Miracle-dz/UniFAR-FTUs huggingface dataset:
The Bloch-Ogus-Gabber theorem

Nacreous pigments comprising crystalline TiO2 platelets, either supported on corresponding plate-like calcium sulfate anhydrite particles or self-supporting. The pigments are prepared by depositing amorphous TiO2 coatings on plate-like gypsum substrate particles, calcining the resulting product to convert the gypsum to the anhydrite and crystallize the TiO2 and, when an unsupported pigment is to be produced, dissolving out the anhydrite substrate. The crystalline TiO2 is produced in the form of either rutile or anatase.
The Development of an IoT-based Indoor Air Monitoring System Towards Smart Energy Efficient Classroom

Indoor air quality has become a crucial issue, specifically during COVID 19 pandemic. The good indoor air quality will lead to occupants’ comfort condition, thus affecting their productivity. Indoor air temperature and relative humidity are two essential components of thermal comfort. This paper presents the development of a temperature and relative humidity monitoring system for the classroom using the Internet of Things (IoT). This system consists of three main components: logger nodes, a gateway logger, and an interconnected cloud server. The logger node (ESP8266 / ESP32 microcontroller and DHT22 sensor) is a device at the edge of the IoT system and is placed at the monitoring location. The logger gateway is built on a Raspberry Pi 4, which serves as an intermediate server. It receives periodic data (temperature and humidity) from the logger nodes through the publish-subscribe MQTT protocol and sends it to the MongoDB Atlas cloud database. The logger gateway saves all received logs into the SQLite database as temporary local storage and then uploads the data to the MongoDB Atlas cloud at a predefined interval. The MongoDB data is then displayed on a monitoring dashboard using MongoDB charts. The logger node with the DHT22 sensor has been adjusted using a linear model and successfully tested to monitor indoor and outdoor air conditions with satisfactory results. The recorded data has also been successfully modeled using the Gaussian Mixture Model and a simple Fuzzy model. These models can capture the dynamic of air condition in the room and predict the state of the cooling system.
Altered Composition of the Oral Microbiota in Depression Among Cigarette Smokers: A Pilot Study

Alterations in the oral microbiota composition may influence mental health. However, linkages between compositional changes in the oral microbiota and their role in mental health among cigarette smokers remain largely unknown. In this study, we used shotgun metagenomics data for the oral microbiome of 105 participants. The data showed Bacteroidota, Fusobacteriota, Firmicutes, Proteobacteria, and Actinobacteria to be the most abundant phyla; Streptococcus, Haemophilus D, and Veillonella are the most abundant genera. Then, we clustered our subjects into avoidance and activation groups based on the behavioral activation for depression scale (BADS). Interestingly, the avoidance group exhibited a higher oral microbiome richness and diversity (alpha diversity). Differential abundance testing between BADS avoidance and activation groups showed the phyla Bacteroidota (effect size 0.5047, q = 0.0037), Campylobacterota (effect size 0.4012, q = 0.0276), Firmicutes A (effect size 0.3646, q = 0.0128), Firmicutes I (effect size 0.3581, q = 0.0268), and Fusobacteriota (effect size 0.6055, q = 0.0018) to be significantly increased in the avoidance group, but Verrucomicrobiota (effect size−0.6544, q = 0.0401), was found to be significantly decreased in the avoidance risk group. Network analysis of the 50 genera displaying the highest variation between both groups identified Campylobacter B, Centipeda, and Veillonella as hub nodes in the avoidance group. In contrast, Haemophilus and Streptococcus were identified as hub nodes in the activation group. Next, we investigated functional profiles of the oral microbiota based on BADS avoidance and activation groups and found Lysine degradations pathway was significantly enriched between both groups (ANCOM-BC, q = 0.0692). Altogether, we provide evidence for the presence of depression-related changes in the oral microbiota of smokers and possible functional contribution. The identified differences provide new information to enrich our understanding of oral microbiota-brain axis interplay and their potential impact on mental health.
INTRODUCTION TO SUSTAINABILITY AS A TRANSVERSAL COMPETENCE IN HIGHER EDUCATION

Sustainability in higher education institutions is little discussed, but there is a large amount of literature that mentions its importance for the awareness of all those who frequent their spaces, whether they are teachers, employees, or students. The understanding, by the human being, of the complex nature of the environment and the perception of the interdependence of the environmental elements in space and time is crucial and, for this reason, the education in this area must be accessible to all and at all levels of education, not matter what subject area. Since 2019, in the Soft Skills Lab of a public university was created a curricular unit called Introduction to Sustainability, with the aim that students and everyone involved with the environment in a higher education institution can be fully aware of what it means to develop sustainability actions, both in the environment and in the very contents of the various courses developed in the institution they are a part of. This exploratory study aims to disseminate this experience with the 1st cycle students and also with workshops made with the employees as a practice that can be replicate in other institutions. The evaluation was made through the process of pedagogical monitoring students make every semester and in the case of the employees through an evaluation survey. It was very motivating to see the commitment that both students and employees have started to show in their daily behaviour and even the creative suggestions for their participation in this matter.
Bringing Subalterns into Speech? Investigating Anarchic Resistance to Hegemonic Modernity

This article aims to critically examine Gayatri Spivak’s (1992) demand to undo subalternity by inserting subalterns into the circuit of hegemonic modernity.For Spivak, working for the subaltern does not demand speaking for them, rather it entails facilitating their speech acts. From the perspective of an anthropology of anarchy, the opening up of political communication towards inclusion of subaltern speech is, on the one hand, an essential goal. It is congruent with the basic democratic principles of consensual decision-making among social groups living outside or at the margins of state influence. On the other hand, the insistence on including subalterns into hegemony entails an inherent paradox: many subalterns, who resort to anarchic ways of life, escape from the state and its communicational structures as a cultural and political survival strategy. My ethnographic example from the Andaman Islands in India addresses this tension. I focus on the subaltern history and resistance practices of the so-called Ranchis, Adivasis (first settlers, Indigenous Peoples) from the Central Indian hill region, who migrated to the Andamans as contract labourers and settled in marginal forests. The Ranchis’ evasion from the state into the margins, enabled by subsistence practices, presents an alternative to Spivak’s compelling demand to bring subalterns into speech: an inclusion of the Ranchis into the circuits of hegemony would moderately benefit them in terms of getting access to the state and the economy, but, at the same time, it would also imply a loss of their partial autarky, as well as cultural and socio-political autonomy from the outside world.
Automatic Detection and Imaging of Rivet Hole Defects for Aircraft Structures With Optimized Sensor Array Using Eddy Current Method and Image Analysis

Fatigue cracking in riveted lap joints on aircraft could cause severe safety consequences in flight. The prior inspection for the integrity of the second layer of aircraft structure with rivet removed is necessary due to the priority of safety. Methods such as radiographic and ultrasonic testing are widely applied but require relatively expensive equipment, mechanical scanning or professional operators. Eddy current method can be adopted for non-destructive testing (NDT) inspection, which is safe and low-cost. In this paper, a hand-held eddy current (EC) sensor array was optimised based on sensitivity map analysis. The EC sensor array was used with a multi-channel EM instrument, which was capable of inspecting defects around the fastener locations covered by the head of rivets. A simulated sample of riveted joint with defects was examined with the eddy current sensing system and the collected data was processed by Hough Circle Transformation (HCT) and texture analysis. The analysis results could detect and locate fatigue cracks on the sample. The presented eddy current sensing system in this paper achieved an automatic, real-time, portable, convenient and accurate inspection method for defects in riveted joints.
Evaluating US Regulations: A Comment on Joseph S. Shapiro’s “Pollution Trends and US Environmental Policy: Lessons from the Past Half Century”

This article is a comment on Shapiro’s “Pollution Trends and US Environmental Policy: Lessons from the Past Half Century. ” It first provides some historical perspective by comparing Shapiro’s analysis and conclusions to an article published 20 years ago by A. Myrick Freeman III. It then discusses (and slightly reframes) Shapiro’s four hypotheses and highlights the importance of understanding the counterfactuals implied by each hypothesis. Finally, it suggests at least one key lesson that emerges from Shapiro’s analysis and its implications for future work—namely, the need not only to improve methods and data for analyzing existing regulations but also to increase efforts to reduce currently unregulated sources of pollution, such as agricultural pollution and greenhouse gas emissions.
Perspectives for Uses of Propolis in Therapy against Infectious Diseases

Propolis has gained wide popularity over the last decades in several parts of the world. In parallel, the literature about propolis composition and biological properties increased markedly. A great number of papers have demonstrated that propolis from different parts of the world is composed mainly of phenolic substances, frequently flavonoids, derived from plant resins. Propolis has a relevant role in increasing the social immunity of bee hives. Experimental evidence indicates that propolis and its components have activity against bacteria, fungi, and viruses. Mechanisms of action on bacteria, fungi, and viruses are known for several propolis components. Experiments have shown that propolis may act synergistically with antibiotics, antifungals, and antivirus drugs, permitting the administration of lower doses of drugs and higher antimicrobial effects. The current trend of growing resistance of microbial pathogens to the available drugs has encouraged the introduction of propolis in therapy against infectious diseases. Because propolis composition is widely variable, standardized propolis extracts have been produced. Successful clinical trials have included propolis extracts as medicine in dentistry and as an adjuvant in the treatment of patients against COVID-19. Present world health conditions encourage initiatives toward the spread of the niche of propolis, not only as traditional and alternative medicine but also as a relevant protagonist in anti-infectious therapy. Production of propolis and other apiary products is environmentally friendly and may contribute to alleviating the current crisis of the decline of bee populations. Propolis production has had social-economic relevance in Brazil, providing benefits to underprivileged people.
Early Effects of Repetitive Transcranial Magnetic Stimulation Combined With Sertraline in Adolescents With First-Episode Major Depressive Disorder

Background Adolescence is a period of high incidence for depression. However, there is a limited treatment option for the adolescent depression. For treatment-resistant major depressive disorder, HF-rTMS of the left dorsolateral prefrontal cortex (DLPFC) appears therapeutically effective. The aim of the study is to explore the early effects of repetitive transcranial magnetic stimulation in combination with sertraline in adolescents with first-episode major depressive disorder. Methods A total of 100 teenage patients with first-episode depression were randomly divided into the study groups. Both groups were treated with sertraline. In addition, the study group was treated with ten sessions of add-on rTMS. The control group was given sertraline only. The depressive symptom and cognitive function were assessed by the Hamilton depression rating scale 17 version (HAMD-17), Children’s Depression Rating Scale-Revised (CDRS-R), Integrated visual and auditory continuous performance test (IVA-CPT), and THINC-it. Results The number of early improvers after 2 weeks of treatment in the study group was statistically significant higher compared to the control group (95.83% vs 73.47%, χ2 = 9.277, P = 0.002). There was significant difference observed in responder rates (62.50% vs. 28.57%, χ2 = 11.262, P = 0.001) or in remission rates (31.25% vs. 6.12%, χ2 = 10.130, P = 0.001) between the two groups at 4 weeks. The score of HAMD-17 and CDRS-R in the study group were significantly lower than the control group (Fgroup = 12.91 vs 10.21, P < 0.05). Attention Quotient (listening, visual and full-scale) attention quotient of IVA-CPT in the study group were higher than those in the control group after treatment, and the differences were statistically significant(P < 0.05). The study group showed higher score in Spotter than the control group after treatment (P < 0.05). Discussion This is the most extensive blinded, randomized clinical study to date examining the efficacy of 10-Hz add-on rTMS for first-onset adolescent depression. Our results support that add-on rTMS accelerates the efficacy of the antidepressants, improving the depressive symptoms and cold cognitive function in first-episode adolescent depression. Clinical Trial Registration [www.ClinicalTrials. gov], identifier [ChiCTR2100048534].
Comparative thermal performance analysis of a shaded and non-shaded Double Skin Façade in hot arid areas.

This paper studies the impact of changing layering configurations, cavity widths and shading apertures of a shading system on the inner cavity surface temperatures of a double skin façade (DSF) in a hot arid climate. Computational fluid dynamics (CFD) thermo-fluid-dynamics modelling is used by coupling RNG k-epsilon turbulence model with Discrete Ordinate (DO) radiation and solar ray-tracing model. The optimized DSF is tested on a peak summer and winter day, at 9:00am, 12:00pm and 15:00pm. A flat-shading with 3 aperture sizes (30%, 50% & 70%) is tested to reduce internal surface heat gains.
Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

Load balancing (LB) is nothing but the systematic distribution of load over different servers. The fog server is handling the maximum data of the cloud server to enhance the advancement of users' requests. The growth in data requests is escalating, and fog computing has intensified the accessibility of the data. Fog computing achieves many challenges according to the demands of the users, but even so, some challenges require more progress. The problem faced by fog computing is LB due to an increase in traffic on the network layer. Various LB techniques have already been proposed in the cloud layer, but until now they have only been in progress in the fog layer. Inefficient LB may cause a decrease in service quality, like delays in response time, processing time, security, and many more. In this survey, several algorithms have been discussed that are based on LB, which works out the issue of overloaded data on the network. Some parameters that authors have focused in LB are latency, bandwidth, deadlines, cost, security, execution time, and response time. Other parameters based on fault tolerance are also discussed with their quality parameter table and algorithm. In addition to this some of the limitations of the author's work, that is discussed in this article.
PerAnSel:  A  Novel Deep Neural Network-Based System for Persian Question Answering

Question answering (QA) systems have attracted considerable attention in recent years. They receive the user's questions in natural language and respond to them with precise answers. Most of the works on QA were initially proposed for the English language, but some research studies have recently been performed on non-English languages. Answer selection (AS) is a critical component in QA systems. To the best of our knowledge, there is no research on AS for the Persian language. Persian is a (1) free word order, (2) right-to-left, (3) morphologically rich, and (4) low-resource language. Deep learning (DL) techniques have shown promising accuracy in AS. Although DL performs very well on QA, it requires a considerable amount of annotated data for training. Many annotated datasets have been built for the AS task; most of them are exclusively in English. In order to address the need for a high-quality AS dataset in the Persian language, we present PASD; the first large-scale native AS dataset for the Persian language. To show the quality of PASD, we employed it to train state-of-the-art QA systems. We also present PerAnSel: a novel deep neural network-based system for Persian question answering. Since the Persian language is a free word-order language, in PerAnSel, we parallelize a sequential method and a transformer-based method to handle various orders in the Persian language. We then evaluate PerAnSel on three datasets: PASD, PerCQA, and WikiFA. The experimental results indicate strong performance on the Persian datasets beating state-of-the-art answer selection methods by 10.66% on PASD, 8.42% on PerCQA, and 3.08% on WikiFA datasets in terms of MRR.
Stage-wise evaluation of integrity risks in public works procurement in India

Abstract Government agencies undertake procurement of works to achieve multiple goals. Like most countries, India has seen a rising volume of public construction projects. Nonetheless, this is an area that is yet to be tamed by the widespread procurement reforms, rendering it prone to corruption. This study introduces integrity risk management to construction management through a stage-wise assessment of integrity risks. Fifty-five major integrity risks are identified and mapped across the nine project stages. Delphi technique involving 11 domain experts yields reliable judgment on each risk item's probability and severity. Prioritization of risks as per the overall impact reveals 30 top risks concentrated in the ‘contract management’ stage, followed by the ‘bid design’ and ‘payments’. A risk heat map is developed for a holistic visual depiction of these risks. Top risks include poor supervision, substitution of materials, mandatory tests not done, hindrances not properly recorded, tailor-made contracts, and non-transparent engagement of sub-contractors. The study demonstrates the application of the Bowtie analysis as a prospective risk management method in construction projects. The dynamic stage-wise integrity risk analysis presented should be of immediate functional utility to policymakers, practitioners, and academicians.
Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction

Considering that the road short-term traffic flow has strong time series correlation characteristics, a new long-term and short-term memory neural network (LSTM)-based prediction model optimized by the improved genetic algorithm (IGA) is proposed to improve the prediction accuracy of road traffic flow. Firstly, an improved genetic algorithm (IGA) is proposed by dynamically adjusting the mutation rate and crossover rate of standard GA. Secondly, the parameters of the LSTM, such as the number of hidden units, training times, gradient threshold and learning rate, are optimized by the IGA. Therefore, the optimal parameters are obtained. In the analysis stage, 5-min short-term traffic flow data are used to demonstrate the superiority of the proposed method over the existing neural network algorithms. Finally, the results show that the Root Mean Square Error achieved by the proposed algorithm is lower than that achieved by the other neural network methods in both the weekday and weekend data sets. This verifies that the algorithm can adapt well to different kinds of data and achieve higher prediction accuracy.
Radiant spectral energy for simulation in the built environment

Fast and accurate daylighting and energy performance simulations are crucial for real-time control systems (RCS) in the built environment. RCS include adaptive and responsive facades, adaptive smart glass, ﬁlm, and HVAC control systems. State-of-the-art building monitoring systems driving such RCS require spectral input in order to take advantage of the visible light and thermal infrared energy of solar and sky radiation. Building designers also require spectral energy for early-stage building and fenestration design and material decisions relating to circadian rhythm daylighting. Unfortunately, modern daylighting and energy simulations often neglect spectral energy in eﬀorts to reduce computation time. We present an accurate, interactive physically-based approach for building performance simulation that utilizes spectral radiance and a radiosity engine to compute a spectral visualization solution in real-time. This approach demonstrates the feasibility of using radiant spectral energy from modern skylight models given real sky viewer captures or sky conditions for daylighting and thermal building performance simulations. This method is intended for building performance simulation, building adaptation, circadian daylighting consideration, and real-time control systems in the built environment.
Distributed control for coordinated tracking of fixed-wing unmanned aerial vehicles subject to velocity constraints

This paper considers a coordinated tracking problem of fixed-wing unmanned aerial vehicles (UAVs), whose kinematics is described as unicycle type with the constraints of both saturated angular speed and bounded linear velocity. A distributed velocity controller based on the consensus theory is designed such that UAVs can converge to the predefined formation and track the target vehicle, while the velocity constraints are satisfied. Moreover, due to the limitation of measurement range of sensors and communication bandwidth of UAV, an observer is designed when the information of neighbors’ velocity cannot be obtained. The stability of the closed-loop system is proved by using a technique lemma, which is developed based on the cascaded system. Finally, the results of numerical simulation compared with another algorithm show the effectiveness of the proposed algorithm, and a hardware-in-loop (HIL) simulation is carried out to prove the practical of the algorithm.