Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of relationship between employee mental health and their job satisfaction in public and private hospital of Karachi

- Our objective of study is to determine effect of hospital sectors on job satisfaction and mental health in tertiary care hospitals of Karachi. To achieve this we targeted two renowned hospitals of Karachi. This study is helpful in understanding that whether employees of government hospital are much satisfied and mentally healthy or private hospital employees are? This will also clarify that how these two factors affect the performance. We selected descriptive research design to build an effect of hospital sector on employees. The population of interest was the employees from all functioning departments; for example, clinical staff (doctors, nurses, technician, etc) and clinical staff (HR staff, administration, finance, etc). This study population comprise of 150 employees from each hospital. We used convenient random sampling technique to draw 150 sample sizes from each sector. The data collection method was tailor made structured questionnaire. For data analysis we used descriptive, standard deviation, and independent t-test. Findings obtained from the study reveal that sector has significant influence on level of job satisfaction of hospital employees and found little higher in public sector hospitals whereas mental health was same in employees of both sector.
Software Ageing Assessment and Indexing Model Based on Software Quality Dimension: The Conceptual Model

Software ageing is an unavoidable phenomenon that can occur with every software product. Software that is unable to evolve and meet current needs will have a limited ability to provide ongoing functionality and relevance. Software performance and quality will degrade when these happen, known as the software ageing phenomena. Even though we cannot escape ageing in software products, it can be delayed and slowed down by understanding which factors contribute to ageing. Software ageing factors can be classified into two categories which are external and internal factors. This study focuses on external factors of software ageing related to or derived from the existing software quality model. Our experience conducting research in software quality and certification motivates us to develop a software ageing assessment and indexing model from the dimension of software quality. This paper presents the background and related issues on software ageing and quality and focuses further on the conceptual model of software ageing.
Earth Radiation Budget Experiment (ERBE) archival and April 1985 results

This paper describes the Earth Radiation Budget Experiment (ERBE) data products being made available to the community. The Science Team used ten validation criteria to judge the acceptability of the data for archival. We list these criteria and present uncertainty estimates based on them for four typical data products. A brief description of the radiation budget for April 1985 from the combined data of ERBE and NOAA-9 concludes this paper.
The structural analysis of a femur bone using a 2D FEM approach

This work presents a methodology for the creation of a two-dimensional geometric model of the femur with data obtained from X-ray images. The aim of this work is to evaluate the stress distribution throughout the femur, in order to understand the behavior of the bone in a healthy condition. The strain will, also, be evaluated throughout the femur bone. The numerical analysis is carried out using the finite element method, considering the trabecular and the cortical bone. The isotropic and elastic model is subjected to three different load cases, consisting of one parabolic distributed load over the joint surface and another parabolic distributed in the trochanter to represent the abductor muscle. The results obtained demonstrate an increase of stress in the cortical bone and an increase of strain mainly in the shaft of the femur.
Microbial Interaction Extraction from Biomedical Literature using Max-Bi-LSTM

Microorganisms play a vital role in various ecosystems, but their complex interaction is still unclear. With the publication of a large number of microbial literatures, many experimentally verified microbial interaction is dispersed therein. Organizing them into a database or knowledge graph can facilitate the development of microbiology research. Text mining technology is able to automatically extract and integrate these microbial interactions, as well as discover implicit information in literatures. For this purpose, we manually annotate a Microbial Interaction Corpus (MICorpus) containing 1005 abstracts, which provide a useful data source for the MIE task. On this basis, we propose an automated MIE extraction system based on Max-Bi-LSTM model. The best result of the system is precision (P) 76.313%, recall (R) of 90.121%, and an F value (F) 82.476%.
Does Economic Policy Uncertainty Encourage Gambling? Evidence from the Chinese Welfare Lottery Market

This paper investigates the effect of economic policy uncertainty (EPU) on gambling activity in China. Based on a theoretical model, we hypothesize that EPU increases the demand for hope which raises the willingness to pay for lottery tickets, resulting in higher lottery sales. We estimate a Panel Autoregressive Distributed Lag model with an Error-Correction form using data on lottery sales in Chinese provinces to estimate the short-and long-run effect of EPU on gambling. Our results suggest that EPU has a significant positive effect on gambling in the short run. In addition, we find that this positive effect is less persistent if the EPU proxy is based on economic policy reports in national newspapers than when the EPU measure is derived from local newspaper reports. This may be explained by the different thematic focus and the different degrees of media censorship of national and local newspapers
The Single and Combined Effects of Prenatal Nonchemical Stressors and Lead Exposure on Neurodevelopmental Outcomes in Toddlers: Results from the CCREOH Environmental Epidemiologic Study in Suriname

The primary aim of this prospective study was to examine the single and combined effect of prenatal exposure to perceived stress, probable depression, and lead on toddlers’ neurodevelopment using the Bayley Scales of Infant and Toddler Development, third edition. Data from 363 mother-toddler pairs enrolled in the Caribbean Consortium for Research in Environmental and Occupational Health prospective cohort study were analyzed. A prenatal lead exposure of ≥3.5 µg/dL was associated with significantly lower receptive (p = 0.008) and expressive (p = 0.006) communication scaled scores. Moderate and severe maternal prenatal probable depression scores were associated with significantly lower fine (p = 0.009) and gross (p = 0.009) motor scaled scores. However, a maternal report of prenatal stress was not associated with neurodevelopmental outcomes. After adjusting for maternal demographics, prenatal stress and lead exposure, prenatal probable depression remained predictive of the toddlers’ gross motor scaled scores (β −0.13, 95% CI [−0.24–−0.02]). Similarly, when adjusting for demographics, prenatal stress and probable depression, prenatal lead exposure remained a significant predictor of their receptive communication scaled scores (β −0.26, 95% CI [−0.49–−0.02]). An analysis testing combined exposure to perceived stress, probable depression, and lead exposure, measured using a cumulative risk index, significantly predicted the child fine motor scaled scores after adjusting for other covariates (β −0.74, 95% CI: [−1.41–−0.01]).
Classification of Magnetic Resonance Imaging (MRI) Scans for Brain Tumor Using Improved EfficientNet Architecture and Transfer Learning

Brain tumor is the abnormal growth of cells formed inside the skull. With an average survival rate of 75.7% for all primary brain tumor patients, early detection of brain tumor can significantly reduce the number and severity of cases. The application of deep learning and neural networks in the area of medical diagnosis has macadamized the path of brain tumor detection. Rapid developments have been successfully achieved by the combined application of various medical imaging innovations and deep learning technologies. Convolutional neural networks (CNNs) are the most widely used machine learning algorithm for visual learning and brain tumor recognition. This paper showcases the use of EfficientNet-B7 model and applying fine tuning on hyperparameters in order to come up with a highly accurate model which extracts the features and detects the presence of tumor in brain. The proposed model is successfully able to break down the images and detect the presence of tumor and if the tumor is present, it shows the type of tumor present namely meningioma tumor, pituitary tumor and glioma tumor inside the brain. An accuracy of 98.188% is achieved using our proposed model. The study also highlights a brief comparison of the proposed model with some of the existing model.
Is perioperative COVID-19 really associated with worse surgical outcomes? A nationwide COVIDSurg propensity-matched analysis

This nationwide analysis shows that patients undergoing surgery with perioperative COVID-19 had higher risks of 30-day mortality & morbidity, especially thromboembolic events, compared to matched patients undergoing surgeries of similar urgency & complexity. BACKGROUND Patients undergoing surgery with perioperative COVID-19 are suggested to have worse outcomes, but whether this is COVID-related or due to selection bias remains unclear. We aimed to compare the postoperative outcomes of patients with and without perioperative COVID-19. METHODS Patients with perioperative COVID-19 diagnosed within 7 days before or 30 days after surgery between February and July 2020 from 68 US hospitals in COVIDSurg, an international multicenter database, were 1:1 propensity score matched to patients without COVID-19 undergoing similar procedures in the 2012 American College of Surgeons National Surgical Quality Improvement Program database. The matching criteria included demographics (e.g., age, sex), comorbidities (e.g., diabetes, chronic obstructive pulmonary disease, chronic kidney disease), and operation characteristics (e.g., type, urgency, complexity). The primary outcome was 30-day hospital mortality. Secondary outcomes included hospital length of stay and 13 postoperative complications (e.g., pneumonia, renal failure, surgical site infection). RESULTS A total of 97,936 patients were included, 1,054 with and 96,882 without COVID-19. Prematching, COVID-19 patients more often underwent emergency surgery (76.1% vs. 10.3%, p < 0.001). A total of 843 COVID-19 and 843 non–COVID-19 patients were successfully matched based on demographics, comorbidities, and operative characteristics. Postmatching, COVID-19 patients had a higher mortality (12.0% vs. 8.1%, p = 0.007), longer length of stay (6 [2–15] vs. 5 [1–12] days), and higher rates of acute renal failure (19.3% vs. 3.0%, p < 0.001), sepsis (13.5% vs. 9.0%, p = 0.003), and septic shock (11.8% vs. 6.0%, p < 0.001). They also had higher rates of thromboembolic complications such as deep vein thrombosis (4.4% vs. 1.5%, p < 0.001) and pulmonary embolism (2.5% vs. 0.4%, p < 0.001) but lower rates of bleeding (11.6% vs. 26.1%, p < 0.001). CONCLUSION Patients undergoing surgery with perioperative COVID-19 have higher rates of 30-day mortality and postoperative complications, especially thromboembolic, compared with similar patients without COVID-19 undergoing similar surgeries. Such information is crucial for the complex surgical decision making and counseling of these patients. LEVEL OF EVIDENCE Prognostic and Epidemiologic; Level IV.
Superradiant cascading effects in rubidium Rydberg levels

The authors report the first observation of superradiant cascading effects in rubidium Rydberg levels. For excited state densities of the order of 1010 cm-3, superradiant emissions drastically modify the radiative behaviour of highly excited states. Some features of a multi-level superradiant system are investigated.
GelStereo Palm: A Novel Curved Visuotactile Sensor for 3-D Geometry Sensing

Recently, visuotactile sensors have shown promising potential in robotics due to their high-resolution sensing ability. Unfortunately, the majority of available visuotactile sensors are limited to flat shapes, which severely limits their application possibilities. In this article, we propose a novel curved visuotactile sensor, the GelStereo Palm, which senses the 3-D contact geometry on a curved surface using a binocular vision system. Meanwhile, to solve the light refraction problem in the binocular stereo vision system under a curved medium, a refractive stereo ray tracing model for GelStereo Palm is presented. Moreover, a 3-D tactile point cloud sensing pipeline is introduced to reconstruct the 3-D contact geometry in real-time. Finally, extensive experiments are conducted to verify the accuracy and robustness of the 3-D contact geometry sensing of our GelStereo Palm sensor.
Intelligent Flood Detection using Traffic Surveillance Images based on Convolutional Neural Network and Image Parsing

An intelligent flood detection system is developed from an existing traffic surveillance structure. Images are captured from closed-circuit television (CCTV) with actual setting conditions - (a) normal, raining and flooding, and (b) day and night. The proposed system applied scene parsing method to avoid the impact of varying the physical setting of CCTV structures. This image parsing method uses pre-trained model, DeepLabv3, to detect objects common to traffic CCTV images such as road and vehicles. Supervised learning is performed to detect floods based on a convolutional neural network (CNN) model. The CNN model is validated ten times by training and testing it with randomly partitioned training and testing datasets, respectively. Initial results show that all validating models perform very close to each other. The best-trained model yields 80.67% accuracy, 86.33% precision, 81% recall, and 79.67% F1-score which shows satisfactory performance. This initial system brings the first step to a more reliable flood monitoring system.
Supporting Bystanders and Other Staff Exposed to Negative Workplace Behaviour: Developing and Testing the IMO Intervention Framework in the Clinical Workplace

Background Medical student and health-care staff bullying remain international concerns. Our understanding about what might solve such problems is still developing. A common approach suggested for bystanders to bullying is that they challenge or “stand up” to the bully. At the same time, the considerable risks should they act (eg, verbal rebuke) are rarely acknowledged, and neither is the potential for considerable cognitive dissonance should they choose not to. Methods Drawing on a substantial literature review, we developed an interventional program, initially with medical student bullying in mind, that generally employed a discourse for values cultivation. We implemented and evaluated this program through 6 cycles of action research methodology in 6 different departments of one tertiary teaching hospital. Data include individual interviews, discussions with key participants plus freehand comments from wider staff’s workshop evaluations. Results Alongside others, an early serendipitous finding was the “IMO” [Indirect, focus on Me, focus on Outcome] framework, intended for bystanders to student bullying. From using this framework, participants reported developing confidence in tackling negative behaviours, that interactions had better outcomes and that the workplace atmosphere felt more relaxed. It’s use also significantly reduced cognitive dissonance. Encouragingly, participants went on to use IMO for staff–staff bullying incidents, and other common negative behaviours. We attribute IMO’s utility to its memorability, simplicity and for catering to the many possible causes of bullying, including values issues, without perpetuating or creating conflict. Conclusion The IMO framework is offered as a widely usable framework for all staff in a clinical environment – a useful addition to popular frameworks used in the clinical workplace, ideas from which might also help these be further refined. Further evaluation would establish whether IMO invites long-term change and its utility in other contexts.
Chaotic Loss Cones and Black Hole Fueling

In classical loss cone theory, stars are supplied to a central black hole via gravitational scattering onto low angular momentum orbits. Higher feeding rates are possible if the gravitational potential near the black hole is nonaxisymmetric and the orbits are chaotic. Motivated by recently published, self-consistent models, we evaluate rates of stellar capture and disruption in triaxial nuclei. Rates are found to substantially exceed those in collisionally resupplied loss cones, as long as an appreciable fraction of the orbits are centrophilic. The mass captured by a black hole after a given time in a steep (ρ ~ r-2) nucleus scales as σ5 with σ the stellar velocity dispersion, and the accumulated mass in 1010 yr is of the correct order to reproduce the M•-σ relation. Triaxiality can solve the "final parsec problem" of decaying black hole binaries by increasing the flux of stars into the binary's loss cone.
Rotational Hysteresis Loss in High‐Field Superconductors

The rotational hysteresis loss in hard type II superconductors is calculated using the critical state model. For materials that can be characterized by a magnetic field‐independent critical current density Jc the surface rotational loss is W(rot)s=[5/4π(6)]1/2(H03/Jc) erg/cm2−cycle. In this expression H0 is the amplitude in gauss of a rotating field applied parallel to the surface of a semi‐infinite slab while Jc is measured in amperes per square centimeter. This loss is 3.84 times the equivalent loss for alternating fields. In addition, it is shown that by measurement of the hysteresis loss or hysteresis torque as a function of field, one may calculate the field dependence of the critical current.
Fringing in tubular permanent-magnet machines: Part I. Magnetic field distribution, flux linkage, and thrust force

This paper extends an analysis developed for tubular permanent-magnet machines to account for the effect of fringing associated with the finite length of the ferromagnetic armature core. The magnetic field distribution, established by using an analytical technique formulated in the cylindrical coordinate system, provides an accurate means of evaluating the effect of the fringing flux on the thrust force and the back-electromotive force. Finite-element calculations confirm the analytically derived results. In Part II of the paper, the analysis is used to predict the cogging force that results from end effects and to facilitate minimization of the force.
Exponential asymptotics and Stokes lines in a partial differential equation

A singularly perturbed linear partial differential equation motivated by the geometrical model for crystal growth is considered. A steepest descent analysis of the Fourier transform solution identifies asymptotic contributions from saddle points, end points and poles, and the Stokes lines across which these may be switched on and off. These results are then derived directly from the equation by optimally truncating the naïve perturbation expansion and smoothing the Stokes discontinuities. The analysis reveals two new types of Stokes switching: a higher-order Stokes line which is a Stokes line in the approximation of the late terms of the asymptotic series, and which switches on or off Stokes lines themselves; and a second-generation Stokes line, in which a subdominant exponential switched on at a primary Stokes line is itself responsible for switching on another smaller exponential. The ‘new’ Stokes lines discussed by Berk et al. (Berk et al. 1982 J. Math. Phys. 23, 988–1002) are second-generation Stokes lines, while the ‘vanishing’ Stokes lines discussed by Aoki et al. (Aoki et al. 1998 In Microlocal analysis and complex Fourier analysis (ed. K. F. T. Kawai), pp. 165–176) are switched off by a higher-order Stokes line.
pH-Responsive Delivery of H2 through Ammonia Borane-Loaded Hollow Polydopamine for Intervertebral Disc Degeneration Therapy

An imbalance in oxidative and inflammatory regulation is the main contributor to intervertebral disc degeneration (IDD). Hydrogen (H2) therapy is a promising antioxidation and anti-inflammatory approach. However, the key to the treatment is how to maintain the long-term effective H2 concentration in the intervertebral disc (IVD). Therefore, we developed a pH-responsive delivery of H2 through ammonia borane-loaded hollow polydopamine (AB@HPDA) for IDD therapy, which has sufficient capacity to control long-term H2 release in an acid-dependent manner in degenerative IVD. The characterization, toxicity, and pH-responsive H2 release of AB@HPDA was detected in vitro. The metabolization of AB@HPDA in the degenerated IVD was tested by in vivo imaging. The therapeutic effect of AB@HPDA on IDD was tested in vivo by X-ray, MRI, water content of the disc, and histological changes. Nuclear extracellular matrix (ECM) components, oxidative stress, and inflammation were also tested to explore potential therapeutic mechanisms. AB@HPDA has good biocompatibility at concentrations less than 500 μg/mL. The H2 release of AB@HPDA was pH responsive. Therefore, AB@HPDAs can provide efficient hydrogen therapy with controlled H2 release in response to the acidic degenerated IVD microenvironment. The metabolization of AB@HPDA in IVD was slow and lasted up to 11 days. HPDA and AB@HPDA significantly inhibited IDD, as tested by X-ray, MRI, disc water content, and histology (P < 0.05). pH-responsive H2 delivery through AB@HPDAs has the potential to efficiently treat IDD by inhibiting ECM degradation and rebalancing oxidative stress and inflammation in degenerative IVDs.
Demonstration of a solid-state optical cooler: An approach to cryogenic refrigeration

We report the successful operation of an optical cooler system. This device achieved 48 °C of cooling from room temperature and a heat lift of 25 mW when it was pumped with 1.6 W of laser light. Its performance as a function of pump laser wavelength and chamber temperature agrees well with theoretical models. This device validates the physics needed for exploiting the laser cooling of solids to develop practical optical refrigerators.
Data-based mechanistic modelling and the rainfall-flow non-linearity.

Although rainfall-flow processes have received much attention in the hydrological literature, the nature of the non-linear processes involved in the relationship between rainfall and river flow still remains rather unclear. This paper outlines the first author's data-based mechanistic (DBM) approach to model structure identification and parameter estimation for linear and non-linear dynamic systems and uses it to explore afresh the non-linear relationship between measured rainfall and flow in two typical catchments. Exploiting the power of recursive estimation, state dependent non-linearities are identified objectively from the time series data and used as the basis for the estimation of non-linear transfer function models of the rainfall - flow dynamics. These objectively identified models not only explain the data in a parametrically efficient manner but also reveal the possible parallel nature of the underlying physical processes within the catchments. The DBM modelling approach provides a useful tool for the further investigation of rainfall-flow processes, as well as other linear and non-linear environmental systems. Moreover, because DBM modelling uses recursive estimation, it provides a powerful vehicle for the design of real-time, self-adaptive environmental management systems. Finally, the paper points out how DBM models can often be interpreted directly in terms of dynamic conservation equations (mass, energy or momentum) associated with environmental flow processes and stresses the importance of parallel processes in this connection.