Reference Benchmark Research Articles

A method for evaluating sediment-induced macroinvertebrate community composition changes in Idaho streams.

Human activities can increase sediment delivery to streams, changing the composition, distribution, and abundance of stream aquatic life. Few U.S. states have numeric water quality standards for streambed sediment under the Clean Water Act, so managers often need to develop local application-specific benchmarks. This study developed stream surface fine sediment <2 mm (sand and fines, SF) and macroinvertebrate fine sediment biotic index (FSBI) benchmarks and an application framework to test for sediment-induced macroinvertebrate community composition changes in 1st-4th order Idaho streams. FSBI reference benchmarks were calculated as the 25th percentile FSBI value among reference sites within three ecoregion-based site classes. Two approaches were used to develop SF benchmarks. Quantile regression was used to define reach-specific SF benchmarks representing an upper bound value expected under reference conditions. In addition, logistic regression was used to predict SF values with 50% and 75% probability that FSBI is worse than reference within each stream order and site class. The strength of association between SF benchmarks and macroinvertebrate community condition was evaluated by calculating relative risk using multiple datasets and examining responses of multiple macroinvertebrate indicators to SF benchmark status. SF reference benchmarks generally had stronger associations with poor macroinvertebrate condition than SF stressor-response benchmarks. Across datasets and macroinvertebrate indicators, poor macroinvertebrate condition was 1.8-3 times more likely when SF reference benchmarks were exceeded than when achieved. We propose rating the strength of evidence for a surface fine sediment-induced macroinvertebrate community composition change at the sample event scale as 'unlikely' if both SF and FSBI reference benchmarks are achieved, having 'mixed evidence' if only one reference benchmark is achieved, and 'likely' if both reference benchmarks are not achieved. We recommend combining ratings with other relevant data in a weight-of-evidence approach to assess if sediment impairs aquatic life.

Taking a deeper look at the priority of agricultural industry efficiency through the use of data envelopment approach

The research aims to analyze the technical efficiency of the chocolate industry, which has outstanding performance in producing chocolate products in East Java, Indonesia. The research sample includes all small and large-scale chocolate industries in East Java, with 42 Decision Making Units (DMUs). Efficiency research uses Data Envelopment Analysis (DEA), which is usually used in agricultural production research. However, in this research, DEA was used to analyze the efficiency of the chocolate industry making this concept as an empirical novelty. The analysis results show that there are industries operating at the Constant Returns to Scale (CRS) level (0.971), with a percentage of 66.67%, consisting of 28 DMUs, and the Variable Returns to Scale (VRS) level (0.992), with a percentage of 85.71%, which is represented by 36 DMUs. The Technical Efficiency (TE) is categorized as full technical efficiency at 85.72%, with a high category at 14.28%, and no DMUs fall under the moderate and low categories. This indicates that the allocation of inputs in each DMU is significantly different. Six DMUs require improvement in the distribution of industrial capital and raw materials input to achieve full efficiency category by making decisions based on the DMU's recommendations as a reference (benchmarks). This article concludes that industrial efficiency is a priority for the establishment of a business to achieve its goals easily. When the industry can control the quality and quantity of its products, it can be highly beneficial. The policy implications required for this case research to maintain and improve the efficiency of the chocolate industry in East Java, Indonesia, are for industries to prioritize joint management, increase the scale of operations, increase production frequency, expand innovative chocolate processing technology, prioritize product quality, and be able to penetrate export markets.

Challenges and Opportunities in the Catalytic Synthesis of Diphenolic Acid and Evaluation of Its Application Potential.

Diphenolic acid, or 4,4-bis(4-hydroxyphenyl)pentanoic acid, represents one of the potentially most interesting bio-products obtainable from the levulinic acid supply-chain. It represents a valuable candidate for the replacement of bisphenol A, which is strongly questioned for its toxicological issues. Diphenolic acid synthesis involves the condensation reaction between phenol and levulinic acid and requires the presence of a Brønsted acid as a catalyst. In this review, the state of the art related to the catalytic issues of its synthesis have been critically discussed, with particular attention to the heterogeneous systems, the reference benchmark being represented by the homogeneous acids. The main opportunities in the field of heterogeneous catalysis are deeply discussed, as well as the bottlenecks to be overcome to facilitate diphenolic acid production on an industrial scale. The regioselectivity of the reaction is a critical point because only the p,p'-isomer is of industrial interest; thus, several strategies aiming at the improvement of the selectivity towards this isomer are considered. The future potential of adopting alkyl levulinates, instead of levulinic acid, as starting materials for the synthesis of new classes of biopolymers, such as new epoxy and phenolic resins and polycarbonates, is also briefly considered.

Exploring CPS-Extrapolated DLPNO-CCSD(T1) Reference Values for Benchmarking DFT Methods on Enzymatically Catalyzed Reactions.

Domain-based local pair natural orbital coupled-cluster singles doubles with perturbative triples [DLPNO-CCSD(T)] is regularly used to calculate reliable benchmark reference values at a computational cost significantly lower than that of canonical CCSD(T). Recent work has shown that even greater accuracy can be obtained at only a small additional cost through extrapolation to the complete PNO space (CPS) limit. Herein, we test two levels of CPS extrapolation, CPS(5,6), which approximates the accuracy of standard TightPNO, and CPS(6,7), which surpasses it, as benchmark values to test density functional approximations (DFAs) on a small set of organic and transition-metal-dependent enzyme active site models. Between the different reference levels of theory, there are changes in the magnitudes of the absolute deviations for all functionals, but these are small and there is minimal impact on the relative rankings of the tested DFAs. The differences are more significant for the metalloenzymes than the organic enzymes, so we repeat the tests on our entire ENZYMES22 set of organic enzyme active site models [Wappett, D. A.; Goerigk, L. J. Phys. Chem. A 2019, 123, 7057-7074] to confirm that using the CPS extrapolations for the reference values has negligible impact on the benchmarking outcomes. This means that we can particularly recommend CPS(5,6) as an alternative to standard TightPNO settings for calculating reference values, increasing the applicability of DLPNO-CCSD(T) in benchmarking reaction energies and barrier heights of larger models of organic enzymes. DLPNO-CCSD(T1)/CPS(6,7) energies for ENZYMES22 are finally presented as updated reference values for the set, reflecting the recent improvements in the method.

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties1–3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions—in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.

Rational Design of Guanidinium-Based Bio-MCOF as a Multifunctional Nanocatalyst in Tumor Cells for Enhanced Chemodynamic Therapy.

Chemodynamic therapy (CDT) has emerged as a promising approach to cancer treatment, which can break the intracellular redox state balance and result in severe oxidative damage to biomolecules and organelles with the advantages of being less dependent on external stimulation, having deep tissue-healing abilities, and being resistant to drug resistance. There is considerable interest in developing CDT drugs with high efficiency and low toxicity. In this study, a new guanidinium-based biological metal covalent organic framework (Bio-MCOF), GZHMU-1@Mo, is rationally designed and synthesized as a multifunctional nanocatalyst in tumor cells for enhanced CDT. The DFT calculation and experimental results showed that due to the ability of MoO42- ion to promote electron transfer and increase the redox active site, Cu3 clusters and MoO42- ions in GZHMU-1@Mo can synergistically catalyze the production of reactive oxygen species (ROS) from oxygen and H2O2 in tumor cells, as well as degrade intracellular reducing substances, GSH and NADH, so as to disrupt the redox balance in tumor cells. Moreover, GZHMU-1@Mo exhibits a potent killing effect on tumor cells under both normal oxygen and anaerobic conditions. Further in vitro and in vivo antiproliferation studies revealed that the GZHMU-1@Mo nanoagent displays a remarkable antiproliferation effect and effectively inhibits tumor growth. Taken together, our study provides an insightful reference benchmark for the rational design of Bio-MCOF-based nanoagents with efficient CDT.

Model-based meta-analysis using latent variable modeling to set benchmarks for new treatments of systemic lupus erythematosus.

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti etal. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose-effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC-220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model-informed development of new investigational agents in SLE.

Comparative Analysis of Usage Patterns and Underlying Determinants for Ride-hailing and Traditional Taxi Services: A Chicago Case Study

As app-based ride-hailing (or e-hailing) services have achieved great success worldwide, the traditional taxi industry faces an unprecedented crisis. To gain a deeper understanding of these two distinct modes that both offer door-to-door mobility services, in this study, we use Chicago as a case to comparatively analyze the travel patterns of the app-based ride-hailing and traditional taxi services and understand how socio-demographic and urban land use attributes affect the usage of two services in temporal and spatial dimensions. We employ the local regression models named geographically and temporally weighted regression (GTWR) models that can capture the spatiotemporal nonstationarity between variables as the primary analysis tool. Four GTWR models that predict the average hourly taxi or ride-hailing trip volume on weekdays or weekends for each community area are carefully constructed with R^2 all above 0.98. The nonstationarity test shows all variables exhibit extra local variations, indicating the necessity of using the GTWR models to explore the attributes’ spatiotemporal impacts. Spatial error models (SEM) and ordinary least square (OLS) models, global regression models, are also built as comparisons and benchmark references. The differences between global and local regression models are illustrated through clustering analysis. To compare and contrast the usage patterns of ride-hailing and taxi services, we analyze the temporal-spatially varying coefficients of GTWR models. Interpretations are provided for our interesting findings, including the strong trip generation power of the population in the South Side area, the attitude of white people towards taxi services is positive but ambiguous towards ride-hailing services, young people's opposing behaviors towards taxi services on weekdays and weekends, and the opposite relationship between transit and taxi or ride-hailing in certain areas. Based on these key findings, we offer some planning and operational suggestions, such as developing mobile apps, implementing price-related strategies, strengthening partnerships with local businesses, building multi-modal transportation, clarifying customer segmentation, providing auxiliary services, etc.

A 3D time-domain solution for the bending of viscoelastic shells

This article presents a time domain analysis of viscoelastic doubly curved shallow shells employing 3D solution based on equilibrium equations. The constitutive equation is transformed into Laplace domain in order to avoid the time domain integration. The partial differential equations are solved for the mid-surface variables by applying the Navier Technique. The thickness equations are solved using the differential quadrature method (DQM). Lagrange interpolation polynomial are employed as basis functions. Each layer of the panel is discretized by the Chebyshev–Gauss–Lobatto grid distribution. The time-domain displacements are obtained by using the inverse Laplace transformation in an approximate numerical manner. The 3D solution is compared with references in the literature. Given the inclusion of comprehensive 3D time domain solutions, these results can be considered as a benchmark for reference.

Formation and conversion of flavour related compounds of thermal cooked Ruditapes philippinarum soup

SummaryThermal reactions affect flavour, but how different thermal reactions influence the flavour of cooked soup has rarely been investigated. Herein, the effect of heat treatment (0–50 min) at different temperatures (120, 140 and 160 °C) on Ruditapes philippinarum soup was studied. The acceptable heating of Ruditapes philippinarum soup was assigned at 140 °C for 30 min by human sensory evaluation and compound analysis. In addition, the detailed relationship between the formation of key volatile compounds and heating temperature was revealed by partial least squares discriminant analysis (PLS‐DA) and the odour coding method. A total of 28 key volatile compounds were selected according to the results of variables important in the projection (VIP) and odour activity value (OAV). Whereas 3‐ethyl‐2,5‐dimethyl pyrazine and 2‐heptylfuran were the most important volatiles produced only in the thermal reaction at 140 °C and associated with cocoa and roasted nut odours. These results indicated the relationship between thermal reactions and flavour formation. The odour coding obtained from OAV could be as a reference benchmark for heating seafood soup products.

Acoustic radiation from a baffled finite shell in an underwater waveguide

Analytical modelling of vibroacoustic systems can help us to understand the physical phenomena involved in more complex problems, and it provides a benchmark solution and reference upon which more complex systems can be built. In the present work, the system of interest consists of a finite elastic cylindrical shell inserted in infinitely rigid cylindrical baffles and immersed in an underwater acoustic waveguide. The latter consists of a finite fluid layer bounded by an upper free surface and a lower rigid floor. In such a fluid domain, the acoustic waves radiated from the excited shell will exhibit reflections off the boundaries. This phenomenon is modelled by the image-source theory and embedded in the fluid loading term, which intervenes in the shell equations. Investigations into the influence on the finiteness of the elastic shell, types of supports (i.e., simply supported, clamped, free, and combinations of these), and depth of the waveguide on the shell’s acoustic radiation are presented.

On-Board Fuel Consumption Meter Field Testing Results

This paper aims to investigate and compare the fuel consumption data obtained from on-board fuel consumption meters (OBFCMs) from approximately 1000 vehicles through field testing. Furthermore, this research aims not only to compare the OBFCM readings but also to juxtapose them against the fuel consumption specifications provided by the respective vehicle manufacturers. To collect data, a cost-effective on-board diagnostics (OBD) reader and a user-friendly mobile app were employed, providing an accessible and efficient method for fuel consumption analysis. Field testing involved a diverse range of vehicles, covering various makes, models, and years of production. The OBCFM readings were recorded over a 9-month period, probably capturing a wide range of driving conditions and patterns. In order to ensure the reliability of the OBCFM readings, the fuel consumption measurements obtained from the manufacturers specifications were utilized as a reference benchmark. Preliminary data analysis indicates that there are noticeable variations in the fuel consumption data obtained from the OBCFM and the manufacturer specifications. These differences can be attributed to various factors. The novelty of the presented data lies in using a new feature implemented in EU cars since 2019. The study capitalizes on this feature, allowing for the collection of data from a broad spectrum of vehicles throughout the country under genuine driving conditions.

Impact of COVID-19 lockdown on injury risk in Qatar's professional football

ObjectivesTo compare injury incidence, burden and characteristics between the pre- and post-COVID-19 lockdown periods in Qatari professional football. DesignProspective cohort study. MethodsInjury and exposure data for two post-COVID-19 lockdown periods [early post-lockdown period: short-term ~2 months (54 matches) and late post-lockdown period: long-term 8-months (183 matches)] were compared to the benchmark of the same periods from the three previous seasons (2017/18–2019/20). ResultsWe observed no difference in overall, training or match incidence between early post-lockdown period and the benchmark reference. However, this short-term period resulted in lower burden for overall- (RR 0.80, P < 0.0001), training- (RR 0.73, P < 0.0001) and match-injuries (RR 0.40, P < 0.0001) compared to the benchmark. During late post-lockdown period match injury incidence (RR 0.72, P = 0.0010) and match injury burden (RR 0.69, P < 0.001) were lower than the benchmark. In contrast, both overall- (RR 1.30, P < 0.001) and training-injury burden (RR 1.65, P < 0.001) were higher. A significant increase in adductor strains in both post-lockdown periods was observed. ConclusionsImmediately after the COVID-19 lockdown (short-term effect), there was no difference in injury incidence but a lower injury burden compared to benchmark. Moreover, the rapid return to competition for the successive season (long-term effect) was associated with a higher overall- and training-injury burden, but a lower match-injury burden compared to the benchmark.

Funding startups using contingent option of value appreciation: theory and formula

PurposeThis paper provides a structural model to value startup companies and determine the optimal level of research and development (R&D) spending by these companies.Design/methodology/approachThis paper describes a new variant of float-the-money options, which can act as a financial instrument for financing R&D expenses for a specific time horizon or development stage, allowing the investor to share in the startup's value appreciation over that duration. Another innovation of this paper is that it develops a structural model for evaluating optimal level of R&D spending over a given time horizon. The paper deploys the Gompertz-Cox model for the R&D project outcomes, which facilitates investigation of how increased level of R&D input can enhance the company's value growth.FindingsThe author first introduces a time-varying drift term into standard Black-Scholes model to account for the varying growth rates of the startup at different stages, and the author interprets venture capital's investment in the startup as a “float-the-money” option. The author then incorporates the probabilities of startup failures at multiple stages into their financial valuation. The author gets a closed-form pricing formula for the contingent option of value appreciation. Finally, the author utilizes Cox proportional hazards model to analyze the optimal level of R&D input that maximizes the return on investment.Research limitations/implicationsThe integrated contingent claims model links the change in the financial valuation of startups with the incremental R&D spending. The Gompertz-Cox contingency model for R&D success rate is used to quantify the optimal level of R&D input. This model assumption may be simplistic, but nevertheless illustrative.Practical implicationsOnce supplemented with actual transaction data, the model can serve as a reference benchmark valuation of new project deals and previously invested projects seeking exit.Social implicationsThe integrated structural model can potentially have much wider applications beyond valuation of startup companies. For instance, in valuing a company's risk management, the level of R&D spending in the model can be replaced by the company's budget for risk management. As another promising application, in evaluating a country's economic growth rate in the face of rising climate risks, the level of R&D spending in this paper can be replaced by a country's investment in addressing climate risks.Originality/valueThis paper is the first to develop an integrated valuation model for startups by combining the real-world R&D project contingencies with risk-neutral valuation of the potential payoffs.

Time, climate, and soil settings set the course for reclamation outcomes following dryland energy development

AbstractSoil attributes, climate, and time since reclamation have important implications for oil and gas reclamation success on drylands. It is uncertain if reclaimed well pads, on highly degraded drylands, can successfully regain ecological function or meet indicator benchmarks for reclamation. Here, our goals were to assess patterns in reclamation outcomes relative to (1) soil attributes, climate, and time since reclamation; and (2) plant and soil reference benchmarks. We collected a suite of plant, soil, and landscape characteristics from 134 reclaimed well pads on the Colorado Plateau that spanned climate and soil gradients and ranged from 2 to 20 years post‐reclamation, then compared characteristics to those from 583 reference plots. On the reclaimed pads, less saline soils, more mesic climates, and longer time since reclamation were associated with favorable reclamation outcomes based on metrics of plant species richness and diversity, plant structure, and cover of important plant functional groups. However, most pads had major departures in 1/4 indicators, suggesting unsuccessful reclamation. Arid warm locations with shallow soils had the greatest percentage of major departures, and noxious plant richness had the greatest departures across soil and climate settings. Our assessment indicates reclamation is failing in key metrics for most pads, and the results highlight the role of environmental conditions in driving reclamation outcomes. These new insights are valuable for ascertaining possible reclamation outcomes and success rates following energy related disturbance, including orphaned wells and renewable energy development. Importantly, the approach used here has applicability for setting benchmarks and outcome standards.

A Study on the Characteristics of Middle-aged Chinese Female Users Based on Clothing Needs

With the growing number of middle-aged population in China, the clothing demand of middle-aged women has received increasing attention, so the research on user characteristics of this group has important value. This article first establishes the research variables through the literature, user interview, expert evaluation method, and then conducts analytical research through the questionnaire and data analysis of their characteristics, and finally establishes the middle-aged female user characteristics system framework, divided into physical change, maturity and stability, conservatism and decency, age adaptability, psychological pressure and cultural differences. The establishment of the system framework further deepens the understanding of the middle-aged female user groups, provides a clear reference benchmark for clothing enterprises, and also lays a theoretical foundation for the subsequent relevant researches.

Initial core loading pattern optimization studies using estimation of distribution algorithm for VVER type cores

Innovative fuel management schemes based on evolutionary algorithms (EAs) now form an integral part of design and operation for nuclear reactors. In the present study, Estimation of Distribution Algorithm (EDA) has been developed for light water reactors (LWRs) and as a case study EDA has been applied to optimize the core loading pattern of VVER X2 benchmark’s initial core. EDA belongs to class of EAs where the optimization solution evolves through progress of generations and in each generation sampling of best candidates of previous generation is done. The main objectives of this study is firstly to develop and test EDA to optimize core loading pattern (LP) for VVER type cores and secondly to generate new optimized LPs having same safety features but with improved fuel utilization with respect to reference benchmark case. The most suitable values for internal parameters for EDA like population size (N), percentage best candidates (M) and weighting factor ‘α’ were evaluated for reaching a reasonable optimized solution in computationally efficient way. During this study, a number of optimized initial core loading patterns were generated, where the target parameters and safety limits are met. A comprehensive analysis for selected LPs has been carried out for full cycle and a comparison has been done for the reported safety and operational characteristics. It is observed that a few of the optimized LPs have better fuel economy than the benchmark LP.

Cleaner production auditing for plastic recycling industry in Pakistan: A baseline study

Recycling plastics is a good alternative to manage the plastic waste generated in Pakistan. Unfortunately, the country lacks efficient system to manage or recycle the plastic waste it generates. Lack of government support, absence of standard operating procedures, negligence towards health and safety of workers, increasing costs of raw materials and poor quality of the recyclates are some of the issues currently faced by plastic recyclers in Pakistan. Considering the need of cleaner production audits in plastic recycling industries, this study was carried out to establish an initial reference benchmark. Production processes in 10 recycling industries were evaluated from cleaner production perspective. The study showed the average water consumption of a recycling industry as high as 3315 L/ton. All the consumed water is wasted in the nearby community sewer while, only 3 recyclers recycled between 70 and 75% of the treated wastewater. In addition, a recycling facility, on an average, consumed 172.5 kWh of power for processing 1 ton of plastic waste. The average temperature was observed to be 36.5 °C and noise levels exceeded the permissible limits. Moreover, the industry is male-dominated, workers are mostly underpaid and have no access to good healthcare facilities. Recyclers lack standardization and have no national guidelines to follow. Guidelines and standardization of recycling process, wastewater treatment, use of renewable energy, water reuse etc, are direly needed for uplifting this sector and reducing its impacts on the environment.

Avoiding Optimal Mean Robust and Sparse BPCA with L1-norm Maximization

&lt;p&gt;Recently, the robust PCA/2DPCA methods have achieved great success in subspace learning. Nevertheless, most of them have a basic premise that the average of samples is zero and the optimal mean is the center of the data. Actually, this premise only applies to PCA/2DPCA methods based on L2-norm. The robust PCA/2DPCA method with L1-norm has an optimal mean deviate from zero, and the estimation of the optimal mean leads to an expensive calculation. Another shortcoming of PCA/2DPCA is that it does not pay enough attention to the instinct correlation within the part of data. To tackle these issues, we introduce the maximum variance of samples&amp;rsquo; difference into Block principal component analysis (BPCA) and propose a robust method for avoiding the optimal mean to extract orthonormal features. BPCA, which is generalized from PCA and 2DPCA, is a general PCA/2DPCA framework specialized in part learning, can makes better use of the partial correlation. However, projection features without sparsity not only have higher computational complexity, but also lack semantic properties. We integrate the elastic network into avoiding optimal mean robust BPCA to perform sparse constraints on projection features. These two BPCA methods (non-sparse and sparse) make the presumption of zero-mean data unnecessary and avoid optimal mean calculation. Experiments on reference benchmark databases indicate the usefulness of the proposed two methods in image classification and image reconstruction.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Methods for multi-segment continuous cable analysis

Cables are invaluable members for some applications of engineering. The specialty is due to its behavior under transverse loads. Having almost no rigidity in transverse direction makes cables different from other structural elements. In most applications, cables are assumed to be two force members. However, not only its weight but also its application with roller supports makes them different structural elements. Generally, cables are assembled as single-segmented cables (SSC) where they are fixed at their ends. However, in most of the SSC applications, cables have intermediate supports which can be rollers or sliders. These type of cable applications are called as multi-segment continuous cables (MSCC). In MSCC systems, the cable fixed at its ends and supported by a number of intermediate rollers. Total length of cable is constant, and the intermediate supports are assumed to be frictionless and stationary. In this problem, the critical issue is to find the distribution of the cable length among the segments in the final equilibrium state, so reactions at all supports can be found. Two methods are proposed for the segment length adjustment based on the stress continuity among the cable. These methods are named as direct stiffness method and tension distribution method (relaxation method). Results calculated from the proposed methods are verified by both the reference benchmark problems and commercial finite element program.