Individual Input Research Articles

Enriched global horizontal irradiance prediction using novel ensemble improved backpropagation neural network

Penetration of solar energy into the power grid and smart grid is becoming an urge because of the continuous progress in industrialization and advancement. Requires a high accurate Global Horizontal Irradiance (GHI) prediction to achieve effective penetration of solar energy. This paper proposes a novel Ensemble Improved Backpropagation Neural Network (EIBPNN) with enhanced generalization ability because it is developed based on the various inputs’ individual improved backpropagation neural networks. Hence, the variance of individual IBPNN and input parameters based uncertainty are overcome and has the generic performance capability. The comparative analysis imparts the proposed prediction model results improved GHI prediction than the existing models. The proposed model has enriched GHI prediction with better generalization.

Design of Ridge Waveguide Radial Combiners

This article presents a comprehensive treatment to the subject of designing ridge waveguide radial combiners. A theoretical framework for the design process is first presented paving the way to a straightforward design procedure for radial combiners with both moderate and very wide bandwidths (BWs) realized in ridge waveguides. Two types of ridge waveguide radial combiners are presented, one with linearly tapered ridged sections and the other with staircase-like ridged sections. The methodology to design the radial combiner is presented. It starts with designing the individual ridge waveguide input ports, and then designing an equivalent transformer section that provides matching between the individual ridge waveguide ports and a common coaxial line output port. Design considerations for the different cross sections make it possible to achieve the required impedance levels for good matching across wide BWs. Three complete design examples are presented. Two proof-of-concept prototypes were designed, simulated, fabricated, and tested demonstrating excellent measured results without any tuning.

MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET.

Inter-frame patient motion introduces spatial misalignment and degrades parametric imaging in whole-body dynamic positron emission tomography (PET). Most current deep learning inter-frame motion correction works consider only the image registration problem, ignoring tracer kinetics. We propose an inter-frame Motion Correction framework with Patlak regularization (MCP-Net) to directly optimize the Patlak fitting error and further improve model performance. The MCP-Net contains three modules: a motion estimation module consisting of a multiple-frame 3-D U-Net with a convolutional long short-term memory layer combined at the bottleneck; an image warping module that performs spatial transformation; and an analytical Patlak module that estimates Patlak fitting with the motion-corrected frames and the individual input function. A Patlak loss penalization term using mean squared percentage fitting error is introduced to the loss function in addition to image similarity measurement and displacement gradient loss. Following motion correction, the parametric images were generated by standard Patlak analysis. Compared with both traditional and deep learning benchmarks, our network further corrected the residual spatial mismatch in the dynamic frames, improved the spatial alignment of Patlak Ki/Vb images, and reduced normalized fitting error. With the utilization of tracer dynamics and enhanced network performance, MCP-Net has the potential for further improving the quantitative accuracy of dynamic PET. Our code is released at https://github.com/gxq1998/MCP-Net.

Optimization on Powder Metallurgy Process Parameters on Nano Boron Carbide and Micron Titanium Carbide Particles Reinforced AA 4015 Composites by Taguchi Technique

Aluminium metal matrix composite is developed using powder metallurgy, with number of different operational factors considered. The three most important operational factors are sintering time, sintering temperature, and compaction pressure. Investigations are conducted using L9 orthogonal array as the experimental design. Density, Vickers hardness, and compressive strength are determined through experiments. The S/N ratio based on Taguchi’s law and a number of anomalies accomplished were used to determine the effect of individual input parameters (ANOVA). The main effect plots identified the optimal parameter settings for obtaining a less density, a higher hardness, and a higher compressive strength. In addition, the ANOVA analysis confirmed that the best metal matrix composite material is produced at the optimal sintering time and average temperature and compaction pressure for generally classified levels.

Agriculture Land Price Setting Systems, and Possible Directions for Their Updating

The aim of this article is to assess the effect of the applied land valuation system on the price of land, and as a consequence of the implied links to compare the valuation of soil units in Slovakia and the Czech Republic. In order to fulfil this objective, an econometric approach was chosen in the form of a hedonic price model, whereby the heteroscedastic corrected linear regression model was specifically applied. The estimated model shows a very high explanatory ability, together with a high parametric significance for most fictitious variables (land characteristics), and at the same time also fulfils the theoretical and logical consistency of general assumptions. From the results, it is evident that, in the case of Slovakia, ESEU [evaluated soil ecological unit] valuation is affected primarily by the climatic region and the main soil unit. When comparing the results of the effect of individual input factors on the price of land in the Czech Republic and Slovakia, it can be stated that the results are slightly different in the effect of certain factors. The characteristics of soils located in warm climatic regions with a valuable soil type of chernozem and low slope are significant and positively intensive factors.

Master Plan Design and Implementation of e-Library System

A study shows that the successful use of e-library system in implementation depended on the speed of access, accuracy and appropriate searching collection access (Umukoro and Tiamiyu 2017). Of course, the incomplete design and implementation of the e-Library System at IAIN Batusangkar had become a challenge for the library unit in implementing the e-Library system optimally to retrieval information easil and fast. The purpose of this study was to see the extent of the implementation of the master plan design and that of the e-Library system at library unit of IAIN Batusangkar. This research was a qualitative, library research. this research, used some references to analyze the strengths and weaknesses of the e-Library system. The results showed that the development of the e-Library or digital library at the library unit of IAIN Batusangkar was a way out to overcome various obstacles in the individual input and Information retrieval process, location distance, high social environmental demands and up to date information that is fast, precise, and accurate without having to visit the library and data collection needs which couls be accessed via one touch by connecting to the internet network for Search to e-Library (e.g using web: Zi-shof model e-Campus ).

Effect of Elementary School Students' Personal and School Characteristics on Career Development

이 연구는 초등학생의 개인 및 학교 특성이 진로발달에 미치는 영향을 알아보기 위한 목적으로 수행되었다. 이를 위해서 질문지법에 의해 조사를 실시하였으며, 빈도분석, 기술통계, t검정, 상관분석, 회귀분석 등을 활용하였다. 연구 결과에 따른 주요한 결론은 다음과 같다. 첫째, 초등학생의 개인 특성 중, 진로발달에 학년별로는 5학년의 진로발달 수준보다 6학년이 높았으며, 통계적으로 유의미하였다. 거주지역별로는 도시지역 거주 학생의 진로발달 수준이 그렇지 않은 학생보다 높았으며, 통계적으로 유의미하였다. 한편, 성별로는 여학생이 남학생보다 진로발달 수준이 높았으나 통계적으로는 유의미하지 않았다. 둘째, 초등학생의 학교 특성 중, 진로발달에 대해 학교생활적응 정도, 진로교육 만족 정도, 학업성취 정도에 따라 통계적으로 유의미하였다. 각 수준 정도가 높은 학생이 그렇지 않은 학생보다 진로발달 점수가 높았다. 셋째, 회귀분석 결과, 초등학생의 개인 특성 변인보다 학교 특성 변인이 진로발달에 더 많은 영향을 미치고 있는 것으로 나타났다. 진로발달 전체 점수 및 진로발달 하위영역 3개 모두에서 모형1(개인 특성 변인 투입)보다 모형2(개인 특성 변인+학교 특성 변인 투입)의 설명력이 높았다. ‘학교생활적응’, ‘진로교육 만족도’, ‘학업성취’ 등 3개의 학교 특성 변인이 모두 통계적으로 유의미하였다. 제언은 다음과 같다. 첫째, 초등학생의 학교 특성 변인의 수준을 높이는 방안이 필요하다. 둘째, 초등학생의 다양한 개인, 학교, 지역사회 변인 등을 추가하여 연구를 진행할 필요가 있다. 셋째, 초등학생 대상의 진로발달을 측정할 수 있는 도구개발과 관련된 연구가 필요하다.The purpose of this study was to investigate the effect of elementary school students personal and school characteristics on their career development. To this end, the survey was conducted using the questionnaire method, and frequency analysis, descriptive statistics, t-test, correlation analysis, and regression analysis were used. The main conclusions according to the study results are as follows. First, among the individual characteristics of elementary school students, the 6th grader was higher than the 5th grader by grade in career development, which was statistically significant. By residential area, the level of career development of students living in urban areas was higher than that of those who did not, and it was statistically significant. On the other hand, in terms of gender, female students had a higher level of career development than male students, but it was not statistically significant. Second, among the school characteristics of elementary school students, career development was statistically significant according to the degree of adaptation to school life, the degree of satisfaction with career education, and the degree of academic achievement. Third, as a result of the regression analysis, it was found that the school characteristic variable had a greater influence on career development than the personal characteristic variable of elementary school students. The explanatory power of Model 2 (personal characteristic variable + school characteristic variable input) was higher than Model 1 (individual characteristic variable input) in the overall career development score and all three career development sub-domains. All three school-specific variables such as adaptation to school life , career education satisfaction , and academic achievement were statistically significant. Suggestions are as follows. First, it is necessary to increase the level of school characteristics variables in elementary school students. Second, it is necessary to conduct research by adding various personal, school, and community variables of elementary school students. Third, there is a need for research related to the development of tools that can measure the career development of elementary school students.

Predicting the Fasting Plasma Glucose Level Using Two Linear Regression Equations from Body Temperature and Weight in the Early Morning as Inputs over a 14-Month Period for a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 558)

The author is a 27-year type 2 diabetes (T2D) patient, who has self-studied and researched diabetes, endocrinology, and chronic disease induced complications since 2010. He is a mathematician and engineer but not a medical doctor; therefore, he does his best to derive some mathematical equations or formulas with sufficient accuracy to describe the observed biomedical or biophysical phenomena. His medical research work started with the task of collecting big data on his own biomarker values and lifestyle details. To date, he has collected and processed nearly 3 million data related to his health. The data in this article covers a few categories. Since 1/1/2012, he has accumulated data on his body weight in the early morning. Beginning on 1/2/2013, he measures his finger-piercing fasting plasma glucose (FPG) at the wakeup moment in the morning. In addition, starting on 5/8/2018, he measures his FPG using a continuous glucose monitoring (CGM) sensor device at 15-minute time intervals. His sensor FPG uses the average glucose value between 12:00 midnight and 07:00 AM for a total of 29 glucose values. Incidentally, the difference between his average finger FPG (104.6 mg/dL) and average sensor FPG (106.8 mg/dL) over the 3.5-year period from 5/8/2018 to 11/27/2018 is a mere 2%. In addition, since 10/1/2020, he measures his body temperature (BT) and finger blood oxygen levels at the wake-up moment in the morning as biomarkers to monitor for possible COVID-19 infection. Currently, he has over one year’s worth of data on his BT and wondered which primary biomarkers would have a connection. Through a quick and easy time-domain analysis, he identified that his FPG has an extremely high correlation with BT, using the 90-days moving average data, finger FPG vs. BT at 73%, and using the 90-days moving average data, sensor FPG vs. BT at 85%, over the one-year period from 11/21/2020 to 11/21/2021. Several years ago, he identified a strong correlation (≥ 90%) existing between his finger FPG and body weight (Weight). In this article, he decided to use his CGM sensor FPG as the dependent variable Y and BT along with body weight as the independent variables X to conduct a space-domain regression analysis. The purpose is to develop a series of linear equations using BT and Weight as the inputs to quickly determine the desired output, his guesstimate sensor FPG value having a high prediction accuracy percentage without applying either finger-piercing device or CGM sensor device to measure his FPG level in the morning. In summary, there are 3 observed conclusions as follows: (1) There are no observed high correlation (-43%) existing between his measured BT and Weight. Furthermore, from the two-regression predicted FPG via BT and Weight as individual inputs, both have different and are not highly correlated (69% for FPG via BT and 12% for FPG via Weight) with his measured FPG over this 14-month period. However, the average regression predicted FPG, i.e. (FPG via BT + FPG via weight) / 2, has a high correlation of 82% (variance 67%) with his measured FPG over the same period.(2) The regression model derived three average predicted FPG data: FPG via BT, FPG via Weight, and average predicted FPG. They have extremely high prediction accuracies (96%-99%) in comparison with the average measured FPG value. This means that the 3 predicted FPG equations are accurate enough for the time period. The regression predicted FPG equations are listed as follows: Body Weight Case Predicted finger FPG (Y) = 4.314 * Weight (X) - 629.34 Body Temperature Case Predicted sensor FPG (Y) = 51.85 * BT (X) - 4970.28 (3) In conclusion, the average predicted FPG equation is the most suitable for predicting his FPG value (either sensor FPG or finger FPG values). Therefore, he can guesstimate an accurate FPG value using his BT and Weight as inputs, without applying either finger-piercing or CGM sensor devices for measuring his FPG value in the morning.

Controlling foamability of polypropylene/γ-irradiated ethylene acrylic elastomer blends by extent of crosslinking and domain microstructure of elastomer

Foams of polypropylene/elastomer blends can often lead to softer foams which may not be desirable every time. Incorporating rigidity to the foams can often be made possible by preferentially crosslinking the elastomer phase prior to blending. Although foamability of polypropylene/elastomer blends has been understood in the scientific community, the influence of the extent of crosslinking in the elastomer phase is not yet understood well. The purpose of this investigation is to identify the influence of the extent of elastomer crosslinking and the blend morphological attributes (achieved by varying screw speed during melt mixing) on foamability of polypropylene/partially crosslinked elastomer blends. Crosslinking of ethylene-acrylic elastomer is carried out using gamma radiation with several doses (0, 12.5, 25, 50 kGy) before melt blending and, subsequently, 10 wt.% of the irradiated elastomers (prior optimized) are mixed with polypropylene in a micro-compounder at three different screw speeds. The microstructure development in blends is characterized by scanning electron microscopy. Frequency sweep rheological analysis is done for selected blends to identify the ease of foamability among the series of blends. Foaming of blends is done with supercritical carbon dioxide in batch mode at three different temperatures. The density reduction along with the microcellular morphology development of blends with foaming is analyzed with the screw speed, the extent of crosslinking, and foaming temperature; furthermore, the individual input parameters (the elastomer domain size, controlled by the screw speed and the extent of crosslinking, controlled by gamma radiation dose) are optimized based on the foam morphology. A uniform and good foamability were achieved at 155 and 160°C for blends with elastomers, irradiated at 12.5 and 25 kGy radiation doses. The lowest density foam (0.37 g/cc) was obtained for polypropylene with 12.5 kGy irradiated crosslinked elastomer mixed at 200 rpm at 160°C foaming temperature. The final elastomer domain dispositions within the foam morphologies are characterized and the plausible foaming mechanism is proposed.

Callisthenic Productivity. A Challenge for the Traditional Economic Productivity Indicators

Measuring economic productivity is a challenging task, mainly because we cannot isolate completely the effects produced by individual inputs, nor can we measure precisely how their interaction affects the overall result. In this paper we will test a new way of measuring economic productivity at country level that takes into account also the social dimension and the sustainability over time - we call it callisthenic productivity. The indicator has been built by measuring economic productivity as a sum of labor and capital productivity, which afterwards we adjust by using the Gini coefficient, the human development index, the biocapacity and the ecologic footprint of consumption. The end result is not necessarily a better way to represent a country’s productivity, but rather a better way to compare the ability of countries to generate effective, reliable and sustainable economic productivity.

Modeling the Dynamics of Spiking Networks with Memristor-Based STDP to Solve Classification Tasks

The problem with training spiking neural networks (SNNs) is relevant due to the ultra-low power consumption these networks could exhibit when implemented in neuromorphic hardware. The ongoing progress in the fabrication of memristors, a prospective basis for analogue synapses, gives relevance to studying the possibility of SNN learning on the base of synaptic plasticity models, obtained by fitting the experimental measurements of the memristor conductance change. The dynamics of memristor conductances is (necessarily) nonlinear, because conductance changes depend on the spike timings, which neurons emit in an all-or-none fashion. The ability to solve classification tasks was previously shown for spiking network models based on the bio-inspired local learning mechanism of spike-timing-dependent plasticity (STDP), as well as with the plasticity that models the conductance change of nanocomposite (NC) memristors. Input data were presented to the network encoded into the intensities of Poisson input spike sequences. This work considers another approach for encoding input data into input spike sequences presented to the network: temporal encoding, in which an input vector is transformed into relative timing of individual input spikes. Since temporal encoding uses fewer input spikes, the processing of each input vector by the network can be faster and more energy-efficient. The aim of the current work is to show the applicability of temporal encoding to training spiking networks with three synaptic plasticity models: STDP, NC memristor approximation, and PPX memristor approximation. We assess the accuracy of the proposed approach on several benchmark classification tasks: Fisher’s Iris, Wisconsin breast cancer, and the pole balancing task (CartPole). The accuracies achieved by SNN with memristor plasticity and conventional STDP are comparable and are on par with classic machine learning approaches.

International acquisitions and efficiency and productivity of the Serbian cement industry

PurposeThis paper explores the impact of international acquisitions on the efficiency and productivity of the cement industry in an emerging economy.Design/methodology/approachThe data envelopment analysis (DEA) and Malmquist index (MI) are used to calculate the partial efficiency and productivity of individual inputs (materials, labour and fixed assets), as well as the total factor efficiency and productivity during the period 2000–2018. DEA and MI are combined with bootstrapping to perform succinct statistical inferences for determining the accuracy of results. In this paper we apply the input-oriented CCR DEA Window model. With respect to the level of analysis, data was collected from individual companies and then aggregated data at the industry level.FindingsThe research results show that international acquisitions positively affect efficiency of the cement industry in the long term. Efficiency of capital is lower in the short period after acquisitions. Additionally, international acquisitions positively affect partial productivity, as well as total factor productivity of the cement industry.Practical implicationsThe results of the study may be significant for managers and policy makers to design appropriate strategies for the improvement of the cement industry performance over time.Originality/valueResearch in emerging economies related to subject matter is limited, and this is one of the earliest research studies which explore change in efficiency and productivity at the level of Serbian cement industry.

The emergence of collective knowledge and cumulative culture in animals, humans and machines.

The emergence of collective knowledge and cumulative culture in animals, humans and machines.

Experimental Investigation on Weld Joint Characterization of Thin Wall Structure Mild Steel Tube at Diverse Loading Condition

This paper aims to investigate the effect of input parameters on plateau stress and specific energy of thin-wall structure circular mild steel (MS) tube. 32 samples of tube have been fabricated in two sets, one is without weld (WOW) and another is with weld (WW), having same dimensions. The tube was made up of available commercial MS (AISI 1010). During compression test the value of plateau stress and specific energy are obtained at varying input parameters and compared the results between WOW and WW tube samples. And its chemical composition verified with the help of Energy-dispersive X-ray spectroscopy test of the welded joint and parent material. Three optimization techniques are used in this study to validate the experimental results namely Taguchi method, Analysis of variance (ANOVA) and Artificial Neural Network (ANN). Taguchi L16 orthogonal array are used to identify the most effective input parameters which affect the energy absorption behaviour. Percentage contribution of individual input parameters analyzed by ANOVA method and also ANN was performed for non-linear mapping of the input and output parameters which are influenced by compression test. Experimental results have been validated with the optimization technique results and found to be in good agreement with them.

150. Updated Clinical Guidelines for Treatment and Prophylaxis of Plague

BackgroundPlague still occurs naturally in the western United States, Latin America, Asia, and Africa. Yersinia pestis, the causative agent of plague, is a Tier 1 bioterrorism agent due to its potential for aerosol release and high fatality rates. Recommendations for treatment and post-exposure prophylaxis (PEP) of plague were published in 2000 and included limited first-line options for treating plague, namely streptomycin or gentamicin. Doxycycline or ciprofloxacin were recommended for PEP. However, since 2000 new human clinical data and animal data have become available, and the FDA has approved additional antimicrobials for plague. MethodsCDC developed updated, evidence-based guidelines for treatment and prophylaxis of plague using a comprehensive process. To collect evidence on relative efficacy of various antimicrobials for treatment of plague, the guidelines team conducted systematic literature reviews and analyzed U.S. surveillance data. Results of these investigations were published in Clinical Infectious Diseases in 2020. We also hosted several meetings with subject matter experts and clinical organizations (IDSA, AAP, etc.), federal agencies, and others to review relevant data and gather individual input on treatment and prophylaxis of plague. ResultsThe forthcoming plague guidelines will include several important updates. First-line treatment options have been expanded to include ciprofloxacin, levofloxacin, and moxifloxacin in addition to streptomycin and gentamicin. For PEP, levofloxacin and moxifloxacin are now first-line options in addition to doxycycline and ciprofloxacin. Trimethoprim-sulfamethoxazole is now one of several new alternative options for PEP. The updated guidelines also include recommendations for treatment of clinical forms of plague other than pneumonic. Additional special populations such as immunocompromised persons and neonates are also covered. ConclusionPlague remains a threat, both as a naturally occurring disease and as a potential bioterrorism weapon, and preparedness and early recognition are key to effective response. The updated clinical guidelines will be a useful tool for clinicians to manage antimicrobial treatment and PEP for plague. Disclosures All Authors: No reported disclosures

HyperNeRF

Neural Radiance Fields (NeRF) are able to reconstruct scenes with unprecedented fidelity, and various recent works have extended NeRF to handle dynamic scenes. A common approach to reconstruct such non-rigid scenes is through the use of a learned deformation field mapping from coordinates in each input image into a canonical template coordinate space. However, these deformation-based approaches struggle to model changes in topology, as topological changes require a discontinuity in the deformation field, but these deformation fields are necessarily continuous. We address this limitation by lifting NeRFs into a higher dimensional space, and by representing the 5D radiance field corresponding to each individual input image as a slice through this "hyper-space". Our method is inspired by level set methods, which model the evolution of surfaces as slices through a higher dimensional surface. We evaluate our method on two tasks: (i) interpolating smoothly between "moments", i.e., configurations of the scene, seen in the input images while maintaining visual plausibility, and (ii) novel-view synthesis at fixed moments. We show that our method, which we dub HyperNeRF , outperforms existing methods on both tasks. Compared to Nerfies, HyperNeRF reduces average error rates by 4.1% for interpolation and 8.6% for novel-view synthesis, as measured by LPIPS. Additional videos, results, and visualizations are available at hypernerf.github.io.

Adolescent Brain Development and Contextual Influences: A Decade in Review.

Adolescence is a developmental period characterized by substantial psychological, biological, and neurobiological changes. This review discusses the past decade of research on the adolescent brain, as based on the overarching framework that development is a dynamic process both within the individual and between the individual and external inputs. As such, this review focuses on research showing that the development of the brain is influenced by multiple ongoing and dynamic elements. It highlights the implications this body of work on behavioral development and offers areas of opportunity for future research in the coming decade.

The PIOLab: Building global physical input–output tables in a virtual laboratory

Informed environmental-economic policy decisions require a solid understanding of the economy's biophysical basis. Global physical input–output tables (gPIOTs) collate a vast array of information on the world economy's physical structure and its interdependence with the environment, which can help to monitor progress toward a sustainable circular economy. However, building gPIOTs requires dealing with mismatched and incomplete primary data with high uncertainties, which makes it a time-consuming and labor-intensive endeavor. We address this challenge by introducing the PIOLab: A virtual laboratory for building gPIOTs. This represents the newest branch of the industrial ecology virtual laboratory (IELab) concept, a cloud-computing platform and collaborative research environment through which participants can pool resources to assemble individual input–output tables that target specific research questions. To overcome the lack of primary data, the PIOLab builds extensively upon secondary data derived from a variety of models commonly used in industrial ecology. We use the case of global iron-steel supply chains to describe the architecture of the PIOLab and highlight its analytical capabilities. A major strength of the gPIOT is its ability to provide mass-balanced indicators on both apparent/direct and embodied/indirect flows, for regions and disaggregated economic sectors. We present the first gPIOTs for 10 years (2008–2017), covering 32 regions, 30 processes, and 39 types of iron/steel flows. Diagnostic tests of the data reconciliation show a good level of adherence between raw data and the values realized in the gPIOT. We conclude with elaborating on how the PIOLab will be extended to cover other materials and energy flows. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

Issues surrounding consumer-bought food-allergy testing.

Allergy is increasingly reported by patients and members of the public, and there is evidence that the prevalence is increasing. Not all diagnoses have been made by clinicians, as direct-to-consumer (DTC) allergy tests are widely available online. To determine if DTC allergy tests are processed in accredited laboratories and utilize validated methods, while providing an overview of the DTC allergy tests available. Internet searches using 'allergy test kit' and 'intolerance test' were performed to identify DTC food-allergy tests. Each company was contacted to enquire if they had ISO15189 accreditation, what methods of testing they used and what was the extent of individual clinical input used to guide the test requested or result interpretation. In total, 24 online companies providing DTC food-allergy testing were identified, of which 22 were contactable. One laboratory had ISO15189 accreditation, which was also the only laboratory using clinically recognized specific IgE testing and had a clinician involved in the process. Other laboratories used bioresonance or IgG and involved a nutritionist at most. Online DTC food-allergy tests are largely misleading to the consumer and provided by unaccredited laboratories using controversial methodology. The dermatologist must politely discount these results and assess the role of food allergy in a patient's skin disease on the merit of clinical history, supported by specific IgE testing as appropriate.

Evaluating anti-poverty policy efficiencies in China: meta-frontier analysis using the two-stage data envelopment analysis model

PurposeThis study examines the relative efficiencies of anti-poverty policies implemented in 28 Chinese provinces.Design/methodology/approachThis study uses meta-frontier undesirable dynamic two-stage data envelopment analysis. The authors divide the poverty reduction process into two stages: agricultural production and poverty reduction. Public expenditure is the input for the second stage, and the population below the poverty line is the undesirable output. The authors compute the efficiencies (overall efficiency, efficiency of each stage and the efficiencies of individual inputs and outputs) using meta-frontier analysis for the 28 provinces.FindingsThe results show that: (1) a significant imbalance exists between the eastern and western regions in terms of input-output efficiencies; (2) the poverty reduction stage generally fared better than the agricultural production stage did. In particular, most provinces saw increases in poverty reduction efficiencies between 2013 and 2017; (3) the place-based poverty relief policies introduced in recent years are effective at reducing the poverty rate and reaching the government-set goals and (4) while disposable income has increased steadily over the past few years, income inequality has been exacerbated.Research limitations/implicationsThe results show that: (1) a significant imbalance exists between the eastern and western regions in terms of input-output efficiencies; (2) the poverty reduction stage generally fared better than the agricultural production stage did. In particular, most provinces saw increases in poverty reduction efficiencies between 2013 and 2017; (3) the place-based poverty relief policies introduced in recent years are effective at reducing the poverty rate and reaching the government-set goals and (4) while disposable income has increased steadily over the past few years, income inequality has exacerbated.Originality/valueA large amount of attention and public resources are devoted to fighting poverty and associated market failures in China. The extant literature focuses either on the agricultural production itself or the relationship between human capital and productivity levels. Making use of recent developments of the DEA method, the authors propose a new framework for evaluating the efficiencies of the poverty reduction process. Such a framework has the advantage of giving researchers and policymakers a more detailed diagnosis with regard to the components in the endeavor to eliminate poverty and providing useful information for policymakers to optimize public funds use. Methodologically, the framework is flexible enough to be employed for future research in similar appraisals, at different geographic and scale aggregation levels, for public projects including but not limited to poverty reduction.