Miracle-dz/UniFAR-FTUs huggingface dataset:
An In-Depth Survey Demystifying the Internet of Things (IoT) in the Construction Industry: Unfolding New Dimensions

In this digital era, many industries have widely adopted the Internet of Things (IoT), yet its implementation in the construction industry is relatively limited. Integration of Construction 4.0 drivers, such as business information modeling (BIM), procurement, construction safety, and structural health monitoring (SHM), with IoT devices, provides an effective framework for applications to enhance construction and operational efficiencies. IoT and Construction 4.0 driver integration research, however, is still in its infancy. It is necessary to understand the present state of IoT adoption in the Construction 4.0 context. This paper presented a comprehensive review to identify the IoT adoption status in the Construction 4.0 areas. Furthermore, this work highlighted the potential roadblocks to IoT’s seamless adoption that are unique to the areas of Construction 4.0 in developing countries. Altogether, 257 research articles were reviewed to present the current state of IoT adoption in developed and developing countries, as well as the topmost barriers encountered in integrating IoT with the key Construction 4.0 drivers. This study aimed to provide a reference for construction managers to observe challenges, professionals to explore the hybridization possibilities of IoT in the context of Construction 4.0, and laymen to understand the high-level scientific research that underpins IoT in the construction industry.
Accuracy of Techniques for Predicting Gas Production by Ruminants Associated with Diet

The aim of this study was to compare the gas production profiles and nutrient degradability of two diets using automatic and semiautomatic in vitro gas production techniques. A randomized block design in a 2 × 2 factorial arrangement was adopted, with two diets comprising different proportions of nonfiber carbohydrates (NFCs) (low-NFC vs. high-NFC diets), two gas production measurement techniques (automatic vs. semiautomatic) and four replicates. The blocks represent three in vitro runs. Gas production from the fermentation of fiber carbohydrates (Vf2) was 22% higher when measured with the automatic technique than with the semiautomatic technique. The Vt of the low-NFC diet differed between techniques and was 22.9% higher using the automatic technique. A highly positive correlation (r = 0.96) was observed between the techniques, with a high coefficient of determination between the techniques (R2 = 0.93). There was greater degradability of dry matter (DMD) and organic matter (OMD) with the automatic technique. In both diets, the degradability of crude protein (CPD) was greater with the semiautomatic technique (p < 0.0001). The high-NFC diet resulted in a lower pH and lower NH3-N in the incubation medium than in that of the low-NFC diet, whereas the degradability of DM, OM and CP increased. The automatic and semi-automatic techniques similarly estimated the kinetic parameters and the profiles of total gas production, demonstrating the potential of both techniques for assessing the nutritional value of diets with different proportions of NFCs.
Efficiency Improvement with Data Center Monitoring Based on Building Information Modeling on the Facility Management Stage

Building Management Systems can be used for effective monitoring, management, planning, and improving the quality of work on large objects (office buildings, factories). Moreover, these systems can reduce costs during the operational phase. One of the technically complex and expensive facilities in which functions of Building Management Systems are used are data centers. To achieve a high level of efficiency at the Facility Management stage, it is important to work with data centers from the design stage. Data center design issues can be solved using Building Information Modeling, but during the transition to the operation of the facility some problems arise, namely the correct data export for Facility Management, the connection of real sensors with the model, and the receipt of data. All of these also affect the efficiency of the data center systems management. This article introduces a novel methodology for interconnecting Building Information Models with real sensors in data centers, which can provide a basis for further optimization. Furthermore, it can result in a more efficient operation in the operational phase. The proposed method is implemented and the experience gained as a result of the application is described.
The wavelet transform on the two-sphere and related manifolds: a review

In a first part, we discuss several properties that seem desirable for any type of wavelet, such as smoothness, orthogonality, local support, Riesz stability, or vanishing moments. Then we review the construction of the spherical continuous wavelet transform based on the stereographic projection. Next we turn to the discrete wavelet transform. We review the various existing constructions and compare them in the light of the requirements listed above. Finally, we briefly describe the continuous wavelet transform on a two-sheeted hyperboloid and give some hints concerning the case of a general conic section.
The smoothness of CO line profiles in Orion - Implications for clumpiness

Spectra with very high SNR and spectral resolution of the (C-12)O and (C-13)O J = 1 - 0 transitions toward several positions in the Orion region have been obtained. These observations test the extent to which the clumpy structure of this region is refelcted in the velocity structure of the spectra. The smoothness of the profiles is such that if the cloud is assumed to have a constant clump volume filling factor, an unrealistically large number of clumps is necessary to reproduce the observations. A volume filling factor which increases from a small value at the surface to a large value at the core may be more appropriate for this source. 15 refs.
Testing homoscedasticity in nonparametric regression

We consider the nonparametric regression model with random design and derive an asymptotic \alpha -test for testing the hypothesis that the conditional variance of the observations is constant against the alternative that it depends on the'design. The test statistic is based on a L_{2} -distance between nonparametric variance estimators in both models - the underlying heteroscedastic model and in the hypothetical homoscedastic model. The proposed test is a consequence of a limit theorem for the weighted Integrated Square Error of nonparametric estimators of the conditional variance. Power considerations complete the approach.
A global uniqueness theorem for an inverse boundary value problem

In this paper, we show that the single smooth coefficient of the elliptic operator LY = v yv can be determined from knowledge of its Dirichlet integrals for arbitrary boundary values on a fixed region 2 C R', n ? 3. From a physical point of view, we show that an isotropic conductivity can be determined by steady state measurements at the boundary.
NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction With Implicit Neural Representations

Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, where a robot is required to explore a scene and plan a view path for the reconstruction, has not been studied. In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one. To solve the challenges, firstly, a proxy of Peak Signal-to-Noise Ratio (PSNR) is proposed to quantify a viewpoint quality; secondly, the proxy is optimized jointly with the parameters of an implicit neural network for the scene. With the proposed view quality criterion from neural networks (termed as Neural Uncertainty), we can then apply implicit representations to autonomous 3D reconstruction. Our method demonstrates significant improvements on various metrics for the rendered image quality and the geometry quality of the reconstructed 3D models when compared with variants using TSDF or reconstruction without view planning.
The Triterpenoid CDDO-Methyl Ester Redirects Macrophage Polarization and Reduces Lung Tumor Burden in a Nrf2-Dependent Manner

The NRF2/KEAP1 pathway protects healthy cells from malignant transformation and maintains cellular homeostasis. Up to 30% of human lung tumors gain constitutive NRF2 activity which contributes to cancer cell survival and chemoresistance, but the effects of NRF2 activation in immune cells within the tumor microenvironment are underexplored. Macrophages can promote cancer progression or regression depending on context, and NRF2 activation affects macrophage activity. The NRF2 activator CDDO-Methyl ester (CDDO-Me or bardoxolone methyl) reprogrammed Nrf2 wild-type (WT) tumor-educated bone marrow-derived macrophages (TE-BMDMs) from a tumor-promoting to a tumor-inhibiting phenotype, marked by an increase in M1 markers TNFα, IL-6, and MHC-II and a decrease in the tumor-promoting factors VEGF, CCL2, and CD206. No changes were observed in Nrf2 knockout (KO) TE-BMDMs. CDDO-Me decreased tumor burden (p < 0.001) and improved pathological grade (p < 0.05) in WT but not Nrf2 KO A/J mice. Tumor burden in Nrf2 KO mice was 4.6-fold higher (p < 0.001) than in WT mice, irrespective of treatment. CDDO-Me increased the number of lung-infiltrating macrophages in WT mice but lowered CD206 expression in these cells (p < 0.0001). In summary, Nrf2 KO exacerbates lung tumorigenesis in A/J mice, and CDDO-Me promotes an Nrf2-dependent, anti-cancer macrophage phenotype.
First-principles study of point defects in an fcc Fe-10Ni-20Cr model alloy

The influence of the local environment on vacancy and self-interstitial formation energies has been investigated in a face-centered-cubic (fcc) Fe-10Ni-20Cr model alloy by analyzing an extensive set of first-principle calculations based on density functional theory. Chemical disorder has been considered by designing special quasirandom structures and four different collinear magnetic structures have been investigated in order to determine a relevant reference state to perform point defect calculations at 0 K. Two different convergence methods have also been used to characterize the importance of the method on the results. Although our fcc Fe-10Ni-20Cr would be better represented in terms of applications by the paramagnetic state, we found that the antiferromagnetic single-layer magnetic structure was the most stable at 0 K and we chose it as a reference state to determine the point defect properties. Point defects have been introduced in this reference state, i.e., vacancies and Fe-Fe, Fe-Ni, Fe-Cr, Cr-Cr, Ni-Ni, and Ni-Cr dumbbell interstitials oriented either parallel or perpendicular to the single layer antiferromagnetic planes. Each point defect studied was introduced at different lattice sites to consider a sufficient variety of local environments and analyze its influence on the formation energy values. We have estimated the point defect formation energies with linear regressions using variables which describe the local environment surrounding the point defects. The number and the position of Ni and Cr first nearest neighbors to the point defects were found to drive the evolution of the formation energies. In particular, Ni is found to decrease and Cr to increase the vacancy formation energy of the model alloy, while the opposite trends are found for the dumbbell interstitials. This study suggested that, to a first approximation, the first nearest atoms to point defects can provide reliable estimates of point defect formation energies.
Metabolomics as a Potential Chemotaxonomical Tool: Application on the Selected Euphorbia Species Growing Wild in Serbia

Chemotaxonomy presents various challenges that need to be overcome in order to obtain valid and reliable results. Individual genetic and environmental variations can give a false picture and lead to wrong conclusions. Applying a holistic approach, based on multivariate data analysis, these challenges can be overcome. Thus, a metabolomics approach has to be optimized depending on the subject of research. We used 1H NMR-based metabolomics as a potential chemotaxonomic tool on the selected Euphorbia species growing wild in Serbia. Principal components analysis (PCA), soft independent modeling by class analogy (SIMCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) were used to analyze obtained NMR data in order to reveal chemotaxonomic biomarkers. The standard protocol for plant metabolomics was optimized aiming to extract more specific metabolites, which are characteristic for the Euphorbia genus. The obtained models were validated, which revealed that variables unique for each species were associated with certain classes of molecules according to literature data. In E. salicifolia, acacetin-7-O-glycoside (not found before in the species) was detected, and the structure of the aglycone part was solved based on 2D NMR data. In the presented paper, we have shown that metabolomics can be successfully used in Euphorbia chemotaxonomy.
A Framework to Address Mobility Management Challenges in Emerging Networks

The key enablers for emerging cellular networks (such as densification, concurrent operation at multiple bands, and harnessing mmWave spectrum), give birth to a peculiar set of new network management challenges. One such key challenge is user mobility management. In this article, we identify key issues that render current mobility management paradigm inadequate for delivering the expected Quality of Experience (QoE) and resource efficiency in emerging and future cellular networks. Together, these challenges call for a paradigm shift in the way mobility is managed in cellular networks. We present an Advanced Mobility Management and Utilization Framework (A-MMUF) that can enable this paradigm shift by transforming mobility management from being a reactive to a proactive process. The core idea of A-MMUF is to build upon Mobility Prediction Models (MPMs) to predict various user mobility attributes and traffic patterns, such as next candidate cell for handover (HO), HO time, and future cell loads. These predictions are then leveraged to not only improve HO process for better QoE and less signaling overhead, but also to enable proactive automation to further maximize network performance (in terms of energy and spectrum efficiency). Understandable, however, the A-MMUF gains offered hinge on the MPMs accuracy. Those gains may vary, not only with underlying machine learning technique choice, but also with training data volume, variety and fidelity. We analyze the potential A-MMUF gains through three case studies: namely proactive HO, proactive Mobility Load Balancing (P-MLB), and proactive Energy Savings (P-ES). In addition to demonstrating significant gains in all three case studies, the results provide useful insights into the agility versus accuracy tradeoff – that can be leveraged for choosing optimal machine learning models for practical A-MMUF deployment.
Irradiation effects in Cu/W multilayers: Ion beam mixing and structural evolution

Irradiation effects in the immiscible Cu/W system are investigated for helium 70 keV and krypton 340 keV irradiations at liquid-nitrogen temperature. Evolution of the [(3.1 nm Cu/4.2 nm W)×12] multilayer was characterized by x-ray diffraction measurements. Limits of solubility were observed in both matrixes for both irradiations. As expected, a strong thermal spike effect is evidenced in copper layers under krypton irradiation where almost no solubility is observed. For helium irradiations very similar limits of solubility are observed in both matrixes. These latter results can be analyzed by a model with two atomic fluxes: one due to ballistic displacement and the other due to chemically driven thermally activated transport. A structural study shows a transition to an amorphous state for heavy ion irradiation in the large cascades above a critical solubility threshold.
Increased Intake of Omega-3 Polyunsaturated Fatty Acids Is Associated with Reduced Odds of Low Hand Grip Strength in Korean Adults

Nutritional status is thought to be one of the modifiable risk factors for muscle health. This study investigates the association between dietary omega-3 polyunsaturated fatty acids (PUFA) intake and hand grip strength (HGS) in Korean adults. The cross-sectional analysis was performed on 18,278 participants aged ≥19 years enrolled in the Korea National Health and Nutrition Examination Survey from 2016–2019. Omega-3 PUFA consumption was positively linked to the dietary intake of nuts, fish, and shellfish in Korean adults. After adjusting for potential confounders, the results showed that increased omega-3 PUFA intake was associated with a decreased risk of low HGS (odds ratio (OR) for upper quartile (Q4) compared to Q1, men: OR = 1.42 (95% CI: 1.17–1.72), women: OR = 1.61 (1.37–1.89)). This inverse association was reported in people who did no resistance exercise or had an insufficient protein intake. In contrast, this association was not evident in adults who did resistance exercise or had sufficient protein intake. Furthermore, participants with hypertension or type 2 diabetes showed stronger associations between dietary omega-3 PUFA intake and HGS compared with other subgroups. These results suggest that dietary omega-3 PUFA intake positively related with HGS in Korean adults.
Azimuthal variations of the microwave radiation from a slightly non‐Gaussian sea surface

Microwave radiation from the ocean rough surface is considered using a small slope expansion. The emphasis is on azimuthal variations of the brightness temperature, related to the anisotropy of wind‐generated sea waves. The first and second harmonics, describing the upwind‐downwind and upwind‐crosswind differences, respectively, are considered using existing models of the sea spectrum. For modeling of the first harmonic the small deviation from Gaussian statistics of the sea surface is introduced to estimate the effect of longwave asymmetry and the distribution of ripples over the long waves. Numerical analysis shows that the longwave asymmetry cannot explain the observed values of the first harmonic in azimuthal variations of the brightness temperature. The ripple modulation by long waves is a possible mechanism explaining the first harmonic in moderate winds, although foam and wave breaks have to be included in electromagnetic modeling to achieve a quantitative agreement with experiments.
Towards a Novel Cost-Effective and Versatile Bioink for 3D-Bioprinting in Tissue Engineering

3D-bioprinting for tissue regeneration relies on, among other things, hydrogels with favorable rheological properties. These include shear thinning for cell-friendly extrusion, post-printing structural stability as well as physiologically relevant elastic moduli needed for optimal cell attachment, proliferation, differentiation and tissue maturation. This work introduces a cost-efficient gelatin-methylcellulose based hydrogel whose rheological properties can be independently optimized for optimal printability and tissue engineering. Hydrogel viscosities were designed to present three different temperature regimes: low viscosity for eased cell suspension and printing with minimal shear stress, form fidelity directly after printing and long term structural stability during incubation. Enzymatically crosslinked hydrogel scaffolds with stiffnesses ranging from 5 to 50 kPa were produced, enabling the hydrogel to biomimic cell environments for different types of tissues. The bioink showed high intrinsic cytocompatibility and tissues fabricated by embedding and bioprinting NIH 3T3 fibroblasts showed satisfactory viability. This novel hydrogel uses robust and inexpensive technology, which can be adjusted for implementation in tissue regeneration, e.g., in myocardial or neural tissue engineering.
Predictors of Oral Health Behaviors among Dental Students

Background and Objectives: College life is a time when students take more responsibility for health-related behaviors. Aim: To determine the oral health behaviors of dental students, to motivate their transformation into cyanogenic behaviors by applying the planned behavior model, and to determine the degree to which students can modify behavior for oral health. Material and Methods: The results of the initial and final assessment (4 months apart) of the bacterial plaque present by means of the Quigley Hein and API indexes were collected from the student files. The Health-Promoting Lifestyle Profile II -HPLP II and Generalized Self-Efficacy Scale (GSE) questionnaires were used to determine the variables with predictor values that influence the cognitive-perceptual factors. Results: For the “health responsibility” domain, the average total value was 2.53 ± 0.9 (SD). For the “interpersonal relationships” domain, the average value was 2.82 ± 0.79. In the case of the “nutrition” domain, the desire to change the behavior of the participants was moderate (2.53 ± 1.19). “Physical activity” represents an important field in the everyday life of young adults. The results of the study indicated a moderate desire for change (2.52 ± 0.97). In the case of “spiritual growth”, in which self-esteem, the way the individual perceives the environment and the adaptation to it were evaluated, a great desire for change was observed (2.74 ± 0.82). Scale GSE questionnaire showed an α coefficient of 28.52 ± 0.82, a value that indicated a moderate to a high level of efficacy and self-confidence. Conclusions: The results of the study showed that students have a moderate tendency to change in all areas, except for interpersonal relationships, where the desire to change something is increased. The participants have a moderate to a high level of self-efficacy and self-confidence.