Huge Scale Research Articles

Seismic Vulnerability Assessment of Building For Retrofitting Strategies: Simplified Vulnerability Assessment

In India, 85% of existing buildings are unreinforced masonry and 5-7% are reinforced concrete and 5-7% are traditional buildings. And most buildings do not qualify to resist earthquakes, technically it can be said that they would not be able to resist earthquake loads. In the past, it is seen at the time of the earthquake, millions of lives and the economy of that region is affected. Demolition of ageing building mass is not only harmful to the environment but also it is not viable economically to rebuild all vulnerable buildings. Such buildings are recommended to be retrofitted. Recent earthquakes have demonstrated the need to identify and improve the performance of the existing seismic deficient buildings. For this reason, Seismic vulnerability assessment is considered a part of a better strategy to mitigate the risk and improve the resiliency of the country and its infrastructure. Due to the high volume of building archetypes in our country, for Seismic Vulnerability Assessment at a huge scale, a rapid, simplified method is being adopted that can facilitate the assessment procedure in a lesser computational time period. This paper was primarily engrossed in the analysis of some simplified methods proposed in the literature for assessing the simplified seismic vulnerability. A hypothetical building is analyzed using structural software (Etabs) by Nonlinear Dynamic Analysis Procedure. Using Etabs software, various structural parameters like Story Response Drift, Displacement (max) and Base Shear are considered and discussed. By proper interpretation of the result, a suitable measure to minimize vulnerability and ensure sustainability and cost-effectiveness, retrofitting strategies is recommended. Keywords: Seismic vulnerability, Existing Building, Simplified Analytical Method, Rapid Visual Screening, Detailed vulnerability assessment, Etabs, Non-linear Dynamic Analysis, Retrofitting Strategies

Candidate for Catalyst during Water-Oxidation Reaction in the Presence of Manganese Compounds, from Nanosized Particles to Impurities: Sleep with One Eye Open.

Water-oxidation reaction (WOR) catalysts are critical for energy conversion. WOR is a four-electron oxidation and sluggish reaction. WOR needs a high thermodynamic driving force; it is also a kinetically slow reaction. Different compounds have been used for WOR; among these compounds, Mn materials have proven to be interesting because Mn is low-cost and also nontoxic, at least compared to many transition metals. Naturally, it has also been used in the biological water-oxidizing complex (WOC). Indeed, WOR has occurred on a huge scale in natural photosynthesis.For a long time, efforts have been made to design and synthesize various ligands and generate Mn compounds toward WOR catalysts. However, the addition or removal of electrons inside Mn compounds during harsh WOR conditions can lead to the formation or the breakage of bonds and result in the conversion of a precatalyst to a catalyst.Here, our findings on the conversion of Mn compounds to catalysts during WOR are presented. Many Mn compounds have been claimed to be catalysts for WOR in the presence of various chemical oxidants or under electrophotochemical conditions. Currently, the advances in characterization techniques and different spectroscopic methods have enabled a better understanding of catalysts. Different conversions such as that of the Mn complex to Mn oxide and Mn salts to Mn oxide during WOR have been explained. Indeed, the morphology and size of the Mn oxide formed depend on several factors such as the origin compounds, pH, ligands, and conditions. Thus, different Mn compounds show different activities toward WOR. The biomimetic models with Mn-Ca clusters are also decomposed during WOR. On the other hand, stable Mn complexes such as Mn phthalocyanines, which are very stable in the absence of potential, are easily decomposed during WOR. It is noted that for many of these Mn compounds, two steps result in the formation of Mn oxide during WOR: (i) Mn(II) or (III) leaching into the electrolyte and (ii) deposition of the leached Mn ions into the solution.Considering these steps, it can be seen that challenges remain in the area of Mn compounds, given the fact that even in the catalytic cycle at low oxidation numbers no Mn(II) or (III) should be leached to the electrolyte.

Formation and development of cultures in the ancient central asian civilization

Annotation: In this article is written about in the history of human society, the ancient Central Asian civilization served as the center of original early cultures for the emergence of material and spiritual wealth created by our ancestors. This area is not limited to the Turan lowland, but includes a vast region where various Turkic ethnic groups are widespread. In these places, over the past 5-6 thousand years, we can observe the formation and development of the culture of Central Asia and the culture of the Turkic peoples. This civilization represents the cultures of regions and ethnic groups on a huge scale, including the stages of the emergence and development of national cultures in the region before the Arab conquest. In these ancient Central Asian regions, it consisted of the Jaitun culture, the Kaltaminor culture, the Hisar culture, the Chust culture, and was generally called the Turan culture. These cultures served the development of various directions in a single cultural space that united the Turkic peoples. For this reason, industries such as hunting, harvesting, animal husbandry and farming have long served as the basis for the livelihood and subsistence of the inhabitants of the region.

Synthesis of New Type Polymers by Quasi-Living Atom Transfer Radical Polymerization.

Thanks to the polymer revolution of the 20th century, plastics are now part of our everyday lives. We use plastics as naturally as if they had always been an integral part of our lives. However, in the recent past, we were still predominantly using wood, metal, and glass objects, which were replaced by plastic products at an explosive rate. In many cases, this replacement has resulted in products with better physical, chemical, or biological properties. The changeover was too rapid, and the consequences were not recognized in time. This is evidenced by the huge scale of plastic pollution worldwide today. It is therefore in the interests of the future of both humans and animals that we must pay particular attention to the direct and indirect environmental impact of plastics introduced in animal husbandry. Starting from the tetrafunctional initiator produced as the first step of my work, poly(n-butyll acrylate) star polymers of different molecular weights were synthesized by atom transfer radical polymerization, using the so-called “core first” method. The bromine chain end of the produced star polymers was replaced by an azide group using a substitution reaction. Propalgyl telechelic PEGs were synthesized as a result of lattice end modification of poly(ethylene glycol) with different molecular weights. The azidated star polymers were connected with propalgyl telechelic PEGs using Huisgen’s “click” chemical process, and as a result of the “click” connection, amphiphilic polymer networks with several different structures were obtained.

Numbers of Great Cormorants Phalacrocorax carbo Wintering in the Western Palaearctic in January 2013

Great Cormorants were censused on a pan-European level in January 2013. Cormorants were found in an enormous winter range, spanning from the Baltic Sea and Atlantic Ocean in the north to the Mediterranean and coasts of North Africa. This large-scale exercise in which more than 5000 volunteers took part resulted in a total of 641,650 Cormorants counted (630,000–672,000 estimated). Based on the breeding census data of 2012 we estimated a total number of birds in January 2013 of slightly over 1 million, including the birds from the Ukrainian and Russian parts of Black Sea, Sea of Azov and north-western Caspian. Using the summer counts to produce a corrected estimate for the area that was actually covered during the winter count gave an estimated 695,000 individuals for January 2013. Total coverage was considered good and comparison to the previously conducted winter count of 2003 revealed, corrected for coverage, a 15% increase. As both counts were carried out during a period of cold weather, it is unlikely that birds were missed due to movements to the east and north-east of the range, from or into areas that are difficult to assess. Some 33% of all Cormorants were found to occur in areas with a temperature of –5°C or lower, suggesting that many birds can survive under conditions that may be marginal, i.e. at a high risk and/or cost. Compared to the previous count a relatively lower number of Cormorants were found under low temperature conditions, –5 °C down to –10°C, coinciding with the moment of active ice formation of shallow and stagnant freshwaters. It may well be that the actual ice cover in 2013 was such that birds had to leave these areas. In contrast to the opinion that wintering under low temperatures is marginal, it is possible that these northerly wintering grounds are more rewarding in terms of food profitability. This is because cold-blooded fishes congregate at certain spots and are less mobile at low temperatures and thus relatively easy to catch. Given the current and previous work carried out we recommend a long-term monitoring of these processes, which operate at a huge geographic scale. A repeated pan-European count with intervals of about ten years could detect the major patterns, while also providing a useful method of monitoring changes due to the expected further warming of winter conditions.

THE FUTURE OF REMOTE WORK – LEGAL DILEMMAS

The COVID-19 pandemic has changed many areas of life, and has also directly affected ways of performing work. The biggest change has been the expansion of remote working on an unprecedented huge scale. This form of work has allowed many workers to move their work activities online, thus reducing the pandemic threat. Remote working has many benefits for employees, saving the time needed to commute, as well as for employers, who can save the funds needed to maintain offices. Aside from its many advantages, remote working raises numerous legal dilemmas that relate to range of important issues, starting with the right to establish such work. Other problems include the provision of work tools, covering the costs of performing work at the employee’s home, appropriate organisation of working time, together with respect for the principle of work-life balance. Other dilemmas concern the protection of privacy of the employee and his/her family. It is also connected with providing the employer with the possibility to control the performance of work by employees, as well as ensuring an appropriate level of occupational health and safety. This paper indicates such dilemmas, and discusses the key issues related to them. These issues need to be taken into account when developing regulations on remote working, both at international and national levels

Construction of Risk Control Decision-Making Model Platform for Transportation Hub Projects under the Background of Message Technique

With the acceleration of urbanization, the construction and operation of comprehensive transportation hub have promoted the development of regional economy. However, due to its huge scale, high building density, and crowded public space, the safety status of the comprehensive transportation hub is worrying. As the node of various transportation modes, the efficient operation of the transportation system is closely related to it, but many potential risks threaten the operation safety of integrated transportation hub all the time. Therefore, risk management is an important work to ensure the safe operation of integrated transportation hub. With the deepening of technology application, the application scope of information technology has also been expanded to complete the analysis and simulation of technical scheme and construction organization process, find problems in time, and take preventive measures, so as to optimize the design scheme, reduce unnecessary losses, and speed up the construction progress. In this study, information technology is used to construct the risk control decision model of transportation hub project. According to the research, the change of the target value of the algorithm in this study is 29% better than that of the traditional algorithm, and it can get better results and get the optimal solution quickly when applied to the intelligent traffic dispatching management system.

Detecting Outliers in Data Streams Based on Minimum Rare Pattern Mining and Pattern Matching

Outliers are the major factors to influence the accuracy of data-based processing, thus, they must be discovered from collected datasets to guarantee data security. With the widely use of sensors and other monitoring equipment, data streams are becoming the main form of data. However, the huge scale of data streams results in the number of mined rare patterns very large, which makes it hard to effectively detect outliers through pattern-based outlier detection methods. Since minimal rare patterns (MRPs) can represent rare patterns and the number is much smaller, therefore, the use of MRPs on outlier detection can short the time consumption. Based on this idea, this paper proposes an outlier detection approach based on minimal rare pattern, called ODMRP, which is composed of pattern mining phase and pattern matching phase. Specifically, in the pattern mining phase, an improved minimal rare pattern mining algorithm, namely MRPM, is proposed to mine the MRPs from data streams; It first constructs two matrix structures to store information of transactions and frequent 2-patterns, and then apply “pattern extension” operations to extend frequent 2-patterns to longer patterns, at the same time, rare patterns are removed to prevent them participating into “pattern extension” operation to reduce meaningless time cost; In the pattern matching phase, we use the IM-Sunday algorithm to match the mined MRPs with the patterns stored in outlier pattern library, to find potential outliers. Extensive experimental studies show that the proposed ODMRP method can accurately detect outliers from data streams in less overhead.

Research on the Application of VR Technology in Oil and Gas Engineering Education: Taking the Well Control Emergency Rescue Robotic System as an Example

Oil and gas engineering is a talent-intensive, technology-intensive, and capital-intensive field. The final teaching is insufficient and unsatisfactory, and while oil and gas engineering instruction, particularly experimental practise teaching, is hampered and restricted by many working conditions such as huge scale, large equipment, high cost, high safety risk, and enterprise production system, and so on VR technology has developed rapidly in recent years, which has brought great impacts to teaching; this study uses VR technology to build and produce a well control emergency rescue robotic system, combining knowledge points with VR technology and applying the system to the course of Drilling and Well Completion Engineering. This teaching practice makes teaching more vivid and makes the process that cannot be reproduced fully displayed in front of students’ eyes. Such an attempt has achieved such good teaching effects that students can solve problems through interactions.

The Scenario of Textile Industry in Malaysia: A Review for Potentiality

This study explains the current situation and the significance of the textile and clothing industries in Malaysia, as well as how they are depicted. Textiles and clothes are the most important elements in human life and modern civilization. Furthermore, the textile industry is the foundation of industrialization for any country around the globe, and the economy of developing countries today is largely dependent on this sector. Once upon a time, Malaysian textiles and apparel were a major source of foreign cash. It is, however, presently erupting. This paper would be an adequate foundation for establishing the textile and garment business on a huge scale in Malaysia. Textile industrialization, the structure of the Malaysian textile industry, export and import states, gross domestic product (GDP) performance, energy consumption, product mix and customer, health, and safety issues have all been thoroughly addressed in this study. To summarize, the textile and garment industry has a substantial impact on Malaysian GDP, imports, and exports, as well as existing industries that produce items for global customers. Upstream textile industry numbers must be increased in tandem with downstream textile industry numbers so that the textile sector may contribute significantly to Malaysia’s GDP and export–import orientation.

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

MotivationContinuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any). ResultsWe explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (>0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a ‘temporal-modeling approach’ to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.

Literature Survey of Plant Disease Detection using CNN

Abstract: Modern farming practises have the potential to feed the world's 7.6 billion inhabitants. Despite the availability of sufficient food, individuals continue to suffer from malnutrition. Plant diseases reduce both the amount and the quality of total production. Building an image processing model for prediction or classification applications presents several obstacles. We present a deep learning model for illness detection that makes use of CNN and Capsule Network (CapsNet). The backbone of image processing is a convolutional neural network (CNN). The new architecture called as CapsNet is suggested to reduce the limitations and to achieve greater performance than standard neural networks. In this project, we analyze CNN and CapsNet for tomato plant disease datasets. The performance of both the models are measured and analysed. As a result, this approach may be used to a variety of plants and on a huge scale. Keywords: Leaf disease detection; Convolutional Neural Network (CNN); Capsule Network (CapsNet); Performance Evaluation

Role of Media and Indigenous Communities to Combat Climate Change

Since the industrial revolution, the greenhouse gas emissions have been on the constant rise and the global average temperature has increased by 1.1 degree Celsius as compared to pre-industrial era. This alarming climate change situation caused by human activities has already been causing devastating effects everywhere on the planet. The melting of sea ice, thawing of the permafrost, increase in natural hazards and their frequencies, reduction in the biodiversity of ecological areas, increase in sea levels, deterioration of species &amp; wild populations, etc. are all pointing towards a direct threat to human well-being posed by the climate change. This research paper, whose idea germinated in the mind of the author while attending Arctic Circle Assembly 2021 in Reykjavik Iceland, circles around the role and position of media and indigenous communities in climate change mitigation and adaption by exploring key questions like challenges and narratives of climate coverage by media, a requirement for revolutionizing the institution of media, the importance of indigenous peoples and traditional knowledge for combatting climate change to name a few. The paper argues various elements affecting media and indigenous communities and concludes the crucial involvement for media and indigenous peoples to act for saving the planet considering the huge scale of this catastrophic climate change scenario.

FACVSPO: Fractional anti corona virus student psychology optimization enabled deep residual network and hybrid correlative feature selection for distributed denial‐of‐service attack detection in cloud using spark architecture

AbstractCloud computing is an emerging standard in modern days for the purpose of sharing huge data, as it affords numerous user friendly behaviors. Cloud computing services offer an extensive range of resource pool in order to maintain huge scale data. Although, cloud computing model is disposed to several cyber‐attacks and security problems regarding cloud structure, because of the dynamic and distribute character and exposures in virtualization implementation. Distributed denial‐of‐service (DDoS) attack is a type of cyber‐attack, which disturbs the usual traffic of targeted cloud server. Moreover, DDoS produces malicious traffic in cloud structure, and thus consumes cloud resources. In this paper, an effective DDoS attack detection model, named fractional anti corona virus student psychology optimization‐based deep residual network (FACVSPO‐based DRN) is implemented using spark architecture. The devised FACVSPO approach is newly designed by incorporating anti coronavirus optimization (ACVO) algorithm, fractional calculus (FC) and student psychology based optimization (SPBO) model. Moreover, the hybrid correlative scheme is designed for extracting significant features for attack detection. The DRN structure is utilized for performing attack recognition, which categorizes the data as normal or attack. In addition, the DRN classifier is trained by the developed FACVSPO approach. The developed attack detection model outperformed other existing techniques in terms of testing accuracy, true negative rate (TNR), true positive rate (TPR) of 0.9236, 0.9141, and 0.9412, respectively. The testing accuracy of the implemented model is 12.02%, 8.92%, 7.27%, 6.30%, 5.68%, and 1.20% better than the existing methods, such as Taylor‐elephant herd optimisation based deep belief network (TEHO‐DBN), deep learning, deep neural network (DNN), multiple kernel learning, Fuzzy Taylor elephant herd optimisation (EHO)‐based DBN, fractional anti corona virus optimization‐deep neuro fuzzy network (FACVO‐based DNFN), respectively. Similarly, the TNR is 10.14%, 6.88%, 5.94%, 5.46%, 4.25%, and 3.28% and TPR is 12.33%, 9.46%, 8.05%, 7.41%, 6.02%, and 3.04% better than the existing methods.

Multidimensional Dynamics and Forecast Models of Network Public Opinions Based on the Fusion of Smart Transportation and Big Data

With the increase of the world’s population, the means of transportation and vehicles that adapt to the times are still difficult to cope with the increase in traffic volume. The traffic problem can be said to be a worldwide problem. However, with the development of artificial intelligence, the emergence of smart transportation has brought new development to modern transportation, and the application of smart transportation and big data is inseparable. In contemporary society, the widespread use of the Internet allows the public to fully exercise their rights to participate in social management and conduct public opinion supervision, which provides a great impetus for the development of online public opinion. However, due to the huge scale of information, some false and harmful information and opinions will inevitably be mixed into it, which will make the network public opinion unable to perform its due function smoothly. Therefore, it is necessary to carry out highly effective management activities on the network public opinion. This paper studies the multidimensional dynamics and prediction model of network public opinion based on the integration of smart transportation and big data; the aim is to design a simple and effective forecasting model to provide traffic management departments with good public opinion forecasting and analysis methods so as to make better decisions. This paper analyzes the related technologies of smart transportation and network public opinion and designs a prediction model of smart transportation network public opinion. Finally, this paper uses rough set theory to optimize the model and compares the data before and after optimization. The results are as follows: the data correlation coefficient before and after optimization is 0.988, and the two-tailed significance level is 0.471, which proves that the results before and after processing are highly correlated, and the two sets of data have no significant difference, proving that the optimization of the model is effective, simplifies the analysis process, and does not change the results.

Sinian hydrocarbon accumulation conditions and exploration potential at the northwest margin of the Yangtze region, China

Based on outcrop, drilling, logging and seismic data, the reservoir forming conditions, reservoir forming model and exploration potential of the ultra-deep Sinian Dengying Formation at the northwest margin of Yangtze craton region were examined. (1) This area is in craton rifting stage from Sinian to Early Cambrian, characterized by syn-sedimentary faults and rapid subsidence, significant sedimentary differences, and development of Dengying Formation platform margins on both sides of the rift. (2) The Sinian–Cambrian in this area has two sets of high-quality source rocks, Doushantuo Formation and Maidiping–Qiongzhusi Formation; of which, the latter has a thickness of 150–600 m and hydrocarbon generation intensity of (100–200)×108 m3/km2. (3) The mounds and shoals in the platform margin of Sinian Dengying Formation controlled by faults are thick and distributed in rows and zones; they are reformed by contemporaneous–quasi-contemporaneous and supergene karstification jointly, forming pore-type reservoirs with a thickness of 200–400 m. (4) The two sets of source rocks enter oil generation windows from Permian to Early Triassic, and the oil migrates a short distance to the lithologic traps of mounds and shoals to form a huge scale paleo-oil reservoir group; from Late Triassic to Jurassic, the oil in the paleo-oil reservoirs is cracked into gas, laying the foundation of present natural gas reservoirs. (5) The mound–shoal body at the platform margin of Dengying Formation and the two sets of high-quality source rocks combine into several types of favorable source-reservoir combinations, which, with the advantage of near-source and high-efficiency reservoir formation, and can form large lithologic gas reservoirs. The Mianyang–Jiange area is a potential large gas field with trillion cubic meters of reserves. According to seismic prediction, the Laoguanmiao structure in this area has the Deng-2 Member mound-shoal reservoir of about 1300 km2 making it a ultra-deep target worthy of exploration in the near future.

Vulnerability and Risk Assessment to Climate Change in Sagar Island, India

Inhabitants of low-lying islands face increased threats due to climate change as a result of their higher exposure and lesser adaptive capacity. Sagar Island, the largest inhabited estuarine island of Sundarbans, is experiencing severe coastal erosion, frequent cyclones, flooding, storm surges, and breaching of embankments, resulting in land, livelihood, and property loss, and the displacement of people at a huge scale. The present study assessed climate change-induced vulnerability and risk for Sagar Island, India, using an integrated geostatistical and geoinformatics-based approach. Based on the IPCC AR5 framework, the proportion of variance of 26 exposure, hazard, sensitivity, and adaptive capacity parameters was measured and analyzed. The results showed that 19.5% of mouzas (administrative units of the island), with 15.33% of the population at the southern part of the island, i.e., Sibpur–Dhablat, Bankimnagar–Sumatinagar, and Beguakhali–Mahismari, are at high risk (0.70–0.80). It has been concluded that the island has undergone tremendous land system transformations and changes in climatic patterns. Therefore, there is a need to formulate comprehensive adaptation strategies at the policy- and decision-making levels to help the communities of this island deal with the adverse impacts of climate change. The findings of this study will help adaptation strategies based on site-specific information and sustainable management for the marginalized populations living in similar islands worldwide.

Conjugated Gradient with Four Terms for Nonlinear Unconstrained Optimization

The nonlinear conjugate gradient (GJG) technique is an effective tool for addressing minimization on a huge scale. It can be used in a variety of applications., We presented a novel conjugate gradient approach based on two hypotheses, and we equalized the two hypotheses and retrieved the good parameter in this article. To get a new conjugated gradient, we multiplied the new parameter by a control parameter and substituted it in the second equation. a fresh equation for 𝛽𝑘 is proposed. It has global convergence qualities. When compared to the two most common conjugate gradient techniques, our algorithm outperforms them in terms of both the number of iterations (NOIS) and the number of functions (NOFS). The new technique is efficient in real computing and superior to previous comparable approaches in many instances, according to numerical results.

Older adults: Kidney disease or senescent aging? HUGE vs. CKD-EPI scale. The approach to the elderly.

Introduction: The approach to the renal condition of the elderly requires diagnostic tools for this age group, which allow discerning its renal condition between chronicity and senescence. The objective of this research was to analyze whether the HUGE scale is a companion tool to determine the existence of chronic kidney disease in older adult patients seen in outpatient clinics. Methods: This observational, analytical and cross-sectional study was carried out in the nephrology outpatient clinic of the Specialty Hospital of the Armed Forces No. 1 in 2015-2018. Results: We included 285 patients. The prevalence of chronic kidney disease was higher in people older than 75 years (60.7%). With the HUGE formula, a lower proportion of chronic kidney disease was established in 37% of men and 60% of women (P = 0.002) than in the CKD-EPI formula (83% and 89%, respectively). The specificity achieved by the HUGE formula was higher than the CKD-EPI and MDRD formulas (97% vs. 80% vs. 79%, respectively), having the highest discriminative capacity (NPV 70%). Hyperuricemia and diabetes mellitus are associated with the development of chronic kidney disease. Conclusions: In this research, it was shown that the HUGE formula helps discern those older adults with decreased glomerular filtration due to senescent aging in those with kidney disease In addition, The use of the HUGE formula is recommended as a complementary tool in the approach to chronic kidney disease in older adults.

Jointly attention network for crowd counting

Nowadays, crowd counting has shown great practical value in public safety and related fields. Most leading algorithms exploit CNN to generate density maps and have improved the estimation accuracy. However, the counting models still suffer from the challenge of huge scale variations. In order to mitigate this issue, we propose a novel approach named Jointly Attention Network (JANet) for Crowd Counting. It is composed of two major schemes: the Multi-order Scale Attention (MSA) module and the Multi-pooling Relational Channel Attention (MRCA) module. The MSA module explores meaningful high-order statistics and helps the backbone network obtain more discriminative features with rich scale information in an explicit manner. The MRCA module compactly represents the global scope relations and accounts the interdependence among all channel-wise nodes, which is complementary to MSA module. Meanwhile, the Distributed Combinatorial Loss (DCL) is designed to achieve the distributed supervision on intermediate layers at each level. Finally, we conduct extensive studies on multiple crowd counting datasets, the ShanghaiTech, the UCF-QNRF, the JHU-CROWD++, the NWPU-Crowd. The experimental results indicate that our proposed method has achieved superior performance.