Miracle-dz/UniFAR-FTUs huggingface dataset:
A model integrating lean and green practices for viable, sustainable, and digital supply chain performance

ABSTRACT In disrupted environments, the concept of digitalisation, viability, and sustainability have attracted considerable attention for the last decade. Digital, lean, and green capabilities have stimulated enormous advances in the development of industries. This paper discusses the current state of the art literature regarding sustainable and digital supply chain management performance. It reviews the relationship between digital technologies, lean, green, sustainability, and supply chain performance. A literature review approach, combined with a bibliometric analysis was adopted for robust field analysis. A set of 86 papers has been analysed and synthesised. The analysis of these papers has enabled the identification of the impact of Industry 4.0 technologies on the supply chain performance; the relationship of lean, green, and sustainability capabilities and digital supply chain; and the implications of the digital transformation of those capabilities. It presents a roadmap model integrating lean and green for viable, sustainable, and digital supply chain performance. The study is useful for academics and practitioners as it provides a roadmap framework and a solid background for integrating lean and green in the digital supply chain to achieve sustainable and viable performance as it outlines the necessity for incorporating the sustainability and viability performances in the digital supply chains.
Support Based Graph Framework for Effective Intrusion Detection and Classification

Intrusion Detection System is one of the worthwhile areas for researchers for a long. Numbers of researchers have worked for increasing the efficiency of Intrusion Detection Systems. But still, many challenges are present in modern Intrusion Detection Systems. One of the major challenges is controlling the false positive rate. In this paper, we have presented an efficient soft computing framework for the classification of intrusion detection dataset to diminish a false positive rate. The proposed processing steps are described as; the input data is at first pre-processed by the normalization process. Afterward, optimal features are chosen for the dimensionality decrease utilizing krill herd optimization. Here, the effective feature assortment is utilized to enhance classification accuracy. Support value is then estimated from ideally chosen features and lastly, a support value-based graph is created for the powerful classification of data into intrusion or normal. The exploratory outcomes demonstrate that the presented technique outperforms the existing techniques regarding different performance examinations like execution time, accuracy, false-positive rate, and their intrusion detection model increases the detection rate and decreases the false rate.
Development of an analytical method for the simultaneous determination of the 17 EU Watch List compounds in surface waters: a Spanish case study

Environmental context In 2015, the European Union issued a Watch List of organic pollutants resulting from anthropogenic activities. A major obstacle to controlling these pollutants in the environment is the difficulty of their simultaneous measurement at low concentrations. We developed an analytical method for the simultaneous determination in surface water of the 17 pollutants included in the EU Watch List. Abstract A multi-class method was developed for the simultaneous determination of the 17 organic pollutants included in the EU Decision 2015/495 regarding surface waters. The target analytes were three estrogens, four pharmaceuticals, a food additive, a solar filter and eight pesticides. The target compounds were extracted through solid-phase extraction with Oasis HLB cartridges. Determination was carried out by high performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS). All compounds were satisfactorily determined in one single injection, with a chromatographic run time of only 20 min. The limit of detection (LOD) of the proposed method was in the range of 0.08–13 ng L−1. For most of the target compounds, except for 17α-ethinylestradiol, 17β-estradiol and estrone, the LOD values were equal or below those required by the EU Decision 2015/495. Accuracy was in the range of 69–120 %, whereas inter-day variability was below 20 % in all cases. The validated method was applied to assess the presence of these compounds in Mediterranean surface waters. Samples were collected from 18 sampling sites located in the south of Spain. Up to 14 compounds were detected, among them, the pharmaceutical compounds were the pollutants most frequently detected at concentration levels up to 1158 ng L−1 (diclofenac).
Deception

Recent events that revolve around fake news indicate that humans are more susceptible than ever to mental manipulation by powerful technological tools. In the future these tools may become autonomous. One crucial property of autonomous agents is their potential ability to deceive. From this research we hope to understand the potential risks and benefits of deceptive artificial agents. The method we propose to study deceptive agents is by making them interact with agents that detect deception and analyse what emerges from these interactions given multiple setups such as formalisations of scenarios inspired from historical cases of deception.
Comparison of Airborne Electromagnetic and Ground-based Resistivity Observations: Case Study Banda Aceh Basin

Airborne measurements are very useful to cover very large areas. Nine month after the Aceh Tsunami and Earthquake in 2004, BGR (Federal Institute for Geoscience and Natural Resources) conducted a fresh water supply exploration survey within a project called Helicopter Project Aceh (HELP ACEH). The helicopter-borne electromagnetic (HEM) device operates at five frequencies. The HEM can estimate the 1D resistivity models down to a depth of 150 m for the high electrical resistivity areas and 50 m for low electrical resistivity areas. In this paper, the airborne data of 2005 are compared with resistivity data acquired in Banda Aceh basin in 2018. The HEM output consists of 1D resistivity models derived by inversion of the processed data. These 1D resistivity models are compared with the 2D resistivity models derived from ground-based resistivity measurements. However, the 2D models on the ground are transformed into 1D resistivity models so it can be used for comparison. The transformation is conducted by averaging the resistivity values in the each layer, so every layer only has one resistivity value. Both methods are influenced by many factors. For example, resistivity on the ground is affected by local conditions. The airborne measurements are also influenced by objects that are at the surface of the ground. In some cases, the airborne resistivity models have some differences in absolute resistivity values, but they often have the same structural pattern compared with the ground-based resistivity models.
Smart contract sharding with proof of execution

Nowadays,Decentralized Networks based on Blockchain technology are actively researched. A special place in these researches is occupied by Smart Contracts that are widely used in many areas, such as Decentralized Finance (DeFi), real estate, gambling, electoral process, etc. Nevertheless, the possibility of their widespread adoption is still not a solved problem. This is caused by the fact of their limited flexibility and scalability. In other words, Smart Contracts cannot process a large number of contract calls per second, lack of direct Internet access, inability to operate with a large amount of data, etc. This article is devoted to the development of the Sharding Concept for Decentralized Applications (DApps) that are expressed in form of Smart Contracts written in WebAssembly. The aim of the research is to offer a new Concept of Smart Contract that will increase the scaling due to applying the idea of Sharding that allows avoiding doing the same work by all nodes on the Network and flexibility due to thepossibility of interaction with the Internet without special Oracles.During the research, decentralized 0ata storages with the possibility of collective decision-making were developed. The scheme of forming Drives that assumes that each Contract is executed by a set of randomly selected nodes that allows avoiding cahoots and prevents Sybil Attack is offered. Such an approach allowed using Drives as a base layer for Smart Contracts. Moreover, Drives can be used as a standalone solution for decentralized data storing.The features of coordination of results of Contracts execution that greatly expands the possibilities of the Contracts compared to Ethereum Smart Contracts, and, in particular, allow the Contracts to interact with the Internet are described. The Rewards Concept that incentivizes all nodes that honestly execute the Contracts, unlike other systems where only the block producer is rewarded, is developed. It is based on the specially developed Proof of Execution –a special algorithm that allows detecting all the nodes that honestly execute the Contracts. In order to make the Proof of Execution more compact, an extension for the existing discrete logarithm zero-knowledge proofs that makes it possible to consistentlyprove knowledge of dynamically expanding set of values with minimal computational and memory complexity so-called Cumulative Discrete Logarithm Zero-Knowledge Proof is developed.Thus, in this article, the new concept of Smart Contracts Sharding empowered by economic leverages is researched. The main advantages of the proposed approach are the possibility of interaction with the Internet and big data processing. Moreover, the mechanism of incentivizing nodes to honestly execute the Smart Contracts is developed. In addition, the Cumulative Proof that is necessary for the cryptographic strength of the specified mechanism is offered and its correctness is proven. The obtained results can be used to implement Smart Contracts in decentralized systems, in particular, working on the basis of Blockchain technology, especially in the case of demanding high bandwidth and performance
EDUARDO RIBEIRO AVENUE: Rescuing the past as a guide for the tourism in the city of Manaus-AM

This study examines the historical, socio-cultural and tourist elements that are part of the composition of Eduardo Ribeiro Avenue, located in the city of Manaus-AM. Having as its general objective: to investigate how the Eduardo Ribeiro Avenue and the historical and tourist aspects that compose it, can be analysed in the context of heritage interpretation. Being its specific objectives: to identify the history of this avenue and its importance to the formation of cultural manauara's patrimony, raise the current elements that complete the landscape of the street and analyse its relationship with tourism and, suggests a touristic visitation route focused specifically for the Eduardo Ribeiro Avenue having as foundation the principles of patrimonial interpretation. A qualitative research with descriptive and exploratory objectives using bibliographical, documental and field technical procedures. Concerning the results, the Eduardo Ribeiro Avenue is considered the main artery of the centre of Manaus, this street has throughout its extension important historical buildings in addition to being one of the centres of commerce in the capital. The historicity is embodied in its urban layout, in its paving, in its cobblestones, in the century-old buildings that make it up along with its infrastructure, making the Avenue a very relevant space for tourist activity in the capital. Its relationship with tourism is based on the perspective of the rescue of a historical legacy for the manauara society and most importantly, the possibility of scripting the aforementioned avenue by the local tourist trade having as a foundation the principles of patrimonial interpretation. Aceito 26/Abr/2021 Publicado 30/Set/2021 Bachelor in Tourism yara01magabi@gmail.com Master in Tourism and Hospitality Undergraduate student in Tourism DOI: : https://doi.org/10.14210/at.v6n3. p27-40 ©2021 autores. Publicado por Univali Esse artigo é de livre acesso sob a licença
Multi-Armed Bandits With Correlated Arms

We consider a multi-armed bandit framework where the rewards obtained by pulling different arms are correlated. We develop a unified approach to leverage these reward correlations and present fundamental generalizations of classic bandit algorithms to the correlated setting. We present a unified proof technique to analyze the proposed algorithms. Rigorous analysis of C-UCB (the correlated bandit versions of Upper-confidence-bound) reveals that the algorithm end up pulling certain sub-optimal arms, termed as <italic>non-competitive</italic>, only <inline-formula> <tex-math notation="LaTeX">$\mathrm {O}(1)$ </tex-math></inline-formula> times, as opposed to the <inline-formula> <tex-math notation="LaTeX">$\mathrm {O}(\log T)$ </tex-math></inline-formula> pulls required by classic bandit algorithms such as UCB, TS etc. We present regret-lower bound and show that when arms are correlated through a latent random source, our algorithms obtain <italic>order-optimal</italic> regret. We validate the proposed algorithms via experiments on the MovieLens and Goodreads datasets, and show significant improvement over classical bandit algorithms.
A hybridized discontinuous Galerkin method for Poisson-type problems with sign-changing coefficients

In this paper, we present a hybridized discontinuous Galerkin (HDG) method for Poisson-type problems with sign-changing coefficients. We introduce a sign-changing stabilization parameter that results in a stable HDG method independent of domain geometry and the ratio of the negative and positive coefficients. Since the Poisson-type problem with sign-changing coefficients is not elliptic, standard techniques with a duality argument to analyze the HDG method cannot be applied. Hence, we present a novel error analysis exploiting the stabilized saddle-point problem structure of the HDG method. Numerical experiments in two dimensions and for varying polynomial degree verify our theoretical results.
A mixed method evaluation of cooperative learning using jigsaw classroom model in clinical pharmacology course

Purpose To evaluate use of cooperative learning using jigsaw classroom model in clinical pharmacology course for undergraduate university students. Methods A sequential mixed method study design was used. Jigsaw learning was used for teaching selected topics in clinical pharmacology course in addition to traditional lectures used to conduct the remaining topics. At the end of the course, students completed the Jigsaw learning assessment instrument that measured attitude, knowledge, skills and satisfaction. In-depth interviews were conducted to explore their experiences. Test scores of teaching content taught by Jigsaw learning was compared to the content delivered by traditional lectures. Results Lowest mean score depicting strongly positive response were observed for survey item-3 (1.9±0.98), item-13 (1.9±0.88) and item-19 (1.8±0.86) within the subscales measuring attitude, skills and satisfaction respectively. Difference between the test performance in course content delivered by jigsaw classroom technique compared to traditional lectures was statistically significant (p<0.001). Difference between mean responses of high and low test scoring students was significant for survey item-4 (p=0.04), item-8 (p=0.001), item-11, 12 (p=0.01, 0.02) and item-16 (p=0.03). Conclusion Jigsaw classroom model had positive effects on the attitude, knowledge, skills, satisfaction and test performance of students in clinical pharmacology course.
EVOLUTION OF DIAGNOSTIC TECHNIQUES OF DEATH: AYURVEDA PERSPECTIVE AND MODERN TRENDS IN INDIA

Background: Death is the permanent and irreversible cessation of the tripod of life, viz. the brain, the heart and the lungs. However, there exist philosophical, religious and cultural differences in the concept of death. The diagnosis of death has medical and legal implications which have come a long way over the years and in the regions. Method: The references available regarding the diagnostic techniques for declaring death in classical text were studied and compared with the available information in the contemporary medical literatures and published articles. Result: A number of Ayurveda literatures such as Charaka Samhita, Sushruta Samhita, Astangahrdayam, Vishavaidyasarasamuchhaya, Vishavaidya jyotsnika etc. written in different era by authors from different region have put forward their techniques for diagnosing death which are mostly similar, simple and gross. With the advent of technology and the development of Modern medical sciences, the clincians in India have been following the western system mostly the one practiced in United Kingdom for diagnosing death. The focus has shifted the cardio-respiratory cessation to brain-stem death due to the development of artificial ventilator system and scope for organ transplantation. However, there is need for universal guidelines and much progress have been made regarding the same. Some of the governing laws in this regard in India are Transplantation of Organ Act (THO Act 1994, 2011, 2014) and THO Rules 1995. Conclusion: Ayurveda criteria also focused on declaring death based on the cessation of the function of the three vital organs, although using different sets of techniques. Better, more specific and accurate techniques have evolved over the years.
PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel

Linux kernel stable versions serve the needs of users who value stability of the kernel over new features. The quality of such stable versions depends on the initiative of kernel developers and maintainers to propagate bug fixing patches to the stable versions. Thus, it is desirable to consider to what extent this process can be automated. A previous approach relies on words from commit messages and a small set of manually constructed code features. This approach, however, shows only moderate accuracy. In this paper, we investigate whether deep learning can provide a more accurate solution. We propose PatchNet, a hierarchical deep learning-based approach capable of automatically extracting features from commit messages and commit code and using them to identify stable patches. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of commit code, making it distinctive from the existing deep learning models on source code. Experiments on 82,403 recent Linux patches confirm the superiority of PatchNet against various state-of-the-art baselines, including the one recently-adopted by Linux kernel maintainers.
Coalescence estimates for the corner growth model with exponential weights

We establish estimates for the coalescence time of semi-infinite directed geodesics in the planar corner growth model with i.i. d. exponential weights. There are four estimates: upper and lower bounds on the probabilities of both fast and slow coalescence on the correct spatial scale with exponent $3/2$. Our proofs utilize a geodesic duality introduced by Pimentel and properties of the increment-stationary last-passage percolation process. For fast coalescence our bounds are new and they have matching optimal exponential order of magnitude. For slow coalescence we reproduce bounds proved earlier with integrable probability inputs, except that our upper bound misses the optimal order by a logarithmic factor.
$B_c \to B_{s(d)}$ form factors

We present results of the first lattice QCD calculations of $B_c \to B_s$ and $B_c \to B_d$ weak matrix elements. Results are derived from correlation functions computed on MILC Collaboration gauge configurations with lattice spacings between $0.12$ [fm] and $0.06$ [fm] including 2+1+1 flavours of dynamical sea quarks in the Highly Improved Staggered Quark (HISQ) formalism. Form factors across the entire physical $q^2$ range are then extracted and extrapolated to the physical-continuum limit. Two different formalisms are employed for the bottom quark: non-relativistic QCD (NRQCD) and heavy-HISQ. Checking agreement between these two approaches is an important test of our strategies for heavy quarks on the lattice.
Using Visible/Near-Infrared Reflectance Spectroscopy and Chemometrics for the Rapid Evaluation of Two Panamanian Watermelon (Citrullus lanatus) Varieties

In this study, visible/near-infrared (Vis/NIR) reflectance spectroscopy measurement in the 300-900 nm wavelength range, are used as external parameter to evaluate the internal parameters (Brix content and pH) of two local watermelon varieties, Quetzaly (N=8) and Mickylee (N=6), in a noninvasive manner. To assess this relationship three Chemometrics and machine learning methods were used: Principal Component Regression (PCR), Partial Least Squares (PLS) and Multilayer Perceptron Regression (MLPR). Spectral Data augmentation was used to generate more observations from the original spectra. Our results suggest Quetzaly has a higher Brix content and that all three methods can be used to predict it with confidence. However, PLS shows a higher correlation coefficient of 0.98 and standard errors of prediction of ≤0.1, closely followed by PCR with 0.96 and errors of ≤0.01 and ≤0.04, respectively. In the case of pH all methods produce good results, with a PLS achieving correlation coefficient of 0.91-0.94 with errors of prediction on the <0.05; and lower results for the other two methods. This study demonstrates the value of non-invasive Vis/NIR Reflectance spectroscopy as quality assessment in a watermelon classification system. More importantly, this system can be later implemented in local watermelon fruit packing centers in Panama. This can also be used in other export fruits or vegetables, helping local producers.
Optimal Installation of Solar Panels with Price Impact: A Solvable Singular Stochastic Control Problem

We consider a price-maker company which generates electricity and sells it in the spot market. The company can increase its level of installed power by irreversible installations of solar panels. In absence of the company's economic activities, the spot electricity price evolves as an Ornstein-Uhlenbeck process, and therefore it has a mean-reverting behavior. The current level of the company's installed power has a permanent impact on the electricity price and affects its mean-reversion level. The company aims at maximizing the total expected profits from selling electricity in the market, net of the total expected proportional costs of installation. This problem is modeled as a two-dimensional degenerate singular stochastic control problem in which the installation strategy is identified as the company's control variable. We follow a guess-and-verify approach to solve the problem. We find that the optimal installation strategy is triggered by a curve which separates the waiting region, where it is not optimal to install additional panels, and the installation region, where it is. Such a curve depends on the current level of the company's installed power, and is the unique strictly increasing function which solves a first-order ordinary differential equation (ODE). Finally, our study is complemented by a numerical analysis of the dependency of the optimal installation strategy on the model's parameters.