Subpar Performance Research Articles

Integrating Building Information Modeling (BIM) for optimal lifecycle management of complex structures

A fundamental issue in the development and engineering industries is the efficient management of complex structures during their entire lifecycles. Traditional fragmented techniques have been used, and this has led to instances of poor communication and subpar performance, particularly in the context of repair and restoration initiatives. Building information modeling (BIM) emerged as a ground-breaking strategy that offers a digital representation of a building's attributes and facilitates effective collaboration among several stakeholders. The current study examines how BIM impacts complex building management throughout the course of a structure's existence. It focuses on how BIM helps to advance maintenance procedures, environmental initiatives, and project effectiveness. Exploratory factor analysis (EFA), structural equation modeling (SEM), and literature research are the three phases of the study's methodology. A thorough investigation was carried out in Pakistan's Rawalpindi and Islamabad region utilizing a quantitative technique. The poll of 159 participants produced intriguing findings. The process of EFA serves to verify constructs and questions, ensuring their reliability and validity. On the other hand, SEM is used to assess the structural relationships between variables and their impact on BIM based lifecycle management. The findings suggest that the application of BIM is favorably influenced by each phase of a project, namely Design, Construction, Operation and Maintenance, Renovation and Retrofitting, and End-of-Life Decisions. This highlights the need to implement a complete BIM strategy across all stages of a project's lifespan, which may enhance facility management, decrease expenses, and promote sustainability. The results of this study have significant consequences for professionals and politicians, highlighting the crucial need to provide incentives and regulations to promote the use of BIM in building projects.

Advanced Framework for Integrating Risks into an Organizational Setting

Enterprise risk management (ERM) is a systematic approach that strategically assesses risk management from a holistic standpoint, including the whole company or enterprise. The aforementioned approach is a top-down strategic methodology designed to detect, evaluate, and proactively address possible risks, threats, hazards, and other sources of damage that have the potential to impede an organization's operations and goals, or result in negative outcomes. ERM is a prominent framework that assists businesses in the identification, evaluation, and management of hazards at the enterprise level. Scholars identified many elements that serve as motivators for enterprises to participate in the process of ERM. These reasons include the likelihood of encountering financial hardship and the subsequent expenses, subpar profits performance, potential development prospects, and the autonomy of the board. The implementation of an effective risk management plan might potentially serve as a competitive advantage for organizations, facilitating their growth. This elucidates the extensive corpus of research devoted to ERM. This paper examines the fundamental connections between Enterprise Architecture and Risk Management and presents a proposed architectural framework for effectively incorporating risk considerations within the broader organizational context. This article presents a proposed strategy for attaining a comprehensive and shared perspective on hazards throughout an organization.

Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images.

Harnessing the power of deep neural networks in the medical imaging domain is challenging due to the difficulties in acquiring large annotated datasets, especially for rare diseases, which involve high costs, time, and effort for annotation. Unsupervised disease detection methods, such as anomaly detection, can significantly reduce human effort in these scenarios. While anomaly detection typically focuses on learning from images of healthy subjects only, real-world situations often present unannotated datasets with a mixture of healthy and diseased subjects. Recent studies have demonstrated that utilizing such unannotated images can improve unsupervised disease and anomaly detection. However, these methods do not utilize knowledge specific to registered neuroimages, resulting in a subpar performance in neurologic disease detection. To address this limitation, we propose Brainomaly, a GAN-based image-to-image translation method specifically designed for neurologic disease detection. Brainomaly not only offers tailored image-to-image translation suitable for neuroimages but also leverages unannotated mixed images to achieve superior neurologic disease detection. Additionally, we address the issue of model selection for inference without annotated samples by proposing a pseudo-AUC metric, further enhancing Brainomaly's detection performance. Extensive experiments and ablation studies demonstrate that Brainomaly outperforms existing state-of-the-art unsupervised disease and anomaly detection methods by significant margins in Alzheimer's disease detection using a publicly available dataset and headache detection using an institutional dataset. The code is available from https://github.com/mahfuzmohammad/Brainomaly.

Graphene-wrapped Fe2TiO5 nanoparticles with enhanced performance as lithium-ion battery anode

Iron-titanium bimetallic oxide, Fe2TiO5 (FTO), is anticipated to exhibit superior electrochemical properties due to its high theoretical capacity. However, the practical large-scale utilization of FTO is severely hindered by its substantial capacity fading and subpar cycling performance. In this work, we propose a strategy to improve the electrochemical properties of FTO by carbon coating. To implement this strategy, we synthesize FTO nanoparticles (FTO NPs) coated with reduced graphene oxide (rGO) by a solvothermal method. When used as anode materials in lithium-ion batteries, FTO/rGO composite demonstrates a higher specific capacity (498.2 mAh g−1 after 100 cycles) than pristine FTO NPs, which is ascribed to the addition of rGO. The rGO with excellent conductivity facilitates efficient electron and ion transportation, and the composite structure of FTO NPs wrapped with rGO provides buffer space to alleviate the volume expansion during the charge/discharge process.

A Dual Encapsulation Strategy for High-Temperature Micro PCM Particles with High Cyclic Durability.

Addressing critical issues such as high-temperature corrosion, leakage, degradation, and subpar cyclic performance is imperative for phase change materials (PCMs), prompting the development of appropriate encapsulation techniques to surmount these challenges. In this study, a dual encapsulation strategy is proposed for high-temperature micro PCM particles. Al-Si core is microencapsulated via the "solvent evaporation-heating curing" method. Subsequently, TiO2 is employed as the skeleton material for form-stable encapsulation of PCM microcapsules by "cold pressed sintering". Detailed analysis of the crystalline phase transformation mechanism in the sintering synthesis pathway of TiO2 underscore its potential as a robust structural material with exceptional thermal stability. Furthermore, the incorporation of hexagonal boron nitride (hBN) results in a substantial enhancement of the thermal conductivity of the composites, increasing by 121.1-131.3%. The prepared form-stable phase change microcapsules (FSPCMs) are subjected to 5000 thermal cycles in the air atmosphere. There is no observed PCM leakage or composite ruptures in the FSPCM. Moreover, the oxidized mass gain is merely 3.3%, signifying exceptional oxidation resistance. Thermophysical analysis indicates that FSPCM can retain 91.3% of the enthalpy after 2000 cycles, with over 80% preservation after 5000 cycles, underscoring its remarkable cyclic thermal durability.

IS3CQL : Intelligent sleep scheduling of small cells in high density 5G MIMO networks via incremental Deep Q learning optimizations

Effective management of small cell networks is crucial as the demand for fast, lowlatency communication grows as a result of the development of 5G and Massive Multiple Input Multiple Output (M-MIMO) technology. Small cell sleep cycle scheduling in highdensity networks frequently encounter issues as it tries to strike a compromise between energy efficiency, quality of service (QoS), and maintaining a high throughput. Additionally, these traditional methods ignore the complex spatial and temporal communication characteristics, resulting in subpar network performance. In order to overcome these difficulties, we suggest a brand-new intelligent sleep scheduling approach that makes use of both fundamental green computing ideas and incremental deep learning. In order to determine the best sleep schedules for tiny cells, our model takes into account both temporal and spatial communication factors, such as end-to-end delay, packet delivery ratio, throughput, and energy consumption of the nodes. The model adjusts the small cells’ sleep scheduling based on the ongoing spatial-temporal fluctuations in the network, providing the best possible use of resources. Additionally, its focus on green computing helps to lower energy consumption during extensive connections, making it an environmentally beneficial alternative for different scenarios. Under enormous MIMO situations, the results from our model show a significant improvement over the current approaches. These improvements include a notable improvement in communication consistency, a notable increase in throughput, a noticeable increase in packet delivery ratio, and a noticeable decrease in energy consumption and delay. In summary, our concept is a major step forward in the continuous development of intelligent, resilient, and effective 5G MIMO networks.

Green HRM Practices and Employees’ in Role Behavior: Mediating Effect of Organizational Citizenship Behavior towards the Environment in the Banking Sector of Bangladesh

Academics are placing emphasis on the need for personnel in the banking sector to undergo behavioral transformation due to the sector’s subpar and inefficient environmental performance. This study examines how green human resource management (GHRM) practices like green recruitment & selection, green training & development, green performance management, and green pay & rewards affect employees in role behavior in Bangladeshi banks. It also examines how workers’ organizational citizenship behavior towards the environment (OCBE) mediates green HRM practices & employees in role behavior. Data from several private and public banks in Bangladesh was gathered between January to June 2024. Data was collected from bank employees in metropolitan cities of Bangladesh utilizing a semi-structured questionnaire and a quantitative study approach. The participants were selected by a non-probability sampling method known as Convenience sampling. Nevertheless, a total of 288 surveys were duly completed and returned. Structural equation modeling, utilizing Smart-PLS second-generation software, revealed the results. This study found four independent variables and one dependent variable. Additionally, it is shown that OCBE acts as a mediator. The study revealed that five of the components exhibited a direct and significant correlation with the independent variables. In contrast, the remaining four factors have a negative correlation with the independent variables. Green HRM procedures in Bangladesh’s banking business are considered adequate but not very satisfactory. These workers also exhibit OCBE and in-role behavior. This study also found positive and negative effects of green HRM strategies on OCBE. The findings also indicated that although most of the banks have established green targets and goals, incorporated environmental concerns into job descriptions and specifications, implemented comprehensive environmental training programs, provided opportunities for green suggestion schemes, and conducted effective workshops to enhance environmental behavior, their actual implementation is insufficient. Moreover, the outcomes of this study have been scrutinized in relation to previous study results. Ultimately, this study provided guidance for future researchers to extrapolate the findings to a broader context.

Exploring Learner Subpar Performance in Linear Programming at Munkuye Secondary School in Zambia: Hermeneutic Perspective: A Glance on Distance as a Promoter

This paper aims to investigate the impact of distance on learners; linear programming performance, employing a qualitative approach. The author argues that effective learning of linear programming requires an accommodation for tardy learners and consideration of their physical distance from school. To reduce absenteeism, math teachers should motivate tardy learners to put extra effort into their studies rather than penalizing them. Additionally, the school administration can arrange transportation for students who face difficulties commuting to class through providing a bus service. Such measures will encourage active participation in class activities and instill positive attitudes towards education among students. Furthermore, relevant parties need to work together to purchase a school bus that facilitates the transportation of learners efficiently. The study concludes by recommending the establishment of more residential schools near communities offering competitive bursaries as it inspires parents and students to support each other.

Age and Gender Identification through Advanced Deep Learning Techniques

The ability to automatically detect the gender and age of an individual based on their facial features or other attributes is an important task in computer vision and deep learning. The precision and dependability of gender and age detection models have significantly increased with the use of deep learning algorithms and massive datasets. The lack of diversity in the training data, which can result in prejudice and subpar performance on members of underrepresented groups, is only one of several issues that still need to be resolved. Deep learning for gender and age detection also poses ethical issues including the possibility of abuse and prejudice. The goal of this work is to overcome these difficulties and create models that are very accurate at identifying a person's gender and age. The study's objectives include incorporating ethical issues into the creation and use of the models as well as enhancing the models' effectiveness on members of underrepresented groups. The model will also be optimized for real-time uses in this study, including security and marketing.

Finite Element Analysis and Experimental Study of Manufacturing Thin-Walled Five-Branched AISI 304 Stainless Steel Tubes with Different Diameters Using a Hydroforming Process.

This study aims to investigate the feasibility of hydroforming (HF) technology coupled with response surface optimization for producing high-quality five-branched AISI 304 stainless steel tubes with different diameters, addressing the shortcomings of traditional manufacturing processes. Conventional techniques often result in issues with multiple consumables, low precision, and subpar performance. The research focuses on finding optimal forming parameters for a more effective process. Initial attempts at a five-branched tube proved unfeasible. Instead, a multi-step forming approach was adopted, starting with the formation of the upper branch tube followed by the two reducing lower branch tubes, a strategy termed "first three, then five". This method, enhanced by a subsequent solid solution treatment, yielded promising results: the combined height of the upper and lower branches was 141.1 mm, with a maximum thinning rate of 26.67%, reduced to 25.33% after trimming. These outcomes met the product usage requirements. Additionally, the study involved designing and developing dies for manufacturing five-branched tubes with different diameters using servo HF equipment. The effectiveness of the multi-step forming process and parameter combinations was confirmed through experimental validation, aligning closely with the FE simulation results. The maximum thinning rate observed in the experiments was 27.60%, indicating that FE simulation and response surface methodology can effectively guide the production of high-quality parts with superior performance.

A Point Cloud Segmentation Method for Dim and Cluttered Underground Tunnel Scenes Based on the Segment Anything Model

In recent years, point cloud segmentation technology has increasingly played a pivotal role in tunnel construction and maintenance. Currently, traditional methods for segmenting point clouds in tunnel scenes often rely on a multitude of attribute information, including spatial distribution, color, normal vectors, intensity, and density. However, the underground tunnel scenes show greater complexity than road tunnel scenes, such as dim light, indistinct boundaries of tunnel walls, and disordered pipelines. Furthermore, issues pertaining to data quality, such as the lack of color information and insufficient annotated data, contribute to the subpar performance of conventional point cloud segmentation algorithms. To address this issue, a 3D point cloud segmentation framework specifically for underground tunnels is proposed based on the Segment Anything Model (SAM). This framework effectively leverages the generalization capability of the visual foundation model to automatically adapt to various scenes and perform efficient segmentation of tunnel point clouds. Specifically, the tunnel is first sliced along its direction on the tunnel line. Then, each sliced point cloud is projected onto a two-dimensional plane. Various projection methods and point cloud coloring techniques are employed to enhance SAM’s segmentation performance in images. Finally, the semantic segmentation of the entire underground tunnel is achieved by a small set of manually annotated semantic labels used as prompts in a progressive and recursive manner. The key feature of this method lies in its independence from model training, as it directly and efficiently addresses tunnel point cloud segmentation challenges by capitalizing on the generalization capability of foundation model. Comparative experiments against classical region growing algorithms and PointNet++ deep learning algorithms demonstrate the superior performance of our proposed algorithm.

Examining the Correlation Between Performance Appraisal and Legal Termination in Employment

The main objective of this study is to examine the correlation between annual performance evaluations in private hospitals and legal termination. Specifically, the study investigates whether employees are dismissed due to subpar performance following their assessment by performance appraisal management. This study utilized a quantitative approach to evaluate data from four distinct private hospitals in order to ascertain whether employees are terminated due to subpar performance subsequent to evaluation by a performance assessment management system. 100 surveys were distributed, but only 68 were received. The study's findings indicate a deficient and feeble connection between performance appraisal evaluation and legal termination in private institutions.

Automated Credibility Assessment of Web-Based Health Information Considering Health on the Net Foundation Code of Conduct (HONcode): Model Development and Validation Study.

An increasing number of users are turning to web-based sources as an important source of health care guidance information. Thus, trustworthy sources of information should be automatically identifiable using objective criteria. The purpose of this study was to automate the assessment of the Health on the Net Foundation Code of Conduct (HONcode) criteria, enhancing our ability to pinpoint trustworthy health information sources. A data set of 538 web pages displaying health content was collected from 43 health-related websites. HONcode criteria have been considered as web page and website levels. For the website-level criteria (confidentiality, transparency, financial disclosure, and advertising policy), a bag of keywords has been identified to assess the criteria using a rule-based model. For the web page-level criteria (authority, complementarity, justifiability, and attribution) several machine learning (ML) approaches were used. In total, 200 web pages were manually annotated until achieving a balanced representation in terms of frequency. In total, 3 ML models-random forest, support vector machines (SVM), and Bidirectional Encoder Representations from Transformers (BERT)-were trained on the initial annotated data. A second step of training was implemented for the complementarity criterion using the BERT model for multiclass classification of the complementarity sentences obtained by annotation and data augmentation (positive, negative, and noncommittal sentences). Finally, the remaining web pages were classified using the selected model and 100 sentences were randomly selected for manual review. For web page-level criteria, the random forest model showed a good performance for the attribution criterion while displaying subpar performance in the others. BERT and SVM had a stable performance across all the criteria. BERT had a better area under the curve (AUC) of 0.96, 0.98, and 1.00 for neutral sentences, justifiability, and attribution, respectively. SVM had the overall better performance for the classification of complementarity with the AUC equal to 0.98. Finally, SVM and BERT had an equal AUC of 0.98 for the authority criterion. For the website level criteria, the rule-based model was able to retrieve web pages with an accuracy of 0.97 for confidentiality, 0.82 for transparency, and 0.51 for both financial disclosure and advertising policy. The final evaluation of the sentences determined 0.88 of precision and the agreement level of reviewers was computed at 0.82. Our results showed the potential power of automating the HONcode criteria assessment using ML approaches. This approach could be used with different types of pretrained models to accelerate the text annotation, and classification and to improve the performance in low-resource cases. Further work needs to be conducted to determine how to assign different weights to the criteria, as well as to identify additional characteristics that should be considered for consolidating these criteria into a comprehensive reliability score.

Overreliance on inefficient computer-mediated information retrieval is countermanded by strategy advice that promotes memory-mediated retrieval

With ubiquitous computing, problems can be solved using more strategies than ever, though many strategies feature subpar performance. Here, we explored whether and how simple advice regarding when to use which strategy can improve performance. Specifically, we presented unfamiliar alphanumeric equations (e.g., A + 5 = F) and asked whether counting up the alphabet from the left letter by the indicated number resulted in the right letter. In an initial choice block, participants could engage in one of three cognitive strategies: (a) internal counting, (b) internal retrieval of previously generated solutions, or (c) computer-mediated external retrieval of solutions. Participants belonged to one of two groups: they were either instructed to first try internal retrieval before using external retrieval, or received no specific use instructions. In a subsequent internal block with identical instructions for both groups, external retrieval was made unavailable. The ‘try internal retrieval first’ instruction in the choice block led to pronounced benefits (d = .76) in the internal block. Benefits were due to facilitated creation and retrieval of internal memory traces and possibly also due to improved strategy choice. These results showcase how simple strategy advice can greatly help users navigate cognitive environments. More generally, our results also imply that uninformed use of external tools (i.e., technology) can bear the risk of not developing and using even more superior internal processing strategies.

Enhancing the Durability of Green Concrete through the Utilization of Nano-sized Palm Oil Fuel Ash and Nano-sized Eggshell Powder

Concrete is often regarded as a material representing robustness and long-lasting properties in contemporary society. The manufacture and utilization of cement are responsible for environmental contamination. Green concrete, often known as GC, has been increasingly recognized as a key material in the global concrete industry. Palm Oil Ash (POA) is a significant Lightweight Aggregate (LA) used in GC. Palm Oil (PO) processing generates POA as a by-product, which is thrown out in open areas and landfills without treatment, leading to environmental degradation. The pozzolanic activity is enhanced by grinding POA to nanoparticles, known as NPOA. Egg Shells (ES) are a type of biowaste that originates from eateries. They are deposited in landfills, producing toxic gases and inflicting environmental harm. Nano-sized ES Powder (NESP) contains a significant quantity of calcium oxide, which is crucial for hydration in concrete formation. Also, POA can serve as a fractional replacement for cement in concrete. This paper offers a study that utilized NPOA to improve the subpar performance of LA for GC produced with POA aggregate. The study involved replacing a portion of the cement with NPOA at 5%, 10%, and 15% ratios. Additionally, NESP was used as an admixture at a ratio of 1.5% to produce green concrete in cubes and cylinders. The concrete samples (in the shape of cubes and cylinders) are exposed to a curing process for 7, 14, and 28 days, respectively. Afterward, these samples are examined to evaluate their performance and measure their strength. The Conventional concrete is produced using an M20 grade mix. It is then compared to GC, both with and without NPOA concrete, using cube and cylinder models.

Insights into the influence of matrix fragments on FT-IR-based meat and bone meal species-specific analysis

This study proposed, verified and analyzed a potential reason (matrix composition complexity) for the subpar performance of FT-IR-based meat and bone meal (MBM) species discrimination. In order to address the aforementioned issue, the MBM matrix was modeled as a binary mixture comprising meat fragment (MF) and bone fragment (BF). BF and MF samples derived from four species were prepared, and MBM samples were subsequently formulated. The influence of matrix fragments on FT-IR-based MBM species-specific analysis was verified through the analysis of FT-IR spectral absorption peak intensities and comparison of species discrimination analysis models established using MBM, BF and MF samples. Furthermore, the coupling effect of BF and MF on FT-IR-based MBM species-specific analysis was elucidated by conducting correlation analysis between composition of matrix fragments and the prediction scores of species discrimination analysis. Additionally, FT-IR spectral variables that contributed predominantly within the coupling process were investigated in detail.

Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection

ObjectiveThe progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. MethodsTo tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. ResultsUnlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. ConclusionThis research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. SignificanceThe findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.

The d‐band energy level splitting of ferric group (Fe, Co, Ni) metals drives the adsorption‐conversion of polysulfides

AbstractThe notorious lithium polysulfides (LiPSs) shuttle effect, which results in low capacity, subpar rate performance, and quick capacity deterioration, has severely restricted the practical applications of lithium sulfur (Li‐S) batteries. Therefore, it is very important for modified materials to promote thermodynamics and redox kinetics in the entrapping‐conversion process of polysulfides. Density functional theory (DFT) calculations show that ferric group (Fe, Co, Ni) transition metals not only provide moderate binding contacts with LiPSs but also act as an active catalyst in the spontaneous and sequential lithiation of S8 to Li2S by d‐band energy level splitting, and quick migration of Li ions can be operated on their surface, enhancing the utilization of LiPSs. Experimentally, felicitously‐fabricated ferric group (Fe, Co, Ni) transition metals encapsulated in nitrogen‐doped carbon nanotubes (M@NCNT) electrocatalysts were introduced into Li‐S batteries via separator functionalization. Actually, the experiments demonstrated that the excellent shuttle effect hindering was enabled. Consistent with theoretical predictions, Li‐S batteries with Ni@NCNT modified separators had significantly improved rate capacity and cycling stability. The cells with Ni@NCNT were able to achieve a high initial discharge capacity of 1035 mAh g−1 and a capacity retention rate of 70% at 500 discharges at 1.0 C with a 0.060% capacity decay each cycle, performing considerable cycle‐life with state‐of‐the‐art separators. Our work demonstrated a realistic separator‐modified strategy of d‐band energy level splitting from ferric group metals for high‐performance and long‐life Li‐S batteries, further propelling Li‐S battery commercialization.

HLAEquity: Examining biases in pan-allele peptide-HLA binding predictors

Peptide-HLA (pHLA) binding prediction is essential in screening peptide candidates for personalized peptide vaccines. Machine learning (ML) pHLA binding prediction tools are trained on vast amounts of data and are effective in screening peptide candidates. Most ML models report the ability to generalize to HLA alleles unseen during training ("pan-allele" models). However, the use of datasets with imbalanced allele content raises concerns about biased model performance. First, we examine the data bias of two ML-based pan-allele pHLA binding predictors. We find that the pHLA datasets overrepresent alleles from geographic populations of high-income countries. Second, we show that the identified data bias is perpetuated within ML models, leading to algorithmic bias and subpar performance for alleles expressed in low-income geographic populations. We draw attention to the potential therapeutic consequences of this bias, and we challenge the use of the term "pan-allele" to describe models trained with currently available public datasets.

Bank Characteristics and Financial Performance of Commercial Banks Listed at The Nairobi Securities Exchange in Kenya

Commercial banks in Kenya have recently experienced a steady downturn in their financial performance, mostly as a result of strict regulatory requirements and a changing business environment. The subpar financial performance of numerous lower-tier banks has resulted in significant consequences, including the necessity of placing some of these banks under receivership. The purpose of this study was to look into how various factors influence the financial performance of commercial banks listed on the Nairobi Securities market. In particular, this study looked to see how financial performance was impacted by asset quality and liquidity management. The study employed a descriptive methodology and a target population comprised of 11 commercial banks listed on the NSE were included in the population sample for this study. Secondary data was collected and analyzed using descriptive and inferential statistics. The research exhibited that the financial performance of commercial banks is significantly influenced by quality of their assets. Furthermore, the study revealed a positive and statistically significant impact of liquidity on the financial performance of Kenyan commercial banks The study concluded that financial performance was substantially and positively impacted by the quality of assets and liquidity management practices. Consequently, it is recommended that banks maintain a low level of nonperforming loans, as these loans have adverse effects on bank profitability, which in turn affects overall financial performance. Although the statutory ratio is established at 20%, the central bank might think about increasing this ratio by taking into account the general growth of the banking industry in recent years.