Miracle-dz/UniFAR-FTUs huggingface dataset:
Memetic algorithm for solving one-to-one shortest path problem

The shortest path problem (SPP) is widely applied in different fields of application such as transportation, telecommunication and networks. It aims at determining the path between two points with the minimum associated/allocated resource (distance, time, cost…) in a directed or undirected graph. SPP has been well studied over the last 60 years, graph theory experts still endeavor to extract new variants of the basic problem in order to model and provide efficient solutions for several real life issues. Important forms of the SPP may be considered such as finding the shortest paths in a dynamic graph where the arcs’ weights vary as functions of time. Moreover, finding shortest paths subject to some additional constraints like fixing the maximum number of visited nodes or precising the nodes’ types, colors, etc. To deal with the SPP and its different variants, several algorithms have been developed since 1956. The standard solution to the one-to-one shortest path problem, which is the target of our study, is Dijkstra’s algorithm [1]. Its running time depends on the selected data structure. For the average case, one can get an O (|E|+|V|*log|V|) (|V| #Vertices, |E| #Edges). To deal with negative edge weight, we have the Bellman-Ford algorithm. SPP was also solved using MILP technique but its running time is usually higher than standard algorithms. MILP is widely used for solving many variants of SPP when standard algorithms fail for a lack of flexibility to cope with additional constraints but this usually leads to a significant execution time. In practice, several speed-up techniques have been proposed to reduce the search space such as bidirectional search, A* algorithm, preprocessing techniques, etc. Although, those techniques are efficient and fast enough, they become less performant when the size of the network becomes very large or when additional constraints are added such as dynamic arcs weight, multi-criteria paths optimization. To overcome such shortcomings, we believe that meta-heuristics such as GA, local search may be efficient candidates to give near optimal solutions within acceptable time.
Real‐Time and Broadband Terahertz Wave Scattering Manipulation via Polarization‐Insensitive Conformal Graphene‐Based Coding Metasurfaces

Here, for the first time, the real‐time and broadband manipulation of terahertz (THz) waves are acquired by introducing a multifunctional graphene‐based coding metasurface (GBCM). The designed structure consists of subwavelength patterned graphene units whose operational statuses can be dynamically switched between two digital states of “0” and “1”. By engineering the spatial distribution of chemical potentials across the GBCM, various scattering patterns having single, two, four, and numerous reflection beams are elaborately achieved just within one planar structure. To compute the far‐field pattern of GBCM, an inverse discrete Fourier transform (IDFT) is established, providing a fast and efficient design method. The proposed GBCM provides a low reflection bellow −10 dB over a broad frequency band ranging from 1 THz to 1.9 THz. In addition, the metasurface retains its low reflection behavior in a wide range of incident wave angles for both TE and TM polarizations. According to conformal invariance of graphene sheets, the stealth property of GBCM is well preserved while wrapping around a curved object. The proposed technique of real‐time scattering manipulation leads to multifunctional THz devices, opening new routes contributing to numerous applications such as imaging and stealth technology.
Analisis Sentimen penggunaan Mypertamina untuk Pembelian BBM Bersubsidi mengggunakan Algoritma Naive Bayes

-This study aims to analyze the sentiment of using the Mypertamina application in purchasing subsidized fuel oil using the Naive Bayes algorithm. This research involves data pre-processing stages, such as full preprocessing and stopword removal, as well as accuracy testing by varying the distribution of training data and test data. The results showed that by carrying out full preprocessing of the data and using 70% of the training data, the classification model achieved an accuracy of 85%. The use of 80% training data increases accuracy to 87%, while the use of 90% training data results in an accuracy of 89%. This shows that the more training data used, the better the performance of the classification model. Eliminating stopwords also has a significant impact on model accuracy. Without omission of stopwords, the accuracy of the model with a data division of 70%, 80%, and 90% is 80%, 82%, and 84%, respectively. Even though the accuracy is lower than full preprocessing, the model still provides good predictions. Based on the test results, it can be concluded that the application of full preprocessing with more training data tends to produce better model performance. However, removing stopwords also makes a significant contribution to improving accuracy. Therefore, in developing a text classification model, comprehensive pre-processing and appropriate stopword removal need to be considered according to the characteristics of the data and analysis needs. In testing the classification using the Naïve Bayes Classifier method, the distribution of training data and test data also has an effect. The use of 70% training data results in an accuracy of 85%, while the use of 80% and 90% training data results in an accuracy of 87% and 89% respectively. The more training data used, the better the performance of the Naïve Bayes Classifier classification model. In the final conclusion, the proportion of 90% of the training data gives the best performance in classifying the test data with the highest accuracy. However, using a smaller test dataset may lead to a higher variation in results. Therefore, cross-validation methods or tests with more folds can provide more comprehensive information about the performance of the classification model.
Bubble dynamics in a compressible viscous liquid

Microbubble dynamics are associated with cavitation damage to pumps, turbines and propellers, as well as applications in biomedical ultrasonics, sonochemistry and cavitation cleaning. The compressible effects of liquid are essential, which are associated with acoustic radiation at the inception of a bubble and the end of collapse. Viscous effects are important for microbubbles. This thesis is concerned with microbubble dynamics in a viscous compressible liquid near a rigid boundary. The compressible effects are modelled by using the weakly compressible theory. The viscous effects are approximated using the viscous potential flow theory, because the flow field is characterised as being an irrotational flow in the bulk volume but with a thin viscous boundary layer at the bubble surface. Consequently, the phenomenon is modelled by using the boundary integral method. The numerical results are shown in good agreement with the Keller-Miksis equation, experiments and computations based on the Navier-Stokes equations. Numerical studies were carried out for microbubble dynamics near a rigid boundary as well as subject to an acoustic wave. The bubble oscillation, topological transform, jet development and penetration through the bubble and the energy of the bubble system are simulated and analysed regarding the compressible and viscous effects.
The Integration of Technology in Teaching and Learning of Mathematics: ----- The Missing Link

This article was motivated by observations made by the researchers that most mathematics educators were shying away from using technology to teach mathematics even in well-resourced schools while learners were embracing technology and using it as a source of inquiry in their everyday lives. The paper further explores how various strategies can be put in place to motivate educators to use technology
Linear state feedback stabilisation of non linear systems via fuzzy models

A Lyapunov based linear stabilising control design method for non linear systems using fuzzy model is proposed. The linear stabiliser is constructed using a fuzzy design model of the dynamical system to be controlled. The fuzzy design model is represented as an uncertain linear system and then sufficiency conditions for the uncertain system to be globally stabilisable by a linear controller are given. The proposed design method is illustrated on a problem of balancing an inverted pendulum on a cart.
Radial ballistic-diffusive heat conduction in nanoscale

ABSTRACT Heat conduction in radius direction is of great importance to the use of two-dimensional materials and experiments. In this paper, radial ballistic-diffusive heat conduction in nanoscale is investigated by the phonon Monte Carlo (MC) method and phonon Boltzmann transport equation. We find that owing to the two-dimensional nature, the radial heat transport is dominated by two parameters, including the Knudsen number (Kn) and the radius ratio of the two concentric boundaries, the former of which is defined as the ratio of the phonon mean-free-path to the distance of the two boundaries. Compared with the one-dimensional cases, radial ballistic transport not only leads to boundary temperature jumps and the size effect of the effective thermal conductivity, but also results in a nonlinear temperature profile in logarithm radius coordinate, a difference of the inner and outer boundary temperature jumps, a stronger size effect, and a nonuniform local thermal conductivity within the system. When the value of Kn is far less than one, diffusive transport predominates and the effect of the radius ratio is negligible. Whereas, when Kn is comparable to or larger than one, the intensity of ballistic transport compared to diffusive transport will be increased significantly as the radius ratio decreases. In addition, the models for the temperature profile and the effective thermal conductivity are derived by an interpolation of the limit solutions and modification of the previous model, respectively. The good agreements with the phonon MC simulations demonstrate their validity.
The Effect of Foot Landing Type on Lower-extremity Kinematics, Kinetics, and Energy Absorption during Single-leg Landing

Here, landing type is divided into forefoot landing, in the toes the ground first, rearfoot landing, in which the heel touches the ground first. A previous study analyzed video of ACL injuries during athletic competition found that athletes landed rearfoot at the time of injuries, and reported that rearfoot landing Objective: The aim of this study was to examine the effect of foot landing type (forefoot vs. rearfoot landing) on kinematics, kinetics, and energy absorption of hip, knee, and ankle joints. Method: Twenty-five healthy men performed single-leg landings with two different foot landing types: forefoot and rearfoot landing. A motion-capture system equipped with eight infrared cameras and a synchronized force plate embedded in the floor was used. Three-dimensional kinematic and kinetic parameters were compared using paired two-tailed Student's t -tests at a significance level of . 05. Results: On initial contact, a greater knee flexion angle was shown during rearfoot landing ( p < .001), but the lower knee flexion angle was found at peak vertical ground reaction force (GRF) ( p < .001). On initial contact, ankles showed plantarflexion, inversion, and external rotation during forefoot landing, while dorsiflexion, eversion, and internal rotation were shown during rearfoot landing ( p < .001, all). At peak vertical GRF, the knee extension moment and ankle plantarflexion moment were lower in rearfoot landing than in forefoot landing ( p = .003 and p < .001, respectively). From initial contact to peak vertical GRF, the negative work of the hip, knee, and ankle joint was significantly reduced during rearfoot landing ( p < .001, all). The contribution to the total work of the ankle joint was the greatest during forefoot landing, whereas the contribution to the total work of the hip joint was the greatest during rearfoot landing. Conclusion: These results suggest that the energy absorption strategy was changed during rearfoot landing compared with forefoot landing according to lower-extremity joint kinematics and kinetics.
Effects of Homogeneous and Heterogeneous Crowding on Translational Diffusion of Rigid Bovine Serum Albumin and Disordered Alfa-Casein

Intracellular environment includes proteins, sugars, and nucleic acids interacting in restricted media. In the cytoplasm, the excluded volume effect takes up to 40% of the volume available for occupation by macromolecules. In this work, we tested several approaches modeling crowded solutions for protein diffusion. We experimentally showed how the protein diffusion deviates from conventional Brownian motion in artificial conditions modeling the alteration of medium viscosity and rigid spatial obstacles. The studied tracer proteins were globular bovine serum albumin and intrinsically disordered α-casein. Using the pulsed field gradient NMR, we investigated the translational diffusion of protein probes of different structures in homogeneous (glycerol) and heterogeneous (PEG 300/PEG 6000/PEG 40,000) solutions as a function of crowder concentration. Our results showed fundamentally different effects of homogeneous and heterogeneous crowded environments on protein self-diffusion. In addition, the applied “tracer on lattice” model showed that smaller crowding obstacles (PEG 300 and PEG 6000) create a dense net of restrictions noticeably hindering diffusing protein probes, whereas the large-sized PEG 40,000 creates a “less restricted” environment for the diffusive motion of protein molecules.
Waiting times and noise in single particle transport

The waiting time distribution w(τ), i.e. the probability for a delay τ between two subsequent transition (‘jumps’) of particles, is a statistical tool in (quantum) transport. Using generalized Master equations for systems coupled to external particle reservoirs, one can establish relations between w(τ) and other statistical transport quantities such as the noise spectrum and the Full Counting Statistics. It turns out that w(τ) usually contains additional information on system parameters and properties such as quantum coherence, the number of internal states, or the entropy of the current channels that participate in transport.
Adaptive H∞ consensus control of Euler-Lagrange systems on directed network graph

A design method of adaptive consensus control of multi-agent systems composed of fully actuated mobile robots which are described as a class of Euler-Lagrange systems on directed network graphs, is presented in this paper based on the notion of inverse optimal H ∞ control criterion. The proposed control scheme is deduced as a solution of certain H ∞ control problem, where estimation errors of tuning parameters are considered as external disturbances to the process. The resulting control system is shown to be robust to uncertain system parameters and the approximate consensus tracking is achieved via adaptation schemes and L 2 -gain design parameters.
Inductive ensemble clustering using kernel support matching

A novel inductive ensemble clustering method is proposed. In the proposed method, kernel support matching is applied to a co-association matrix that aggregates arbitrary basic partitions in order to detect clusters of complicated shape. It also has the advantage of naturally detecting the number of clusters and assigning clusters for out-of-sample data. In the proposed method, a new similarity is learned from various clustering results of the basic partition, and a kernel support function capable of clustering learning data and test data is constructed. Experimental results demonstrated that the proposed method is effective with respect to clustering quality and has the robustness to induce clusters of out-of-sample data.
Regional spatial planning for implementing the European Green Deal: a new method of assessment applied to the metropolitan area of Florence

ABSTRACT The European Green Deal (EGD) is the most ambitious current challenge for regions and cities of the EU member states. While the role of regional spatial planning in addressing sustainable development and climate change mitigation has been widely investigated, an analysis of its ability to drive the green transition is still missing. The authors propose a new method to analyse how current regional plans meet EGD challenges and apply it to the case of the metropolitan area of Florence. The analysis reveals that, although plans cover EGD issues, there is still a need for more integrated tools and greater effective governance.
Contrastive Learning-Based Bayes-Adaptive Meta-Reinforcement Learning for Active Pantograph Control in High-Speed Railways

The fluctuation of pantograph–catenary contact force seriously affects the current collection quality, maintenance cost, and operation safety of high-speed trains. In recent years, agents developed with deep reinforcement learning (DRL) technology have achieved significant success. However, most of these jobs are restricted to narrow task distributions and stationary environments, which require a large amount of training data and cannot adapt quickly to the new task. We propose a contrastive learning-based Bayes-adaptive meta-reinforcement learning (CBAMRL) algorithm that addresses these limitations, enabling agents to learn new skills from a few transitions and adapt to the new environment. We first introduce a Bayes-adaptive training strategy, achieving zero-shot adaptation in nonstationary environments with high sample efficiency and competitive asymptotic performance. We proposed a contrastive learning-based contextual encoder to represent complex task distributions with similar structures, providing compact and sufficient task representation without modeling irrelevant dependencies. We evaluate the proposed method on a validated pantograph–catenary system (PCS) benchmark. Compared to the state-of-the-art DRL approach and traditional solutions, the experiment result demonstrates that the proposed algorithm can swiftly adapt to new operating circumstances and unknown perturbations with well-structured task representation and zero-shot adaptation.
Karakteristik Manajemen dan Kepemimpinan Transformasional sebagai Penentu Kreativitas Karyawan yang Dimediasi oleh Kepuasaan Kerja

Many organizational top managers are increasingly aware of the importance of innovation as a precondition to maintaining sustainable organizational growth. Moreover, creativity is also often seen as the main driving force for innovation. The aim of this study is to analyze the effects of management characteristics represented by the dimensions of supervisor’s humility and abusive supervision, and transformational leadership on employee creativity mediated by employee job satisfaction. Utilizing a purposive sampling method, this study selected 260 back-office employees who work at banks in Indonesia and the online survey was administered during the period of June to July 2022. Employing PLS-SEM as an inferential analysis tool, this study found that a supervisor’s humility and transformational leadership have positive effects, whereas abusive supervision has a negative effect on employees’ job satisfaction. Furthermore, employee job satisfaction was also found to mediate the effects of supervisor’s humility, abusive supervision, and transformational leadership towards employee creativity.
EFEKTIVITAS PROGRAM APLIKASI MYPERTAMINA DI SPBU 54.651.05 TLOGOMAS KOTA MALANG

BBM subsidi merupakan bahan bakar minyak yang dibantu pemerintah melalui penggunaan dana Anggaran Pendapatan Belanja Negara (APBN). Penelitian ini bertujuan untuk mengetahui, mendeskripsikan dan menganalisis Efektivitas Program Aplikasi MyPertamina di SPBU 54.651. 05 Tlogomas Kota Malang. Fokus penelitian ini adalah Efektivitas Program dengan menggunakan empat aspek pokok meliputi Ketepatan Sasaran Program, Sosialisasi Program, Tujuan Program, dan Pemantauan Program. Penelitian ini menggunakan metode pendekatan kualitatif yang bersifat deskriptif. Data dianalisis melalui teknik wawancara, dokumentasi dan observasi langsung ke lapangan. Hasil penelitian menunjukan bahwa Efektivitas Program Subsidi Tepat pada Aplikasi MyPertamina di SPBU 54.651. 05 Tlogomas Kota Malang sudah efektif sesuai dengan tujuan yang sudah ditetapkan. Aspek ketepatan sasaran program, Pihak SPBU 54.651. 05 sudah tepat sasaran memilih pelanggan yang berhak mendapatkan BBM subsidi yang dipilih melalui regulasi yang sudah dibuat. Aspek sosialisasi program, pihak SPBU 54.651. 05 memberikan sosialisasi secara berkala baik melalui poster, brosur maupun penyampaian secara langsung. Aspek tujuan program, pihak SPBU 54.651. 05 sudah tercapai tujuan program yang telah ditetapkan karena sudah banyak pelanggan yang mendaftarkan kendaraannya. Aspek pemantauan program subsidi tepat dengan cara melakukan evaluasi terhadap pegawai yang mengoperasikan mesin pompa.
CMS ECAL VFE design, production and testing

Maintaining the required performance of the CMS electromagnetic calorimeter (ECAL) barrel at the High-Luminosity Large Hadron Collider (HL-LHC) requires the replacement of the entire on-detector electronics. 12240 new very front end (VFE) cards will amplify and digitize the signals of 62100 lead-tungstate crystals instrumented with avalanche photodiodes. The VFE cards host five channels of CATIA pre-amplifier ASICs followed by LiTE-DTU ASICs, which digitize signals with 160MS/s and 12bit resolution. We present the strategy and infrastructure developed for achieving the required reliability of less than 0.5% failing channels over the expected lifetime of 20 years. This includes the choice of standards, design for reliability and manufacturing, as well as factory acceptance tests, reception testing, environmental stress screening and calibration of the VFE cards.
M2SA: a novel dataset for multi-level and multi-domain sentiment analysis

ABSTRACT People have more channels to express their opinions and feelings about events, products, and celebrities because of the development of social networks. They are becoming rich data sources, gaining attention for many practical applications and in the field of research. Sentiment analysis (SA) is one of the most common uses of this data source. Of the currently available SA datasets, most are only suitable for use in SA corresponding to a specific level, such as document, sentence, or aspect levels. This renders it difficult to develop practical systems that require a combination of sentiment analyzes at all three levels. Additionally, the previous datasets included opinions on only a single domain, although many people often mention multiple domains when expressing their views. This study introduces a new dataset called multi-level and multi-domain (M2SA) for SA. Each sample in M2SA contains a short text with at least two sentences and two aspects with different domains and sentiment polarities. The release of the M2SA dataset will contribute to the promotion of research in the field of SA, primarily by promoting the development and improvement of methods for multi-level SA or multi-aspect, multi-domain SA. The M2SA dataset was tested using state-of-the-art SA methods and was compared with other standard datasets. The results demonstrate that the M2SA dataset is better than the previous datasets in supporting to improve of the performance of SA methods.