Reasonable Interval Research Articles

Empty Pallet Allocation Optimization in Shipbuilding Using a Pallet Pool System

Pallets are an important transportation tool in modern shipbuilding. With shipbuilding now trending towards larger ships, empty pallet allocation needs to meet the demands of having low costs and being sustainable for green shipbuilding. Thus, with the development of a pallet pool system, a new shipbuilding empty pallet pool allocation (SEPPA) pattern is proposed in this study. An integrated framework is developed that combines a mathematical planning model for a SEPPA pattern with a green allocation strategy. For the base case, the operation costs of the traditional shipbuilding empty pallet allocation (TSEPA) pattern and the SEPPA pattern are solved by applying an improved genetic algorithm for different pallet supply and demand situations. The results show that the SEPPA pattern is more cost-efficient than the TSEPA pattern. With increasing imbalances between supply and demand, the operation costs of the SEPPA pattern are lower than that of the TSEPA pattern. In general, the distribution of supply and demand will affect operation costs. Reasonable safety inventory intervals can reduce the operation costs of empty pallet allocation. This research may support decision making by shipbuilding pallet managers as they seek to minimize the costs of their pallet operations, by adopting practices and adapting strategies for their specific conditions.

P057 Is baseline screening enough for long term biologic treatment? Looking for hepatitis in biologic populations

Abstract Background/Aims With biological agents used for inflammatory arthritis (IA) for over 20 years and the more recent addition of targeted synthetic agents, it is no surprise that pre-screening has evolved as we continually learn about these drugs. The BSR biologic guidelines (2018) recommend that we screen hepatitis B (HBV) and hepatitis C (HCV) serology prior to commencing any biologic. However, in long-term treatment, infection risks are posed from both new exposure and from reactivation of latent disease in the context of immunosuppression. The latter has been well recognised for tuberculosis, but we report a case of hepatitis B in a patient treated for 11 years, initially with anti-TNF for 2.5 years and subsequently with rituximab (RTX). We present an 80-year-old female patient diagnosed with rheumatoid arthritis (RA) in 2001, who migrated from Hong Kong in 1972. After initial treatment with gold, sulfasalazine, methotrexate, hydroxychloroquine and leflunomide, eventually etanercept (ETN) was initiated in 2010. In accordance with earlier BSR guidance (2005) we tested for HBsAg and HCV in 2009, prior to ETN, both were undetected. In early 2018, hepatology advice recommended including antibodies to hepatitis B core antigen (anti-HBc) with our screening, later specified by the current 2018 BSR guidance. Initially, this was only undertaken in new patients, however, we decided to re-check all patients on biologics to include anti-HBc in 2019. Prior to this, re-screening was not undertaken routinely at any interval or when switching biologic, unless there were relevant risk factors. The relevant guidance and recommendations, at the time, only recognised prior screening and reactivation of HBV. On re-testing our case in July 2021, anti-HBc was detected. Subsequently the patient was referred to hepatology and commenced on anti-viral treatment (tenofovir). The aim was to review all patients on biologics not previously screened for anti-HBc, especially those in high-risk groups. Methods A retrospective review of the hepatitis screening profiles of 71 patients. These patients were actively receiving RTX or infliximab in the preceding three years, since including anti-HBc. Results HBV was not detected in 55/71 (77.5%). Twelve patients (16.9%) are yet to be re-screened. HBV was detected in four individuals (5.6%). Our index patient, receiving RTX since 2012, underwent additional screen in 2021, likely delayed by the pandemic. Of the other three, one had no viral load detected and the result deemed a false positive. One patient was new to RTX in 2019, and the final patient had HBsAg detected with anti-HBc not detected. Conclusion For patients with IA receiving long-term biologic therapy, there is a need to standardise screening protocols for viral hepatitis and to undertake repeat testing at a reasonable interval, such as every five years and possibly more frequently in high-risk groups. Disclosure A. Eden: None. S. Ellis: None. T. Marianayagam: None.

Retrospective audit on time to surgery delays following the completion of neoadjuvant chemotherapy in breast cancer patients

The ideal time to surgery interval in breast cancer patients following neoadjuvant chemotherapy according to published literature is as low as 21 days, 40 days and up to 8 weeks. The authors' hospital multidisciplinary team (MDT) accepted 28 days as a reasonable time interval to allow recovery from the side‑effects of neoadjuvant chemotherapy. The present retrospective clinical audit presents a review of all breast cancer cases that received neoadjuvant chemotherapy and examined the conformity to the set audit standards. The primary aim of the present audit was to investigate the time interval from the conclusion of neoadjuvant chemotherapy to surgery in breast cancer patients, and to determine the causes of potential delays and identifying means to improve the outcomes of the current process. The secondary objectives were to investigate the timing of tumour coil insertion, the timing of surgical planning multidisciplinary team meeting, the time to addition to the surgical waiting list and whether these patients had adequate tumour monitoring for effective surgical planning. A total of 59 breast cancer patients who had received neoadjuvant chemotherapy from November 1, 2011 to October 31, 2016 were identified. In total, one primary audit standard and six secondary audit standards were derived from the MDT consensus agreement and reasons for non‑conformity to these standards were audited. The primary standard was described as ‘surgery should be performed within 28 days of completion of last cycle of neoadjuvant chemotherapy’. The results revealed that conformity to this standard was 14%. The conformity of secondary standards was also poor, ranging from 20 to 98%. The results of the present audit were not very encouraging. Recommendations were drawn with a plan for a re‑audit following the implementation of changes agreed upon by the multidisciplinary team and a plan of action was prepared. It was concluded that achieving a time to surgery interval of <28 days following the completion of neoadjuvant chemotherapy is an achievable challenging task and only possible with an efficient multidisciplinary team work.

An Early-Warning System to Validate the Soil Profile during TBM Tunnelling

Identification of soil condition at the working face of a tunnel boring machine (TBM) is a key factor for the efficiency and safety of TBM tunneling. The paper presents the first application of the Horizontal-to-Vertical Spectral Ratio (HVSR) method on microtremors induced by a TBM during tunnelling. The innovative application is based on the development of an easy-to-use and economical early-warning system, which aims to confirm, or otherwise, the soil profile established in the design phase of tunnels by comparing the soil natural frequencies obtained from the soil profile carried out during the design phase and the soil natural frequencies coming from the HVSR analysis of the microtremors induced by the TBM during tunnelling. Just one or two geophones are necessary to use the proposed procedure. It can be applied to an area up to about 20 m ahead of the TBM excavation front and constitutes a powerful early warning system. Due to the great heterogeneity of the subsoil, dual-mode TBMs are often used, frequently changing from Open-Face (OF) mode for rock formations to Earth Pressure Balance (EPB) mode for cohesive and incoherent soil. Any “additional” information on the soil, which will be dug in the next days or hours is extremely useful for subsoil with great heterogeneity. The new procedure offers a reasonable time interval in which to modify the excavation method. This, in turn, can avoid damage to the TBM and existing structures and infrastructures. It allows us also to have a valuable geotechnical database for future works on the infrastructural networks. The proposed procedure has been successfully applied during the construction of the new underground lines in Catania (Italy).

Blasting Vibration Monitoring and a New Vibration Reduction Measure

Vibration waves generated by blasting can cause shock to buildings. Different responses occur in different parts of the building. Therefore, a single standard is inaccurate. At the same time, methods to reduce vibration are needed. In this paper, the variation of peak particle velocity (PPV) and principal frequency was analyzed. The energy variation of blast vibration waves was analyzed by wavelet packet decomposition. A numerical model was established to verify the new vibration reduction measure. The results showed that the PPV on the walls increases with their height. The PPV and principal frequency of different structures of single-story brick-concrete buildings are different. The amplification factor of PPV does not change much when the principal frequency ratio is larger than 0.75. Measuring points at different heights have different sensitivities to blasting vibration waves of different principal frequencies. Therefore, different structures will respond differently to the same blasting operation. The PPV can be reduced by waveform interference. However, the cycle of blasting vibration waves decreases with increasing distance. Therefore, it is necessary to determine a reasonable interval to reduce the PPV. This requires further research.

Practical Applications of Private Portfolio Attribution Analysis

In <b><i>Private Portfolio Attribution Analysis</i></b>, published in the Fall 2021 issue of <b><i>The Journal of Alternative Investments</i></b>, <b>Gregory Brown</b> and <b>Frank Ethridge</b> at the <b>University of North Carolina at Chapel Hill</b> and <b>Tyler Johnson</b> and <b>Tom Keck</b> at <b>Stepstone Group</b> address the challenges of applying portfolio performance attribution techniques to private funds. One such challenge is that private equity funds have no periodically reported returns. The authors provide a method for attributing private portfolio performance to commitment timing and sizing, geography and strategy selection, and fund selection skills. They are measured relative to a market benchmark and can be interpreted as alphas. The authors also calculate and illustrate reasonable confidence intervals for these attributes with venture capital and buyout cash flow data. The factors explain how managers’ strategic and tactical allocation decisions impact performance and inform future decision making.

Prediction of interface of geological formations using generalized additive model

ABSTRACT Geological information such as geological interfaces is important for the design of underground excavation and supporting measures. This in turn requires a method to predict accurately the locations of geological interfaces for the gap areas between boreholes. This study presents a generalized additive model (GAM) to predict the location of the geological interfaces. The performance of the GAM method is evaluated using both simulated data and borehole data for the determination of rockhead in two different geological formations in Singapore. The results show that the GAM method can provide a reasonable confidence interval (CI) of the mean trend and the prediction interval (PI) in the sense that the 95% CI covers about 95% of the actual mean curve while the 95% PI covers around 95% of testing data. Furthermore, the geological complexity can be well reflected as the prediction uncertainty in the geologically complex area is larger than that in the geologically regular area. More importantly, the users can impose prior information or personal judgment regarding the shape of the geological profile on the model. This is an important feature to enable further improvement in the accuracy of the prediction. Highlights A generalized additive model is used to predict the location of the geological interfaces The performance of the GAM method is evaluated using both simulated data and actual borehole data from Singapore The results show that the GAM method can provide a reasonable confidence interval of the mean trend and the prediction interval of a prediction Geological complexity can be well reflected in the sense that the prediction uncertainty in the geologically complex area is relatively large Expert judgment or knowledge on the geological profile can be applied to the model, which improves the prediction accuracy

An Approximate Bayesian Approach to Model-assisted Survey Estimation with Many Auxiliary Variables

Model-assisted estimation with complex survey data is an important practical problem in survey sampling. When there are many auxiliary variables, selecting significant variables associated with the study variable would be necessary to achieve efficient estimation of population parameters of interest. In this paper, we formulate a regularized regression estimator in the framework of Bayesian inference using the penalty function as the shrinkage prior for model selection. The proposed Bayesian approach enables us to get not only efficient point estimates but also reasonable credible intervals. Results from two limited simulation studies are presented to facilitate comparison with existing frequentist methods.

Micro-mechanism of Shear Strength and Water Stability Enhancement of Montmorillonite by Microwave Heating

Microwave heating potentially reinforces the muddy intercalation to eliminate slope failure. Montmorillonite has the worst water resistance among the muddy intercalation components, which is a primary facet of inducing muddy intercalation failure. This study investigates the mechanism of shear strength and water stability enhancement of montmorillonite heated by the microwave oven and muffle furnace from room temperature to 800 °C. Results show that montmorillonite mineralogical evolution can be divided into three stages: room temperature-300 °C, 300-600 °C, and 600-800 °C. Microwave heating is more efficient in montmorillonite heat treatment than muffle furnace heating and makes the montmorillonite dehydroxylated earlier. It is worth noting that hot spots formed inside the montmorillonite specimens during the microwave heat treatment. Microwave significantly promotes the shear resistance of montmorillonite, where the maximum increases are 39.94% of cohesion at 600 °C and 20.54% of internal friction angle at 700 °C. This enhancement is due to the rough surfaces and large particles produced by dehydroxylation and Mg-Al spinel synthesis, and the significant degree of disorder state of MMT after dehydroxylation also plays a vital role. The microwave-heated montmorillonite over 500 °C presents good integrity in the water immersion test over 24 h. Considering the shear behavior and water stability, we believe the most reasonable heating interval for microwaves is 500-600°C.

WRMatch: Improving FixMatch With Weighted Nuclear-Norm Regularization for Few-Shot Remote Sensing Scene Classification

Semisupervised learning (SSL), such as FixMatch, has been successfully applied to remote sensing scene classification to relieve the burden of data annotation. However, in some extreme settings, only very few samples available, e.g., one to ten labels per remote sensing scene, can be used. When meeting this &#x201C;few-shot&#x201D; scenario, the deep model may be overfitting and prone to generate confusing predictions due to the lack of labels and strong augmentation-based perturbations. Thus, the prediction&#x2019;s diversity may collapse, and the discriminability exceeds the reasonable interval. How to improve the performance of few-shot learning is underexplored for the remote sensing scene classification in previous studies. In this article, we present a novel framework for the task by utilizing the improved FixMatch and the weighted nuclear-norm regularization (WNNR). Specifically, we regularize the prediction matrix by exploiting the nuclear-norm, which is an approximation of the matrix rank and a relaxed boundary for the Shannon entropy. We further provide two weighting schemes to improve the nuclear-norm-based regularization. First, the random-weighting scheme for nuclear-norm (RWNNR) is proposed based on the Dirichlet distribution to improve the model&#x2019;s generalization. Second, we present the self-weighting scheme (SWNNR) to weight the singular values according to singular values themselves and adjust the relaxed degree for the boundary between the nuclear-norm and the Shannon entropy. Maximizing the weighted nuclear-norm can improve the prediction diversity and optimize the prediction discriminability simultaneously. Combining the advantages of SSL and the aforementioned improvements, we can reliably classify the remote sensing scene image with very limited annotated datasets. To empirically demonstrate the proposed method&#x2019;s effectiveness, we comprehensively evaluate the method on three publicly available benchmark datasets. The results show that the proposed method outperforms the baseline methods by a large margin and achieves superior performance on all three datasets. Our method can be an effective alternative to metalearning in few-shot scene classification, with the advantage lying in the competitive performance and the absence of metatraining stage associated with a large number of labels.

Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study.

A tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes.

Evaluation of Pavement Runoff and Driving Safety on Highway Curve Segment

Runoff depth distribution on the concave and circular curve sections is obtained from a two-dimensional numerical simulating model in order to analyze the temporal and spatial variation of the pavement runoff on the curve section. The two-dimensional model verified by the field data can depict the alignment of pavement more accurately as compared to the empirical equation and a one-dimensional model. The runoff on the concave section and circular curve section is compared for the free water drainage and centerline drainage. Results show that a two-dimensional model is essential for the analysis of the centerline drainage. The runoff depth can be controlled by a reasonable curb height and location interval. The drainage type affects the variation of the runoff depth on the nearside lane, and the maximum water depth can be up to more than 80 mm on the concave section and nearly 60 mm on the circular curve section under centerline drainage. Besides the existing hydroplaning results, the runoff depth difference of the wheel trace should be considered to evaluate driving safety. Sideslip will occur when the depth difference becomes more than 6 mm under condition that the runoff depth is less than the tread depth (7 mm). When the runoff depth is more than the tread depth, sideslip will occur once the depth difference exceeds 4 mm.

A Class of Biocompatible Dye-Protein Complex Optical Nanoprobes.

Molecular organic dyes are classic fluorescent nanoprobes finding tremendous uses in biological and life sciences. Yet, they suffer from low brightness, poor photostability, and lack of functional groups for bioconjugation. Here, we describe a class of biocompatible dye-protein optical nanoprobes, which show long-time photostability, superbrightness, and enriched functional groups. These nanoprobes utilize apoferritin (an intracellular protein for iron stores and release) to encase appropriate molecular organic dyes to produce on-demand fluorescence in aqueous solution. A pH-driven dissociation-reconstitution process of apoferritin subunits allows substantial incorporation of hydrophilic (aggregation caused quenching, ACQ) or hydrophobic (aggregation induced enhancement, AIE) dye molecules into the protein nanocavity (8 nm), producing monodispersed dye-apoferritin nanoparticles (apo-dye-NPs, ∼12 nm). As compared with single dye monomer, single apo-dye-NPs possess hundreds of times larger molar extinction coefficient and 2 orders of magnitude higher absolute luminescence quantum yield (up to 45-fold), multiplying fluorescence brightness up to 2778-fold. We show that varying the type of incorporated dyes entails a precise control over nanoprobe emission profile tunable in a broad spectral range of 370-1300 nm. Mechanical investigations indicate that the diversified microstructures of nanocavity inner surface are able to conform ACQ dyes at reasonable space interval while providing protein-guided-stacking for AIE dyes, thus enhancing fluorescence quantum yield through confining intermolecular quenching and intramolecular rotation. Moreover, apo-dye-NPs are able to emit stable fluorescence (over 13 min) without quenching in confocal imaging of HepG2 cancer cell under ultrahigh laser irradiance (1.3 × 106 W/cm2). These superb properties make them suitable, as demonstrated in this work, for long-term super-resolved structured illumination microscopic cell imaging (spatial resolution, 117 nm) over 48 h, near-infrared (NIR) fluorescence angiography imaging of whole-body blood vessels (spatial resolution, 380 μm), and NIR photoacoustic imaging of liver in vivo.

Study on the Correlation between Linguistic Complexity and Audience Recognition in College English Speech Contests

From the perspective of linguistic complexity, this paper explores the correlation between linguistic complexity and audience recognition in college English speech contests. By adopting the corpus construction and computer visualized data analysis, this study analyzes the speech of contestants at different levels in FLTRP Cup National English peaking Contest 2019-2020, the most authoritative college English speech contest in China. The study shows that: 1) In college English speech contests, the lexical complexity of the speech is negatively correlated with the recognition degree of the audience (i.e. the final ranking of the competition or the success of the speech). 2) The syntactic complexity of the speech exists reasonable interval to ensure good audience recognition. 3) In college English speech contests, the correlation between the lexical complexity and syntactic complexity of speeches and audience recognition is similar to the correlation between the rhetoric and audience recognition in political speeches which is obtained by previous researchers in the field of political speeches. Therefore, we think this study has a certain practical value. It provides evidence of linguistic complexity for predicting the winner of college English speech contests and helping contestants prepare for the contest better.

Real-time rock mass condition prediction with TBM tunneling big data using a novel rock–machine mutual feedback perception method

Real-time perception of rock mass information is of great importance to efficient tunneling and hazard prevention in tunnel boring machines (TBMs). In this study, a TBM–rock mutual feedback perception method based on data mining (DM) is proposed, which takes 10 tunneling parameters related to surrounding rock conditions as input features. For implementation, first, the database of TBM tunneling parameters was established, in which 10,807 tunneling cycles from the Songhua River water conveyance tunnel were accommodated. Then, the spectral clustering (SC) algorithm based on graph theory was introduced to cluster the TBM tunneling data. According to the clustering results and rock mass boreability index, the rock mass conditions were classified into four classes, and the reasonable distribution intervals of the main tunneling parameters corresponding to each class were presented. Meanwhile, based on the deep neural network (DNN), the real-time prediction model regarding different rock conditions was established. Finally, the rationality and adaptability of the proposed method were validated via analyzing the tunneling specific energy, feature importance, and training dataset size. The proposed TBM–rock mutual feedback perception method enables the automatic identification of rock mass conditions and the dynamic adjustment of tunneling parameters during TBM driving. Furthermore, in terms of the prediction performance, the method can predict the rock mass conditions ahead of the tunnel face in real time more accurately than the traditional machine learning prediction methods.

Impact of reservoir geochemistry on low salinity waterflooding: Global sensitivity analysis

Low salinity waterflooding (LSW) has been receiving an increasing attention in the industry as it has several advantages over alternative strategies to enhanced oil recovery (EOR), i.e. i) outperforms conventional EOR methods in terms of additional oil recovery, ii) involves lower chemical costs, iii) it is environmentally friendly, and iv) requires a relatively simple field process implementation. We quantify low salinity effect (LSE) on oil recovery upon employing a mechanistic geochemical model under parametric uncertainty. Our ultimate goal is to characterize the impact of the reservoir rock and fluid properties on the performance of LSW. To this end, we propose a geochemical model of LSW built within a commercial compositional reservoir simulator. The model is designed to account for geochemical processes recently identified through experimental and pore-scale analyses, i.e. double layer expansion, multiple ion exchange and mineral dissolution/precipitation. The geochemical model outputs were first compared with two low salinity coreflood experiments reported in the literature and then applied to predict oil recovery under parametric uncertainty. Our study encompasses the effect of formation water ionic concentrations, rock mineralogy and reservoir temperature on LSW performance. Global sensitivity indices were calculated considering reasonable intervals of variability of the studied parameters. Our analysis focuses on the total oil recovery at three different time scales: 6 months (before water breakthrough), 2 years (after water breakthrough), and 10 years (the end of the injection). The results are discussed in the frame of quantitative screening criteria development, which is necessary to assess whether a given reservoir may be a promising candidate for LSW. Our results indicate that reservoir temperature is the parameter which demonstrated the most significant impact on the LSW performance. Ion concentration also displays a significant influence on oil recovery, and we identify different chemical species in carbonate and sandstone environments. On the contrary, mineral composition shows a limited influence on oil recovery. In particular, our results show that clay presence might be not essential for LSW to be effective in sandstone reservoirs. We discuss the impact of our results in the context of experimental design aimed at an improved constraining of LSW parameterization. Our study suggests that reservoir fluid composition (e.g., concentration of Ca2+, SO42− and Na+), in addition to reservoir temperature, should be prioritized in future experimental campaigns to better understand its influence on LSW under different reservoir and operation conditions.

Practical Applications of Private Portfolio Attribution Analysis

In <b><i>Private Portfolio Attribution Analysis</i></b>, published in the Fall 2021 issue of <b><i>The Journal of Alternative Investments</i></b>, <b>Gregory Brown</b> and <b>Frank Ethridge</b> at the <b>University of North Carolina at Chapel Hill</b> and <b>Tyler Johnson</b> and <b>Tom Keck</b> at <b>Stepstone Group</b> address the challenges of applying portfolio performance attribution techniques to private funds. One such challenge is that private equity funds have no periodically reported returns. The authors provide a method for attributing private portfolio performance to commitment timing and sizing, geography and strategy selection, and fund selection skills. They are measured relative to a market benchmark and can be interpreted as alphas. The authors also calculate and illustrate reasonable confidence intervals for these attributes with venture capital and buyout cash flow data. The factors explain how managers’ strategic and tactical allocation decisions impact performance and inform future decision making.

Probabilistic Prediction Model for the Chloride Diffusion Coefficient of Concrete under Tensile and Compressive Stresses

To accurately describe the uncertainty of chloride diffusion coefficient of concrete under tensile and compressive stresses, an iterative algorithm is proposed to reduce the influence of the uncertainty of prior variance on the posterior results. Subsequently, a probabilistic prediction model is developed to take into account the stress effect on chloride diffusion coefficient by using a Bayesian algorithm and Markov Chain Monte Carlo (MCMC) method. The existing deterministic model is adopted as prior model for this probabilistic model, and 180 sets of chloride diffusion coefficient data under different stresses obtained from 23 published journal papers are used as posterior information for this probabilistic model. The accuracy of the proposed model is validated by comparing with the experimental samples and other existing models. Analysis results show that the proposed probabilistic prediction model provided a reasonable confidence interval of the correction coefficient of stress effect on chloride diffusion coefficient. The provided iterative algorithm reduce the length of confidence interval with the exact prediction precision.

Loss reduction strategy and evaluation system based on reasonable line loss interval of transformer area

As the wiring of distribution network becomes more and more complex, it is difficult to calculate the theoretical line loss of low-voltage distribution transformer area and the evaluation index of transformer line loss is extensive. In this paper, a reasonable interval calculation model of line loss is established based on the data of electricity information acquisition system. Firstly, the collected data are processed in the image format, and then the line loss calculation model is established based on the convolutional neural network. This model can estimate the reasonable line loss interval according to the operation data of different transformers. On this basis, the line loss evaluation system is established and the loss reduction strategy of abnormal transformer is formed. The method in this paper can be used to evaluate the line loss level of distribution transformer area more accurately. It also can improve the quality and efficiency of line loss lean management and produce certain effect for electric energy conservation and improving economic benefit of power supply.

Practical Applications of Private Portfolio Attribution Analysis

In Private Portfolio Attribution Analysis, published in the Fall 2021 issue of The Journal of Alternative Investments, Gregory Brown and Frank Ethridge at the University of North Carolina at Chapel Hill and Tyler Johnson and Tom Keck at Stepstone Group address the challenges of applying portfolio performance attribution techniques to private funds. One such challenge is that private equity funds have no periodically reported returns. The authors provide a method for attributing private portfolio performance to commitment timing and sizing, geography and strategy selection, and fund selection skills. They are measured relative to a market benchmark and can be interpreted as alphas. The authors also calculate and illustrate reasonable confidence intervals for these attributes with venture capital and buyout cash flow data. The factors explain how managers’ strategic and tactical allocation decisions impact performance and inform future decision making.