Miracle-dz/UniFAR-FTUs huggingface dataset:
The environment, social and governance (ESG) activities and profitability under COVID-19: evidence from the global banking sector

PurposeThis study investigated the impacts of the environment, social and governance (ESG) and its components on global bank profitability considering the COVID-19 outbreak.Design/methodology/approachThis study used a system generalized method of moments (GMM) proposed by Arellano and Bover (1995) to investigate the relationship between ESG and bank profitability using an unbalanced sample of 487 banks from 51 countries from 2006 to 2021.FindingsThe findings generally found that ESG activities may reduce bank profitability, thus supporting the trade-off hypothesis that adopting ESG standards could increase bank costs while lowering profitability. In addition, there is a U-shaped relationship between ESG and bank profitability, suggesting that ESG activities can help improve bank performance in the long term. Such effect is the first time observed in the global banking sector. This study’s results are robust across different models and settings (e.g. developed vs developing countries, different levels of profitability, and samples with vs without US banks). Practical implicationsThis study provides empirical evidence to support the sustainable development policy which is implemented by many countries. It also provides empirical incentives for bank managers to be more ESG-oriented in their activities.Originality/valueThis study provides a better understanding of the roles of ESG activity and its components in the global banking system, considering the recent crises.
Spatial Unit Roots

This paper proposes a model for, and investigates the consequences of, strong spatial dependence in economic variables. Our approach and ﬁndings echo those of the corresponding “unit root” time series literature: We suggest a model for spatial I (1) processes, and establish a Functional Central Limit Theorem that justiﬁes a large sample Gaussian process approximation for such processes. We further generalize the I (1) model to a spatial “local-to-unity” model that exhibits weak mean reversion. We char-acterize the large sample behavior of regression inference with independent spatial I (1) variables, and establish that spurious regression is as much a problem with spatial I (1) data as it is with time series I (1) data. We also develop asymptotically valid spatial unit root and stationarity tests, as well inference for the local-to-unity parameter. And ﬁnally, we consider strategies for valid inference in regressions with persistent ( I (1) or local-to-unity) spatial data, such as spatial analogues of ﬁrst-diﬀerencing transformations.
Biogenic CuO and ZnO Nanoparticles as Nanofertilizers for Sustainable Growth of Amaranthus hybridus

The biogenic synthesis of CuO and ZnO nanoparticles (NPs) was carried out by Stenotrophomonas maltophilia. The shape, size, and chemical identity of the CuO and ZnO NPs were determined using FTIR, XRD, SEM, EDX, and TEM analysis. The study aimed to investigate the effects of the CuO and ZnO NPs on Amaranthus hybridus seed germination and plant growth. Two different fertilizer application modes (hydroponics and foliar) were studied with varying concentrations of CuO (0.06 µM, 0.12 µM) and ZnO (0.12 µM, 0.24 µM) nanoparticles with water control and Hoagland’s media control. The hydroponic system of fertilizer application demonstrated better efficiency in terms of plant growth as compared to the foliar application. The agronomic traits, SPAD value, total reducing sugars, antioxidant activity, amount of copper, and zinc ions in root and shoot were analyzed for all experimental plants and found better with the nanoparticle application. The highlight of the study is the application of extremely low concentrations of CuO and ZnO nanoparticles, almost 70% lower than the copper and zinc salts in the Hoagland’s medium for improved plant growth. The use of lower concentrations of nanoparticles can prevent their accumulation in the environment and also lower the production cost. The high antioxidant concentration exhibited by the plants treated with CuO and ZnO nanoparticles ensures the enhanced plant’s resistance to infections and pests while promoting plant growth.
Bio-Fabrication of Euryale ferox (Makhana) Leaf Silver Nanoparticles and Their Antibacterial, Antioxidant and Cytotoxic Potential

Bio-fabrication of green or plant extract-based silver nanoparticles has garnered much praise over the past decade as the methodology is environment-friendly, undemanding, non-pathogenic, and economical. In the current study, leaves of Eurale ferox (Makhana), considered as waste, were used for the bio-fabrication of silver nanoparticles (ELAgNPs). Various analytical techniques including UV–VIS spectroscopy, Field emission scanning electron microscopy equipped with an energy dispersive X-ray spectrometer (FESEM-EDX), Particle size analyzer (PSA), Fourier transform infra-red spectroscopy (FTIR) and high-resolution transmission electron microscopy (HRTEM) were used for their characterization. Their antibacterial efficacy was examined against gram positive bacterium, Bacillus subtilis and gram negative bacterium, Escherichia coli. The antioxidant potential of the ELAgNPs was compassed by 2, 2 diphenyl-1-picryl hydrazyl (DPPH; λmax = 517 nm) assay, H2O2 (λmax = 230 nm) and OH− (λmax = 520 nm)-based radical scavenging assays. The cytotoxicity was checked against the VERO cell line using 3-[4, 5-dimethyl thiazol-2-yl]-2, 5 diphenyl tetrazolium bromide (MTT) assay. A mean particle size of 26.51 ± 8.87 nm with a size distribution of 7.08–53.94 nm was obtained using HRTEM. The ELAgNPs exhibited dose-dependent antibacterial efficacy with a maximum zone of inhibition (ZOI) of 21.98 ± 0.59 mm against B. subtilis and of 16.46 ± 0.22 mm against E. coli at 500 ppm after 24 h of incubation. The median lethal concentration for the cytotoxicity analysis was found to be 9.54 ± 0.35 ppm, 120.9 ± 6.31 ppm, and 20.74 ± 0.63 ppm for ELAgNPs, commercial silver nanoparticles (CAgNPs), and silver nitrate (SN), respectively. The ordinary one-way ANOVA results exhibited a significant decrease in cell viability after 72 h of incubation at p < 0.05, α = 0.05. In conclusion, the ELAgNPs showed good antibacterial, radical scavenging and dose-dependent cytotoxicity against the VERO cells. Therefore, these could be used for biomedical applications. Phyto-constituents present in the plant not only act as reducing agents but also as stabilizing and coating agents, and the availability of a wide range of metabolites makes the green approach more promising.
Discrete GPCR-triggered endocytic modes enable β-arrestins to flexibly regulate 1 cell signaling 2 3

15 β-arrestins are master regulators of cellular signaling that operate by desensitizing ligand-activated G 16 protein-coupled receptors (GPCRs) at the plasma membrane and promoting their subsequent endocytosis. 17 The endocytic activity of β-arrestins is ligand-dependent, triggered by GPCR binding, and increasingly 18 recognized to have a multitude of downstream signaling and trafficking consequences that are specifically 19 programmed by the bound GPCR. However, only one biochemical ‘mode’ for GPCR-mediated triggering of 20 the endocytic activity is presently known– displacement of the β-arrestin C-terminus (CT) to expose CCP21 binding determinants that are masked in the inactive state. Here we revise this view by uncovering a 22 second mode of GPCR-triggered endocytic activity that is independent of the β-arrestin CT and, instead, 23 requires the cytosolic base of the β-arrestin C-lobe (CLB). We further show each of the discrete endocytic 24 modes is triggered in a receptor-specific manner, with GPCRs that bind β-arrestin transiently (‘class A’) 25 primarily triggering the CLB-dependent mode and GPCRs that bind more stably (‘class B’) triggering both 26 the CT and CLB -dependent modes in combination. Moreover, we show that different modes have 27 opposing effects on the net signaling output of receptors– with the CLB-dependent mode promoting rapid 28 signal desensitization and the CT-dependent mode enabling prolonged signaling. Together, these results 29 fundamentally revise understanding of how β-arrestins operate as efficient endocytic adaptors while 30 facilitating diversity and flexibility in the control of cell signaling. 31 32
This paper is included in the Proceedings of the Eighteenth Symposium on Usable Privacy and Security (SOUPS 2022)

Women whoidentifywithIslam in theUnitedStatescomefrom many different race, class, and cultural communities. They are also more likely to be first or second-generation immigrants. This combination of different marginal identities (religious affiliation, gender, immigration status, and race) exposes Muslim-American women to unique online privacy risks and consequences. We conducted 21 semi-structured interviews to understand how Muslim-American women perceive digital privacy risks related to three contexts: government surveillance, Islamophobia, and social surveillance. We find that privacy concerns held by Muslim-American women unfolded with respect to three dimensions of identity: as a result of their identity as Muslim-Americans broadly (e.g., Islamophobic online harassment), as Muslim-American women more specifically (e.g., reputational harms within one’s cultural community for posting taboo content), and as a product of their own individual practices of Islam (e.g., constructing female-only spaces to share photos of oneself without a hijab). We discuss how these intersectional privacy concerns add to and expand on existing pro-privacy design principles, and lessons learned from our participants’ privacy-protective strategies for improving the digital experiences of this community.
COLLISIONLESS WEIBEL SHOCKS AND ELECTRON ACCELERATION IN GAMMA-RAY BURSTS

A study of collisionless external shocks in gamma-ray bursts is presented. The shock structure, electromagnetic field, and process of electron acceleration are assessed by performing a self-consistent 3D particle-in-cell simulation. In accordance with hydrodynamic shock systems, the shock consists of a reverse shock (RS) and forward shock separated by a contact discontinuity. The development and structure are controlled by the ion Weibel instability. The ion filaments are sources of strong transverse electromagnetic fields at both sides of the double shock structure over a length of 30–100 ion skin depths. Electrons are heated up to a maximum energy ϵ ele ≈ ϵ b ? > , where ϵ is the energy normalized to the total incoming energy. Jet electrons are trapped in the RS transition region due to the presence of an ambipolar electric field and reflection by the strong transverse magnetic fields in the shocked region. In a process similar to shock surfing acceleration for ions, electrons experience drift motion and acceleration by ion filament transverse electric fields in the plane perpendicular to the shock propagation direction. Ultimately, accelerated jet electrons are convected back into the upstream.
A Non-cooperative Game-theoretic Distributed Control Approach for Power Quality Compensators

This paper demonstrates that the Game Theory (GT) can be an effective tool for implementing a distributed control scheme for coordinating the compensation efforts of power quality compensators (PQCs) feeding a common unbalanced load. A non-cooperative game is formulated where each PQCs minimises its own interests, defined as the power losses that incur each PQC in carrying unbalanced power from its connection point in the system to the point of common coupling (PCC). By doing this, the whole system will move until it reaches a global equilibrium (the so-called Nash equilibrium). The power losses are calculated based on the conservative power theory (CPT), allowing the implementation of the proposal in the natural abc reference frame. A comparison (via simulations) between the proposed non-cooperative distributed scheme and a cooperative distributed approach based on the consensus theory shows that the proposed non-cooperative game compensates the PCC with fewer overall losses than the consensus-based cooperative approach, improving the efficiency of the whole compensation system.
Deep-Learning fault detection and classification on a UAV propulsion system

A fault detection and identification method using a Deep-Learning classification method is used to identify several faults that may occur on a UAV propulsion system. Training is performed from a dataset acquired from a simplified multiphysics simulation of the system which allows for the generation of large datasets of modular, interconnected and scalable components of various sizes and performances. We aim to provide a model able to identify faults occurring on a propulsion system using a reduced set of input signals.
Toward an E-Learning Adoption: Student Perspectives

The purpose of this study is to investigate the relationship between perceived ease of use, IT infrastructure, e-learning curriculum, educational partners and e-learning adoption. The research was conducted by surveying students who have implemented the concept of e-learning in the city of Bandung, where there were 151 students who provided data through an online questionnaire. Data from respondents was processed using SmartPLS by testing the Inner and Outer models and to confirm the results, the research hypothesis was tested. The results of the study were conveyed that there were two factors that were able to control student e-learning adoption, namely perceived ease of use and e-learning curriculum. Meanwhile, IT infrastructure and educational partners are not able to support the achievement of e-learning adoption. The research findings explain the factors of e-learning users who are able to support e-learning adoption. Information from research can be input for universities that have implemented the concept of e-learning so that e-learning goals can be achieved better.
Dielectric Properties of BaTiO3-Modified BiFeO3 Ceramics

Multiferroic BiFeO3 has attracted extensive interest due to its potential in several applications. However, pure-phase BiFeO3 is very difficult to obtain. Hence, mixing BiFeO3 with other ferroelectrics, such as BaTiO3, is another approach to obtain materials with possible multiferroic properties. In this study, the BiFeO3 was modified with addition of BaTiO3 via a simple solid state reaction method. Dielectric properties of the BaTiO3-modified BiFeO3 ceramics were investigated over wide range of temperature and frequency. The results show that the (1-x)BiFeO3-xBaTiO3 ceramics with x = 0.1 and 0.2 exhibited large dielectric properties with giant-dielectric-like behavior. The dielectric properties were seen to improve with increasing BaTiO3 content, with the highest dielectric constant (ϵr = 19000) observed in 0.8BiFeO3-0.2BaTiO3 ceramic. The results were explained in terms of the pre-dominantly intrinsic BiFeO3 giant-dielectric behavior as a result of Fe multivalent states, as well as a better grain packing density with increasing BaTiO3 content.
Utility of Actimetry to Detect Apathy in Old-Age Depression: a Pilot Study

Apathy is a promising marker of dementia conversion in old-age depression. However, the classical definition of apathy, as a lack of motivation, does not satisfy the criteria of objectivity and validity. The direct measure of subject activity by actigraphy might provide more promising results to characterize patients. In this paper, we evaluate the utility of actimetry to distinguish the activity of apathetic depressed elderly from non-apathetic depressed ones and control population. To this end, six features were extracted to characterize daily activity measured by a wrist-worn actimeter. These features present a statistical significance to discriminate between different groups. Furthermore, the results showed a linear relationship between the physical activity and the clinical severity of apathy. This pilot study suggests that the combination of motion signals parameters provides a promising diagnostic utility in apathetic depressed elderly, with an objective to complete current data with a larger cohort of participants.
Smart watch activity recognition using plot image analysis

Nowadays, many of us wear multiple devices capable of acquiring and storing data related to our everyday activities. Since the computing power of mobile battery-operated devices slowly increases and the power optimizations allow for more and more continuous use, these devices are capable of not only monitoring our activity but analyzing the activity as well. Of these devices, the smartwatch is probably the most inconspicuous, and due to its widespread use, we have used accelerometer data gathered from a smartwatch to identify common user activities by using image generated plots and image recognition machine learning. By leveraging the.Net ML.NET machine learning framework we have managed to obtain a decent recognition rate.
Safety by Construction: Pattern-Based Application of Safety Mechanisms in XANDAR

Considering the design of safety-critical embedded systems for future mobility solutions, the XANDAR project employs the X-by-Construction paradigm to meet non-functional requirements in an automated manner. This paper introduces the pattern library concept developed as part of the project and analyzes three state-of-the-art safety mechanisms for their compatibility with the approach.
SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0

Recent technological advances encompassed by the smart factory concept have fundamentally changed industrial control systems in the way they are structured and how they operate. Majority of these changes affect Supervisory Control And Data Acquisition (SCADA) systems, shifting them to a higher level of interoperability, heterogeneous networks, big data and toward internet technologies and services in general. However, this transformation does not affect all SCADA systems equally. The immediate industrial environment and controlled processes have a significant impact as well. This paper presents a holistic approach to SCADA systems implemented in continuous flow production control within the steel industry production environment. We outline the multi-layer architecture of the SCADA control framework and the aspects of interoperability and interconnectivity within the architecture reference models, together with the research challenges and opportunities arising from the recent rapid increasement of the industrial control systems complexity and digital transformation under the Industry 4.0 paradigm, resulting in disrupting levels of the traditional automation pyramid based on Purdue model toward a higher level of integration and interoperability enabling cross-level data exchange empowered by the Industrial Internet of Things. Furthermore, the paper addresses the problem of proprietary SCADA systems and elaborates the causal correlation between SCADA quality requirements and adoption of new technologies in relation to the specific industrial environment of the steel manufacturing process.
Comparison of High-Power 2-Level and 3-Level Converters in Terms of Power Density, Costs and Performance

This paper compares high-power converters in terms of their power density, costs and performance. 2-level and 3-level NPC phase modules with 1400 A semiconductors are compared. For pulse frequencies higher than 6.75 kHz 3-level NPC converter is twice smaller and 48 % cheaper then 2-level converter, because for 2-level parallel operation of phase modules is necessary. In this case the 3-level NPC has with 0.58 kVA/dm3 a twice higher power density as the 2-level converter. The performance of 3-level NPC is much higher than of the 2-level, because of the twice higher possible pulse frequency of 16 kHz and therefore higher possible control frequency. Because of the higher pulse frequency and the lower voltage ripple the chokes can be quite smaller for the 3-level NPC and so the maximum current raise is a lot higher than in case of a 2-level converter.
Retraining a BERT Model for Transfer Learning in Requirements Engineering: A Preliminary Study

In recent years, advanced deep learning language models such as BERT, ELMO, ULMFiT and GPT have demonstrated strong performance on many general natural language processing (NLP) tasks. BERT, in particular, has also achieved promising results on some domain-specific tasks, including the requirements classification task. However, in spite of its great potential, BERT under-performs on domain specific tasks. In this paper, we present BERT4RE, a BERT-based model retrained on requirements texts, aiming to support a wide range of requirements engineering (RE) tasks, including classifying requirements, detecting language issues, identifying key domain concepts, and establishing requirements traceability links. We demonstrate the transferability of BERT4RE, by fine-tuning it for the task of identifying key domain concepts. Our preliminary study shows that BERT4RE achieved better results than the BERTbase model on the demonstrated RE task.
A Novel Flexible and Scalable Nonintrusive Load Monitoring Approach Using Wavelet Design and Machine Learning

This paper addresses the problem of non-intrusive appliance load monitoring considering the flexibility of adapting to a new load and the scalability when adopting more appliances. The proposed approach in this paper uses the concept of wavelet design to generate new wavelets to match the features in the transient signals of such loads. The detection and the classification processes are then automated through the application of the unsupervised clustering and the decision tree classifier. The proposed concept has been experimentally verified using a real-test system. The flexibility and the scalability of the proposed method have been discussed and have been exemplified through numerical results.
A Comparison of HOG-SVM and SIFT-SVM Techniques for Identifying Brown Planthoppers in Rice Fields

Brown planthoppers (BPH) are insect pests that cause significant damage to rice crop yields throughout the Asia-Pacific region. Early identification of BPH forms has ramifications for forecasting potential outbreaks. To address this, we use Adaboost and Haar features to discover areas of interest in images of rice plants. We apply two separate techniques to identify the BPH in images: we compare a technique that utilizes HOG descriptors and another that utilizes SIFT feature descriptors. To each of these techniques, we apply a Support Vector Machine (SVM) to allow us to classify areas of interest in the images. Our approach achieves a weighted average classification rate of 95.38% for HOG and 96.38% for SIFT, improving upon state-of-the-art BPH detection methods and our findings lay the groundwork for other insect pest identification and detection efforts.
Predicting Latent States of Dynamical Systems With State-Space Reconstruction and Gaussian Processes

Predicting future observations of a system is a clas-sical task in signal processing. However the effects of nonlinear dynamics, unobserved variables and observation noise make this task difficult in practice. We propose a data-driven non-parametric approach to model systems with latent dynamics using state-space reconstruction and Gaussian processes. With this approach, both latent states and future observations can be predicted together. When applicable, this method is efficient even with short time series. We demonstrate the method on synthetic data and then showcase its efficacy and accuracy in predicting brain dynamics on a data set obtained from traumatic brain injury patients.