Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterologous Expression and Evaluation of Novel Plasmodium falciparum Transmission Blocking Vaccine Candidates

Malaria transmission blocking vaccines (TBV) aim to induce antibodies that can interrupt Plasmodium falciparum development in the mosquito midgut and thereby prevent onward malaria transmission. A limited number of TBV candidates have been identified and only three (Pfs25, Pfs230 and Pfs48/45) have entered clinical testing. While one of these candidates may emerge as a highly potent TBV candidate, it is premature to determine if they will generate sufficiently potent and sustained responses. It is therefore important to explore novel candidate antigens. We recently analyzed sera from naturally exposed individuals and found that the presence and/or intensity of antibodies against 12 novel putative surface expressed gametocyte antigens was associated with transmission reducing activity. In this study, protein fragments of these novel TBV candidates were designed and heterologously expressed in Drosophila melanogaster S2 cells and Lactococcus lactis. Eleven protein fragments, covering seven TBV candidates, were successfully produced. All tested antigens were recognized by antibodies from individuals living in malaria-endemic areas, indicating that native epitopes are present. All antigens induced antigen-specific antibody responses in mice. Two antigens induced antibodies that recognized a native protein in gametocyte extract, and antibodies elicited by four antigens recognized whole gametocytes. In particular, we found that antigen Pf3D7_0305300, a putative transporter, is abundantly expressed on the surface of gametocytes. However, none of the seven novel TBV candidates expressed here induced an antibody response that reduced parasite development in the mosquito midgut as assessed in the standard membrane feeding assay. Altogether, the antigen fragments used in this study did not prove to be promising transmission blocking vaccine constructs, but led to the identification of two gametocyte surface proteins that may provide new leads for studying gametocyte biology.
Preventing Knowledge Hiding Behaviors Through Workplace Friendship and Altruistic Leadership, and Mediating Role of Positive Emotions

Studies related to knowledge hiding prevention are limited and need attention. Hence, the present study attempts to measure the direct impact of workplace friendship and altruistic leadership on preventing the knowledge hiding behavior; and also, in the presence of positive emotions. The study has also checked the mediating role of positive emotions in these relationships. The target population of the study is the employees working in the government sector (sample size of 496). The present study has employed quantitative research techniques for testing the hypotheses. Smart-PLS 3 software has been employed to run the partial least square structural equation modeling. Findings of the study have given major indications about the positive role of workplace friendship and altruistic leadership in preventing the hiding of knowledge among employees. It has also been revealed that positive emotions play a significant role in augmenting the relationship of workplace friendship and altruistic leadership with knowledge hiding behavior. This study adds a significant contribution to the body of knowledge by measuring the mediating role of positive emotions in decreasing the knowledge hiding behavior in the presence of workplace friendship and altruistic leadership.
Construction of Artificial Intelligence Application Model for Supply Chain Financial Risk Assessment

An artificial intelligence integrated application model of supply chain financial risk assessment is constructed. Based on the financial data and supply chain data of listed companies in China’s new energy electric vehicle industry, the supply chain financial credit risk evaluation index system is constructed. The data samples are preprocessed by PCA as the input data of the support vector machine, which effectively solves the problem of high-dimensional data in supply chain finance. By improving the inertia weight of particle swarm optimization and introducing mutation operation, a dynamic mutation particle swarm optimization algorithm is proposed to avoid the problem of particles falling into a local minimum in the process of optimization. Finally, the improved optimization algorithm is used to optimize the parameters of SVM and input AdaBoost integration as a weak classifier to build an integrated model with good performance in many aspects. The model has been successfully applied to the credit risk assessment of China’s new energy vehicle supply chain finance. The comparison with other models shows that the constructed model has certain advantages in performance.
Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

This paper presents a two-level control scheme for the energy management of an electricity prosumer node equipped with controllable loads, local generation, and storage devices. The main control objective is to optimize the prosumer’s energy bill by means of intelligent load shifting and storage control. A generalized tariff model including both volumetric and capacity components is considered, and user preferences as well as all technical constraints are respected. Simulations based on real household consumption data acquired with a sampling period of 1 s are discussed. The proposed control scheme bestows the prosumer node with the flexibility needed to support smart grid use cases such as bill optimization (i.e., local energy trading), control of the profile at the point of connection with the grid, demand response, and reaction to main supply faults (e.g., islanding operation), etc.
A real 3D scene rendering optimization method based on region of interest and viewing frustum prediction in virtual reality

ABSTRACT As an important technology of digital construction, real 3D models can improve the immersion and realism of virtual reality (VR) scenes. The large amount of data for real 3D scenes requires more effective rendering methods, but the current rendering optimization methods have some defects and cannot render real 3D scenes in virtual reality. In this study, the location of the viewing frustum is predicted by a Kalman filter, and eye-tracking equipment is used to recognize the region of interest (ROI) in the scene. Finally, the real 3D model of interest in the predicted frustum is rendered first. The experimental results show that the method of this study can predict the frustrum location approximately 200 ms in advance, the prediction accuracy is approximately 87%, the scene rendering efficiency is improved by 8.3%, and the motion sickness is reduced by approximately 54.5%. These studies help promote the use of real 3D models in virtual reality and ROI recognition methods. In future work, we will further improve the prediction accuracy of viewing frustums in virtual reality and the application of eye tracking in virtual geographic scenes.
Optimum design of a recharge filter for recharging the dug wells: a field application

Artificial recharging of the abandoned and/or functional dug wells through a recharge filter is an efficient and economical method for mitigating and maintaining depleting water tables. An optimum sizing of the recharge filter is necessary to avoid its over-design or under-design and has a minimum cost. Optimum sizing of the recharge filter for the dug well recharging has received very little attention, especially in India. In this paper, analytical expressions for designing the recharge filter were derived based on the concept of maximization of the hydrological water balance equation. Optimum sizing of the recharge filter based on the minimum cost was formulated using the optimization model. The optimization model was solved using the Snopt non-linear programming optimization model of the AMPL software. The results of the optimization model were also compared with the analytical framework. Sensitivity analyses using an analytical framework indicated that size, time of filling and empty time for the recharge filter were more sensitive to the changes in watershed area and watershed condition as compared to the diameter of the discharge pipe, the porosity of the filter material and depth of the recharge filter. It could be concluded that the derived optimization model incorporating the derived analytical expressions can successfully be used for optimum sizing and evaluation of the effectiveness of the recharge filter of any shape and locations in different hydro-climatic regions.
Dampak Penerapan PSAK 10 (Revisi 2010) Mengenai Pengaruh Perubahan Kurs Valuta Asing terhadap Daya Informatif Laba

This study aims to examine the consequences of the implementation of PSAK No. 10 (2010) regarding The Effect of Changes in Foreign Exchange Rates on the informativeness of earnings. Our research sample consists of companies listed on Indonesia Stock Exchange (IDX) from 2010 until 2013. Using panel least square linear regression, this study finds that the implementation of PSAK No. 10 (2010) improves the informativeness of earnings which is measured by Earnings Response Coefficient (ERC). This is the first study that investigates the impact of the implementation of PSAK No. 10 (2010) on ERC, therefore this study contributes on providing evidence of the impact of accounting standard revision implementation on capital market aspect. --- Penelitian ini bertujuan untuk menguji konsekuensi penerapan PSAK No. 10 (Revisi 2010) mengenai Pengaruh Perubahan Kurs Valuta Asing terhadap daya informatif laba. Riset ini menggunakan sampel yang terdiri dari perusahaan yang terdaftar di Bursa Efek Indonesia (BEI) dari 2010 sampai 2013. Dengan menggunakan regresi data panel, penelitian ini menemukan bahwa penerapan PSAK No. 10 (Revisi 2010) meningkatkan daya informatif laba yang diukur menggunakan Koefisien Respon Laba (Earnings Response Coefficient/ERC). Penelitian ini merupakan penelitian pertama yang menginvestigasi dampak penerapan PSAK No. 10 (Revisi 2010) terhadap ERC sehingga memiliki kontribusi dalam menyediakan bukti dampak penerapan revisi standar akuntansi terhadap aspek pasar modal.
Implementation of CS coefficient calculation based on dual-operator engines in multimode spaceborne SAR imaging systems

To provide an optimised implementation aimed at calculating the chirp scaling (CS) coefficients used in state-of-the-art spaceborne synthetic aperture radar (SAR) imaging systems. Built on the core concept of breaking down the CS coefficient calculation into basic operations, we propose a new architecture that adopts a dual-operator engine as the basic processing unit and seeks to optimise the switching network of operator engines through an appropriately interconnected grouping of various calculations. A wide variety of SAR imaging architectures based on fast Fourier transformation-CS operations may benefit from this work. The proposed architecture is implemented in a Xilinx Virtex-7 FPGA process; a comparison between test results and results obtained from an off-the-shelf counterpart confirms the efficacy of the proposed architecture.
Improved Detection of Malicious Domain Names Using Gradient Boosted Machines and Feature Engineering

Malicious domain names have been commonly used in recent years to launch different cyber-attacks. There are a large number of malicious domains that are registered every day and some of which are only active for brief periods of time. Therefore, the automated malicious domain names detection is needed to provide security for individuals and organisations. As new technologies continue to emerge, the detection of malicious domain names remains a challenging task. In this study, we propose a model to effectively detect malicious domain names. This is done by evaluating the performance of several machine learning algorithms and feature importance measures using a recent DNS dataset. Based on the empirical evaluation, the gradient boosted machines GBM classiﬁcation with a combination of lexical and host-based features produce the most accurate detection rates of 98.8% accuracy and a low false positive rate of 0.003. In terms of feature importance, measures used in this study agree on the importance of six features, ﬁve of which are lexical in nature. Furthermore, to make the best out of these relevant features, we apply automatic feature engineering. Our results show that preprocessing the dataset using deep feature synthesis and then reducing the dimensionality improves the classiﬁcations performance as compared to using raw features. The results of this study are then veriﬁed using a challenging category of domain names, the domain generation algorithm dataset, and consistent results are obtained.
Challenges For Regional Governance In Turkey: The Role Of Development Agencies

This article examines the functioning of regional development agencies (DAs) in Turkey from a policy instruments approach with a view to uncovering their intended and unintended consequences for regional governance. The policy instruments approach challenges the functionalist view that instruments are merely technical and neutral devices for realizing policy aims in the most effective way. Instead, the article shows that as policy instruments DAs are highly political with important political consequences. The article is based on original data collected and generated from an extensive review of official documents and reports and semi-structured interviews with local stakeholders in six different regions conducted between 2011 and 2014. On the basis of the findings, the article argues that the DAs do not have the potential to institutionalize regions as legitimate political forms and have weak potential to act as centerpieces for the empowerment of local actors and mobilization of endogenous capacities. Findings show that the DAs in Turkey are largely failing to promote developmental cross-sectoral alliances among local actors. The short-term inward looking concerns of the local political and business elites in the distribution of the DAs’ financial resources are helping to consolidate existing local power structures rendering it even more difficult to transform local power relations in the future.
High-performance flexible oxide TFTs: optimization of a-IGZO film by modulating the voltage waveform of pulse DC magnetron sputtering without post treatment

We propose a facile and scalable approach to fabricate high performance flexible a-IGZO thin film transistors (TFTs) by adopting the waveform modulation of pulse DC magnetron sputtering (PDCMS) to rationally optimize the film quality of semiconductors without post treatment. The voltage waveform was modulated by rationally altering the frequency and duty cycles, and, consequently, an optimum film quality of a-IGZO film was obtained that resulted in the outstanding performance of the flexile oxide TFTs. A series of characterizations (TEM, XRR AFM, XPS, μ-PCD etc.) were carried out to understand the mechanism of a-IGZO semiconductor film growth. The flexible TFT with an optimum a-IGZO film exhibited a mobility (μsat) of 20.9 cm2 V−1 s−1 and good stability under bending strain. This work provides an alternative approach to fabricate high performance flexible a-IGZO TFTs on an industrial scale.
Elastocaloric Properties of Polycrystalline Samples of NiMnGaCu Ferromagnetic Shape Memory Alloy under Compression: Effect of Improvement of Thermoelastic Martensitic Transformation

Shape memory alloys (SMAs) and ferromagnetic shape memory alloys (FeSMAs) have recently attracted interest for solid state refrigeration applications. Among NiMnGa-based quaternary systems, NiMnGaCu exhibits an interesting giant magnetocaloric effect thanks to the overlapping of the temperatures related to the magnetic transition and the thermoelastic martensitic transformation (TMT); in particular, for compositions with Cu content of approximately 6 at%. In the present work, we investigated the improvement effect of TMT on the total entropy change (ΔS) in the elastocaloric performances of polycrystalline Ni50Mn18.5Cu6. 5Ga25 at% alloy samples, just above room temperature. We report an extensive calorimetric and thermomechanical characterization to explore correlations between microstructural properties induced by the selected thermal treatment and elastocaloric response, aiming at providing the basis to develop more efficient materials based on this quaternary system. Both ΔT and ΔS values obtained from mechanical curves at different temperatures and strain recovery tests under fixed load vs. T were considered. Maximum values of ΔS = 55.9 J/KgK and ΔT = 4.5 K were attained with, respectively, a stress of 65 MPa and strain of 4%. The evaluation of the coefficient of performance (COP) was carried out from a cyclic test.
Analysis and Design of a Wide-Area Damping Controller for Inter-Area Oscillation With Artificially Induced Time Delay

One of the major issues in an interconnected power system is the low damping of inter-area oscillations. A proportional-derivative (PD) compensator is a popular control method which is extensively being used as an active damping control in various domains such as robotics, smart structure, etc. However, existence of noise in wide-area signal makes an ideal PD compensator unrealisable in power systems. In this endeavour, a new methodology christened as time-delayed control is proposed where artificial delay is used to provide derivative action which acts as a damping component of wide-area controller. The delay margin is computed for the closed loop system using Razumikhin approach. It is observed that the proposed controller is able to damp the oscillations adequately with reduced sensitivity to the noise in the signals. Further, the proposed controller is able to provide robustness against uncertainties arising from the system dynamics and inherent delay in communication channel.
Uma Contribuição do Design Emocional para o Design de Superfície: um estudo de caso da ciclovia Van Gogh-Roosegaarde

This paper addresses the contribution from Emotional Design to Surface Design. By considering the Donald Norman’s Three Levels of Design theory (2008), it describes the Van Gogh-Roosegaarde cycle path, Holland. Results point to the use of luminescent materials as an interactive resource between the surface-medium, besides causing emotional impact to users as it modifies the urban landscape in a sustainable way. The conceptual aspects and the technical resources that have guaranteed the success of the project are demonstrated. In addition, it is concluded that the phosphorescent paint was critical to satisfy the three levels of design, which characterizes a good project.
Digital Transformation and Rule of Law Based on Peak CO2 Emissions and Carbon Neutrality

The promotion and implementation of carbon neutrality against peaking carbon dioxide emissions urgently need the support of science and technology and the backing provided by a guarantee of rule of law. The proposition, logic, and progression of digital responses to peaking carbon dioxide levels in the search for carbon neutrality are clearly reflected in the current era, employing big data to address the problems of inadequate central–local coordination and interaction, the inadequate application of the rule of law, campaign-style “carbon reduction” promotion, and scientific and technological support in the promotion and implementation of peak carbon dioxide emissions and carbon neutrality. We need to pay attention to the coordination of digital technology, the rule of law response, and the protection of people’s rights. First, in the process of digital carbon dioxide peaking and carbon neutrality, it is necessary to improve the credibility of carbon dioxide peaking and carbon neutrality through the “whole-process trace” and storage mechanism techniques that are made possible by blockchain technology. Second, it is necessary to refine the management of peak carbon dioxide emissions and carbon neutrality through “decentralization” and consensus mechanisms. Third, it is necessary to improve the effectiveness of governance in the management of peak carbon dioxide emissions and carbon neutrality through “non-falsifiability” and collaboration mechanisms. Fourth and finally, the conclusions of this paper are offered. First, from the aspect of smart city construction, it is necessary to promote the coordinated construction of a low-carbon city and smart city and explore the legal ramifications of low-carbon development in urban governance. Second, in corporate governance, we need to build a low-carbon-development digital platform to promote the integration of digital technology and corporate compliance. Third, in terms of global governance, we need to promote the rule of law in cyberspace to address global climate change, the low-carbon development of digital technology, and the low-carbon construction of a cyber society. Fourth, we need to emphasize the rights and obligations of different parties in the implementation mechanism of the rule of law on digital carbon peaks and carbon neutrality.
The provincial border, information costs, and stock price crash risk

ABSTRACT Based on externalities in the allocation of interprovincial resources, we examine how geographic location affects firms’ access to resources and thus their information disclosure and stock price crash risk. The results show that border firms have a higher stock price crash risk than non-border firms. Mechanism tests find that border firms have lower available credit, higher financing costs, smaller fiscal subsidies, higher accrual earnings management, lower accounting conservatism, and a more positive tone in the management discussion and analysis section of the annual report. This indicates that resource shortages and aggressive information disclosure are important drivers of their higher stock price crash risk. Additional tests find no border effect in the borders of integrated areas or borders of areas adjacent to municipalities. Accelerating the digital transformation of the government and the information infrastructure construction, and strengthening external governance can partially alleviate the stock price crash risk of border firms.
A Hierarchy-Based Energy-Efficient Routing Protocol for LoRa-Mesh Network

LoRa is one of the most promising techniques for the Internet of Things (IoT), and it has attracted considerable attention. Most existing LoRa applications follow the LoRaWAN specification and adopt a star topology. However, LoRaWAN networks are typically limited by their scalability due to the use of a centralized control strategy. The recent development of LoRa-Mesh networks can be a potential solution to this limitation. In this article, we propose a hierarchical-based energy-efficient (HBEE) routing protocol as a desirable method for building LoRa-Mesh networks. The HBEE exhibits the following advantages: 1) it can quickly build the network structure during the network formation stage by using contention-free concurrent transmission; 2) during the routing exploration phase, end-device nodes (ENs) can locate parent ENs with a minimized overhead; and 3) at the data collection stage, packet collision is minimized using multipath and multichannel routing method. Simulations are conducted to evaluate the performance of HBEE, and the results show that HBEE outperforms conventional ad hoc on-demand distance vector routing (AODV) protocol in terms of energy efficiency and transmission delay. Finally, proof-of-concept experiments are implemented in the real world. The results show that the packet reception rate of 95.7% and 89.8% can be achieved during network formation and normal operation phases, respectively.