Accurate identification of drug-targets in human body has great significance for designing novel drugs. Compared with traditional experimental methods, prediction of drug-targets via machine learning ...algorithms has enhanced the attention of many researchers due to fast and accurate prediction. In this study, we propose a machine learning-based method, namely XGB-DrugPred for accurate prediction of druggable proteins. The features from primary protein sequences are extracted by group dipeptide composition, reduced amino acid alphabet, and novel encoder pseudo amino acid composition segmentation. To select the best feature set, eXtreme Gradient Boosting-recursive feature elimination is implemented. The best feature set is provided to eXtreme Gradient Boosting (XGB), Random Forest, and Extremely Randomized Tree classifiers for model training and prediction. The performance of these classifiers is evaluated by tenfold cross-validation. The empirical results show that XGB-based predictor achieves the best results compared with other classifiers and existing methods in the literature.
The study examined the impact of different forces on carbon dioxide emissions for ten newly industrialized countries (NIC's) by applying the extended STIRPAT model for the period 1991-2013. Moreover, ...study utilizes the regression of group mean dynamic common correlated estimator (DCCE) to analyze the relationship between carbon dioxide emissions (CO2), population, affluence, technology and extended factors such as labor productivity, urban employment level, population carrying intensity of real economy, energy mix, trade openness and energy intensity. The results from DCCE approach are compared with fully modified ordinary least square and dynamic ordinary least square techniques for robustness. Results of the study suggest that population, GDP per capita and CO2 emission intensity along with energy intensity are main contributors for CO2 emissions for NIC's, while population carrying capacity of real economy have no significant impact on CO2 emission level. Furthermore, energy mix and trade openness have marginal contribution in CO2 emissions. The policymakers can use these results to develop appropriate policies for economic growth through industrial development by curtailing the level of CO2 emissions.
This paper posits that the conventional arms trade between China and Pakistan is the most durable aspect of their relationship since the mid‐1960s. It carries out a succinct review of the relations ...to identify changes in key areas compared to continuity in the arms trade. The paper then looks into China's key motives as a supplier and Pakistan's as a recipient while focusing on political, economic, and technological factors; conversely, it excluded geopolitical reasons. This paper found that a mutuality of interests supports this arms trade. In return for its arms exports to Pakistan, China gained access to Western defense technology, earned revenue, and gained entry into the Islamic world. In addition, Pakistan proved itself as a marketer and testing ground of Chinese weapons. Islamabad initially approached Beijing to make up for the disruption caused by abrupt US sanctions but soon found China the trustiest supplier that also transferred technology, granted licenses for local production, trained engineers, and assisted in building defense industries to gain autarky. Both countries have become irreplaceable allies. The trajectory, marked by joint productions, joint military exercises, and advancement in China's weapon systems, coupled with congruence on regional security issues, indicates further strengthening of this arms trade.
Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and ...resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory.
In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF.
The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew's-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process.
The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .
This paper argues that during the last two decades, China and Pakistan have strengthened their maritime cooperation in the Indian Ocean to their mutual benefit. Based upon its geostrategic location ...and vast maritime experience, Pakistan has promoted China's growing interests in the Indian Ocean and received China's economic, technological and military assistance in return. India has responded to these developments by expanding its naval power, adding a nuclear component and aligning with like-minded states. The paper concludes that a lack of institutional mechanisms, coordination and trial among the three can potentially expand their rivalry seawards, triggering a new naval arms race.
Purpose
The purpose of this paper is to investigate the perceptions of faculty members about the influence of family motivation on their self-efficacy and organizational citizenship ...behavior-individual (OCBI).
Design/methodology/approach
The proposed model was tested on a sample of 353 faculty members from different public and private universities of Pakistan. Partial least squares structural equation modeling was used to analyze data.
Findings
Surprisingly, results reveal that family motivation was not positively related to faculty members’ OCBI; instead, this relationship is fully mediated by self-efficacy. The findings suggest that it is employees’ self-efficacy belief through which their family motivation translates to their increased OCBI. This study also finds that supporting the family is a powerful source of motivation to work, offering meaningful practical and theoretical implications for policy-makers, leaders, managers and researchers on the new dynamics of work and family engagements.
Originality/value
The study contributes to human resource management (HRM) and organizational behavior (OB) literatures by providing some useful practical implications for managers and HRM and OB consultants who are interested in understanding the underlying psychological mechanisms (i.e. self-efficacy) through which employees’ family motivation results in the increased OCBI.
Display omitted
•Pyrolysis kinetics of waste polystyrene was studied.•Ea and A-factor were determined using various kinetics models.•The waste was decomposed in an indigenously made furnace.•Fuel ...properties of the oil obtained were compared with commercial oils.
In the present study polystyrene waste (PS) was collected from a drop off site in a local market and pyrolyzed at heating rates of 5, 10, 15 and 20 °C/min and temperature range 40–600 °C under nitrogen condition. The apparent activation energy (Ea) and pre-exponential factor (A) were determined using 6 different kinetic methods. Activation energy and pre-exponential factor were found in the range of 82.3 – 202.8 kJmol−1 and 3.5 × 106–7.6 × 1014 min−1 respectively. The results demonstrated that the calculated values of Ea and A vary with fraction of conversion, heating rates and the applied model. Moreover, pyrolysis of waste polystyrene was carried out in an indigenously manufactured furnace at temperatures ranging from 340 to 420 °C. The composition of liquid and gaseous fractions was determined using gas chromatography-mass spectrometry. Temperature and reaction time were optimized and the results revealed that temperature of 410 °C and exposure time of 70 min are the best conditions for maximum fuel oil production. Methane and ethane were found as the main products in the gas phase constituting about 82% of the gaseous fraction. The liquid products composed of broad range of C2 – C15 hydrocarbons depending on the pyrolytic parameters. A comparison of the composition of pyrolysis oil with standard parameters of diesel, gasoline and kerosene oil suggested that pyrolysis oil from polystyrene waste holds great promise for replacing fuel oil.
In extremely cold environments, living organisms like plants, animals, fishes, and microbes can die due to the intracellular ice formation in their bodies. To sustain life in such cold environments, ...some cold-blooded species produced Antifreeze proteins (AFPs), also called ice-binding proteins. AFPs are not only limited to the medical field but also have diverse significance in the area of biotechnology, agriculture, and the food industry. Different AFPs exhibit high heterogeneity in their structures and sequences. Keeping the significance of AFPs, several machine-learning-based models have been developed by scientists for the prediction of AFPs. However, due to the complex and diverse nature of AFPs, the prediction performance of the existing methods is limited. Therefore, it is highly indispensable for researchers to develop a reliable computational model that can accurately predict AFPs. In this connection, this study presents a novel predictor for AFPs, named AFP-CMBPred. The sequences of AFPs are formulated via four different feature representation methods, such as Amphiphilic pseudo amino acid composition (Amp-PseAAC), Dipeptide Deviation from Expected Mean (DDE), Multi-Blocks Position Specific Scoring Matrix (MB-PSSM), and Consensus Sequence-based on Multi-Blocks Position Specific Scoring Matrix (CS-MB-PSSM) to collect local and global descriptors. In the next step, the extracted feature vectors are evaluated via Support Vector Machine (SVM) and Random Forest (RF) based classification learners. The prediction performance of both classifiers is further assessed using three validation methods i.e., jackknife test, 10-fold cross-validation test, and independent test. After examining the prediction rates of all validation tests, it was found that our proposed model achieved the higher prediction accuracies of ∼2.65%, ∼2.84%, and ∼3.37% using jackknife, K-fold, and independent test, respectively. The experimental outcomes validate that our proposed “AFP-CMBPred” predictor secured the highest prediction results than the existing models for the identification of AFPs. It is further anticipated that our proposed AFP-CMBPred model will be considered a valuable tool in the research academia and drug development.
•Designed a novel predictor named AFP-CMBPred for prediction of Antifreeze proteins.•The local and global discriminative features are explored by Amp-PseAAC, DDE, MB-PSSM, and CS-MB-PSSM.•SVM and RF are used as classification algorithms.•AFP-CMBPred predictor secured the highest prediction results for AFPs identification.
The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) attacks ...emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantity and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms (EAs) Optimization with Tree-based Pipelines Optimization Tool (TPOT)-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using EAs, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.
Purpose
This paper aims to examine the consequences for innovative work behavior (IWB) of top-down knowledge hiding – that is, supervisors’ knowledge hiding from supervisees (SKHS). Drawing on social ...learning theory, the authors test the three-way moderated-mediation model in which the direct effect of SKHS on IWB is first mediated by self-efficacy and then further moderated by supervisor and supervisee nationality (locals versus foreigners).
Design/methodology/approach
The authors collected multi-sourced data from 446 matched supervisor-supervisee pairs working in a diverse range of organizations operating in the Kingdom of Saudi Arabia. After initial data screening, confirmatory factor analysis was conducted to test for the factorial validity of the used measures with AMOS. The hypothesized relationships were tested in regression analysis with SPSS.
Findings
Results showed that SKHS had both direct and mediation effects, via the self-efficacy mediator, on supervisee IWB. The mediation effect was further moderated by supervisor and supervisee nationality (local versus foreigners), which highlighted that the effect was stronger for supervisor–supervisee pairs that were local-local or foreigner-foreigner than for pairs that were local-foreigner or foreigner-local.
Originality/value
This study contributes to both knowledge hiding and IWB literature and discusses the useful theoretical and practical implications of the findings.
PDF
