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For the last two decades, the aromatic aldehyde 5‐hydroxymethyl‐furfural (5‐HMF) has been the subject of several investigations for its pharmacologic potential. In 2004, the Safo group reported that 5‐HMF has potent antisickling activity by targeting and ameliorating the primary pathophysiology of hypoxia‐induced sickling of erythrocytes (red blood cells [RBC]). Following the encouraging outcome of the preclinical and phase I/II clinical studies of 5‐HMF for the treatment of sickle cell disease (SCD), there have been multiple studies suggesting 5‐HMF has several other biological or pharmacologic activities, including anti‐allergic, antioxidant, anti‐hypoxic, anti‐ischemic, cognitive improvement, anti‐tyrosinase, anti‐proliferation, cytoprotective, and anti‐inflammatory activities. The wide range of its effects makes 5‐HMF a potential candidate for treating a variety of diseases including cognitive disorders, gout, allergic disorders, anemia, hypoxia, cancers, ischemia, hemorrhagic shock, liver fibrosis, and oxidative injury. Several of these therapeutic claims are currently under investigation and, while promising, vary in terms of the strength of their evidence. This review presents the research regarding the therapeutic potential of 5‐HMF in addition to its sources, physicochemical properties, safety, absorption, distribution, metabolism, and excretion (ADME) profiles. [ABSTRACT FROM AUTHOR]

When multi-dynamic target UAVs escape, the uncertainty of the formation method and the external environment causes difficulties in rounding them up, so suitable solutions are needed to improve the roundup success rate. However, traditional methods can generally only enable the encirclement of a single target, and when the target is scattered and escaping, this will lead to encirclement failure due to the inability to sufficiently allocate UAVs for encirclement. Therefore, in this paper, a real-time roundup and dynamic allocation algorithm for multiple dynamic targets is proposed. A real-time dynamic obstacle avoidance model is established for the roundup problem, drawing on the artificial potential field function. For the escape problem of the rounding process, an optimal rounding allocation strategy is established by drawing on the linear matching method. The algorithm in this paper simulates the UAV in different obstacle environments to round up dynamic targets with different escape methods. The results show that the algorithm is able to achieve the rounding up of multiple dynamic targets in a UAV and obstacle scenario with random initial positions, and the task UAV, which is able to avoid obstacles, can be used in other algorithms for real-time rounding up and dynamic allocation. The results show that the algorithm is able to achieve the rounding up of multi-dynamic targets in scenarios with a random number of UAVs and obstacles with random locations. It results in a 50% increase in the rounding efficiency and a 10-fold improvement in the formation success rate. And the mission UAV is able to avoid obstacles, which can be used in other algorithms for real-time roundup and dynamic allocation. [ABSTRACT FROM AUTHOR]

X-ray communication is a kind of space communication technology which uses X-ray as information carrier. In order to improve the information transmission capacity, communication rate and anti-interference ability of X-ray communication, we proposes to design a novel multi-target X-ray source. The source is composed of a fast switching module of light channels based on FPGA technology and four photoelectric X-ray tubes with different target materials: Cr, Fe, Ni, and Cu. Using Geant4 software, we determined the optimal target thickness for each material, which enabled us to fully leverage the characteristic X-rays for multi-channel signal modulation transmission. Moreover, using CST software for particle trajectory simulation and optimization of the electron beam revealed that at a tube voltage of 20 kV, the focus area measures approximately 1.2 mm×1.2 mm. The simulations show that four kinds of spectra with high distinctiveness can be generated from the Cr, Fe, Ni, and Cu targets. Within a single modulation period, these spectra can be combined in various ways to create 16 different X-ray spectra signals, thereby increasing the number of communication elements and enhancing the information transmission rate. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to generate a range-Doppler (RD) map and detect multiple targets on an frequency modulated interrupted continuous wave (FMICW) radar using tripod waves with two different pulse repetition frequencies (PRFs). By applying the likelihood to the FMICW radar to generate a 2D high-resolution RD Map and cumulating it using the periodicity of the RD Map by PRF, the multi-target detection time and detection performance can be improved. Our simulation results show the successful detection of five targets. [ABSTRACT FROM AUTHOR]

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation. [ABSTRACT FROM AUTHOR]

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The looking-down mode of space airship bistatic radars faces complex sea–land clutter, and the mode of wide-range surveillance and the over-sight detection of the satellite platform generates a low SNR and range–Doppler ambiguity. The method traditionally used involves the transmission of multiple Pulse Repetition Frequencies (PRFs) and correlating them to solve the ambiguity. However, with a low SNR, the traditional disambiguation fails due to the large number of false alarms and target omissions. In order to solve this problem, a new algorithm for multi-target joint detection and range–Doppler disambiguation based on an ambiguity matrix is presented. Firstly, all possible state values corresponding to the ambiguous sequence are filled into the ambiguity matrix one by one. Secondly, the state values in the matrix cell are divided into several groups of subsequences according to the PRF. By disambiguating multiple sets of subsequences, performing subsequence fusion, and then undertaking point aggregation, the targets can be effectively detected in scenarios with a strong clutter rate, the false alarms can be suppressed, and the disambiguation of the range and Doppler is completed. The simulation shows that the proposed algorithm has the strong ability to detect targets and perform ambiguity resolution in the scenario of a multi-target and multi-false alarm. [ABSTRACT FROM AUTHOR]

A series of 31 hybrid of phenylsulfonyl furoxan and phenstatin (1a-j, 2a-j, 3a-j, 4 and 5) derivatives, were computationally studied as potential anti-cancer inhibitors against four cell lines, i.e. A2780, MDA-MB-231, HCT-116 and A549. In this work, the 2D-QSAR approach combining the multiple linear regression (MLR) model, and internal and external cross-validation, showed a satisfactory quality factor: R2 = 0.85, 0.74, 0.82 and 0.75 for the four cell lines, respectively. The binding affinity of the hybrid agents towards the four 4GL7, 6GUE, 1M17 and 4XL7 anti-tumoral targets was further evaluated using molecular docking and dynamics simulations (0–200 ns). The dynamics assessment parameters indicated the formation of satisfactorily stable complexes. In addition, all considered data sets show that the best binding affinity, including the highest docking score, hydrogen bond energy and amino acid steric interactions are well predicted for the best-selected complexes. The developed 2D-QSAR model was leveraged to design and predict the biological activity of 12 new hybrid compounds (N1–N12) based on the best in-vivo inhibitor, namely, the 3 h ligand of the formula: (4-((1-(2-((4-((3crylamidophenyl)amino)quinazolin-2-yl)thio)acetyl)piperidin-4-yl)oxy)-3-(phenylsulfonyl)-1,2,5-oxadiazole 2-oxide). Multitargeting docking scores and dynamics simulations show that they exhibit satisfactorily potent anti-tumoral inhibition abilities towards the four proteins. Our in-silico outcomes would be combined with in-vitro and in-vivo studies to provide a perspective on the validation of their anti-cancer activity. In particular, the ADMET predictions indicate that four new designed ligands have demonstrated a good drug-like profile and can be considered prospective candidates for future anti-cancer therapies. [ABSTRACT FROM AUTHOR]

After the decline of the COVID-19 pandemic, health systems were challenged by the simultaneous prevalence of different respiratory viruses causing a wide overlap in symptoms. This increased the demand for multi-virus diagnostic tests which require suitable pre-analytical workflow solutions in order to receive valid diagnostic results. In this context, the effects of specimen storage duration and temperature on the RNA/DNA copy number stability of influenza A/B, RSV A/B, SARS-CoV-2 and adenovirus were examined for four commercially available transport swab systems and saliva collection devices. The respiratory viruses were more stable in the saliva collection devices than in the transport swab systems when stored at RT or 37 °C for up to 96 h. Moreover, no differences between viral nucleic acid stability of enveloped and non-enveloped viruses were observed. The infectivity of all enveloped viruses could be inactivated by the saliva collection device from PreAnalytiX. The Norgen saliva device completely inactivated influenza A/B, while RSV A/B were partially inactivated. The non-enveloped adenovirus was inactivated by a reduction factor of 10E+ 4 in both saliva collection devices. All respiratory viruses remained infectious in the transport swab systems. Two possible transport medium additives were tested which inactivated or strongly reduced viral replication of tested enveloped viruses but had no effect on the non-enveloped adenovirus. Finally the implementation of multi-target detection procedures involving a direct amplification approach was successfully tested by spike-in of all enveloped viruses simultaneously into transport swab systems. This fast and reproducible setup presents a valuable solution for future implementations in multi-virus testing strategies. • Specimen collection, storage, transport and handling influence diagnostic results. • Multi-target detection assays could aid rapid identification of causative pathogens. • Tested saliva collection devices inactivated respiratory viruses used here. • Tested swab systems do not inactivate respiratory viruses used here. • Inactivating additives can reduce the risk of infection for healthcare personnel. [ABSTRACT FROM AUTHOR]

With the rapid development of photovoltaic power generation, how to improve the photovoltaic grid connection rate is an urgent problem to be solved. This article proposes an optimized scheduling method for the water and photovoltaic complementary system, taking into account the operation strategy of pump stations to improve the photovoltaic grid connection rate. Firstly, a multiobjective optimization scheduling model is constructed to consider both power generation and output fluctuation, and the uncertainty of photovoltaic power generation is analyzed from multiple perspectives. Then, taking the cascade hydropower stations and surrounding photovoltaic power stations in a river basin in Sichuan as an example, the operation strategy of pump stations is introduced into the water and photovoltaic complementary system, considering different weather scenarios, to reduce the photovoltaic curtailment rate. The study verifies that the introduction of pump stations can effectively increase the photovoltaic grid connection rate, and quantitatively analyzes the pump station capacity configuration under different photovoltaic penetration rates. [ABSTRACT FROM AUTHOR]

We are now well-positioned to comprehend carcinogenesis at a molecular level in greater detail due to significant technological advancements. Additionally, we are now able to rationally design and develop drug molecules with the ability to either selectively enhance or disrupt important biological processes, maximizing their therapeutic potential. This has heralded a new era in drug design. The heterometallic ruthenium–platinum complexes can be used as anticancer, photodynamic therapy, diabetes treatment, and molecular sensors for thiol-containing peptides due to their multifunctional interactions with nuclear DNA, mitochondrial DNA, RNA, and proteins. Compared to cisplatin and its Ru-based monometallic precursors, a significant number of reported ruthenium–platinum complexes exhibit enhanced cytotoxicity and tumor selectivity. Due to the covalent binding of the cis-PtIICl2 moiety to DNA, photoactive Ru(II)–Pt(II) complexes were designed to prelocalize a photodynamic therapy agent at the site of action. The development of ruthenium–platinum-based heterometallic complexes has recently advanced, opening up new avenues for the development of drugs that are more efficient. Metal complexes' potential as important cancer therapeutic agents will be the primary focus of this review. The development of ruthenium and platinum-based mono and mixed-metal complexes with therapeutic and biomedical applications are discussed in detail in this article. [ABSTRACT FROM AUTHOR]

Insulators play a pivotal role in power transmission lines, and the timely detection of defects in insulators is crucial to prevent potentially catastrophic consequences in terms of human lives and property. This paper proposes an insulator defect detection algorithm, named Insulator Lack-You Only Look Once (IL-YOLO), addressing the limitations observed in existing research concerning the complex background and multi-target challenges in insulator detection. The IL-YOLO algorithm focuses on detecting insulator defects within the intricate background of power transmission lines. To enhance its functionality, we propose three improved modules. Firstly, the Insulator Lack-Global Attention Mechanism (IL-GAM) addresses issues such as the mutual influence of weights and loss of detailed information in the original module. Secondly, the Insulator Lack-C3 (IL-C3) module is designed to emphasize key information while preserving feature extraction and fusion. Lastly, the Insulator Lack-SPPFCSPC (IL-SPPFCSPC) module enhances attention to both key and global information while extracting effective information from multi-scale features. Experimental results demonstrate that IL-YOLO achieves a detection accuracy of 91.2%, marking a 3.6% improvement compared to the YOLOv5 algorithm. Furthermore, precision improves by 0.5%, recall increases by 6.3%, and the F1 score sees a boost of 3.8%. Notably, IL-YOLO achieves a frame rate of 90 frames per second (FPS), showcasing its capacity for real-time detection. Additional experiments affirm IL-YOLO’s accuracy in completing insulator defect detection tasks in both general and complex backgrounds, highlighting its substantial advantages in addressing complex background and multi-target challenges.

Shu-Tong Huang,1,2,* Jin-Chong Luo,1,3,* Guo-Hui Zhong,1,2 Li-Ping Teng,4 Cai-Yan Yang,4 Chun-Li Tang,1 Lin Jing,1 Zhong-Bo Zhou,4 Jing Liu,1,3 Neng Jiang1,2 1Department of Pharmacy, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China; 2Pharmaceutical College, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 3School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, People’s Republic of China; 4School of Pharmacy, Youjiang Medical University for Nationalities, Baise, Guangxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jing Liu, School of Pharmacy, Jiangxi University of Chinese Medicine, No. 56, Yangming Road, Donghu District, Nanchang, Jiangxi, 330006, People’s Republic of China, Email liujing860828@163.com Neng Jiang, Department of Pharmacy, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Qingxiu District, Nanning, Guangxi, 530021, People’s Republic of China, Tel/Fax +86-17307711726, Email cpujnh@163.comPurpose: Alzheimer’s disease (AD) is the most common neurodegenerative disease, and its multifactorial nature increases the difficulty of medical research. To explore an effective treatment for AD, a series of novel tacrine-selegiline hybrids with ChEs and MAOs inhibitory activities were designed and synthesized as multifunctional drugs.Methods: All designed compounds were evaluated in vitro for their inhibition of cholinesterases (AChE/BuChE) and monoamine oxidases (MAO-A/B) along with their blood-brain barrier permeability. Then, further biological activities of the optimizing compound 7d were determined, including molecular model analysis, in vitro cytotoxicity, acute toxicity studies in vivo, and pharmacokinetic and pharmacodynamic property studies in vivo.Results: Most synthesized compounds demonstrated potent inhibitory activity against ChEs/MAOs. Particularly, compound 7d exhibited good and well-balanced activity against ChEs (hAChE: IC50 = 1.57 μM, hBuChE: IC50 = 0.43 μM) and MAOs (hMAO-A: IC50 = 2.30 μM, hMAO-B: IC50 = 4.75 μM). Molecular modeling analysis demonstrated that 7d could interact simultaneously with both the catalytic active site (CAS) and peripheral anionic site (PAS) of AChE in a mixed-type manner and also exhibits binding affinity towards BuChE and MAO-B. Additionally, 7d displayed excellent permeability of the blood-brain barrier, and under the experimental conditions, it elicited low or no toxicity toward PC12 and BV-2 cells. Furthermore, 7d was not acutely toxic in mice at doses up to 2500 mg/kg and could improve the cognitive function of mice with scopolamine-induced memory impairment. Lastly, 7d possessed well pharmacokinetic characteristics.Conclusion: In light of these results, it is clear that 7d could potentially serve as a promising multi-functional drug for the treatment of AD. Keywords: Alzheimer’s disease, cholinesterase, monoamine oxidase, multi-target, designed

When multi-dynamic target UAVs escape, the uncertainty of the formation method and the external environment causes difficulties in rounding them up, so suitable solutions are needed to improve the roundup success rate. However, traditional methods can generally only enable the encirclement of a single target, and when the target is scattered and escaping, this will lead to encirclement failure due to the inability to sufficiently allocate UAVs for encirclement. Therefore, in this paper, a real-time roundup and dynamic allocation algorithm for multiple dynamic targets is proposed. A real-time dynamic obstacle avoidance model is established for the roundup problem, drawing on the artificial potential field function. For the escape problem of the rounding process, an optimal rounding allocation strategy is established by drawing on the linear matching method. The algorithm in this paper simulates the UAV in different obstacle environments to round up dynamic targets with different escape methods. The results show that the algorithm is able to achieve the rounding up of multiple dynamic targets in a UAV and obstacle scenario with random initial positions, and the task UAV, which is able to avoid obstacles, can be used in other algorithms for real-time rounding up and dynamic allocation. The results show that the algorithm is able to achieve the rounding up of multi-dynamic targets in scenarios with a random number of UAVs and obstacles with random locations. It results in a 50% increase in the rounding efficiency and a 10-fold improvement in the formation success rate. And the mission UAV is able to avoid obstacles, which can be used in other algorithms for real-time roundup and dynamic allocation.

With the increasing use of the strategy and group target attack method in the modern battlefield, multi-target inverse synthetic aperture radar (ISAR) imaging simultaneously with high efficiency draws more and more attention, which gives a promising prospect for aerospace target detection and recognition in the multi-target scenario. To overcome the shortcomings of traditional multi-target imaging with one beam at one pulse repetition time (PRT) based on phase array radar (PAR), this paper proposes a novel multi-target imaging waveform design method based on the newly full digital array radar (DAR). Firstly, we propose using radar waveform diversity with 2D orthogonality to realize multi-target ISAR imaging with high imaging quality and efficiency. Then, to meet the constant modulus requirement for maximizing the transmitting power, orthogonal frequency division multiplexing (OFDM) chirp theory is proposed to directly generate the transmit waveform instead of the traditional optimization method with the nonconvex problem for waveform design. Based on time-variant weighted and time diversity technology, a of group transmit waveforms is designed, which can form multiple beams simultaneously and make the signals arriving at different targets approximately orthogonal. Finally, simulations and experiments are carried out to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Progressive cognitive decline in Alzheimer's disease (AD) is a growing challenge. Present therapies are based on acetylcholinesterase inhibition providing only temporary relief. Promising alternatives include butyrylcholinesterase (BuChE) inhibitors, multi-target ligands (MTDLs) that address the multi-factorial nature of AD, and compounds that target oxidative stress and inflammation. Cinnamate derivatives, known for their neuroprotective properties, show potential when combined with established AD agents, demonstrating improved efficacy. They are being positioned as potential AD therapeutic leads due to their ability to inhibit Aβ accumulation and provide neuroprotection. This article highlights the remarkable potential of cinnamic acid as a basic structure that is easily adaptable and combinable to different active groups in the struggle against Alzheimer's disease. Compounds with a methoxy substitution at the para-position of cinnamic acid display increased efficacy, whereas electron-withdrawing groups are generally more effective. The effect of the molecular volume is worthy of further investigation. [ABSTRACT FROM AUTHOR]

Molecular hybridization has emerged as a promising approach in the treatment of diseases exhibiting multifactorial etiology. With regard to this, dual cyclooxygenase-2/lipoxygenase (COX-2/LOX) inhibitors could be considered a safe alternative to traditional non-steroidal anti-inflammatory drugs (tNSAIDs) and selective COX-2 inhibitors (coxibs) for the treatment of inflammatory conditions. Taking this into account, six novel pyrrole derivatives and pyrrole–cinnamate hybrids were developed as potential COX-2 and soybean LOX (sLOX) inhibitors with antioxidant activity. In silico calculations were performed to predict their ADMET (absorption, distribution, metabolism, excretion, toxicity) properties and drug-likeness, while lipophilicity was experimentally determined as RM values. All synthesized compounds (1–4, 5–8) could be described as drug-like. The results from the docking studies on COX-2 were in accordance with the in vitro studies. According to molecular docking studies on soybean LOX, the compounds displayed allosteric interactions with the enzyme. Pyrrole 2 appeared to be the most potent s-LOX inhibitor (IC50 = 7.5 μM). Hybrids 5 and 6 presented a promising combination of in vitro LOX (IC50 for 5 = 30 μM, IC50 for 6 = 27.5 μM) and COX-2 (IC50 for 5 = 0.55 μM, IC50 for 6 = 7.0 μM) inhibitory activities, and therefore could be used as the lead compounds for the synthesis of more effective multi-target agents. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the main cause of dementia in older age. The prevalence of AD is growing worldwide, causing a tremendous burden to societies and families. Due to the complexity of its pathogenesis, the current treatment of AD is not satisfactory, and drugs acting on a single target may not prevent AD progression. This review summarizes the multi-target pharmacological effects of thiazolidinediones (TZDs) on AD. TZDs act as peroxisome proliferator-activated receptor gamma (PPARγ) agonists and long-chain acyl-CoA synthetase family member 4 (ACSL4) inhibitors. TZDs ameliorated neuroinflammation and ferroptosis in preclinical models of AD. Here, we discussed recent findings from clinical trials of pioglitazone in the treatment of AD, ischemic stroke, and atherosclerosis. We also dissected the major limitations in the clinical application of pioglitazone and explained the potential benefit of pioglitazone in AD. We recommend the use of pioglitazone to prevent cognitive decline and lower AD risk in a specific group of patients. [ABSTRACT FROM AUTHOR]

Have monetary policies asymmetrically responded to targets besides output and inflation? Multiple targets have been found to exist for monetary authority, and uncertainty is having a growing impact on monetary policy in China. Using the nonlinear autoregressive distribution lag (NARDL) model and data from China from 2001Q1–2022Q3, we build a multi-target monetary policy rule with uncertainty and verify the monetary authority's asymmetric reactions to the exchange rate, housing price, and uncertainty. Specifically, the Monetary authority of China responds more to a decline than increase in the output gap, and responds more to an increase than decrease in the inflation gap, exchange rate gap, housing price gap, and uncertainty. This practice supports the view of expanding the targets of the Taylor rule, while also providing innovative ideas for asymmetric monetary policy responses for countries with an economic structure or population density comparable to China. [ABSTRACT FROM AUTHOR]

Adopting a longitudinal person-centred perspective, we examined the profiles of employee commitments to the organization, supervisor, occupation, work team, and family in a diversified sample of employees (N = 1459) surveyed three times at one-month intervals during the COVID-19 pandemic. In line with recent developments in research on commitment towards multiple targets, these profiles were estimated while considering employees' global levels of commitment to their work life (across targets) as well as the specific nature of their commitment to each target. Our results revealed six distinct commitment profiles differing quantitatively and qualitatively from one another and defined by employees' global and target-specific levels of commitment. These profiles were replicated across the three measurement points. Profile membership was moderately to highly stable over time but also demonstrated some malleability. Lastly, we found that employees' levels of basic psychological need satisfaction at work and perception of work meaningfulness predicted membership into more favourable commitment profiles. [ABSTRACT FROM AUTHOR]

The role of copper element has been an increasingly relevant topic in recent years in the fields of human and animal health, for both the study of new drugs and innovative food and feed supplements. This metal plays an important role in the central nervous system, where it is associated with glutamatergic signaling, and it is widely involved in inflammatory processes. Thus, diseases involving copper (II) dyshomeostasis often have neurological symptoms, as exemplified by Alzheimer’s and other diseases (such as Parkinson’s and Wilson’s diseases). Moreover, imbalanced copper ion concentrations have also been associated with diabetes and certain types of cancer, including glioma. In this paper, we propose a comprehensive overview of recent results that show the importance of these metal ions in several pathologies, mainly Alzheimer’s disease, through the lens of the development and use of copper chelators as research compounds and potential therapeutics if included in multi-target hybrid drugs. Seeing how copper homeostasis is important for the well-being of animals as well as humans, we shortly describe the state of the art regarding the effects of copper and its chelators in agriculture, livestock rearing, and aquaculture, as ingredients for the formulation of feed supplements as well as to prevent the effects of pollution on animal productions.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Its treatment includes antiarrhythmic drugs (AADs) to modulate the function of cardiac ion channels. However, AADs have been limited by proarrhythmic effects, non-cardiovascular toxicities as well as often modest antiarrhythmic efficacy. Theoretical models showed that a combined blockade of Nav1.5 (and its current, INa) and Kv1.5 (and its current, IKur) ion channels yield a synergistic anti-arrhythmic effect without alterations in ventricles. We focused on Kv1.5 and Nav1.5 to search for structural similarities in their binding site (BS) for flecainide (a common blocker and widely prescribed AAD) as a first step for prospective rational multi-target directed ligand (MTDL) design strategies. We present a computational workflow for a flecainide BS comparison in a flecainide-Kv1.5 docking model and a solved structure of the flecainide-Nav1.5 complex. The workflow includes docking, molecular dynamics, BS characterization and pattern matching. We identified a common structural pattern in flecainide BS for these channels. The latter belongs to the central cavity and consists of a hydrophobic patch and a polar region, involving residues from the S6 helix and P-loop. Since the rational MTDL design for AF is still incipient, our findings could advance multi-target atrial-selective strategies for AF treatment.

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.

Introduction: Nut quality detection is of paramount importance in primary nut processing. When striving to maintain the imperatives of rapid, efficient, and accurate detection, the precision of identifying small-sized nuts can be substantially compromised. Methods: We introduced an optimized iteration of the YOLOv5s model designed to swiftly and precisely identify both good and bad walnut nuts across multiple targets. The M3-Net network, which is a replacement for the original C3 network in MobileNetV3’s YOLOv5s, reduces the weight of the model. We explored the impact of incorporating the attention mechanism at various positions to enhance model performance. Furthermore, we introduced an attentional convolutional adaptive fusion module (Acmix) within the spatial pyramid pooling layer to improve feature extraction. In addition, we replaced the SiLU activation function in the original Conv module with MetaAconC from the CBM module to enhance feature detection in walnut images across different scales. Results: In comparative trials, the YOLOv5s_AMM model surpassed the standard detection networks, exhibiting an average detection accuracy (mAP) of 80.78%, an increase of 1.81%, while reducing the model size to 20.9 MB (a compression of 22.88%) and achieving a detection speed of 40.42 frames per second. In multitarget walnut detection across various scales, the enhanced model consistently outperformed its predecessor in terms of accuracy, model size, and detection speed. It notably improves the ability to detect multi-target walnut situations, both large and small, while maintaining the accuracy and efficiency. Discussion: The results underscored the superiority of the YOLOv5s_AMM model, which achieved the highest average detection accuracy (mAP) of 80.78%, while boasting the smallest model size at 20.9 MB and the highest frame rate of 40.42 FPS. Our optimized network excels in the rapid, efficient, and accurate detection of mixed multi-target dry walnut quality, accommodating lightweight edge devices. This research provides valuable insights for the detection of multi-target good and bad walnuts during the walnut processing stage. [ABSTRACT FROM AUTHOR]

In this paper, we consider the cooperative decision-making problem for multi-target tracking in multi-agent systems using multi-agent deep reinforcement learning algorithms. Multi-agent multi-target pursuit has faced new challenges in practical applications, where pursuers need to plan collision-free paths and appropriate multi-target allocation strategies to determine which target to track at the current time for each pursuer. We design three feasible multi-target allocation strategies from different perspectives. We compare our allocation strategies in the multi-agent multi-target pursuit environment that models collision risk and verify the superiority of the allocation strategy marked as POLICY3, considering the overall perspective of agents and targets. We also find that there is a significant gap in the tracking policies learned by agents when using the multi-agent reinforcement learning algorithm MATD3. We propose an improved algorithm, DAO-MATD3, based on dynamic actor network optimization. The simulation results show that the proposed POLICY3-DAO-MATD3 method effectively improves the efficiency of completing multi-agent multi-target pursuit tasks. [ABSTRACT FROM AUTHOR]

To better consume high-density photovoltaics, in this article, the application of energy storage devices in the distribution network not only realizes the peak shaving and valley filling of the electricity load but also relieves the pressure on the grid voltage generated by the distributed photovoltaic access. At the same time, photovoltaic power generation and energy storage cooperate and have an impact on the tidal distribution of the distribution network. Since photovoltaic output has uncertainty, the maximum photovoltaic output in each scenario is determined by the clustering algorithm, while the storage scheduling strategy is reasonably selected so the distribution network operates efficiently and stably. The tidal optimization of the distribution network is carried out with the objectives of minimizing network losses and voltage deviations, two objectives that are assigned comprehensive weights, and the optimization model is constructed by using a particle swarm algorithm to derive the optimal dispatching strategy of the distribution network with the cooperation of photovoltaic and energy storage. Finally, a model with 30 buses is simulated and the system is optimally dispatched under multiple scenarios to demonstrate the necessity of conducting coordinated optimal dispatch of photovoltaics and energy storage. [ABSTRACT FROM AUTHOR]

In radar detection of multiple targets, the large difference in echo energy of coexisting targets leads to the phenomenon that strong targets cover weak targets. To tackle this problem, a peak self-seeking track before detect (TBD) algorithm based on parallel line coordinate transformation is proposed. By comparing the sum of the maximum energy of measurement point in the parameter unit where each measurement point is located after accumulation, the unit with the maximum energy accumulation value is found, by which a new binary accumulation result is formed. Then peak value extraction, track back-tracking, and track correction are performed with the threshold. The simulation results show that the improved algorithm has a high global detection probability for multiple targets, and can initiate multiple targets with different echo energies. [ABSTRACT FROM AUTHOR]

Introduction: Visceral leishmaniasis (VL) is caused by Leishmania donovani. The purine and pyrimidine pathways are essential for L. donovani. Simultaneously inhibiting multiple targets could be an effective strategy to eliminate the pathogen and treat VL. Objective: We aimed to target the essential enzymes of L. donovani and inhibit them using a multi‐target approach. Materials and methods: A systematic analytical method was followed, in which first reported inhibitors of two essential enzymes (adenine phosphoribosyl‐transferase [APRT] and dihydroorotate dehydrogenase [DHODH]) were collected and then ADMET and PASS analyses were conducted using the Lipinski rule and Veber's rule. Additionally, molecular docking between screened ligands and proteins were performed. The stability of complexes was analyzed using molecular dynamics (MD) simulations and MMPBSA analysis. Results: Initially, 6,220 unique molecules were collected from the PubChem database, and then the Lipinski rule and Veber's rule were used for screening. In total, 203 compounds passed the ADMET test; their antileishmanial properties were tested by PASS analysis. As a result, 15 ligands were identified. Molecular docking simulations between APRT or DHODH and these 15 ligands were performed. Four molecules were found to be plant‐derived compounds. Lig_2 and Lig_3 had good docking scores with both proteins. MD simulations were performed to determine the dynamic behavior and binding patterns of complexes. Both MD simulations and MMPBSA analysis showed Lig_3 is a promising antileishmanial inhibitor of both targets. Conclusion: Promising plant‐derived compounds that might be used to combat VL were obtained through a multi‐target approach. The public health hazard posed by visceral leishmaniasis is significant, and there are currently no effective medications. A multi‐target approach utilizing various screening strategies was used to obtain promising ligands. Together, the screening of molecules, molecular docking, MD simulations, and MMPBSA investigation suggest that Lig_3 (PubChem CID‐44610153) can be considered a lead inhibitor compound of APRT and DHODH of L. donovani. The findings of this study may serve as a reference in the field of medicine and other related research. [ABSTRACT FROM AUTHOR]

The more information obtained about the driving environment, the more ensures driving safety. Due to the complex driving environment of farmland roads, targets beside the road sometimes have an important impact on driving safety. To achieve this goal, a novel real-time detection and prediction algorithm of targets was proposed. The whole image was divided into four parts by RCM: driving region, crossroad region, roadside region, and the other region. In addition, a safety policy for every part was enforced by the algorithm, which was based mainly on the combination of the YOLACT and GPM. On this basis, a self-collected data set of 5000 test samples is used for testing. The detection accuracy of the algorithm in the data set could reach up to 90%, and the processing speed to 30.4 fps. In addition, experiments were carried out on actual farmland roads, and the results showed that the proposed algorithm was able to detect, track, and predict targets on the farmland road, and alarm to driver in time before the targets rush into the road. This study provides an important reference for the safe driving of agricultural vehicles. [ABSTRACT FROM AUTHOR]

The looking-down mode of space airship bistatic radars faces complex sea–land clutter, and the mode of wide-range surveillance and the over-sight detection of the satellite platform generates a low SNR and range–Doppler ambiguity. The method traditionally used involves the transmission of multiple Pulse Repetition Frequencies (PRFs) and correlating them to solve the ambiguity. However, with a low SNR, the traditional disambiguation fails due to the large number of false alarms and target omissions. In order to solve this problem, a new algorithm for multi-target joint detection and range–Doppler disambiguation based on an ambiguity matrix is presented. Firstly, all possible state values corresponding to the ambiguous sequence are filled into the ambiguity matrix one by one. Secondly, the state values in the matrix cell are divided into several groups of subsequences according to the PRF. By disambiguating multiple sets of subsequences, performing subsequence fusion, and then undertaking point aggregation, the targets can be effectively detected in scenarios with a strong clutter rate, the false alarms can be suppressed, and the disambiguation of the range and Doppler is completed. The simulation shows that the proposed algorithm has the strong ability to detect targets and perform ambiguity resolution in the scenario of a multi-target and multi-false alarm.

The COVID-19 pandemic evolves constantly, requiring adaptable solutions to combat emerging SARS-CoV-2 variants. To address this, we created a pentameric scaffold based on a mammalian protein, which can be customized with up to 10 protein binding modules. This molecular scaffold spans roughly 20 nm and can simultaneously neutralize SARS-CoV-2 Spike proteins from one or multiple viral particles. Using only two different modules targeting the Spike's RBD domain, this construct outcompetes human antibodies from vaccinated individuals' serum and blocks in vitro cell attachment and pseudotyped virus entry. Additionally, the multibodies inhibit viral replication at low picomolar concentrations, regardless of the variant. This customizable multibody can be easily produced in procaryote systems, providing a new avenue for therapeutic development and detection devices, and contributing to preparedness against rapidly evolving pathogens. [ABSTRACT FROM AUTHOR]

Inverse Synthetic Aperture Radar (ISAR) is widely used as an all-weather imaging radar system in military and civilian fields. However, for complex moving targets, the presence of their velocity, acceleration, and jerk leads to range migration and Doppler shift, which causes difficulties in imaging, especially in multi-target scenarios. In this paper, the adjacent cross-correlation function (ACCF) is used to reduce the migration order. Based on the idea of Lv transform and Hough transform (HT), an ISAR imaging algorithm for complex maneuvering multi-target is proposed. The algorithm proposed in this paper is simple and fast and can be accomplished by complex multiplication, fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) without the searching process. The simulation results show that the proposed algorithm can effectively compensate for the range migration (RM) and Doppler migration (DM) caused by the target motion parameters, and form a focused ISAR image. Compared with the existing methods, the algorithm has lower computational complexity and achieves ideal results in both anti-noise performance and imaging. [ABSTRACT FROM AUTHOR]

Traditional Chinese Medicine (TCM) prescriptions are organically composed of compatible herbs according to the TCM theory. The complex ingredients of TCM could act on multiple targets through various pathways simultaneously to exert pharmacological effects, making TCM an unrivaled gem in the medical world. However, due to a lack of comprehensive and standard study methods, the research of TCM products has been quite limited. A novel paradigm that could aid in the discovery of the material basis and fully clarify the mechanism of TCM prescriptions is urgently needed. In this study, a similarity analysis based on molecular fingerprints was adopted to explore the representative molecules of the Tiaoxin recipe, a Chinese patent formula approved by the National Medical Products Administration (NMPA) for the treatment of mild-to-moderate Alzheimer's disease (AD), and 38 out of 1047 chemicals were finally screened out. Next, we tried to define a new concept of a "functional molecule cluster" for chemicals with similar pharmacological effects to elucidate how the chemical mixture from TCMs produce their therapeutic effects. Four anti-AD functional molecule clusters from the Tiaoxin recipe were identified: an anti-inflammatory cluster, an anti-ROS cluster, an anti-AChE activity cluster, and an anti-A β aggregation cluster. Furthermore, the chemicals from the anti-inflammatory cluster and anti-ROS cluster were proved to display their multi-target and multi-pathway roles partially or mainly through molecules of the TLR4-MYD88-NF- κ B and Keap1-Nrf2-ARE pathways. The functional molecule clusters may be vital to the explanation of the efficacy of the Tiaoxin recipe, which could give us a more profound understanding of TCM prescriptions. Our paradigm may open a novel path for TCM research. [ABSTRACT FROM AUTHOR]

Radar is a common means of tracking a target, and with active enemy interference, it often causes the target to lose its track, thus causing the radar to lose continuous tracking of the target. To improve the tracking effect, a multi-sensor cooperative detection target tracking method based on radar photoelectric linkage control was established. The study is based on radar photoelectric linkage, constant velocity (CV), constant acceleration (CA) and current statistical model (CSM) as the mathematical model of moving targets for this study. Improved interactive multi-model (IMM) and standard IMM were compared for targets in different motion states, as well as single sensor electronic support measures (ESM) and multi-sensor electronic support measures (ESM), infrared search and track (IRST). The research results show that in variable speed motion, the improved IMM algorithm and multiple sensors are used for target tracking. The azimuth and elevation tracking errors of the target are low, which can effectively solve the problem of model mismatch during the conversion of motion modes such as CV and CA. The azimuth and elevation image curves fluctuate smoothly, and have high stability. This method can achieve better tracking results. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the path generated by the artificial potential field (APF) method, such as local minimum, target unreachability, and low path smoothness, an improved artificial potential field method is proposed. First, to reduce the collision risk and planning difficulty, based on known environmental information such as the location of obstacles and targets, the area with fewer obstacles is selected as the priority area for path planning. Second, to improve the path smoothness and reduce the computation amount, an adaptive step-size adjustment method based on the distance and angle relationship with obstacles within the prediction range is proposed. Third, in view of the effect on each other between obstacle, local minimum, and unsmooth path, a multi-target model considering the size and influence range of obstacles and an improved potential field function are proposed on the basis of the identified planning priority area. Finally, in order that the path is smooth enough to be tracked by autonomous mobile robots, a safe driving corridor without collision with obstacles is constructed on the planned path, and a trajectory fully constrained to the safe driving corridor is generated using the quadratic programming method. The simulation comparison experiments are carried out on matlab simulation software and the smoothness of IAPF is improved by an average of 97.3% as compared to traditional APF and 45.19% as compared to DWA. The sum of the proposed IAPF path planning and optimization time is improved by 45.1% on average compared to DWA path planning time.

Abstract In the environment of electronic warfare, the countermeasure between radar and jammer has become a hot issue. Finding the solution to optimal waveforms between them is very great significance. Aiming at the problem of multi-target detection in clutter environment, the authors propose the waveform optimization method based on signal-to-interference-plus-noise ratio (SINR) criterion for radar and jammer, respectively. When radar is the dominant player, the maxmin strategy is used to establish the optimization model to optimize the radar transmission waveform. When jammer is the dominant player, the minmax strategy is used to establish the optimization model to optimize the jammer waveform. When dealing with random multi-target, the authors also propose an adaptive weight method to solve the problem of summation of weighted multiple targets. Then, an improved method combined with Jensen’s inequality for radar and jammer is proposed, respectively, which could improve the detection performance of the radar system and reduce the computational complexity of maximum SINR-based method. The simulation results show that the proposed maximum SINR-based methods could adaptively allocate the transmission energy to improve the performance of the radar. The energy allocation of radar and jammer is slightly different under maxmin strategy and minmax strategy. The proposed improved methods could output the higher SINR than the maximum SINR-based methods. Both of the proposed methods could improve the performance of radar detecting targets.

Vision-based target detection and segmentation has been an important research content for environment perception in autonomous driving, but the mainstream target detection and segmentation algorithms have the problems of low detection accuracy and poor mask segmentation quality for multi-target detection and segmentation in complex traffic scenes. To address this problem, this paper improved the Mask R-CNN by replacing the backbone network ResNet with the ResNeXt network with group convolution to further improve the feature extraction capability of the model. Furthermore, a bottom-up path enhancement strategy was added to the Feature Pyramid Network (FPN) to achieve feature fusion, while an efficient channel attention module (ECA) was added to the backbone feature extraction network to optimize the high-level low resolution semantic information graph. Finally, the bounding box regression loss function smooth L1 loss was replaced by CIoU loss to speed up the model convergence and minimize the error. The experimental results showed that the improved Mask R-CNN algorithm achieved 62.62% mAP for target detection and 57.58% mAP for segmentation accuracy on the publicly available CityScapes autonomous driving dataset, which were 4.73% and 3.96%% better than the original Mask R-CNN algorithm, respectively. The migration experiments showed that it has good detection and segmentation effects in each traffic scenario of the publicly available BDD autonomous driving dataset. [ABSTRACT FROM AUTHOR]