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Iron-Heart Cunninghamia lanceolata, a wild relative of Chinese fir with valuable genetic and breeding traits, has been limited in genetic studies due to a lack of genomic resources and markers. In this study, we conducted transcriptome sequencing of Iron-Heart C. lanceolata leaves using Illumina NovaSeq 6000 and performed assembly and analysis. We obtained 45,326,576 clean reads and 115,501 unigenes. Comparative analysis in five functional databases resulted in successful annotation of 26,278 unigenes, with 6,693 unigenes annotated in all databases (5.79% of the total). UniProt and Pfam databases provided annotations for 22,673 and 18,315 unigenes, respectively. Gene Ontology analysis categorized 23,962 unigenes into three categories. KEGG database alignment annotated 10,195 unigenes, classifying them into five categories: metabolism, genetic information, biological systems, cellular processes, and environmental information processing. From the unigenes, we identified 5,645 SSRs, with dinucleotides repeats being the most common (41.47%). We observed variations in repeat numbers and base compositions, with the majority of markers ranging from 12 to 29 bp in length. We randomly selected 200 primer pairs and successfully amplified 15 pairs of polymorphic SSR primers, which effectively distinguished Chinese fir plants of different origins. This study provides insights into the genetic characteristics of Iron-Heart C. lanceolata and offers a foundation for future molecular marker development, breeding programs, genetic diversity analysis, and conservation strategies. [ABSTRACT FROM AUTHOR]

Economic globalization has swept the whole world. To focus on their main business, enterprises that are referred to as original equipment manufacturers (OEMs) outsource non-core production activities to contract manufacturers (CMs). By constructing a two-level supply chain consisting of two competing OEMs and one upstream CM, the strategic interaction of the OEMs between outsourcing and purchasing is studied. Specifically, the CM can offer custom- and predefined modes of original equipment manufacturing (namely, CO mode and PO mode, respectively). The former mode enables OEMs to determine product quality, while the latter only allows them to purchase from several quality configurations. The results show that, first, since the CO mode allows the adopter to lead the product design, whether to choose this mode depends on the required R&D cost. Interestingly, however, a lower R&D cost does not necessarily result in the adoption of the CO mode if the product quality difference is small under the PO mode. Second, the optimal purchasing strategy of an OEM is indifferent to the outsourcing mode (CO and PO) of its rival but significantly affected by the quality cost. However, compared to the PO mode, choosing the CO mode would cause the competitor to suffer more profit losses. Third, differing from the prior literature, this paper finds that when the downstream OEM can make quality decisions, although this may lead to profit loss of the contract manufacturer in some channels, it could benefit the CM overall. [ABSTRACT FROM AUTHOR]

The determination of the weights of decision makers (DMs) is an important problem in multi-attribute group decision making. Many approaches have been presented to determine DMs’ weights. However, the computed weight vectors of DMs are usually assumed to be constant in existing studies, and this may cause irrationalities in the decision results. Therefore, this article proposes a novel method to determine DMs’ weights based on variable weights theory in which the evaluation information is described as intuitionistic fuzzy sets (IFSs). First, DMs provide their assessment with IFSs, and the intuitionistic fuzzy weighted averaging (IFWA) operator is applied to obtain weighted decision matrix based on the prior given DMs’ and attributes’ weights. Second, the DMs’ weights are obtained based on variable weights theory, and an alternative decision can be computed. Finally, the converted value of the achieved IFS of each alternative is calculated, and the best appropriate alternative is acquired. Two illustrative examples and the comparisons with exsiting approaches are also used to reflect the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In view of the rapid development and application of blockchain technology, this paper considers a secondary supply chain system consisting of a single upstream supplier and a downstream e-tailer that implements blockchain technology and a traditional e-tailer that does not implement blockchain technology. We establish the demand function of two channels based on consumers' sensitivity to the blockchain and use the Stackelberg game model to compare and analyse the e-tailers' profits from the two channels. For the basic properties, interestingly, we find that there exists a critical threshold on the cost sensitivity to effort that helps each e-tailer decide whether to implement effort. If the cost sensitivity to effort is high, the two-sided e-tailers will reduce their effort as much as possible to obtain greater profits. Conversely, if the cost sensitivity to effort is low, they will increase their effort to obtain more benefits. We also discuss the role of blockchain technology in competition between e-tailers and analyse the impact of the product brand effect brought by the traceability characteristic of blockchain on the competition between e-tailers. To check the robustness of the core results and to investigate different forms of supply chain configurations, this paper further develops the analysis of the supplier entering agency agreements with two-sided e-tailers. Under this scenario, the supplier sells their products directly to consumers through two-sided e-platforms and shares revenue with e-tailers as platform agency fees. These core ideas remain valid in the extended model. [ABSTRACT FROM AUTHOR]

Sustainability, as a trend of social development and the embodiment of corporate social responsibility, has begun to receive more attention. To achieve this goal, sustainable supplier selection (SSS) and order allocation (OA) are seen as the crucial activities in corporate management. In the process of SSS, the psychological behavior of decision-makers (DMs) could play a critical role in the evaluation results. Therefore, introducing it into the decision-making process may lead to decision in line with the actual situation. In the uncertain multi-criteria group decision-making (MCGDM) problem described by probability linguistic term sets (PLTS), the DMs can evaluate the criteria of each supplier based on his own preference and hesitation, which is useful to avoid the loss of information. For this reason, this study develops a novel multi-criteria group decision-making combined with fuzzy multi-objective optimization (MCGDM-FMOO) model for SSS/OA problems by considering the triple bottom line (TBL) in which includes economic, environmental and social factors. The proposed method includes four stages. (1) the best-worst method (BWM) and entropy weight method are utilized to assign the weights of criteria to obtain the comprehensive weight. According to the output weights, the an acronym for interactive and multi-criteria decision-making in Portugese (TODIM) approach is applied to rank the suppliers under PLTS environment; (2) a FMOO model that can effectively deal with uncertainties and dynamic nature of parameter is formulated for allocating optimal order quantities; (3) two novel approaches are utilized to solve the FMOO model in order to obtain the richer Pareto frontier; and (4) the final OA solution is achieved by technique for order preference by similarity to ideal solution (TOPSIS) method. Finally, the validity and practicability of proposed MCGDM-FMOO model are verified by an example and comparative analysis with other classical MCGDM methods. [ABSTRACT FROM AUTHOR]

Background: Falls are the leading cause of injury in stroke patients. However, the cause of a fall is complicated, and several types of risk factors are involved. Therefore, a comprehensive model to predict falls with high sensitivity and specificity is needed. Methods: This study was a prospective study of 112 inpatients in a rehabilitation ward with follow-up interviews in patients’ homes. Evaluations were performed 1 month after stroke and included the following factors: (1) status of cognition, depression, fear of fall and limb spasticity; (2) functional assessments [walking velocity and the Functional Independence Measure (FIM)]; and (3) objective, computerized gait and balance analyses. The outcome variable was the number of accidental falls during the 6-month follow-up period after baseline measurements. Results: The non-faller group exhibited significantly better walking velocity and FIM scale compared to the faller group (P < .001). The faller group exhibited higher levels of spasticity in the affected limbs, asymmetry of gait parameters in single support (P < .001), double support (P = .027), and step time (P = .003), and lower stability of center of gravity in the medial-lateral direction (P = .008). Psychological assessments revealed that the faller group exhibited more severe depression and lower confidence without falling. A multivariate logistic regression model identified three independent predictors of falls with high sensitivity (82.6%) and specificity (86.5%): the asymmetry ratio of single support [adjusted odds ratio, aOR = 2.2, 95% CI (1.2–3.8)], the level of spasticity in the gastrocnemius [aOR = 3.2 (1.4–7.3)], and the degree of depression [aOR = 1.4 (1.2–1.8)]. Conclusions: This study revealed depression, in additional to gait asymmetry and spasticity, as another independent factor for predicting falls. These results suggest that appropriate gait training, reduction of ankle spasticity, and aggressive management of depression may be critical to prevent falls in stroke patients. [ABSTRACT FROM AUTHOR]