Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 𝑒 -6 and (PSO)=3.4948 𝑒 -4 .
A front‐fixing method for American option pricing on zero‐coupon bond under the Hull and White model

A new efficient numerical method is proposed for valuation of American option on zero‐coupon bond using Hull and White model. By applying the front‐fixing transformation suggested by Holmes and Yang, the original free boundary problem is transformed into a new fixed boundary partial differential equation (PDE) problem, where the optimal stopping boundary is one of the unknowns of the problem. The numerical finite difference scheme for the transformed problem is constructed. Stability and convergence rate is studied empirically. Numerical simulation of the computation of both the option price and the optimal stopping boundary are illustrated with examples and the comparison with the Hull and White tree method.
Unobtrusive human activity classification based on combined time-range and time-frequency domain signatures using ultrawideband radar

In this proposed approach to unobtrusive human activity classification, a two ‐ stage machine learning–based algorithm was applied to backscattered ultrawideband radar signals. First, a preprocessing step was applied for noise and clutter suppression. Then, feature extraction and a combination of time ‐ frequency (TF) and time ‐ range (TR) domains were used to extract the features of human activities. Then, feature analysis was performed to determine robust features relative to this kind of classification and reduce the dimensionality of the feature vector. Subsequently, different recognition algorithms were applied to group activities as fall or non ‐ fall and categorise their types. Finally, a performance study was used to choose the higher accuracy algorithm. The ensemble bagged tree and fine K ‐ nearest neighbour methods showed the best performance. The results show that the two ‐ stage classification was more accurate than the one ‐ stage. Finally, it was observed that the proposed approach using a combination of TR and TF domains with two ‐ stage recognition outperformed reference approaches mentioned in the literature, with average accuracies of 95.8% for eight ‐ activities classification and 96.9% in distinguishing between fall and non ‐ fall activities with efficient computational complexity.
Acute Toxicity of Nano-sized Zinc Oxide in ICR Mice via Intratracheal Instillation

[Objective] To make a preliminary assessment on the acute toxicity of intratracheally instilled nano-sized zinc oxide(N-ZnO)in ICR mice. [Methods] 50 healthy male adult ICR mice were randomly grouped in 5 by their body weight. 20μg(HG),10μg(MG),5μg(LG)of N-ZnO were intratracheally instilled into lungs of each mouse as test groups and PBS was instilled as negative control(NG). Another group without any treatment was used as blank control(BG). The BALF and blood were collected for analysis on the 7th day after treatment.Histopathologic evaluation of the main organs were also performed. [Results] There were only one mouse left in HG on ? th day postinstillation,so this group was not included for statistical analysis.The healthy conditions of mice from HG and MG were generally poor,loss of body weight were significantly higher than that of BG and NG from the 1st day to the 5th day(P0.05);RBC of MG or LG was lower than that of BG and NG while content of total protein, hydroxyproline and activity of AKP in BALF,MCV,MCH,RDW,MPV of blood were higher than those of BG and NG (P0.05). MOD% of MG was also higher than that of BG,NG and LG,but MCHC was lower(P0.05). While the differences of body weight loss,activity of AKP in BALF and RDW,MPV,MOD% on 3rd and 5th day were also observed among the treated groups. Histopathological observation showed that serious pulmonary inflammation,proliferation and thickening of alveolar walls existed in the lungs of all treated mice groups,which became more serious as dose increased. [Conclusion] Intratracheal instillation of N-ZnO induced significant pulmonary inflammation and marked body weight loss accompanied with anemia.
The Influence of the CEO on Auditor Choice in Private Firms: An Interplay of Willingness and Ability

Reliable financial reporting is highly important when aiming for sustainable development and the long-term financial stability of the entire economy. An external audit is one of the main monitoring mechanisms to warrant this reliability. While auditing serves as an independent monitoring mechanism towards management, studies indicate that management is often the driving force behind auditor appointments and terminations, especially if it is willing to drive auditor choice. While this raises questions about an auditor’s independence and resulting audit quality, willingness will only have an impact when management is also able to exert its will. This study, therefore, examines to what extent ability strengthens the CEO’s willingness to appoint a non-Big Four auditor. Using a dataset of 316 private firms, regression results show that when the CEO is willing to appoint a non-Big Four auditor and also has sufficient power, it is less likely that a Big Four auditor is actually appointed, at least when the control effectiveness of the board is weak such that the CEO can exert his/her power. This emphasizes the need for both shareholders and legislators to ensure that the independence of the auditor is guaranteed and to implement complementary monitoring mechanisms like a strong board.
Thermal decay of laser-induced long living metastable electronic states in Na2[Fe(CN)5NO]·2H2O single crystals

And the decay was measured in the temperature range of 106 K-300 K. All metastable states decay thermally according to an exponential law. We could not observe any emission of radiation in the energy range of 5 eV-0.3 eV during the thermal decay. We have evaluated the activation energies E A and frequency factors Z assuming the validity of the Arrhenius law and a first order reaction for three different metastable states. They all can be completely annihilated with red light. A strong temperature dependence with respect to the generation of the metastable states was observed
Distributed Detection for Centralized and Decentralized Millimeter Wave Massive MIMO Sensor Networks

Multi-sensor millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs) relying on both distributed (D-MIMO) and centralized (C-MIMO) configurations are conceived. Hybrid combining based low complexity fusion rules are constructed for the fusion center (FC) for both D-MIMO and C-MIMO systems employing a partially connected structure (PCS) and a fully connected structure (FCS), respectively. The decision rules are based on the transmission of local binary sensor decisions and also take into account the accuracy of local detection at the individual sensors. Closed-form analytical expressions are derived for the probabilities of false alarm and correct detection to analyze the system's performance. Furthermore, the asymptotic distributed detection (DD) performance corresponding to both antenna architectures is analyzed in the large-scale antenna regime along with the pertinent power scaling laws. Additionally, digital signaling matrices are designed for enhancing the system performance. Our simulation results quantify the performance gains of the proposed architectures, which closely match the analytical results.
Lead Isotopes to Identify Underwater Ceramic Contamination: The Example of the Kyrenia Shipwreck (Cyprus)

We present the lead isotopic composition of ceramic fragments from the Kyrenia shipwreck (Cyprus), selected from three chemical groups related to the Rhodes and Alimos regions (Greece). Fragments of the lead sheathing covering the ship’s hull and biogenic material formed on some of the ceramic sherds, were analyzed along with sherds from the Rhodes and Alimos source areas for comparison. The objective of this paper was to evaluate the impact of the seawater environment on the isotopic signature of the ceramics, and on our ability to use lead isotopes to source ceramics recovered from seawater. The results showed that the lead isotopic composition of the shipwreck ceramics was modified by its prolonged proximity to the lead hull sheathing. The lead signature of filtering marine organisms encrusted on the ceramics provided support for this hypothesis.
A Computer Vision Sensor for Efficient Object Detection Under Varying Lighting Conditions

Convolutional neural networks (CNNs) have attracted much attention in recent years due to their outstanding performance in image classification. However, changes in lighting conditions can corrupt image segmentation conducted by CNN, leading to false object detection. Even though this problem can be mitigated using a more extensive CNN training set, the immense computational and energy resources required to continuously run CNNs during always‐on applications, such as surveillance or self‐navigation, pose a serious challenge for battery‐reliant mobile systems. To tackle this longstanding problem, a vision sensor capable of autonomously correcting for sudden variations in light exposure, without invoking any complex object detection software, is proposed. Such video preprocessing is efficiently achieved using photovoltaic pixels tailored to be insensitive to specific ranges of light intensity alterations. In this way, the pixels behave similarly to neurons, wherein the execution of object detection software is only triggered when light intensities shift above a certain threshold value. This proof‐of‐concept device allows for efficient fault‐tolerant object detection to be implemented with reduced training data as well as minimal energy and computational costs and demonstrates how hardware engineering can complement software algorithms to improve the overall energy efficiency of computer vision.
Unveiling the Evolution of Innovation Ecosystems: An Analysis of Triple, Quadruple and Quintuple Helix Model Innovation Systems in European Case-studies

Despite the rising interest in smart city initiatives worldwide, governmental theories along with the managerial perspectives of city planning are a great lack in the literature. It is definitely understandable that the adoption of configurational pathways towards the ‘smart’ ‘governance’ models is required as key factor and smartness’ facilitator in modern cities. In this manuscript, we display an exhaustive analysis on the importance of the n-Helix models along with a benchmarking critical approach through selected European case-studies. The study, through the literature review, revealed the lack of exhaustive analyses for the methodological investigation, identification and adoption of the most appropriate governance model and collaborative approaches per project and collaborative approaches and create modular frameworks to address efficiently the continuous urban challenges, such as the rapid urbanization or the climate change.
Simultaneous Bayesian Estimation of Non‐Planar Fault Geometry and Spatially‐Variable Slip

Large earthquakes are usually modeled with simple planar fault surfaces or a combination of several planar fault segments. However, in general, earthquakes occur on faults that are non‐planar and exhibit significant geometrical variations in both the along‐strike and down‐dip directions at all spatial scales. Mapping of surface fault ruptures and high‐resolution geodetic observations are increasingly revealing complex fault geometries near the surface and accurate locations of aftershocks often indicate geometrical complexities at depth. With better geodetic data and observations of fault ruptures, more details of complex fault geometries can be estimated resulting in more realistic fault models of large earthquakes. To address this topic, we here parametrize non‐planar fault geometries with a set of polynomial parameters that allow for both along‐strike and down‐dip variations in the fault geometry. Our methodology uses Bayesian inference to estimate the non‐planar fault parameters from geodetic data, yielding an ensemble of plausible models that characterize the uncertainties of the non‐planar fault geometry and the fault slip. The method is demonstrated using synthetic tests considering slip spatially distributed on a single continuous finite non‐planar fault surface with varying dip and strike angles both in the down‐dip and along‐strike directions. The results show that fault‐slip estimations can be biased when a simple planar fault geometry is assumed in presence of significant non‐planar geometrical variations. Our method can help to model earthquake fault sources in a more realistic way and may be extended to include multiple non‐planar fault segments or other geometrical fault complexities.
Estimating location parameters in sample-heterogeneous distributions

Estimating the mean of a probability distribution using i.i. d. samples is a classical problem in statistics, wherein finite-sample optimal estimators are sought under various distributional assumptions. In this paper, we consider the problem of mean estimation when independent samples are drawn from ddimensional non-identical distributions possessing a common mean. When the distributions are radially symmetric and unimodal, we propose a novel estimator, which is a hybrid of the modal interval, shorth, and median estimators, and whose performance adapts to the level of heterogeneity in the data. We show that our estimator is near-optimal when data are i.i. d. and when the fraction of “low-noise” distributions is as small as Ω ( d logn n ) , where n is the number of samples. We also derive minimax lower bounds on the expected error of any estimator that is agnostic to the scales of individual data points. Finally, we extend our theory to linear regression. In both the mean estimation and regression settings, we present computationally feasible versions of our estimators that run in time polynomial in the number of data points.
The emergence of systematic argument distinctions in artificial sign languages

Word order is a key property by which languages indicate the relationship between a predicate and its arguments. However, sign languages use a number of other modality-specific tools in addition to word order such as spatial agreement, which has been likened to verbal agreement in spoken languages, and role shift, where the signer takes on characteristics of propositional agents. In particular, data from emerging sign languages suggest that, though some use of a conventional word order can appear within a few generations, systematic spatial modulation as a grammatical feature takes time to develop. We experimentally examine the emergence of systematic argument marking beyond word order, investigating how artificial gestural systems evolve over generations of participants in the lab. We find that participants converge on different strategies to disambiguate clause arguments, which become more consistent through the use and transmission of gestures; in some cases, this leads to conventionalized iconic spatial contrasts, comparable to those found in natural sign languages. We discuss how our results connect with theoretical issues surrounding the analysis of spatial agreement and role shift in established and newly emerging sign languages, and the possible mechanisms behind its evolution.
Occupational stress in University academics in Australia and New Zealand

ABSTRACT Occupational stress has increased in higher education academic staff over several decades, and this has been particularly acute in Australia and New Zealand. This scoping review sought to understand the causes and impacts of occupational stress among Australian and New Zealand academics. Eight EBSCO databases were searched for key terms: academic and occupational stress and Australia and New Zealand. Twenty relevant papers were sourced, from which five common themes were extracted: (i) balancing an academic workload, (ii) casualisation of the workforce, (iii) the managerialism phenomenon, (iv) transition from field of practice to academia, and (v) academic and other staff. Further research in the Australian and New Zealand context is required to identify the nature of specific stressors and how these impact health and well-being.
Promoter Variations Strongly Enhance the Transcription Level of the BoLMI1 Gene, Causing Lobed Leaves in Ornamental Kale (Brassica Oleracea L. Var. Acephala)

Bin Zhang Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Wendi Chen Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Xing Li Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Wenjing Ren Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Li Chen Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Fengqing Han Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Zhiyuan Fang Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Limei Yang Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Mu Zhuang Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Honghao Lv Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Yong Wang Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers Yangyong Zhang (  zhangyangyong@caas.cn ) Chinese Academy of Agricultural Sciences Institute of Vegetables and Flowers
Blockchain-Enabled Transaction Scanning Method for Money 
Laundering Detection

Currently, life cannot be imagined without the use of bank cards for purchases or money transfers; however, their use provides new opportunities for money launders and terrorist organizations. This paper proposes a Blockchain-enabled transaction scanning (BTS) method for the detection of anomalous actions. The BTS method specifies the rules for outlier detection and rapid movements of funds, which restrict anomalous actions in transactions. The specified rules determine the specific patterns of malicious activities in the transactions. Furthermore, the rules of the BTS method scan the transaction history and provide a list of entities that receive money suspiciously. Finally, the Blockchain-enabled process is used to restrict the money laundering. To validate the performance of the proposed BTS, the Spring Boot application is built based on the Java programming language. Based on experimental results, the proposed BTS method automates the process of investigating transactions and restricts the money laundering incident.
Differential speciation rates, colonization time and niche conservatism affect community assembly across adjacent biogeographical regions

To test the importance of evolutionary and biogeographical processes in shaping the assembly of local frog communities in two adjacent regions (hereafter, coastal and inland regions) with different historical signatures. We asked two main questions: (1) why does the coastal region harbour more frog species than the inland region? and (2) how do these processes affect the spatial variation in taxonomic, phylogenetic and functional diversities within and across these regions?
Dual roles of granzyme B

Granzyme B (GZMB) was first identified as a factor that could eliminate cells such as tumour cells and bacteria/virus‐infected cells. However, comprehensive studies conducted on GZMB have gradually revealed the negative extracellular functions of GZMB; for example, it is an aetiology and a factor responsible for the aggravation of several diseases. This review focuses on the positive role of GZMB (in the elimination of tumour cells, bacteria, parasites and viruses, and in the identification, diagnosis and treatment of diseases) and the negative effects of GZMB (with regard to extracellular function and other characteristics). The current research on GZMB inhibitors has been briefly introduced, and few studies on GZMB fusion proteins and several GZMB material delivery systems have been discussed.
An Analysis of Impact of Age on Task Performance among Non-managerial Employees in State Bank of India

The National economy of a country is determined largely in terms of the financial growth of its financial institutions. The employee performance has always been the back bone of the organizational performance. The State bank of India, as being the oldest and the most trusted public sector bank especially among rural people, has always tried to live up to the expectations of its customers. This trust on the bank is drawn from the performance of its employees, especially the non-managerial employees such as clerks, accountants, cashiers, window operators, etc. as they are the face of the bank and represent the bank to the customer and to the society. They account for 75-85 percent of total work force of the bank. The purpose of this paper is to study the impact of age on task performance among non-managerial employees in State Bank of India. A sample of 283 employees was selected during 2018-19 through simple random sampling from branches of the bank in the Raipur region. Raipur is the financial hub of Chhattisgarh state and it is a growing capital. The data was analyzed using SPSS-20 software tool. The results revealed that there is a statistical significant difference in task performance of employees on the basis of their age groups where younger employees or new joiners were found to show higher level of task performance than other age groups. It is observed that the non managerial posts include more physically challenging jobs than the managerial posts, as such the experienced employees were seen to be inspiring the subordinates and providing financial guidance to the younger employees, the younger employees are full of energy in carrying out their assigned task significantly. This study has a clear implication where policymakers can frame policies to enhance the performance level evenly among all the age groups.