Recognition Process Research Articles

Environmental Management Strategies for Cost-Effective Waste Management in Hospitals

General background: Waste management in hospitals is crucial due to its environmental impact. Specific background: RSU Al Islam HM Mawardi Krian struggles with waste management while adhering to PSAP No. 1 of 2010 and maintaining efficiency. Knowledge gap: Limited research exists on applying environmental management accounting and operational strategies for hospital waste management in line with accounting standards. Aims: This study analyzes the impact of environmental management accounting and strategies on waste management costs at RSU Al Islam HM Mawardi Krian, assessing compliance with PSAP standards. Results: The hospital has implemented processes of identification, recognition, measurement, recording, presentation, and disclosure per PSAP No. 1 of 2010, managing waste effectively and incurring environmental costs. Novelty: Offers insights into aligning hospital environmental cost management with government standards. Implications: Structured environmental accounting and strategies can enhance waste management, improve hospital reputation, ensure regulatory compliance, and potentially increase revenue. Highlights: RSU Al Islam HM Mawardi complies with PSAP No. 1 of 2010 in managing waste costs. Proper environmental accounting improves transparency and operational efficiency. Structured strategies can boost hospital reputation and revenue. Keywords: Environmental Management Accounting, Operational Strategies, Waste Management Costs, Hospital Compliance

Self-organizing maps of unbiased ligand-target binding pathways and kinetics.

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design.

Color Recognition Achieved in Multiwavelength Controlled Plasmonic Optoelectronic Memristor for Neuromorphic Visual System

AbstractAn optoelectronic synaptic device with the color‐recognition ability is highly desired for achieving the high‐efficient neuromorphic color visual system. In order to realistically implement the functionality of retina (i.e., bipolar cells and cones photoreceptors), the exploration of multiwavelength controlled optoelectronic synaptic devices with fully light tunable ability would be sorely needed. Here, a multiwavelength controlled plasmonic optoelectronic memristor based on the size‐dependent localized surface plasmon resonance (LSPR) of metallic nanostructures is developed. The distinguishable red (R), green (G), and blue (B) light response behaviors can be achieved in this single device, which enables the realization of color image perception and memory functions. The RGB light‐strengthened synaptic plasticity can be depressed by ultraviolet light stimulations, relying on the effects of LSPR and optical excitation in WOx:Ag nanocomposites. Moreover, the visual attention for color (color discrimination) is realized in this memristor, which promotes the improvement of recognition accuracy during the color image recognition process. A new approach in developing multiwavelength controlled synaptic devices is provided here for highly efficient neuromorphic vision.

An empirical study of AI techniques in mobile applications

The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. Despite existing research exploring how mobile apps exploit AI techniques, they have the following main limitations: (1) Most existing studies focus on DL-based apps, with limited research on ML-based apps. (2) Existing research typically focuses on investigating the apps and the technologies utilized in the apps, lacking user-level analysis. (3) The number of apps studied is limited, with only 1,000 to 2,000 ML/DL apps identified after filtering. To fill the gap, in this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real-world AI applications, focusing on three crucial perspectives: (1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; (2) Framework and model analysis, where we analyze AI framework usage and AI model protection; (3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.

Cell membrane-inspired COF/AAO hybrid nanofluidic membrane with ion current rectification properties for ultrasensitive detection of E. coli

Nanofluidic membranes hold great prospects in sensitive and rapid detection of bioanalysts, while remaining lacking robust and general approaches for specificity. In this work, we introduce a cell membrane-inspired nanofluidic membrane with excellent ion current rectification properties as a simple and robust platform for label-free and ultrasensitive biosensing. The asymmetric nanofluidic membrane is prepared by coupling a two-dimensional covalent organic framework (COF) membrane to an anodic aluminum oxide (AAO) nanochannel via hydrogen bonding. The prepared COF/AAO membrane presents selective ion transport due to the asymmetric structure and anion selectivity of COF. With the large surface area and abundant functional modification sites of COF, further grafting of the recognition unit can be easily achieved to mimic the recognition process of receptors and ligands on cell membranes. As a proof of concept, the application in bacterial detection is demonstrated by covalently bonded Concanavalin A as recognition units. The highly amplified ionic current based on ion current rectification properties of the COF/AAO membrane allows sensitive and selective bioanalysis. Specifically, the proposed asymmetric nanofluidic membrane presents a linear detection range from 10 to 106 CFU/mL with an ultralow detection limit of 7 CFU/mL for E. coli. This work reveals the great potential of COF/AAO hybrid nanofluidic membrane as a sensor for rapid and precise bioanalysis.

Factors influencing educators’ implementation of quality teaching practices in Australian engineering education

ABSTRACT Improving teaching quality within engineering education is a long-standing challenge, made more difficult by the complex economic, political, and social environment in which educators operate. Understanding the enablers and barriers to implementing quality educational practices from a holistic perspective is thus critical for developing evidence-based actions for improving practice. This study addresses the research question: ‘what key factors influence engineering educators’ implementation of quality educational practices, and how?’. Using Bourdieu’s Theory of Practice as the theoretical lens, interviews were conducted with 21 engineering educators from 11 distinct Australian universities. Conceptualising the field as the engineering departments of Australian universities, data were thematically analysed to identify key themes relating to capital (what is valued within the field) and habitus (characteristics influencing individuals’ perceptions). Five factors emerged relating to capital: knowledge of engineering subject matter, knowledge of quality educational practices, recognition of teaching quality, resource allocation for teaching, and collegial network. These interacted with four factors relating to educators’ habitus: role context, personal motivation towards teaching, subject and student characteristics, and institutional context. Recommendations for educational leaders include increasing the value of educational and industry acquired engineering knowledge, developing enhanced impact evaluation approaches aligned to recognition processes, and supporting engagement between educators.

Novel Chiral Self-Assembled Nano-Fluorescence Materials with AIE Characteristics for Specific Enantioselective Recognition of L-Lysine.

In this paper, two aggregation-induced emission (AIE) chiral fluorescent materials, S-1 and S-2, were synthesized. The two materials are based on BINOL and H8-BINOL backbones, respectively, and large electron-absorbing groups are attached to the chiral backbones through the Knoevenagel reaction. At the same time, the CD signals of these two chiral fluorescent materials are gradually weakened (fw gradually increases) as they continue to aggregate. However, S-2 underwent a flip-flop from a negative to positive chiral CD signal at fw ≥ 90. And both materials also showed significant enantioselective recognition of lysine, demonstrating their potential as novel chiral fluorescent probes. Among them, the enantioselective fluorescence enhancement ratios (ef) of S-1 and S-2 for lysine were 10.0 and 10.3, respectively, while different degrees of blue shifts were produced by the ICT mechanism during the recognition process. In addition, the self-assembled morphology of the two nanomaterials is different; S-1 comprises hollow-core vesicles that are more likely to aggregate to form larger self-assembled vesicles, whereas S-2 is a solid block structure. When L/D-lysine was added alone, the morphology of S-1 was more distinctly different compared to S-2. With the addition of L-lysine, S-1 was dispersed and regularly spherical, whereas with the addition of D-lysine, S-1 itself remained in the form of aggregated large vesicles. This suggests that both S-1 and S-2 are important in the fields of chiral optics, chiral recognition, and nanoscale self-assembly.

Image sensor fusion for multimodal biometric recognition in mobile devices

AimMultimodal biometric recognition authenticates or identifies someone using various biometric features or modalities. Multimodal biometric systems use different biometric qualities to improve the recognition process's accuracy, security, and reliability rather than depending solely on one biometric property, such as fingerprint or facial recognition. Sensor fusion techniques can merge data from several biometric sensors, including fingerprint scanners and facial recognition cameras, to boost the accuracy and dependability of biometric authentication in the context of multimodal biometric recognition in mobile devices. The process of combining data from numerous image sensors to enhance a system's overall performance is known as image sensor fusion. ChallengesChallenges includeintegration difficulties in achieving accurate and secure multimodal biometric recognition on mobile devices. The study goal was Image sensor fusion for multimodal biometric recognition in mobile devices. MethodologyThe study uses a specially made database of 450 face photos roughly 450 fingerprints.Weiner filter (WF) is used for preparing data. Battle Royale optimized with deep convolutional neural network (BRO-DCNN) is proposed to improve the overall performance of fingerprint and facial information. FindingsAccuracy, sensitivity, specificity, and precision are used to measure the effectiveness of the proposed BRO-DCNN system. Study results show that BRO-MLANN solves the many characteristics of smart cities and is an efficient approach compared to other current methods in recognition rate, 98 % accuracy, 99 % precision, 96 % specificity, and 94 % sensitivity.

Application of machine learning based genome sequence analysis in pathogen identification.

Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combining Raman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5 min. These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety.

Object Detection Using Capsule Neural Network: An Overview

Appropriate remedies for tasks like language translation, object identification, object segmentation, picture recognition, and natural language processing are needed for today's computer vision tasks. This paper explores the use of Capsule neural Networks (Caps Nets) in object identification and offers a thorough analysis of their developments and uses. It presents an analysis of the transformative impact of Caps Nets on object identification tasks. This distinctive the architecture of neural network is presented as an alternative to convolutional neural networks (CNNs). The paper highlights that how Caps Nets are unique in capturing spatial correlations and hierarchical patterns in visualization data by analyzing the fundamental ideas of the technology. Additionally, it demonstrates how Caps net out perform CNNs in terms of generalization, interpretability, and last in the resistance to spatial distortions, confirming their goodness at object detection. By integrating the Caps networks with the latest scientific findings and advances, the paper shows the current status and potential future paths for object detection methods that use this leading neural network architecture.

Transcriptomic Signature of Spatial Navigation in Brains ofDesert Ants.

Navigation is crucial for central-place foragers to locate food and return to the nest. Cataglyphis ants are renowned for their advanced navigation abilities, relying on landmark cues and path integration. This study aims to uncover the transcriptomic basis of exceptional spatial learning in the central nervous system of Cataglyphis niger. Ants navigated a maze with a food reward, and we examined expression changes linked to correct decisions in subsequent runs. Correct decisions correlated with expression changes in the optic lobes, but not the central brain, showing a downregulation of genes associated with sucrose response and Creb3l1. The latter gene is homologous to Drosophila crebA, which is essential for long-term memory formation. To understand how ants use distance information during path integration, we analyzed expression shifts associated with the last distance traveled. We uncovered a transcriptomic footprint in the central brain, but not in the optic lobes, with genes enriched for energy consumption and neurological functions, including neuronal projection development, synaptic target inhibition, and recognition processes. This suggests that transcriptional activity in the central brain is necessary for estimating distance traveled, which is crucial for path integration. Our study supports the distinct roles of different brain parts for navigation in Cataglyphis ants.

Towards a Switchable Nanoparticle Behavior Using Inverse Electron-Demand Diels-Alder Chemistry and Ectoenzyme-Based Ligand Activation

Nanoparticles (NPs) as drug delivery platforms encounter numerous obstacles on their journey from administration to the target site. Often, diametrically opposing particle properties are desirable to overcome biological and physical barriers. Therefore, stimuli-responsive NPs have been developed to allow for specific particle adaptation. In this work, it was demonstrated that NPs can be rendered switchable with respect to their interaction with a receptor through an external chemical stimulus. A combination of the inverse electron-demand Diels-Alder (iEDDA) reaction for subsequent NP functionalization and ectoenzyme-based ligand activation allowed for specific particle tailoring. Building on this, a two-step process for target cell recognition was developed: First, NPs were functionalized with Angiotensin-I (Ang-I) as inactive ligand using iEDDA chemistry. At the target site, the ligand was enzymatically processed to Angiotensin-ll (Ang-II) by cellular ectoenzymes. Ang-ll binds as active ligand to the angiotensin ll type 1 (AT1) receptor on the target cell surface. This enzymatic activation aims to minimize the biological effect of the ligand prior to particle binding, while the NP target cell specificity is increased by a two-step recognition with enzymatic processing and receptor binding.

THE ROLE OF GEOSPATIAL ARTIFICIAL INTELLIGENCE (GEOAI) IN SMART BUILT ENVIRONMENT MAPPING: AUTOMATIC OBJECT DETECTION OF RASTER TOPOGRAPHIC MAPS IN MALAYSIA

Smart built environment mapping is integrating Geospatial Artificial Intelligence (GeoAI) to enable advanced analysis, pattern recognition, and decision-making processes. This shift in understanding, planning, designing, and managing the built environment is paving the way for a smarter, more sustainable future. This commentary explores the current role of AI in enhancing technology use within the geospatial field, focusing specifically on the application of GeoAI in mapping the built environment. Additionally, the paper presents a selection of case studies related to the implementation of AI in developing automatic vectorization, particularly for geospatial mapping in built environments. This research demonstrates the effectiveness of using Convolutional Neural Network (CNN) models for sorting objects in scanned, old topographic maps of the built environment. The findings of this study are valuable for making informed decisions, devising effective strategies, and identifying opportunities for further research and exploration within the dynamic field of GeoAI in smart built environment mapping and applications.

Impact of Disciplinary Variations on Second Language Mental Lexicon

This study investigates how academic disciplines impact second language (L2) lexical competencies. Prior L2 research has often overlooked the broader effects of disciplinary backgrounds on lexical development. To address this gap, this study utilized lexical decision, memory, and semantic fluency tasks to examine lexicon recognition, memory, and storage processes among L2 Chinese learners from various academic fields. The study participants comprised 16 students from the Humanities and Social Sciences (HSS) disciplines and 11 from the Science and Engineering (S&E) disciplines, all having passed HSK level 4. The tasks were conducted using E-Prime 2.0. The findings revealed distinct cognitive strategies and thinking patterns among the participants from different disciplines . These strategies and patterns affected lexical task performance in terms of accuracy and response times. Although lexical memory exhibited no significant variation among the groups, discipline-specific tendencies were observed in the formation of semantic networks. Learners from S&E disciplines preferred mastering field-relevant Chinese terminology, whereas HSS learners tended to acquire words outside their specific disciplines. This observation highlights how learners’ disciplinary backgrounds influence their vocabulary acquisition and the organization of semantic networks in L2.

Self-supervised learning-based re-parameterization instance segmentation for hazardous object in terahertz image

Recently, terahertz imaging technology has shown widespread potential applications in the public security screening field. Previous terahertz hazardous object detection techniques primarily relied on manual recognition and image processing methods. Some solutions incorporating deep learning technologies depend on large quantities of high-quality data, making it challenging to achieve low-cost and high-performance detection. To solve the issue of dependency on large amounts of high-quality data, we propose a diversified dynamic structure You Only Look Once (DDS-YOLO) model based on masked image modeling and structural reparameterization for the instance segmentation of hazardous objects in terahertz security inspection images. To address the scarcity and annotating difficulty problems of terahertz security inspection image samples, we combine automatic data strategies with masked image modeling for self-supervised learning. We propose a multilevel feature refinement fusion mechanism to enhance the quality of learned feature representations. The backbone parameter transfer and fine-tuning training strategies are employed to achieve hazardous object instance segmentation on the terahertz dataset. To address the low detection accuracy issue caused by the poor terahertz image quality, we develop a reparameterizable hierarchical structure for the backbone, improve a multi-scale feature-integrating neck, and design a dynamically decoupled head with lower computational requirements to enhance the performance of the instance segmentation model. Experimental results demonstrate that the proposed model accurately outputs detection boxes, categories, and segmentation masks for hazardous objects with minimal training samples. The comparative experimental results indicate that the proposed model outperforms existing state-of-the-art methods in terms of detection performance. The proposed DDS-YOLO model achieves 59.3 % in mask mean Average Precision (mAP) and 61.3 % in box mAP, and the model parameters and computational requirements also meet practical application scenarios.

Sentiment Analysis and Facial Expression Recognition in Customer Service Interactions

In the evolving landscape of digital customer service, the need for advanced methods to accurately understand and respond to customer emotions has become critical. Traditional systems often rely solely on textual data, missing non-verbal cues that significantly contribute to the customer's emotional state. This study proposes a combined approach integrating Facial Expression Recognition (FER) and Natural Language Processing (NLP) to enhance emotion detection accuracy in customer service interactions. The FER component employs Convolutional Neural Networks (CNNs) to analyze facial expressions, while the NLP component uses Long Short-Term Memory (LSTM) networks to process textual data. This multimodal system aims to provide a comprehensive understanding of customer emotions by capturing both verbal and non-verbal cues. Experiments demonstrate that the integrated FER and NLP model significantly outperforms standalone models, achieving an accuracy of 92.3%, compared to 85.2% for FER-only and 87.4% for NLP-only models. The results highlight the benefits of a multimodal approach, showing substantial improvements in both training and validation performance. This study also compares the proposed model with other state-of-the-art models such as the Deep Learning Assisted Semantic Text Analysis (DLSTA) and Multimodal Emotion Recognition using Deep Belief Networks (DBN). While DLSTA achieves higher accuracy in text-based emotion detection, and DBNs provide robust emotion classification by integrating various modalities, our model effectively balances the strengths of both visual and textual data. The findings suggest that integrating FER and NLP can significantly enhance the quality of customer service by enabling more empathetic and effective interactions. Future work will focus on optimizing computational efficiency, addressing data variability, and ensuring adaptability across diverse customer service scenarios.

Harnessing the Power of AI in Salesforce: An Empirical Study on Enhanced Data Insights and Organizational Efficiency

Abstract: This article examines the integration of Artificial Intelligence (AI) in Salesforce platforms to enhance business analytics and decision-making processes. We explore how AI-powered solutions are revolutionizing data analysis, providing organizations with actionable insights, and improving market responsiveness. The article investigates key AI techniques implemented in Salesforce, including machine learning algorithms for pattern recognition and natural language processing for unstructured data analysis. We analyze the impact of these technologies on identifying trends, forecasting, threat detection, and opportunity identification for sustainable growth. Through case studies and empirical analysis, we demonstrate the effectiveness of AI-driven Salesforce solutions in streamlining data processing, enabling real-time market adaptation, and enhancing organizational efficiency. The article also addresses the technological infrastructure supporting these AI solutions, discussing cloud-based computing resources, integration challenges, and data security considerations. Our findings suggest that AIpowered Salesforce solutions significantly improve business analytics capabilities, allowing companies to make more informed decisions and maintain a competitive edge in rapidly evolving markets. This article contributes to the growing body of knowledge on AI applications in customer relationship management and business intelligence systems

Mechanism of Dual-Site Recognition in a Classic DNA Aptamer.

Nucleic acid aptamers possess unique advantages in specific recognition. However, the lack of in-depth investigation into their dynamic recognition mechanisms has restricted their rational design and potential applications in fields such as biosensing and targeted therapy. We herein utilized enhanced sampling molecular dynamics to address affinities of adenosine monophosphate (AMP) to the dual binding sites in the DNA aptamer, focusing on the dynamic recognition mechanism and pathways. The present results indicate that in addition to the widely known intermolecular interactions, inequivalence of chemical environments of the two binding sites leads to slightly higher stability of AMP binding to the site proximal to the aptamer terminus. In the presence of two AMPs captured by the two sites, each binding free energy is enhanced. In particular, an additional hydrogen bond of AMP to A10 is introduced in the dual-site binding complex, which increases the binding energy from -4.25 ± 0.47 to -9.48 ± 0.33 kcal mol-1 in the site close to the loop. For the dual-site recognition process, the free energy landscape and minimum free energy pathway calculations elucidate the crucial role of electrostatic interactions between the AMP phosphate groups and Na+ ions in positively cooperative binding mechanisms.

The impact and application of artificial intelligence technology on mental health counseling services for college students

With the intensification of social pressure and the enhancement of mental health awareness, the mental health issues of college students have become increasingly prominent, attracting social attention. Mental health counseling services, as an important way to alleviate students’ psychological stress, are facing the dual challenges of a shortage of professionals and growing service demands. In recent years, the application of artificial intelligence (AI) technology in the field of mental health has gradually risen, and its advantages in data analysis, pattern recognition, and natural language processing provide new solutions for mental health counseling services. However, existing research still faces problems such as insufficient understanding and limited emotional interaction capabilities in practical applications. This paper delves into the application of AI technology in mental health counseling services for college students and innovates and improves upon the deficiencies in existing research. The study focuses on two main areas: First, word vector generation technologies based on statistics and language models are used according to different application scenarios, and their effectiveness in the analysis of mental health counseling texts is compared. Second, an improved Seq2Seq model is proposed to enhance the emotional understanding and interaction capabilities of emotional dialogue generation algorithms in mental health counseling. This study not only provides technological support for college mental health counseling services but also opens new research directions and perspectives for the application of AI in the field of mental health.

Approaching Pharmacological Space: Events and Components.

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.