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Purpose The purpose of this paper is to study the important factors which help explain consumer intention and use behavior in mobile banking (m-banking) adoption. All constructs of the unified theory of acceptance and use of technology 2 are studied. Non-monetary value is studied through perceived value. Trust and perceived risk are also included to predict intention. Design/methodology/approach A questionnaire was utilized to evaluate customer responses on a five-point Likert scale. A convenience sampling technique was used to collect data from a sample of 490 respondents in Pakistan. The data were analyzed using AMOS and SPSS for Cronbach’s α, CR, CMV, AVE, Harmon’s single factor test, correlation and structural equation modeling. Findings The results of the study show that most of the predictors of intention, including perceived value, performance expectancy, habit, social influence, effort expectancy, hedonic motivation (except for facilitating condition), perceived risk and trust, are significant. All predictors of usage behavior are significant. Research limitations/implications A cross-sectional study was conducted due to time constraints. Practical implications Bank managers must focus on improving customers’ intentions to use m-banking as well as on providing facilitating conditions to increase its actual use. To boost mobile banking, banks’ management must consider the customers’ habits while designing their m-banking products. Originality/value The findings of this paper are not only interesting in terms of boosting m-banking diffusion rate, but also in terms of financial inclusion of the vast majority of mobile users. Further the impact of intention, facilitating condition and habit were checked on actual use behavior since people tend not always to act upon their intentions.

We present a 1986 through March 2020 estimate of Greenland Ice Sheet ice discharge. Our data include all discharging ice that flows faster than 100 m yr−1 and are generated through an automatic and adaptable method, as opposed to conventional handpicked gates. We position gates near the present-year termini and estimate problematic bed topography (ice thickness) values where necessary. In addition to using annual time-varying ice thickness, our time series uses velocity maps that begin with sparse spatial and temporal coverage and end with near-complete spatial coverage and 12 d updates to velocity. The 2010 through 2019 average ice discharge through the flux gates is ∼487±49 Gt yr−1. The 10 % uncertainty stems primarily from uncertain ice bed location (ice thickness). We attribute the ∼50 Gt yr−1 differences among our results and previous studies to our use of updated bed topography from BedMachine v3. Discharge is approximately steady from 1986 to 2000, increases sharply from 2000 to 2005, and then is approximately steady again. However, regional and glacier variability is more pronounced, with recent decreases at most major glaciers and in all but one region offset by increases in the northwest region through 2017 and in the southeast from 2017 through March 2020. As part of the journal's living archive option and our goal to make an operational product, all input data, code, and results from this study will be updated as needed (when new input data are available, as new features are added, or to fix bugs) and made available at https://doi.org/10.22008/promice/data/ice_discharge (Mankoff, 2020a) and at https://github.com/mankoff/ice_discharge (last access: 6 June 2020, Mankoff, 2020e).

Due to their unique layered microstructure, the presence of various functional groups at the surface, earth abundance, and attractive electrical, optical, and thermal properties, MXenes are considered promising candidates for the solution of energy- and environmental-related problems. It is seen that the energy conversion and storage capacity of MXenes can be enhanced by changing the material dimensions, chemical composition, structure, and surface chemistry. Hence, it is also essential to understand how one can easily improve the structure–property relationship from an applied point of view. In the current review, we reviewed the fabrication, properties, and potential applications of MXenes. In addition, various properties of MXenes such as structural, optical, electrical, thermal, chemical, and mechanical have been discussed. Furthermore, the potential applications of MXenes in the areas of photocatalysis, electrocatalysis, nitrogen fixation, gas sensing, cancer therapy, and supercapacitors have also been outlooked. Based on the reported works, it could easily be observed that the properties and applications of MXenes can be further enhanced by applying various modification and functionalization approaches. This review also emphasizes the recent developments and future perspectives of MXenes-based composite materials, which will greatly help scientists working in the fields of academia and material science.

Supercapacitors (SCs) have received much interest due to their enhanced electrochemical performance, superior cycling life, excellent specific power, and fast charging–discharging rate. The energy density of SCs is comparable to batteries; however, their power density and cyclability are higher by several orders of magnitude relative to batteries, making them a flexible and compromising energy storage alternative, provided a proper design and efficient materials are used. This review emphasizes various types of SCs, such as electrochemical double-layer capacitors, hybrid supercapacitors, and pseudo-supercapacitors. Furthermore, various synthesis strategies, including sol-gel, electro-polymerization, hydrothermal, co-precipitation, chemical vapor deposition, direct coating, vacuum filtration, de-alloying, microwave auxiliary, in situ polymerization, electro-spinning, silar, carbonization, dipping, and drying methods, are discussed. Furthermore, various functionalizations of SC electrode materials are summarized. In addition to their potential applications, brief insights into the recent advances and associated problems are provided, along with conclusions. This review is a noteworthy addition because of its simplicity and conciseness with regard to SCs, which can be helpful for researchers who are not directly involved in electrochemical energy storage.

The objective of the study is to examine the impact of natural disasters on external migration, price level, poverty incidence, health expenditures, energy and environmental resources, water demand, financial development, and economic growth in a panel of selected Asian countries for a period of 2005–2017. The results confirm that natural disasters in the form of storm and flood largely increase migration, price level, and poverty incidence, which negatively influenced country’s economic resources, including enlarge healthcare expenditures, high energy demand, and low economic growth. The study further presented the following results: i) natural resource depletion increases external migration, ii) FDI inflows increase price level, iii) increase healthcare spending and energy demand decreases poverty headcount, iv) poverty incidence and mortality rate negatively influenced healthcare expenditures, v) industrialization increases energy demand, and vi) agriculture value added, fertilizer, and cereal yields required more water supply to produce greater yield. The study emphasized the need to magnify the intensity of natural disasters and create natural disaster mitigation unit to access the human and infrastructure cost and attempt quick recovery for global prosperity.

Smart cities assure the masses a higher quality of life through digital interconnectivity, leading to increased efficiency and accessibility in cities. In addition, a huge amount of data is being exchanged through smart devices, networks, cloud infrastructure, big data analysis and Internet of Things (IoT) applications in the various private and public sectors, such as critical infrastructures, financial sectors, healthcare, and Small and Medium Enterprises (SMEs). However, these sectors require maintaining certain security mechanisms to ensure the confidentiality and integrity of personal and critical information. However, unfortunately, organizations fail to maintain their security posture in terms of security mechanisms and controls, which leads to data breach incidents either intentionally or inadvertently due to the vulnerabilities in their information management systems that either malicious insiders or attackers exploit. In this paper, we highlight the importance of data breaches and issues related to information leakage incidents. In particular, the impact of data breaching incidents and the reasons contributing to such incidents affect the citizens’ well-being. In addition, this paper also discusses various preventive measures such as security mechanisms, laws, standards, procedures, and best practices, including follow-up mitigation strategies.

Wastewater from the textile industries contaminates the natural water and affects the aquatic environment, soil fertility and biological ecosystem through discharge of different hazardous effluents. Therefore, it is essential to remove such dissolved toxic materials from water by applying more efficient techniques. We performed a comparative study on the removal of rhodamine B (RhB) and Nile blue (NB) from water through a catalytic/photocatalytic approach while using a CuO–SiO2 based nanocomposite. The CuO–SiO2 nanocomposite was synthesized through a sol–gel process using copper nitrate dihydrate and tetraethylorthosilicate as CuO and SiO2 precursors, respectively, with ammonia solution as the precipitating agent. The synthesized nanocomposites were characterized, for their structure, morphology, crystallinity, stability, surface area, pore size and pore volume, by using a scanning electron microscope (SEM), transmission electron microscope (TEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and Brunauer–Emmett–Teller (BET) techniques. The CuO–SiO2 nanocomposite was used for potential environmental applications in the terms of its catalytic and photocatalytic activities toward the degradation of rhodamine B (RhB) and Nile blue (NB) dyes, in the presence and absence of light, while monitoring the degradation process of dyes by UV-Visible spectroscopy. The catalytic efficiency of the same composite was studied and discussed in terms of changes in the chemical structures of dyes and other experimental conditions, such as the presence and absence of light. Moreover, the composite showed 85% and 90% efficiency towards the removal of rhodamine B and Nile blue dyes respectively. Thus, the CuO–SiO2 nanocomposite showed better efficiency toward removal of Nile blue as compared to rhodamine B dye while keeping other experimental variables constant. This can be attributed to the structure–property relationships and compatibility of a catalyst with the molecular structures of dyes.

Autonomous harvesters can be used for the timely cultivation of high-value crops such as strawberries, where the robots have the capability to identify ripe and unripe crops. However, the real-time segmentation of strawberries in an unbridled farming environment is a challenging task due to fruit occlusion by multiple trusses, stems, and leaves. In this work, we propose a possible solution by constructing a dynamic feature selection mechanism for convolutional neural networks (CNN). The proposed building block namely a dense attention module (DAM) controls the flow of information between the convolutional encoder and decoder. DAM enables hierarchical adaptive feature fusion by exploiting both inter-channel and intra-channel relationships and can be easily integrated into any existing CNN to obtain category-specific feature maps. We validate our attention module through extensive ablation experiments. In addition, a dataset is collected from different strawberry farms and divided into four classes corresponding to different maturity levels of fruits and one is devoted to background. Quantitative analysis of the proposed method showed a 4.1% and 2.32% increase in mean intersection over union, over existing state-of-the-art semantic segmentation models and other attention modules respectively, while simultaneously retaining a processing speed of 53 frames per second.

The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system’s performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system’s improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.

This study responds to a current phenomenon where individuals experience distress and exhaustion caused by cyberbullying and social overload while using social networking sites (SNSs). Social cognitive theory suggests that this phenomenon is caused by the interactive influences of environmental, personal, and behavioral factors, which are key unique drivers of SNS discontinuous usage intentions. This study focuses on how cyberbullying and social overload (environmental) induce distress and SNS exhaustion (personal), thereby affecting an individual’s intention to voluntarily abandon the use of SNSs (behavioral). The purpose model is tested through a sample of 314 SNS users. Empirical results indicate that cyberbullying and social overload exert a considerable impact on distress and SNS exhaustion, both of which further increase users’ intention to discontinue their usage of SNSs. This article concludes with several theoretical and practical contributions, limitations, and future research directions.