Excellent Recognition Research Articles

Tailoring Chirality and Optimizing Enantioselective Recognition in Strategic Defect Engineering of Chiral Metal-Organic Frameworks.

Introducing chiral molecules into metal-organic frameworks (MOFs) to obtain chiral MOFs (CMOFs), the tunability of their structures makes them a highly anticipated class of chiral materials for electrochemical sensing. However, the structure of CMOFs is often limited by synthesis challenges, and introducing chiral molecules into MOFs often leads to a decrease in their internal space. This study introduces a defect engineering strategy into the synthesis of CMOFs to obtain CMOFs with defects, which is an efficient synthesis method. The two CMOFs constructed with different structures not only have more chiral recognition sites but also greatly increase the substrate capacity due to the defects, making them have a wide range of substrates and enhancing the enantioselective recognition effect of the two defective CMOFs. In addition, using MOF as a chiral carrier greatly overcomes the problem of low conductivity of chiral molecules. Based on the advantages of defective CMOFs, we have designed a novel chiral electrochemical sensor with an excellent enantiomer recognition performance. This study provides a simple and scalable synthetic method for constructing CMOFs with defects and high stability.

Scaling and spreading age-friendly care: Early lessons from the VA National Age-Friendly Action Community.

The Age-Friendly Health System (AFHS) initiative seeks to improve care for older adults through assessing and acting on the 4Ms (What Matters, Medication, Mentation, Mobility). The Department of Veterans Affairs (VA) joined the initiative in 2020, and from 2022 to 2023, VA led its first Age-Friendly Action Community, a 7-month online educational series to teach clinicians about implementing the 4Ms across VA care settings. The VA Action Community was designed to spread awareness about Age-Friendly care for older Veterans, improve interprofessional team knowledge for providing care guided by the 4Ms, and support AFHS implementation across multiple care settings. The VA Action Community included online synchronous webinars, Community of Practice coaching calls, and office hours. A learner experience questionnaire was administered at the completion of the Action Community. Totally 186 care teams enrolled in the VA Action Community, representing 78/171 (45.6%) VA medical centers (VAMCs), across 36 US states and 20 types of care settings. Participants reported high rates of satisfaction and confidence in their ability to apply the knowledge and skills learned. Overall, 58 Action Community teams earned Level 1, Participant recognition, and 43 teams also earned Level 2, Committed to Care Excellence recognition from the Institute for Healthcare Improvement. The VA Action Community facilitated learning about the 4Ms, supported interprofessional teams in earning AFHS recognition, and offers a promising model for spreading AFHS in other large health systems. Further work is underway to evaluate the impact of this educational experience on clinical process and outcomes measures.

Construction of a regulable chiral recognition platform based on the photothermal effect of popcorn-like gold nanoparticles/bovine serum albumin.

Although the chiral recognition ability of nanomaterials is known to play a crucial role in the chiral discrimination of enantiomers, it remains challenging to precisely regulate the chiral recognition ability predictably and on demand. In this work, a regulable chiral recognition platform is designed based on the photothermal effect of popcorn-like gold nanoparticles (AuNPs)/bovine serum albumin (BSA). First, AuNPs with excellent chiral recognition ability are prepared for the discrimination of tyrosine (Tyr) enantiomers. Subsequently, AuNPs can be combined with BSA through the Au-S bond. The obtained AuNPs/BSA exhibits the opposite chiral recognition ability to AuNPs due to the inherent chirality of BSA. Interestingly, BSA can also be destroyed relying on the photothermal effect of AuNPs and the excitation of near-infrared (NIR) light. Therefore, the resulting AuNPs/BSA/NIR reversibly displays the same chiral recognition ability as AuNPs. A regulable chiral recognition platform is constructed for chiral discrimination of Tyr enantiomers, which can satisfy the purpose of predictable and on-demand regulation of the recognition ability of nanomaterials and might be a potential candidate for detection and therapy in vivo.

An efficient surface electromyography-based gesture recognition algorithm based on multiscale fusion convolution and channel attention

In the field of rehabilitation, although deep learning have been widely used in multitype gesture recognition via surface electromyography (sEMG), their higher algorithmic complexity often leads to low computationally inefficient, which compromise their practicality. To achieve more efficient multitype recognition, We propose the Residual-Inception-Efficient (RIE) model, which integrates Inception and efficient channel attention (ECA). The Inception, which is a multiscale fusion convolutional module, is adopted to enhance the ability to extract sEMG features. It uses fast dimensionality reduction, asymmetric convolution decomposition, and pooling to suppress the accumulation of parameters, then reducing the algorithmic complexity; The ECA is adopted to reweight the output features of Inception in different channels, enabling the RIE model to focus on information that is more relevant to gestures. 52-, 49-, and 52-class gesture recognition experiments are conducted on NinaPro DB1, DB3, and DB4 datasets, which contain 14,040, 3234, and 3120 gesture samples, respectively. RIE model proposed in this study achieves accuracies of 88.27%, 69.52%, and 84.55% on the three datasets, exhibiting excellent recognition accuracy and strong generalization ability. Moreover, this method reduces the algorithmic complexity from both spatial and temporal aspects, rendering it smaller in size and faster in computation compared to other lightweight algorithms. Therefore, the proposed RIE model possesses both lightweight computational requirements and reliable performance, providing an efficient deep learning method for gesture recognition based on sEMG.

Framework Nucleic Acid-Nanobody Fusion Probe-Based Pharmacokinetics Modulation and Analysis for Efficient Positron Emission Tomography Imaging.

Nanobodies are promising for immunoPET imaging due to their excellent antigen recognition and tumor targeting, yet rapid clearance limits their tumor accumulation. Although multimerization and albumin binding can extend their circulation time and improve tumor targeting, a simple and universal method for creating protein multimers is still needed. Here, we leveraged the facile synthesis, controllable size, and precise assembly of DNA nanotechnology to construct CD47-targeted framework nucleic acid-nanobody fusion probes with multiple valences and sizes. Following comprehensive structural characterization, in vitro specificity assessment and in vivo PET/CT imaging analysis were conducted on a colorectal cancer LS174T mouse model. Furthermore, a pharmacokinetic model was developed and fitted with considerable in vivo data to prove its rationality, followed by testing the effects on tumor uptake prediction by changing different pharmacokinetic parameters. Indeed, by manipulating the size of the nucleic acid scaffolding and the number of attached nanobodies, we could precisely modulate the accumulation of probes at the tumor site. Overall, this study not only developed an efficient strategy for constructing nanobody multimers but also provided a pharmacokinetic model, allowing profound insight into the multidimensional data obtained experimentally and informing the design of future imaging probes with predictable delivery efficacies.

Enhanced quasi-meshing hotspot effect integrated embedded attention residual network for culture-free SERS accurate determination of Fusarium spores.

Determination of Fusarium spores is essential for precision control of Fusarium head blight and ensuring agri-food safety. As a highly sensitive real-time detection technique with rich fingerprint information and little influence from water, surface-enhanced Raman spectroscopy (SERS) has been widely applied in the determination of microorganisms. However, fungi determination faces significant challenges including low sensitivity, and poor specificity. The enhanced quasi-meshing hotspot effect (EQMHE) integrated with the embedded attention residual network (EARNet) was proposed to realize a label-free and accurate SERS determination of various Fusarium spores. The EQMHE can promote the binding of spores and nanoparticles to form numerous hotspots, significantly enhancing signal quality and improving the detection limit by at least four orders of magnitude. The key factors inducing EQMHE were validated through various characterization techniques. Moreover, due to its excellent feature extraction and recognition capabilities, EARNet successfully overcomes the limitations of spectral similarity, achieving determination accuracies of 100% in the training set, 98.33% in the validation set, and 100% in the prediction set for three Fusarium species from actual samples. EARNet requires only a small amount of training data and provides rapid and accurate diagnostics. Throughout the process, the spores do not require culturing or lysing, providing an effective determination method for the practical determination of mixed fungal spores. Overall, the proposed strategy effectively addresses challenges such as the need for fungal spore cultivation, difficulties in SERS hotspot formation, and suboptimal signal quality, and it holds significant promise for applications in disease control, food safety, and agricultural production.

Preparation of a 1,1'-Binaphthyl-based Chiral Polyimine Macrocycle Bonded Chiral Stationary Phase by Thiol-ene Click Reaction and Its Enantioseparation Performance in High-Performance Liquid Chromatography.

Chiral macrocycles have emerged as attractive media for chromatographic enantioseparation due to their excellent host-guest recognition properties. In this study, a new chiral stationary phase (CSP) based on 1,1'-binaphthyl chiral polyimine macrocycle (CPM) was reported. The CPM was synthesized by one-step aldehyde-amine condensation of (S)-2,2'-dihydroxy-[1,1'-binaphthalene]-3,3'-dicarboxaldehyde with 1,2-phenylenediamine and bonded on thiolated silica via the thiol-ene click reaction to afford the CSP. The enantioseparation performance of the CSP was evaluated by separating different types of racemates including alcohols, esters, ketones, amides, organic acids, and ethers in both normal-phase (NP) and reversed-phase (RP) elution modes. As a result, enantioseparations of 10 and 15 racemates were achieved in the two elution modes, respectively. Meanwhile, the effects of chromatographic conditions on separation, such as mobile phase composition and injection mass, were studied in detail. Moreover, a comparison of the proposed CSP for the separation of the tested racemates with commercial Chiralcel OD-H and Chiralpak AD-H columns was also conducted, and results revealed that the proposed CSP can achieve some enantioseparations that cannot be achieved by the two commercial columns. This study indicates that the chiral macrocycle is a promising chiral selector for high-performance liquid chromatography.

Chiral Differentiation of Chiral Lactides and Chiral Diketones on Native and Phenylcarbamoylated Cyclodextrin Chiral Stationary Phases.

Inclusion complexation, hydrogen bonds, π-π interaction, dipole-dipole interaction, and steric hindrance effect all contribute to the enantioseparation ability of cyclodextrin (CD) or CD derivatives. In this work, one native cationic CD chiral stationary phases (SHCDCSP) and four derivatized CD-CSPs, namely, per(4-trifluoromethyl) phenylcarbamoylated-β-CD CSP (SFPhCDCSP), per(4-chloro) phenylcarbamoylated-β-CD CSP (SCPhCDCSP), per(4-bromo) phenylcarbamoylated-β-CD CSP (SBPhCDCSP), and per(4-methyl) phenylcarbamoylated-β-CD CSP (SMPhCDCSP), were prepared via thioether linkage and applied for the enantioseparation of chiral lactides and chiral diketones in both reverse phase(RP) and normal phase (NP) modes. Most of the studied chiral lactides were found to be well resolved (Rs > 1.5) under RP mode, especially Met-Sty-Ibn (compound 11) is observed to display highest resolutions (Rs = 5.09, Rs = 3.17) among all the analytes on the SFPhCDCSP and SMPhCDCSP. In NP mode, SFPhCDCSP showed excellent chiral recognition ability towards chiral lactides. The comparison study of CD-CSPs reveals the structure of CSPs play a significant role on the enantioselectivities.

Fluorescence sensor array for highly sensitive pattern recognition of biothiols in food based on tricolor upconversion luminescence metal-organic frameworks

Fluorescence nanomaterial sensors have exhibited excellent application potential in biothiols analyses. The fluorescence sensor arrays constructed from upconversion luminescence metal-organic frameworks nanocomposites (LMOFs) can provide impressive discrimination and exquisite fingerprinting capabilities for extremely similar analytes. Herein, an upconversion fluorescence sensor array based on LMOFs featuring UiO-type metal-organic frameworks-functionalized lanthanide-doped upconversion nanoparticles was proposed, wherein Cu2+ can make the fluorescence quenching of LMOFs and preferentially bind biothiols to recover fluorescence in different degrees forming unique fingerprinting. The fluorescence sensor array displayed an excellent pattern recognition for five biothiols (glutathione, homocysteine, N-acetylcysteine, and L/D-cysteine) even at 50 µM by linear discriminant analysis, and the discernment for the enantiomers of L/D-cysteine, as well as the accurate identification (90.0% accuracy) of biothiols in food samples (tea beverage and white wine). Such fluorescence sensor array might provide a simple and efficient detection method for biothiols.

QiandaoEar22: a high-quality noise dataset for identifying specific ship from multiple underwater acoustic targets using ship-radiated noise

Target identification of ship-radiated noise is a crucial area in underwater target recognition. However, there is currently a lack of multi-target ship datasets that accurately represent real-world underwater acoustic conditions. To tackle this issue, we conducted experimental data acquisition, resulting in the release of QiandaoEar22—a comprehensive underwater acoustic multi-target dataset. This dataset encompasses 9 h and 28 min of real-world ship-radiated noise data and 21 h and 58 min of background noise data. To demonstrate the availability of QiandaoEar22, we executed two experimental tasks. The first task focuses on assessing the presence of ship-radiated noise, while the second task involves identifying specific ships within the recognized targets in the multi-ship mixed data. In the latter task, we extracted eight features from the data and employed six deep learning networks for classification, aiming to evaluate and compare the performance of various features and networks. The experimental results reveal that ship-radiated noise can be identified from background noise in over 99% of cases. For the specific identification of individual ships, the optimal recognition accuracy achieves 99.56%. Finally, we found using spectrum and MFCC as feature inputs and DenseNet as classifier can achieve excellent recognition performance. Considering the computational efficiency, CRNN and ECAPA-TDNN are also good choices. Our work not only establishes a benchmark for algorithm evaluation but also inspires the development of innovative methods to enhance underwater acoustic target detection (UATD) and underwater acoustic target recognition (UATR).

DNA Nanomaterial-Based Electrochemical Biosensors for Clinical Diagnosis.

Sensitive and quantitative detection of chemical and biological molecules for screening, diagnosis and monitoring diseases is essential to treatment planning and response monitoring. Electrochemical biosensors are fast, sensitive, and easy to miniaturize, which has led to rapid development in clinical diagnosis. Benefiting from their excellent molecular recognition ability and high programmability, DNA nanomaterials could overcome the Debye length of electrochemical biosensors by simple molecular design and are well suited as recognition elements for electrochemical biosensors. Therefore, to enhance the sensitivity and specificity of electrochemical biosensors, significant progress has been made in recent years by optimizing the DNA nanomaterials design. Here, the establishment of electrochemical sensing strategies based on DNA nanomaterials is reviewed in detail. First, the structural design of DNA nanomaterial is examined to enhance the sensitivity of electrochemical biosensors by improving recognition and overcoming Debye length. In addition, the strategies of electrical signal transduction and signal amplification based on DNA nanomaterials are reviewed, and the applications of DNA nanomaterial-based electrochemical biosensors and integrated devices in clinical diagnosis are further summarized. Finally, the main opportunities and challenges of DNA nanomaterial-based electrochemical biosensors in detecting disease biomarkers are presented in an aim to guide the design of DNA nanomaterial-based electrochemical devices with high sensitivity and specificity.

PDeT: A Progressive Deformable Transformer for Photovoltaic Panel Defect Segmentation.

Defects in photovoltaic (PV) panels can significantly reduce the power generation efficiency of the system and may cause localized overheating due to uneven current distribution. Therefore, adopting precise pixel-level defect detection, i.e., defect segmentation, technology is essential to ensuring stable operation. However, for effective defect segmentation, the feature extractor must adaptively determine the appropriate scale or receptive field for accurate defect localization, while the decoder must seamlessly fuse coarse-level semantics with fine-grained features to enhance high-level representations. In this paper, we propose a Progressive Deformable Transformer (PDeT) for defect segmentation in PV cells. This approach effectively learns spatial sampling offsets and refines features progressively through coarse-level semantic attention. Specifically, the network adaptively captures spatial offset positions and computes self-attention, expanding the model's receptive field and enabling feature extraction across objects of various shapes. Furthermore, we introduce a semantic aggregation module to refine semantic information, converting the fused feature map into a scale space and balancing contextual information. Extensive experiments demonstrate the effectiveness of our method, achieving an mIoU of 88.41% on our solar cell dataset, outperforming other methods. Additionally, to validate the PDeT's applicability across different domains, we trained and tested it on the MVTec-AD dataset. The experimental results demonstrate that the PDeT exhibits excellent recognition performance in various other scenarios as well.

Encoding human activities using multimodal wearable sensory data

Human Activity Recognition (HAR) is the task to automatically analyze and recognize human body gestures or actions. HAR using time-series multi-modal sensory data is a challenging and important task in the field of machine learning and feature engineering due to its increasing demands in numerous real-world applications such as healthcare, sports and surveillance. Numerous daily wearable devices e.g., smartphones, smartwatches, and smart glasses can be used to collect and analyze the human activities on an unprecedented scale. This paper presents a generic framework to recognize the different human activities using continuous time-series multimodal sensory data of these smart gadgets. The proposed framework follows the channel of Bag-of-Features which consists of four steps: (i) Data acquisition and pre-processing, (ii) codebook computation, (iii) feature encoding, and (iv) classification. Each step in the framework plays a significant role to generate an appropriate feature representation of raw sensory data for efficient activity recognition. In the first step, we employed a simple overlapped-window sampling approach to segment the continuous time-series sensory data to make it suitable for activity recognition. Secondly, we build a codebook using k-means clustering algorithm to group the similar sub-sequences. The center of each group is known as codeword and we assume that it represents a specific movement in the activity sequence. The third step consists of feature encoding which transform the raw sensory data of activity sequence into its respective high-level representation for the classification. Specifically, we presented three reconstruction-based encoding techniques to encode sensory data, namely: Sparse Coding, Local Coordinate Coding, and Locality-constrained Linear Coding. The segmented activity sub-sequences are transformed to high-level representation using these techniques and earlier computed codebook. Finally, the encoded features are classified using a simple Random Forest classifier. The proposed HAR framework using three different encoding techniques is evaluated on three large benchmark datasets: UniMiB-SHAR dataset, MHEALTH dataset and WISDM dataset and their results are compared with most recent state-of-the-art techniques. It outperforms the existing techniques and achieves 98.39%, 99.5%, and 99.4% recognition scores on UniMiB-SHAR dataset, MHEALTH dataset, and WISDM dataset respectively. The excellent recognition results and computational analysis confirms the effectiveness and efficiency of the proposed framework.

Synthesis of a novel β-cyclodextrin chiral stationary phase and its application to the evaluation of the enantioselective bioaccumulation and elimination behavior of tebuconazole in Rana nigromaculata tadpoles

BackgroundThe increased production and use of chiral pesticides will enhance their exposure in the environment. Chiral pesticides typically exhibit varied biological effects among these enantiomers. Therefore, it is very essential to develop and validate chiral analytical methods to investigate their potential ecological risks from a stereoselective perspective. Current separation of pesticides enantiomers relies extensively on chiral stationary phases (CSPs), while the development of β-Cyclodextrin derivatives CSPs become the research focus due to their great modifiability and excellent chiral recognition capabilities. ResultsA novel chiral stationary phase, 3,5-dichlorophenylaminomethyl-6-phenylenediamine-β-cyclodextrin chemically bonded silica gel (MPDCDA), was successfully prepared. Based on that, a stereoselective HPLC-MS/MS method was developed and validated for the determination of tebuconazole enantiomers in Rana nigromaculata tadpoles. After extraction by QuEChERS, the tebuconazole enantiomers were completely separated with the resolutions of 1.63 using the mobile phase of methanol-water (70/30, v/v). Good linearity (r > 0.9990) for both enantiomers over a concentration range of 0.20–500.0 ng/mL was obtained with the accuracy ranged from 6.7 % to 9.3 % and the intra-day and inter-day precisions below 6.2 % at three quality control levels. The proposed method was successfully applied in evaluating the enantioselective bioaccumulation and elimination profiles of tebuconazole in tadpoles. At the tested conditions, there was no significantly enantioselective difference in the bioaccumulation process for S- tebuconazole and R-tebuconazole. However, the elimination process of tebuconazole enantiomers was enantioselective with R-tebuconazole preferentially degraded. SignificanceThis work provided an accurate risk assessment of chiral pesticides to non-target aquatic organisms from a stereoselective perspective. These findings would deepen our understanding of the potential ecological risks of chiral pesticides on aquatic organisms and provide scientific support for the protection of aquatic organisms and their ecological environments, as well as sustainable development.

HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.

EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging. In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects. Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% ± 1.25% (SEED), 98.93% ± 1.45% (DEAP-arousal), and 98.57% ± 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology. The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition.

Efficient multi‐perspective jamming feature perception network for suppressive jamming recognition with limited training samples

AbstractRecognising suppressive jamming signals is crucial for radar systems to counteract this type of jamming, highlighting the importance of research in this area. Current deep learning‐based methods for identifying suppressive jamming signals suffer from reduced effectiveness with limited training samples and issues related to high parameter counts and computational complexity. To address these challenges, the authors propose a jamming recognition method based on an efficient multi‐perspective jamming feature perception network. This method extracts features from the time‐frequency spectrum of jamming signals from multiple perspectives, including local, multi‐scale, cross‐space, and global, to obtain more robust and discriminative jamming features and improve recognition under limited training sample conditions. Additionally, the authors design efficient modules for local jamming feature extraction, multi‐scale jamming feature down‐sampling, and global jamming feature representation. The lightweight design of these modules enables the proposed method to maintain excellent jamming recognition performance while reducing parameters and computational complexity. Simulation experiment outcomes highlight the exceptional effectiveness of the proposed technique across multiple metrics compared to eight other approaches. Furthermore, the proposed method exhibits significantly fewer parameters and lower computational complexity than its deep learning‐based counterparts.

Exploring barriers to Quality 4.0 implementation: a multivariate analysis

PurposeIndustry 4.0 (I4.0) technologies and practices are changing the way quality is managed in organizations, giving rise to the movement known as “Quality 4.0” (Q4.0). In this context, this study aims to analyze the barriers organizations face when trying to implement Q4.0 and to examine whether there are significant differences according to size, sector, level of excellence and degree of readiness for Q4.0 implementation.Design/methodology/approachThe sample comprises 107 Spanish organizations with some form of European Foundation for Quality Management (EFQM) excellence recognition system. The methodology includes multivariate techniques (factor analysis and mean difference analysis).FindingsThe study has managed to summarize the ten main barriers to the implementation of Q4.0 identified in the literature in three groups: organizational (five barriers), knowledge (two barriers) and technological (three barriers). The main barriers are the high investment in technology required to implement Q4.0 and the need for more training and competence of workers. In addition, SMEs encounter more significant technological barriers when implementing Q4.0 than large companies. However, organizational and knowledge barriers are the same. Organizations in the service sector come across more organizational and knowledge barriers to implementing Q4.0 than industrial organizations; no differences were identified for technological barriers. The higher the EFQM seal of excellence achieved, the lower the knowledge barriers faced, with no differences in organizational and technological barriers. Finally, it is observed that the more prepared organizations are to implement Q4.0, the lower the barriers they face.Research limitations/implicationsThe study helps managers identify the barriers they may encounter when implementing Q4.0, which helps design short- and long-term plans to mitigate or break down these barriers.Originality/valueThe study provides empirical evidence on an organization’s barriers to implementing Q4.0. On the other hand, the barriers are related to some of the relevant characteristics that define the organizations, such as their size, the sector of activity, their level of excellence and their degree of preparation for implementing Q4.0.

2024 AMCA MEMORIAL LECTURE HONOREE: MIR SUBHAN MULLA 1925-20231.

From 1979 to 2024, 51 individuals have been recognized as AMCA honorees for their lifetime achievements in mosquito and vector control. Mir Subhan Mulla (1925-2023) was the most recent honoree recognized in March 2024. Mulla received a scholarship in 1948 to attend Cornell University, where he obtained his bachelor's degree in entomology and parasitology. Mulla received his Ph.D. in entomology at the University of California, Berkeley in 1956, and then joined the University of California, Riverside (UC Riverside) faculty the same year. He retired in 2006 after 50 years of service. As one of the founding faculty in entomology at UC Riverside, Mulla created undergraduate and graduate courses and seminars in medical and veterinary entomology. As a leader in UC Riverside's world-renowned medical entomology graduate program, he advised 27 Ph.D. students, three master's students, 20 postdoctoral fellows, and 30 visiting scholars from many countries. Mulla contributed significantly to research and developments in biology, ecology, and control of arthropods of public health and economic importance, including but not limited to mosquitoes, flies, eye gnats, midges, blackflies, and stored product beetles. His work on biorational pesticides was remarkable. He published more than 550 scientific papers in peer-reviewed journals. Mulla served the scientific community as a consultant, advisor, member, or chairman with numerous national and international organizations. He guided many universities in the development of their medical entomology programs. Mulla also served as one of the founding scientists of the Society for Vector Ecology (SOVE). He also provided financial support for facilities, faculty, and students at UC Riverside. Additionally, Mulla served as a leader in the Riverside Muslim community. He and his wife Lelia founded the Islamic Society of Riverside and Orange Counties in California. In recognition of Mulla's excellence in teaching, mentorship, and research, he received numerous prestigious recognitions from universities, communities, and professional organizations. He will be greatly missed by his loving and devoted family, friends, former students, and colleagues here in the USA and abroad.

Editorial: Recognition of excellence

Editorial: Recognition of excellence

Radio frequency fingerprint recognition method based on prior information

The open wireless communication environment is vulnerable to various malicious attacks. Wireless communication hardware devices have unique physical layer characteristics. As an inherent unique feature of wireless signals, radio frequency fingerprints provide a guarantee for the identification and verification of wireless signals. Most of the existing radio frequency fingerprint identification methods only extract fingerprints from one of the steady-state signals or transient signals. Neglecting the connection between the two wireless communication signals results in low identification accuracy of the radio frequency fingerprint identification method under the condition of a low signal-to-noise ratio. Aiming at the respective characteristics of these two signals, a radio frequency fingerprinting method combining transient and steady-state signals based on prior information of wireless signals is proposed. This method combines the characteristic stability of steady-state signals and the integrity characteristics of transient signals, which can effectively identify and classify wireless signals and achieve excellent recognition under low signal-to-noise ratio conditions. The effectiveness of the proposed method is verified by experimental comparison with the traditional radio frequency fingerprinting method on the LFM signal dataset.