Miracle-dz/UniFAR-FTUs huggingface dataset:
A Descriptive Study on Multiple Health-Risk Behaviors among Chinese Adults in Hong Kong

This study investigated the prevalence and clustering patterns of multiple health-risk behaviors and their associations with non-communicable diseases among Chinese adults in Hong Kong. A large sample survey was conducted in all 18 districts of Hong Kong between 21 June and 31 August 2021. A total of 5737 adults completed the survey. Overall, 4605 (80.3%) had at least one health-risk behavior and 2696 (47.0%) had two or more health-risk behaviors. Multiple health-risk behaviors were more prevalent among men. The prevalence of smoking and alcohol consumption among Hong Kong Chinese women was considerably lower than in most Western countries. In contrast to previous findings, this study revealed that a high proportion of adults with high educational attainment or household income had multiple health-risk behaviors. In addition, this study revealed that the health-risk behaviors in Chinese adults co-occurred in clusters, with smoking and alcohol consumption co-occurring with other health-risk behaviors. Those who smoked or consumed alcohol had the highest proportion of multiple health-risk behaviors and the highest proportion of non-communicable diseases. The findings of this study add further evidence that health-risk behaviors co-occur in clusters and can contribute to non-communicable diseases.
Analysis of Engineering Students Acceptance and Usage of 5G Technology: A Case Study of Gannon University

It has been around 20-25 years since the internet was first developed for public use, and since then the number of users has been increasing exponentially. In a recent report, there are around 313 million internet users, out of which 276.8 million are mobile internet users and internet penetration is 90.3% (Johnson, 2021). With the development of wireless telecommunication technology and mobile devices, use of the internet access has been increasing. From the first generation (1G) of cellular networks to the current 5th Generation (5G), there has been a huge improvement in the data rate, coverage, and security that made it possible to have the internet on mobile phones. The internet in mobile devices has existed since 2G and was used for checking emails and browsing the web (Yamauchi et al., 2005). It is important that users should accept new technology. In this study, acceptance, and usage of the 5G technology that was investigated in a survey of students from the engineering discipline of Gannon University. This work describes the usage of a statistical technique called the technology acceptance model to determine the engineering students perception of the degree to which the 5G technology is accepted and useful. It aims to answer the research questions of whether perceived usefulness and perceived ease of use a ect the actual usage of 5G technology among engineering students, as they tend to bend towards new technology because of the high involvement of technology in engineering studies. This work aims to answer, to what extent perceived usefulness and perceived ease of use determine the usage of 5G technology among the selected group of participants.
Self-Supervised Fully Automatic Learning Machine for Intelligent Retail Container

Intelligent retail containers (IRCs) are a representative application of object detection in open-world scenarios. However, their wide application is limited because of labor-intensive data labeling and frequently updated retail goods categories. To address the two challenges, we propose a self-supervised fully automatic learning machine (SFLM), which consists of a self-paced active learning (SPAL) human–machine cooperation module and a clustering autolearning (CAL) module. Based on active learning and self-paced learning, we use the SPAL module to handle the labor-intensive data labeling task, which speeds up data labeling and reduces its cost. We further use the CAL module to overcome the challenge of frequently updated goods categories in intelligent retail. Considering the lack of large-scale datasets in real retail container scenarios, we propose a new challenging IRC dataset. This dataset consists of 35000 images, with 370000 bounding boxes and instance segmentation masks, covering 200 common categories of goods for different data acquisition settings. Extensive experimental results on the proposed dataset show that the proposed SFLM greatly reduced the labeling cost and time consumption and achieved superior performance in recognizing new categories. The dataset will be available at https://github.com/SunYM2020/IRC.
Unveiling living dead: characteristics and consequences of zombie firms

Abstract The increasing number of zombies is becoming a thread for industrial growth due to their inefficiency and lack of performance. Previous studies show that zombies inhibit the growth of non-zombies and prevent the emergence of new ones. However, there is still a lack of research on the consequences of zombies on industrial growth. Therefore, this study aims to investigate the potential characteristics of the likelihood of zombie firms and analyze their impact on industrial growth. Data were extracted from publicly traded companies on the Pakistan Stock Exchange from 2009–2019. Results confirm the presence of zombie firms in the Pakistani context with low liquidity, low solvency, non-profitability, inefficiency, and relatively younger. Moreover, the findings demonstrated a negative link between zombie firms and industrial growth. It states zombie enterprises negatively impact industrial growth as high industrial indebtedness decreases the chances of industrial growth. These findings may be helpful to policymakers when developing insolvency rules to ensure a stable environment for industrial growth.
Possible alternative to Rλ-scaling of small-scale turbulence statistics

Traditionally, trends of universal turbulence statistics are presented versus Rλ, which is the Reynolds number based on Taylor's scale λ and the root-mean-square (rms) of a component of velocity urms. λ and urms, and hence Rλ, do not have the attribute of universality. The ratio of rms fluid-particle acceleration to the rms of the acceleration caused by the viscous force, Ra, is an alternative to Rλ. Ra is a Reynolds number. It is composed of statistics of the small scales of turbulence. It can be evaluated with single-wire hot-wire anemometry. Like Rλ, it can be partially evaluated by means of flow similarity. Direct measurement of Ra is challenging; therefore, Ra is not a replacement for Rλ. For isotropic turbulence the relationship between Ra and Rλ is given. Anisotropic turbulence is discussed.
Knowledge and innovation management in agribusiness: A study in the Dominican Republic

To date and worldwide, most business activities in the agribusiness sectors have focused on increasing productive efficiency to the detriment of the environmental impact of these activities, despite close connection that exist between agribusinesses and natural resources. This paper argues that knowledge and innovation management (KIM) can play a key role in fostering and managing creativity in the agribusiness sector. To test this assumption, an empirical model linking KIM with employee creativity, responsible green innovation, and performance in the agribusiness sector of the Dominican Republic is proposed and tested with data from 110 agribusiness companies. We found that strengthening the relationships between the above constructs is needed, with a view to readjusting the meaning of firm performance in the light of emerging (post-pandemic) circumstances for agribusinesses. The statistical analyses yield important implications and recommendations for practice, management, and policy making in these areas in this country and elsewhere.
On the fundamentals of coherence theory

In the thesis I study various quantum coherence phenomena and create some of the foundations for a systematic coherence theory. So far, the approach to quantum coherence in science has been purely phenomenological. In my thesis I try to answer the question what quantum coherence is and how it should be approached within the framework of physics, the metatheory of physics and the terminology related to them. It is worth noticing that quantum coherence is a conserved quantity that can be exactly defined. I propose a way to define quantum coherence mathematically from the density matrix of the system. Degenerate quantum gases, i.e., Bose condensates and ultracold Fermi systems, form a good laboratory to study coherence, since their entropy is small and coherence is large, and thus they possess strong coherence phenomena. Concerning coherence phenomena in degenerate quantum gases, I concentrate in my thesis mainly on collective association from atoms to molecules, Rabi oscillations and decoherence. It appears that collective association and oscillations do not depend on the spin-statistics of particles. Moreover, I study the logical features of decoherence in closed systems via a simple spin-model. I argue that decoherence is a valid concept also in systems with a possibility to experience recoherence, i.e., Poincare recurrences. Metatheoretically this is a remarkable result, since it justifies quantum cosmology: to study the whole universe (i.e., physical reality) purely quantum physically is meaningful and valid science, in which decoherence explains why the quantum physical universe appears to cosmologists and other scientists very classical-like. The study of the logical structure of closed systems also reveals that complex enough closed (physical) systems obey a principle that is similar to Godel’s incompleteness theorem of logic. According to the theorem it is impossible to describe completely a closed system within the system, and the inside and outside descriptions of the system can be remarkably different. Via understanding this feature it may be possible to comprehend coarse-graining better and to define uniquely the mutual entanglement of quantum systems.
High Carbon Mineralization Rates in Subseafloor Hadal Sediments—Result of Frequent Mass Wasting

In the past 20 years, the exploration of deep ocean trenches has led to spectacular new insights. Even in the deepest canyons, an unusual variety of life and unexpectedly high benthic oxygen consumption rates have been detected while microbial processes below the surface of the hadal seafloor remains largely unknown. The information that exist comes from geophysical measurements, especially related to seismic research, and specific component analyses to estimate the carbon export. In contrast, no information is available on metabolic activities in deeper buried sediments of hadal environment. Here we present the first pore water profiles from 15 up to 11 m long sediment cores recovered during three expeditions to two hadal zones, the Japan Trench and the Atacama Trench. Despite low levels of organic debris, our data reveal that rates of microbial carbon turnover along the trench axes can be similar to those encountered in much shallower and more productive oceanic regions. The extreme sedimentation dynamics, characterized by frequent mass wasting of slope sediments into the trenches, result in effective burial of reactive, microbially available, organic material. Our results document the fueling of the deep hadal biosphere with bioavailable material and thus provide important understanding on the function of deep‐sea trenches and the hadal carbon cycle.
Crustal‐Scale Fault Systems in the Korean Peninsula Unraveled by Reflection Seismic Data

An approximately 40‐km long high‐resolution reflection seismic profile (P3) was acquired in the metropolitan area of Seoul in South Korea for the purpose of fault system imaging in a highly noisy and challenging urban environment. Two 12t seismic vibrators (mini‐vibs) were used as the seismic source. Data were recorded using a dual element seismic spread; 20 m spaced 421 wireless seismic recorders connected to 10 Hz geophones and 20 micro‐electro‐mechanical‐based landstreamer sensors (2 m sensor spacing) attached to one of the vibrators. The purpose of the dual spread employed was to delineate both near‐surface and deep structures. The processing results show good quality and the processing work was complemented by different analysis to further constraints the geological interpretation. The survey results provide evidence for the 3D geometry of three fault systems, including Chugaryeong, Pocheon, and Wangsukcheon faults. A gently westerly dipping set of reflectivity underlying a dome‐shaped package of reflectivity is interpreted as a fault, and could project to the known surface position of the Pocheon fault. The dome‐shaped reflectivity is interpreted as folded and faulted dyke or sill systems. Downward continuation of the interpreted fault intersects the sub‐vertical Chugaryeong fault in a zone where the current seismicity is observed, suggesting that these two major fault systems may have jointly evolved in the form of splay faults. Reflections from the Wangsukcheon fault are also present in the data and interpreted to dip approximately 60° to the east, in an opposite direction to the two other faults.
Evaluation of assisted reproductive technology treatment outcomes based on stimulation dosages and anti-Mullerian hormone levels

Background: Serum levels of anti-Müllerian hormone (AMH) have a significant association with ovarian response in the event of assisted reproductive technology (ART) treatment cycles. The present study was performed to evaluate the ovarian stimulation protocols selection with respect to serum AMH levels in patients and its impact on ART outcome. Methods: Based on the serum AMH, 191 women were classified as low, normal and high responders. Follicle Stimulating Hormone (FSH) and Human Menopausal Gonadotropin (HMG) dosages in controlled ovarian stimulation were determined. Retrieval, maturation, fertilization and utilization rates were measured. Subgroup analysis was done using one-way ANOVA followed by post hoc analysis. The relationship between AMH levels and fertilization was determined using Pearson’s correlation analysis. A p-value <0.05 was considered as statistically significant. Results: According to AMH levels, the majority of the patients were normal responders (52.4%) followed by high responders (29.8%) and low responders (17.8%). The average FSH, HMG per day and number of days HMG required was found to be significant among the different responders. Further, retrieval rate and blastulation rate was found to be significant among the different responders. A positive correlation was found between AMH and the number of oocytes retrieved and number of oocytes successfully fertilized, which was found to be significant. In addition, there was a significant inverse relationship between AMH levels and age. Conclusions: The study outcomes revealed that the rates of retrieval, maturation, fertilization, embryo utilization and blastulation were higher among the high responders compared with the low responders. Thus, proper increase in dosages of ovarian stimulation protocol is required among the low responders to achieve quality embryos.
Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System

Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem). The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems. However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process. Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems. The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems. The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%. The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively. Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively. Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems. The developed biometric system can be adapted by any organization that requires an ultra-secure identification system. O.S. O. :
Variational calculation of ground-state energy of iron atoms and condensed matter in strong magnetic fields. [at neutron star surfaces]

We present variational calculations of the binding energies of iron atoms and condensed matter in strong magnetic fields (B> or approx. =10/sup 12/ gauss). These calculations include the electron exchange energy. The cohesive energy of the condensed matter, which is the difference between these two binding energies, is of interest in pulsar theories and in the description of the surfaces of neutron stars. We find the cohesive energy to range from 2.6 keV at B/sub 12/=1 to 8.0 keV at B/sub 12/=5; these are considerably lower values than reported in earlier work (Chen, Ruderman, and Sutherland).
Flat metrics are local strict minimizers for the polynomial entropy

As we have proved in [L], the geodesic flows associated with the flat metrics on T^2 minimize the polynomial entropy. In this paper, we show that, among the geodesic flows that are Bott integrable and dynamically coherent, the geodesic flows associated to flat metrics are local strict minima for the polynomial entropy. To this aim, we prove a graph property for invariant Lagrangian tori in near-integrable systems.
Too Much of a Good Thing? Associations among Parenting Profiles and Helicopter Parenting

Helicopter parenting is defined by popular culture as a set of parenting practices characterized by overinvolvement. However, empirical research has not adequately defined the construct, and it is unclear how helicopter parenting may relate to existing parenting behaviors and parenting styles. Participants included 341 caregivers from across the United States who completed a survey about their parenting beliefs and behaviors. Latent class analysis was used to determine combinations of parenting behaviors, and the identified classes of parents were then used to conduct one-way ANOVAs to examine differences in helicopter parenting between the different classes. Results suggest that parents who fit the authoritative parenting profile endorse greater helicopter parenting. Further research needs to be conducted to determine the “tipping point” for when positive parenting practices, which are often associated with positive outcomes, begin to produce negative outcomes.
Gravity and Remote Sensing Methods as a Solution in Identifying Geothermal Reservoirs on Volcanoes

Tiris Village, Lamongan Volcano complex is an area that has geothermal potential. The existence of geothermal potential in this study was identified using the gravity method combined with remote sensing methods. Remote sensing method is used to obtain a map of the distribution pattern of the soil surface temperature and a map of the continuity of the straightness and fault structure. The data used in the remote sensing method is secondary data in the form of Landsat-8 imagery and DEM SRTM 1 arc. Processing of the soil surface temperature map was obtained by using thermal infrared processing NDVI. The form of continuity of the straightness or fault structure is obtained by processing Landsat-8 multispectral image data in the form of 567 band composite and DEM SRTM data form of 3D topographic modeling. The data from the composite band 567 was reviewed visually using a 3D topographic model. The gravity method is the main method in this research because it is used to obtain the residual Bouguer anomaly distribution. The residual Bouguer anomaly data was obtained by using the moving average through spectrum analysis. The residual Bouguer anomaly distribution can be used for qualitative interpretation of the distribution of the earth’s gravitational acceleration at the measurement location. The quantitative interpretation of the gravity data is based on the surface density estimation graph using the Parasnis method and 3D inversion modeling, using residual Bouguer anomaly data. The results of 3D inversion modeling provide four types of subsurface rock layers based on the density distribution value, namely, the first rock layer ρ 1 = 2.52 – 2.67 g/cm3, the second rock layer ρ 2 = 2.72 – 2.77 g/cm3, the third rock layer ρ 3 = 2.80 – 2.83 g/cm3, and the fourth rock layer ρ 4 = 2.84 – 2.86 g/cm3. The third rock layer is assumed to be reservoir rock and is estimated to be at a depth of 500–800 meters below the measurement point. The fourth rock layer is assumed to be intrusive igneous rock, which penetrates dominantly in the middle of the measurement location, which is oriented southwest, south to north, which is indicated by the high anomaly continuity on the regional Bouguer anomaly map. Based on the results of 3D inversion modeling and spectrum analysis of the average depth of the regional Bouguer anomaly, it is found that the intrusive rock layer is thought to be at a depth of 350 meters below the measurement point.
Activity Identification and Local Linear Convergence of Inertial Forward-Backward Splitting

We consider the class of inertial Forward--Backward (iFB) proximal splitting algorithms, to minimize the sum of two proper lower semi-continuous convex functions, one of which having a Lipschitz continuous gradient and the other being partly smooth relative to an active manifold $\mathcal{M}$. Special cases of this class include the FB and, for an appropriate choice of the inertial parameter, FISTA-like schemes. We propose a unified analysis, under which we show that iFB-type splitting, (i) correctly identifies the active manifold $\mathcal{M}$ in a finite number of iterations, and then (ii) enters a local (linear) convergence regime, which is characterised precisely. This gives a grounded justification to the typical behaviour that has been observed numerically for many problems encompassed in our framework, including the Lasso, the group Lasso, total variation minimization and the nuclear norm regularization to name a few. These results may have numerous applications including in signal/image processing processing and machine learning.