Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the global diffusion of B2B E-commerce (B2B EC): An integrated model

Using institutional theory, this study offers an integrated framework that describes the diffusion of business-to-business e-commerce within a country. The model specifies the role of national institutional frameworks, international institutional pressures, and market complexity in business-to-business e-commerce diffusion. We test this model using archival, cross-sectional data from 146 countries for the period from 2013 to 2016. The study also compares the roles of these factors in business-to-business e-commerce diffusion across developed and developing countries. The results suggest that national institutional frameworks, international institutional pressures, and market complexity contribute positively to business-to-business e-commerce diffusion and that the influence of these variables varies according to the degree of a country’s development. Theoretical, research, and managerial implications of the study are also discussed.
Customer Engagement in Higher Education: How Important the Role of Social Media Marketing, E-Service Quality and E-Satisfaction for Generation Z Students?

The industrial era 4.0 had lead big changes for companies in managing their business, such as carrying out marketing and service strategies. Social media and digital technology provided faster and more responsive service to customers. The use of social media was considered important to increase engagement of students from Generation Z and to improve academic achievement. The objective of the research was to examine the effect of social media marketing, e-service quality, and e-satisfaction on customer engagement. The research was conducted at Bina Nusantara University (Binus) with a sample of 100 students from Generation Z using quantitative approach with data collection by distributing questionnaires to be analyzed using path analysis. The findings show that social media marketing and e-service quality significantly have impacts on e-satisfaction and customer engagement, while the e-satisfaction does not significantly impact customer engagement. Therefore, the development of digital technology in higher education becomes the main focus for supporting services and activities.
Enhanced Hydrocarbons Biodegradation at Deep-Sea Hydrostatic Pressure with Microbial Electrochemical Snorkels

In anaerobic sediments, microbial degradation of petroleum hydrocarbons is limited by the rapid depletion of electron acceptors (e.g., ferric oxide, sulfate) and accumulation of toxic metabolites (e.g., sulfide, following sulfate reduction). Deep-sea sediments are increasingly impacted by oil contamination, and the elevated hydrostatic pressure (HP) they are subjected to represents an additional limitation for microbial metabolism. While the use of electrodes to support electrobioremediation in oil-contaminated sediments has been described, there is no evidence on their applicability for deep-sea sediments. Here, we tested a passive bioelectrochemical system named ”oil-spill snorkel” with two crude oils carrying different alkane contents (4 vs. 15%), at increased or ambient HP (10 vs. 0.1 MPa). Snorkels enhanced alkanes biodegradation at both 10 and 0.1 MPa within only seven weeks, as compared to nonconductive glass controls. Microprofiles in anaerobic, contaminated sediments indicated that snorkels kept sulfide concentration to low titers. Bulk-sediment analysis confirmed that sulfide oxidation by snorkels largely regenerated sulfate. Hence, the sole application of snorkels could eliminate a toxicity factor and replenish a spent electron acceptor at increased HP. Both aspects are crucial for petroleum decontamination of the deep sea, a remote environment featured by low metabolic activity.
Groundwater Depth Forecasting Using a Coupled Model

Accurate and reliable prediction of groundwater depth is a critical component in water resources management. In this paper, a new method based on coupling wavelet decomposition method (WA), autoregressive moving average (ARMA) model, and BP neural network (BP) model for groundwater depth forecasting applications was proposed. The relative performance of the proposed coupled model (WA-ARMA-BP) was compared to the regular autoregressive integrated moving average (ARIMA) and BP models for annual average groundwater depth forecasting using leave-one-out cross-validation (LOO-CV). The variables used to develop and validate the models were average groundwater depth data recorded from 1981 to 2010 in Jinghui Canal Irrigation District in the northwest of China. It was found that the WA-ARMA-BP model provided more accurate annual average groundwater depth forecasts compared to the ARIMA and BP models. The results of the study indicate the potential of the WA-ARMA-BP model in forecasting nonstationary time series such as groundwater depth.
Gender diversity of boards and executives on real earnings management in the bull or bear period: Empirical evidence from China

This study investigates whether gender diversity in the roles of executives, boards of supervisors and management impacts real earnings management during the bullish and bearish periods from 2000 to 2017. The sample is separated by bullish (11,616 firm-year observations) and bearish (14,436 firm-year observations) periods. The results suggest that female participation on supervisory boards and executives tend to kerb real earnings management during bearish and bullish periods. Another important finding is that female CEOs are more cautious in real earnings manipulation due to their risk-averse nature during the bearish period, while female CFOs are more likely to constrain real earnings management in the bullish period. This study failed to find the relation of gender diversity on boards of management to real earnings management; how-ever, the study has found that female executives (excluding CEOs and CFOs) who are also directors are more likely to limit earnings manipulation.
Dynamic Carriage Reserving for an Over-crowded Metro Junction Station

ABSTRACT At metro junction stations located in transportation hubs, a great number of passengers need to transfer to the metro system from other transportation modes during peak hours. This paper proposes a new train operation strategy—reserving carriages dynamically on each service train at the departure station for the metro junction station, which aims to relieve the serious passenger accumulation at the target station. We construct a nonlinear integer programming model which determines the number of reserved carriages on each train, and the total risk levels of passenger accumulation and passenger waiting time are both taken into account in the objective function. Furthermore, the latest arrival time of passengers on board is introduced to calculate effective passenger loading times analytically. At last, a set of numerical experiments from Beijing Metro Line 4 are implemented to verify the performance and effectiveness of the proposed approach.
Nonsteroidal Mineralocorticoid Receptor Antagonists: Exploring Role in Cardiovascular Disease

Abstract: Aldosterone, a mineralocorticoid hormone, plays a role in the pathophysiology of many cardiovascular disease states. Mineralocorticoid receptor antagonists (MRAs) have been shown to improve clinical outcomes in select patient populations. However, use of available steroidal receptor antagonists, eplerenone and spironolactone, is often limited by the risk or development of hyperkalemia. Nonsteroidal MRAs have been designed to overcome this limitation. The nonsteroidal MRAs have been studied in patients with heart failure with reduced ejection fraction, hypertension, and to lower the risk of cardiac and renal outcomes in those with type 2 diabetes and renal disease. In this review, the pharmacology of the MRAs is compared, the data evaluating the use of nonsteroidal MRAs are examined, and the place of this new generation of therapy is discussed. At this time, it seems that there could be a future role for nonsteroidal MRAs to reduce the risk of renal outcomes in high-risk individuals.
Methods for Constructing Complex Solutions of Nonlinear PDEs Using Simpler Solutions

This paper describes a number of simple but quite effective methods for constructing exact solutions of nonlinear partial differential equations that involve a relatively small amount of intermediate calculations. The methods employ two main ideas: (i) simple exact solutions can serve to construct more complex solutions of the equations under consideration and (ii) exact solutions of some equations can serve to construct solutions of other, more complex equations. In particular, we propose a method for constructing complex solutions from simple solutions using translation and scaling. We show that in some cases, rather complex solutions can be obtained by adding one or more terms to simpler solutions. There are situations where nonlinear superposition allows us to construct a complex composite solution using similar simple solutions. We also propose a few methods for constructing complex exact solutions to linear and nonlinear PDEs by introducing complex-valued parameters into simpler solutions. The effectiveness of the methods is illustrated by a large number of specific examples (over 30 in total). These include nonlinear heat equations, reaction–diffusion equations, wave type equations, Klein–Gordon type equations, equations of motion through porous media, hydrodynamic boundary layer equations, equations of motion of a liquid film, equations of gas dynamics, Navier–Stokes equations, and some other PDEs. Apart from exact solutions to ‘ordinary’ partial differential equations, we also describe some exact solutions to more complex nonlinear delay PDEs. Along with the unknown function at the current time, u=u(x,t), these equations contain the same function at a past time, w=u(x,t−τ), where τ>0 is the delay time. Furthermore, we look at nonlinear partial functional-differential equations of the pantograph type, which, in addition to the unknown u=u(x,t), also contain the same functions with dilated or contracted arguments, w=u(px,qt), where p and q are scaling parameters. We propose an efficient approach to construct exact solutions to such functional-differential equations. Some new exact solutions of nonlinear pantograph-type PDEs are presented. The methods and examples in this paper are presented according to the principle “from simple to complex”.
Slab Dehydration in Sumatra: Implications for Fast and Slow Earthquakes and Arc Magmatism

The catastrophic 2004 Sumatra‐Andaman Mw9.1 earthquake associated with destructive tsunamis has characterized Sumatra as one of the most dangerous convergence zones. Nevertheless, the effects of the thermohydrous state on the strongly coupled megathrust of the incoming plate remain enigmatic. By using a three‐dimensional (3‐D) thermomechanical model to compute the temperature variation and the complicated phase transition process of the water‐bearing descending plate which generates unstable thrust slips, we find that Sumatran earthquakes at varying depths are likely under the control of the inter or intraplate hydrothermal regime, which occurs in or close to the petrological metamorphism transition area. The slab dehydration of the water‐rich mid‐ocean‐ridge basalts (MORB) and the ultramafic rocks in the oceanic lithosphere releases a large amount of fluid to the continental wedge and further facilitates arc magmatism. The fluids are prone to upwelling following the subduction channel along the plate interface and thus contribute to the clustering of earthquakes updip of the dehydration front beneath offshore Sumatra. Brittle failure and dehydration embrittlement at depth, along with the temperature differences caused by variant slab geometry, are conjectured to greatly influence the occurrence of fast and slow earthquakes in Sumatra.
Extending Libraries of Extremely Localized Molecular Orbitals to Metal Organic Frameworks: A Preliminary Investigation

Libraries of extremely localized molecular orbitals (ELMOs) have been recently assembled to reconstruct approximate wavefunctions of very large biological systems, such as polypeptides and proteins. In this paper, we investigate for the first time the possibility of using ELMO transferability to also quickly obtain wavefunctions, electron densities, and electrostatic potentials of three-dimensional coordination polymers such as metal organic frameworks (MOFs). To accomplish this task, we propose a protocol that, in addition to exploiting the usual exportability of extremely localized molecular orbitals, also takes advantage of the novel QM/ELMO (quantum mechanics/extremely localized molecular orbital) approach to properly describe the secondary building units of MOFs. As a benchmark test, our technique has been applied to the well-known metal organic framework HKUST-1 ({Cu3(BTC)2}n, with BTC=1,3,5-benzenetricarboxylate) to quickly calculate electrostatic potential maps in the small and large cavities inside the network. On the basis of the obtained results, we envisage further improvements and applications of this strategy, which can be also seen as a starting point to perform less computationally expensive quantum mechanical calculations on metal organic frameworks with the goal of investigating transformation phenomena such as chemisorption.
TiO2 Nanostructures and Nanocomposites for Sustainable Photocatalytic Water Purification

Water, together with energy and food, has been addressed as one of the main urgent problems of humanity. The conventional wastewater treatments suffer some limita‐ tions related to the effectiveness in decontamination (mechanical filtration), in the heavy use of chemicals (chlorination), or in elevation of operational costs and energy require‐ ments (desalination and reverse osmosis). In this sense, new materials such as nanocom‐ posites may overcome these issues taking advantage of the peculiar properties of materials at nanoscale. Research on novel nanotechnologies must bring advances in order to contrast and prevent water scarcity and pollution. In order to be effective, these nanotechnolo‐ gies should run at low operational cost, even in places unequipped by strong infrastruc‐ tures and in concert with conventional cheap methodologies. Among the alternative water purification methods, TiO 2 -based photocatalysis has attracted great attention due to material stability, abundance, non-toxicity and high decontamination efficiency. In this material, electron–hole pairs generated by light absorption separate from each other and migrate to catalytically active sites at the surface of the photocatalyst. Photogenerated carriers are able to induce the decomposition of organic pollutants as well as the deactivation of bacteria and viruses. The main deficien‐ cy of this material, related to its large bandgap, is that only the UV fraction of the solar spectrum which is effective to this purpose. Several approaches have been proposed to overpass this issue and, among them, the use of metal–TiO 2 nanocomposites with proper nanostructuration seems very promising for water purification strategies.
Sharing Machine Learning Models as Indicators of Compromise for Cyber Threat Intelligence

Cyber threat intelligence (CTI) sharing is the collaborative effort of sharing information about cyber attacks to help organizations gain a better understanding of threats and proactively defend their systems and networks from cyber attacks. The challenge that we address is the fact that traditional indicators of compromise (IoC) may not always capture the breath or essence of a cyber security threat or attack campaign, possibly leading to false alert fatigue and missed detections with security analysts. To tackle this concern, we designed and evaluated a CTI solution that complements the attribute and tagging based sharing of indicators of compromise with machine learning (ML) models for collaborative threat detection. We implemented our solution on top of MISP, TheHive, and Cortex—three state-of-practice open source CTI sharing and incident response platforms—to incrementally improve the accuracy of these ML models, i.e., reduce the false positives and false negatives with shared counter-evidence, as well as ascertain the robustness of these models against ML attacks. However, the ML models can be attacked as well by adversaries that aim to evade detection. To protect the models and to maintain confidentiality and trust in the shared threat intelligence, we extend our previous research to offer fine-grained access to CP-ABE encrypted machine learning models and related artifacts to authorized parties. Our evaluation demonstrates the practical feasibility of the ML model based threat intelligence sharing, including the ability of accounting for indicators of adversarial ML threats.
Efficient, automated and robust pollen analysis using deep learning

Pollen analysis is an important tool in many fields, including pollination ecology, paleoclimatology, paleoecology, honey quality control, and even medicine and forensics. However, labour‐intensive manual pollen analysis often constrains the number of samples processed or the number of pollen analysed per sample. Thus, there is a desire to develop reliable, high‐throughput, automated systems. We present an automated method for pollen analysis, based on deep learning convolutional neural networks (CNN). We scanned microscope slides with fuchsine stained, fresh pollen and automatically extracted images of all individual pollen grains. CNN models were trained on reference samples (122,000 pollen grains, from 347 flowers of 83 species of 17 families). The models were used to classify images of different pollen grains in a series of experiments. We also propose an adjustment to reduce overestimation of sample diversity in cases where samples are likely to contain few species. Accuracy of a model for 83 species was 0.98 when all samples of each species were first pooled, and then split into a training and a validation set (splitting experiment). However, accuracy was much lower (0.41) when individual reference samples from different flowers were kept separate, and one such sample was used for validation of models trained on remaining samples of the species (leave‐one‐out experiment). We therefore combined species into 28 pollen types where a new leave‐one‐out experiment revealed an overall accuracy of 0.68, and recall rates >0.90 in most pollen types. When validating against 63,650 manually identified pollen grains from 370 bumblebee samples, we obtained an accuracy of 0.79, but our adjustment procedure increased this to 0.85. Validation through splitting experiments may overestimate robustness of CNN pollen analysis in new contexts (samples). Nevertheless, our method has the potential to allow large quantities of real pollen data to be analysed with reasonable accuracy. Although compiling pollen reference libraries is time‐consuming, this is simplified by our method, and can lead to widely accessible and shareable resources for pollen analysis.
Rethinking Offline Personalized Advertising: Challenges and System Design

Online personalized advertisements on a smartphone have shown a great impact on both user experience and advertiser income. This type of personalized advertising is possible due to existence of easily traceable features when user is online. Online advertising agencies such as Google AdSense can determine appropriate ads for a particular user based on their behavior on the internet. Therefore, these advertising agencies inherently depend long interactions between user and the device to get a decent ad recommendation. This paper focuses on interactions of users with electronic devices which are very short and need-based. Examples of these devices would be gaming arenas, selfie stations in malls or self check-in booths at the airport. The paper throughout considers these types of interactions as ”offline” since there is no way to track user’s behavior here like it is possible in ”online” scenario. Main objective of the paper is to discuss challenges in recommending ads in offline interaction scenario and develop methods to overcome these challenges. Finally, the paper presents a method to recommend ads using fashion based features with the help of computer vision and demonstrates its working.
Temperature Effects on Tensile Deformation Behavior of a Medium Manganese TRIP Steel and a Quenched and Partitioned Steel

Third-generation advanced high-strength steels (AHSS) containing metastable retained austenite are being developed for the structural components of vehicles to reduce vehicle weight and improve crash performance. The goal of this work was to compare the effect of temperature on austenite stability and tensile mechanical properties of two steels, a quenched and partitioned (Q&P) steel with a martensite and retained austenite microstructure, and a medium manganese transformation-induced plasticity (TRIP) steel with a ferrite and retained austenite microstructure. Quasi-static tensile tests were performed at temperatures between −10 and 85 °C for the Q&P steel (0.28C-2.56Mn-1.56Si in wt.%), and between −10 and 115 °C for the medium manganese TRIP steel (0.14C-7.14Mn-0.23Si in wt.%). X-ray diffraction measurements as a function of strain were performed from interrupted tensile tests at all test temperatures. For the medium manganese TRIP steel, austenite stability increased significantly, serrated flow behavior changed, and tensile strength and elongation changed significantly with increasing temperature. For the Q&P steel, flow stress was mostly insensitive to temperature, uniform elongation decreased with increasing temperature, and austenite stability increased with increasing temperature. The Olson–Cohen model for the austenite-to-martensite transformation as a function of strain showed good agreement for the medium manganese TRIP steel data and fit most of the Q&P steel data above 1% strain.
MGMT promoter gene methylation and neurological scale improvement in glioma: a cohort study

Background: Glioma is one of the most common primary brain tumours and conveys a dismal prognosis despite aggressive treatment. Several biomarkers have been studied in the hope of yielding better diagnostic accuracy and improving patient management. Besides survival, functional and neurological disability are concerns that have no lesser importance. In 2017, a disease-specific assessment tool – the Neurologic Assessment in Neuro-Oncology (NANO) scale – was developed to measure neurologic function in neuro-oncology cases. We sought to determine biomarkers that might be associated with neurological scale improvement in glioma patients. Methods: Glioma grade II-IV patients were recruited from three major hospitals in Jakarta-Tangerang. Isocitrate dehydrogenase (IDH) mutation and O6-methylguanine-DNA methyltransferase (MGMT) promoter gene methylation were tested, as well as patients’ neurological function before surgery and three months after. Improvement in neurological scale (NANO scale) was considered positive if there was a decrement of ≥1 of the scale. Results: There were 54 patients included in the study. Mean age was 43.63 (14.723) years old, and 61.1% were male. As much as 16 (29.6%) carried a mutation in codon 132 of the IDH1 gene, and 33 (61.1%) were MGMT methylated. Median NANO scale score before and three months after surgery was 4 (0-12) and 3 (0-12), respectively. Neurological improvement was found in 44 (81.5%) of the patients. Among patients with MGMT promoter gene methylation, 90.9% showed neurological improvement (p=0.035; OR=5; 95%CI 1.122-22.272). Conclusions: Gliomas with MGMT promoter gene methylation are more likely to show neurological improvement three months after surgery.