Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Supervised Exercise on the Development of Hypertensive Disorders of Pregnancy: A Systematic Review and Meta-Analysis

Hypertensive disorders of pregnancy (HDP) are the most common medical complication in pregnancy, affecting approximately 10–15% of pregnancies worldwide. HDP are a major cause of maternal and perinatal morbidity and mortality, and each year, worldwide, around 70,000 mothers and 500,000 babies die because of HDP. Up-to-date high-quality systematic reviews quantifying the role of exercise and the risks of developing HDP are currently lacking. Physical exercise is considered to be safe and beneficial to pregnant women. Supervised exercise has been shown to be safe and to be more beneficial than unsupervised exercise in the general population, as well as during pregnancy in women with obesity and diabetes. Therefore, we undertook a systematic review and meta-analysis to investigate the effects of women performing supervised exercise during pregnancy compared to a control group (standard antenatal care or unsupervised exercise) on the development of HDP. We searched Medline, Embase, CINHAL, and the Cochrane Library, which were searched from inception to December 2021. We included only randomized controlled trials (RCTs) investigating the development of HDP compared to a control group (standard antenatal care or unsupervised exercise) in pregnant women performing supervised exercise. Two independent reviewers selected eligible trials for meta-analysis. Data collection and analyses were performed by two independent reviewers. The PROSPERO registration number is CRD42020176814. Of 6332 articles retrieved, 16 RCTs met the eligibility criteria, comparing a total of 5939 pregnant women (2904 pregnant women in the intervention group and 3035 controls). The risk for pregnant women to develop HDP was significantly reduced in the intervention compared to the control groups, with an estimated pooled cumulative incidence of developing HDP of 3% in the intervention groups (95% CI: 3 to 4) and of 5% in the control groups (95% CI: 5 to 6), and a pooled odds ratio (OR) comparing intervention to control of 0.54 (95% CI:0.40 to 0.72, p < 0.001). A combination of aerobic and anaerobic exercise, or yoga alone, had a greater beneficial effect compared to performing aerobic exercise only (mixed-OR = 0.50, 95% CI:0.33 to 0.75, p = 0.001; yoga-OR = 0.28, 95% CI:0.13 to 0.58, p = 0.001); aerobic exercise only-OR = 0.87, 95% CI:0.55 to 1.37, p = 0.539). Pregnancy is an opportunity for healthcare providers to promote positive health activities, thus optimizing the health of pregnant women with potential short- and long-term benefits for both mother and child. This systematic review and meta-analysis support a beneficial effect of either structured exercise (combination of aerobic, strength, and flexibility workouts) or yoga for preventing the onset of HDP. Yoga, considered a low-impact physical activity, could be more acceptable and safer for women in pregnancy in reducing the risk of developing HDP.
A quantitative analysis of supplier quality in the engineer-procure-construct industry

OF THE THESIS A Quantitative Analysis of Supplier Quality in the EngineerProcure-Construct Industry by Yoshua Neuman Master of Science in Civil Engineering San Diego State University, 2014 This thesis is part of an extensive study conducted by Research Team 308 Achieving Zero Rework Through Effective Supplier Quality Practices from the Construction Industry Institute (CII). The main objectives of this study are centered on the belief that owners, contractors, and suppliers in the construction industry engage in multiple supplier quality (SQ) practices to ensure that project components are procured to the site with the highest quality possible in order to avoid any rework associated with components that are defective or do not conform to the required specifications. Furthermore, data obtained for this study focuses on four different types of material: tagged/engineered equipment, fabricated goods (structural steel and pipe spools), and manufactured/bulk goods (non-engineered/bulk valves). The first outcome of this study was the development of a Supplier Quality Process Map (SQPM) that illustrates how the process of procuring components to the project site with sufficient quality is currently being implemented by the Engineer-Procure-Construct (EPC) industry. A detailed description of the SQPM is provided and depicts five major stages within the process. The first two stages illustrate the supplier selection process and the development of a quality plan. These stages are known as planning and selection and execution. The last three stages pertain to key milestones where measurements for the quality of the production process occur; these stages are release from shop, received on site, and mechanical completion. The second outcome was a statistical analysis of how SQ practices are used by EPC contractors and how these SQ practices impact the resulting quality of goods supplied based on the number of non-conformances (NCs) throughout the process outlined in the SQPM. The statistical analysis resulted in several findings related to the SQ process: More NCs are found when more time is spent observing and inspecting work; developing a quality control/inspection and testing (QC/IT) plan, and projecting the cost of the inspection effort with the supplier helps find problems earlier in the SQ process. Moreover, for meetings held before execution (specifically before any manufacturing, fabrication, or assembly takes place) results suggest that NCs will appear at the shop (upstream), which is a good way of avoiding higher costs associated with the correction of NCs on site (downstream). Moreover, the use and implementation of registered or certified quality management systems (QMS) does not affect the number of NCs found throughout the SQPM.
Review of Methods for Biological Treatment of Food Processing Wastewater

The food processing industries are increasingly faced with the problem of treating their wastewater before releasing to municipal wastewater treatment plants or to the receiving environment. Food processing wastewaters are almost always amenable to biological treatments. The selection of the most suitable biological treatment methods is important. The purposes of this paper are to review the available literature on different treatment methods for food processing wastewaters and to compare their differences. The methods reviewed include activated sludge processes, aerobic or anaerobic lagoon, trickling filter, anaerobic digestion, anaerobic filter, soft fiber packing media contact oxidation process (SFPMCOP), anaerobic - anoxic - oxic (AAO) process, anaerobic - aerobic system, oxidation ditch system, upflow anaerobic sludge blanket (UASB) reactor, sequencing batch reactor (SBR), anaerobic fluidised bed reactor (AFBR), aerobic jet loop reactor, biobed expandad grandular sludge bed (EGSB) reactor and batch reactor (CASSTM). Their efficiencies in treating wastewater from food processing industries such as meat packing, dairy, brewery, sugar beet, seafood processing and potato chips and snack processing are evaluated.
Theranostics Nanomedicine: Recent Advancement, Applications and Challenges

Many advancements have been made in treatment and diagnosis tools with the progress in modern medicine and technology. Theronostics are multifunctional nanomaterials that combine therapeutic as well as imaging functions. Theranostic nanomedicines are used for checking drug release and efficacy. The amalgamation of diagnostic and therapeutic agents within a single system provides the target site localization and accumulation of nanomedicines in organs.It provides a transition from conventional medicine to personalized medicine. Many different types of nanomedicines have been evaluated over the years. Theragnostics approach includes pharmacogenomics, personalized medicine and imaging to develop efficient new targeted therapies which will help in better and optimize drug selection along with monitoring the therapy response to increase drug safety and efficacy. This review summarizes the recent development and various nanocarriers developed for nanotheranostics, includes inorganic Nanoparticles, Polymeric, dendrimers, micelles, liposomes, metallic, and carbon nanotubes.
A Modular Approach to Design Multi-channel Bistable Valves for Integrated Pneumatically-driven Soft Robots via 3D-printing

A pneumatic system that transmits power via the force of compressed air is an essential component of an air-driven soft robot. Pneumatic valves are one of the key parts of this system. However, the development of soft or electronics-free valves for soft robotic applications is in its infancy, with only a few 2/2 way valves developed. Previous research has shown demands for a complex pneumatic system that can regulate the airflow in multiple channels or switching the pressure within a chamber between multiple states. Hardware redundancy is found in such complex pneumatic circuits if only 2/2 way valves are available for the system design. To increase the design freedom, this paper presents a modular approach that integrates multi-channel modular valve units and bi-stable structures for the conversion of pneumatic signals. By utilising soft-material 3D printing, the 3/2-way valve, 4/2-way valve and 5/2-way valve design are proposed in this paper to control multiple air channels simultaneously. The modular design of these 3D printed multi-port valves allows quick design and fabrication solutions of a complex electronics-free pneumatic system by reassembling different modular units of the valve. Experiment characterization of the multi-channel valves shows maximum allowable pressure at 187.2 kPa and a flow rate of 7.42 L/min under 50 kPa pressure loss. A demonstration of controlling four states of a dual-chamber soft robotic arm with only two modular multi-chamber valves was included, showing reduced valve units and overall weight compared to conventional electronics-free 2/2 way valves.
Dictionary-based and machine learning classification approaches: a comparison for tonality and frame detection on Twitter data

ABSTRACT Automated text analysis methods have made it possible to classify large corpora of text by measures such as frames and tonality, with a growing popularity in social, political and psychological science. These methods often demand a training dataset of sufficient size to generate accurate models that can be applied to unseen texts. In practice, however, there are no clear recommendations about how big the training samples should be. This issue becomes especially acute when dealing with texts skewed toward categories and when researchers cannot afford large samples of annotated texts. Leveraging on the case of support for democracy, we provide a guide to help researchers navigate decisions when producing measures of tonality and frames from a small sample of annotated social media posts. We find that supervised machine learning algorithms outperform dictionaries for tonality classification tasks. However, custom dictionaries are useful complements of these algorithms when identifying latent democracy dimensions in social media messages, especially as the method of elaborating these dictionaries is guided by word embedding techniques and human validation. Therefore, we provide easily implementable recommendations to increase estimation accuracy under non-optimal condition.
Strain Signals Governed by Frictional‐Elastoplastic Interaction of the Upper Plate and Shallow Subduction Megathrust Interface Over Seismic Cycles

The behavior of the shallow portion of the subduction zone, which generates the largest earthquakes and devastating tsunamis, is still insufficiently constrained. Monitoring only a fraction of a single megathrust earthquake cycle and the offshore location of the source of these earthquakes are the foremost reasons for the insufficient understanding. The frictional‐elastoplastic interaction between the megathrust interface and its overlying wedge causes variable surface strain signals such that the wedge strain patterns may reveal the mechanical state of the interface. To contribute to this understanding, we employ Seismotectonic Scale Modeling and simplify elastoplastic megathrust subduction to generate hundreds of analog seismic cycles at a laboratory scale and monitor the surface strain signals over the model's forearc across high to low temporal resolutions. We establish two compressional and critical wedge configurations to explore the mechanical and kinematic interaction between the shallow wedge and the interface. Our results demonstrate that this interaction can partition the wedge into different segments such that the anelastic extensional segment overlays the seismogenic zone at depth. Moreover, the different segments of the wedge may switch their state from compression/extension to extension/compression domains. We highlight that a more segmented upper plate represents megathrust subduction that generates more characteristic and periodic events. Additionally, the strain time series reveals that the strain state may remain quasi‐stable over a few seismic cycles in the coastal zone and then switch to the opposite mode. These observations are crucial for evaluating earthquake‐related morphotectonic markers and short‐term interseismic time series of the coastal regions.
An in vitro 3D annulus fibrosus cell culture model with type I collagen: An examination of cell–matrix interactions

Disorders of the intervertebral disc (IVD) are widely known to result in low back pain; one of the most common debilitating conditions worldwide. As a multifaceted condition, both inflammatory environment and mechanical factors can play a crucial role in IVD damage, and in particular, in the annulus fibrosus (AF), the highly collagenous outer ring of the IVD. As a result, a better understanding of how cells from the IVD, and specifically the AF, interact and respond to their environment is imperative.
Effect of Cigarette Smoking on Location of Infarction in Patients Presented with ST-Segment Elevation Myocardial Infarction

Background: In an acute ST-elevation myocardial infarction, transmural myocardial ischemia causes myocardial damage or necrosis (STEMI). In order to establish myocardial ischemia damage, the current clinical definition of myocardial infarction (MI) in 2018 includes abnormal cardiac biomarkers. STEMI is linked to a greater risk of mortality in the hospital compared to non-ST segment elevation acute coronary syndromes. The study's purpose was to investigate and assess the effect of cigarette smoking on the location of infarction in people who had ST-segment elevation myocardial infarction. Methods: The present case-control research investigated 100 individuals who had a first ST segment elevation acute myocardial infarction and got split into two groups based on the smoking status: group 1 (50 patients): Group 1 (50 patients) were smokers, whereas group 2 (50 patients) were nonsmokers. Results: Smokers were significantly younger than non-smokers (53.86±7.77) years vs. (58.20 ± 7.80), P = 0. 006. Gender distribution: Smokers were significantly more likely to be male (82.0% vs. 58.0%, P = 0.009). inferior STEMI was statistically significant difference among the two groups (P value =0.045*). Myocardial Infarction Localization by electrocardiogram was statistically significant different among the two groups (P value =0.045*). Ejection fraction (EF) was statistically significant different among the two groups (smoker and nonsmokers respectively 54.86 ± 8.58% vs. 49.04 ± 6.61%) (P value =0.001*). Conclusions: Smoking raises STEMI risk, and smokers are more likely than non-smokers to develop coronary artery disease at an earlier age.
Generative Modeling of Future Precipitation Patterns

Neural networks are currently under utilized in climate science. This is unfortunate given that they have potential to learn the patterns underlying climate change from many types of long term data. We use PredNet, a convolutional recurrent neural network made to predict the next frame in a video, with an input of 33 years of United States precipitation “images” from 1981 to 2013 to predict that of 2014 (1). While our results were less revealing than was desired, we illustrate that next frame video prediction has the possibility to be used to great effect in field of climate science in the future.
Filtering based multi-sensor data fusion algorithm for a reliable unmanned surface vehicle navigation

When considering the working conditions under which an unmanned surface vehicle (USV) operates, the navigational sensors, which already have inherent uncertainties, are subjected to environment influences that can affect the accuracy, security and reliability of USV navigation. To combat this, multi-sensor data fusion algorithms will be developed in this paper to deal with the raw sensor measurements from three kinds of commonly used sensors and calculate improved navigational data for USV operation in a practical environment. Unscented Kalman Filter, as an advanced filtering technology dedicated to dealing with non-linear systems, has been adopted as the underlying algorithm with the performance validated within various computer-based simulations where practical, dynamic navigational influences, such as ocean currents, provide force against the vessel’s structure, are to be considered.
Predicting Respiratory Illness using Chest X-rays

Chest X-rays are cheap, accessible, and commonly used as first tests in diagnosing many lung diseases. Many hospital systems have vast amounts of X-ray data that can be leveraged by AI systems to supplement radiologist predictions and improve patient outcomes. In particular, radiologists pore over large numbers of x-rays, CT scans, and MRI images every day [7], implying that a detection framework which can serve as a pre-filter system, drawing bounding boxes around likely areas of disease, would both significantly reduce a radiologist’s workload and help them ignore negatives and hone in on positives, increasing their own diagnostic capacity. To this end, we train both a multi-label classifier (ResNet-18) and a diseased-area detector (Faster R-CNN + MobileNet backbone) on the NIH Kaggle dataset, and are able to achieve 95% validation Acc IoU (defined later) with our classifier and pixel IoU of . 206 with our object detection model.
Shock interpretation and noninvasive hemodynamic monitoring with transthoracic echocardiography in an intensive care unit: a concise review

Circulatory failure and shock are common in critically ill patients, and the cause of shock is usually multifactorial. Transthoracic echocardiography is a noninvasive method to determine the contribution of various factors toward a patient’s circulatory failure. Such factors include fluid status, cardiac contractility as well as vasomotor tone. Advances in echocardiographic measurements allow for the accurate estimation of a patient’s loading status and cardiac contractility, which help to guide treatment strategy and monitor treatment response. This article offers an overview of echocardiographic and clinical parameters and aims to incorporate these findings into a methodical approach to the clinical management of shock. Key words: Transthoracic echocardiogram; Shock; Hemodynamic monitoring; ICU Citation: Tang KB, Lau CWM, Li KC. Shock interpretation and noninvasive hemodynamic monitoring with transthoracic echocardiography in an intensive care unit: a concise review. Anaesth. pain intensive care 2021;25(6):819–827; DOI: 10.35975/apic.v25i6. 1713 Received: October 5, 2021; Reviewed: October 10, 202; Accepted: October 25, 2021
EQUIVALENCE CLASSES OF SKEW DYCK PATHS MODULO SOME PATTERNS

For any pattern p ∈ { U,L,UU,UD,DU,DD } , we enumerate the equivalence classes of skew Dyck paths, where two skew Dyck paths of the same semilength are p equivalent whenever the positions of the occurrences of the pattern p are the same. In this paper we use generating functions, bijective arguments, and recurrence relations to obtain the main results.
Shear wave tensiometry tracks reductions in collateral ligament tension due to incremental releases

Surgeons routinely perform incremental releases on overly tight ligaments during total knee arthroplasty (TKA) to reduce ligament tension and achieve their desired implant alignment. However, current methods to assess whether the surgeon achieved their desired reduction in the tension of a released ligament are subjective and/or do not provide a quantitative metric of tension in an individual ligament. Accordingly, the purpose of this study was to determine whether shear wave tensiometry, a novel method to assess tension in individual ligaments based on the speed of shear wave propagation, can detect changes in ligament tension following incremental releases. In seven medial and eight lateral collateral porcine ligaments (MCL and LCL, respectively), we measured shear wave speeds and ligament tensions before and after incremental releases consisting of punctures with an 18‐gauge needle. We found that shear wave speed squared decreased linearly with decreasing tension in both the MCL (average coefficient of determination (R2avg) = 0.76) and LCL (R2avg = 0.94). We determined that errors in predicting tension following incremental releases were 26.2 and 14.2 N in the MCL and LCL, respectively, using ligament‐specific calibrations. These results suggest shear wave tensiometry is a promising method to objectively measure the tension reduction in released structures. Clinical Significance: Direct, objective measurements of the tension changes in individual ligaments following release could enhance surgical precision during soft tissue balancing in total knee arthroplasty. Thus, shear wave tensiometry could help surgeons reduce the risk of poor outcomes associated with overly tight ligaments, including residual knee pain and stiffness.
Cloud Based Databases- A Changing Trend

Today is the era of web scale applications which are growing drastically. Internet is becoming the most essential and popular entity in everyday's life. There are more than 2 billion internet users. The size of data being generated regularly through facebook, Mobile apps, RFID is in zettabytes. To handle a large pool of data there is a high need for technology. The advance web browsers should follow the properties like user-friendliness, usability and availability. Changing need of applications and databases proved that the traditional RDBMS are not effective for distributed environment where as The requirements of cloud computing are such as high availability, high throughput, maximum and proven scalability, disaster recovery. NoSQL databases provide elasticity and scalability along with the capability to store huge data. This provides opportunity to work with the cloud computing systems. This makes the NoSQL system extremely popular. The paper discusses the effectiveness of NoSQL databases over the relational databases, being schema-free and following the BASE properties.