Miracle-dz/UniFAR-FTUs huggingface dataset:
Message Passing and Metabolism

Active inference is an increasingly prominent paradigm in theoretical biology. It frames the dynamics of living systems as if they were solving an inference problem. This rests upon their flow towards some (non-equilibrium) steady state—or equivalently, their maximisation of the Bayesian model evidence for an implicit probabilistic model. For many models, these self-evidencing dynamics manifest as messages passed among elements of a system. Such messages resemble synaptic communication at a neuronal network level but could also apply to other network structures. This paper attempts to apply the same formulation to biochemical networks. The chemical computation that occurs in regulation of metabolism relies upon sparse interactions between coupled reactions, where enzymes induce conditional dependencies between reactants. We will see that these reactions may be viewed as the movement of probability mass between alternative categorical states. When framed in this way, the master equations describing such systems can be reformulated in terms of their steady-state distribution. This distribution plays the role of a generative model, affording an inferential interpretation of the underlying biochemistry. Finally, we see that—in analogy with computational neurology and psychiatry—metabolic disorders may be characterized as false inference under aberrant prior beliefs.
A Compression-Based Method for Detecting Anomalies in Textual Data

Nowadays, information and communications technology systems are fundamental assets of our social and economical model, and thus they should be properly protected against the malicious activity of cybercriminals. Defence mechanisms are generally articulated around tools that trace and store information in several ways, the simplest one being the generation of plain text files coined as security logs. Such log files are usually inspected, in a semi-automatic way, by security analysts to detect events that may affect system integrity, confidentiality and availability. On this basis, we propose a parameter-free method to detect security incidents from structured text regardless its nature. We use the Normalized Compression Distance to obtain a set of features that can be used by a Support Vector Machine to classify events from a heterogeneous cybersecurity environment. In particular, we explore and validate the application of our method in four different cybersecurity domains: HTTP anomaly identification, spam detection, Domain Generation Algorithms tracking and sentiment analysis. The results obtained show the validity and flexibility of our approach in different security scenarios with a low configuration burden.
The Stochastic Nature of Functional Responses

Functional responses are non-linear functions commonly used to describe the variation in the rate of consumption of resources by a consumer. They have been widely used in both theoretical and empirical studies, but a comprehensive understanding of their parameters at different levels of description remains elusive. Here, by depicting consumers and resources as stochastic systems of interacting particles, we present a minimal set of reactions for consumer resource dynamics. We rigorously derived the corresponding system of ODEs, from which we obtained via asymptotic expansions classical 2D consumer-resource dynamics, characterized by different functional responses. We also derived functional responses by focusing on the subset of reactions describing only the feeding process. This involves fixing the total number of consumers and resources, which we call chemostatic conditions. By comparing these two ways of deriving functional responses, we showed that classical functional response parameters in effective 2D consumer-resource dynamics differ from the same parameters obtained by measuring (or deriving) functional responses for typical feeding experiments under chemostatic conditions, which points to potential errors in interpreting empirical data. We finally discuss possible generalizations of our models to systems with multiple consumers and more complex population structures, including spatial dynamics. Our stochastic approach builds on fundamental ecological processes and has natural connections to basic ecological theory.
Entanglement and Photon Anti-Bunching in Coupled Non-Degenerate Parametric Oscillators

We analytically and numerically show that the Hillery-Zubairy’s entanglement criterion is satisfied both below and above the threshold of coupled non-degenerate optical parametric oscillators (NOPOs) with strong nonlinear gain saturation and dissipative linear coupling. We investigated two cases: for large pump mode dissipation, below-threshold entanglement is possible only when the parametric interaction has an enough detuning among the signal, idler, and pump photon modes. On the other hand, for a large dissipative coupling, below-threshold entanglement is possible even when there is no detuning in the parametric interaction. In both cases, a non-Gaussian state entanglement criterion is satisfied even at the threshold. Recent progress in nano-photonic devices might make it possible to experimentally demonstrate this phase transition in a coherent XY machine with quantum correlations.
How robust is the association between youth unemployment and later mental health? An analysis of longitudinal data from English schoolchildren

Background Several studies show that youth unemployment is associated with worse mental health later in life. However, existing studies report results for only one model, or a few models, and use regression adjustment to support causal claims. We use two novel methods to address these gaps in the literature. Methods We use data from Next Steps, a cohort study of English schoolchildren who entered the labour market in the aftermath of the 2008–2009 global financial crisis, and measure mental health using the 12-item General Health Questionnaire (GHQ-12) at age 25. We use specification curve analysis and a negative control outcome design (a form of placebo test) to test whether associations between youth unemployment and later GHQ-12 scores are sensitive to model specification or are likely to be confounded by unobserved factors. Results We find that the association between unemployment and later GHQ-12 is qualitatively similar across 99.96% of the 120 000 models we run. Statistically significant associations with two placebo outcomes, height and patience, are not present when regression adjustments are made. Conclusions There is clear evidence that youth unemployment is related to later mental health, and some evidence that this cannot be easily explained by unobserved confounding.
Multi-strategy intervention increases school implementation and maintenance of a mandatory physical activity policy: outcomes of a cluster randomised controlled trial

Objectives To assess if a multi-strategy intervention effectively increased weekly minutes of structured physical activity (PA) implemented by classroom teachers at 12 months and 18 months. Methods A cluster randomised controlled trial with 61 primary schools in New South Wales Australia. The 12-month multi-strategy intervention included; centralised technical assistance, ongoing consultation, principal’s mandated change, identifying and preparing school champions, development of implementation plans, educational outreach visits and provision of educational materials. Control schools received usual support (guidelines for policy development via education department website and telephone support). Weekly minutes of structured PA implemented by classroom teachers (primary outcome) was measured via teacher completion of a daily log-book at baseline (October–December 2017), 12-month (October–December 2018) and 18-month (April–June 2019). Data were analysed using linear mixed effects regression models. Results Overall, 400 class teachers at baseline, 403 at 12 months follow-up and 391 at 18 months follow-up provided valid primary outcome data. From baseline to 12-month follow-up, teachers at intervention schools recorded a greater increase in weekly minutes of PA implemented than teachers assigned to the control schools by approximately 44.2 min (95% CI 32.8 to 55.7; p<0.001) which remained at 18 months, however, the effect size was smaller at 27.1 min (95% CI 15.5 to 38.6; p≤0.001). Conclusion A multi-strategy intervention increased mandatory PA policy implementation. Some, but not all of this improvement was maintained after implementation support concluded. Further research should assess the impact of scale-up strategies on the sustainability of PA policy implementation over longer time periods. Trial registration number Australian New Zealand Clinical Trials Registry (ACTRN12617001265369).
SEIPS 101 and seven simple SEIPS tools

© Author(s) (or their employer(s)) 2021. Reuse permitted under CC BYNC. No commercial reuse. See rights and permissions. Published by BMJ. In the past 15 years, SEIPS (Systems Engineering Initiative for Patient Safety) and related conceptual models 5 were developed to study and improve healthcare. These theoretical models depict how work systems affect healthrelated outcomes, such as patient safety, and can be used to guide research and improvement efforts. Various versions of the SEIPS model have been used by academics and practitioners, but a recent review argued that broader use and benefits can be achieved through ‘an easytouse version of the model and simplified tools for model application’. This concords with repeated requests we have received for simple, practical tools to apply SEIPS and with the general critique regarding the dearth of easy to use systems engineering tools. Accordingly, we offer SEIPS 101, a simplified, practiceoriented SEIPS model meant for easy use by practitioners, researchers and others, regardless of prior familiarity with SEIPS. We also for the first time present seven simple SEIPS tools virtually anyone can use offtheshelf.
Physicochemical properties of banana peel powder in functional food products

Saba banana is one of the most favourite banana varieties in Malaysia. It also generates a concerning amount of waste as the only value part is the pulp. Banana peel has been proven to have a lot of nutritional content that should not be ignored and wasted. Conversion of banana peel to powder will convert it into a value-added product in the food industry. Preliminary study on the protein, moisture, crude fibre and fat was carried out for 2 different stages of maturity which are stage 2 (light green) and stage 5 (yellow with green tips). Both stages have almost similar proximate analysis values and hence stage 2 of banana was chosen for the next analysis, due to the high waste of banana peel in stage 2 in SME (Small Medium Enterprise) in the production of banana chips. This study characterized the banana peel powder for storage application at different sizes of particles which are 1-125 μm, 126-250 μm and >250 μm. As the particles of the banana peel powder increases, the flowability of the powder decreases. It was found that green banana peel (stage 2) at sample A (1-125 μm) has the highest true density, but sample C (>250 μm) has the highest bulk density. The pH mean value of the three sizes also ranges from 6.15 to 6.46. The powder’s pH (6.15-6.46) is in the standard wheat flour acidity range. Moreover, the study on the water holding capacity at different temperatures identifies that banana peel powder has a high water holding capacity ranging from 3.83 to 6.45 g water/g dry sample. In short, this study is a success in identifying the potential of banana peel powder in food products based on its characteristics.
Properties of Poly(vinyl alcohol) Plasticized by Magnesium Chloride

The plasticized poly(vinyl alcohol)(PVA) films were prepared by the casting method using magnesium chloride(MgCl2) as the plasticizer.The interaction between MgCl2 and PVA was analysised by Fourier transform infrared(FTIR) spectroscopy.The change of the crystalline and thermal properties of PVA with the content of MgCl2 were measured by X-ray diffraction measurements(XRD),differential scanning calorimetry and thermogravimetric analysis(TGA) respectively.The mechanical properties were measured as a function of MgCl2 content.It was found that MgCl2 could form considerable interaction with PVA and form a complex.The XRD showed that the crystallinity of PVA film decreased with the content of MgCl2,which would be ascribed to the interaction of MgCl2 with PVA molecules,causing the destroying of the intra/inter molecular hydrogen bonding between PVA chains.The glass transition temperature of PVA also decreased with the content of MgCl2.The TGA results show that the thermal stabilities of PVA decreased with the addition of MgCl2 and the thermal degradation process was changed.The mechanical testing showed that PVA become soft,with lower tensile strength and higher elongation at break after modification.
THE EFFECT OF INTEGRAL STIFFENING ON THE FATIGUE OF THIN-WALLED PLATES SUBJECTED TO SHEAR

The paper presents results of experimental investigations of fatigue behaviour of thin-walled integrally stiffened plates. Structures under consideration was treated as the skin part of the semi-monocoque construction and subjected to shear load what led to the post-buckling state of deformation. Tested plate was manufactured as an integral construction from 2024-T3 aluminium alloy with use of the CNC milling machine. Experimental results of preliminary static test in the form of displacement fields obtained by the digital image correlation method were compared to the results of numerical calculations. On the basis of this comparison, the correctness of the stress distribution obtained by numerical method was found and the load level for fatigue test was chosen. The conducted experimental tests showed a significant influence of the stiffener arrangement on the form of fatigue failure.
Tile Type C Pelvic Fractures: Initial Management, Surgery and Rehabilitation Results of 12 Cases with Literature Review

We report the results of 12 patients followed for Tile type C pelvic fractures treated with percutaneous sacroiliac screwing combined to anterior ring reduction and fixation provided by plate or external fixator during the last 4 years. this study was carried out in the light of literature review to focus on the therapeutic principles of the initial management, the different surgical techniques described for the definitive treatment of bone lesions, the current attitude regarding the rehabilitation of patients and the perspectives under development.
Analysis on Regional Economic Development Efficiency in China : Based on DEA Model and Malmquist Index

Based on the establishment of evaluation index system of efficiency, this paper employs DEA model and malmquist index to investigate the relative efficiency of economic development in 30 province-level regions in China from 2015 to 2019. The results show that the resource allocation in most regions are not reasonable, which leads to lower economic development efficiency. In addition, this paper also finds that Chinese regional economic development is unbalanced. Therefore, it is crucial to attach great importance to technological innovation and deepen the reform in an all round way to promote economic development efficiency.
Convolution Neural Network Based Enhanced Computerized Technique for Brain Tumour Detection

Recent years have witnessed how medical imaging is growing these days. Brain Tumour is considered as the world's 10th largest fatal human disease. In this perspective, it is a challenging task for the neurosurgeons to reduce the death rates that occur due to brain tumour. For reducing the death rates, it is the primary duty to identify the tumour cells in their early stage. It is very difficult for the tumour cells to be identified with naked eye for by looking to the MR images. In this paper, an automatic cancer cell detection system is proposed to minimize the burden of the neurosurgeons to identify the cancer cells. Here, CNN (Convolution Neural Network) based system with Median filtering and texture feature extraction is proposed and it gives results with 93 % accuracy and at present it has been proved as the better model for brain tumour cells detection.
A System for Fake News Detection by using Supervised Learning Model for Social Media Contents

The evolution of ICTs has dramatically increased the number of people with internet access, which has altered the way the information is consumed. As a result, fake news has become one of the main concerns because it could destabilize governments and put them at risk for contemporary society. This can be seen in the example, consider the United States. Electoral campaign, where the term “fake news” became famous as a result of the hoaxes impact on the final outcome. This research work studies the possibility of using deep learning techniques to discriminate against counterfeit news on the Internet using only their text. To do so, three different neural network architectures are suggested, one on the basis of BERT, Google's modern linguistic model that achieves cutting-edge results. In this project are the applications for the detection of ‘fake news,’ which is misleading news stories from reputable sources of the NLP (Natural Language Processing) methods. This approach has been implemented and examined in the form of a software system. Can you build a prototype that can differentiate between “real” and “fake” news? In this novel fake news detection approach by SVM achieved the accuracy of 92 % and Naive Bayes achieved the accuracy of 73%. In this study SVM can better than Naive Bayes classifier model in new prediction approach.
Identifying Considerable Anomalies and Conflicts in ABAC Security Policies

Nowadays security of shared resources and big data is an important and critical issue. With the growth of information technology and social networks, data and resources are shared in the distributed environment such as cloud and fog computing. Various access control models protect the shared resources from unauthorized users or malicious intruders. Despite the attribute-based access control model that meets the complex security requirement of todays' new computing technologies, considerable anomalies and conflicts in ABAC policies affect the efficiency of the security system. One important and toughest task is policy validation thus to detect and eliminate anomalies and conflicts in policies. Though the previous researches identified anomalies, failed to detect and analyze all considerable anomalies that results vulnerable to hacks and attacks. The primary objective of this paper is to study and analyze the possible anomalies and conflicts in ABAC security policies. We have discussed and analyzed considerable conflicts in policies based on previous researches. This paper can provide a detailed review of anomalies and conflicts in security policies.
An Incremental Iterative Acceleration Architecture in Distributed Heterogeneous Environments With GPUs for Deep Learning

The parallel computing capabilities of GPUs have a significant impact on computationally intensive iterative tasks. Offloading part or all of the deep learning tasks from the CPU to the GPU for execution is mainstream. However, a large number of redundant iterative calculations exist in the iterative process of computing tasks. Therefore, we propose a GPU-based distributed incremental iterative computing architecture that can make full use of distributed parallel computing and GPU memory structure. The architecture supports deep learning and other computationally intensive iterative applications by optimizing data placement and reducing redundant iterative calculations. To support block-based data partitioning and coalesced memory access on GPUs, we propose GDataSet, an abstract data set. The GPU incremental iteration manager called GTracker is designed to be responsible for GDataSet cache management on the GPU. In order to solve the limitation of on-chip memory size, we propose a variable sliding window mechanism. It improves the hit rate of cache access and the speed of data access by realizing the best block arrangement between on-chip memory and off-chip memory. Besides, a communication channel based on an incremental iterative model is designed to support data transmission and task communication in cluster computing. Finally, we implement the proposed architecture based on Spark 2.4. 1 and CUDA 10.0. Comparative experiments with widely used computationally intensive iterative applications (K-means, LSTM, etc.) show that the incremental iterative acceleration architecture can significantly improve the efficiency of iterative computing.
Robust Security Constrained Unit Commitment considering AC power flow constraints

The uncertainty of the output of renewable energy units will affect the safe operation of the power system. At present, many unit commitment models based on DC power flow consider the uncertainty. These studies only consider the active power flow, which can improve the efficiency of the solution, but it is difficult to apply to the research fields related to voltage and reactive power. Therefore, the unit commitment considering uncertainty based on AC power flow constraints is worthy of further study. This paper introduces the AC power flow constraint into the robust security-constrained unit commitment (SCUC) model, uses the second-order cone relaxation technology to deal with the nonlinear constraint. The uncertainty set of polyhedrons is used to describe the uncertainty of wind power, and a robust SCUC model based on second-order cone programming is established. The proposed model is solved by Column and constraint generation algorithm, and the reliability of the proposed model is proved by case study.
China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program

In this article, we introduce the Intelligent Vehicles Future Challenge of China (IVFC), which has lasted 12 years. Some key features of the tests and a few interesting findings of IVFC are selected and presented. Through the IVFCs held between 2009 and 2020, we gradually established a set of theories, methods, and tools to collect tests? data and efficiently evaluate the performance of autonomous vehicles so that we could learn how to improve both the autonomous vehicles and the testing system itself.
An Optimized Deep Learning Framework for Network Intrusion Detection System (NIDS)

Securing the network and associated infrastructure is a never-ending process. Researchers and network administrators are continuously developing new tools and technologies to protect the network and related infrastructure. Intrusion Detection System (IDS) is one of them. Artificial neural networks can learn and improve its performance if appropriately trained with the optimal feature vectors. In this paper, a Recurrent Neural Networks (RNN) and LSTM based machine learning model is proposed to detect and classify the type of intrusion depending upon the input data. Once trained, the proposed method will remember the classification task and subsequently become able to classify the attack whenever a new set of inputs are provided to the pre-trained model. The proposed RNN and LSTM model has been tested over the NSL-KDD data-set and evaluated for varying different parameters (i.e., layers with a fixed number of neurons) and the width of the proposed models (i.e., number of neurons on a single hidden layer). The results proved that the proposed model performs better than the existing models in terms of accuracy.