Miracle-dz/UniFAR-FTUs huggingface dataset:
Transverse and longitudinal magneto-optical effects with a functionalized microstructured optical fiber

We present a novel approach which could be useful for real-time all-fiber measurements of magnetic fields applied either longitudinally or transversally. We used a microstructured optical fiber, whose suspended core is coated with a nanocomposite magneto-optic thin film. It is obtained by doping a sol-gel solution with cobalt ferrite nanoparticles based on an innovative microfluidic deposition process. When any magnetic field is applied, several effects are induced by the appearance of off-diagonal elements in the nanocomposite material permittivity tensor. It results in a variation of the emerging light polarization state measured for the C-band telecom spectral range, at 1550 nm. One of the best-known magneto-optic effects is the Faraday rotation, observed with longitudinal magnetic fields. Therefore, our functionalization greatly exalts this effect already present in commercial fibers due to the Verdet constant of silica. A 300mT and 1 cm long longitudinal magnetic field creates up to 15° of rotation compared to 0.1° for a classical optical fiber, while for a transverse magnetic field, conventional optical fibers are almost totally insensitive to it. However, in our configuration different off-diagonal elements of permittivity tensor will be activated causing non-reciprocal variation of the propagation constant of the eigenmodes as well as another process leading to a reciprocal magneto-refractive effect. It results in an additional polarization variation of several degrees per centimeter in transversal magnetic field configuration. The interest of such a highly sensitive functionalized optical fiber, is found where conventional magnetic probes cannot be used. Above all, the optical fibers are relatively unaffected by radiations, unlike conventional electronic sensors, so it is conceivable to use them in these exclusion zones where the sensitive part is deported from the electronic measurement system.
The Effect of Pregnancy on Engagement with Politics. Toward a Model of the Political Consequences of the Earliest Stages of Parenthood

How do pregnancy and childbirth affect engagement in politics and society? Our data from a large-scale citizen panel record political engagement before, during, and after pregnancy for (future) mothers and fathers. We find that women demobilize from politics and societal issues during pregnancy. This disengagement is strongest for indicators of political participation and seeking of political news. Our analysis also shows that gender gaps in political engagement are not only strengthened but also partly created in the earliest stages of parenthood. Although the effects are relatively minor, they are robust to various analysis techniques. Some effects also last until the child grows older. Pregnancy and childbirth rarely lead to political mobilization, and when they do, they concern child-related activities, such as attempts to change daycare providers, but only at later stages of early parenthood.
Investigation of the Critical Factors Influencing Low-Cost Green Sustainable Housing Projects in Iraq

Iraq oil rich country is in the condition of economic chaos and confronting an intense scarcity for the low-cost housing in the capital as migration from the surrounding cities flooded where country poor economic condition has pushed the housing price hiked in cities. Therefore, this study is conducted with aims to determine critical factors affecting the affordable housing in Iraq. Quantitative study consisting close ended questionnaire of four sections cover all critical factors and randomly distributed questionnaire among the construction industry players to determine the objectives of the study. The questionnaire was translated into Arabic/local language so the every responded understand the questions, total distributed questionnaire is 175 and in response 134 received and data is analysed using SPSS software. Study findings shows that sustainable land management, easy and low interest rate loan arrangement to overcome on the financial constraints are the critical problem followed by unsteady or interpreted supply of construction materials, pricing control significantly help to keep lower the cost of affordable housing. Study also concluded that smaller size houses are no longer the key barriers of acceptance to the Iraqi community. Thus, study strongly recommend government stakeholders to develop framework and properly manage the land for housing purpose. Lastly, steady supply chain management for all the essential construction materials such as steel, technology, cement, electrical and sanitary appliance also control the pricing so low-cost housing dream of the people become true.
A Game-Theoretic Approach for Increasing Resource Utilization in Edge Computing Enabled Internet of Things

Edge computing is a new paradigm that reduces latency and saves bandwidth by deploying edge servers in different geographic locations. This technology plays a crucial role in the rapidly growing apps market for IoT devices as app vendors can hire computing resources on edge servers to serve their app users. An effective allocation of edge computing resources to different apps is needed to maximize resource utilization and serve the most app users at the lowest cost. We refer to this as an Edge Resource Allocation (ERA) problem. In this paper, we propose an Edge Resource Allocation Game (ERAGame), a game-theoretic approach that formulates the ERA problem by appropriately pricing the multi-tenant edge servers. The proposed approach gives a Pure Nash Equilibrium (PNE) solution to the ERA problem. For this, we design an ERA algorithm using ERAGame under which the system converges to PNE. For fast convergence to PNE, the edge servers are partitioned into different groups, enabling the ERA algorithm to run in parallel on all edge servers within each group. We prove that ERAGame is a potential game that guarantees at least one PNE under the ERA algorithm. We evaluate that the price of stability of ERAGame is at most O(log n). The performance of the proposed algorithm is examined through simulation.
Asymptotic tests for Hardy–Weinberg equilibrium in hexaploids

Abstract Hexaploids, a group of organisms containing three complete sets of chromosomes in a single nucleus, are of utmost importance to evolutionary studies and breeding programs. Many studies have focused on hexaploid linkage analysis and QTL mapping in controlled crosses, but little methodology has been developed to reveal how hexaploids diversify and evolve in natural populations. We formulate a general framework for studying the pattern of genetic variation in autohexaploid populations through testing deviation from Hardy–Weinberg equilibrium (HWE) at individual molecular markers. We confirm that hexaploids cannot reach exact HWE but can approach asymptotic HWE at 8–9 generations of random mating. We derive a statistical algorithm for testing HWE and the occurrence of double reduction for autopolyploids, a phenomenon that affects population variation during long evolutionary processes. We perform computer simulation to validate the statistical behavior of our test procedure and demonstrate its usefulness by analyzing a real data set for autohexaploid chrysanthemum. When extended to allohexaploids, our test procedure will provide a generic tool for illustrating the genome structure of hexaploids in the quest to infer their evolutionary status and design association studies of complex traits.
Biofabrication of Collagen Tissue-Engineered Blood Vessels with Direct Co-Axial Extrusion

Cardiovascular diseases are considered one of the worldwide causes of death, with atherosclerosis being the most predominant. Nowadays, the gold standard treatment is blood vessel replacement by bypass surgery; however, autologous source is not always possible. Thereby, tissue-engineered blood vessels (TEBVs) are emerging as a potential alternative source. In terms of composition, collagen has been selected in many occasions to develop TEBVs as it is one of the main extracellular matrix components of arteries. However, it requires specific support or additional processing to maintain the tubular structure and appropriate mechanical properties. Here, we present a method to develop support-free collagen TEBVs with co-axial extrusion in a one-step procedure with high concentrated collagen. The highest concentration of collagen of 20 mg/mL presented a burst pressure of 619.55 ± 48.77 mmHg, being able to withstand perfusion of 10 dynes/cm2. Viability results showed a high percentage of viability (86.1 and 85.8% with 10 and 20 mg/mL, respectively) of human aortic smooth muscle cells (HASMCs) and human umbilical vein endothelial cells (HUVEC) after 24 h extrusion. Additionally, HUVEC and HASMCs were mainly localized in their respective layers, mimicking the native distribution. All in all, this approach allows the direct extrusion of collagen TEBVs in a one-step procedure with enough mechanical properties to be perfused.
Dextrose Effects on Platelet Count and Volume: Implications for Regenerative Medicine

Platelet-rich plasma (PRP) and hypertonic dextrose solutions are commonly used injectates in regenerative medicine, sometimes used simultaneously. The effects of hypertonic dextrose on platelet lysis and activation have not been previously reported. We tested the effects of escalating dextrose concentration on cell counts and cell volume of platelets and red cells in PRP and whole blood (WB). A prompt partial reduction in platelet count occurred with all dextrose admixtures with either PRP or whole blood, consistent with partial lysis. After the first minute, platelet counts remained stable, suggesting a rapid accommodation of residual platelets to extreme (>2000 mOsm) hypertonicity. A 25% or higher dextrose concentration caused a significant increase in mean platelet volume (MPV), which suggests an early phase of platelet activation. Further investigation is warranted to confirm if platelet lysis or activation has occurred and whether additive clinical benefit may result from hypertonic dextrose injection alone or in combination with PRP.
Research on automatic identification technology of the safety helmet based on machine vision

Due to the lack of safety awareness of workers in the construction area,the workers often do not wear the safety helmet. In view of this situation,it uses the method of machine vision to develop the automatic identification technology of safety helmet. Based on image preprocessing,it mixtures Gaussian model to detect the foreground,then determines the connected domain to judge whether it belongs to the human body or not. Finally,it locates the head region of human and realizes the automatic recognition of safety helmet. The result of construction field experiments shows that the method is of high accuracy and good environment adaptability,and is suitable for application in the construction area.
Views on Scientific Research Methods: A Study of North Indian Engineering Faculty Members

Scientific research is a significant component in the field of education. It is a systematic effort not only to get proficiency in teaching but also to stimulate independent search for new knowledge, to unravel scientific issues and to identify how to study the information. This study aimed to investigate the views of North Indian engineering faculty members on the scientific research methods (SRM). The research data was collected through a structured questionnaire and interviews. The faculty members were asked questions about scientific research methods and their opinion about how to improve the effectiveness of engineering faculty in teaching and research. To this aim, the main effects of the scientific research methods on academics and research have been studied. The research work data was collected from more than five hundred faculty members and was analysed with the descriptive method. The findings indicate a positive relationship between teaching pedagogy and scientific research methods. The results also revealed that the engineering faculty members lacked knowledge about scientific research methods which further impedes the systematic teaching-learning process.
How do I model my system? A Qualitative Study on the Challenges that Modelers Experience

Model-Driven Software Engineering relies both on domain-expertise as well as software engineering expertise to fully grasp its representative power in modeling complex systems. As is typical in the development of any system, modelers face similar challenges to classic software developers, whether with general modeling concepts or specific features of existing tools such as the Eclipse Modeling Framework. In this work, we aim to understand the issues that modelers face by analyzing discussions from Eclipse's modeling tool forums, MATLAB Central, and Stack Overflow. By performing a qualitative study using an open-coding process, we created a taxonomy of common issues faced by modelers. We considered both difficulty experienced when modeling a system and issues faced using existing modeling tools; these form the basis of our two research questions. Based on the taxonomy, we propose nine suggestions and enhancements, in three overarching groups, to improve the experience of modelers, at all levels of experience.
DramatVis Personae: Visual Text Analytics for Identifying Social Biases in Creative Writing

Implicit biases and stereotypes are often pervasive in different forms of creative writing such as novels, screenplays, and children’s books. To understand the kind of biases writers are concerned about and how they mitigate those in their writing, we conducted formative interviews with nine writers. The interviews suggested that despite a writer’s best interest, tracking and managing implicit biases such as a lack of agency, supporting or submissive roles, or harmful language for characters representing marginalized groups is challenging as the story becomes longer and complicated. Based on the interviews, we developed DramatVis Personae (DVP), a visual analytics tool that allows writers to assign social identities to characters, and evaluate how characters and different intersectional social identities are represented in the story. To evaluate DVP, we first conducted think-aloud sessions with three writers and found that DVP is easy-to-use, naturally integrates into the writing process, and could potentially help writers in several critical bias identification tasks. We then conducted a follow-up user study with 11 writers and found that participants could answer questions related to bias detection more efficiently using DVP in comparison to a simple text editor.
CASE STUDY Phosphorus recovery from domestic sewage sludge in the presence of waste grape pruning biochar

BACKGROUND AND OBJECTIVES: Phosphorus is an essential and limiting nutrient for all living organisms. Although phosphorus is a finite resource on earth, it is usually wasted today. Precipitation of struvite from waste residues is mainly carried out to recover phosphorus. This study aimed to investigate the percentage of phosphorous recovery from sewage sludge in the presence of grape-biochar via the formation of biochar/struvite precipitates. METHODS: Different amounts of grape-biochar were applied to recover nutrients (phosphorus, nitrogen, and magnesium) from sewage sludge via the formation of struvite by digestion of sewage sludge with H 2 SO 4 and the molar ratio of magnesium/ ammonium/ phosphorus in 2:1:1 at pH=8.5. Solubility and release properties of the precipitates were determined and the equations, such as first-order, parabolic diffusion, power function, and simple Elovich models, were fitted to the kinetic data. FINDINGS: The phosphorus recovery from sewage sludge increased by application of grape biochar in the precipitation system, and the accumulation release of nutrients (phosphorus, nitrogen, and magnesium) from samples increased in the presence of grape biochar, especially in high amounts. Increasing the remove and recovery of phosphorus from sewage sludge by application of grape-biochar decreased the incidence of eutrophication, as an environmental dilemma, and provided the requirement for phosphorus-fertilizers by solid waste management. The solubility of the samples was 0.5 mole per liter hydrochloric acid > in 20 gram per liter citric acid > water. The results showed that the phosphorus - cumulative - release of composites in water good fitted the parabolic kinetic model (R 2 =0.97-0.99), whereas it followed the simple Elovich model (R 2 =0.86-0.92) in 0.5 mole per liter hydrochloric acid and first-order kinetics model (R 2 =0.76-0.92) in 20 gram per liter critic acid. CONCLUSION: The results indicated that the presence of grape-biochar for recovery of phosphorus from sludge as struvite had a good potential for increasing the release of nutrients for the formation of struvite, and these precipitates had a high potential to be used as a slow-release phosphorus-fertilizer.
Learning the Low-level Policy for Robot Air Hockey

The current approaches for solving robotics with reinforcement learning often lack safety guarantees, which are required for real-world usage. A solution to this is safe exploration algorithms that respect a set of constraints during the whole training process. In this work, we are going to evaluate the performance of a specific safe exploration algorithm introduced by Liu et al. [18]. To do that we are going to extend the experiments of the original paper to more general tasks in the air hockey domain. We chose this air hockey domain because it is a high speed, dynamic environment, in which it is important to fulfil constraints. Furthermore, air hockey has a small state space which results in fast convergence of RL algorithms. We will evaluate the performance of a simple planar robot and an IIWA industrial robot. Both of these agents respect the constraints at all times when trained with the safe exploration algorithm. Additionally, their performance with and without the safe exploration algorithm is comparable. Thus we conclude that the safe exploration algorithm can fulfil constraints while not negatively impacting performance. Furthermore, we analyse the learned behaviours of the planar robot to show that we designed sufficiently informative reward functions. Moreover, we show that it is possible to combine the tasks into a general air hockey player, that can play against itself.
Features Analysis of the Research and Development Industry in Indonesia

R&D is one of the key drivers of technological progress and contributes to increased productivity and profit growth. Indonesian percentage of Gross Domestic Expenditure on R & R&D (GERD) to GDP in 2018 is one of the Global Competitiveness Index indicators, only reaches 0.28% and is dominated by the government sector, while the industrial sector is only 7.34%. One of the reasons for this small value is that the data collection of R&D on the business sector in Indonesia has not been carried out optimally. A classification model is needed to determine the data collection target so that the results are more optimal. The main objective of this study is to classify R&D industries actors in Indonesia using XGBoost and then analyze the features for R&D industries actors using SHAP. XGBoost is one of the black-box models that is difficult to interpret, and SHAP is one of the interpretation methods. The classification results using XGBoost obtained the accuracy, AUC, and F1-Score values of 79.61%, 0.7646, and 84.44%, respectively. Based on the Shapley value of the SHAP method, it was found that the average growth in R&D expenditure had the highest contribution. The feature's contribution to the estimation will be even higher if the mean of R&D expenditure growth is higher (more than 0). The other one is the ratio of researchers to R&D human resources. If the ratio is more than 75%, it will negatively contribute. Finally, exports and State-Owned Enterprise (BUMN) feature with the smallest contribution.
Scaling Learned Indexes on Persistent Memory ∗

1 MOTIVATION Byte-addressable persistent memory (PM) such as Intel Optane DCPMM promises persistence, low cost, high capacity and low latency on the memory bus. This opens up new possibilities for indexes that operate and persist data directly on PM, with desirable features like instant recovery while maintaining high performance. Recent learned indexes [4] exploit data distribution and have shown great performance for some workloads. The main intuition behind learned indexes is that if keys are continuous integers (e.g., 0-100 million), the value mapped by key k can be accessed by array [k]. Such model-based search givesO(1) complexity and the entire index is as simple as a linear function. Of course, the data distribution of real world data could be much more complex so that current learned indexes [2, 4] typically use a hierarchy of models that form a tree-like structure to improve accuracy. We observe learned indexing is a natural t for PM: Real PM (Optane DCPMM) exhibits ∼4× higher latency and ∼3–14× lower bandwidth than DRAM [10], whereas model-based search is especially good at reducing memory accesses. However, learned indexes were designed based on DRAM without considering PM properties, and existing PM-based indexes [8, 9] typically evolve B+-trees or hash tables which are agnostic to data distribution. Therefore, it remains challenging for learned indexes to work well on PM. Challenge 1: Scalability and Throughput. Although learned indexes are frugal in bandwidth usage for lookups, they still exhibit excessive PM accesses for inserts. This is because learned indexes [2, 4] require key-value records be maintained in sorted order, which may cause existing records to be shifted during inserts. Since PM exhibits asymmetric read/write bandwidth with writes being 3–4× lower, frequent record shifting can easily exhaust write bandwidth and eventually limit insert scalability and throughput. A common solution is to use unsorted nodes [9] that accept inserts in an append-only manner at the cost of linear search. This is reasonable for small B+-tree nodes (e.g., 256B–1KB), but for model-based operations to work well, it is critical to use large nodes (e.g., up to 16MB in ALEX [2]) with sorted data. This in turn makes structural modi cation operations (SMOs), such as node splits, more expensive with more PM accesses and higher synchronization cost: typically only one thread can work on a node during an SMO. Challenge 2: Persistence and Crash Consistency. A key feature of persistent indexes is to ensure correct recovery across restarts and power cycles. Simply running a learned index on PM does not guarantee consistency. Any operations that involvewriting more than eight bytes could result in inconsistencies as currently only 8-byte writes on PM are atomic. Although recent work [3, 5] provides easy ways to convert DRAM indexes to work on PM with crash consistency, they are either not general-purpose, or incur very high overhead due to heavyweight logging internally.
A Domain Based Approach to Semantic Lexicon Expansion

Current approaches to the expansion of semantic lexicons for corpus annotation are somewhat ad hoc in nature and do not generally offer a systematic means of identifying areas for development within one’s lexicon. The present paper sets forward a domain based approach to semantic lexicon expansion, targeting UCREL’s Semantic Analysis System (USAS). First, an updated version of the lexicon is compared to representative corpora to ascertain areas of underrepresentation in a novel method which we call K-FLUX analysis. Second, an example set of underrepresented types are targeted for development using domain specific corpora. Collectively, the results show that some corpora are more successful than others in supplementing the existing USAS lexicon. The paper discusses the various factors that should be borne in mind when utilising the proposed method before concluding with how findings might inform future developments of the lexicon, and crucially, the semantic system on which it is based.