Miracle-dz/UniFAR-FTUs huggingface dataset:
Standard Molar Enthalpies of Formation for Ammonium/3d-Transition Metal Phosphates NH4MPO4·H2O (M = Mn2+, Co2+, Ni2+, Cu2+)

Ammonium/transition metal phosphate monohydrate NH4MPO4·H2O (M = Mn2+, Co2+, Ni2+, Cu2+) compounds were synthesized by solid-state reaction at low temperature and characterized by X-ray diffraction (XRD), FT-IR, and chemical analysis. Based on Hess’s law, the thermochemical cycles were designed for measuring the dissolution enthalpies of reactants and products using a solution-reaction isoperibol calorimeter at 298.15 K. From the dissolution enthalpies, the molar enthalpies of the five reactions designed were calculated individually. With these data and other auxiliary theromodynamic data, the standard molar enthalpies of formation of the title compounds were concluded as: ΔfHmΘ[NH4MnPO4·H2O] = (−1925.01 ± 0.25) kJ·mol−1, ΔfHmΘ[NH4CoPO4·H2O] = (−1754.91 ± 0.43) kJ·mol−1, ΔfHmΘ[NH4NiPO4·H2O] = (−1755.48 ± 0.39) kJ·mol−1, and ΔfHmΘ[NH4CuPO4·H2O] = (−1636.41 ± 0.36) kJ·mol−1.
EqBench: A Dataset of Equivalent and Non-equivalent Program Pairs

Equivalence checking techniques help establish whether two versions of a program exhibit the same behavior. The majority of popular techniques for formally proving/refuting equivalence are evaluated on small and simplistic benchmarks, omitting "difficult" programming constructs, such as non-linear arithmetic, loops, floating-point arithmetic, and string and array manipulation. This hinders efficient evaluation of these techniques and the ability to establish their practical applicability in real scenarios. This paper addresses this gap by contributing EqBench – the largest and most comprehensive benchmark for equivalence checking analysis, which contains 147 equivalent and 125 non-equivalent cases, in both C and Java languages. We believe EqBench can facilitate a more realistic evaluation of equivalence checking techniques, assessing their individual strength and weaknesses. EqBench is publicly available at: https://osf.io/93s5b/.
Passive Control with Blade-End Slots and Whole-Span Slot in a Large Camber Compressor Cascade

Suitable slot structure of the compressor blade can generate high-momentum jet flow through pressure difference between the pressure and suction surface, it has been proved that the slot jet flow can reenergize the local low-momentum fluid to effectively suppress the flow separation on the suction surface. In order to explore a slotted method for better comprehensive suppressing effects on the boundary layer separation near blade midspan and the three-dimensional corner separation, a diffusion stator cascade with large camber angle is selected as the research object. Firstly, the Slotted_1 and Slotted_2 whole-span slotted schemes are set up, then the Slotted_3 scheme with whole-span slot and blade-end slots is proposed, finally the performance of original cascade and slotted cascades is computed under a wide range of incidence angles at the Mach number of 0.7. The results show that: in the full range of incidence angles, compared with the whole-span slotted cascades, the development of the endwall secondary flow on the suction surface of Slotted_3 cascade is effectively suppressed, the degree of the mutual interference between the secondary flow and the main flow is reduced. Besides, on the suction surface of Slotted_3 cascade, the boundary layer separation near blade midspan and the corner separation are basically eliminated. As a result, compared with those of original cascade, the total pressure losses of Slotted_3 cascade are reduced in the full range of incidence angles, and its operating range of incidence angles is broadened. Moreover, compared with the whole-span slotted schemes, Slotted_3 scheme has a better adaptability to wide range of incidence angles.
Rigid 3D calibration in a surgical scenario

—Autonomy is the next frontier of research in robotic surgery and its aim is to improve the quality of surgical procedures in the next future. One fundamental requirement for autonomy is advanced perception capability through vision sensors. In this paper, we propose a novel calibration technique for a surgical scenario with a da Vinci ® Research Kit (dVRK) robot. Camera and robotic arms calibration are necessary to precise position and emulate expert surgeon. The novel calibration technique is tailored for RGB-D cameras. Different tests performed on relevant use cases prove that we signiﬁcantly improve precision and accuracy with respect to state of the art solutions for similar devices on a surgical-size setups.
The Topological Properties of the Knowledge Structure in MIS Research

- This study proposes that keyword networks can be formulated from research publications and used to examine how the keywords have been connected with each other to create new concepts. For the purpose of empirical analysis, the keyword networks are organized based on the MIS publication information from 1999 to 2008, the analysis of which reveals a number of notable results. First, the MIS field has changed so quickly that many new concepts and keywords have been introduced, and the connection of keywords is highly clustered. Second, the keyword network shows a clear power-law degree distribution, indicating that, the more popular a keyword becomes, the more that the keyword is selected by researchers and it is associated with other keywords or concepts to create new ideas. This evidence implies that the important keywords analyzed from a network perspective are different from those analyzed from a popularity perspective. Also, the interdisciplinary keywords have the potential to create new research fronts in the MIS field. This suggests that analysis from a network perspective is needed to determine the changes in important keywords over time and to better understand the process of knowledge evolution.
Towards a Blockchain-based Identity Provider

—The emerging technology blockchain is under way to revolutionize various ﬁelds. One signiﬁcant domain to apply blockchain is identity management. In traditional identity management, a centralized identity provider, representing a trusted third party, supplies digital identities and their attributes. The identity provider controls and owns digital identities instead of the associated subjects and therefore, constitutes a single point of failure and compromise. To overcome the need for this trusted third party, blockchain enables the creation of a decentralized identity provider serving digital identities that are under full con-trol of the associated subject. In this paper, we outline the design and implementation of a decentralized identity provider using an unpermissioned blockchain. Digital identities are partially stored on the blockchain and their attributes are modelled as veriﬁable claims, consisting of claims and attestations. In addition to that, the identity provider implements the OpenID Connect protocol to promote seamless integration into existing application landscapes. We provide a sample authentication workﬂow for a user at an online shop to show practical feasibility.
ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR ATTACK PREDICTION IN THE NETWORKS

With the rise of the Internet, the number of attacks has skyrocketed, and Intrusion Detection Systems (IDS) have emerged as a critical component of information security. The aim of an intrusion detection system (IDS) is to assist computer systems in dealing with attacks. This anomaly detection system builds a database of regular behavior and deviations from it, which it uses to activate when intrusions occur. IDS is divided into two types depending on the data source: host-based IDS and network-based IDS. Individual packets flowing through the network are analyzed in network-based IDS, while activities on a single device or server are analyzed in host-based IDS. IDS' feature selection aids in the reduction of classification time.In this paper, a new framework is proposed with Adaptive Neuro Fuzzy Inference System (ANFIS) for an IDS, to find the risk severity of the attacks. The proposed framework is composed Pre-Processing, Classification and Risk Severity Prediction. In this research work, the proposed ANFIS network is designed to predict the risk severity of the attacks in the IDS.
Distributed Training for Reinforcement Learning

Reinforcement learning (RL) has scaled up immensely over the last few years through the creation of innovative distributed training techniques. This paper discusses a rough time-line of the methods used to push the ﬁeld forward. I begin by summarizing the problem of reinforcement learning and general solution methods. I then discuss the training environments used to evaluate model performance. I walk through a timeline of breakthroughs in distributed training used to scale up RL models, as well as other innovations in RL training. Finally, I take a look at exciting applications of distributed training processes in complex games like Go, Dota 2, and StarCraft II
Trends in Nanophysics-ReadingSample

The success of many nanotechnologies depends on our ability to understand and control the mechanics of nano objects, such as nanotubes and nanobelts. Because of the numerous experimental difficulties encountered at this scale, simulation can emerge as a powerful predictive tool. For this, new multiscale simulation methods are needed in which a continuum model emerges from a precise, quantum mechanical description of the atomic scale. Because computing nanomechanical responses requires large systems, computationally affordable but less accurate classical atomistic treatments of the atomic scale are widely adopted and only multiscale classical atomistic-to-continuum bridging is achieved. As a first step towards achieving accurate multiscale models for nano objects, based on a quantum-mechanical description of chemical bonding, here we present an ingenious symmetry-adapted atomistic scheme that performs calculations under helical boundary conditions. The utility of the microscopic method is illustrated with examples discussing the nanomechanical response of carbon nanotubes and thermodynamical stability of silicon nanowires.
Enhance Strategy Promotion University of Medan Area to Increasing the Number of New Students during the COVID-19 Pandemic

Private and State Universities are still faced with competition between universities to increase the number of students especially during the Covid 19 period. This research aims to analyze the effect of each of the variables of e-service quality, e-satisfaction, positive Word of Mouth / WoM, either directly or indirectly. Data were collected from 200 students from one of the best private universities in North Sumatra (Medan), and processed using Structural Equation Modeling / SEM. From the results of data processing, all tested hypotheses can be accepted, and research found that to encourage positive WoM, PTS must be sensitive to the creation of student satisfaction. Positive WoM can be an effective advertising promotion tool for PTS to get new student candidates through existing student recommendations. In addition, to increase e-customer satisfaction, PTS is required to always improve service quality that is delivered through online services.
Effects of Land-Use Intensity and Land Management Policies on Evolution of Regional Land System: A Case Study in the Hengduan Mountain Region

In the last few decades, land use/land cover (LULC) has changed significantly under the influence of local planning and policy implementation, and this has had a profound impact on the regional ecological environment. By taking the Hengduan Mountain region as the study area, this study considered the demands of various commodities and services and applied the CLUMondo model to predict the trajectory of change in the land system for the years 2010–2030. The results indicate that the forest system expands significantly in this time, while the grassland and cropland systems are projected to develop intensively under the three scenarios. The high demand for livestock products is the main cause of the intensification of the grassland system under the TREND scenario, the demand for forests leads to the expansion of the forest land system under the FOREST scenario, and the significant intensification of the cropland system under the CONSERVATION scenario is closely related to an increase in the area of ecological land. The results of this study can provide a scientific reference for the optimal management of land systems in other mountainous areas.
A Review On Human Activity Recognition Based On Smart Phone Using Supervised Learning

: Human activity recognition systems are in wide diversity of applications and also in a large field of research and development. Identifying complicated activities will continue as a challenge in research area currently with focusing mainly on advanced machine learning algorithms. This article concentrates on activity recognition based on a particular dataset of UCI Machine learning repository . Some basic activities like walking, walking up stairs, walking down stairs, sitting, standing, laying are being focused. A comparison is also made between different approaches . The aim of this paper is to discuss about the different approaches which can be applied on HAR dataset.
The Quality of Relationship with Stakeholders, Performance Risk and Competitive Advantage in the Hotel, Restaurant and Café Market

While emerging markets have become an opportunity for companies in the less populated and saturated markets to expand their business, they also impose challenges for foreign partners’ competitive behavior. To offer the value that would be competitive in emerging markets, companies need to improve the quality of their relationship with business partners. Relationship quality may enhance the probability of continued interchange between companies and their stakeholders, leading to increased attractiveness for the emerging markets’ economy. This research explores antecedents (communication and relationship longevity) of relationship quality with stakeholders (suppliers, customers, and employees) and how the relationship quality with three stakeholders impacts the company’s performance risk and competitive advantage in the Lithuanian hotel, restaurant, and cafe market. The findings suggest that communication and relationship longevity have a positive effect on relationship quality with all three stakeholders. A higher level of relationship quality with stakeholders has a more positive effect on competitive advantage and a more significant negative effect on performance risk. The study expands the understanding of relationship quality antecedents (communication and relationship longevity) and relationship quality with customers, suppliers, and employees in terms of competitive advantage and performance risk in the less populated and saturated hotel, restaurant, and cafe market seeking expansion to emerging markets.
Factors Affecting Bank-specific and Macroeconomic profitability Determinants of Islamic and Conventional Banks in Pakistan

This empirical research aims to look at the factors that mark the financial performance of Islamic and traditional banking sector in Pakistan. The results of the data signify that gross domestic product and inflation is connected to the financial performance of Islamic banks in all aspects of profitability ratios and gross domestic product has insignificant relation with the return on equity and assets of conventional banks. Inflation has a weighty effect on return of assets of conventional banks. Further results indicate a negative impact of bank size on return of assets Islamic and positive impact on conventional banks. A negative impact of bank size on return on equity of Islamic and positive impact on conventional banks. There is no performance impact of bank size on price earnings ratio. Results also indicate that a negative performance impact of age of bank on return on assets of Islamic and traditional banks.
Study of the cold oxygen corona with IUVS/MAVEN

The Mars Atmosphere and Volatile Evolution Mission (MAVEN) has observed the Martian upper atmosphere for several months. The Imaging Ultraviolet Spectrograph (IUVS) is able to measure the resonance line of the atomic oxygen at 130.4 nm. From this emission we are able to derive information of the oxygen content in the Martian upper atmosphere and its variability. Atomic oxygen is a key species to better understand the chemistry, heating, dynamics and escape of the Martian upper atmosphere.
Non-Linear DCM Trajectory Optimization with Variable Center of Mass Height

—This manuscript presents an algorithm for planning the divergent component of motion trajectory. In particular, the proposed approach takes into account a variable center of mass height while considering the zero moment point as a contact feasibility criterion. The algorithm is then framed in a non-linear optimization problem. Tests have been performed in the Gazebo simulation environment using the one-meter-tall iCub humanoid robot model. Results show the effectiveness of the approach in generating feasible trajectories while walking on steps.
Integrating emotional affect into bear viewing management and bear safety education

The popularity of viewing wildlife, specifically brown bears (Ursus arctos), is increasing rapidly throughout North America. In addition, population distributions of both humans and brown bears are expanding, creating larger areas of overlap and an increased possibility of human-bear interactions. In order to prevent negative encounters and injury to either species, park managers must continue to work to encourage appropriate behavior among local citizens as well as park visitors. Human behavior, however, is a result of many complex factors, including emotion and cognition. Despite this, the effects of emotions on human-wildlife conflict remain unstudied and therefore may limit success of any mitigation efforts. In this study we employed a quantitative self-assessment questionnaire, distributed online to a representative sample of the general U.S. public, to understand the relationship between emotion and behavior within the context of human encounters with bears. Questionnaires used video clips as visual methods to illustrate a variety of brown bear encounter scenarios based on setting, the bear’s age or sex class, and bear behavior. Following each video, respondents were asked to rate the intensity of their affective responses using the Positive and Negative Affect Schedule and then rate the likelihood of performing several listed actions as well as the perceived appropriateness of each action. Results demonstrate significant variation in negative affect and relative consistency in positive affect across brown bear encounter scenarios. In general, respondents seemed to be aware of appropriate behavior during encounters with brown bears, but affective responses may limit their ability to behave accordingly. Further, feelings of fear and hostility increased the impact of current emotion on in-the-moment decisions. These results and suggestions provided by respondents were then used to create a set of meaningful recommendations to improve the efficacy of current bear management and safety education.
GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices Based on Fine-Grained Structured Weight Sparsity

It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices, because even the powerful modern mobile devices are considered as “resource-constrained” when executing large-scale DNNs. It necessitates the sparse model inference via weight pruning, i.e., DNN weight sparsity, and it is desirable to design a new DNN weight sparsity scheme that can facilitate real-time inference on mobile devices while preserving a high sparse model accuracy. This paper designs a novel mobile inference acceleration framework GRIM that is General to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and that achieves Real-time execution and high accuracy, leveraging fine-grained structured sparse model Inference and compiler optimizations for Mobiles. We start by proposing a new fine-grained structured sparsity scheme through the Block-based Column-Row (BCR) pruning. Based on this new fine-grained structured sparsity, our GRIM framework consists of two parts: (a) the compiler optimization and code generation for real-time mobile inference; and (b) the BCR pruning optimizations for determining pruning hyperparameters and performing weight pruning. We compare GRIM with Alibaba MNN, TVM, TensorFlow-Lite, a sparse implementation based on CSR, PatDNN, and ESE (a representative FPGA inference acceleration framework for RNNs), and achieve up to <inline-formula><tex-math notation="LaTeX">$14.08\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>14</mml:mn><mml:mo>. </mml:mo><mml:mn>08</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="niu-ieq1-3089687.gif"/></alternatives></inline-formula> speedup.
ISOZYME DIFFERENTIATION OF PZNUS MUG0 TURRA AND PZNUS

The genetic structure of two genera from the Pinus mugo complex was studied based on starch gel electrophoresis of dormant buds. In total 7 populations have been scored. Three subpopulations of Pinus mugo Turra originating from the Giant Mountains (KrkonoSe Mts. Northern part of the Czech Republic) and four subpopulations of Pinus x pseudopumilio (Willk.) Beck (a stabilized hybrid between l? mugo Turra and P rotundatu Link) originating from the Bohemian Forest peat bogs (summa Mts. Southwestern part of the Czech Republic), in particular. The genotypic frequencies at 16 polymorphic enzyme loci of MDH, PGM, SKDH, LAP, FEST, PEROX, DIA, GOT, PEP, and GDH were scored. After our results P mugo and l? x pseudopumilio are clustered into two clearly divorced groups, corresponding with the taxonomic status of these species and/or subspecies and geographic site. The Got-A, allele was observed with low frequency in P mugo. The Lup-A 1 occurred with relative high frequency in hybrid l? x pseudopumilio. Occurrence of some Pgm, Skdh-A, Mdh-C, Fest-A and Got-B alleles might be considered more like site-specific than species-specific.
Weaponizing Middleboxes for TCP Reflected Amplification

Reﬂective ampliﬁcation attacks are a powerful tool in the arsenal of a DDoS attacker, but to date have almost exclusively targeted UDP-based protocols. In this paper, we demonstrate that non-trivial TCP-based ampliﬁcation is possible and can be orders of magnitude more effective than well-known UDP-based ampliﬁcation. By taking advantage of TCP-non-compliance in network middleboxes, we show that attackers can induce middleboxes to respond and amplify network traf-ﬁc. With the novel application of a recent genetic algorithm, we discover and maximize the efﬁcacy of new TCP-based reﬂective ampliﬁcation attacks, and present several packet sequences that cause network middleboxes to respond with substantially more packets than we send. We scanned the entire IPv4 Internet to measure how many IP addresses permit reﬂected ampliﬁcation. We ﬁnd hundreds of thousands of IP addresses that offer ampliﬁcation factors greater than 100 × . Through our Internet-wide measurements, we explore several open questions regarding DoS attacks, including the root cause of so-called “mega ampliﬁers”. We also report on network phenomena that causes some of the TCP-based attacks to be so effective as to technically have inﬁnite ampliﬁcation factor (after the attacker sends a constant number of bytes, the reﬂector generates trafﬁc indeﬁnitely). We have made our code publicly available.