Miracle-dz/UniFAR-FTUs huggingface dataset:
Opinion formation on evolving and adaptive networks

Members in a social group may change over time: a new member is recruited; a current member stops his or her position; a candidate supersedes a member of the group. Here we investigate the effects of changes of nodes on the dynamics of opinion formation. In our model, opinion formation is in two stages. In the first stage, a network evolves through a voting process that is used to select agents to add into the network, to remove from the network or to be replaced by other agents. Special rules for the voting process are necessary in the first stage. We focus on two rules, namely, majority rule and random rule. In the second stage, opinion formation takes place. We find that the majority rule generally foster consensus formation in comparison to the random rule. We also investigate the effects of changes of agents on two-party systems, in which there are two parties and opinions of members in the same party are close to each other. In particular, the effects of diversity of opinions for two-party systems are studied. A counterintuitive finding is that the diversity of opinions, instead of hindering consensus formation, facilitates consensus formation.
Strategi Komunikasi dalam Operasional Pelayanan pada Masa Pandemi Covid-19

This study aims to determine the extent to which the role of the public relations strategy of PT. Train Commuter Indonesia is related to the dissemination of information regarding services and operations of commuter line trains and local trains during the Covid-19 pandemic. Conducting a study to describe the implementation of PT. Kereta Commuter Indonesia during the Covid-19 pandemic, analyzed the supporting and inhibiting factors related to the implementation of this communication strategy. As well as analyzing the way of determining and implementing the Public Relations communication strategy of PT. Kereta Commuter Indonesia, and Describe the influence of the public relations strategy of PT. Train Commuter Indonesia is in operation of train trips during the Covid-19 pandemic. This study uses a qualitative method approach with interview techniques to obtain primary data from reliable and credible sources and with literature study techniques to obtain additional data on the object being studied. The results of the study show that the public relations communication strategy plays an important role in conveying information related to Commuter Train operations and services during the Covid-19 pandemic. Submission of information is carried out in two methods, namely through offline by delivering information directly and online by utilizing social media. Keywords: Commuter Line, Covid-19, Communication, Strategy, Transportation
Kinetics and Thermodynamics of Sorption of Methylene Blue Dye Onto Raw Groundnut Shell Biosorbent

Dyes are one of the hazardous chemicals found in wastewater produced by textile and allied industries that need to be removed from the wastewater before it is released into water bodies. The sorption efficiency of raw groundnut shell (RGS), an agricultural waste, was tested on methylene blue (MB) dye through a batch adsorption experiment. Physicochemical analysis of the RGS indicated good biosorbent properties. Fourier transform infrared (FTIR) spectroscopy analysis of the RGS revealed the presence of heterofunctional groups. The effect of various biosorption parameters such as initial dye concentration, contact time, and biosorbent dosage was systematically investigated. The quantity of MB adsorbed increased as the initial concentration and contact time increased and decreased with an increase in biosorbent dose. A maximum uptake capacity of 29.3 mg/g was recorded for 50 mg/L MB initial concentration. The equilibrium data from the batch adsorption experiments were fitted well into Freundlich isotherm and pseudo-second-order kinetic models. The results from this study indicate that RGS can serve as an alternative, low-cost, eco-friendly and efficient adsorbent for the removal of MB dye from wastewater.
H pylori and functional dyspepsia

Editor,—We read with great interest the leading article by Hellier and Morris ( (2000) Gut 47:459–60; ) regarding the future of endoscopic retrograde cholangiopancreatography (ERCP) training in the UK. They discuss the importance of restricting training in ERCP to some trainees and training only in centres that are accredited for training by the Joint Advisory Group (JAG) in Endoscopy. JAG, at the request of the Conference of Royal Colleges, produced endoscopy training guidelines.1 For ERCP training, these state that a centre should be performing a minimum of 250 procedures per annum (with specialist centres performing 500 per annum). They should also have regular meetings to discuss radiological findings with a radiologist with special interest in ERCP. While we certainly agree with the importance of ensuring satisfactory training, we are concerned that the guidelines base competence for training in ERCP solely on the number of procedures carried out in individual centres. We have noted that larger centres often have several practitioners performing ERCP and several trainees learning. To determine the likely impact of the JAG recommendations on ERCP training and whether trainees were likely to have greater individual exposure to procedures in larger units, we performed a questionnaire based study, which was mailed to all acute UK hospitals. A total of 178 of 292 units replied …
Identity-Based Key Agreement Algorithm for Smart Meter Technology to ensure Anonymity

The adoption of smart grids is happening quickly due to the rapid growth of technology. Smart meters are essential for measuring and controlling energy use in these systems. Smart meter technology does, however, raise a number of security and privacy issues, particularly in relation to the anonymity of user data. Despite the fact that some technique for the key establishment has been provided, these protocols are subject to a variety of security risks. This paper will examine the previously presented solutions and attempt to identify their flaws. We will try to design an Identity-Based Cryptographic solution for key agreements for smart meters based on the findings, which will assure the users’ anonymity.
Fault Ride Through Improvement Employing Individual Phase Linked Dynamic Braking Resistor for Wind Energy Integrated MMC-HVDC Transmission Network

Modular multilevel converter (MMC) based high voltage DC transmission (HVDC) is competitive for grid integration of remotely placed renewable energy sources. Usually, a dynamic braking resistor (DBR) is placed in parallel with the transmission line to protect the HVDC link from overvoltage during severe AC side low voltage faults. However, during moderate low voltage fault at the point of common coupling of AC grids, it introduces multiple switching and ripples in the HVDC link voltage. This work presents an individual phase-linked dynamic braking resistor-based control method to control the HVDC link voltage at the point of common coupling during AC side low voltage faults. In this study, a permanent magnet synchronous generator (PMSG) for integrating wind energy is considered. Real-time digital simulation (RTDS) has been used to build the entire system. The proposed technique redudces multiple switching and ripples in the HVDC link voltage during symmetrical and asymmetrical low voltage disturbances.
Computational Complexity Analysis of Beamspace Transformation for Dual Array Antennas

5G mobile communication using a high carrier frequency can operate a massive array antenna. A large-scale antenna element is a major factor that increases the hardware and software complexity of an array antenna. In such a massive array antenna, beamspace conversion is a very efficient method to reduce the high complexity of element space. This paper presents a mathematical model that analyzes the total number of addition/subtraction and multiplication/division of beamspace transformation formulas for a dual array antenna among various massive array antennas and provides results of computational complexity analysis.
The Intergenerational Correlation of Health: Evidence from Africa

This paper investigates the intergenerational transmission of health in Africa. Our measure of maternal health is height and we explore two alternative measures of child health, namely infant mortality risk and child height. We use individual survey data on 1.26 million children born to 250000 mothers in 28 African countries during 19702000 which we match to aggregate macroeconomic data by region and birth cohort. Our main findings are as follows. Maternal and child health exhibit a positive correlation in every country in the sample. On average, children born to mothers who are at least one standard deviation below the mean height in their region are 1.1 percentage points [9.9% of the sample infant mortality rate] more likely to suffer infant death than children born to mothers of mean height. The gain associated with having a tall mother is only half as large as the penalty attached to having a short mother. If the correlation were purely genetic then this sort of asymmetry would be unlikely. Since height is positively correlated with socioeconomic status, it seems likely that poor women are constrained in the investments they can make in child health. We therefore investigate whether economic development weakens the average intergenerational coefficient. The idea is that, with development, fewer women are liquidity constrained and public services improve so that child health is less tied to family wealth. We find that it does - the intergenerational correlation of health is weaker in richer countries and especially so amongst shorter women. This result is more pronounced when estimated on between-country variation than when estimated on within -country cohort/year variation, which suggests that it is longer term development that matters. Given evidence that the variation in heights in early childhood is largely determined by environme ntal factors, with the role of genes kicking in later in life, we investigated how the relationship between child and mother height changes with child age in the range 0-5. We find some tendency for the relationship to strengthen with age. We argue that although this is an interesting result in itself, there are alternative interpretations of it.
Tunable Circular Dichroism Based on Graphene-Au Chiral Metasurface Structure

In this study, we present a chiral metasurface structure based on graphene-Au elliptical hole arrays. Circular dichroism of up to 0.91 is achieved by varying the rotation angle of the elliptical hole. Circular dichroism (CD) typically expresses the chiral response. The structure has excellent absorption of right circularly polarized light while reflecting left circularly polarized light, and it is theoretically analyzed using the Jones matrix. By incorporating graphene between the gold hole array and the dielectric, the CD can be finely tuned. The CD response decreases from 0.88 to 0.55 when the Fermi energy level of graphene is changed from 0.3 eV to 1.0 eV, as indicated by computational studies. In addition, the periodic cell structure based on this metasurface can realize the digital imaging function and the dynamic modulation of the imaging state can be achieved by adjusting the Fermi energy level, which has potential applications in fields like communication encryption.
Self-regulation of learning in the context of modern technology: a review of empirical studies

Purpose Self-regulation is the level of learning where the learner becomes an active agent in their learning process in terms of activity and aspects of motivation and metacognition. The current paper mostly deals with the metacognitive aspect. The purpose of this study is to gain insight into self-regulation of learning in the context of modern technology in higher education. This study also aims to highlight the direction, tendencies and trends toward which self-regulation of learning is moving in relation to modern technologies. Design/methodology/approach The review study was compiled via searches in three databases: Scopus, Web of Science and ERIC. A filter was used to search for empirical studies solely in English, published over the past decade on the topics of self-regulation of learning and technology in higher education. Findings The findings clearly show a correlation between self-regulation of learning and modern technology, especially after a significant event such as the Covid-19 pandemic. However, in the wake of this change, the field of education has seen the emergence of methods and new platforms that can provide support for the development of self-regulated learning strategies. Originality/value The originality of the study lies in the fact that it focuses on the link between self-regulation of learning and modern technologies in higher education, including some predictions of the future direction of self-regulation of learning in this context.
Variations in snowpack melt on the Greenland ice sheet based on passive-microwave measurements

Abstract A simple microwave-emission model is used to simulate 37 GHz brightness temperatures associated with snowpack-melt conditions for locations across the Greenland ice sheet. The simulated values are utilized as threshold values and compared to daily, gridded SMMR and SSM/I passive-microwave data, in order to reveal regions experiencing melt. The spatial extent of the area classified as melting is examined on a daily, monthly and seasonal (May-August) basis for 1979–91. The typical seasonal cycle of melt coverage shows melt beginning in late April, a rapid increase in the melting area from mid-May to mid-July, a rapid decrease in melt extent from late July through mid-August, and cessation of melt in late September. Seasonal averages of the daily melt extents demonstrate an apparent increase in melt coverage over the 13 year period of approximately 3.8% annually (significant at the 95% confidence interval). This increase is dominated by statistically significant positive trends in melt coverage during July and August in the west and southwest of the ice sheet. We find that a linear correlation between microwave-derived melt extent and a surface measure of ablation rate is significant in June and July but not August, so caution must be exercised in using the microwave-derived melt extents in August. Nevertheless, knowledge of the variability of snowpack melt on the Greenland ice sheet as derived from microwave data should prove useful in detecting climate change in the Arctic and examining the impact of climate change on the ice sheet.
The emerging and diverse roles of sirtuins in cancer: a clinical perspective

: Sirtuins are a highly conserved family of nicotinamide adenine dinucleotide (NAD + )-dependent protein lysine modifying enzymes with deacetylase, adenosine diphosphate-ribosyltransferase and other deacylase activities. Mammals have seven sirtuins, namely SIRT1-7. They are key regulators for a wide variety of cellular and physiological processes such as cell proliferation, differentiation, DNA damage and stress response, genome stability, cell survival, metabolism, energy homeostasis, organ development, aging, and cancer. Here we present an extensive literature review of the roles of mammalian sirtuins, particularly SIRT1 as that is the most studied sirtuin, in human epithelial, neuronal, hematopoietic, and mesenchymal malignancies, covering breast, prostate, lung, thyroid, liver, colon, gastric, pancreatic, ovarian, and cervical cancers, tumors of the central nervous system, leukemia and lymphoma, and soft tissue sarcomas. Collective evidence suggests sirtuins are involved in both promoting and suppressing tumorigenesis depending on cellular and molecular contexts. We discuss the potential use of sirtuin modulators, especially sirtuin inhibitors, in cancer treatment.
Simulated Annealing Aided Artificial Hummingbird Optimizer for Infinite Impulse Response System Identification

Infinite impulse response (IIR) systems, with their ability to model intricate system behaviors, have proven to be a powerful class of digital filters. However, accurately identifying the optimal filter parameters for system emulation remains a challenge. Metaheuristic algorithms have emerged as effective tools for parameter optimization in IIR filter design, allowing for the exploration of parameter spaces and the discovery of suitable filter sets. This paper introduces a novel adaptive algorithm, named simulated annealing aided artificial hummingbird optimizer (AHA-SA), which combines the strengths of the artificial hummingbird algorithm (AHA) and simulated annealing (SA). The synergistic integration of AHA and SA in the AHA-SA optimizer enables efficient search space exploration, rapid convergence, and the attainment of precise solutions. Extensive experiments demonstrate the superiority of the AHA-SA optimizer over competitive algorithms, both in terms of solution quality and convergence speed. The proposed optimizer presents a promising solution for optimization problems in various domains, with its simplicity, intuitive workflow, and potential for widespread adoption.
Adaptive Multi-Domain Dialogue State Tracking on Spoken Conversations

The main objective of the task-oriented dialogue system is to identify the intent and needs of human dialogue. Many existing studies are conducted under the setting of written dialogue, but there always exists a difficulty in coping with real-world spoken dialogues. To this end, DSTC10 challenge organizers propose the task of building robust dialogue state tracking (DST) models on spoken dialogues. With the powerful existing DST model (i.e., MinTL), this article suggests integral components for building a dialogue state tracker; 1) Data augmentation effectively enhances the capability of the model to catch the entities that exist in the evaluation dataset. 2) Levenshtein post-processing aims to prevent the distortion in model prediction caused by automatic speech recognition errors. To validate the effectiveness of our methods, we evaluate our model on DSTC10 datasets and conduct qualitative analysis by ablating each component of the model. Experimental results show that our model significantly outperforms baselines in all evaluation metrics and took 3rd place in the challenge.
Parameterizing Internal Wave Boundary Mixing in a Canyon

Abstract : This project, in collaboration with Eric Kunze at the University of Victoria, aimed to increase our understanding of (primarily tidal) internal wave processes and how they affect diapycnal mixing in the coastal ocean, with an eventual goal of improved representation of baroclinic tides and mixing parameterizations in numerical models. Our work is expected to add significantly to the knowledge of the internal wave field, its interactions with topography, and implications for mixing of tracers and momentum. Both the wave field and the topographic interactions are generally not well-represented in numerical models, resulting in a great degree of uncertainty over appropriate mixing coefficients or parameterizations. Our measurements will permit validation of existing parameterizations in a small highly-resolved region, as well as aid in the development of new parameterizations. Our work will continue on our new grant N00014-08-1-0983, "Analysis of Internal Tide Observations from the August 2006 AESOP Experiment. "
Intelligent Traffic Management System: AI-Enabled IoT Traffic Lights to Mitigate Accidents and Minimize Environmental Pollution

Reducing traffic and pollution is crucial for the well-being of both people and the planet. These issues mainly affect developing nations due to rapid urbanization and limited infrastructure. This paper emphasizes the development of smart traffic management systems that monitor and control traffic flow, lessen congestion, and shorten travel times using IoT and LoRa. Lowering the number of vehicles on the road can lower fuel use and emissions. IoT can also gather real-time data on air quality, which can help pollution reduction efforts and inform air quality policies.
Early Detection of Breast Cancer using Diffuse Optical Probe and Ensemble Learning Method

In this paper, we propose using the diffuse optical breast scanning (DOB-Scan) probe, which employs an ensemble learning method to enable earlier detection of breast cancer. For this, we utilized an ensemble of nine models with various regression algorithms as base estimators to predict optical properties for liquid breast-mimicking phantoms. These regression models included Polynomial Regression, Support Vector, Random Forest, K-Nearest Neighbors, Decision Tree, Multi-layer Perceptron, XGBoost, CatBoost, and Extra Trees Regressors. We evaluated the performance of our models based on accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). Our analysis revealed that the Extra Trees model had the highest accuracy of 93%, making it the best regression model. Additionally, the Bagging with the KNN model achieved 100% accuracy in classifying the optical properties into healthy and unhealthy categories. These results suggest that the DOB-Scan probe, utilizing an ensemble learning approach, has the potential to detect breast cancer at an earlier stage.
Self-Defined Protocols for Ubiquitous Networks

The paper presents a novel approach to the dynamic deployment of both networking and application software in ubiquitous networks. The proposed approach introduces the concept of first-class objects, enabling the dynamic construction and configuration of software components. This leads to the capability of dynamically deploying software components for defining both networking and application functions at nodes by utilizing network protocols specified within software components. The dynamic deployment improves the reliability and resilience of the network by actively managing network resources, configuring network components, and monitoring network conditions. Furthermore, the approach offers the added advantage of allowing the use of non-standard protocols, as components for defining protocols can be dynamically deployed at nodes using other component-based protocols.
Vehicle Detection and Traffic Control Using Sensor Technology

Intelligent, autonomous driving units are the new trend in the world of transportation. Risk mitigation, zero carbon emission, fuel scarcity and avoiding accidents due to driver behaviors are the key factors that are pushing us towards autonomous driving. Achieving this requires advanced sensor technologies, fusion theories, machine learning models for perceiving the environment around Autonomous Vehicles (AV). The intelligence, communication, and security of these systems are the separate entities which are under improvement and play a major role in calling a system to be totally intelligent. The study conducted on afore mentioned areas strives to provide an insight to the researchers on the various sensors of AV’s while it also notices some of the state-of-the-art processes in the field to propose a safe scene understanding.