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PurposeThe purpose of this study is to explore the potential for the development of a country logistics performance assessment approach based upon textual big data analytics.Design/methodology/approachThe study employs design science principles. Data were collected using the Global Perspectives text corpus that describes the logistics systems of 20 countries from 2006–2014. The extracted texts were processed and analysed using text analytic techniques, and domain experts were employed for training and developing the approach.FindingsThe developed approach is able to generate results in the form of logistics performance assessments. It contributes towards the development of more informed weights of the different country logistics performance categories. That said, a larger text corpus and iterative classifier training is required to produce a more robust approach for benchmarking and ranking.Practical implicationsWhen successfully developed and implemented, the developed approach can be used by managers and government bodies, such as the World Bank and its stakeholders, to complement the Logistics Performance Index (LPI).Originality/valueA new and unconventional approach for logistics system performance assessment is explored. A new potential for textual big data analytic applications in supply chain management is demonstrated. A contribution to performance management in operations and supply chain management is made by demonstrating how domain-specific text corpora can be transformed into an important source of performance information.

Over the last few decades, the Indonesian government has designed a partnership model for the farming system called an inti-plasma model. An inti or a core was a large private company, while the plasmas were smallholders that collaborated with the core. In the subsector of animal husbandry, the animal housing system has been considered the main regulation to achieve a contract between the core and plasmas. This study aimed to evaluate the performance comparison of broiler chickens kept in the closed house (CH) versus those kept in the stage slatted-floor open-sided house (OH) at broiler farms in Desa Simpang Jambe, Indonesia, in partnership with Charoen Pokphan Jaya Farm. This study was conducted in a completely randomized design (CRD) with two treatments and ten replications. The treatments were broilers reared in CH, and those in the OH. Each replication was an experimental unit with 4,800 birds pen-1 in the CH and 600 birds pen-1 in the OH. Twenty birds were randomly sampled from each pen for body weight (BW) and feed intake (FI), resulting in 200 sampling birds from each house. Data were analyzed using a t-test analysis. The results of this study showed that during the starter period, BW, body weight gain, feed intake, mortality, and depletion were significantly better at p < 0.05 and p < 0.01 in the OH than in the CH, but feed conversion ratio was not significantly different at p > 0.05 between both houses. However, during the grower/finisher and overall periods, all performance parameters were significantly different (p < 0.01) by house type. In conclusion, producing broilers at 4 or 5 weeks of marketed ages in the closed house performed better performance index than those in the stage slatted-floor open-sided house.

The beaver-like underwater robot can crawl and swim, playing a significant role in the exploration of amphibious environments. The robot's fan-shaped tail offers partial forward propulsion and enhances motion stability. However, a comprehensive method for modeling this tail motion is lacking. This paper introduces a segmented approach to dynamic modeling, building both propulsion and lift models for the robot's beaver-like tail. The approach integrates hydrodynamics with material mechanics to formulate a fan-shaped flexible dynamics theory. A performance index is established to quantify the tail's motion performance, offering a theoretical foundation for its evaluation. To validate the dynamic model and assess motion performance under varying swing amplitude coefficients, both simulations and experiments are undertaken. An optimal swing amplitude coefficient of 2 is identified, setting the stage for future developments in robotic motion control. This work may also advance dynamic modeling and analysis in other biomimetic underwater robots.

Epoxy resins are widely used polymers in the automotive and aerospace fields. Different blends of novel biodegradable resins have been studied in the last years in order to provide sustainability while maintaining the same properties of epoxy resins. Bio-based thermoset resins made with acrylated epoxidized soybean oil are well-studied in different vat polymerization techniques. The present work compares a bio-based resin and a petroleum-based resin. A benchmark with different features was designed and manufactured by a VAT photopolymerization process using both materials; measured with an optical scanning device; thus, the dimensional deviations were analyzed through inspection software. Tensile and flexural specimens were manufactured with the same procedure and tested with a dynamometer machine. Therefore, the comparison between a biodegradable resin and a petroleum-based resin is discussed in terms of the quality and mechanical performances of manufactured parts, considering the use of identical printing conditions. Some parts are required to satisfy both the requirements at the same time, such as the gears. Therefore, dimensional accuracy and mechanical strength need to be controlled and evaluated in a unique final quantification. This work proposes a novelty performance index to quantify dimensional accuracy and mechanical strength simultaneously. By combining the two aspects it is possible to define the overall performance obtained with the used material, optimizing the manufacturing process by choosing the proper material for each purpose.

Air pollution is one of the major global environmental issues urgently needed attention for its control through sustainable approaches. The release of air pollutants from various anthropogenic and natural processes imposes serious threats to the environment and human health. The green belt development using air pollution-tolerant plant species has become popular approach for air pollution remediation. Plants’ biochemical and physiological attributes, especially relative water content, pH, ascorbic acid, and total chlorophyll content, are taken into account for assessing air pollution tolerance index (APTI). In contrast, anticipated performance index (API) is assessed based on socio-economic characteristics including “canopy structure, type, habit, laminar structure, economic value and APTI score” of plant species. Based on previous work, plants with high dust-capturing capacity are identified in Ficus benghalensis L. (0.95 to 7.58 mg/cm 2 ), and highest overall PM accumulation capacity was observed in Ulmus pumila L. (PM 10  = 72 µg/cm 2 and PM 2.5  = 70 µg/cm 2 ) in the study from different regions. According to APTI, the plant species such as M. indic a (11 to 29), Alstonia scholaris (L.) R. Br. (6 to 24), and F. benghalensis (17 to 26) have been widely reported as high air pollution-tolerant species and good to best performer in terms of API at different study sites. Statistically, previous studies show that ascorbic acid ( R 2  = 0.90) has good correlation with APTI among all the parameters. The plant species with high pollution tolerance capacity can be recommended for future plantation and green belt development. Graphical Abstract

The operating performance index (OPI) was developed by Chen and Yang (J Comput Appl Math 343:737–747, 2018) from the Six Sigma process quality index. The fact that OPIs include unknown parameters means that they must be formulated using estimates based on sample data. Unfortunately, cost and effectiveness considerations in practice have led to sample size limitation and measurement uncertainty. In this study, we sought to enhance testing accuracy and overcome the uncertainties in measurement by applying confidence intervals of OPI to derive a fuzzy number and membership function for OPI. We developed a one-tailed fuzzy test method to determine whether performance reaches the required level. We also developed a two-tailed fuzzy testing method based on two OPIs to serve as a verification model for the effectiveness of improvement measures. Both fuzzy testing methods are proposed based on confidence intervals of the indices to reduce the risk of misjudgment caused by sampling errors and enhance testing accuracy.

Background Sorghum ( Sorghum bicolor ) is a promising opportunity crop for arid regions of Africa due to its high tolerance to drought and heat stresses. Screening for genetic variability in photosynthetic regulation under salt stress can help to identify target trait combinations essential for sorghum genetic improvement. The primary objective of this study was to identify reliable indicators of photosynthetic performance under salt stress for forage yield within a panel of 18 sorghum varieties from stage 1 (leaf 3) to stage 7 (late flowering to early silage maturity). We dissected the genetic diversity and variability in five stress-sensitive photosynthetic parameters: nonphotochemical chlorophyll fluorescence quenching (NPQ), the electron transport rate (ETR), the maximum potential quantum efficiency of photosystem II (F V /F M ), the CO 2 assimilation rate (A), and the photosynthetic performance based on absorption (PI ABS ). Further, we investigated potential genes for target phenotypes using a combined approach of bioinformatics, transcriptional analysis, and homologous overexpression. Results The panel revealed polymorphism, two admixed subpopulations, and significant molecular variability between and within population. During the investigated development stages, the PI ABS varied dramatically and consistently amongst varieties. Under higher saline conditions, PI ABS also showed a significant positive connection with A and dry matter gain. Because PI ABS is a measure of plants’ overall photosynthetic performance, it was applied to predict the salinity performance index (SPI). The SPI correlated positively with dry matter gain, demonstrating that PI ABS could be used as a reliable salt stress performance marker for forage sorghum. Eight rubisco large subunit genes were identified in-silico and validated using qPCR with variable expression across the varieties under saline conditions. Overexpression of Rubisco Large Subunit 8 increased PI ABS , altered the OJIP, and growth with an insignificant effect on A. Conclusions These findings provide insights into strategies for enhancing the photosynthetic performance of sorghum under saline conditions for improved photosynthetic performance and potential dry matter yield. The integration of molecular approaches, guided by the identified genetic variability, holds promise for genetically breeding sorghum tailored to thrive in arid and saline environments, contributing to sustainable agricultural practices.

Air pollutants adversely affect the physiological, biochemical parameters, and productivity of the crops, but scarce and meager reports are available to know the certain impact of air pollution on crops. The aim of the present study was to assess environmental impact of air pollutants on biochemical parameters of the crops by monitoring two important indicators, i.e., Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API). These two indicators provide the sensitivity and the tolerance level of the crops towards the air pollutants. Seven different crops were selected in four different locations in the vicinity of a thermal power plant. The results depicted the maximum aerial particulate matter deposition on crop canopy (ADCC) in barley ( Hordeum vulgare 2.15 mg/cm 2 ) and wheat ( Triticum aestivum 2.21 mg/cm 2 ). The maximum APTI value was found in berseem ( Trifolium alexandrinum , 9.45 and 11.44) during the first and second year of study, respectively. Results indicated that all crops were sensitive to air pollution in the selected area, but berseem ( Trifolium alexandrinum ) was less sensitive in comparison to other crops. API value showed that wheat ( Triticum aestivum) and rice ( Oryza sativa) were best-suited crops in the selected study area as compared to other crops. It has been found in the study that the API and APTI are two important indicators for the selection of crops in the severe air polluting area.

The Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API) of Azadirachta indica, Ficus benghalensis, and Ficus religiosa were compared to assess their tolerance to air pollution in different environmental setups. The study was conducted at six different locations with different environmental setups, including Urban, Industrial, Institutional and Agricultural. The parameters used for APTI were pH, relative water content, total chlorophyll content, and ascorbic acid content in the leaves, while API was calculated using APTI along with the socio-economic characteristics of the targeted species. Three species were selected, with nine replicates of each species from each setup (i.e., 6×9×3, which means a total of 162 samples) were analyzed for APTI during the winter season, when there is a lower mixing height that prevents the dispersion of pollutants and makes the environment highly polluted, and trees show high tolerance in a polluted environment. The APTI values of all the targeted species were higher in industrial setups than in the other environmental setups, i.e., 20.42 ± 1.65 for A. indica, 14.75 ± 0.53 for F. benghalensis, and 13.39 ± 1.11 for F. religiosa. The sample t-test showed a significant difference in the APTI of the industrial setup and other setups (p=0.0000). A. indica was found to be a tolerant species, and F. benghalensis and F. religiosa were intermediate-tolerant species based on APTI. F. benghalensis and F. religiosa fall under the excellent and A. indica falls under the very good category based on API. Based on these two indices, the best tree species were identified for plantation and the abatement of air pollution in industrial areas.

Tree species (including Eucalyptus camaldulensis , Ziziphus spina-christi , Albizia lebbeck , Prosopis juliflora , Pithecellobium dulce , and Ficus altissima ) were investigated to elucidate their appropriates for green belt application. Leaf samples were collected from four different locations in Riyadh: (1) residential; (2) dense traffic; (3) industrial; and (4) reference sites located approximately 20 km away from the city of Riyadh. Leaves collected from the industrial site showed the highest leaf area reduction. The smallest reduction of leaf areas was observed for F. altissima (11.6%), while the highest reduction was observed for P. juliflora (34.8%). Variations in the air pollution tolerance index (APTI) coupled with the anticipated performance index (API) for each species were examined. The APTI value of Z . spina-christi was highest (58.5) at the industrial site while the lowest APTI value was for P. juliflora (14) at the reference site. Correlation coefficient and linear regression analyses determined that the correlation between the ascorbic acid content and APTI is positive and significantly strong. Our findings indicate that urban green planning in Riyadh should include growing F. altissima on roadsides as well as in heavy industrial locations followed by Z. spina-christi and A. lebbeck according to their API and APTI performances.