Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automatic Fake News Identification System using Machine Learning Techniques

With the evolvement in technology and social media, the prevalence of fake news is rapidly increasing. It has become a new research field that is gaining popularity and requires attention. However, due to a scarcity of resources such as insufficient and invalid datasets along with analysis techniques, there are various challenges such as the flourishment of fake news, that are faced. It has a considerable influence on everyday lives, as well as in almost every single field, especially politics, and education. Hence, this condition requires attention to detect fake news for reducing distrust in the government systems. This article introduces a solution to fake news detection by implementing a model using various classification techniques. This work has been implemented with Decision Tree, Random Forest, Logistic Regression, and Passive Aggressive Classifier for identifying fake news. However, the outcome of the passive-aggressive classifier has resulted in the highest accuracy of 93.05%. Furthermore, this work can help in the real-time identification of fake news leading to maintaining people’s trust on social media and government systems.
Understanding biases when fitting disk truncations

Abstract Truncations in the stellar population at the edges of disk galaxies are thought to be a common morphological feature (e.g., Erwin et al. 2005; and more recently Marino et al. 2016). In fact, using imaging data from the SDSS, Pohlen & Trujillo (2006) showed that only ~ 10% of face-on to intermediate inclined, nearby, late-type (Sb-Sdm) spiral galaxies have a normal/standard purely exponential disk down to the noise limit. In situations like these, the simultaneous fit of two lines, joined or not at an intermediate point (the break radius), constitutes a natural step towards the modelling of radial variation in surface brightness, metallicity, or any other relevant parameter. This work shows the results of simple simulations in which the simultaneous fit to two joined lines is compared to the simultaneous fit of two independent lines (i.e., two lines that do not necessarily coincide at an intermediate point), and also to the traditional single ordinary least squares fit. These simulations reveal some biases that should be taken into account when facing these kind of fitting procedures.
Research and scholarly methods: Studies of workplace well‐being and other organizational phenomena

As research in pharmacy increasingly focuses on work‐related topics such as burnout, workforce management, and diversity, equity, and inclusion, there is a growing need for scholars to understand and use organizational research methods. However, these skills are not typically taught in pharmacy schools or postgraduate training programs, posing a challenge to researchers and peer reviewers alike. To address this gap, we aim to provide a practical introduction to organizational research methods with a focus on deductive, quantitative approaches. Given the growing interest in studying pharmacists' workplace well‐being, we use this as a theme for our review. We begin by discussing the importance of a thorough literature review and using theory to frame the research, develop study hypotheses, and select an appropriate research design. We also provide a list of example theories that researchers could use to study the topic of workplace well‐being. Next, we discuss key considerations for defining and measuring organizational constructs, including validity and reliability, and highlight potential pitfalls to avoid in survey research. Using several phenomena of interest in studies of workplace well‐being, we provide examples of construct definitions and illustrate how they could be operationalized as variables in a research study. Finally, we discuss important considerations for analyzing organizational data and describe skills that researchers should acquire or seek in potential collaborators. To aid researchers and peer reviewers, we provide a checklist summarizing our recommendations and references to additional resources. By equipping scholars with a better understanding of organizational research methods, we aim to enhance our understanding of pharmacists' work experiences and generate evidence to support workplace improvements that will benefit pharmacists and their efforts to provide patient care.
Foreign Direct Investment Impact on Emerging and Transition Economies: A Sustainability Perspective

Purpose: In this article it is intended to analyze the impact of Foreign Direct Investment on the adoption of sustainability practices in Emerging and Transition Economies. In this context, it is adopted a perspective where value creation encompasses the capacity of an enterprise to generate value to its stakeholders, considering the influence of its activities on the environmental and social domains. Design/methodology/approach: Accordingly, it was developed a case study that is divided into two parts. On the first part, it is evaluated social and environmental indicators in Poland, a Transition Economy adopting a comparative perspective that focus on the relative performance of the country in comparison to Portugal, a developed economy. This evaluation was done under the lenses of the Sustainable Development Goals (SDGs) articulated under the 2030 Agenda for Sustainable Development. On the second part of the study, it was reviewed the impact of multinational enterprises activities on environmental and social welfare. Relative to this, the article adopts a innovative comparative approach, as it is the first study to analyze McDonald’s corporate sustainability practices in two different markets: Poland and Portugal. Besides this, it provides a disruptive analysis on cross cultural sustainability and its impact on international sustainability dynamics, using an approach that focuses on the contribution of Multinational’s Sustainability Practices on the accomplishment of Sustainable Development Goals. Findings: It is concluded that, under a globally adopted strategy including relevant economic and social agents, that Foreign Direct Investment contributes to enhance environmental and social sustainability. Besides this, it is also highlighted the role of strategy flexibility so that sustainability practices achieve a significant impact on local context as well. Originality: This study analyses the impact of Foreign Direct Investment in host countries by considering a cross-country sustainability analysis, that focuses on Multinational Enterprises strategic behavior.
Efficient Self-Attention Model for Speech Recognition-Based Assistive Robots Control

Assistive robots are tools that people living with upper body disabilities can leverage to autonomously perform Activities of Daily Living (ADL). Unfortunately, conventional control methods still rely on low-dimensional, easy-to-implement interfaces such as joysticks that tend to be unintuitive and cumbersome to use. In contrast, vocal commands may represent a viable and intuitive alternative. This work represents an important step toward providing a viable vocal interface for people living with upper limb disabilities by proposing a novel lightweight vocal command recognition system. The proposed model leverages the MobileNet2 architecture, augmenting it with a novel approach to the self-attention mechanism, achieving a new state-of-the-art performance for Keyword Spotting (KWS) on the Google Speech Commands Dataset (GSCD). Moreover, this work presents a new dataset, referred to as the French Speech Commands Dataset (FSCD), comprising 4963 vocal command utterances. Using the GSCD as the source, we used Transfer Learning (TL) to adapt the model to this cross-language task. TL has been shown to significantly improve the model performance on the FSCD. The viability of the proposed approach is further demonstrated through real-life control of a robotic arm by four healthy participants using both the proposed vocal interface and a joystick.
Continuous Adaptive Fast Terminal Sliding Mode-Based Speed Regulation Control of PMSM Drive via Improved Super- Twisting Observer

A continuous adaptive fast terminal sliding mode based speed regulation control is proposed for a permanent magnet synchronous motor (PMSM) drive, where an improved super-twisting algorithm with linear terms (STLT) is constructed in the super-twisting speed observer. First, the STLT observer introduces a first-order term on the basis of conventional super-twisting sliding mode observer to improve the speed estimation convergence rate and to reduce the estimation error. Second, an adaptive function is added to the reaching law for the continuous fast terminal sliding mode speed controller to produce a faster response speed and stronger control robustness. In addition, the total disturbance of the system is estimated via Luenberger disturbance observer, which contributes to the feedforward compensation control component. The closed-loop stability analysis is rigorously given by the Lyapunov stability theory. Finally, real-time experiments on a PMSM platform are carried out to show the effectiveness and superior performance of the proposed control approach.
Multi-Joint Bionic Mechanism Based on Non-Circular Gear Drive

Aiming at the nonlinear expansion/contraction drive problem between different cables in multi-joint cable drive mechanisms, a mechanical drive method based on a non-circular gear drive was proposed, which could replace the servo-sensing control system and minimize the system’s complexity and cost. A multi-joint single-degree-of-freedom (DOF) bending mechanism was constructed with several T-shaped components and cross-shaped components. The principle of the multi-joint mechanism driven by non-circular gears was clarified. The corresponding relationships between the joint bending angle, cables’ extension/retraction amount and non-circular gear transmission ratio were established. Using the Bowden cable driving, a multi-DOF bending mechanism decoupling scheme was proposed. Considering the adverse effect of non-circular gear hysteresis on the motion of multi-joint mechanisms, a non-circular gear backlash elimination method was proposed. The expression of the backlash of the non-circular gear with respect to the axial movement amount was deduced, which could enable the precise control of the backlash. A two-DOF multi-joint bionic mechanism driven by the non-circular gear was developed. The experimental results show that the mechanism can achieve coordinated bending motion by precisely controlling the line extension/contraction through non-circular gears. This multi-joint bionic mechanism driven by non-circular gears has the characteristics of reliable structure and simple control, and it is expected to be applied to bionic fish and bionic quadruped robots in the future.
Impedance measurements for demineralized tooth lesions assessment

This work deals with the design and development of a non invasive though rather effective solution to detect carious lesions. The main aim of the study is to develop a simple automatic approach for the assessment of the tooth demineralization, which represents the earlier stage of the dynamic carious process, by means of impedance spectroscopy. In particular, impedance measurements were carried out on 50 extracted human teeth. Teeth were demineralized in-vitro according to a validated protocol; then, a morphological analysis of the tooth surface was performed by scanning electron microscopy to confirm enamel demineralization. The proposed approach tries to take advantage of the change of impedance phase due to the demineralization process. Data analysis confirmed that the best frequency for discriminating between demineralized and non-demineralized teeth is about 15 Hz, and this parameter can be used for building up an automatic classifier based on Multilayer Perceptron (MLP) topology. Impedance data were processed by using a modified single neuron, which allows classifying demineralized and sound teeth with an error rate of about 7%, estimating therefore the presence of carious lesions in progress. The presented work can be considered as a feasibility study with a final goal to conceive a simple and low-cost measurement system to identify caries at an early stage
A Rail-to-Rail Transconductance Amplifier Based on Current Generator Circuits

In this brief, two current generator circuits are used to design a self-biasing transconductance amplifier. The current generators are configured using two n-channel and p-channel cascode current mirrors by which a high input dynamic range is achieved. Since such a topology creates positive feedback, the transconductance of the circuit is also increased causing higher performance. To ensure the stability of the circuit, constant current sources can be paralleled with the current mirror topologies, which of course are implemented using input drivers. Therefore, two n-channel and p-channel input differential pairs are added to the current generator circuits by which not only a rail-to-rail operation is achieved but also the amplifier is stabilized. The proposed circuit was fabricated in the TSMC 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process with a silicon area of <inline-formula> <tex-math notation="LaTeX">$54.1\times 71\,\,\mu \text{m}$ </tex-math></inline-formula>. Under a 1.8-V supply voltage, the experimental results showed a high input common-mode range (ICMR), while a gain bandwidth (GBW) of 83.9 MHz was measured for a capacitive load of <inline-formula> <tex-math notation="LaTeX">$2\times6$ </tex-math></inline-formula> pF. In addition, a dc gain and a slew rate (SR) of 68.4 dB and 71.7 V/<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, respectively, were achieved.
Is Amazon deforestation decreasing the number of thunderstorms over South America?

Lightning activity has been predicted to increase with global warming, though estimates of lightning sensitivity to a change of temperature vary widely. Since lightning is a small‐scale process, it must be represented by parametrizations in climate models. This article uses large‐scale meteorological parameters tied to thunderstorm generation to improve existing empirical models that simulate regional thunderstorm behaviour. The response of the number of thunderstorms (as presented here) to climate change is rarely analysed in global studies. This study focuses on Tropical America, and uses the ERA5 higher‐resolution reanalysis data (ERA5) to develop our empirical model. Thunderstorm data were taken from the World Wide Lightning Location Network (WWLLN) and processed using the clustering algorithm developed by Mezuman et al. (2014). The two meteorological parameters that correlated best with thunderstorm clusters in Tropical America were specific humidity (SH) and convective available potential energy (CAPE). The resulting empirical model was run from 1979 to 2019 using ERA5 reanalysis data as input. This approach approximates the long‐term trends in the behaviour of thunderstorms in the regions, in the absence of a complete historical lightning record. To our surprise, Tropical American thunderstorms exhibited a negative trend over this period, with an 8% decrease in thunderstorm clusters since the 1980s even with a rise of 1 K in temperature over the same period. The regions of largest decreases in thunderstorm activity align well with estimates of deforestation. We estimate that for every 1 Tg C lost due to deforestation, there is a 10% decrease in thunderstorm number.
Heterogeneity of adaptation strategies to climate shocks: Evidence from the Niger Delta region of Nigeria

There is overwhelming evidence to suggest that climate shocks undermine food security and livelihood well-being of the climate-impacted Niger Delta region of Nigeria. Employing survey data collected from farming and fishing households in the Niger Delta region of Nigeria, the study investigated the range of adaptation practices prevalent in the region, as well as factors influencing the adoption of these adaptation strategies. Five hundred and three (503) households (252 fishing households and 251 farming households) were selected using multi-stage sampling techniques. Multinomial logit model was used to determine factors affecting the household choice of adaptation strategies. The results show that adaptation strategies adopted by farming households were livelihood diversification (78.5%), crop management (77.7%), and soil and water management (64.5%). Factors influencing their choice of adaptation strategies were age, gender, household size, education, extension, and farm size. The adaptation strategies employed by the fishing households were livelihood diversification (83.61%) and intensification [which include the use of improved fishing gears (80.33%), varying fishing locations (67.21%), and expanding area of fishing (40.98%)]. Uncovering the heterogeneity in adaptation and resilience aspects to climate shocks has immense practical significance, particularly in providing targeted assistance for the two livelihood groups’ adoption.
Modelling ALMA observations of strong gravitational lenses detected by Herschel

We have modelled ALMA imaging of six strong gravitationally lensed galaxies detected by the Herschel Space Observatory. Both the cleaned image plane data and the directly observed interferometric visibilities have been modelled, enabling comparison of both approaches. In the majority of cases, the recovered lens models are consistent between modelling methods, although a few systems show some small differences, particularly those with more poorly resolved Einstein rings. Our modelling recovers mass properties of the lensing galaxies and, by determining magnification factors, intrinsic properties of the lensed sub-millimetre sources. The mass density profiles of all six lenses are close to isothermal. We find that the lensed sources all have high ratios of star formation rate to dust mass, consistent with or higher than the mean ratio for high redshift sub-millimetre galaxies and low redshift ultra-luminous infra-red galaxies. Most reconstructed sources show disturbed morphologies.
Turbulence Treatment in Steady and Unsteady Turbomachinery Flows

The adequate representation of turbulence is crucial for the predictive accuracy of the computational method used in the turbomachinery design process. While unsteady simulations are of ever-increasing importance, steady Reynolds-Averaged Navier-Stokes computations will remain the workhorse of industrial CFD for some time. Turbulence treatments for both cases are presented. Since standard linear eddy-viscosity models exhibit some well-known deficiencies, a twofold strategy is pursued in the DLR-code TRACE: While the inclusion of turbomachineryspecific extensions to two-equation eddy-viscosity models forms the backbone of the turbulence modelling effort, Explicit Algebraic Reynolds Stress Models are currently validated for turbomachinery flows. With the continued increase in available computer power, unsteady computations are no longer beyond the realm of industrial CFD. In particular for flow features such as largescale separation and wakes, hybrid methods become increasingly popular. Here, Detached-Eddy Simulation is used, where a RANS treatment is retained for attached boundary layers while the separated areas of the flow are treated in an LES-type fashion. The results demonstrate the improved predictive accuracy as well as the feasibility of the presented approaches.
Pattern synthesis for multi‐layered perforated dielectric superstrate loaded patch antenna with equal
 E
 /
 H
 beamwidth

The pattern synthesis based on the spectral Green's functions for multi-layered perforated dielectric loaded patch antenna is presented. It is conducted by optimising the dielectric superstrate parameters, i.e. relative permittivity (e r) and thickness. To solve the problem of the optimised e r unavailable from market, perforated dielectric superstrate (PDS) is utilised. The equivalence between PDS and homogeneous dielectric superstrate (HDS) in spectral domain is investigated and the effective relative permittivity (e reff) of PDS is studied. Moreover, by introducing e reff, the general optimisation procedure for HDS loaded antenna is also applicable for PDS loaded antenna, which reduces the computational cost significantly. To validate their method, a two-layer PDS loaded patch antenna with E/H beamwidth equalisation was designed, fabricated and measured. Simulations and measurements show that the synthesised antenna has satisfactory performances over the ITU-RS.1068-1994 band from 13.75 to 14 GHz. The half 10 dB E/H beamwidths of 19°/20°, −27 dB cross polarisation and −15.5 dB side lobe level have been achieved.
Exploration of Transferable Deep Learning-Aided Radio Frequency Fingerprint Identification Systems

Radio frequency fingerprint identification (RFFI) shows great potential as a means for authenticating wireless devices. As RFFI can be addressed as a classification problem, deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance. RFFI is suitable for securing the legacy existing Internet of Things (IoT) networks since it does not require any modifications to the existing end-node hardware and communication protocols. However, most deep learning-based RFFI systems require the collection of a great number of labelled signals for training, which is time-consuming and not ideal, especially for the IoT end nodes that are already deployed and configured with long transmission intervals. Moreover, the long time required to train a neural network from scratch also limits rapid deployment on legacy IoT networks. To address the above issues, two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning. More specifically, they rely on fine-tuning and distance metric learning, respectively, and only require only a small amount of signals from the legacy IoT network. As the dataset used for transfer is small, we propose to apply augmentation in the transfer process to generate more training signals to improve performance. A LoRa-RFFI testbed consisting of 40 commercial-off-the-shelf (COTS) LoRa IoT devices and a software-defined radio (SDR) receiver is built to experimentally evaluate the proposed approaches. The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another IoT network with less than ten signals from each LoRa device. The classification accuracy is over 90%, and the augmentation technique can improve the accuracy by up to 20%.
An Integrated Sensing and Communication System for Fall Detection and Recognition Using Ultrawideband Signals

Fall detection and recognition play a crucial role in enabling timely medical interventions for people who are at risk of falls, especially among vulnerable populations like older adults and those with mobility limitations. In this article, a cost-effective integrated sensing and communication system, namely, FallDR, is presented for fall detection and recognition using ultrawideband communication. First, we collected the time of flight information of falls (four types) and nonfall events by 10 participants using FallDR. We then proposed a convolutional neural network incorporated with squeeze-and-excitation blocks to detect and recognize falls based on fall trajectories. It proves that the proposed model is accurate, energy-efficient, and lightweight to achieve 100% accuracy in fall detection and recognition. Our proposed solution is proven to be highly robust against environmental changes, such as interference, distance, and direction changes. Further tests in an office showed that FallDR could achieve nearly 100% accuracy, even when the environment was changed. FallDR efficiently employs the characteristics of fall trajectory and the advanced modeling ability of the neural network. We have published our archived data sets and code for comparisons and improvements.