Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Sustainable Performance through Green Supply Chain Management Practices: A Study of Malaysian Manufacturing Firms

This paper investigates the effects of green supply chain management practices on sustainable performance. Green supply chain management practices have become an important agenda among researchers and yet there are still limited studies that investigate the effects of green supply chain management practices towards sustainable performance, especially in the context of Malaysia. Further studies are needed in the area because of the immense impact it can bring to the economic, environmental and social performance of firms. A survey was sent to 616 Malaysian ISO 14001 certified manufacturing companies. With a total of 152 usable questionnaires, the response rate was 24.68%. The findings showed that green purchasing and eco-design were found to improve all the components of sustainable performance. While reverse logistics was found to have a positive impact on social performance only. These relationships indicate that Green Supply Chain Management Practices can be of value to organizations specifically, as well to the external environment at large.
It takes two to tango: complementarity of bonding and bridging trust in alleviating corruption in cities

ABSTRACT Petty administrative corruption (bribery) can be seen as a regional phenomenon and investigated in the spatial context. By applying Heckman selection models with instrumenting to the European Bank for Reconstruction and Development’s (EBRD) Life in Transition Survey (LiTS) data, it is found that larger cities appear more prone to corruption. However, capital cities exhibit lower corruption, owing to differences in social and political structures and government accountability. Furthermore, the paper illustrates the importance of the meso-level social environment, exploring complementarity between bridging (across broader local society by institutional trust) and bonding trust (within more embedded communities) in alleviating bribery.
What would the economy with credit-enhanced interest rates look like? The case of Turkey

ABSTRACT We verify the potential impact of credit-enhanced monetary policy rule on inflation, GDP and credit dynamics in Turkey (2002Q1—2018Q3), where the Turkish Cental Bank takes into account financial stability in its inflation-targeting strategy. We estimate interest rates from two monetary policy rules (classic and augmented with the exogenous credit-to-GDP gap) with time-varying parameters (TVP) and assess their performance in a TVP-VAR model. The results show that after the global crisis both observed interest rates and those estimated from the Taylor-rule impact inflation and the GDP in a similar way. The central bank’s monetary policy after 2010 was in line with the Taylor rule and took into account cyclical systemic risk with increased strength in the monetary transmission mechanism. Yet, before the crisis, both types of Taylor rules indicate that the bank’s monetary policy should have been more restrictive.
Salmonella-Mediated Cancer Therapy: An Innovative Therapeutic Strategy

Bacterial-mediated cancer therapy (BMCT) has become a hot topic in the area of antitumor treatment. Salmonella has been recommended to specifically colonize and proliferate inside tumors and even inhibit tumor growth. Salmonella typhimurium (S. typhimurium) is one of the most promising mediators, which can be easily manipulated. S. typhimurium has been engineered and designed as cancer-targeting therapeutics, and can be improved by combining with other therapeutic methods, e.g. chemotherapy and radiotherapy, which regulate the tumor microenvironment synergistically. In view of all these strengths, the engineered attenuated strains have significant advantages for tumor diagnosis and treatment. This treatment has also been approved by the FDA for clinical trial. In this review, we summarized the recent progress and research in the field of Salmonella -mediated cancer therapy.
Computational Biology

STATISTICAL MECHANICS Equilibrium Statistical Mechanics Z: The Partition Function Relation to Thermodynamics Computing Z Nonequilibrium Statistical Mechanics Stochastic Processes The Master Equation Fluctuation Theorems The Fokker-Planck and Langevin Equations Sequence Alignment: A Nonequilibrium Phase Transition BIOMOLECULES Molecules, Code and Representation DNA and RNA: The Building Blocks Representing RNA Structure Proteins DNA Thermal Stability of DNA: The Melting Transition The Melting Profiles of Genomic DNA and cDNA Hybridizing DNA RNA Computing RNA Secondary Structure: Combinatorics The RNA Partition Function RNA Phase Behavior and Folding Kinetics Proteins Proteins: Folding Proteins: Docking Electrostatics Chromatin Bacterial Chemotaxis: Cooperativity Once More NETWORKS Network Dynamics I: Deterministic Deterministic Dynamics: ? -Repressor Expression The Turing Insight The Min System Network Dynamics II: Fluctuations Noise in Signaling Stochastic Cascades Stochastic Focusing Fluctuating Gene Network Dynamics Extrinsic vs. Intrinsic Noise Networks: Structure Networks as Graphs Probability Generating Functions and Network Characteristics Statistical Mechanics of Networks Network Growth INDEX
Relationships between lower extremity strength and the multi-directional reach test in children aged 7 to 12 years

Objective: This study investigates the relationships between the Multi-Directional Reach Test (MDRT) and lower extremity strength in typical children. Methods: The MDRT including forward, backward, leftward, and rightward directions was measured in 60 children aged between 7 and 12 years old with typical development. The lower extremity muscle groups were measured using a hand-held dynamometer. Results: The reaching score in each direction had positive relationships with the strengths of several lower extremity muscle groups (r=0.26 to 0.52,p<0.05). Only the strengths of the hip flexor and knee flexor muscles significantly correlated with the MDRT scores in all directions (r=0.26 to 0.50,p<0.05). Conclusion: This study highlights the strength of the hip and knee flexor muscle groups as being important domain to control balance in all directions. These findings may be used for therapists in planning a balance program to improve the limits of stability.
On the impact of connected automated vehicles in freeway work zones: a cooperative cellular automata model based approach

Purpose Freeway work zones have been traffic bottlenecks that lead to a series of problems, including long travel time, high-speed variation, driver’s dissatisfaction and traffic congestion. This research aims to develop a collaborative component of connected and automated vehicles (CAVs) to alleviate negative effects caused by work zones. Design/methodology/approach The proposed cooperative component is incorporated in a cellular automata model to examine how and to what scale CAVs can help in improving traffic operations. Findings Simulation results show that, with the proposed component and penetration of CAVs, the average performances (travel time, safety and emission) can all be improved and the stochasticity of performances will be minimized too. Originality/value To the best of the authors’ knowledge, this is the first research that develops a cooperative mechanism of CAVs to improve work zone performance.
Force Aware Haptic Rendering for Intubation Simulation

We introduce a new haptic rendering method for neonatal endotracheal intubation simulation. The challenging procedure involves multiple models of different material properties, and is performed in the narrow oral cavity involving multiple contacts with tongue, lips, and laryngoscope. Our method first sets up simple collision detection mechanism with adaptive inner sphere tree structure for deformable tongue tissue. Then a collision response is handled with a novel force aware projective position correction. Our method is proved to be effective for heterogeneous simulation environment, therefore can be applied to surgical simulation with similar difficult settings.
Parents’ and teachers’ perspectives on children’s sexual health education: a qualitative study in Makwanpur Nepal

Sex education is taught to secondary school students in Nepal. Nevertheless, there are concerns that the school-based sexual health education is not effective and adequate to address young people’s necessities. We carried out a qualitative study comprising key informant interviews with teachers (n=8) and parents (n=6) in Makwanpur district in Nepal. Interviews were recorded, transcribed and translated into English. Thematic analysis was performed to identify patterns or themes within the qualitative data. Most participants (both teachers and parents) had thought of delivering sex education preferably from grade seven to avoid the effects of globalised mass media and the internet. The practical aspects of school sex education programme and the importance of parent-child communication were of major concerns. Comprehensive training to health teachers, an informal approach to teaching sex education and seeking outside health professionals, such as health facilitators were the frequently reported issues. There is a need to offer sexual health services along with sex education to protect young people from potential dangers of STIs including HIV infection. Particularly, health teachers should be trained properly to mitigate the social and cultural impacts, and to allow a smooth sex education discussion in the classroom. The curriculum for sex education should be relevant, engaging and developmentally suitable with clear progressive avenues for learning experience.
Sound based Steganalysis for Waveform Audio File Format (WAV) and Audio File
Format (AU)

Developing of technology today brings to ensure the security of data in the digital media with it. Coding and steganography techniques include the solution techniques which are used to ensure this security. The soundness of those techniques are also tested with the analysis techniques. In this study, steganalysis methods, which are used to reveal the hidden data within sound files, have been examined and the results, which are obtained from those methods, have been compared. The soundness of sound based steganography solutions has been tested via software which was developed in this study. Chi-square attack has been used for the testing of software and the outcomes have been assessed. Very successful outcomes have been obtained in the method which was developed in the sound based steganalysis. However, no good outcomes have been obtained for the sounds which included the prediction in the noisy sounds.
Multi-Attributes Web Objects Classification based on Class-Attribute Relation Patterns Learning Approach

The amount of Web data increases with the proliferation of a variety of Web objects, primarily in the form of text, images, video, and music data files. Each of these published objects has some properties that support defining its class properties. Because of their diversity, using these attributes to learn and generate patterns for precise classification is very complicated. Even learning a set of attributes that clearly categorize the categories is very important. Existing attribute learning methods only learn attributes that are closely related to multiple similar objects, but if similar class objects have different attributes, this problem is difficult to learn and classify them. In this paper, a Multi-attributes Web Objects Classification (MA-WOC) based on Class-attribute Relation Patterns Learning Approach is being proposed, which generates a class-attribute with its multi relations patterns. The MA-WOC calculates the relationship probabilities of the attributes and the associated values of the class to understand the degree of association of the construction of classification pattern. To evaluate the effectiveness of the classifier, this will compare to an existing classifier that supports a multi-attribute data set, which shows improvisation of precision with a significant minimum Hamming loss. To evaluate the effectiveness of MA-WOC classification a comparison among the classifiers that are supported to the multi-attribute dataset are being performed to measure the accuracy and hamming loss.
The Future Mobility Survey: Overview and Preliminary Evaluation

Technological advances, such as GPS devices and smartphones, have had numerous impacts in the realm of travel surveys. These advances bring both challenges and opportunities as we work to balance the potential to collect nearly unlimited amounts of data with the need to not overburden transport survey participants. This paper addresses the steps currently taken to manage this balance in the context of the Future Mobility Survey (FMS), a smartphone based travel survey being developed in the Singaporean context, and presents a preliminary evaluation by comparison with a traditional survey which is being made simultaneously.
Perturbation of Quorum Sensing in Pseudomonas aeruginosa by Febuxostat

Quorum sensing is a signal-based communication system in bacteria. It is an attractive target because it regulates the production of virulence factors in Pseudomonas aeruginosa. As a result, interference with quorum sensing could result in inhibition of virulence of Pseudomonas aeruginosa with the merit of lack of selective pressure on growth that leads to development of resistance. This study investigated the anti-quorum sensing and anti-virulence activities of febuxostat in Pseudomonas aeruginosa PAO1 strain. At 1/8 MIC of febuxostat, the production of the quorum-sensing regulated violacein pigment of Chromobacterium violaceum CV026 was significantly reduced. Moreover, it markedly reduced pyocyanin, hemolysin, protease and elastase production. Significant inhibitory activities were also found against biofilm, swimming, twitching and swarming motilities. Molecular docking showed the ability of febuxostat to inhibit quorum sensing by competing with the autoinducers to bind with LasR and RhlR receptors. Febuxostat could bind to both receptors by hydrogen bonding and hydrophobic interaction. From the Molecular docking scores, febuxostat is a very promising quorum sensing inhibitor. Febuxostat could also significantly decrease the level of expression of all QS genes LasI, LasR, RhlI, RhlR, PqsA and PqsR that regulate the production of virulene factors as confirmed by qRT-PCR.
Farsighted Manipulation and Exploitation in Networks

Economic agents with an increased sophistication sometimes use their advantage to exploit their more naive counterparts. In public goods games played on networks, such an agent will attempt to manipulate as many of his neighbors as possible to produce the public good. We study the exploitation of a myopic population by a single farsighted player in such games. We show the existence and payoff-uniqueness of optimal farsighted strategies in every network structure. In the long run, the farsighted player’s effects are only felt locally. A simple dependence-withdrawal strategy reaches the optimal outcome for every network if the starting state is unfavorable, and reaches it for every starting state if the farsighted player is linked to all opponents. We characterize the lower and upper bounds of long-run payoffs the farsighted player can attain in a given network and make comparative statics with respect to adding a new link. The farsighted player always benefits from linking to more opponents (sociability) and is always harmed by his neighbors linking to each other (jealousy).
Electroencephalogram Emotion Recognition Based on Empirical Mode Decomposition and Optimal Feature Selection

Electroencephalogram (EEG) emotion recognition based on a hybrid feature extraction method in empirical mode decomposition domain combining with optimal feature selection based on sequence backward selection is proposed, which can reflect subtle information of multiscale components of unstable and nonlinear EEG signals and remove the reductant features to improve the performance of emotion recognition. The proposal is tested on DEAP dataset, in which the emotional states in the Valance dimension and Arousal dimension are classified by both ${K}$ -nearest neighbor and support vector machine, respectively. In the experiments, temporal windows of different length and three kinds of rhythms of EEG signal are taken into account for comparison, from which the results show that EEG signal with 1s temporal window achieves highest recognition accuracy of 86.46% in Valence dimension and 84.90% in Arousal dimension, respectively, which is superior to some state-of-the-art works. The proposed method would be applied to real-time emotion recognition in multimodal emotional communication-based humans–robots interaction system.
A Novel Framework for Parameter and State Estimation of Multicellular Systems Using Gaussian Mixture Approximations

Multicellular systems play an important role in many biotechnological processes. Typically, these exhibit cell-to-cell variability, which has to be monitored closely for process control and optimization. However, some properties may not be measurable due to technical and financial restrictions. To improve the monitoring, model-based online estimators can be designed for their reconstruction. The multicellular dynamics is accounted for in the framework of population balance models (PBMs). These models are based on single cell kinetics, and each cellular state translates directly into an additional dimension of the obtained partial differential equations. As multicellular dynamics often require detailed single cell models and feature a high number of cellular components, the resulting population balance equations are often high-dimensional. Therefore, established state estimation concepts for PBMs based on discrete grids are not recommended due to the large computational effort. In this contribution a novel approach is proposed, which is based on the approximation of the underlying number density functions as the weighted sum of Gaussian distributions. Thus, the distribution is described by the characteristic properties of the individual Gaussians, like the mean and covariance. Thereby, the complex infinite dimensional estimation problem can be reduced to a finite dimension. The characteristic properties are estimated in a recursive approach. The method is evaluated for two academic benchmark examples, and the results indicate its potential for model-based online reconstruction for multicellular systems.
Code variants and their retrieval using knowledge discovery based approaches

Code variants represent alternative implementations of a code snippet, where each alternative provides the same functionality, but has different properties that make some of them better suited to the overall project requirements. Developers routinely need to analyze existing code, find better reuse alternatives, and look to develop high-quality code that meets some desired properties. However, searching for such code variants over the web has several challenges. In this dissertation, we address this problem. This dissertation presents new techniques to search for code variants. Classical program analysis techniques do not scale well to analyze partial programs at webscale. Hence, we apply search techniques to mine code variants using human annotated natural language descriptions found in the posts of Stack Overflow1 (SO) which is a popular discussion forum. Here, we make four major contributions. Unlike clones and examples, existing literature lacks a rigorous characterization of code variants. So, as our first contribution, we present a characterization of code variants where we discuss the code context, desired properties, and types of variants along with implications for tool builders. With this knowledge about variants, we propose techniques to search for variants in SO, as our second major contribution. We propose a novel structural model for source code which is based on developers’ perspective of similarity. To leverage the text and code components that we index from SO, we adapt an existing state of the art term-weighting method to propose a Multi-Component Multi-Aspect Term Frequency Inverse Document Frequency (MCMATF-IDF) model to retrieve code variants. Existing text-retrieval models do not work well on source code. Expressing natural language queries on source code is an open problem. Many query terms in natural language have multiple surface forms in source code. We address this problem by perceiving source code as a collection of entities. This becomes our third major contribution. Further, as a bottleneck to the success of our approaches, we notice that our work depends on parsing code snippets in SO. We observe that only 31.3% of code snippets in SO parse. Hence, in our fourth contribution, we apply grounded theory approach to study these parsing problems. Based on this study, we develop a tool which increases the code snippets that can generate Abstract Syntax Trees for 63% of the code snippets in SO. Overall, the ability to perform semantic search over source code snippets assisted by developer knowledge in the form of discussion forum data opens up a new way to solve several important problems. It can lead to improvements in a variety of software engineering tasks and tools such as semantic clone detection, code comprehension and defect detection. Apart from supporting software engineering applications, as future work, we plan to explore enhancing static analysis over source code snippets using the data from discussion forums. http://stackoverflow.com/
Immediate implant replacement after Partial explantation of malpositioned dental implant: Case report and follow-up

Aim It is not common to report in the literature about complications during intraoral dental implant removal and its immediate approach during implant surgery. This report describes the approach of an intraoperative accident during an explantation and its follow up. Case report In this case, an immediate implant was placed in the presence of an implant remnant left after its partial explantation due to implant fracture during the procedure. A cone-beam CT scan was taken after six months post rehabilitation and revealed bone formation surrounding the replaced implant and the remaining titanium fragment. Results There were no functional or aesthetic alterations that affected the outcome of the treatment after 2 years of follow-up. Conclusions This satisfactory result suggests that this immediate solution can be used to obtain adequate results when failed dental implants cannot be completely eliminated, which reduces the duration of the treatment and the number of surgical procedures.
Classifying facts and opinions in Twitter messages: a deep learning-based approach

ABSTRACT Massive social media data present businesses with an immense opportunity to extract useful insights. However, social media messages typically consist of both facts and opinions, posing a challenge to analytics applications that focus more on either facts and opinions. Distinguishing facts and opinionss may significantly improve subsequent analytics tasks. In this study, we propose a deep learning-based algorithm that automatically separates facts from opinions in Twitter messages. The algorithm outperformed multiple popular baselines in an experiment we conducted. We further applied the proposed algorithm to track customer complaints and found that it indeed benefits subsequent analytics applications.