Miracle-dz/UniFAR-FTUs huggingface dataset:
FloodVis: Visualization of Climate Ensemble Flood Projections in Virtual Reality

Anthropogenic greenhouse gas emissions are leading to accelerating climate change, forcing politicians and administrations to take actions to mitigate climate change and adapt to its impacts, such as changes in ﬂood regimes. For European countries, an increasing frequency and severity of extreme rainfall and ﬂood events is expected. However, studies on future ﬂood risks caused by climate change are associated with various uncertainties. The risk simulations are elaborate as they consider (i) climate data ensembles (temperature, precipitation), (ii) hydrological modeling (ﬂood generation), (iii) hydrodynamic modeling (ﬂood conveyance), and (iv) vulnerability modeling (damage assessment) involving a huge amount of data and their handling with Big Data methods. The results are difﬁcult to understand for decision makers. Therefore, FloodVis offers a means of visualizing possible future ﬂood risks in Virtual Reality (VR). The presentation of the results in a VR especially supports the user in understanding the complexity of the dynamics of the risk system enabling the feeling of presence. In FloodVis the user enters into a virtual surrounding to interact with the data, examine the temporal evolution, and compare alternative development pathways. Critical structures that require improved protection can be identiﬁed. The user can follow the inundation process in hourly resolution. We evaluated FloodVis through an online and ofﬂine user study on the context of whether VR can provide a better visualization of ensemble ﬂood risk data and whether the sense of presence in VR
AN EFFICIENT DIRECT MPPT FOR PV SYSTEM UNDER EXTREMELY FAST CHANGING IRRADIANCE

— Photovoltaic cells require of Maximum Power Point Tracking (MPPT) algorithms to ensure the amount of power extracted is maximized. True seeking, direct duty cycle control MPPT algorithms are a simple and straightforward solution that can provide high tracking efficiency. In these algorithms the duty cycle is traditionally modified to reach a new steady state prior performing a new MPPT iteration. Therefore, the MPPT update period must be larger than the converter’s settling time to reach a new steady state, which limits the dynamic tracking performance. This work proposes a novel direct duty cycle control method that does not require the converter to achieve steady state in between MPPT updates. The proposed method benefits from the natural oscillations occurring in the converter to obtain extreme dynamic tracking improvements while maintaining simple implementation with no need of employing temperature or irradiance sensors. The scheme being introduced combines MPPT concepts with large-signal geometric control to achieve a reliable, high-performance solution very suitable for applications with rapidly changing irradiance such as wearable technology and rooftop EV. The theoretical analysis is supported by detailed mathematical procedures and validated by simulations and experimental results.
A Study on Active Learning for Graphs

This is a comprehensive study of active learning methods on real-world graph datasets. Active Learning for interconnected data has been studied over the years and has gained increasing importance in recent times, especially due to its applications in tasks where labeling is laborious and requires human experts, such as drug discovery and protein-protein interaction prediction. In this work, various active learning strategies are compared across 6 real-world datasets from different domains for the task of node classification. The main goal of this evaluation is to benchmark a range of active learning strategies against state-of-the-art and identify the ones that consistently perform well. We also propose a simple strategy for selecting nodes for training the node classifier, and our experiments show promising results of this strategy.
Advanced Smart Road DeformationDetection System

-Aging roads and poor road-maintenance systems resulted many potholes, whose numbers increase over time. Potholes jeopardize road safety and transportation efficiency. Moreover, they are often a contributing factor to car accidents. To address the problems associated with potholes, the locations and size of potholes must be determined quickly to find the location a data base as to be developed, which requires a specific pothole-detection system that can collect pothole information at low cost and over a wide area. However, pothole repair has long relied on manual detection efforts causing loss of time and money to government. Thus, in this paper, we introduced a pothole-detection system using a commercial “Road sepoy”. The proposed system detects potholes using vision-based tracking system and MATLAB algorithm specifically designed to work with road sepoy camera giving us accurately in real-time environment. Geo-mapping the pothole in google maps helps the exact location.
Metamaterial-Based Highly Isolated MIMO Antenna for Portable Wireless Applications

: In this paper, a metamaterial structure is presented to lower the mutual coupling between the closely spaced microstrip patch antenna elements. Two elements Multiple Input Multiple Output (MIMO) antenna is closely placed with each other at edge to edge separation 0f 0.13of wavelength isolation improvement of 9dB is achieved by keeping the metamaterial structure in between the MIMO elements. with the proposed structure, the isolation is achieved by around -24.5dB. Due to low ECC, high gain, low channel capacity loss and very low mutual coupling between elements, the proposed antenna is a good candidate for the MIMO applications. The proposed antenna is fabricated and tested.A reasonable agreement between stimulated and measured results is observed.
POLYMERS USED IN TRANSDERMAL DRUG DELIVERY: AN OVERVIEW

Transdermal drug delivery systems (TDDS) are dosage forms that involve drug transport to viable epidermal and or dermal tissues of the skin for local therapeutic effect while a very major fraction of drug is transported into the systemic blood circulation. It is one of the best pharmaceutical dosage forms for patients, who cannot take medicaments orally. On the application of transdermal patches, the delivery of the drug across the dermis gives the systemic effect. Advances in transdermal delivery systems (TDS) and the technology involved have been rapid because of the sophistication of polymer science which now allows the incorporation of polymeric additives in TDS in adequate quantity. [1] Careful polymer selection is essential to control the drug loading, release rate, and duration of release. Many polymers can be formulated into various drug delivery systems to address the controlled release, Targeted delivery and solubility enhancement strategies enable improved drug efficacy. The most critical factor in polymer selection is considering the interaction of the drug and polymer. Polymer selection will determine the mechanism of drug release (bulk erosion, system degradation) and the choice of polymer properties (molecular weight, surface charge) will influence release rate and impact pharmacokinetics. This article summarizes the factors contributing to polymers for the formulation aspects of the transdermal drug delivery system and emphasizes the selection criteria and various release patterns of polymers. In addition, Applications, Manufacturing considerations and recent developments of TDDS have also been reviewed. [2]
An Ant Colony Optimization Algorithm for Stacking Sequence Design of Composite Laminates

The study reported in this paper explores the potential of Ant Colony Optimization (ACO) metaheuristic for stacking sequence optimization of composite laminates. ACO is a recently proposed populationbased search approach able to deal with a wide range of optimization problems, especially of a combinatorial nature, and inspired by the natural foraging behavior of ant colonies. ACO search processes, in which the activities of real ants are simulated by means of artificial agents that communicate and cooperate through the modification of the local environment, were implemented in a specifically developed numerical algorithm aimed at the lay-up optimization (based on a strain energy criterion) of laminated plates subject to in-plane and out-of-plane loads. Numerical analyses were conducted to investigate the quality and reliability of the metaheuristic search procedure under various load cases, geometry configurations and constraint conditions. The analyses indicated that the proposed ACO algorithm is able to achieve reasonably good solutions within very few iterations, and extremely high-quality solutions within a limited number of runs, with respect to the total number of possible solutions, for both uncostrained and constrained optimization lay-up problems. The results obtained during the investigation point out the robustness and effectiveness of the procedure and suggest the use of ACO-based search techniques as practical design tools for laminate lay-up configuration. keyword: Ant Colony Optimization (ACO), Metaheuristic, Composite laminates, Lay-up optimization.
Visual-Language Transformer for Referring Video Object Segmentation

Referring Video Object Segmentation task (R-VOS) aims to segment the target object in all video frames referred with a language expression. In this work, we present a Visual-Language Transformer (VLFormer), a query-based network to tackle the R-VOS task. Its key component is the visual-language transformer block (VLB), which associates visual and linguistic features with the object queries effectively and simultaneously. We use the object queries as a set of candidate objects, the Transformer decoder with VLB blocks that guide and interact with candidate objects to find the referred target object. The object tracking is achieved automatically by linking the corresponding queries across frames. Afterwards, a post-processing technique is used to refine and re-track the mask prediction among all the frames. Our model ranks 6 th place on Track 3: Referring Video Object Segmentation of the CVPR 2022 Youtube-VOS Challenge.
A Broad Survey on Electric Springs for Flexible Load Management in Smart Grid

— Smart grid technologies are emerging technology and it is said to be the integration of electrical network and communication technology. This paper deals with survey of various problems related to the Smart Grids and recently proposed concept of electric springs used to compensate those problems. Flexible Alternating Current Transmission Systems (FACTS) devices helps in present transmission and distribution lines to deliver maximum power and helps to stabilize the grid with precise voltage control. Electric springs are said to be built from Hooke’s law published three centuries ago. They are power electronics-based reactive power controllers which are used to regulate the power and voltage and overcome the disadvantages in Facts devices. This concept is mainly used in the power grid which are connected to the distributed energy sources as they are intermittent in nature.
CATEGORICAL, PROTOTYPICAL AND GRADIENT THEOFIIES OF SPEECH: REACTION TIME DATA

How do we make phonetic decisions? Categorical, prototypical, and gradient theories were tested using the times to identify a Isa/48ml continuum created by inserting varying amounts of silence into a [53/ syllable or deleting silence from :1 [Sta]. The gradient model requires 6-8 times as many parameters as the others, and so is difficult to compare. Two variants of a prototypical model and a simple categorical one accounted for some of the variance in the reaction times. but a modified categorical model with the same number of parameters accounts for more. In identification, it seems that all unambiguous syllables elicit identical reaction times, but §yllablcs farther from that range elicit mmasingly longer times.
Should I Trust You? Incorporating Unreliable Expert Advice in Human-Agent Interaction

—A major concern in reinforcement learning, especially as it is applied to real-world and robotics problems, is that of sample-efﬁciency given increasingly complex problems and the difﬁculty of data acquisition in certain domains. To that end, many approaches incorporate external advice in the learning process in order to increase the rate at which an agent learns to solve a given problem. However, these approaches typically rely on a single reliable information source; the problem of learning with information from multiple, potentially unreliable sources is still an open question in assisted reinforcement learning. We present CLUE (Cautiously Learning with Unreliable Experts), a framework for learning single-stage decision problems with policy advice from multiple, potentially unreliable experts. We compare CLUE against an unassisted agent and an agent that na¨ıvely follows advice, and our results show that CLUE exhibits faster convergence than an unassisted agent when advised by reliable experts, but is nevertheless robust against incorrect advice from unreliable experts.
Representation Learning Using a Multi-Branch Transformer for Industrial Time Series Anomaly Detection

In recent years, due to the rapid expansion of the Industrial Internet of Things (IIoT), substantial amounts of high-dimensional industrial time series data have been generated. Anomaly detection in such industrial time series data is a challenging task due to complex temporal dynamics. In this paper, we propose multi-branch transformer with Gaussian mixture model (MBTGMM), a novel transformer-based framework to address some of these challenges. In the framework, normal representations are learned with a multi-branch transformer architecture that comprises of a convolution branch and a multi-head attention branch in order to learn both short-and long-term temporal dependencies in the time series data. These representations are then fed into a Gaussian mixture model for density estimation and anomaly detection task. Experimental results on public industrial datasets show the effectiveness of our proposed framework, and the ablation studies clearly demonstrate the efficacy of our design choices.
Fiscal Decentralisation and Economic Growth Across States: New Evidence from Malaysia

The link between fiscal decentralisation and state-level economic growth has often been overlooked, particularly in Malaysia, with its highly centralised federal fiscal system. Thus, using data from all 13 Malaysian states from 2006 to 2018, this study evaluated the degree of fiscal decentralisation and examined its effects on state economic growth. This study employed a new proxy for fiscal decentralisation (FD), a composite variable FD comprising two components: fiscal autonomy (FA) and fiscal importance (FI). The model was examined using the fixed effects technique with robust standard error panel analysis. The empirical results demonstrated that FI and FD were significant and positively impacted economic growth across states. The results also showed the significant and negative impact of budget balances resulting from persistent fiscal deficits on state economic growth, signalling states' heavy reliance on intergovernmental grants and borrowings (soft budget constraints). Thus, fiscal decentralisation has enabled the state governments to alleviate the soft budget constraint problem and reduce the negative impact of deficits on local economic growth. Overall, the results supported prior findings that fiscal decentralisation had a significant positive effect on state economic growth.
The challenge of redundancy on multi-agent value factorisation

In the field of cooperative multi-agent reinforcement learning (MARL), the standard paradigm is the use of centralised training and decentralised execution where a central critic conditions the policies of the cooperative agents based on a central state. It has been shown, that in cases with large numbers of redundant agents these methods become less effective. In a more general case, there is likely to be a larger number of agents in an environment than is required to solve the task. These redundant agents reduce performance by enlarging the dimensionality of both the state space and and increasing the size of the joint policy used to solve the environment. We propose leveraging layerwise relevance propagation (LRP) to instead separate the learning of the joint value function and generation of local reward signals and create a new MARL algorithm: relevance decomposition network (RDN). We find that although the performance of both baselines VDN and Qmix degrades with the number of redundant agents, RDN is unaffected.
ASLR: Arabic Sign Language Recognition Using Convolutional Neural Networks

Sign language is the native language of deaf and hearing-impaired people which they use on their daily life. Few interpreters are available to facilitate communication between deaf and vocal people. However, this is neither practical nor possible for all situations. Advances in information technology encouraged the development of systems that can facilitate the automatic translation between sign language and spoken language, and thus removing barriers facing the integration of deaf people in society. Objective: The main objective of this paper is to present an Arabic Sign Language recognition system that automatically recognizes 28 letters using a CNN model taking RGB images as input. Methods: In this work, we propose a new framework based on convolutional neural networks, fed with a real dataset, which will automatically recognize letters of Arabic Sign Language. In order to validate our scheme, we have performed a comparative analysis that demonstrates the efficacy and robustness of our proposed method compared to conventional methods. Results: we tested the CNN model on (10810 images), we measured the accuracy of the model as well as the error rate, the accuracy increases, the error decreases during the training and testing phases, and we achieved 92.9% of recognition accuracy.
The Study of Interrelationship between Financial Performance and CSR Expenditure of Power Sector PSUs

This study intends to analyze the trend and pattern of CSR and ﬁnancial variable and their corresponding relationship in Power sector PSUs for the time period from 2014-15 to 2019-20. The data have been gathered from the annual reports of the companies and the intra-company and intra-sector analysis for determining relationship have been carried out through correlation. It has been found that there has been no consistency in CSR expenditure and ﬁnancial performance over the years and there is no signiﬁcant relationship found between CSR expenditure and ﬁnancial performance in any company except PGCI Ltd. in which positively signiﬁcant relationship has been found. The study recommendsthat it is signiﬁcant to maintain consistency in CSR expenditure and ﬁnancial performance by adequate functioning and address the needs of the society around rather than emphasizing on compliance of standards.
SCAMPER or RAFT: A comparative Study in Improving EFL Students' Creative Writing

: The study aimed to compare the effectiveness of both SCAMPER and RAFT strategies in improving EFL students' creative writing. The participants of the study consisted of (50) female students, who were divided into two groups; the first one consisted of (25) students taught using SCAMPER, the other one consisted of (25) students taught using RAFT. To determine the effectiveness level of the instructional strategy, a pre-post achievement test was employed. The results indicated that there were statistically significant differences in the effect of both SCAMPER and RAFT on students’ creative writing, in favor of the group taught using SCAMPER strategy which indicates that SCAMPER was more effective. In light of the results, some recommendations were provided.
PROBABILISTIC FAULT DISPLACEMENT HAZARD ANALYSIS – SENSITIVITY ANALYSES AND RECOMMENDED PRACTICES FOR DEVELOPING DESIGN FAULT DISPLACEMENTS

Probabilistic fault displacement hazard analysis (PFDHA) is a relatively new methodology to use with deterministic fault displacement estimates to characterize expected fault displacement for design and analysis of new and existing structures and infrastructure. While the methodology for assessing probabilistic displacements on normal faults was published more than a decade ago, comparable methodologies for assessing probabilistic displacements on strike slip and reverse faults was not available until recently. We developed probabilistic displacement hazard codes based on the methodologies for each fault slip type, and prepared comparisons of the effects of the components of PFDHA including site location along the fault, occurrence of surface rupture, site location along the rupture, and for fault slip type and earthquake return period. Based on these comparisons as well as comparisons of probabilistic and deterministic displacement estimates, we characterize the sensitivity of the displacement estimates to the approaches and input variables. We also identify issues and recommend approaches in performing PFDHA and in selection of design displacements. ABSTRACT Probabilistic fault displacement hazard analysis (PFDHA) is a relatively new methodology to use with deterministic fault displacement estimates to characterize expected fault displacement for design and analysis of new and existing structures and infrastructure. While the methodology for assessing probabilistic displacements on normal faults was published more than a decade ago, comparable methodologies for assessing probabilistic displacements on strike slip and reverse faults was not available until recently. We developed probabilistic displacement hazard codes based on the methodologies for each fault slip type, and prepared comparisons of the effects of the components of PFDHA including site location along the fault, occurrence of surface rupture, site location along the rupture, and for fault slip type and earthquake return period. Based on these comparisons as well as comparisons of probabilistic and deterministic displacement estimates, we characterize the sensitivity of the displacement estimates to the approaches and input variables. We also identify issues and recommend approaches in performing PFDHA and in selection of design displacements.
Our experience in cell therapy in surgical problems

Aim: To study the effect and efficacy of autologous bone marrow cell therapy in various surgical pathologies of varied etiology which were refractory to conventional methods of management. Material and method: Ethical committee clearance and informed consent of patients were taken. Two groups of each surgical pathologies were selected randomly. The patients in study group were treated by conventional methods followed by autologous bone marrow cell application locally, whereas patients in control group were treated by conventional methods only. The patients were then followed post-operatively and analyzed by statistical methods. Result: Post-operatively the patients in study group experienced faster recovery by appearing good quality granulation tissue and rapid epithelisation and there was also significant reduction in local pain. Biopsy showed improved fibroblast, macrophages, neovascularisation and less necrosis per HPF in study group as comparison to control group. Conclusion: Autologous bone marrow cell therapy is a novel method in the management of various surgical pathologies.