Extreme Region Research Articles

Birefringence learning: A new global optimization technology model based on birefringence principle in application on artificial bee colony

Optimization technologies play a key role in addressing sophisticated optimization problems. However, with the increasing scale and dimension of problems, the performance of optimization technologies has been greatly challenged. To better address them, a new global optimization model is proposed inspired by the birefringence phenomenon, namely a birefringence learning (BRL) model which simulates the birefringence phenomenon of two refracted beams formed by the incident beam in the nature when the natural light enters some certain mediums. To substantiate the optimization performance of the model, it is applied to the artificial bee colony algorithm (ABC) to enhance its global optimization performance further and make its local optimization (exploitation) and global optimization (exploration) balance to some extent. In ABC, the BRL model as a mutation operator is employed in the phase of the scout bee to decrease the probability of ABC trapping into the local extreme region and then a novel ABC algorithm using the birefringence learning (NABC-BRL) is proposed. Via conducting abundant numerical experiments on some universally known benchmark functions, the experimental results and analysis indicate that it can obtain higher accuracy of solutions and faster convergence on majority of benchmark functions compared with some well-acknowledged algorithms, which also proves the effectiveness of global optimization of the BRL model.

Automated segmentation and classification of hand thermal images in rheumatoid arthritis using machine learning algorithms: A comparison with quantum machine learning technique

The aims and objectives of the study were to i) perform image segmentation using a color-based k-means clustering algorithm and feature extraction using binary robust invariant scalable key points (BRISK), maximum stable extremal regions (MSER), features from accelerated segment test (FAST), Harris, and orientated FAST and rotated BRIEF (ORB); ii) compare the performance of classical machine learning techniques such as LogitBoost, Bagging, and SVM with a quantum machine learning technique. For the proposed study, 240 hand thermal images were acquired in the dorsal view and ventral view of both the right and left-hand regions of RA and normal subjects. The hot spot regions from the thermograms were segmented using a color-based k-means clustering technique. The features from the segmented hot spot region were extracted using different feature extraction methods. Finally, normal and RA groups were categorized using LogitBoost, Bagging, and support vector machine (SVM) classifiers. The proposed study used two testing methods, such as 10-fold cross-validation and a percentage split of 80–20%. The LogitBoost classifier outperformed with an accuracy of 93.75% using the 10-fold cross-validation technique compared to other classifiers. Also, the quantum support vector machine (QSVM) obtained a prediction accuracy of 92.7%. Furthermore, the QSVM model reduces the computational cost and training time of the model to classify the RA and normal subjects. Thus, thermograms with classical machine learning and quantum machine learning algorithms could be considered a feasible technique for classifying normal and RA groups.

Comparison of shoulder muscle strength, shoulder range of motion and scapular motion in men with ankylosing spondylitis and healthy men: a case-controlled study

Background/Aims Ankylosing spondylitis mainly affects the vertebral column, meaning that the assessment of the physical function of the upper extremity and scapulothoracic region in ankylosing spondylitis is often overlooked. Therefore, the aim of this study was to investigate shoulder muscle strength, shoulder active range of motion and three-dimensional scapular motion in patients with ankylosing spondylitis. Methods The study included 18 men with ankylosing spondylitis, and 16 age-matched typically healthy men as the control group. Muscle strength, active range of motion, and three-dimensional scapular motion were assessed with a digital hand-held dynamometer, an inclinometer, and an electromagnetic tracking device respectively. Results A decrease in shoulder flexion and abduction muscle strength on both the dominant side (P<0.001, P=0.001) and non-dominant side (P<0.001, P<0.001) was found in the ankylosing spondylitis group compared to the control group. A decrease in shoulder flexion, abduction, and external and internal rotation active range of motion on both the dominant side (P=0.001, P<0.001, P=0.015, P<0.001, respectively) and non-dominant side (P=0.001, P<0.001, P=0.017, P<0.001 respectively) was observed in the ankylosing spondylitis group. There was an increase in the upward rotation of the scapula on the non-dominant side at 30, 60, and 90° humeral elevations in the ankylosing spondylitis group compared to the control group (P=0.018, P=0.003, P=0.001 respectively). The other parameters of the scapular motion did not differ between groups (P>0.05). Conclusions This study showed that shoulder muscle strength and active range of motion were lower, and the upward rotation of the scapula was greater in men with ankylosing spondylitis compared to typically healthy men. The kinetics and kinematics parameters of the shoulder and scapula should be considered when assessing the physical function of patients with ankylosing spondylitis.

The Laser Infrastructure at the SXP instrument of the European XFEL

This contribution presents the laser infrastructure concept of the Soft X-ray Port (SXP) instrument at the European X-ray Free Electron Laser (XFEL) for day one operation. This scientific platform is conceived as an open port complementing the scientific scope of the other two, already operating baseline instruments at the SASE 3 soft x-ray undulator focusing on atomic, molecular and non-linear optical (SQS) as well as condensed matter physics (SCS). The main driving force behind SXP originates from the time-resolved X-ray photo-electron spectroscopy community contemplating key questions in the dynamics of materials science at interfaces. Nonetheless, proposals to investigate high-valent metal intermediates in biological and inorganic catalysts for chemical bond activation by means of fluorescence spectroscopy as well as research on highly charged ions in the light of astrophysics are also pursued. The outstanding capabilities of the European XFEL pave the way for ultrafast pump-probe investigations at the SXP instrument combining intense and tunable soft X-rays with versatile optical laser capabilities, which are provided by two synchronized femtosecond laser systems, whose wavelength ranges can be extended into the infrared as well as extreme ultraviolet region.

On the economic impact of wax deposition on the oil and gas industry

• Wax deposition blockages are known as a billion-dollar problem in the O&G industry. • A case-specific methodology for assessing its economic impact was developed. • The economic impact of wax deposition has an annual expected value of 47.62 billion USD. • The annual economic impact might range from 14.74 to 330.59 billion USD. Partial and total blockages due to wax deposition in wells and pipelines gather consensus as a billion-dollar problem in the oil and gas industry. However, an exact quantification is still required for production shifts towards heavy and paraffinic oils, extreme climate regions, and ultra-deep offshore oilfields. Motivated by such a problem, this research endeavour developed a case-specific methodology for assessing the economic impact at stake based on state-of-the-art methodologies. It is deemed to perform meta-analyses upon the current scientific and technical literature. It has been found that the economic impact of wax deposition has an expected value of 47.62 billion USD/year and may oscillate between 14.74 and 330.59 billion USD/year. Moreover, the upper bound is deeply influenced by the costs of potential environmental impacts, with a trend for assuming new record values with each new significant spill.

Study of ruthenium film grown in oxygen environment for x-ray optics application

Ru shows high damage threshold as compared to other standard materials Au, Rh and Pt for high heat load applications in high brilliance synchrotron radiation source and x-ray free electron laser. It is also a promising candidate to be used as a protective capping layer in multilayers for increasing their life time. In the present study, Ru thin films of 500Å thickness were deposited in oxygen environment at different substrate temperatures varying from 70°C to 500°C. In reactive ion beam sputtering process, the flow of Ar and reactive O2 gas was maintained in 4:1 ratio keeping total flow 3 SCCM constant. The deposited films were characterized using grazing incidence x-ray reflectivity (GIXRR), grazing incidence x-ray diffraction (GIXRD) and secondary ion mass spectroscopy (SIMS) techniques. A significant change in GIXRR profile was observed in the sample grown at 100°C and 500°C whereas the GIXRD measurements indicated no significant formation of ruthenium oxide at different temperatures. However, a strong peak of Ru2Si3 (222) at 500°C was observed indicating a strong interfacial reaction at ruthenium/ substrate interface.The experimental data suggested a weak signature of RuO2 phase in the films deposited at RT, 70°C and 100°C and therefore the effect of ruthenium oxide with 10% composition in pure ruthenium was calculated on optical performance in extreme ultra violet region of photon energy (85-115 eV).

An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection

The street sign text information from natural scenes usually exists in a complex background environment and is affected by natural light and artificial light. However, most of the current text detection algorithms do not effectively reduce the influence of light and do not make full use of the relationship between high-level semantic information and contextual semantic information in the feature extraction network when extracting features from images, and they are ineffective at detecting text in complex backgrounds. To solve these problems, we first propose a multi-channel MSER (Maximally Stable Extreme Regions) method to fully consider color information in text detection, which separates the text area in the image from the complex background, effectively reducing the influence of the complex background and light on street sign text detection. We also propose an enhanced feature pyramid network text detection method, which includes a feature pyramid route enhancement (FPRE) module and a high-level feature enhancement (HLFE) module. The two modules can make full use of the network’s low-level and high-level semantic information to enhance the network’s effectiveness in localizing text information and detecting text with different shapes, sizes, and inclined text. Experiments showed that the F-scores obtained by the method proposed in this paper on ICDAR 2015 (International Conference on Document Analysis and Recognition 2015) dataset, ICDAR2017-MLT (International Conference on Document Analysis and Recognition 2017- Competition on Multi-lingual scene text detection) dataset, and the Natural Scene Street Signs (NSSS) dataset constructed in this study are 89.5%, 84.5%, and 73.3%, respectively, which confirmed the performance advantage of the method proposed in street sign text detection.

Üniversite Öğrencilerinin COVID-19 Pandemisi Sırasında Yaşadıkları Boyun ve Üst Ekstremite Ağrısı ve Engelliliği: Tanımlayıcı Araştırma

AbstractObjective: Musculoskeletal system problems emerged in students who had to attend classes with a computer or smartphone during the weekly course hours, due to the fact that the universities providing face-to-face education switched to the online method of distance education due to the Covid-19 pandemic.Purpose of the study; To determine the severity of the pain experienced by the students, to reveal its relationship with disability, and to find the factors that induce pain and disability in daily life.Material and Methods: One hundred students between the ages of 18-25, who attended distance education for at least 2 academic semesters and met the study criteria were included in the study. After the Sociodemographic Data Questionnaire was applied to the students included in the study, the Arm, Shoulder and Hand Problems Quick Questionnaire (Q-DASH), Neck Disability Index and NRS tests were applied.Results: As a result of the Quick Questionnaire for Arm, Shoulder and Hand Problems (Q-DASH), 43% of the students included in the study had moderate disability, and according to the Neck Disability Index, 37% of the study group had complete disability. According to multivariate regression analysis, gender (p<0.05) and duration of computer or smart phone use (p<0.05) were the variables that most affected disability. A significant correlation was found between the frequency of pain and disability in the upper extremity (p<0.001).Conclusion: It is seen that excessive computer or smart phone use during distance education causes disability due to pain and joint movement limitations in the upper extremity and cervical region.

Patient Characteristics and Clinical Outcomes Associated With Conservative Treatment for Spine Pain in Women Experiencing Socioeconomic Challenges

ObjectiveThe purpose of this study was to describe patient demographics and pain changes for women over the course of care in a chiropractic program. MethodsWe performed a retrospective cross-sectional analysis of a prospective quality assurance database from the Mount Carmel Clinic (MCC) in Winnipeg, Manitoba, Canada. Pain scores were reported on an 11-point Numeric Rating Scale. Baseline and discharge Numeric Rating Scale scores were compared for each spinal and extremity region through Wilcoxon signed rank tests to determine if clinically meaningful or statistically significant differences were present. ResultsThe sample population attained was 348 primarily middle-aged (mean = 43.0, SD = 14.96) women with obesity (body mass index = 31.3 kg/m2, SD = 7.89) referred to the MCC chiropractic program by their primary care physician (65.2%) for an average of 15.6 (SD = 18.49) treatments. Clinically meaningful median baseline to discharge changes in pain by spine region were observed (Cervical = −2, Thoracic = −2, Lumbar = −3, Sacroiliac = −3), each of which yielded statistical significance (P < .001). ConclusionThis retrospective analysis found that the MCC chiropractic program serves middle-aged women with obesity experiencing socioeconomic challenges. Pain reductions were reported, regardless of the region of complaint, temporally associated with a course of chiropractic care.

Assimilation of global positioning system radio occultation refractivity for the enhanced prediction of extreme rainfall events in southern India

AbstractHere, we investigated the impact of assimilating the satellite‐based product of Global Positioning System (GPS) radio occultation (RO) refractivity profiles data on the simulation of selected extreme rainfall events in three states of southern India: Tamil Nadu, Telangana, and Kerala. We assimilated the GPS RO data into the weather research and forecasting model using a 3DVar assimilation technique and evaluated the results against unassimilated (control) simulations. Various observations (e.g., rainfall measurements from AWS/rain‐gauge) and observation‐based gridded rainfall were used. The assimilation of the data yielded improved prediction of the spatial distributions of extreme rainfall regions and the amounts of rainfall. The analysis of the simulated dynamical and thermodynamic processes indicated that the assimilation of the data enabled the model to simulate significantly deep convection, high instability, and strong vertical motions. A vorticity budget analysis confirmed the marginally strengthened low‐level convergence. The vertical motions because of assimilation facilitated an increased vertical advection of vorticity, which enhanced the extreme conditions in storms. Moreover, the assimilation of the data resulted in enhanced water vapor condensation and high levels of ice, cloud, and rain water in clouds, all of which contributed to extreme rainfall.

Dark photon Dark Matter without Stueckelberg mass

We study the scenario of dark photon Dark Matter where the mass is generated through the Higgs mechanism rather than the constant Stueckelberg mass. In this construction the dark sector contains necessarily extra degrees of freedom and interactions that lead to non-trivial dynamics including thermalization, phase transitions, cosmic string production. As a consequence the predictions of Stueckelberg theories are vastly modified, strongly depending on the couplings to curvature and on the scale of inflation HI compared to the scale f of spontaneous symmetry breaking. We find in particular that only in extreme regions of parameter space the phenomenology of Stueckelberg dark photon is reproduced. These scenarios are strongly constrained by isocurvature perturbations unless the dark sector is approximately Weyl invariant.

Isolated Severe Pelvic Fractures Treated With or Without Resuscitative Endovascular Balloon Occlusion of the Aorta: A Matched Cohort Study

INTRODUCTION: Previous studies have revealed resuscitative endovascular balloon occlusion of the aorta (REBOA) may be correlated with higher mortality and acute kidney injury rates in severely injured patients. This study investigated outcomes in severe pelvic fracture patients with or without REBOA. METHODS: This was a retrospective study of patients with severe pelvic fracture (pelvis AIS 3 or greater) in the TQIP database (2013 to 2018). Patients were excluded if transferred from another hospital, without sign of life in the emergency department, died in the emergency department, there was a penetrating mechanism, or other than lower extremity region AIS greater than 3. Propensity score matching was performed with age, vital signs, Injury Severity Score, comorbidities, each AIS region scores, and hollow viscus injuries. Outcomes were mortality and complications, including acute kidney injury, ARDS, venous thromboembolism, deep venous thrombosis, severe sepsis, and ventilator-associated pneumonia or pneumonia. RESULTS: A total of 376 patients were matched: 94 patients with REBOA and 282 patients without. REBOA patients were more likely to undergo laparotomy (62.8% vs 42.9%, p < 0.001) and had higher transfusion rates (12 vs 5 units PRBC, p < 0.001). The REBOA group had worse 24-hour mortality (23.1% vs 13.2%; p = 0.024), worse in-hospital mortality (31.9% vs 21.3%, p = 0.036), higher venous thromboembolism rate (13.8% vs 6.7%, p = 0.033) and higher deep venous thrombosis rate (11.7% vs 6.3%, p = 0.009; Table). Table. - Postmatched Outcome Variable Total, N = 376 No REBOA, n = 282 (75%) REBOA, n = 94 (25%) p Value HLOS, days * 15.0 (6.0–28.0) 15.0 (7.0–27.0) 18.0 (2.0–28.0) 0.947 ICU LOS, days* 7.0 (3.0–15.0) 7.0 (3.0–15.0) 7.5 (3.0–14.0) 0.676 AKI 30 (8%) 19 (6.7%) 11 (11.7%) 0.124 ARDS 12 (3.2%) 10 (3.5%) 2 (2.2%) 0.738 VTE 32 (8.5%) 19 (6.7%) 13 (13.8%) 0.033 DVT 23 (6.1%) 12 (4.3%) 11 (11.7%) 0.009 VAP or pneumonia 19 (5.1%) 15 (5.3%) 4 (4.3%) 0.792 24-h mortality* 58 (15.6%) 37 (13.2%) 21 (23.1%) 0.024 In-hospital mortality 90 (23.9%) 60 (21.3%) 30 (31.9%) 0.036 AKI, acute kidney injury; DVT, deep venous thrombosis; HLOS, hospital length of stay; LOS, length of stay; REBOA, resuscitative endovascular balloon occlusion of the aorta; VAP, ventilator-associated pneumonia; VTE, venous thromboembolism. CONCLUSION: REBOA use in severe pelvic trauma patients is correlated with higher rates of mortality, venous thromboembolism, and deep venous thrombosis compared with no REBOA. Further randomized controlled studies should be designed to verify the benefits of REBOA use in severe trauma patients.

Predicting necessity of daily online adaptive replanning based on wavelet image features for MRI guided adaptive radiation therapy

PurposeOnline adaptive replanning (OLAR) is generally labor-intensive and time-consuming during MRI-guided adaptive radiation therapy (MRgART). This work aims to develop a method to determine OLAR necessity during MRgART. MethodsA machine learning classifier was developed to predict OLAR necessity based on wavelet multiscale texture features extracted from daily MRIs and was trained and tested with data from 119 daily MRI datasets acquired during MRgART for 24 pancreatic cancer patients treated on a 1.5 T MR-Linac. Spearman correlations, interclass correlation (ICC), coefficient of variance (COV), t-test (p < 0.05), self-organized map (SOM) and maximum stable extremal region (MSER) algorithm were used to determine candidate features, which were used to build the prediction models using Bayesian classifiers. The model performance was judged using the AUC of the ROC curve. ResultsSpearman correlation identified 123 features that were not redundant (r < 0.9). Of them 82 showed high ICC for repositioning > 0.6, 67 had a COV greater than 9% for OLAR. Among the 38 features passed the t-test, 25 passed the SOM and 12 passed the MSER. These final 12 features were used to build the classifier model. The combination of 2–3 features at a time was used to build the classifier models. The best performing model was a 3-feature combination, which can predict OLAR necessity with a CV-AUC of 0.98. ConclusionsA machine learning classifier model based on the wavelet features extracted from daily MRI for pancreatic cancer was developed to automatically and objectively determine if OLAR is necessary for a treatment fraction avoiding unnecessary effort during MRgART.

Scene Text detection and Recognition by Using Multi-Level Features Extractions Based on You Only Once Version Five (YOLOv5) and Maximally Stable Extremal Regions (MSERs) with Optical Character Recognition (OCR)

Textual information within scene images is very important in computer vision applications such asimage retrieval based on its content, Tourist translator, and Navigation systems assistant. This paper presented ascene text recognition system based on YOLO. The main steps of the suggested methodology are: text detectionand localization (using YOLOv5), text segmentation (using Morphological processing as a new method), featuresextraction (using MSERs), character segmentation and word segmentation (using bounding boxes with graph), andfinally character recognition (using OCR). In this work we create a new dataset model that includes most of thetext challenges such as Font type, Font size, Font color, and Font. The proposed system gives higher performancefor detection, localization, and recognition when using dataset containing many challenges, the results were 80%,96%, and 87.6 for precision, recall, and F-score respectively. Comparing with other similar works it was better.The accuracy of (OCR) is more than 99%.

Assessing the effectiveness of quarantine measures during the COVID-19 pandemic in Chile using Bayesian structural time series models.

BackgroundWith the emergence of the COVID-19 pandemic, all existing health protocols were tested under the worst health crisis humanity has experienced since the Black Death in the 14th century. Countries in Latin America have been the epicenter of the COVID-19 pandemic, with more than 1.5 million people killed. Worldwide health measures have included quarantines, border closures, social distancing, and mask use, among others. In particular, Chile implemented total or partial quarantine measures depending on the number of infections in each region of the country. Therefore, it is necessary to study the effectiveness of these quarantines in relation to the public health measures implemented by government entities at the national level.ObjectiveThe main objective of this study is to analyze the effectiveness of national- and region-level quarantines in Chile during the pandemic based on information published by the Chilean Ministry of Health, and answers to the following question are sought: Were quarantine measures in Chile effective during the COVID-19 pandemic?MethodsThe causal effect between the rates of COVID-19 infections and the population rates in Phase 1 and Phase 2 quarantines in the period from March 2020 to March 2021 in different regions of Chile were evaluated using intervention analyses obtained through Bayesian structural time series models. In addition, the Kendall correlation coefficient obtained through the copula approach was used to evaluate the comovement between these rates.ResultsIn 75% of the Chilean regions under study (12 regions out of a total of 16), an effective Phase 1 quarantine, which was implemented to control and reduce the number of cases of COVID-19 infection, was observed. The main regions that experienced a decrease in cases were those located in the north and center of Chile. Regarding Phase 2, the COVID-19 pandemic was effectively managed in 31% (5 out of 16) of the regions. In the south-central and extreme southern regions of Chile, the effectiveness of these phases was null.ConclusionThe findings indicate that in the northern and central regions of Chile, the Phase 1 quarantine application period was an effective strategy to prevent an increase in COVID-19 infections. The same observation was made with respect to Phase 2, which was effective in five regions of northern Chile; in the rest of the regions, the effectiveness of these phases was weak or null.

Variation characteristics of different plant functional groups in alpine desert steppe of the Altun Mountains, northern Qinghai-Tibet Plateau.

In grassland ecosystems, the plant functional group (PFG) is an important bridge connecting individual plants to the community system. The grassland ecosystem is the main ecosystem type on the Qinghai-Tibet Plateau. Altun Mountain is located in the key grassland transcontinental belt of the northern Qinghai-Tibet Plateau. The composition and changes in the PFG in this ecosystem reflect the community characteristics in the arid and semi-arid extreme climate regions of the Plateau. The main PFGs were forbs and grasses, and the importance values (IVs) accounted for more than 50%. Plant species diversity of the community was influenced by the IV of the legumes, and the increase in legumes would promote the increase in plant community diversity. The C, N, and P contents of plant communities were mainly influenced by forbs and grasses, and the relationship between forbs and C, N, and P was opposite to that of grasses. However, under the influence of different hydrothermal conditions, forbs and grasses as dominant functional groups had a stronger correlation with community and soil nutrients. This indicates that the dominant PFGs (forbs and grasses) can dominate the C, N, and P contents of the community and soil, and legumes affect community composition and succession. In this study, we analyzed the changing characteristics of functional groups in dry and cold extreme environments and the difference in their impacts on community development compared with other grassland ecosystem functional groups.

Detection of a new crescent moon using the Maximally Stable Extremal Regions (MSER) technique

Visibility of a new crescent moon is crucial for Muslims when declaring the beginning of the Islamic calendar. For many years, astronomers had observed the visibility of the new crescent moon using various techniques, such as the naked eye and optical instruments. In line with technological developments, astronomers have changed their observation technique to using astronomical imaging with the aid of a digital camera attached to a telescope. This helps to capture and save the images of the new crescent moon. However, the new crescent moon image has presented difficulties to astronomers as it visibility in the image is difficult to detect due to a low contrast with the sky. Hence, the captured image could not be presented as evidence of its visibility. This study proposes the use of Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement. Then, the bilateral filter for image denoising was applied and lastly, the Maximally Stable Extremal Regions (MSER) detector was used to detect the new crescent moon region in the image. The result shows that this proposed method has acceptable practical results. The new crescent moon images can be enhanced successfully using CLAHE and the bilateral filter technique even when the new crescent moon is not visible to the naked eye. The new crescent moon region can be well detected with the MSER detector although MSER does not perform well on the blurry region of new crescent moon corner. This technique can help astronomers detect the new crescent moon’s visibility for declaring the beginning month of the Islamic calendar.

Utilization of chiropractic services in patients with osteoarthritis and spine pain at a publicly funded healthcare facility in Canada: A retrospective study.

Osteoarthritis (OA) is one of the most prevalent and disabling musculoskeletal diseases worldwide. There is preliminary evidence from experimental studies and consensus documents that chiropractic management may alleviate spine and/or extremity OA related pain in the short term. This research explores the potential relationship of a pragmatic course of care, including soft tissue therapy, spinal manipulation, and other treatments commonly delivered by chiropractors, to spine and extremity pain in patients with OA. A retrospective analysis of prospectively collected data from the chiropractic program at a publicly funded healthcare facility was conducted. The primary outcome measures for patients diagnosed with spine and/or extremity OA (n= 76) were numeric pain scores of each spinal and extremity region at baseline and discharge, and a change score was determined. Statistically significant improvements that exceed a clinically meaningful difference in pain numeric rating scale scores were demonstrated by point change reductions from baseline to discharge visits. Change scores exceeding a minimally clinically important difference of "2-points" were present in the sacroiliac (-2.91), extremity (-2.84), cervical (-2.73), thoracic (-2.61), and lumbar (-2.59) regions. Patients diagnosed with OA in a socioeconomically disadvantaged community demonstrated reductions in mean pain scores in both a clinically meaningful and statistically significant manner concurrent with a course of chiropractic care.

Measurement Error Prediction of Power Metering Equipment Using Improved Local Outlier Factor and Kernel Support Vector Regression

The measurement error evaluation of power metering equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially under extreme environmental stresses. However, actual measurement error assessment is often disturbed by the environmental noise and insufficient input information. To address this problem, an improved local outlier factor (ILOF) method is first presented to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on box plot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely kernel support vector regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions.

Green energy and environmental sustainability on Vernacular Architecture. The case of Ayllus of Atacama Desert and stilt houses of Chiloé

The main objective of this study is to explore the development of vernacular techniques capable of facilitate a green and sustainable architecture today’s. An ayllu in San Pedro de Atacama and a stilt house in Chiloé are selected as a comparative case, since (1) both are part of the vernacular architecture of its respective place, (3) both cases belong to extreme regions of the country, with both geographical characteristics totally opposite, the ayllu in the extreme north with a desert climate and the stilt houses in the country’s south with low temperatures and maritime-rainy climate, (4) both cases are works of green and sustainable architecture, wooden on stilt houses and adobe on ayllu. The methodology is carried out through three points, contamination levels (1), materiality (2) and energetic design and climate adaptation (3). It is concluded that the development of particular vernacular architecture, can introduce new logics to develop green energy and being closer to environmental sustainability, as has occurred in the palafitos and in the ayllu.