Miracle-dz/UniFAR-FTUs huggingface dataset:
A USER PREFERABLE K-SHORTEST PATH ALGORITHM FOR INTERMODAL NETWORK

This study discusses seamlessly integrating multiple modes of transportatio n networks and calculating the User Preferable K-Shortest paths in the network between origin and destination with a new algorithm based on improved Link Penalty. A projection method has been used to connect different networks. Three aspects of users ' preferences have been evaluated to set the rate of link penalties in each step that k-shortest paths will be generated. Link similarities are considered respectively in different mode and Partial Overlap has been introduced to be a criterion for iteration. Users can set their constraints and will get very precise and efficient travel information through GIS. Due to the simplification of the topological structure and flexibility of the k-shortest path algorithm, it's very easy for developers to update their existing traveler information system.
Mechanical Characterisation and Numerical Modelling of TPMS-Based Gyroid and Diamond Ti6Al4V Scaffolds for Bone Implants: An Integrated Approach for Translational Consideration

Additive manufacturing has been used to develop a variety of scaffold designs for clinical and industrial applications. Mechanical properties (i.e., compression, tension, bending, and torsion response) of these scaffolds are significantly important for load-bearing orthopaedic implants. In this study, we designed and additively manufactured porous metallic biomaterials based on two different types of triply periodic minimal surface structures (i.e., gyroid and diamond) that mimic the mechanical properties of bone, such as porosity, stiffness, and strength. Physical and mechanical properties, including compressive, tensile, bending, and torsional stiffness and strength of the developed scaffolds, were then characterised experimentally and numerically using finite element method. Sheet thickness was constant at 300 μm, and the unit cell size was varied to generate different pore sizes and porosities. Gyroid scaffolds had a pore size in the range of 600–1200 μm and a porosity in the range of 54–72%, respectively. Corresponding values for the diamond were 900–1500 μm and 56–70%. Both structure types were validated experimentally, and a wide range of mechanical properties (including stiffness and yield strength) were predicted using the finite element method. The stiffness and strength of both structures are comparable to that of cortical bone, hence reducing the risks of scaffold failure. The results demonstrate that the developed scaffolds mimic the physical and mechanical properties of cortical bone and can be suitable for bone replacement and orthopaedic implants. However, an optimal design should be chosen based on specific performance requirements.
Artificial gauge field for photons in coupled cavity arrays

We propose and characterize solid-state photonic structures where light experiences an artificial gauge field. A nontrivial phase for photons tunneling between adjacent sites of a coupled cavity array can be obtained by inserting optically active materials in the structure or by inducing a suitable coupling of the propagation and polarization degrees of freedom. We also discuss the feasibility of observing strong gauge field effects in the optical spectra of realistic systems, including the Hofstadter butterfly spectrum.
TASI lectures: Collisions in anti-de Sitter space, conformal symmetry, and holographic superconductors

In four lectures, delivered at the TASI 2010 summer school, I cover selected topics in the application of the gauge-string duality to nuclear and condensed matter physics. On the nuclear side, I focus on multiplicity estimates from trapped surfaces in AdS_5, and on the consequences of conformal symmetry for relativistic hydrodynamics. On the condensed matter side, I explain the fermion response to the zero-temperature limit of p-wave holographic superconductors.
Wearable Fabric μ Brain Enabling On-Garment Edge-Based Sensor Data Processing

Advances in artificial intelligence (AI) is an enabler for innovative wearable on-garment edge-driven AI. Such AI technologies are leveraging bioinspired methods to develop spiking neural networks (SNNs) that mimic the workings of the human brain to produce higher performance edge-based processing capabilities. Such advancements are driving the development and emergence of neuromorphic computing with SNN architectural designs. Taking inspiration from these, this article proposes and demonstrates a wearable <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Brain multilayer SNN smart garment, with an event-driven artificial SNN embedded into the garments fabric. The fabric-based multilayer SNN is interchangeable and scalable through its fabric hidden layer (FHL) and fabric patch design and can be interconnected with textile-based analog monitoring sensors. The prototype was tested to check the functionality of the individual and collective nodes of the SNN as well as evaluating the wearable <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Brain for fault tolerance scenarios (ripping and fraying) proving the prototype remained intact following the disconnection of fabric node Patches 5 and 8. Two application demonstrators were identified and tested. The first application focused on the detection of touch sensation on a forearm using the wearable <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Brain; here, real-world use cases were checked against the expected output. Fluctuations were encountered when voltage output readings were above and below the set threshold level of 2.3 V. The second application interfaced the wearable <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Brain to live neurons to demonstrate how textile pressure sensors on the surface can lead to stimulation of neurons, when validated we observed an 11-s timeframe where the brain slice was stimulated.
Impact of deficit irrigation strategies on water use and productivity for vegetable crops in a semi-arid context of Tunisia

Irrigated agriculture in arid and semi-arid environments is characterized by acute imbalance between rainfall and evapotranspiration and tough competition for water. It is against this back-drop that water use efficiency is becoming a must. This study is targeted to evaluate the effect of deficit irrigation on water requirements, yield and water productivity of potato and tomato crops under average and very high climatic conditions of 2016 and 2017 respectively. The case study is the Cherfech irrigation district located in the northern of Tunisia. For this purposes, the FAO/ CROPWAT irrigation scheduling and simulation model was used to identify appropriate deficit irrigation strategies for improving water conservation with accep table impacts on yields. Deficit irrigation strategies were evaluated through parameters of irrigation, relative crop evapotranspira- tion, relative yield loss as well as water productivity and economic water productivity. Results indicate that deficit irrigation is practicable under average water demand for both potato and tomato crops. For tomato, adoption of deficit irrigation is less feasible particularly under very high demand. For a relative yield losses threshold of 25%, results show that for potato, optimal season irrigation could be reduced by 43.3% and 31.6%, respectively for average and very high climatic conditions. For tomato crop, optimal irrigation requirements could be reduced by 33.3% for average demand and 31% for very high water demand. Regarding the water use indexes, results show that water conservation due to deficit irrigation strategies improves water productivity under the average water demand more than the very high demand. This improvement was more noticeable for potato than tomato. Furthermore, economic water productivity is more affected by the difference in potato and tomato prices for average (2016) and very high (2017) water demand conditions.
Extrinsic Calibration of Multiple 3D LiDAR Sensors by the Use of Planar Objects

Three-dimensional light detection and ranging (LiDAR) sensors have received much attention in the field of autonomous navigation owing to their accurate, robust, and rich geometric information. Autonomous vehicles are typically equipped with multiple 3D LiDARs because there are many commercially available low-cost 3D LiDARs. Extrinsic calibration of multiple LiDAR sensors is essential in order to obtain consistent geometric information. This paper presents a systematic procedure for the extrinsic calibration of multiple 3D LiDAR sensors using plane objects. At least three independent planes are required within the common field of view of the LiDAR sensors. The planes satisfying the condition can easily be found on objects such as the ground, walls, or columns in indoor and outdoor environments. Therefore, the proposed method does not require environmental modifications such as using artificial calibration objects. Multiple LiDARs typically have different viewpoints to reduce blind spots. This situation increases the difficulty of the extrinsic calibration using conventional registration algorithms. We suggest a plane registration method for cases in which correspondences are not known. The entire calibration process can easily be automated using the proposed registration technique. The presented experimental results clearly show that the proposed method generates more accurate extrinsic parameters than conventional point cloud registration methods.
Retrieval of Stratospheric Ozone Profiles from Limb Scattering Measurements of the Backward Limb Spectrometer on Chinese Space Laboratory Tiangong-2: Preliminary Results

The Backward Limb Spectrometer (BLS) onboard the Tiangong-2 (TG-2) space laboratory, the first spaceborne limb sounding instrument of China, was successfully launched on 15 September 2016, and its measurements of scattered photons of sunlight along the limb line-of-sight (LOS) in the 290–1000 nm range could be used to derive the vertical distribution of stratospheric ozone with high vertical resolution. Ozone profiles with a vertical resolution of one km in 10–40 km and 30–50 km were retrieved by the triplet and pair methods, respectively, and the ozone profiles retrieved by the BLS were compared with the ozone sounding data over four sounding stations. Meanwhile, the Ozone Mapping and Profiler Suite Limb Profiler (OMPS/LP) version 2.5 (v2.5) stratospheric ozone profile product was also introduced for comparison. The retrieval results showed a good agreement with the ozone profiles of ozone sounding and the OMPS/LP v2.5 product, and the differences were basically within 25% above 20 km, while relatively larger differences occasionally occurred below 20 km. The case studies over four sites worldwide demonstrate that the BLS is capable of measuring stratospheric ozone profiles with high vertical resolution.
A Review Study for Arabic Machine Learning and Deep Learning Methods

The fields of Natural Language Processing (NLP), Computer vision and speech processing witnessed great breakthroughs caused by the continuous advances in Deep Learning (DL). The implementation of DL techniques in Online Social Networking and Sentiment Analysis have proved to provide a state-of-the-art result in these areas. In this research, we survey the papers that claims to use the DL methods in NLP. We focus on the research that is related to the Arabic language due to the scares resources in Arabic NLP. We concluded that most of the early research in Arabic NLP focusses of OCR-Digitization and most recently is focusing on applying DL methods in Sentiment Analysis, diacrization and machine translation. We present this survey to provide the growing community of researchers in ANLP in the hope of bridging the gap between the ANLP and the English NLP.
Oxygen Isotopes in Carbonate and Phosphate of Modern Mammal Bioapatite: New Data and Critical Revision after about 25 Years from the First Recognitions

Oxygen and carbon isotopes of well-preserved skeletal remains give relevant support to archaeological and environmental reconstructions. However, the preservation of the skeletal remains must be preliminarily checked. About twenty-five years ago, a diagnostic method based on the oxygen isotope ratio in the phosphate, and carbonate, of bioapatite of modern mammals was proposed: for well-preserved samples, the and should plot near the regression line on obtained for modern mammals. In the last twenty years, techniques of analysis have changed. In the past, or were precipitated from dissolved bioapatite and analysed with the fluorination technique, whereas at present, temperature reduction (HTR) in a glassy carbon reactor with release is commonly used. Taking into account the HTR technique, for some modern mammals, we report a new + 1 on + 1 regression line, and related dispersion of the data that, in addition to mineralogical and structural methods, may be used to select samples reliable for archaeological use. In the past, other similar regression lines on modern mammals were defined by several authors. However, statistical results indicate that data used for these regression lines cannot be pooled because the hypothesis of a similar elevation is rejected.
Potential interactions between metal-based phenanthroline drugs and the unfolded protein response endoplasmic reticulum stress pathway

Abstract The unfolded protein response has recently been implicated as a mechanism by which 1,10-phenanthroline-containing coordination compounds trigger cell death. We explored the interaction of two such compounds—one containing copper and one containing manganese—with endoplasmic reticulum (ER) stress. Pretreatment with anisomycin significantly enhanced the cytotoxic activity of both metal-based compounds in A2780, but only the copper-based compound in A549 cells. The effects of pretreatment with tunicamycin were dependent on the nature of the metal center in the compounds. In A2780 cells, the cytotoxic action of the copper compound was reduced by tunicamycin only at high concentration. In contrast, in A549 cells the efficacy of the manganese compound cells was reduced at all tested concentrations. Intriguingly, some impact of free 1,10-phenanthroline was also observed in A549 cells. These results are discussed in the context of the emerging evidence that the ER plays a role in the cytotoxic action of 1,10-phenanthroline-based compounds.
Network Intrusion Detection in Encrypted Traffic

Traditional signature-based intrusion detection systems inspect packet headers and payloads to report any malicious or abnormal traffic behavior that is observed in the network. With the advent and rapid adoption of network encryption mechanisms, typical deep packet inspection systems that focus only on the processing of network packet payload contents are gradually becoming obsolete. Advancing intrusion detection tools to be also effective in encrypted networks is crucial. In this work, we propose a signature language indicating packet sequences. Signatures detect events of possible intrusions and malicious actions in encrypted networks using packet metadata. We demonstrate the effectiveness of this methodology using different tools for penetrating vulnerable web servers and a public dataset with traffic that originates from IoT malware. We implement the signature language and we integrate it into an intrusion detection system. Using our proposed methodology, the generated signatures can effectively and efficiently report intrusion attempts.
Stable numerical methods for two classes of SDEs with multiplicative noise: bilinear and scalar

We introduce a new methodology for solving bilinear systems of stochastic differential equations (SDEs), which allows us to design first weak order numerical schemes that preserve for any step-size the almost sure exponential stability of the unknown solutions, under general conditions. Moreover, the new numerical methods also keep intact, roughly speaking, the possible property of being distant from 0. To achieve our main goal, we develop a new stable method for non-linear scalar SDEs. The good performance of the new schemes is illustrated by many numerical experiments.
Low phase noise 300 GHz generation from laser diodes injection-locked to a dissipative Kerr soliton microcomb

We investigate injection-locking low cost laser diodes (LD's) to a dissipative Kerr soliton (DKS) frequency comb. The locked LD's improve the optical signal to noise of the comb teeth by over 35 dBm/nm while maintaining the low phase noise and frequency stability of the microcomb repetition rate. The resulting laser lines can be photomixed down to produce precision THz radiation.
A cone approach to the quantum separability problem

Exploiting the cone structure of the set of unnormalized mixed quantum states, we offer an approach to detect separability independently of the dimensions of the subsystems. We show that any mixed quantum state can be decomposed as $\rho=(1-\lambda)C_{\rho}+\lambda E_{\rho}$, where $C_{\rho}$ is a separable matrix whose rank equals that of $\rho$ and the rank of $E_{\rho}$ is strictly lower than that of $\rho$. With the simple choice $C_{\rho}=M_{1}\otimes M_{2}$ we have a necessary condition of separability in terms of $\lambda$, which is also sufficient if the rank of $E_{\rho}$ equals 1. We give a first extension of this result to detect genuine entanglement in multipartite states and show a natural connection between the multipartite separability problem and the classification of pure states under stochastic local operations and classical communication (SLOCC). We argue that this approach is not exhausted with the first simple choices included herein.
On the accuracy with which the lower boundary conditions can be determined in numerical models of the atmosphere

Since the prediction of climate is mainly considered as a prediction of second kind, it is indispensable to assess the accuracy with which these boundary conditions can be determined so that we can find a reasonable answer, whether climate is predictable with a sufficient degree of accuracy or not. Therefore, our contribution is mainly focused on the accuracy with which the fluxes of sensible heat and water vapor, required for predicting the lower boundary conditions in numerical models of the atmosphere using the coupled set of energy and water flux balance equations for the Earth's surface, can be determined. The parameterization schemes for the interfacial sublayer in the immediate vicinity of the Earth's surface and for the fully turbulent layer above presented and discussed here document that an appreciable degree of uncertainty exists. It is shown that the great uncertainty inherent in the universal functions of the Monin-Obukhov similarity laws on which the parameterization schemes for the fully turbulent atmospheric surface layer are based is reflected by the considerable scatter in the results of sophisticated field campaigns. This uncertainty affects also the results for the gradient-Richardson number, the turbulent Prandtl number, Pr_t, and the turbulent Schmidt number, Sc_{t,q}, (and the turbulent Lewis-Semenov number, LS_{t,q}) for water vapor used in such parameterization schemes. It is argued that the inherent uncertainty prevents that climate is predictable with a sufficient degree of accuracy.
Can Gamification Help in Increasing Motivation, Engagement, and Satisfaction? A gamified experience in teaching CS to students from other disciplines

The digital revolution is causing the new generations to demand new ways of learning. Motivating students is one crucial element of the learning-teaching process to achieve students’ learning, which is the main objective of education. Using typical gaming elements in non-game environments, gamification transforms the learning process into a more motivating and enjoyable experience. Therefore, gamification offers adequate solutions for the educational needs of the new generation of students. Although most studies have found benefits from gamification, the results have not been encouraging in some cases. Several review studies suggest that more empirical studies are needed to investigate the motivating effects of using gamification in different educational settings and for specific student types. This study wants to answer whether using the proposed gamification design affects motivation, engagement, and satisfaction in teaching computer science to university students from other disciplinary areas such as journalism and audiovisual communication. The experiment consists of a gamified experience in a virtual classroom in Moodle for four weeks on general computer science content. A control group and a test group were used. The difference between the two was the use of gamified elements (points, badges, levels, task unlocking.). From the data obtained in this study, no significant differences were found in motivation and satisfaction between the different groups or the different methodologies. Our results demonstrate a specific positive correlation between engagement, motivation, and satisfaction. It is worth noting the positive values obtained in this type of course among all the students who were part of this experience and methodology.
Robustness Evaluation of the Open Source Product Community Network Considering Different Influential Nodes

With the rapid development of Internet technology, the innovative value and importance of the open source product community (OSPC) is becoming increasingly significant. Ensuring high robustness is essential to the stable development of OSPC with open characteristics. In robustness analysis, degree and betweenness are traditionally used to evaluate the importance of nodes. However, these two indexes are disabled to comprehensively evaluate the influential nodes in the community network. Furthermore, influential users have many followers. The effect of irrational following behavior on network robustness is also worth investigating. To solve these problems, we built a typical OSPC network using a complex network modeling method, analyzed its structural characteristics and proposed an improved method to identify influential nodes by integrating the network topology characteristics indexes. We then proposed a model containing a variety of relevant node loss strategies to simulate the changes in robustness of the OSPC network. The results showed that the proposed method can better distinguish the influential nodes in the network. Furthermore, the network’s robustness will be greatly damaged under the node loss strategies considering the influential node loss (i.e., structural hole node loss and opinion leader node loss), and the following effect can greatly change the network robustness. The results verified the feasibility and effectiveness of the proposed robustness analysis model and indexes.