Balance Condition Research Articles

Machine Learning Supporting Virtual Reality and Brain–Computer Interface to Assess Work–Life Balance Conditions for Employees

The widespread adoption of the Industry 5.0 paradigm puts people and their applications at the center of attention and, with the increasing automation and robotization of work, the need for workers to acquire new, more advanced skills increases. The development of artificial intelligence (AI) means that expectations for workers are further raised. This leads to the need for multiple career changes from life and throughout life. Belonging to a previous generation of workers makes this retraining even more difficult. The authors propose the use of machine learning (ML), virtual reality (VR) and brain–computer interface (BCI) to assess the conditions of work–life balance for employees. They use machine learning for prediction, identifying users based on their subjective experience of work–life balance. This tool supports intelligent systems in optimizing comfort and quality of work. The potential effects could lead to the development of commercial industrial systems that could prevent work–life imbalance in smart factories for Industry 5.0, bringing direct economic benefits and, as a preventive medicine system, indirectly improving access to healthcare for those most in need, while improving quality of life. The novelty is the use of a hybrid solution combining traditional tests with automated tests using VR and BCI. This is a significant contribution to the health-promoting technologies of Industry 5.0.

On the issue of reliability of power transmission (on the example of the branch of PJSC Rosseti Volga – Penzaenergo)

The article is a continuation of a series of publications focusing on the analytical assessment of specifics of power transmission in various structural divisions of PJSC Rosseti and is dedicated to the consideration of specific features of electric energy transport in the PJSC Rosseti Volga – Penzaenergo branch electric networks over the period of 2018 – 2023. The characteristic of the structural and balance state of the company's electrical networks for the period under study is presented. Based on the data published in the open press, the analysis of the number of emergency shutdowns and their consequences in electric networks of all levels of rated voltage was carried out. In accordance with the accepted classification, the intensity of these outages was analyzed depending on the amount of electrical energy undersupplied to consumers. The percentage ratio of the value of electrical energy not delivered as a result of emergency shutdowns is analyzed according to the accepted criteria of the intensity level of these failures. It was found that the smallest number of failures of extreme intensity accounts for more than 25% of undelivered electrical energy. The percentage ratio of the amount of electricity not delivered as a result of emergency outages is compared with losses of electricity as a result of unauthorized connections. Specific groups of causes of emergency shutdowns have been identified and the percentage ratio of the number of failures due to these causes to the total number of failures during the period under study has been determined. General scientific methods and methods of numerical analysis were used in the study. The technologies of Excel, Paint 3D software shells, MATLAB graphics editor, as well as author's software solutions were used as a tool base. The results obtained may be of interest to the management of the power grid structures of the Russian Federation, as well as to engineers and researchers engaged in research in the field of improving the reliability of power supply to consumers.

NCOG-41. COMPARATIVE ANALYSIS OF MORTALITY AND SHORT-TERM SIDE EFFECTS IN PATIENTS WITH GLIOBLASTOMA MULTIFORME TREATED WITH TEMOZOLOMIDE VERSUS TEMOZOLOMIDE PLUS BEVACIZUMAB CHEMOTHERAPY

Abstract INTRODUCTION Glioblastoma multiforme (GBM) is a highly aggressive primary brain tumor in adults with limited treatment options and poor prognosis. Temozolomide (TMZ) chemotherapy is the standard treatment for GBM, but alternative therapies have shown limited success. Bevacizumab (BEV), a monoclonal antibody targeting vascular endothelial growth factor (VEGF), is used to restrict GBM growth and spread. This study evaluates the efficacy of combining TMZ with BEV compared to TMZ alone in managing GBM. METHODS A retrospective cohort study was conducted using TriNetX database data, focusing on patients diagnosed with GBM and receiving chemotherapy. Cohorts were defined by treatment: TMZ plus BEV versus TMZ alone. Propensity-score matching balanced baseline characteristics, including demographics and health conditions. Primary outcomes included mortality rates and short-term side effects within the first year of treatment, such as cognitive communication deficits, headache, dizziness, insomnia, and nausea/vomiting. RESULTS After matching, 922 patients were included in each cohort. There were no significant demographic differences post-matching. The mortality rate was higher in the TMZ plus BEV group compared to TMZ alone (51.30% vs. 36.98%, RR: 1.387, 95% CI: 1.249-1.541). However, TMZ plus BEV was associated with a lower incidence of cognitive communication deficits (1.95% vs. 4.34%, RR: 0.45, 95% CI: 0.26-0.779) and dizziness (7.27% vs. 10.52%, RR: 0.691, 95% CI: 0.513-0.93). No significant differences were found in headache, insomnia, or nausea/vomiting rates between the groups. CONCLUSION This study indicates that combining bevacizumab with temozolomide may increase mortality in GBM patients but reduce certain side effects such as cognitive deficits and dizziness. These findings emphasize the need to carefully consider the benefits and risks of different chemotherapy regimens for GBM and highlight the necessity for further research to optimize treatment strategies and improve patient outcomes.

The Hydrophobic Amino Acid-Rich Fish Collagen Peptide Ameliorates Dextran Sulfate Sodium-Induced Ulcerative Colitis in Mice via Repairing the Intestinal Barrier, Regulating Intestinal Flora and AA Metabolism.

The incidence of ulcerative colitis (UC) is increasing annually, but treatment option is limited. Fish collagen peptide (FCP) is a food source collagen peptide that has shown promise in alleviating UC symptoms. However, its impact on the intestinal barrier and intestinal metabolic homeostasis in UC remains unclear. This study aimed to analyze the peptide sequences and absolute amino acid (AA) content of FCP, assessing its effects on UC in mice induced by dextran sulfate sodium (DSS). FCP was examined by liquid chromatography and tandem mass spectrometry (LC-MS/MS) analysis. The 3% DSS was utilized to induce UC in murine models, followed by the assessment of the therapeutic efficacy of FCP. Clinical manifestations of UC mice were meticulously evaluated and scored. Subsequently, samples were procured for histological examination and intestinal epithelial barrier integrity analysis as well as macrogenomic and metabolomic profiling. Here, it shows that abundant peptide sequences and AAs were in FCP, particularly enriched in hydrophobic AAs (HAAs). Furthermore, it was observed that FCP effectively reversed colon shortening and reduced the extent of histological damage. Additionally, FCP suppressed the abnormal expression of inflammatory factors and intestinal barrier proteins and modulated the dysbiosis of gut microbiota toward a balanced state. These alterations led to the activation of intestinal alkaline AA and various AA metabolisms, ultimately contributing to the mitigation of UC symptoms. In summary, the diverse peptide sequences and high AAs in FCP, particularly rich in HAAs, can alleviate DSS-induced UC via preserving intestinal barrier integrity, regulating gut microbiota, and modulating AA metabolism.

Wisdom of age is the main psychological geroprotector in older people (analytical review)

The article presents an analytical review of the problem of aging from the standpoint of social and spiritual forms of human life. The intensive growth in the number of elderly people has sharply reduced the quality of public attitude towards them. The foundations of social relations between elderly people and the state, intergenerational, family, and other relations have acquired a new colouring, in connection with which the importance of wisdom in solving these problems increases. The wisdom in an elderly person is potent of keeping his inner world in a state of balance. A wise person perceives his presence in life as a paradigm of integration, which determines the coexistence of all age phases during his life. It helps to cast aside regret about past failures, unfulfilled hopes, to find a lot of good even in a far from easy life. Wisdom is the universe to which a person should come in old age. Wisdom will always find something that will please and strengthen the spiritual strength of a person, make his life positions stable, lead away from despondency and a sense of helplessness. The wisdom of age creates self-confidence in an elderly person, pride in the years lived with dignity, in the benefits created, which are used by the young. The concept of “conscious aging” is based on inner strength and spiritual stability over old age. Of course, old age is full of sorrows, but life experience gives it prudence to endure them with dignity. Wisdom serves as a support for an aging person for the normal perception of all life changes, it constantly reminds him that life does not close its doors to him.

Designing Multi-axis Compliant Mechanisms With Lockable Decoupled Inputs: A Tip–Tilt Case Study

Abstract This research is about designing multi-degree-of-freedom (multi-DOF) compliant mechanisms with decoupled inputs that can be independently locked/unlocked using bistable switches to achieve different combinations of DOFs. A case study mechanism achieving two decoupled rotational DOFs (tip and tilt) is designed, fabricated, and characterized. It can be triggered using two pairs of bistable switches, achieving drastically different states of torsional stiffness for each DOF in four sets of DOF combinations—no DOFs, a tip DOF, a tilt DOF, and both tip and tilt DOFs. Bistability and stiffness cancelation principles are exploited to achieve the desired changes in stiffness. Two flexure elements can be identified—the switch providing a negative stiffness and the cross-axis-flexural-pivot (CAFP) producing a positive stiffness. The mechanism is tuned to achieve static balancing, reaching a near-zero stiffness over much of its range. The pseudo-rigid body model and two-dimensional (2D) finite element model (FEM) are combined defining a fast method to dimension the system. The 3D FEM is simulated to validate the obtained results. For each DOF, the system is tested in two configurations (stiff and compliant) for three cycles over a ±10 deg rotation, achieving a stiffness reduction of around 99%. Comparable stiffness values were measured after triggering the switches more than once, repetitively reaching the required two states of stiffness, confirming the system's usability in practical applications. The positive stiffness provided by the CAFP is measured and compared to the device's overall stiffness, highlighting the stiffness cancelation concept.

Balancing the elementary steps via photo-assisted thermal catalysis for boosting propane dehydrogenation

Balancing kinetics behavior is a significant priority in chemical reactions especially involving two or more elementary steps. Propane dehydrogenation (PDH), usually involves a slower 2nd C-H activation step at a low temperature, resulting in heat waste for overcoming a higher energy barrier of the 2nd dehydrogenation. Herein, we develop one Photo-assisted Thermal PDH process (PTPDH) based on PtZn clusters encapsulated in porous MFI zeolite, which effectively balances the kinetic elementary steps via exciting the ground state electrons of the active sites to the positively charged -C3H7 fragments, achieving a 23.4 % growth ratio (from 39.6 to 51.3·10−8 mol·g−1·s−1) with the assistance of ∼5 suns light intensity at 400 °C. The comparison between the temperature-dependent and light intensity-dependent kinetic parameters confirmed that the non-thermal effect instead of the photo-to-thermal effect dominates the PTPDH process. Furthermore, the Lewis acidity-activity correlation experiments, in-situ radiation mass spectrometry and the H2 co-feeds experiment indicate that the hot electrons instead of the holes play a more significant role in propylene production. Last, the in-situ Drifts-MS technique and DFT calculations demonstrate light radiation conduce to the electron transition from metal to the unpopulated electronic states of -C3H7 fragments, while not conducing to the adsorbed C3H8 molecule, thus selectively promoting the 2nd dehydrogenation process and achieving a balanced chemical kinetic condition. Our findings demonstrate that Photo-assisted Thermal PDH is a promising method for balancing the 1st and 2nd dehydrogenation processes.

Unexpected normal ROTEM results in protein C deficiency with IVC thrombosis

Protein C deficiency is a hereditary disorder that increases the risk of thrombotic events but has unclear effects on rotational thromboelastometry (ROTEM) analysis. A man in his 60s with a history of protein C deficiency and multiple thrombotic events underwent inferior vena cava (IVC) filter removal, iliocaval thrombectomy and infrarenal IVC placement for IVC-related complete iliocaval and common femoral vein thromboses. A ROTEM analysis showed normal coagulation in NATEM and EXTEM and only a slight shortening in A10 and A20 in INTEM, which was unexpected given his diagnosis of protein C deficiency. Normal results indicate that there is complexity and variability of coagulation to maintain a balanced state even in individuals with underlying coagulation disorders.

Predictive Analytics Technique based on hybrid Sampling to manage Unbalanced Data in Smart Cities

A smart city is deemed smart enough because it has the capability to make decisions on its own. Artificial intelligence needs a lot of data from the physical world to make correct decisions. IoT sensor devices collect data from the surroundings, which is further used for predictive analytics. Collected data may be balanced or imbalanced. Unbalanced data used for decision-making without any pre-processing may lead to ravaging results. This paper proposes a novel predictive analytical technique to manage unbalanced data. A pipeline is designed using Principal Component Analysis (PCA), a hybrid sampling method, and a Machine Learning (ML) prediction method. SMOTE + ENN, a hybrid data balancing method, is used to specify imbalanced data to a balanced state. ML method is applied to form clusters and make predictions over the dataset. A large Smart City IoT dataset having 4,05,184 records has been used in this study. The proposed technique is used to predict the presence of a person in the vicinity of IoT devices. Evaluation parameters such as accuracy, precision, recall, F1-score, and Area Under Curve (AUC)/Receiver Operating Characteristic (ROC) curve are used to evaluate the proposed approach. Accuracy, Precision, Recall, F1-score, and AUC obtained using the proposed technique for cluster 0 are 0.79, 1.0, 0.79, 0.87, and 0.88 and for cluster 1 are 0.86 0.99, 0.86, 0.92, and 0.92, respectively. In view of the encouraging results, the proposed technique may prove to be a good choice to help in decision-making in different application domains in real life.

Cyber attacks detection and mitigation in hybrid power system using type-2 fuzzy controller

Abstract This paper presents a novel control technique using Fuzzy Type-2 controller for regulating the frequency deviations in an independent Hybrid Power System (HPS). The HPS comprises of a wind system and a solar photovoltaic (PV) system as primary energy sources, along with Fuel Cells (FCs) as additional generation systems and Double Layer Capacitor (DLC) Banks as storage devices to meet the load demand. The frequency variations occur in the HPS due to the intermittency in power outputs from the primary sources, the load changes as well as any attacks in the system. The frequency regulation is achieved by effective coordination control of FC and DLC with the aid of a Fuzzy Type-2 controller and a high pass filter (HPF). The DLC technology adequately satisfies the residual power of hybrid systems resulting from the slow dynamics of FCs. The Fuzzy Type-2 controller is trained using an input-output data set of a PID controller whose gains are tuned to their optimal values using Monte-Carlo state estimators. The proposed system is simulated using real weather data to validate the capability of the Hybrid Power System (HPS) in supplying isolated loads while maintaining power frequency balance conditions. Also the HPS is subjected to attacks in daily load variation and the results are compared with the system without attacks. The simulation results show that the peak over shoots are reduced considerably but the settling time is 0.2 seconds in the system with no attacks where as the settling time is prolonged to 1.2 seconds with the attacks. This paper demonstrates the effectiveness of the Fuzzy Type-2 controller for the coordination control of DLC and FC to achieve generation-load balance condition in comparison with that of the PID controller for HPS system with and without attacks.

The Effect of Work-Life Balance and Work Environment on Employee Job Satisfaction

Job satisfaction is an attitude and a sense of fulfillment that employees feel towards their jobs, which arises from the company's ability to meet all employee needs adequately. Employee job satisfaction is an essential component of human resource organizations and serves as a benchmark that companies need to improve to foster trust in the organization and ensure peak performance. This is also the case with PT. Bank Tabungan Negara Tbk Bintaro Jaya Branch, which has experienced suboptimal work-life balance and work environment conditions, causing employees to feel uncomfortable in their jobs. This study aims to analyze the influence of work-life balance and work environment in enhancing job satisfaction among employees at PT Bank Tabungan Negara Tbk Bintaro Jaya. A sample size of 53 respondents was taken using a non-probability sampling technique with a saturated sample method. This research employs a quantitative method with data analysis conducted using multiple linear regression analysis with the assistance of SPSS version 25.0. The results show that work-life balance has a positive and significant effect on job satisfaction, and the work environment also has a positive and significant effect on job satisfaction.

Denoising diffusion probabilistic model enhanced tool condition monitoring method under imbalanced conditions

Abstract In recent years, tool condition monitoring (TCM) based on deep learning has been widely considered and achieved remarkable success. However, these methods typically require relatively large training samples to produce significant results, which are both imbalanced and rather troublesome to obtain in the practical application of TCM. To address this issue, a novel TCM method combined with multiscale permutation entropy (MPE), denoising diffusion probabilistic model (DDPM) and a residual network (ResNet) is proposed under conditions of sample imbalance. First, the one-dimensional sensing signal data is converted to a grayscale recurrence plot (RP) by minimizing the MPE of the signals in each channel. Second, combine and splice these grayscale RPs from different channels in each sample into color RPs. After that, the generated RP images using DDPM are added to the imbalanced dataset to augment the data to achieve a balanced state of the dataset. Finally, the balanced mixed data set of real and fake samples is input into a ResNet for recognition and monitoring tool conditions. TCM experiments are conducted to verify the performance of the proposed method with imbalanced dataset, and the results of experimental investigation demonstrate that the accuracy of the proposed method improved by 2%–18.8% compared to that of the other four sample augmentation methods using ResNet18 when the imbalance rate is 1:200.

Interactions between a toroidal bubble and a free surface

Toroidal bubbles (TBs) represent cases of vortex rings with a gas–liquid interface where a gas vortex ring is encased within a liquid vortex ring, and can serve as effective media for mass conveyance, process mixing, noise reduction and reaction regulation. In this study, we carry out a systematic study on the interaction between a TB and a free surface. According to the high-speed photographic images from the experiments, we identify strong and weak interactions in terms of the normalized maximum free surface deformation $h_{max}^*$ . Then, we perform numerical simulations based on the volume of fluid (VOF) method in the OpenFOAM platform. Based on both the experimental and the numerical results, we conclude that the Froude number, $Fr$ , determines the main characteristics during the interaction process. The TB–free surface interaction is essentially the interaction between the liquid vortex ring enveloping the TB and the free surface, supplemented by the TB's complex behaviour. Next, we establish the scaling law of $h_{max}^*$ based on the energy balance condition. Based on this, we provide the critical $Fr$ and the slenderness of the TB, $\eta$ , for identifying the strong and weak interactions, and a parametric plot of the interactions in terms of $Fr$ and $\eta$ .

Effect of capital market on economic growth: An analysis using the autoregressive distributed lag (ARDL) approach

The limits testing method known as ARDL (Autoregressive Distributed Lag) examines Nigerian capital market improvement and monetary development. Relevant indicators about the capital market are taken into account during analysis while considering influencing factors as well. This study aims to explore the relationship between the capital market and financial development in Nigeria. The study’s theoretical framework, which is based on the ARDL framework, includes both short- and long-term dynamics. The data used in this analysis was collected from the Central Bank of Nigeria (CBN), the World Bank database, and the Nigerian Stock Exchange (NSE). In this study, the Nigerian All-Share Index, foreign direct investment, currency exchange rates, and inflation rates are the free factors, while the increase in GDP is the dependent factor. Through cointegration analysis using the ARDL framework, it was discovered that in Nigeria there’s a lengthy equilibrium correlation between economic expansion and financial development. The coefficients determined allow for a deeper understanding of how capital market factors affect economic growth over time. Furthermore, utilizing an error correction model derived from the ARDL analysis offers insight into both brief dynamics and modified speed toward reaching a lasting state of balance. By utilizing the ARDL approach, this study adds to the continuing discourse surrounding how capital markets impact growth in Nigeria. Its empirical evidence provides valuable knowledge for policymakers and stakeholders looking to utilize capital markets effectively toward achieving long-lasting economic development within the country.

Pluricanonical cycles and tropical covers

We extract a system of numerical invariants from logarithmic intersection theory on pluricanonical double ramification cycles, and show that these invariants exhibit a number of properties that are enjoyed by double Hurwitz numbers. Among their properties are (i) the numbers can be efficiently calculated by counts of tropical curves with a modified balancing condition, (ii) they are piecewise polynomial in the entries of the ramification vector, and (iii) they are matrix elements of operators on the Fock space. The numbers are extracted from the logarithmic double ramification cycle, which is a lift of the standard double ramification cycle to a blowup of the moduli space of curves. The blowup is determined by tropical geometry. We show that the traditional double Hurwitz numbers are intersections of the refined cycle with the cohomology class of a piecewise polynomial function on the tropical moduli space of curves. This perspective then admits a natural, combinatorially motivated, generalization to the pluricanonical setting. Tropical correspondence results for the new invariants lead immediately to the structural results for these numbers.

State-Space Basins for Monopedal Jumping with Stable Landing

Abstract Maintaining stability during jumping remains a challenge due to its hybrid dynamics. Despite recent advancements in the control of jumping robots, existing research lacks an accurate and comprehensive criterion for the stability of jumping. This study proposes a general criterion for jumping that incorporates the system’s nonlinear dynamics and environmental constraints. The stability basins are formulated as a state-space partition based on a rigorous definition extending the balanced state basin (BSB) concept, which has been validated as a stability criterion for biped robots in legged stance. A hybrid approach, using a combination of analytical and optimization methods, solves the flight and stance phases of jumping as independent sub-problems. The basins are computed for a monopedal jumping robot, Salto-1P, and validated with simulations. To demonstrate its general applicability, two distinct flight-to-stance tasks are considered: targeted jumping and cat-like righting. Analysis of each task’s stability basins and the landing BSB reveals the inherent trade-offs between stability and the task requirements for variations in the initial state variables such as speed, orientation, and height. The stability basins represent the system’s fundamental controller-independent stability characteristics and can be applied to the mechanical design, control, and fall prediction of jumping robots.

Motor delay image recognition based on deep learning and human skeleton model

Children with developmental delays often exhibit gross motor delay during their early childhood. Identifying such delay typically requires standardized assessments, but these assessments are susceptible to subjective interpretation. To enhance objectivity and reliability in these assessments, this study proposes a novel method utilizing an AI model for identifying gross motor delay in children. The proposed method divides the assessment of gross motor skills into two components: static balance and dynamic balance. First, the Yolo target detection model and the Mediapipe human posture detection model are employed for preprocessing to determine skeletal joints and body positioning information. Subsequently, dimensionality reduction techniques are applied to extract key features. A sliding window method is then used to extract action features identifying static balance features of the human body (e.g., standing on one foot, standing on the left foot) and dynamic balance features (e.g., lifting the heels while walking, jumping on a mat). The machine learning classifier and LSTM-CNN are then used to categorize the static and dynamic balance feature data. Experimental results demonstrate that the proposed method achieved accuracy scores of 80 % and 87 % for static and dynamic balancing skills, respectively.

Current Status of Nursing Home Distribution in China: ANational Cross-Sectional Study.

Population aging is a challenge that the whole world is facing, especially in China. This study aims to investigate the current distribution status of nursing homes in China using spatial epidemiology methods. Cross-sectional study in China. The data were obtained from the Integrated Civil Affairs Government Service Platform 'China County Statistical Yearbook' for 2020 and the 'China City Statistical Yearbook' for 2020. Using global Moran's index to test the clustering of nursing homes, inter-group and intra-group Theil index was utilised to differentiate the sources of differences. The coupling coordination analysis was conducted to explore the coordination. Geographically weighted regression was utilised to investigate the impact of economic development and aging on nursing home resources. All analyses were conducted by Arcgis 10.8 and R Studio 4.3.2. Global Moran's index indicated that the distribution of nursing homes in China exhibited clustering. The Theil index values for institutions and beds at the national county were 0.32450 and 0.30675. However, upon comparing provinces and regions, it was found that the differences across the country mainly stem from within provinces (contribution rate: institutions 65.0%; beds 73.0%) and within regions (contribution rate: institutions 99.0%; beds 91.0%). The majority of districts and counties had a coupling coordination index of institutions and bed numbers, both of which were < 0.5. The development of nursing home resources in China has been rapid, generally presenting a balanced state, but further optimisation is needed. This study established a foundation for the targeted distribution of essential public services, focusing on nursing home healthcare resources tailored to the needs of older persons. The study underscored the urgent need for targeted elderly care policies, emphasising the optimisation of resource distribution to enhance the overall quality of care provided to the aging population. The study did not involve humans.

Analysis of the employment structure of ukraine’s population during the war

Structural shifts in employment in the modern Ukrainian economy are caused by factors such as the military situation, technological progress, demographic changes, and economic instability. These changes affect the sectoral, gender, and regional distribution of workers and require adaptation from labor market participants and social policies to ensure the stability and efficiency of employment. Addressing structural shifts in employment in Ukraine requires a comprehensive approach, which includes stimulating the development of new economic sectors, supporting entrepreneurship, modernizing education and training, and providing social protection for workers during reorientation and retraining processes. Such an approach will help achieve a balanced state of supply and demand in the labor market, thereby ensuring long-term societal protection and well-being.

Generalization of Biasing Particle Insertion and Deletion for Grand Canonical Monte Carlo Simulation.

A general formulation of biasing particle insertion/deletion for the grand canonical Monte Carlo (GCMC) simulation is presented. The proposed formulation unifies the concepts of the conventional cavity- and configurational-type biasing techniques and modifies a cavity biasing method to strictly satisfy the detailed balance condition. Numerical examples of this modified cavity bias method are presented for Lennard-Jones fluid and hydrogen adsorption on a platinum surface. The results obtained using this method are compared with those obtained using various GCMC methods. Furthermore, a rigorous on-the-fly on-lattice GCMC simulation for surface adsorption is formulated and performed for comparison. The numerical examples clearly demonstrate the efficiency and accuracy of the proposed formulation.