Miracle-dz/UniFAR-FTUs huggingface dataset:
Combination of topical liposomal amphotericin B and Glucantime in comparison with glucantime alone for the treatment of anthroponotic cutaneous leishmaniasis (ACL) caused by Leishmania tropica: study protocol for a randomized, controlled trial

Background and Objectives: Cutaneous leishmaniasis (CL) treatment is a challenging issue, although numerous modalities have been introduced as candidate treatment for CL yet only antimonial agents are commonly used to treat CL, a different form of amphotericin B is used to treat visceral form of leishmaniasis but the efficacy against CL is not high. There are a few reliable clinical trials on CL, the main reason is the nature of the disease which required a well design protocol to evaluate the efficacy of any candidate treatment against CL. In this study, a protocol was developed and used to evaluate a topical formulation of a nano-liposomal form of amphotericin B in addition to glucantime to treat CL caused by L. tropica. Materials and Methods: This study is a phase 3, randomized, double-blind, placebo-controlled trial to evaluate the efficacy and safety of topical nano-liposomal amphotericin B (SinaAmpholeish 0.4%) in combination with intralesional injections of meglumine antimoniate in the treatment of ACL caused by L. tropica. Overall, 130 patients, aged 12-60 years, with a diagnosis of ACL caused by L. tropica are recruited and treated according to the protocol. Results: A total of 130 patients with CL lesion will be recruited and doubleblind randomly treated with received intralesional injections of Glucantime weekly or Glucantime plus SinaAmpholeish for 4 weeks. Conclusion: The results of this study showed that the protocol works well and the treatment was tolerated by both groups of patients.
Manhattan‐world urban building reconstruction by fitting cubes

The Manhattan‐world building is a kind of dominant scene in urban areas. Many existing methods for reconstructing such scenes are either vulnerable to noisy and incomplete data or suffer from high computational complexity. In this paper, we present a novel approach to quickly reconstruct lightweight Manhattan‐world urban building models from images. Our key idea is to reconstruct buildings through the salient feature ‐ corners. Given a set of urban building images, Structure‐from‐Motion and 3D line reconstruction operations are applied first to recover camera poses, sparse point clouds, and line clouds. Then we use orthogonal planes detected from the line cloud to generate corners, which indicate a part of possible buildings. Starting from the corners, we fit cubes to point clouds by optimizing corner parameters and obtain cube representations of corresponding buildings. Finally, a registration step is performed on cube representations to generate more accurate models. Experiment results show that our approach can handle some nasty cases containing noisy and incomplete data, meanwhile, output lightweight polygonal building models with a low time‐consuming.
Target detection and tracking with HF radar using reciprocal SAR techniques

Sustained research and development at Memorial University of Newfoundland has led to an operational High Frequency Ground Wave Radar (HF-GWR) system for coastal surveillance. This radar system has demonstrated over-the-horizon detection of targets such as vessels, ice hazards and low-flying aircraft, and performed ocean parameter measurements over a large area. The industrial developers of offshore hydrocarbon reserves in ice infested regions have an urgent requirement for the long range detection and tracking of icebergs from their production platforms. However, due to space restrictions, a rig- or ship-based system can only accommodate a compact antenna array. The uniform trajectory and low velocity of icebergs is ideal for Reciprocal Synthetic Aperture Radar (RSAR) processing with long target dwell times. The proven ice detection capability of HF-GWR systems, coupled with the compact antennas suggested by the RSAR technique, can be used to develop a rig- or ship-based all-weather surveillance device for ice hazards. It is also anticipated that the results of this research will allow the use of shorter antenna arrays for many other applications. Preliminary results using real data from the operational HF-GWR system are presented.
Self-aligned positioning of microoptical components by precision prismatic grooves impressed into metals

Grooves embossed into metallic substrates are proposed for self-aligned positioning of microoptical components like microlenses, optical fibers, glass rods with mirrors or filters, etc. As substrates, both ductile metals such as aluminum or copper, and hard alloys like CrNi steel can be used, which allows adaptation to requirements concerning thermal expansion coefficient, thermal conductance, hardness, mechanical strength, solderability, weldability, etc. Compared to other methods such as anisotropic etching in silicon substrates and LIGA, this technique offers more design flexibility and much lower investment cost and is well suited for medium production numbers. By the embossing process, the shape and surface quality of the embossing die are virtually transferred into the groove. The residual deviation between die and groove is in the order of 5-10 /spl mu/m. The reproducibility is better than 5 /spl mu/m, thus allowing self-aligned positioning with multimode fiber applications. Without reconditioning, several thousand embossing cycles can be made with the same die. The manufacturing of embossing dies is based on precision tooling techniques. The paper concludes with two application examples of optical communication components.
Package performance testing of dangerous goods in high‐altitude shipments

This paper discusses the impact of high‐altitude shipments on package integrity. High‐altitude shipments are encountered when trucks travel over high mountain passes or when cargo and feeder aircraft transport packages in non‐pressurized or partially‐pressurized cargo holds. Both these types of transport methods will result in severe changes in pressure as compared to packages being transported close to sea level. The testing of packages under these conditions is critical since package integrity may be compromised. The current shipping tests performed in test laboratories do not account for pressure changes and vibration together. This study showed that combination packages for dangerous goods and hazardous materials that are tested to existing UN, ICAO and US DOT requirements are limited, and can result in significant number of leaks. Testing under combined vibration and pressure changes is necessary. Copyright © 2003 John Wiley & Sons, Ltd.
Identification of the reinforcing steel's corrosion state in RC beams based on electrochemical sensor

Purpose – The purpose of this paper is to present a novel five‐electrode electrochemical corrosion sensor. Health degradation by the corrosion of steel in civil engineering is a persistent problem. Structural health monitoring (SHM) techniques, including embedded sensors, can greatly improve the quantification of the steel corrosion information, which can lead to promote assessments of structural safety and serviceability. To integrate the corrosion monitoring system in future, the corrosion sensor and the monitoring methods have been explored here in advance. Also, the corrosion monitoring system has been applied preliminarily in the investigation of reinforcing concrete (RC) beams.Design/methodology/approach – First, a novel five‐electrode electrochemical corrosion sensor has been developed as the hardware to provide the platform for corrosion monitoring methods. Second, half‐cell potential of the RC beams has been measured before and after corrosion. Third, galvanostatic step method has been used to ex.
ANALISIS KELAYAKAN FINANSIAL INDUSTRI RUMAHTANGGA PANGAN (STUDI KASUS USAHA TAHU ALFARIA, KOTA KEFAMENANU)

This study aims to determine the financial feasibility analysis of the Alfaria Food home industry. This research was conducted in August - September 2020 at the Home Industry of Tofu Alfaria Jln. El-Tari km 7 Kefamenanu. This research uses qualitative data methods and quantitative data. Qualitative data is in the form of non-financial aspects in the form of descriptive descriptions, tables, charts, and pictures while quantitative data is used to determine the state of the tofu industry financially such as Net Present Value (NPV), Internal Rate Of Return (IRR), Payback Period (PP), B. / C Ratio, Break Event Point (BEP) and Return Of Investment (ROI). The results of this study indicate that Alfaria Tofu Business is a kind of home industry. The results of the financial feasibility analysis show that the NPV value is Rp. 1.462. 032.734. 59, which means the NPV value> 1, therefore this company deserves to be continued, the IRR value is 53.8% which means the interest rate obtained from the tofu business is> compared to the KUR interest rate of 6.0% so that this company deserves to be continued and the B / C Ratio value is 8.8 which means that every 1 rupiah of costs incurred can provide a profit of 8.0 rupiah and the Payback Period is 11.41 which means that within 11 months 12 days of initial capital used to invest has been returned. Meanwhile, the analysis of Return of Investment (ROI) is 79.52, which means that the ability to measure the company's overall activities in the company, the higher this ratio, the better the company. Thus the Alfaria tofu business industry can be developed because it provides potential benefits.
A systematic coupled approach for electromagnetic susceptibility analysis of a shielded device with multilayer circuitry

This paper presents a systematic coupled approach for electromagnetic susceptibility (EMS) analysis of a shielded electronic device in the presence of an ambient electromagnetic interference (EMI). A full-wave electromagnetic modeling technique coupled with a circuit-based method is developed for the overall EMS performance analysis. The mixed-potential electric field integral equation (MPIE) is formulated via the method of moments to model the electromagnetic properties of an arbitrary structure consisting of wires and surfaces with any applied excitation. With numerical analysis, the effect of an ambient electromagnetic noise is characterized, and an equivalent circuit model is extracted for further analysis of EMI to internal high-speed sensitive circuits. Numerous examinations exhibit that this systematic coupled approach is a computationally efficient method to address the EMS problems at system level, in which the external EMI effects are fully considered and the circuit-based simulator is also exploited.
Android Custom Permissions Demystified: A Comprehensive Security Evaluation

Permission is the fundamental security mechanism for protecting user data and privacy on Android. Given its importance, security researchers have studied the design and usage of permissions from various aspects. However, most of the previous research focused on the security issues of <italic>system permissions</italic>. Overlooked by many researchers, an app can use <italic>custom permissions</italic> to share its resources and capabilities with other apps. However, the security implications of using custom permissions have not been fully understood. In this paper, we systematically evaluate the design and implementation of Android custom permissions. Notably, we built an automatic fuzzing tool, called <monospace>CuPerFuzzer+</monospace>, to detect custom permission related vulnerabilities existing in the Android OS. <monospace>CuPerFuzzer+</monospace> treats the operations of the permission mechanism as a black-box and executes massive targeted test cases to trigger privilege escalation. In the experiments, <monospace>CuPerFuzzer+</monospace> discovered 5,932 effective cases with 47 critical paths successfully. Through investigating these vulnerable cases and analyzing the source code of Android OS, we further identified a series of severe design shortcomings lying in the Android permission framework, including <italic>dangling custom permission</italic>, <italic>inconsistent permission-group mapping</italic>, <italic>custom permission elevating</italic>, <italic>inconsistent permission definition</italic>, <italic>dormant permission group</italic>, and <italic>inconsistent permission type</italic>. Exploiting these shortcomings, a malicious app can access unauthorized platform resources. On top of these observations, we propose three general design guidelines to secure custom permissions. Our findings have been acknowledged by the Android security team and assigned <monospace>CVE-2020-0418</monospace>, <monospace>CVE-2021-0306</monospace>, <monospace>CVE-2021-0307</monospace>, and <monospace>CVE-2021-0317</monospace>.
Posture Monitoring and Warning System

In this era computers are becoming a necessity in most of the office environments. So people working in front of computer for long hours is a common scenario which exposes them to health risks such as eye strain as well as musculoskeletal issues such as neck, shoulder pain, etc. In this system, we will be using integrated as well as external webcams available in the workplace to provide correct ergonomic feedback to the workers. We use a webcam to track a user's face with the help of face tracking algorithms thereby calculating the distance from the computer screen, his head movements as well as blink rate. Other camera can be used to capture social interaction of the workers. For this purpose every user need to calibrate the webcam according to its body features where we capture users image and mark the central reference line . We will monitor the user's posture, if there is any variation from the central line for a long period of time, we will notify the user that he has been sitting in a bad posture. We can suggest the user to take a break periodically. So our software can be used in every scenario where people need to work before the computers for a longer period of time. It will help in maintaining good posture and reduce many ergonomic problems.
Joint Coordinated Beamforming and Power Splitting Ratio Optimization in MU-MISO SWIPT-Enabled HetNets: A Multi-Agent DDQN-Based Approach

This paper proposes a multi-agent double deep Q network (DDQN)-based approach to jointly optimize the beamforming vectors and power splitting (PS) ratio in multi-user multiple-input single-output (MU-MISO) simultaneous wireless information and power transfer (SWIPT)-enabled heterogeneous networks (HetNets), where a macro base station (MBS) and several femto base stations (FBSs) serve multiple macro user equipments (MUEs) and femto user equipments (FUEs). The PS receiver architecture is deployed at FUEs. An optimization problem is formulated to maximize the achievable sum information rate of FUEs under the constraints of the achievable information rate requirements of MUEs and FUEs and the energy harvesting (EH) requirements of FUEs. Since the optimization problem is challenging to handle due to the high dimension and time-varying environment, an efficient multi-agent DDQN-based algorithm is presented, which is trained in a centralized manner and runs in a distributed manner, where two sets of deep neural network parameters are jointly updated and trained to tackle the problem and avoid overestimation. To facilitate the presented multi-agent DDQN-based algorithm, the action space, the state space and the reward function are designed, where the codebook matrix is employed to deal with the complex transmit beamforming vectors. Simulation results validate the proposed algorithm. Notable performance gains are achieved by the proposed algorithm due to considering the beam directions in the action space and the adaptability to the Doppler frequency shifts. Besides, the proposed algorithm is shown to be superior to other benchmark ones numerically.
RUBoost-Based Ensemble Machine Learning for Electrode Quality Classification in Li-ion Battery Manufacturing

As a typical mechatronics system, the battery manufacturing chain becomes a hot research topic because it directly determines electrode quality, further affecting manufactured battery performance. Due to the complexity of battery manufacturing, an effective sensitivity analysis solution that could quantify variable importance or correlations and explore impact variables toward resulting the electrode quality is urgently needed. This article scrutinizes the effects of component parameters from the mixing stage on the manufactured results of Li-ion battery electrode via classification modeling. Specifically, an effective RUBoost-based ensemble learning framework is proposed to compensate for class imbalance issue and well classify three key quality indicators including the electronic conductivity, thickness, and half-cell capacity for both LiFePO <inline-formula><tex-math notation="LaTeX">$_4$</tex-math></inline-formula>- and Li<inline-formula><tex-math notation="LaTeX">$_4$</tex-math></inline-formula> Ti<inline-formula><tex-math notation="LaTeX">$_5$</tex-math></inline-formula>O<inline-formula><tex-math notation="LaTeX">$_{12}$</tex-math></inline-formula>-based electrode. Experimental results reveal that the proposed models could well handle the class imbalance issues and accurately classify/predict the qualities of the manufactured electrode. Moreover, the importance weights of variables and the correlations of variable pairs could be effectively quantified. Due to the superiority in terms of accuracy, interpretability, and data-driven nature, the proposed ensemble learning approach could not only help to conduct reliable multiclassification of manufactured electrode but also benefit smarter battery manufacturing.
Money is not everything: funding investment and dual performance of key universities in China

ABSTRACT In order to clarify whether, how and when the increasing S&T fund investment to improve innovation performance, based on the resource-based view (RBV) and panel data of key universities in China, we construct basic research intensity and experimental development intensity to analyse the functional mechanism between ‘fund investment– different intensity of research types—economic and social performance’. We found: (1) the increasing fund investment cannot improve economic performance, but has a positive relationship with social performance. (2) The fund investment of key universities can rapidly improve economic performance only if they are invested in experimental development, while investing in both basic research and experimental development can improve social performance. (3) The effect of fund investment on economic performance needs at least lagged 1 year to turn positive, while its promoting effect on social performance is significantly positive only in the first 2 years. Our research further enriches the subject category and core argument of RBV.
Automated photolithography critical dimension controls in a complex, mixed technology, manufacturing fab

Critical dimension (CD) control is one of the most difficult challenges in semiconductor manufacturing. In photolithography, factors associated with individual technologies, lithography tools, product levels, reticles, and product specifications dictate unique exposure conditions for each lithography tool set-up. In a multi-part number mixed technology environment, sophisticated controls are required to ensure that correct exposure conditions are used for each wafer processed. A process control system was developed by IBM Microelectronics using simple process rules and feedback to provide CD control. The system provides the control necessary to meet customization requirements in a highly automated low maintenance environment. It is driven by defined rules based on documented specifications of each technology. With documentation in place, new products can be introduced into the manufacturing facility without engineering assistance. The rules-based system eliminates traditional tool-setting tables by capturing engineering strategies for selecting relevant feedback information. A specific rule defines strategies for selecting the best available sample of historical tool setting and metrology data. CDs for each product are centered to their respective targets by the system's use of feedback algorithms which are integrated with a centralized specification database. Customized CD targets can be entered into the specification system for immediate customization of a particular part. A key feature of the system is its ability to learn the character of each reticle and the differences between each lithography tool.
Global economic sanctions, global value chains and institutional quality: Empirical evidence from cross-country data

The present paper applies a modified gravity model to examine the relationship between global economic sanctions (GES) and global value chains (GVC) by using a dyad panel dataset of 38 developed and 28 developing countries during the 2005–2015 period and the rich and updated database of the Global Sanction Database version 2 (Felbermayr, G., A. Kirilakha, C. Syropoulos, E. Yalcin, and Y. V. Yotov. 2020. “The Global Sanctions Data Base.” European Economic Review 129: 1–23. doi:10.1016/j.euroecorev.2020.103561; Kirilakha, A., G. Felbermayr, C. Syropoulos, E. Yalcin, and Y. V. Yotov. 2021. The Global Sanctions Data Base: An update that includes the years of the Trump presidency. The Research Handbook on Economic Sanctions, Edited by Peter AG van Bergeijk.). GVC participation is classified into the source of the value-added as incorporated in exports, looking both backward and forward from the view of a reference country. With distinct econometric techniques and empirical estimation strategies, our empirical results document a negative association between GESs and GVC, including backward and forward linkages. This marginal effect of sanction on backward GVC is more sizable than those on forward GVC. Furthermore, the GES-GVC nexus is only evident in the case of developing countries, while the effects of sanctions are found to be small or statistically insignificant in the developed countries. However, these marginal effects of sanctions become less sizable if the target country develops a well-developed institutional system.
Complex Data: Learning Trustworthily, Automatically, and with Guarantees

Machine Learning (ML) achievements enabled automatic extraction of actionable information from data in a wide range of decision-making scenarios. This demands for improving both ML technical aspects (e.g., design and automation) and human-related metrics (e.g., fairness, robustness, privacy, and explainability), with performance guarantees at both levels. The aforementioned scenario posed three main challenges: (i) Learning from Complex Data (i.e., sequence, tree, and graph data), (ii) Learning Trustworthily, and (iii) Learning Automatically with Guarantees. The focus of this special session is on addressing one or more of these challenges with the ﬁnal goal of Learning Trustworthily, Automatically, and with Guarantees from Complex Data.
CHARACTER ASSOCIATION AND PATH ANALYSIS IN GROUNDNUT (ARACHIS HYPOGAEA L.)

Correlation and path co-efficient analysis were carried out for pod yield and its component characters in 50 genotypes of groundnut. The genotypic correlation co-efficients were found to be of relatively higher magnitude than the corresponding phenotypic correlation co-efficients, indicating strong inherent association between the characters. Pod yield displayed significant positive association with kernel yield per plant, mature pods per plant, total pods per plant, harvest index, 100-seed weight, root weight, plant height and shoot weight. Path co-efficient analysis revealed high direct effects of kernel yield per plant and harvest index on pod yield. Hence, it would be rewarding to give due importance on the selection of these characters for rapid improvement in pod yield of groundnut. Key words: Character Association, Path Analysis, Groundnut.