion strength effect) and quantum chemical calculations revealed the microscopic adsorption mechanism involves each single component, of which the hydrogen bond (O/N…HO) and π-π electron donor acceptor (π-π EDA) interactions of mGOP endowed favourable adsorption of ZIF-67/mGOP, and mechanisms of the pore filling and Co-O chelation of ZIF-67 played synergistic effect.

Development and validation of nomogram of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumor markers.

Background: Peritoneal metastasis (PM) is not uncommon in patients with gastric cancer(GC), which affects clinical treatment decisions, but the relevant examination measures are not efficiently detected. Our goal was to develop a clinical radiomics nomogram to better predict peritoneal metastases.

Methods: A total of 3480 patients from 2 centers were divided into 1 training, 1 internal validation, and 1 external validation cohort(1949 in the internal training set, 704 in the validation set, and 827 in the external validation cohort) with clinicopathologically confirmed GC.

Neural Network-Based Autonomous Search Model with Undulatory Locomotion Inspired by .

() exhibits sophisticated chemotaxis behavior with a unique locomotion pattern using a simple nervous system only and is, therefore, well suited to inspire simple, cost-effective robotic navigation schemes. Chemotaxis in involves two complementary strategies: klinokinesis, which allows reorientation by sharp turns when moving away from targets; and klinotaxis, which gradually adjusts the direction of motion toward the preferred side throughout the movement. In this study, we developed an autonomous search model with undulatory locomotion that combines these two chemotaxis strategies with its body undulatory locomotion.

Diabetes mellitus promoted lymph node metastasis in gastric cancer: a 15-year single-institution experience.

Background: Previous studies have revealed that diabetes mellitus (DM) promotes disease progress of gastric cancer (GC). This study aimed to further investigating whether DM advanced lymph nodes (LNs) metastasis in GC.

Methods: The clinicopathologic data of GC patients with >15 examined LN (ELN) between October 2004 and December 2019 from a prospectively maintained database were included.

Multiple Power Allocation Game Schemes for Spectrum Coexistence Model Between Multistatic MIMO Radar Sensors and MU Communication.

The normal operations of radar systems and communication systems under the condition of spectrum coexistence are facing a huge challenge. This paper uses game theory to study power allocation problems between multistatic multiple-input multiple-output (MIMO) radars and downlink communication. In the case of spectrum coexistence, radars, base station (BS) and multi-user (MU) have the working state of receiving and transmitting signals, which can cause unnecessary interferences to different systems.

Frequency analysis of light field sampling for texture information.

Light field sampling (LFS) theory can properly reduce minimum sampling rate while ensuring that novel views are not distorted for image-based rendering (IBR). The minimum sampling rate is determined by spectral support of light field. The spectral support of light field has studied the influence of the following factors: the minimum depth and the maximum depth, non-Lambertian reflections, whether the scene surfaces are flat, maximum frequency of painted signals.