Time Cost Research Articles

Factors influencing adherence to Physical Exercise in patients with multiple sclerosis: a systematic review focusing on Exercise over General Physical Activity.

While the barriers and facilitators of physical activity adherence are well-defined, there is a limited number of studies focusing specifically on exercise adherence, which is crucial to identify in patients with multiple sclerosis (MS) due to its known disease-modifying effect. To investigate the factors affecting adherence to physical exercise in MS patients. Literature search was conducted in PubMed, Cochrane, Web of Science, and Scopus electronic databases up to the current date. Methodological quality was assessed using the Newcastle-Ottawa Scale (NOS) for quantitative studies and the McMaster Critical Appraisal Tool for qualitative studies, with evidence synthesis conducted using the Modified Bakker Scale. The systematic review incorporated thirteen studies, consisting of seven qualitative and six quantitative ones. These studies' methodological quality was high; 12 were high, and the other was medium (unclear). In all reviewed studies, the primary patient-related and environmental barriers with high levels of evidence regarding exercise adherence included fatigue (38%), false beliefs (18%), cost (18%), and lack of time (15%). Barriers identified in very few studies included cognitive problems (3%), bladder-bowel problems (3%), and family support and advice (3%), indicating a necessity for further research to reveal their impact on exercise adherence. The scarcity of studies on exercise barriers in MS is partly due to the absence of reliable assessment methods for evaluating exercise adherence, along with the prevalent emphasis on physical activity. Future research should prioritize the development of adherence scales tailored to physical exercise and shifting focus towards less explored barriers.

Features of Structural Hardware Transformation of Information in Cryptosystems

The article considers types, functions and some features of cryptosystems, as well as circuit design options for expanding their functionality. Options for generating keys and ciphers used in cryptosystems with a typical structural organization, and technologies for creating encryption chains are presented. An encryption scheme is described, in the recurrent formula of the algorithm of which the previous blocks of both encryption and plaintext are used. This scheme reliably protects against any unauthorized modification of the encrypted text. Structural diagrams of the organization of symmetric and asymmetric cryptosystems are given. A variant of implementing a decoding procedure in a threshold MIMA cryptomodule for sharing a secret with a masking transformation is proposed, in which the necessary time and hardware costs for performing the procedure of reconstructing the original secret are minimized. The presented material can be the part of the original sections of a necessary and sufficiently provided in mathematical terms textbook on the basics and modern problems of cryptography.

Dynamic Optimization of Exclusive Bus Lane Location Considering Reliability: A Case Study of Beijing

For metropolises like Beijing, heavy congestions cause transit passengers’ unreliable travel time, including in vehicle time and waiting time. Comparing with other managerial measures, designing a lane for bus use only is an effective method to improve travel reliability, for it can eliminate the influence on bus-driving conditions. This paper proposes a reliable and practical method to determine exclusive bus lanes (EBL). A reliability-based optimization model is established, in which the tradeoff among bus and private car passengers’ travel time, reliability, and EBL construction cost are considered. Based on the actual network, a user equilibrium demand assignment model is applied to estimate the dynamic bus flow distribution. Since the model is nonlinear, a two-step method is proposed where tangent lines are introduced to constitute an envelope curve to linearize the model. This work conducts the statistical modeling and fitting analysis with actual bus trajectory data, collected on EBL in Beijing during peak hours. Passenger travel time distributions are fitted to estimate the statistical passenger travel time; Lognormal distribution and Gaussian distribution are the best fit. The optimization results indicate that the passenger travel time reliability can be improved by 5.5% by the optimized EBL location scheme. This study will provide a theoretical basis and methodological support for improving the service level of the public transportation system in large cities through the scientific planning of exclusive bus lanes.

Features of Structural Hardware Transformation of Information in Cryptosystems

The article considers types, functions and some features of cryptosystems, as well as circuit design options for expanding their functionality. Options for generating keys and ciphers used in cryptosystems with a typical structural organization, and technologies for creating encryption chains are presented. An encryption scheme is described, in the recurrent formula of the algorithm of which the previous blocks of both encryption and plaintext are used. This scheme reliably protects against any unauthorized modification of the encrypted text. Structural diagrams of the organization of symmetric and asymmetric cryptosystems are given. A variant of implementing a decoding procedure in a threshold MIMA cryptomodule for sharing a secret with a masking transformation is proposed, in which the necessary time and hardware costs for performing the procedure of reconstructing the original secret are minimized. The presented material can be the part of the original sections of a necessary and sufficiently provided in mathematical terms textbook on the basics and modern problems of cryptography.

AI-Augmented Fault Detection and Diagnosis in VLSI Circuits: A Step toward Intelligent Chip Design

Fault detection and diagnosis are critical challenges in Very Large Scale Integration (VLSI) circuits, where even minor defects can cause significant performance degradation or system failure. Traditional fault detection methods often struggle with scalability and real-time analysis as circuits grow increasingly complex. This paper proposes an AI-augmented framework that leverages machine learning algorithms and neural networks to enhance the fault detection process in VLSI circuits. The proposed model not only identifies and classifies faults with high accuracy but also provides root-cause diagnostics, significantly reducing testing time and maintenance costs. Simulation results demonstrate the effectiveness of the AI-based approach in comparison with conventional techniques, showcasing improved fault coverage, faster detection, and scalability for modern chip designs. This study serves as a step toward the integration of intelligent diagnostics into chip manufacturing, paving the way for more robust and self-healing electronic systems.

Budget impact analysis on the use of Sysmex PA-100 AST system as a point of care for uncomplicated urinary tract infections detection and treatment in Spanish females

Objective Efficient diagnostic methods, such as the Sysmex PA-100, provide rapid antimicrobial susceptibility test results directly from urine samples with high sensitivity and specificity, and improve treatment recommendations. Our study analyzes the economic impact of integrating the Sysmex PA-100 to assess its potential benefits and cost implications for uncomplicated urinary tract infection (uUTI) management in Spain. Methodology A budget impact model was developed using a decision tree framework to compare current UTI management practices with a new scenario incorporating the Sysmex PA-100. The clinical inputs were extracted from published literature. The model, representing adult females with uUTIs, assessed direct medical and indirect costs over three years, including diagnostic procedures, treatment costs, staff time costs, and productivity loss. The cost data was captured from two Spanish hospitals. Two scenario analyses were conducted in the model: scenario A included costs for antimicrobial resistance (AMR) emergence in patients receiving unnecessary empirical antibiotics, whereas scenario B assumed no AMR costs. Results In scenario A, 100% implementation of PA-100 within various Spanish healthcare settings over one year resulted in total cost savings of EUR 323,657,712 (EUR 119.10 per patient per year). The total overall cost savings attributable to the implementation of PA-100 over three years were EUR 970,973,137. In scenario B, the total annual savings were EUR 4,254,795 (EUR 1.57 per patient per year), and the 3-year savings were EUR 12,764,385. Conclusion The adoption of this point-of-care system results in overall cost savings, driven by reductions in direct and indirect costs related to UTI management. The model highlights potential savings over three years owing to appropriate rapid management (reduction in time to diagnosis and treatment, which leads to fewer complications and secondary infections) and reduced staff time and productivity loss costs. These findings support the potential benefits of adopting the Sysmex PA-100 to enhance UTI management and allocate healthcare resource utilization in Spain.

Facile Electrical DNA Genosensor for Human Papillomavirus (HPV 58) for Early Detection of Cervical Cancer

For decades, a Pap smear test has been applied as a conventional method in detecting Human Papillomavirus caused cervical cancer. False-positive results were also recorded while using it as conventional method. Current biosensor such as Hybrid (II) Capture resulted in higher time consumption and cost. s Meanwhile, in this study we provided facile, mini, rapid, highly sensitive, eco-friendly, and cost-effective sensing system focusing on HPV strain 58 (HPV58) in a nano-size lab-on-chip technology genosensor. 30-mer of virus ssDNA designed and analyzed as a probe via bioinformatics tools such as GenBank, Basic Local Alignment Searching Tools (BLAST) and ClustalW. Nanotechnology-developed colloidal Gold-nanoparticles (AuNPs) are used in the biosensor fabrication to produce high stability and electron efficient transmission during electrical measurement. AuNPs-APTES modified on active sites of IDEs, followed by immobilization of specific probe ssDNA for HPV 58. Hydrogen binding during hybridization with its target produce electrical signals measured by KEITHLEY 2450 (Source Meter). The genosensor validated with different types of targets such as complimentary, non-complementary and single mismatch oligonucleotides. The serial dilution of target concentration has been experimented triplicate (n=3) range from 1fM to 10μM. The slope of calibration curve resulted 2.389E-0 AM-1 with regression coefficient (R2) = 0.97535.

The brain hierarchically represents the past and future during multistep anticipation

Memory for temporal structure enables both planning of future events and retrospection of past events. We investigated how the brain flexibly represents extended temporal sequences into the past and future during anticipation. Participants learned sequences of environments in immersive virtual reality. Pairs of sequences had the same environments in a different order, enabling context-specific learning. During fMRI, participants anticipated upcoming environments multiple steps into the future in a given sequence. Temporal structure was represented in the hippocampus and across higher-order visual regions (1) bidirectionally, with graded representations into the past and future and (2) hierarchically, with further events into the past and future represented in successively more anterior brain regions. In hippocampus, these bidirectional representations were context-specific, and suppression of far-away environments predicted response time costs in anticipation. Together, this work sheds light on how we flexibly represent sequential structure to enable planning over multiple timescales.

Pedestrian Path Choice in Crowded Indoor Environments

Abstract. Indoor crowds can be regarded as dynamic obstacles and they could impede the movement of pedestrians. For instance, the scope of crowds could significantly impact accurate path choices. There are few discussions from an individual’s perspective to reveal the decision-making process of reasonable path choices. In this paper, we aim to address how an indoor pedestrian would walk in a crowded indoor environment. We propose a method to simulate a pedestrian’s path choices during wayfinding process. It is wrapped in a workflow that can capture near-real-time crowd uncertainty. First, the measurement errors of crowds are alleviated with a Bayesian filter. According to crowd locations at each moment, the method of Kernel Density Estimation (KDE) is applied to a grid model of the building to obtain the uncertainty map of the crowds. Based on a two-level spatial model, the logical path represented by a room sequence can be derived from a ’room-door’ network, while detailed path choices on the ’operational’ level are refined in each room. By considering both crowd uncertainty and time cost, path choices for a user are determined by using the A* algorithm at each time slot. By iterating the optimal path to the next door/the target in the current room, the user location of the next moment can be inferred. Our test initially validated the feasibility of the method on the path choices of a pedestrian. In the future, we plan to further conduct field tests with different users in crowded indoor environments.

An Entropy-based Method to Evaluate the Appropriate Spatial Resolution

Abstract. Geospatial data is available at increasingly finer spatial resolutions. However, such data can also be problematic because it may result in increased financial, resource and time cost. It might also be unnecessary if the phenomenon of interest does not vary at this scale or if the scientific application does not require it. This paper explores the scale effects associated with increased spatial resolution using the entropy-based local indicator of spatial association (LISA-ELSA). Results are illustrated for two datasets a digital elevation model (30 m ASTER GDEM) and for a raster PM2.5 air pollution map (1000 m). It is shown that increasing the size of the local window for the LISA can be used to explore the effect of reducing the spatial resolution. It is possible to identify areas where the spatial association is invariant with increasing window size and which could be represented with coarser pixels.

An One-step Triple Enhanced weakly supervised semantic segmentation using image-level labels.

Weakly supervised semantic segmentation, based on image-level labels, abandons the pixel-level labels relied upon by traditional semantic segmentation algorithms. It only utilizes images as supervision information, thereby reducing the time cost and human resources required for marking pixel data. The prevailing approach in weakly supervised segmentation involves two-step method, introducing an additional network and numerous parameters, thereby complicating the model structure. Furthermore, image-level labels typically furnishes only category information for the entire image, lacking specific location details and accurate target boundaries during model training. We propose an innovative One-Step Triple Enhanced weakly supervised semantic segmentation network(OSTE). OSTE streamlines the model structure, which can accomplish both pseudo-labels generation and semantic segmentation tasks in just one step. Furthermore, we augment the weakly supervised semantic segmentation network in three key aspects based on the class activation map construction method, thereby enhancing segmentation accuracy: Firstly, by integrating local information from the activation map with the image, we can enhance the network's localization and expansion capabilities to obtain more accurate and rich location information. Then, we refine the seed areas of the class activation map by exploiting the correlation between multi-level feature. Finally, we incorporate conditional random field theory to generate pseudo-labels with higher confidence and richer boundary information. In comparison to the prevailing two-step weakly supervised semantic segmentation schemes, the segmentation network proposed in this paper achieves a more competitive mean Intersection over Union (mIoU) score of 58.47% on Pascal VOC. Additionally, it enhances the mIoU score by at least 5.03% when compared to existing end-to-end schemes.

Adoption, acceptance, and use of a decision support tool to promote timely investigations for cancer in primary care.

The complexities of diagnosing cancer in general practice has driven the development of quality improvement (QI) interventions, including clinical decision support (CDS) and auditing tools. Future Health Today (FHT) is a novel QI tool, consisting of CDS at the point-of-care, practice population-level auditing, recall, and the monitoring of QI activities. Explore the acceptability and usability of the FHT cancer module, which flags patients with abnormal test results that may be indicative of undiagnosed cancer. Interviews were conducted with general practitioners (GPs) and general practice nurses (GPNs), from practices participating in a randomized trial evaluating the appropriate follow-up of patients. Clinical Performance Feedback Intervention Theory (CP-FIT) was used to analyse and interpret the data. The majority of practices reported not using the auditing and QI components of the tool, only the CDS which was delivered at the point-of-care. The tool was used primarily by GPs; GPNs did not perceive the clinical recommendations to be within their role. For the CDS, facilitators for use included a good workflow fit, ease of use, low time cost, importance, and perceived knowledge gain. Barriers for use of the CDS included accuracy, competing priorities, and the patient population. The CDS aligned with the clinical workflow of GPs, was considered non-disruptive to the consultation and easy to implement into usual care. By applying the CP-FIT theory, we were able to demonstrate the key drivers for GPs using the tool, and what limited the use by GPNs.

The Impact of Mergers and Acquisitions on Technological Innovation of Enterprises: Taking Apple as an Example

As market competition becomes increasingly fierce, in order to maintain a leading position in the industry, companies must enhance their core competitiveness to gain priority discourse power in the market. Corporate mergers and acquisitions are the act of acquiring property rights of other legal entities through certain economic means on the basis of equality, voluntariness, and equal compensation. They are a major form of capital operation and management for enterprises. This article aims to study the impact of mergers and acquisitions on technological innovation in enterprises, by analyzing the financial statements of enterprises and exploring the impact of mergers and acquisitions on their main business. Research has found that mergers and acquisitions are a business tool that can effectively enhance a company's technological level without increasing profits, while using less time cost. This provides a reference for enterprises to enhance their market competitiveness, differentiate their products, and adjust their business strategies.

A Realistic Ingeniously Designed Cleft Palate Simulator for Training of Residents: Bridging the Gap Between Theory and Practice.

Cleft palate is a congenital deformity that presents significant challenges in surgical correction. Proper training with an educational model for can greatly enhance the learning curve and improve patient outcomes. This article discusses the development and evaluation of an economical cleft palate model designed to enhance surgical skills and confidence among residents. The model was constructed using dental casts, acrylic resin, foam sheets, double-sided adhesives, infant feeding tubes, and red dye. Tertiary care center. Surgical residents. The assembly time and material costs were recorded. To evaluate its effectiveness, participants' feedbacks were collected and analyzed. The cleft palate model was produced at a cost of 250 INR per unit, with assembly taking approximately 15 minutes. Overall, 32 residents completed the simulation exercise of cleft surgery on the model. Participants gave the model an average score of 4 (±0.60) for realistic anatomical appearance, 3.5 (±1.3) for replicating the tactile nature of human tissue. Anatomical accuracy and ability to manipulate tissue and perform suturing received a rating of 3.4 (±1.1) and 3.7 (±0.81), respectively. The model's value as a training tool scored 4.4 (±0.51), with an overall satisfaction rating of 4.6 (±0.22) among residents. Our cleft palate model offers an economical, practical, and accessible solution for surgical training. This model is a viable alternative to more complex and expensive training tools and potentially enhancing the training of healthcare professionals in cleft palate repair.

Thematic control and criteria-based assessment of foreign language writing skills using artificial intelligence technologies

Importance. Currently, there is a tendency that when teaching a foreign language, teachers are increasingly resorting to the use of artificial intelligence technologies to plan training sessions, generate educational content, as well as to conduct automated testing of formed communication skills. The monitoring of academic achievements and assessment is one of the key components of the organization of the educational process. Traditional methods of control and assessment require significant time and labor-intensive costs from the teacher, while artificial intelligence technologies make it possible to simplify and automate routine tasks: check tests and written papers, analyze them and identify mistakes, provide feedback. Thanks to the integration of natural language processing (NLP) technologies into chatbots and adaptive intelligent learning systems, it becomes possible on a daily basis to check texts created by students, evaluate them from the point of view of grammatical and lexical correctness, as well as identify stylistic and factual errors in real time. The purpose of this work is to test the applicability of artificial intelligence technologies for thematic control and criteria-based assessment of educational achievements using the example of productive and reproductive writing.Research Methods. In carrying out this study, the following groups of methods were used: theoretical methods aimed at familiarizing with scientific and methodological literature on the topic of research, analysis and classification of theoretical and methodological material for conducting thematic control and criterion assessment in foreign language classes, as well as empirical methods that allowed modeling pedagogical control and assessment processes using artificial intelligence technologies, observation, analysis and description of the results obtained.Definition of Concepts. The main concepts used in the study are “control of educational results” and its variety “thematic control”, “criterion assessment”.Results and Discussion. In the course of the study, various types of monitoring educational results using artificial intelligence technologies were considered: preliminary, current, intermediate, thematic, final. The choice of thematic control is due to the opportunity to trace its applicability within the framework of a single lesson in a foreign language and identify the main difficulties in using this form of control. The following criteria for evaluating writing skills are used to evaluate learning outcomes using artificial intelligence technologies: a) the structure of the written text; b) compliance with the main topic; c) coherence; d) relevance; e) grammatical correctness; f) lexical correctness; g) ethics of writing and stylistic correctness.Conclusion. Artificial intelligence technologies at the present stage have a high degree of adaptability and include a wide range of software and hardware solutions that allow for such important pedagogical procedures as monitoring educational achievements and evaluation in accordance with user-defined evaluation criteria. The obtained results are proposed to be used in research devoted to the study of modern methods of monitoring educational achievements in the methodology of teaching foreign languages using artificial intelligence technologies.

Sponsor Reputation and Capital Structure Dynamics in Leveraged Buyouts

We examine whether leveraged buyout (LBO) sponsors’ reputations as borrowers affect the refinancing terms of their portfolio companies. In 510 U.S. LBOs for which we can reconstruct debt financing activity, 67% of financing events occur one quarter before the earliest existing debt maturity. These On Time events generally improve borrowing terms, whereas Early events feature more dividends, leverage, and higher cost. In each case, dividend issuance decreases and cost increases with the proportion of the sponsors’ recent exits that are failures. Effects are absent for matched firms and prior to the LBO and are procyclical; sponsors with recent failures miss opportunities to decrease financing costs in good times. This paper was accepted by Bo Becker, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.00971 .

Cost-effectiveness of prophylactic ramosetron in the prevention of postoperative nausea and vomiting.

This study was conducted to assess the cost-effectiveness of prophylactic use of ramosetron compared to no antiemetic medications for the prevention of postoperative nausea and vomiting (PONV) from the healthcare payer and societal perspectives in South Korea. A decision analytic model was constructed to assess the cost-effectiveness of prophylactic ramosetron use versus no antiemetic therapy at 24-hour and 48-hour periods post-surgery over a 5-day duration. The model was populated using costs and utility parameters from published studies as well as from surveys of an expert panel of physicians using structured questionnaires. The cost parameters included the costs of drugs, treatment, patient time, productivity loss, and transportation. Effectiveness was measured using quality adjusted life years (QALYs). The study outcome was the incremental cost-effectiveness ratio (ICER). The parameter uncertainties were addressed using deterministic and probabilistic scenario analyses. The base-case analysis showed that, on average, patients treated with prophylactic ramosetron had lower costs from both the healthcare payer (US$16.88 vs US$17.33) and societal (US$16.89 vs US$18.72) perspectives and higher QALYs (0.0121 vs 0.0114) over the 5-day study duration compared to patients without any antiemetic medications. Deterministic and probabilistic sensitivity analyses were conducted to examine the robustness of results for the parameters included in the model. The acceptability curve probability showed that treating patients with ramosetron compared to no antiemetic medications was more than 99% cost-effective at a willingness-to pay threshold of US$5,000/QALY from both payer and societal perspectives. The results demonstrated that prophylactic use of ramosetron compared to no antiemetic therapy is highly cost-effective to prevent PONV for patients undergoing surgery from both healthcare payer and societal perspectives. The cost effectiveness is the result of the decrease in the incidence of PONV and the direct treatment costs of severe PONV with improved patient quality of life.

Endoscopic injection vs anti-reflux surgery for moderate- and high-grade vesicoureteral reflux in children: a cost-effectiveness international study

Even if vesicoureteral reflux is a common condition in children, there are no guidelines about the best therapeutic approach. This study aims to compare the results of endoscopic injection and ureteral reimplantation in children with grade III, IV and V VUR. A multicenter retrospective study included children with grade III, IV and V VUR treated from 2003 to 2018 at three Departments of Pediatric Surgery. Patients were divided into Group A (endoscopic injections) and Group B (anti-reflux surgery), B1 (open, OUR), B2 (laparoscopic, LUR) and B3 (robot-assisted laparoscopic RALUR). Follow-up was at least 5 years. 400 patients were included, 232 (58%) in group A and 168 (42%) in group B. Mean age at surgery was 38.6 months [3.1–218.7]. Mean follow-up was 177.8 months [60–240]. Group A had shorter operative time than group B (P < 0.01); lower analgesic requirement (p < 0.05), shorter hospital stay (P < 0.05) and lower overall costs (p < 0.05), but higher postoperative PNPs (p < 0.01), lower success rate (p < 0.01) and higher redo-surgery percentage (p < 0.01). No differences in terms of postoperative complications, success rate and mean radiation exposure between the two groups. Endoscopy is associated with shorter operative time, shorter hospitalization and lower cost, also in case of multiple injections. Recurrence rate after surgery is lower meaning lower rate of re-hospitalization and radiation exposure for children.

Design of Connector Assembly Equipment for the Automotive Industry

The automotive industry is one of the most demanding sectors of all manufacturing industries due to its competitiveness. It is necessary to innovate through the implementation of automated and robotic equipment, leading to cycle time and labor cost reduction. This work aims to design semi-automatic equipment to assemble cabling connectors used in the automotive sector, replacing a manual process currently taking place in an automotive components company. In the proposed equipment, the operator places a connector in the equipment, and the components (pins and seals) are automatically inserted. A vision sensor with artificial intelligence then confirms the correct application. The equipment operation defined as Finite Element Method (FEM) was applied for structural verification; the materials and fabrication processes were detailed; the associated costs were calculated, and the equipment subsets were validated. The design was successfully accomplished, and the imposed requirements were fulfilled, with significant advantages over the current process, providing new knowledge on how semi-automatic systems can be deployed to enhance the productivity and quality of manufacturing processes. The design principles and insights gained from this work can be applied to other automation challenges, particularly where manual processes need to be replaced by more efficient semi-automatic or automatic systems. The modularity of the overall solution and the design concepts of the component inserter, component feeder, and assembly process allow for its use in different assembly scenarios beyond the automotive sector, such as electronics or aerospace, providing a contribution to increased competitiveness and survival in the global market.

PMGNet: Disentanglement and entanglement benefit mutually for compositional zero-shot learning

Compositional zero-shot learning (CZSL) aims to model compositions of two primitives (i.e., attributes and objects) to classify unseen attribute-object pairs. Most studies are devoted to integrating disentanglement and entanglement strategies to circumvent the trade-off between contextuality and generalizability. Indeed, the two strategies can mutually benefit when used together. Nevertheless, they neglect the significance of developing mutual guidance between the two strategies. In this work, we take full advantage of guidance from disentanglement to entanglement and vice versa. Additionally, we propose exploring multi-scale feature learning to achieve fine-grained mutual guidance in a progressive framework. Our approach, termed Progressive Mutual Guidance Network (PMGNet), unifies disentanglement–entanglement representation learning, allowing them to learn from and teach each other progressively in one unified model. Furthermore, to alleviate overfitting recognition on seen pairs, we adopt a relaxed cross-entropy loss to train PMGNet, without an increase of time and memory cost. Extensive experiments on three benchmarks demonstrate that our method achieves distinct improvements, reaching state-of-the-art performance. Moreover, PMGNet exhibits promising performance under the most challenging open-world CZSL setting, especially for unseen pairs.