Miracle-dz/UniFAR-FTUs huggingface dataset:
An approach to derive seismic fragility curves

Seismic risk assessment is needed for the evaluation of disaster management, emergency preparedness and prior retrofitting. Based on feasible collapse mechanisms and calculation of their associated seismic parameter Peak Ground Displacement, the failure analysis of the structure is identified. Further, the outputs are manipulated to produce a measure vulnerability obtained from failure analysis. In regions of moderate and high seismicity, Zone – III Fragility analysis is used. An investigation is made on low rise Unreinforced masonry (URM) structure located in Zone – III, particularly in Chennai. To obtain capacity curve and bi-linear capacity curve the structure is modelled as Equivalent Frame Model (EFM), and non – linear static pushover analysis (NPOA). The structure damages, under various level of seismic excitation was developed by an analytical seismic fragility curve. For prioritizing retrofit, pre-earthquake planning, and loss estimation tools such as cost-benefit analysis this curve is utilized. The damage states considered for determining probability of exceeding a specified damage state involved in fragility curve generation are slight, moderate, extreme and collapse.
Numerical evaluation of the slab effective width in steel-concrete composite box girder bridges

ABSTRACT: Bridges are structures built to overcome obstacles and allow continuity of transport routes. In the search for efficient construction systems, steel-concrete composite structures, especially box girder bridges, emerged. The American AASHTO and European EN 1994-2-2 standards present design procedures for this type of bridge, but there are still doubts about which portion of the concrete slab contributes to each web strength in composite section (effective width). The present paper aims to evaluate, by numerical analysis in the Abaqus® software, the concrete slab effective width and to verify whether the expressions for its calculation in composite I-girder bridges are valid for box girder sections. After analyzing the results, it was found that the standards recommendations for composite I-girder cannot be applied directly to box girders. Finally, based on the numerical analysis, an equation was proposed to calculate the effective width of steel-concrete composite box girder bridges.
Social Sensing in IoT Applications: A Review

The proliferation of the Internet and mobile devices has led to users increasingly communicating with each other and expressing themselves on a variety of digital channels. Social sensing refers to the collection, processing and analyses of data from such social media and Internet communications. This new form of sensing modality gives insights into user interactions, and thus can play a central role in the Internet-of-Things (IoT). In this review, we first present a range of IoT applications enabled by social data. We then identify core social sensing techniques and inherent challenges. A specific case study of social sensing driven connected landmark lighting is provided to showcase how social sensing techniques can be used to realize an IoT application in practice. We conclude by laying out new directions in social sensing to address challenges like diverse and fast-changing nature of social data, pernicious influencing and data privacy.
Noise Intensity Estimation Method Based on PCA and Weak Textured Block Selection for Neutron Image

Noise intensity estimation has a very important application in image denoising. In image processing, the denoising method can achieve an ideal denoising effect under the assumption that the Gaussian noise intensity in the image is known. But in real denoising applications, especially the neutron image, the noise level is unknown, which will greatly affect the denoising effect of neutron image processing. In this paper, a method which combined the principal component analysis with weak texture block selection is proposed for noise intensity estimation of neutron images. The experimental results show that the proposed method can accurately estimate the Gaussian noise in the neutron image. Compared with the existing noise intensity estimation methods, the qualitative and quantitative results show that the proposed method has higher accuracy and stability.
Garbage Segregation System using Support Vector Machine

The garbage bins are full, half full or empty. These bins include various types of garbage ranging from metals, plastics to glasses. The collection of these dump waste are not segregated and so when the eliminating method is proposed the method is not efficient. The bio waste also gets dumped into landfills. To make the system efficient this paper proposes method to segregate system at its early stage. The system uses a Machine Learning Algorithm called Support Vector Machine (SVM) which performs image comparison in the vector form. Also, capacitive proximity sensors is used for next level of segregation which identifies type of waste either wet or dry with dielectric effect. Thus, this system collaboratively separates the waste using Machine learning and capacitance effect. The separated bio waste is converted to bio fuel for economic purposes. The plastic wastes can be given to scrap industries.
A Self-Power Method for a Converter-Level on-State Voltage Measurement Concept

This article discloses part of an invention on plug-and-play converter-level on-state voltage measurement methods for power semiconductor devices. To exclude the external power supply required in on-state voltage measurement circuits, a self-power solution is proposed to provide the required bidirectional low-voltage power sources. The application of the measurement circuit with the proposed self-power solution is demonstrated for a single-switch, a single-phase inverter, and a three-phase inverter.
Judge, jurors, and gendered instructions to disregard evidence: stereotype-congruent judicial instructions increase compliance

ABSTRACT An umbrella study, consisting of two university samples (N = 196), examined mock jurors’ decisions after being presented with a trial summary (and accompanying audio) about the murder of the defendant’s wife and neighbor. As part of the simulated trial, jurors were asked to disregard incriminating, but unreliable, wiretap evidence from a judge who was male or female. When the judge simply admonished jurors to disregard the evidence, participants in the female judge condition were more likely to rate the defendant as guilty compared to the male judge condition, suggesting the former considered the inadmissible evidence more. However, this pattern was reversed when, in addition to the disregard instruction, an collaborative explanation for why one should disregard the evidence was provided: specifically, participants in the female judge condition rated the defendant as less guilty than those in the male judge condition. These findings suggest that gender-congruent behaviors (i.e. female judge providing a collaborative explanation) by an individual occupying an incongruent social role may elevate that individual’s level of authority.
Inkjet-Printed CNT/PEDOT:PSS Temperature Sensor on a Textile Substrate for Wearable Intelligent Systems

The presented work focused on the development of temperature sensors based on specially formulated carbon nanotube (CNT) and poly(3,4-ethylene dioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) based inks which are deposited directly onto the adhesive polyamide-based taffeta fabric with low-cost office-type inkjet printer. Stable and uniform dispersions of CNT and PEDOT:PSS with Triton X-100 were formulated to be used as temperature sensitive inkjet inks. Three kinds of temperature sensors were fabricated using sensitive materials including CNT ink, PEDOT:PSS ink, and CNT/PEDOT:PSS composite ink. Developed sensors are connected to the measuring devices via silver yarns and silver adhesive paste. Sensors are covered with translucent polyurethane welding tape for packaging and protection. All the sensors exhibit negative temperature coefficient (NTC) behavior and sensitivity of 0.15 %/°C for CNT, 0.41 %/°C for PEDOT:PSS, and 0.31 %/°C for CNT/PEDOT:PSS for temperatures varying from room temperature to 50 °C. CNT/PEDOT:PSS composite ink printed sensor achieved better sensing repeatability and demonstrated higher bending stability with a resistance change of 0.3 % up to 1000 cycles than CNT printed and PEDOT:PSS printed sensors. The softness, lightness, and capability to work on flexible or curved surfaces make the developed CNT/PEDOT:PSS composite ink printed sensor a robust candidate for wearable temperature sensor applications, especially in situations requiring continuous body temperature monitoring while satisfying comfort of the user.
Distributed Traffic Engineering for Multi-Domain SDN Without Trust

In software defined networking, the flat design of distributed control plane enables the management of multi-domain networks that are incapable of deploying a root controller. However, it is very difficult to avoid policy conflicts between independent local controllers due to the lack of centralized arbitration. Moreover, domains without trust may not be always cooperative and could even cheat to maximize their own interests. In this article, we first consider the cooperative scenario and address the problem of traffic engineering in a flat distributed control plane. We propose a fully distributed algorithm, called DisTE, which can provide max-min fair bandwidth allocation for flows and maximize resource utilization. DisTE also preserves the local topology of each domain and achieves policy consistency by multiple rounds of synchronization. We then consider the non-cooperative scenario, where selfish domains may discriminate bandwidth requests from other domains or overstate theirs owns to squeeze more bandwidths.
Fiber-Based Sensors and Energy Systems for Wearable Electronics

Wearable electronics have been receiving increasing attention for the past few decades. Particularly, fiber-based electronics are considered to be ideal for many applications for their flexibility, lightweight, breathability, and comfortability. Furthermore, fibers and fiber-based textiles can be 3D-molded with ease and potentially integrated with everyday clothes or accessories. These properties are especially desired in the fields of bio-related sensors and energy-storage systems. Wearable sensors utilize a tight interface with human skin and clothes for continuous environmental scanning and non-invasive health monitoring. At the same time, their flexible and lightweight properties allow more convenient and user-friendly experiences to the wearers. Similarly, for the wearable devices to be more accessible, it is crucial to incorporate energy harvesting and storage systems into the device themselves, removing the need to attach an external power source. This review summarizes the recent applications of fibers and fiber-based textiles in mechanical, photonic, and biomedical sensors. Pressure and strain sensors and their implementation as electronic skins will be explored, along with other various fiber sensors capable of imaging objects or monitoring safety and health markers. In addition, we attempt to elucidate recent studies in energy-storing fibers and their implication in self-powered and fully wireless wearable devices.
Performance of a Shunt Active Power Filter for Unbalanced Conditions Using Only Current Measurements

Shunt active power filters (APFs) are used to address power quality issues such as harmonic distortion and power system imbalance. Many control systems for shunt APFs demand measurements of voltages and currents for the determination of instantaneous real and reactive power signals. From these signals, the reference currents of the shunt APF are calculated. A method of control for the APF shunt that only measures currents is proposed in this paper. The control method includes a modified technique to extract the positive and negative sequence components in the time domain. The reference currents in the shunt APF are calculated by the negative sequence components of the measured currents. Simulation and experimental results confirm the efficiency of this control scheme under ideal and non-ideal conditions.
The Importance of Local Investments Co-Financed by the European Union in the Field of Renewable Energy Sources in Rural Areas of Poland

Local investments for the development of renewable energy sources (RESs) constitute an important element of sustainable rural development. They are conducive to the social and economic development of the said areas, and improve the environmental values and living conditions of their inhabitants. However, such advancement in rural areas is not possible without adequate financial support, including the funds from the EU budget. Therefore, the main objective of the research is to assess the scale, scope and importance of local investments in renewable energy sources in rural areas of Poland in 2014–2020, cofinanced from EU funds. The study covered 1117 projects, whose beneficiaries were rural and urban–rural municipalities. Evaluation of the municipal investment activities in acquiring EU subsidies in the area of environmentally friendly energy was conducted using selected methods of descriptive statistics and the analysis of variance. Subsequently, with the use of logistic regression, the study identified the main socioeconomic, financial and environmental conditions of the investment activities of the local government entities in RES in rural areas. Empirical studies allowed for the positive verification of the research hypothesis, which assumed that “The highest investment activity in the field of local projects co-financed from EU funds, related to the development of RES in rural areas, may be attributed to municipalities performing primarily agricultural functions, located in Eastern Poland”. The municipalities’ own income potential and investment activity are of major importance for the acquisition of EU funds used in RES financing. Municipalities at a lower development level demonstrated a greater activity in accessing these funds. They view the development of RES as an opportunity for accelerated growth.
Effects of cropping systems under no‐till agriculture on arbuscular mycorrhizal fungi in Argentinean Pampas

Here, we compare arbuscular mycorrhizal fungi (AMF) communities and fatty acids in soils under different no‐till (NT) agricultural managements over two seasons in two consecutive years. Two NT practices with different agricultural managements were compared: crop rotation (CR) and soya bean monoculture (MC). Soils of natural grasslands (NGs) were used as a reference. Treatments were tested along a regional gradient (four geographical locations) across a 400‐km transect of the Argentinean Pampas. We identified 46 morphospecies. Several morphospecies occurred abundantly at all soils; others appeared to be restricted to specific situations. At the regional scale, CR maintained the same richness levels of AMF spores, whereas MC showed less richness, when compared with the NG. Although AMF spore density was clearly affected by cropping practices in the four locations, we could observe some changes in AMF species richness, and similar diversity under agricultural and natural soils. Fatty acid concentrations (whole‐cell, phospholipid and neutral lipid fatty acids) revealed differences between soil managements and showed similar patterns of variation in all locations. Spore density positively correlated with all soil lipids fractions. The results suggest that AMF spore communities and fatty acids in soils are suitable indicators of soil management involving different levels of crop rotation. Spore richness measured at a regional scale proved to be sensitive to different NT agricultural managements. Moreover, certain morphospecies could be good bioindicators for NT practices based on cropping systems on the Argentinean Pampas.
Exact solutions for unsteady free convection flow of carbon nanotubes over an oscillating vertical plate

The purpose of this paper is to study the unsteady convection flow of carbon nanotubes (CNTs) induced by free convection with oscillating plate condition. Single-wall CNTs are used with water as base fluids. The governing partial differential equations and boundary conditions are transformed into a set of ordinary differential equations using suitable dimensionless variables. These equations are solved analytically using Laplace transform technique to obtain the velocity and temperature profiles. Results for velocity and temperature are shown in various graphs and discussed for the embedded flow parameters in detail. It is observed that, velocity decreases with increasing CNTs volume fraction and an increase in CNTs volume fraction increases the nanofluid temperature, which leads to an increase in the heat transfer rates.
Hybrid liquid crystals tetrazolyl and isoxazolyl cinnamates

ABSTRACT In this work, a series of isoxazolyl and tetrazolylcinnamic ester hybrid liquid crystals (HLCs) were synthesised and their mesomorphic behaviour was analysed. Cinnamic acid derivatives were prepared by Knoevenagel condensation of malonic acid and arylaldehydes. Five-membered tetrazoles and isoxazoles were prepared by [3 + 2] 1,3-dipolar cycloaddition. Tetrazoles were synthesised by sodium azide addition to arylnitrile, while isoxazoles were synthetised by arylnitrile oxide addition to alkenes to form isoxazolines, followed by MnO2-oxidation. Tetrazolyltolane compounds were also synthesised and their LC behaviour compared with cinnamic esters. HLCs containing isoxazole rings displayed a large mesophase range with high clearing temperature (Tc), with predominance of smectic mesophases SmA and SmC. The HLCs decomposed upon heating due to their high clearing temperature (>250°C). HLCs with tetrazole rings showed a narrow nematic mesophase range with enantiotropic or monotropic behaviour. GRAPHICAL ABSTRACT
A unified moment magnitude earthquake catalog for Northeast India

Abstract Earthquake-related studies on seismicity and seismic hazard assessment need a homogenous earthquake catalog for the region studied. A homogenous earthquake catalog for Northeast India region was compiled using derived regional and global empirical relationships between different magnitudes and moment magnitude based on an improved error-corrected methodology suggested in the recent literature. To convert smaller magnitude earthquakes, global empirical equations were derived and used. A procedure is suggested to change different magnitudes into moment magnitude. A homogenous earthquake catalog of 9845 events was compiled for the time period 1897–2012. Entire magnitude range (EMR) was found to be the most reasonable method for estimating magnitude of completeness. Derived local and global empirical equations are useful for every seismic hazard or seismicity study. A complete and consistent homogenized earthquake catalog prepared in this study could provide good data for studying earthquake distribution in Northeast India. By carefully converting these original magnitudes into homogenized Mw magnitudes, an obstacle is removed for the consistent assessment of seismic hazards in Northeast India.
A Birnbaum Importance and regression analysis based heuristic algorithm for multi-type component assignment problem

In this paper, a new kind of multi-type component assignment problem (MCAP) called the two types of component of component assignment problem (TCAP) is proposed. The problem is to assign two types of components to three types of positions in a system in order to obtain high system reliability. The number of each type of components equals the number of positions in the system where the components can be placed in. A new heuristic algorithm, called the REG-ZK method, based on Birnbaum Importance and regression analysis for solving the new MCAP, is illustrated in the paper. Comprehensive numerical experiments on small systems and large systems are conducted to evaluate the optimization effect of the method. Compared to the enumeration and randomization method, the REG-ZK method proves to be applicable and efficient with good optimization quality.
Evaluating the Risk of Disclosure and Utility in a Synthetic Dataset

: The advancement of information technology has improved the delivery of financial services by the introduction of Financial Technology (FinTech). To enhance their customer satisfaction, Fintech companies leverage artificial intelligence (AI) to collect fine-grained data about individuals, which enables them to provide more intelligent and customized services. However, although visions thereof promise to make customers’ lives easier, they also raise major security and privacy concerns for their users. Differential privacy (DP) is a common privacy-preserving data publishing technique that is proved to ensure a high level of privacy preservation. However, an important concern arises from the trade-off between the data utility the risk of data disclosure (RoD), which has not been well investigated. In this paper, to address this challenge, we propose data-dependent approaches for evaluating whether the sufficient privacy is guaranteed in differentially private data release. At the same time, by taking into account the utility of the differentially private synthetic dataset, we present a data-dependent algorithm that, through a curve fitting technique, measures the error of the statistical result imposed to the original dataset due to the injection of random noise. Moreover, we also propose a method that ensures a proper privacy budget, i.e., (cid:15) will be chosen so as to maintain the trade-off between the privacy and utility. Our comprehensive experimental analysis proves both the efficiency and estimation accuracy of the proposed algorithms.
Sliding Mean Value Subtraction-Based DC Drift Correction of B-H Curve for 3D-Printed Magnetic Materials

This paper presents an algorithm to remove the DC drift from the B-H curve of an additively manufactured soft ferromagnetic material. The removal of DC drift from the magnetization curve is crucial for the accurate estimation of iron losses. The algorithm is based on the sliding mean value subtraction from each cycle of calculated magnetic flux density (B) signal. The sliding mean values (SMVs) are calculated using the convolution theorem, where a DC kernel with a length equal to the size of one cycle is convolved with B to recover the drifting signal. The results are based on the toroid measurements made by selective laser melting (SLM)-based 3D printing mechanism. The measurements taken at different flux density values show the effectiveness of the method.
Highly Selective Production of Biobased p-Xylene from 2,5-Dimethylfuran over SiO2–SO3H Catalysts

Aimed at highly selective production of p-xylene (PX) from biomass-derived 2,5-dimethylfuran with a high carbon balance, SiO2 supported with sulfonic acid was especially designed and prepared. As one kind of the most widely used Bronsted acid, sulfonic acid groups were chosen as the active sites of probes. The density of sulfonic acid groups in the whole composite was highly adjustable, ranging from 4.7–540.6 μmol/g. The catalytic activity was strongly related to the acid concentration and location: the one with active sites mainly on the exterior surface exhibited much better catalytic activity by facile mass transfer through mesopores. The suitable acid species and structures of the as-prepared catalyst contributed to the improved activity: a selectivity of 89% and a carbon balance of 95% were achieved at 523 K. Overall, this new reusable catalyst provided an alternative for highly efficient production of biobased PX.