Differences in computer exposure between university administrators and CAD draftsmen

This study utilized an external logger system for onsite measurements of computer activities of two professional groups—twelve university administrators and twelve computer-aided design (CAD) draftsmen. Computer use of each participant was recorded for 10 consecutive days—an average of 7.9 ± 1.8 workdays and 7.8 ± 1.5 workdays for administrators and draftsmen, respectively. Quantitative parameters computed using recorded data were daily dynamic duration (DD) and static duration, daily keystrokes, mouse clicks, wheel scrolling counts, mouse movement and dragged distance, average typing and clicking rates, and average time holding down keys and mouse buttons. Significant group differences existed in the number of daily keystrokes (p < 0.0005) and mouse clicks (p < 0.0005), mouse distance moved (p < 0.0005), typing rate (p < 0.0001), daily mouse DD (p < 0.0001), and keyboard DD (p < 0.005). Both groups had significantly longer mouse DD than keyboard DD (p < 0.0001). Statistical analysis indicates that the duration of computer use for different computer tasks cannot be represented by a single formula with same set of quantitative parameters as those associated with mouse and keyboard activities. Results of this study demonstrate that computer exposure during different tasks cannot be estimated solely by computer use duration. Quantification of onsite computer activities is necessary when determining computer-associated risk of musculoskeletal disorders. Other significant findings are discussed.     

Sleep in a live-in mining operation: The influence of start times and restricted non-work activities

The amount of sleep obtained between shifts is influenced by numerous factors including the length of work and rest periods, the timing of the rest period relative to the endogenous circadian cycle and personal choices about the use of non-work time. The current study utilised a real-world live-in mining environment to examine the amount of sleep obtained when access to normal domestic, family and social activities was restricted. Participants were 29 mining operators (26 male, average age 37.4 ± 6.8 years) who recorded sleep, work and fatigue information and wore an activity monitor for a cycle of seven day shifts and seven night shifts (both 12 h) followed by either seven or fourteen days off. During the two weeks of work participants lived on-site. Total sleep time was significantly less (p < 0.01) while on-site on both day (6.1 ± 1.0 h) and night shifts (5.7 ± 1.5 h) than days off (7.4 ± 1.4 h). Further, night shift sleep was significantly shorter than day-shift sleep (p < 0.01). Assessment of subjective fatigue ratings showed that the sleep associated with both days off and night shifts had a greater recovery value than sleep associated with day shifts (p < 0.01). While on-site, participants obtained only 6 h of sleep indicating that the absence of competing domestic, family and social activities did not convert to more sleep. Factors including shift start times and circadian influences appear to have been more important.     

Knowledge sharing and social media: Altruism,perceived online attachment motivation,and perceived online relationship commitment

Social media, such as Facebook and Twitter, have become extremely popular. Facebook, for example, has more than a billion registered users and thousands of millions of units of information are shared every day, including short phrases, articles, photos, and audio and video clips. However, only a tiny proportion of these sharing units trigger any type of knowledge exchange that is ultimately beneficial to the users. This study draws on the theory of belonging and the intrinsic motivation of altruism to explore the factors contributing to knowledge sharing behavior. Using a survey of 299 high school students applying for university after the release of the public examination results, we find that perceived online attachment motivation (β = 0.31, p < 0.001) and perceived online relationship commitment (β = 0.49, p < 0.001) have positive, direct, and significant effects on online knowledge sharing (R² 0.568). Moreover, when introduced into the model, altruism has a direct and significant effect on online knowledge sharing (β = 0.46, p < 0.001) and the total variance explained by the extended model increases to 64.9%. The implications of the findings are discussed.     

Human lower extremity joint moment prediction: A wavelet neural network approach

Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics.To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient (ρ).Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE < 10%, ρ > 0.94) compared to FFANN (NRMSE < 16%, ρ > 0.89). A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation.     

Data mining using clinical physiology at discharge to predict ICU readmissions

Patient readmissions to intensive care units (ICUs) are associated with increased mortality, morbidity and costs. Current models for predicting ICU readmissions have moderate predictive value, and can utilize up to twelve variables that may be assessed at various points of the ICU inpatient stay. We postulate that greater predictive value can be achieved with fewer physiological variables, some of which can be assessed in the 24 h before discharge. A data mining approach combining fuzzy modeling with tree search feature selection was applied to a large retrospectively collected ICU database (MIMIC II), representing data from four different ICUs at Beth Israel Deaconess Medical Center, Boston. The goal was to predict ICU readmission between 24 and 72 h after ICU discharge. Fuzzy modeling combined with sequential forward selection was able to predict readmissions with an area under the receiver-operating curve (AUC) of 0.72 ± 0.04, a sensitivity of 0.68 ± 0.02 and a specificity of 0.73 ± 0.03. Variables selected as having the highest predictive power include mean heart rate, mean temperature, mean platelets, mean non-invasive arterial blood pressure (mean), mean spO2, and mean lactic acid, during the last 24 h before discharge. Collection of the six predictive variables selected is not complex in modern ICUs, and their assessment may help support the development of clinical management plans that potentially mitigate the risk of readmission.     

Thermodynamic study of the cerium–cadmium system

Cadmium vapor pressures were determined over Ce–Cd samples by an isopiestic method. The measurements were carried out in the temperature range from 690 to 1080 K and over a composition range of 48–85 at% Cd. From the vapor pressures thermodynamic activities of Cd were derived for all samples at their respective sample temperatures, and partial molar enthalpies of Cd were obtained from the temperature dependence of the activities. With these partial molar enthalpies the Cd activities were converted to a common temperature of 823 K. By means of a Gibbs–Duhem integration Ce activities were calculated, using a corresponding literature value for the two-phase field (CeCd₁₁+L) as integration constant. Finally integral Gibbs energies were calculated for the composition range 48–100 at% Cd with a minimum value of −37 kJ g-atom⁻¹ at 823 K in the phase CeCd. Phase boundaries of the intermetallic compounds CeCd, CeCd₂, Ce₁₃Cd₅₈, and CeCd₁₁ were estimated from the vapor pressure measurements and from SEM analyses.     

Human activity recognition with smartphone sensors using deep learning neural networks

Human activities are inherently translation invariant and hierarchical. Human activity recognition (HAR), a field that has garnered a lot of attention in recent years due to its high demand in various application domains, makes use of time-series sensor data to infer activities. In this paper, a deep convolutional neural network (convnet) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics of activities and 1D time-series signals, at the same time providing a way to automatically and data-adaptively extract robust features from raw data. Experiments show that convnets indeed derive relevant and more complex features with every additional layer, although difference of feature complexity level decreases with every additional layer. A wider time span of temporal local correlation can be exploited (1 × 9–1 × 14) and a low pooling size (1 × 2–1 × 3) is shown to be beneficial. Convnets also achieved an almost perfect classification on moving activities, especially very similar ones which were previously perceived to be very difficult to classify. Lastly, convnets outperform other state-of-the-art data mining techniques in HAR for the benchmark dataset collected from 30 volunteer subjects, achieving an overall performance of 94.79% on the test set with raw sensor data, and 95.75% with additional information of temporal fast Fourier transform of the HAR data set.     

Empirical evaluation of the effects of mixed project data on learning defect predictors

ContextDefect prediction research mostly focus on optimizing the performance of models that are constructed for isolated projects (i.e. within project (WP)) through retrospective analyses. On the other hand, recent studies try to utilize data across projects (i.e. cross project (CP)) for building defect prediction models for new projects. There are no cases where the combination of within and cross (i.e. mixed) project data are used together.ObjectiveOur goal is to investigate the merits of using mixed project data for binary defect prediction. Specifically, we want to check whether it is feasible, in terms of defect detection performance, to use data from other projects for the cases (i) when there is an existing within project history and (ii) when there are limited within project data.MethodWe use data from 73 versions of 41 projects that are publicly available. We simulate the two above-mentioned cases, and compare the performances of naive Bayes classifiers by using within project data vs. mixed project data.ResultsFor the first case, we find that the performance of mixed project predictors significantly improves over full within project predictors (p-value < 0.001), however the effect size is small (Hedges′ g = 0.25). For the second case, we found that mixed project predictors are comparable to full within project predictors, using only 10% of available within project data (p-value = 0.002, g = 0.17).ConclusionWe conclude that the extra effort associated with collecting data from other projects is not feasible in terms of practical performance improvement when there is already an established within project defect predictor using full project history. However, when there is limited project history, e.g. early phases of development, mixed project predictions are justifiable as they perform as good as full within project models.     

Bivariate quality control using two-stage intelligent monitoring scheme

In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), whereby selection of statistical process control (SPC) scheme becomes more critical. Nevertheless, the existing traditional SPC schemes for bivariate quality control (BQC) were mainly designed for rapid detection of unnatural variation with limited capability in avoiding false alarm, that is, imbalanced monitoring performance. Another issue is the difficulty in identifying the source of unnatural variation, that is, lack of diagnosis, especially when dealing with small shifts. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Design consideration involved extensive simulation experiments to select input representation based on raw data and statistical features, artificial neural network recognizer design based on synergistic model, and monitoring–diagnosis approach based on two-stage technique. The study focused on bivariate process for cross correlation function, ρ = 0.1–0.9 and mean shifts, μ = ±0.75–3.00 standard deviations. The proposed two-stage intelligent monitoring scheme (2S-IMS) gave superior performance, namely, average run length, ARL₁ = 3.18–16.75 (for out-of-control process), ARL₀ = 335.01–543.93 (for in-control process) and recognition accuracy, RA = 89.5–98.5%. This scheme was validated in manufacturing of audio video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC.     

A molecular dynamic study on the dissociation mechanism of SI methane hydrate in inorganic salt aqueous solutions

Gas hydrate is not only a potential energy resource, but also almost the biggest challenge in oil/gas flow assurance. Inorganic salts such as NaCl, KCl and CaCl₂ are widely used as the thermodynamic inhibitor to reduce the risk caused by hydrate formation. However, the inhibition mechanism is still unclear. Therefore, molecular dynamic (MD) simulation was performed to study the dissociation of structure I (SI) methane hydrate in existence of inorganic salt aqueous solution on a micro-scale. The simulation results showed that, the dissociation became stagnant due to the presence of liquid film formed by the decomposed water molecules, and more inorganic ions could shorten the stagnation-time. The diffusion coefficients of ions and water molecules were the largest in KCl system. The structures of ion/H₂O and H₂O/H₂O were the most compact in hydrate/NaCl system. The ionic ability to decompose hydrate cells followed the sequence of: Ca²⁺ > 2K⁺ > 2Cl⁻ > 2Na⁺.     

3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification

A dataset of 237 human Ether-à-go-go Related Gene (hERG) potassium channel inhibitors (180 of which were used for model building and validation, whereas 57 constituted the “true” external prediction set) collected from 22 literature sources was modeled by 3D-SDAR. To produce reliable and reproducible classification models for hERG blocking, the initial set of 180 chemicals was split into two subsets: a balanced modeling set consisting of 118 compounds and an unbalanced validation set comprised of 62 compounds. A PLS bagging-like algorithm written in Matlab was used to process the data and assign each compound to one of the two (hERG+ or hERG-) activity classes. The best predictive model evaluated on the basis of a fully randomized hold-out test set (comprising 20% of the modeling set) used 4 latent variables and a grid of 6 ppm × 6 ppm × 1 Å in the C-C region, 6 ppm × 30 ppm × 1 Å in the C-N region, and 30 ppm × 30 ppm × 1 Å in the N-N region. An overall accuracy of 0.84 was obtained for both the hold-out test set and the validation set. Further, an external prediction set consisting of 57 drugs and drug derivatives was used to estimate the true predictive power of the reported 3D-SDAR model – a slight reduction of the overall accuracy down to 0.77 was observed. 3D-SDAR map of the most frequently occurring bins and their projection on the standard coordinate space of the chemical structures allowed identification of a three-center toxicophore composed of two aromatic rings and an amino group. A U test along the distance axis of the most frequently occurring 3D-SDAR bins was used to set the distance limits of the toxicophore. This toxicophore was found to be similar to an earlier reported phospholipidosis (PLD) toxicophore.     

The effect of sinusoidal rolling ground motion on lifting biomechanics

The objective of this study was to quantify the effects of ground surface motion on the biomechanical responses of a person performing a lifting task. A boat motion simulator (BMS) was built to provide a sinusoidal ground motion (simultaneous vertical linear translation and a roll angular displacement) that simulates the deck motion on a small fishing boat. Sixteen participants performed lifting, lowering and static holding tasks under conditions of two levels of mass (5 and 10 kg) and five ground moving conditions. Each ground moving condition was specified by its ground angular displacement and instantaneous vertical acceleration: A): +6°, −0.54 m/s²; B): +3°, −0.27 m/s²; C): 0°, 0 m/s²; D): −3°, 0.27 m/s²; and E): −6°, 0.54 m/s². As they performed these tasks, trunk kinematics were captured using the lumbar motion monitor and trunk muscle activities were evaluated through surface electromyography. The results showed that peak sagittal plane angular acceleration was significantly higher in Condition A than in Conditions C, D and E (698°/s² vs. 612–617°/s²) while peak sagittal plane angular deceleration during lowering was significantly higher in moving conditions (conditions A and E) than in the stationary condition C (538–542°/s² vs. 487°/s²). The EMG results indicate that the boat motions tend to amplify the effects of the slant of the lifting surface and the external oblique musculature plays an important role in stabilizing the torso during these dynamic lifting tasks.     

Free-living inferential modeling of blood glucose level using only noninvasive inputs

The goal of this work is to present a causation modeling methodology with the ability to accurately infer blood glucose levels using a large set of highly correlated noninvasive input variables over an extended period of time. These models can provide insight to improve glucose monitoring, and glucose regulation through advanced model-based control technologies. The efficacy of this approach is demonstrated using real data from a type 2 diabetic (T2D) subject collected under free-living conditions over a period of 25 consecutive days. The model was identified and tested using eleven variables that included three food variables as well as several activity and stress variables. The model was trained using 20 days of data and validated using 5 days of data. This gave a fitted correlation coefficient of 0.70 and an average absolute error (AAE) (i.e., the average of the absolute values for the measured glucose concentration minus modeled glucose concentration) of 13.3 mg/dL for the validation data. This AAE result was significantly better than the subject’s personal glucose meter AAE of 15.3 mg/dL for replicated measurements.     

Hands-free administration of subjective workload scales: Acceptability in a surgical training environment

IntroductionSubjective workload measures are usually administered in a visual–manual format, either electronically or by paper and pencil. However, vocal responses to spoken queries may sometimes be preferable, for example when experimental manipulations require continuous manual responding or when participants have certain sensory/motor impairments. In the present study, we evaluated the acceptability of the hands-free administration of two subjective workload questionnaires – the NASA Task Load Index (NASA-TLX) and the Multiple Resources Questionnaire (MRQ) – in a surgical training environment where manual responding is often constrained.MethodSixty-four undergraduates performed fifteen 90-s trials of laparoscopic training tasks (five replications of 3 tasks – cannulation, ring transfer, and rope manipulation). Half of the participants provided workload ratings using a traditional paper-and-pencil version of the NASA-TLX and MRQ; the remainder used a vocal (hands-free) version of the questionnaires. A follow-up experiment extended the evaluation of the hands-free version to actual medical students in a Minimally Invasive Surgery (MIS) training facility.ResultsThe NASA-TLX was scored in 2 ways – (1) the traditional procedure using participant-specific weights to combine its 6 subscales, and (2) a simplified procedure – the NASA Raw Task Load Index (NASA-RTLX) – using the unweighted mean of the subscale scores. Comparison of the scores obtained from the hands-free and written administration conditions yielded coefficients of equivalence of r = 0.85 (NASA-TLX) and r = 0.81 (NASA-RTLX). Equivalence estimates for the individual subscales ranged from r = 0.78 (“mental demand”) to r = 0.31 (“effort”). Both administration formats and scoring methods were equally sensitive to task and repetition effects. For the MRQ, the coefficient of equivalence for the hands-free and written versions was r = 0.96 when tested on undergraduates. However, the sensitivity of the hands-free MRQ to task demands (η_partial² = 0.138) was substantially less than that for the written version (η_partial² = 0.252). This potential shortcoming of the hands-free MRQ did not seem to generalize to medical students who showed robust task effects when using the hands-free MRQ (η_partial² = 0.396). A detailed analysis of the MRQ subscales also revealed differences that may be attributable to a “spillover” effect in which participants’ judgments about the demands of completing the questionnaires contaminated their judgments about the primary surgical training tasks.ConclusionVocal versions of the NASA-TLX are acceptable alternatives to standard written formats when researchers wish to obtain global workload estimates. However, care should be used when interpreting the individual subscales if the object is to make comparisons between studies or conditions that use different administration modalities. For the MRQ, the vocal version was less sensitive to experimental manipulations than its written counterpart; however, when medical students rather than undergraduates used the vocal version, the instrument’s sensitivity increased well beyond that obtained with any other combination of administration modality and instrument in this study. Thus, the vocal version of the MRQ may be an acceptable workload assessment technique for selected populations, and it may even be a suitable substitute for the NASA-TLX.     

Assessment of grape yield and composition using the reflectance based Water Index in Mediterranean rainfed vineyards

In rainfed vineyards water deficits play a major role in determining berry yield and composition. Therefore, reliable indicators of vine water status might be of great value for the optimization of grape yield and quality. In the present study the feasibility of using hyperspectral reflectance indices related to plant biophysical properties at predicting berry yield and quality attributes in rainfed vineyards is assessed. The study was conducted on Vitis vinifera cv. Chardonnay in commercial vineyards in the D.O. Penedès region (Catalonia, Spain) over two consecutive years (2007–2008). Field measurements of fractional intercepted Photosynthetic Active Radiation (fIPAR), canopy reflectance, predawn water potential (Ψ_p) and the canopy to air temperature difference at midday (ΔT_midday) were conducted at the stage of veraison. Yield, Total Soluble Solids (TSS), Titratable Acidity (TA) and the ratio TSS/TA (maturation index, IMAD) were determined at harvest. Contrasted water availability among vineyards prompted considerable variation in berry yield and quality attributes. Across years, higher yield was accompanied by higher TA (r = 0.59, p < 0.01) and lower IMAD (r = ? 0.63, p < 0.01) while no significant relationship was observed between yield and TSS. Yield was related to canopy vigor (fIPAR) in a variable extend: in 2007, yield was positively related to fIPAR (r = 0.71, p < 0.05) while yield was found to decrease along with increasing fIPAR in 2008 (r = ? 0.62, p < 0.05). Contrastingly, NDVI provided consistent estimates of yield across years (r = 0.57, p < 0.05). These results suggest that NDVI might be more appropriate to characterize the effects of varying water availability on yield than fIPAR. In addition, yield was found to be related to ΔT_midday (r = ? 0.63 and r = ? 0.66, in 2007 and 2008, respectively). Accordingly, the Water Index (WI), an indicator of vine water status, provided robust estimates of yield across years (r = 0.61, p < 0.01). The strength of the correlation between NDVI and WI vs. yield suggests that yield was influenced by changes in both leaf area (intercepted light) and photosynthesis (stomatal aperture) in a variable extent according to the timing and severity of water deficits in the years of study. Berry quality attributes did not show significant relationships against fIPAR but were related to ΔT_midday. Accordingly, NDVI did not show significant correlation with berry quality attributes, while WI was found to be consistently related to TA (r = 0.70, p < 0.01) and IMAD (r = ? 0.71, p < 0.01) across years. The results obtained suggest that the WI might provide reliable estimates of berry quality attributes in vineyards experiencing moderate to severe water deficits with potential application in precision viticulture activities such as selective harvesting according to grape quality attributes as well as for ripening assessment.     

Bounds for the Roots of Lacunary Polynomials

A polynomial P(X)  = X^d + a_d − 1X^d − 1 + ⋯ is called lacunary when a_d − 1 =  0. We give bounds for the roots of such polynomials with complex coefficients. These bounds are much smaller than for general polynomials.     

Revisiting the Task-Media Fit Circumflex: A further examination of negotiation tasks

McGrath and Hollingshead's Task-Media Fit Circumflex, which proposed that the negotiations tasks “best fit” highly information-rich channels, is widely accepted for more than two decades. However, this study argues that according to the task nature, different negotiation tasks shall “best fit” different media channels. A 2 × 3 laboratory experiment was conducted to test this argument. Findings suggested that higher process satisfaction, lower cognitive effort and higher negotiation efficiency would be created when text-based systems were used to execute more analyzable tasks. In contrast, less analyzable tasks best fit face-to-face or audio/video-based systems. Managerial implications are discussed accordingly.     

Damaged corporate reputation: Can celebrity Tweets repair it?

These days, many corporations engage in Twitter activities as a part of their communication strategy. Corporations can use this medium to share information with stakeholders, to answer customer questions, or to build on their image. In this study we examined the extent to which celebrity Tweet messages can be used to repair a damaged corporate reputation, and how this message should be designed and what celebrity should be ‘used’.In two experiments, a 2 × 2 (attractive celebrity versus intelligent celebrity) × (personal message versus general message) design was used. In total, 163 respondents first expressed their feelings regarding the two organisations in a baseline reputation measurement (M = 4.72 on 7 point Likert scale). After that a news items was presented communicating a big fraud and mismanagement, resulting in a decreased reputation score (M = 4.10). In the final stage one of the four experimental Tweets was presented, aimed at repairing the damaged reputation, which succeeded (M = 4.43). For both organisations, the crisis prime significantly decreased reputation scores, and the Tweet significantly increased reputation score again. The analysis of variance shows a main effect for type of celebrity. In our experiment the intelligent celebrity’s Tweet was best to use.The study reveals that celebrities’ Tweets can restore a positive public opinion about corporations. This study shows that when it comes to serious matters, an intelligent celebrity, who has the best fit with the topic, is of best impact. Consequences for corporate communication and future research are discussed.     

A statistical model for robust integration of narrowband cues in speech

We investigate a statistical model for integrating narrowband cues in speech. The model is inspired by two ideas in human speech perception: (i) Fletcher’s hypothesis (1953) that independent detectors, working in narrow frequency bands, account for the robustness of auditory strategies, and (ii) Miller and Nicely’s analysis (1955) that perceptual confusions in noisy bandlimited speech are correlated with phonetic features. We apply the model to detecting the phonetic feature [ +  /  − sonorant] that distinguishes vowels, approximants, and nasals (sonorants) from stops, fricatives, and affricates (obstruents). The model is represented by a multilayer probabilistic network whose binary hidden variables indicate sonorant cues from different parts of the frequency spectrum. We derive the Expectation-Maximization algorithm for estimating the model’s parameters and evaluate its performance on clean and corrupted speech.     

Designing of potential inhibitors against Staphylococcus aureus sortase A: Combined analogue and structure based approach with in vitro validation

Staphylococcus aureus sortase A is an attractive target of Gram-positive bacteria that plays a crucial role in anchoring of surface proteins to peptidoglycan present in bacterial cell wall. Inhibiting sortase A is an elementary and essential effort in preventing the pathogenesis. In this context, in silico virtual screening of in-house database was performed using ligand based pharmacophore model as a filter. The developed pharmacophore model AAHR 11 consists of two acceptors, one hydrophobic and one ring aromatic feature. Top ranked molecule KKR1 was docked into the active site of the target. After profound analysis, it was analyzed and optimized based on the observations from its binding pose orientation. Upgraded version of KKR1 was KKR2 and has improved docking score, binding interactions and best fit in the binding pocket. KKR1 along with KKR2 were further validated using 100 ns molecular dynamic studies. Both KKR1 and KKR2 contain Indole-thiazolidine moiety and were synthesized. The disk diffusion assay has good initial results (ZI of KKR1, KKR2 were 24, 38 mm at 10 μg/mL and ZI of Ampicillin was 22 at 10 μg/mL) and calculated MICs of the molecules (KKR1 5.56 ± 0.28 μg/mL, KKR2 1.32 ± 0.12 μg/mL, Ampicillin 8 ± 1.1 μg/mL) were in good agreement with standard drug Ampicillin. KKR1 has shown IC₅₀ of 1.23 ± 0.14 μM whereas the optimized lead molecule KKR2 show IC₅₀ of 0.008 ± 0.07 μM. Results from in silico were validated by in vitro studies and proved that indole-thiazolidine molecules would be useful for future development as lead molecules against S. aureus sortase A.