Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Printing Technologies in Architectural Design and Construction: A Systematic Literature Review

The proliferation of digital technologies considerably changed the field of architecture. Digital fabrication pushes architecture into an unexpected new domain of previously unachievable complexity, detail, and materiality. Understanding these technologies’ impact can help direct future research, innovate design and construction processes, and improve the education of future professionals. However, comprehensive reviews offering a holistic perspective on the effects of 3D printing technologies on architecture are limited. Therefore, this study aims to provide a systematic review of state-of-the-art research on 3D printing technologies in architectural design and construction. The review was performed using three major databases, and selected peer-reviewed journal articles published in the last ten-year period were included in quantitative and qualitative analyses. Using bibliometric analysis, the research progress is summarized through the identified trend of the annual number of articles, prominent authors and co-authorship network, and key topics in the literature organized in three clusters. Further, content analysis of selected articles enabled coding cluster themes. Moreover, the analysis differentiated two categories of 3D printing technologies based on the scale of the system, elaborating their peculiarities in terms of materials, methods, and applications. Finally, challenges and promising directions for future work and research challenges are discussed.
Language use shapes cultural norms: Large scale evidence from gender

Cultural norms vary dramatically across social groups. Here we use large scale data to examine the extent to which language plays a role in shaping one such norm—the gender norm to associate men with careers and women with family. We measure cross-cultural variability in this gender bias using previouslycollected estimates from the Implicit Association Task (IAT; N = 663,709). We then try to predict bias variability by the way that gender is encoded in language semantics and grammar. We quantify gender bias in semantics using word-embedding models trained on different languages. Our data suggest that the linguistic encoding of gender predicts the degree of speakers’ gender bias in the IAT, pointing to a causal role for language in shaping gender norms.
Persistence on challenging tasks mediates the relationship between childhood poverty and mental health problems

Childhood disadvantage is associated with psychological distress throughout the lifespan. Poor children are alleged to give up more often than their more privileged peers when facing challenges. Yet little research has examined the role of task persistence in poverty and mental health. We test whether poverty-related deficits in persistence contribute to the well-documented link between childhood disadvantage and mental health. We used growth curve modeling to analyze three waves (ages 9, 13, and 17) of data assessing the trajectories of persistence on challenging tasks and mental health. Childhood poverty is the proportion of time participants lived in poverty from birth to age 9. We found that individuals experiencing more poverty in early childhood demonstrate less persistence and deteriorated mental health from ages 9 to 17. As expected, task persistence accounts for a portion of the robust childhood poverty—worsening mental health association. Clinical research on childhood disadvantage is in the early stages of unpacking underlying reasons why childhood poverty is bad for psychological well-being throughout life, revealing potential points of intervention.
Curiosity: The effects of feedback and confidence on the desire to know.

In 10 experiments, we investigated the relations among curiosity and people's confidence in their answers to general information questions after receiving different kinds of feedback: yes/no feedback, true or false informational feedback under uncertainty, or no feedback. The results showed that when people had given a correct answer, yes/no feedback resulted in a near complete loss of curiosity. Upon learning they had made an error via yes/no feedback, curiosity increased, especially for high-confidence errors. When people were given true feedback under uncertainty (they were given the correct answer but were not told that it was correct), curiosity increased for high-confidence errors but was unchanged for correct responses. In contrast, when people were given false feedback under uncertainty, curiosity increased for high-confidence correct responses but was unchanged for errors. These results, taken as a whole, are consistent with the region of proximal learning model which proposes that while curiosity is minimal when people are completely certain that they know the answer, it is maximal when people believe that they almost know. Manipulations that drew participants toward this region of "almost knowing" resulted in increased curiosity. A serendipitous result was the finding (replicated four times in this study) that when no feedback was given, people were more curious about high-confidence errors than they were about equally high-confidence correct answers. It was as if they had some knowledge, tapped selectively by their feelings of curiosity, that there was something special (and possibly amiss) about high-confidence errors. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
The Severity of COVID-19 Affects the Plasma Soluble Levels of the Immune Checkpoint HLA-G Molecule

The non-classical histocompatibility antigen G (HLA-G) is an immune checkpoint molecule that has been implicated in viral disorders. We evaluated the plasma soluble HLA-G (sHLA-G) in 239 individuals, arranged in COVID-19 patients (n = 189) followed up at home or in a hospital, and in healthy controls (n = 50). Increased levels of sHLA-G were observed in COVID-19 patients irrespective of the facility care, gender, age, and the presence of comorbidities. Compared with controls, the sHLA-G levels increased as far as disease severity progressed; however, the levels decreased in critically ill patients, suggesting an immune exhaustion phenomenon. Notably, sHLA-G exhibited a positive correlation with other mediators currently observed in the acute phase of the disease, including IL-6, IL-8 and IL-10. Although sHLA-G levels may be associated with an acute biomarker of COVID-19, the increased levels alone were not associated with disease severity or mortality due to COVID-19. Whether the SARS-CoV-2 per se or the innate/adaptive immune response against the virus is responsible for the increased levels of sHLA-G are questions that need to be further addressed.
Interference Alignment for Transceiver Design in Multi-User MIMO Relay System

A multi-user multiple-input multiple-output (MU-MIMO) relay system using macrocell and femtocells has numerous advantages, such as optimal coverage, better fairness in the resource allocation and spectral efficiency. Despite these advantages, interference management remains an important challenge especially for the cell edge (CE) users. In this article, a linear transceiver design for CE macrocell user equipments (CE-MUEs) and amplify-and-forward (A-F) relay node (RN) dealing with the interference in perfect channel state information (CSI) case is proposed. The proposed scheme is based on the interference alignment (IA) technique, which is formulated as a sum mean square error (MSE) problem using the minimum MSE (MMSE) criterion. In comparison with the conventional solutions, the simulation results of the proposed IA scheme show a marked improvement of the system performance in terms of average sum-rate.
ADMM for Hybrid Precoding of Relay in Millimeter-Wave Massive MIMO System

Beamforming with multiple data streams, or named as precoding, plays a significant role in millimeter-wave massive MIMO systems. If properly designed, the precoding can improve spectral efficiency of the system and compensate for the degradation caused by channels. Although the classical full-digital precoding can achieve the optimal performance, it is too expensive for implementation. To reduce cost and power consumption, some hybrid structures of precoding are recently proposed while their precoding schemes have not been extensively studied. In this paper, the mathematical model of the precoding for relay assisted system is reconstructed. The objective function is highly non-convex and has some complex constraints. Especially the block-diagonal constraint and constant-modulus constraint make the classical precoding methods based on singular value decomposition not applicable. We design an algorithm based on alternating direction method of multipliers to optimize the objective function. By deliberating over the iteration order of the variables, we give the convergent condition of the proposed algorithm. This paper focused on sub-connected precoding structures since they have more constraints. Meanwhile, the proposed algorithm can be applied to full-connected structures simply. We present the simulation results which indicate that the proposed algorithm can provide a near optimal solution to the original precoding problem.
An Exploration of the Contingency Influence on Chinese Customers’ Selection of Online Hotel Reservation Channels

Hotel companies have tried to sell rooms through their direct online reservation channels, but consumers still purchase a substantial number of rooms through indirect channels operated by online travel agencies (OTAs). This study explores when and under what circumstances consumers choose either a hotel channel or an OTA channel drawing on the theoretical frameworks of tourist information search strategy. Results reveal that travelers who book the hotels for business purposes, who have visited the destination before, and who are more familiar with the destinations and hotels seem to be more likely to choose hotels’ direct online reservation channels. These findings provide novel perspectives on understanding why hotel customers prefer one channel to another. Hotel managers could use the findings of this study to increase reservations through direct channels.
antioxidant of Anethum graveolens (Dill) on some biochemical parameter in blood serum of male rats

Liver toxic is an acute effect of paracetamol (500 mg/kg) Consequently, we investigated how paracetamol toxicity affected liver cell enzymes in this study aspartate aminotransferase (GOT) and alanin aminotransferase catalyzes (GPT), Glutation (GSH( and total protein ,glucose , liver and body weight of male rats that drenched Anethum graveolens (Dill) . were used in this research 40 adult male rats, weight 172-311 gm, age 8-12 weeks, isolated in a environment at temperature 25ºC in an animal house in university of karbala ,They were given food, male rats were divided into four groups (G). (G1) the control group has gaves the food and normal saline 0,9% only, (G2) injection of paracetamol 500 mg/kg body weight (B.W). (G3) injection 100 mg/kg B.W A. graveolen extract + 500 mg/kg B.W of paracetamol. (G4) given 150mg/kg B.W of flavonoid compound extracted + 500 mg/kg B.W of paracetamol. The present study found ability of Dill and flavonoid extract to antioxidant and protective liver enzyme against the paracetamol overdose by significant decrease GPT, GOT, glucose.
Pediatric abdominal trauma in a National Referral Hospital

Background Trauma is a major contributor to pediatric morbidity and mortality. Injury and violence are a major killer of children throughout the world. Unintentional injuries account for almost 90% of these cases. They are the leading cause of death for children aged 10–19 years. More than 95% of all injury deaths in children occur in low income and middle-income countries. Abdominal trauma is present in approximately 25% of pediatric patients with major trauma and is the most common cause of unrecognized fatal injury in children. Objectives To describe the patterns, the management and outcomes of pediatric abdominal trauma. This was a descriptive retrospective study. Data was extracted from the Pediatric surgery Unit database from January 2012 to July 2019 on all abdominal trauma admissions to the unit. Results Falls were the commonest (51.3%) mechanism for trauma on the unit. Most (84%) of the admissions had blunt abdominal trauma, with the majority (77%) managed non operatively. Only 16% had penetrating trauma, with the majority (84%) of these managed operatively. The average length of hospital stay for most (71.9%) of the patients was less than 7 days, with 96.1% of all admitted patients being discharged upon recovery. Conclusion Blunt abdominal trauma is the most common pattern of pediatric abdominal trauma, with majority of these patients being managed non-operatively with good outcomes. Selective non-operative management for penetrating pediatric abdominal trauma has good patient outcomes as well.
Design and analysis of a label-free water-soluble glucose sensor based on suspended hybrid plasmonic micro-ring resonator

In this paper, the design and analysis of a label-free water-soluble glucose sensor based on a suspended hybrid plasmonic micro-ring resonator with an external radius of 940 nm in the third telecommunication window to sense the concentration of water-soluble glucose is presented. The simulation results obtained using three-dimensional FDTD show that the sensitivity and the figure of merit values obtained for glucose measurements with concentrations of 0 to 50% in water solution are 218.5 (nm/RIU) and 16.11 (1/RIU), respectively. Comparing the results of the proposed sensor with the previous work, which has four times more footprint, shows that the structure's sensitivity has increased by 17 percent.
Dry-type High Voltage Capacitors

High voltage capacitors are important components of electrical network. The current technology is based foil-laminar coil impregnated in a liquid dielectric. However, there is an interest to develop technologies allowing to replace mineral or synthetic oil due both to health and environmental issues. The feasibility of replacing the impregnating liquid by a solid encapsulating material for HV capacitors is presented in this paper.
AI in Breast Cancer Imaging: A Survey of Different Applications

Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role. This paper surveys different applications of AI in Breast Imaging. First, traditional Machine Learning and Deep Learning methods that can detect the presence of a lesion and classify it into benign/malignant—which could be important to diminish reading time and improve accuracy—are analyzed. Following that, researches in the field of breast cancer risk prediction using mammograms—which may be able to allow screening programs customization both on periodicity and modality—are reviewed. The subsequent section analyzes different applications of augmentation techniques that allow to surpass the lack of labeled data. Finally, still concerning the absence of big datasets with labeled data, the last section studies Self-Supervised learning, where AI models are able to learn a representation of the input by themselves. This review gives a general view of what AI can give in the field of Breast Imaging, discussing not only its potential but also the challenges that still have to be overcome.
An Ensemble Voting Method of Pre-Trained Deep Learning Models for Skin Disease Identification

Millions of confirmed cancer cases have been reported worldwide as a result of the development of skin disease. One of the most essential stages in preventing disease development is early diagnosis and treatment. Nevertheless, due to similarities in appearance, location, color, and size, diagnosing skin lesions is a challenging feat which requires high standard human resources in the medical system. To address this problem, a machine-based skin disease diagnosis is introduced as a first step to aid in patient classification. Recently, deep learning in medical imaging is becoming a cutting-edge research trend in a variety of applications. In this research, an ensemble network from the pre-trained models ResNet50, MobileNetV3, and EfficientNet is proposed to classify skin diseases. Thanks to the major voting step, the advantages of distinct models are combined to improve the diagnosis of the classification process. The observations and results are based on the experiments performed with the HAM10000 dataset, which includes 7 different forms of skin disease. In comparison to the initial pre-trained models, the proposed model has a 98.3 % average accuracy and other assessment metrics indicate improved results.
Exploration of Test Anxiety Profiles in U.S. Undergraduate Students

The present study was conducted using latent profile analysis to determine whether homogeneous test anxiety groups could be identified among 625 undergraduates and to determine whether gender, self-critical perfectionism, and rigid perfectionism were significant predictors of class membership in the identified test anxiety groups. The students completed two multidimensional instruments, measures of test anxiety and perfectionism, online. Results of the latent profile analysis identified a test anxiety profile with three homogeneous groups as the best model. The three homogeneous groups were labeled low, medium, and high. Moreover, gender, self-critical perfectionism, and rigid perfectionism were found to predict class membership differentially in the three homogeneous groups. Implications of the results are discussed in relation to prevention and intervention in the fields of perfectionism and test anxiety.
Immunomodulatory Effects of Radon Inhalation on Lipopolysaccharide-Induced Inflammation in Mice

Typical indications for radon therapy include autoimmune diseases such as rheumatoid arthritis (RA). We had previously reported that radon inhalation inhibits Th17 immune responses in RA mice by activating Th1 and Th2 immune responses. However, there are no reports on how radon inhalation affects the activated Th1 and Th17 immune responses, and these findings may be useful for identifying new indications for radon therapy. Therefore, in this study, we investigated the effect of radon inhalation on the lipopolysaccharide (LPS)-induced inflammatory response, focusing on the expression of related cytokines and antioxidant function. Male BALB/c mice were exposed to 2000 Bq/m3 radon for one day. Immediately after radon inhalation, LPS was administered intraperitoneally at 1.0 mg/kg body weight for 4 h. LPS administration increased the levels of Th1- and Th17-prone cytokines, such as interleukin-2, tumor necrosis factor-α, and granulocyte-macrophage colony-stimulating factor, compared to no treatment control (sham). However, these effects were suppressed by radon inhalation. IL-10 levels were significantly increased by LPS administration, with or without radon inhalation, compared to sham. However, radon inhalation did not inhibit oxidative stress induced by LPS administration. These findings suggest that radon inhalation has immunomodulatory but not antioxidative functions in LPS-induced injury.
A Newton-Based Method for Nonconvex Optimization with Fast Evasion of Saddle Points

Machine learning problems such as neural network training, tensor decomposition, and matrix factorization require local minimization of a nonconvex function. This local minimization is challenged by the presence of saddle points, of which there can be many and from which descent methods may take an inordinately large number of iterations to escape. This paper presents a second-order method that modifies the update of Newton's method by replacing the negative eigenvalues of the Hessian by their absolute values and uses a truncated version of the resulting matrix to account for the objective's curvature. The method is shown to escape saddles in at most $1 + \log_{3/2} (\delta/2\varepsilon)$ iterations, where $\varepsilon$ is the target optimality and $\delta$ characterizes a point sufficiently far away from the saddle. This base of this exponential escape is $3/2$ independently of problem constants. Adding classical properties of Newton's method, the paper proves convergence to a local minimum with probabil.
Evaluation of the Technical Efficiency of Taiwan’s Milkfish Polyculture in Consideration of Differences in Culturing Models and Environments

Taiwan has cultured milkfish for longer than hundreds of years. Given its long and narrow terrain surrounded by sea and location in a subtropical monsoon area, it has developed a unique culture bounded by the Erren River in the south. The Yunjianan area in the north is cold in winter and thus follows the “current year harvest” culturing model. In contrast, the Gaoping area in the south is warmer in winter and follows the “overwinter harvest” culturing model. This paper evaluated the production efficiency by using the stochastic metafrontier production model and the multi-input-multi-output distance function using input from in-person interviews with 100 current year harvest farmers and 70 overwinter harvest farmers from 2017 to 2019. In the first stage, the environmental variables of various regions were internalized into the model to obtain the group technical efficiency (GTE) of different farming models. In the second stage, the common environmental variables were re-internalized to evaluate the metafrontier technical efficiency (MTE) of both culturing modes. In this way, the technical efficiency and production technology of the two different culturing model are reasonably evaluated by taking into account not only the difference between their input and output, but also their environmental difference during their farming periods. The results show that in spite of the environmental difference between th two culturing models, shallower pond, smaller size of fish fry and lower shrimp density should make culturing more technically efficient. When cultured in a common environment, the lower the temperature, the worse the culture efficiency is. It indicated that milkfish are highly sensitive to low temperature. Therefore, the MTE and technical gap rate (TGR) of current year harvest farming are significantly higher than those of overwinter harvest farming. Finally, the regression analysis showed that the younger the farmers were, the lower the average pond age was, the larger the freshwater culture area was, and the greater the experience in fish farming was. Thus, the relatively better the MTE is; the younger the farmers, the higher the education level is and the more years of experience in fish farming they have, thus the relatively better the production technology level is.