Proteomics in biopharmaceutical industries.

In the absence of an internationally coordinated management strategy, continued exploitation of the North Sea is expected to exacerbate underwater radiated noise (URN), heightening risks of adverse impacts on marine life. Identifying indicator species and their habitats is a fundamental step in the EU framework for setting a scientifically grounded underwater noise limit value (UNLV). While past research has primarily emphasized marine mammals, there is an increasing effort to highlight that the impacts of URN extend to fishes and invertebrates. To support indicator species selection in the North Sea for URN risk assessment, a trait-based vulnerability scoring system for marine mammals, fishes and invertebrates was developed. Each scoring system evaluates multiple attributes related to a species' capacity to detect and produce sound, as well as the documented impacts from both impulsive and continuous anthropogenic noise, and highlights species of particular concern and socio-ecological significance. Five potential indicator species were identified from each of the three taxonomic groups (marine mammals, fishes and invertebrates) for URN risk assessment. The proposed vulnerability scoring system serves as an adaptive framework, open to iterative refinement as bioacoustics knowledge advances. Although data gaps persist, the establishment of regional UNLV to safeguard vulnerable species should not be delayed. By linking URN exposure with key habitats of identified indicator species, this approach facilitates an ecosystem-based management of URN in the North Sea and provides a transferable framework for other regions.

Using a human resource (HR) attributions lens, we uncover 16 attributions that employees make about the reasons why their organization provides wellness programs. We also examine the implications of such attributions for employee outcomes (i.e., psychological strain, job burnout, job satisfaction). Recognizing employees can hold multiple HR attributions at the same time, an employee-centered approach was used. Through latent profile analysis with a sample of 517 Australian employees, four wellness program attribution profiles emerged: indifferent, favorable, unfavorable and ambivalent. Overall, employees with a favorable profile of wellness program attributions experienced better health and well-being outcomes compared to those with an unfavorable profile. In addition, employees in the unfavorable profile were found to have higher psychological strain, higher job burnout, and lower job satisfaction compared with their indifferent counterparts. Furthermore, employees with a profile of unfavorable attributions had higher psychological strain and lower job satisfaction compared to those with more mixed views about their wellness program. Past participation was unrelated to employee outcomes, but past participation predicted profile membership, suggesting that it is the attributions employees hold about their wellness programs that matter more than actual usage. Overall, we advance our understanding of HR attributions within the specific context of wellness programs and offer novel insights into a far greater number of attributions than previously studied for any HR practice. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Therapeutic proteins are an effective and highly selective, yet complex class of drugs for the treatment of diseases to improve the quality of life of patients. Their manufacturability is subject to evaluation to ensure product quality, safety, and efficacy. To this end, a need exists within the biopharmaceutical industry for a streamlined platform method that allows end-to-end therapeutic protein comparability assessment of multiple critical quality attributes. The implementation of a single platform method would de-risk development and accelerate speed to market. Herein, a novel method for therapeutic protein identification has been proposed that can be used to monitor regulated drug products throughout their lifecycle. The innovative streamlined method evaluates the intact protein in its formulation conditions. This breakthrough technology comprises a quantum cascade laser microscope with a heated accessory, an innovative slide cell array with a fixed path-length that allows for experimental flexibility and quantitative analysis, and dedicated software. Two-trace two-dimensional correlation spectroscopy (2T2D) was proven useful for the determination of spectral differences between two samples. The weighted absorbance differences, defined using the correlation between the cross-peaks, can be used to determine substantial molecular information, including secondary structure, the extent of glycosylation, amino acid content, and solvent accessibility, which is directly related to the therapeutic protein's critical quality attributes. Four different comparability assessments are discussed to demonstrate the usefulness of the platform method for the confirmation of protein identification and the comprehensive evaluation of the extent of glycosylation.

The importance of supply chain management lies in its direct influence on efficiency, customer satisfaction, and competitiveness. These issues are addressed, enabling organizations to minimize expenses, enhance reliability, respond to market dynamics, and sustain growth. Therefore, it is not an easy task to assess the most appropriate supply chain management system based on multiple attributes. The theory of multi-attribute decision-making (MADM) is a valuable mechanism that simplifies the analysis of complex information involving multiple sets of alternatives and attributes. The rough t-spherical fuzzy set (Rt-SFS) framework is a fairly standard approach to aggregating fuzzy information, and the theory of Dombi operations will broaden its scope. Through the generalized version of t-norm (TNM) and t-conorm (TCNM) operations, we define the Dombi t-norm (DTNM) and Dombi t-conorm (DTCNM), which provide a versatile framework for aggregating incomplete and fuzzy information. In earlier eras, many decision-making problems were not evaluated accurately due to deficiencies in the MADM models developed at the time. But after the introduction of the Rt-SFS framework, the data aggregation process is more precise and accurate than before. The theory of Rt-SFS is the most general of all existing extension fuzzy set frameworks due to the presence of lower and upper approximation spaces. To apply MADM and Rt-SFS models, this paper introduces a new model, utilizing a novel conception of rough t-spherical fuzzy Dombi weighted averaging (Rt-SFDWA) and rough t-spherical fuzzy Dombi weighted geometric (Rt-SFDWG) operators. We have given an MADM algorithm based on the proposed theory. To examine the accuracy of management in relation to the suggested theory of supply chain management, we address a numerical problem based on real-world situations. The sensitivity analysis is conducted to test the validity of the proposed theory. To demonstrate the authenticity of the developed theory, we made comparisons with other methods available in the literature. At last, we concluded.

The Certified in Disease Intervention (CDI) certification aims to reinforce and expand the expertise of those working at the community level to prevent the spread of infectious disease. To support the recertification process and promote continuing education of CDI competencies, a resource repository of relevant, high-quality materials is being developed. Interdisciplinary subject matter experts at Indiana University's School of Public Health-Bloomington, a Council on Education for Public Health-accredited and Association of Schools and Programs of Public Health member school, developed a systematic approach to identify and vet training resources. The result was an evidence-informed rubric for assessing multiple attributes of online training materials (including Course Overview and Introduction, Learning Objectives, Learning Assessment and Activities, Instructional Materials, Social Awareness, Scenario-Based Learning, Timeliness of Content, Course Technology, Learner Support, and Accessibility and Usability), a list of resources with evaluation outcomes, and metadata tags for populating a searchable database. The team successfully curated a robust repository of high-quality educational resources. However, the list is not exhaustive, and future work is needed to capture and evaluate more resources and update when new trainings are released for disease intervention professionals in the United States.

The evaluation of food quality in the Free Nutritious Meal Program (MBG) requires an objective and systematic approach to ensure nutritional standards and service effectiveness. This study applies a data mining technique using the K-Means clustering algorithm to classify food quality based on multiple attributes, including Calories, Protein, Fat, Carbohydrates, Freshness, Cleanliness, Serving Temperature, and Eligibility. This research utilizes RapidMiner to perform data preprocessing, involving data preprocessing through normalization, clustering with K-Means, and performance evaluation using the Davies-Bouldin Index (DBI) and Average Within Centroid Distance. Two clustering scenarios, K=3 and K=5, were evaluated to determine the optimal number of clusters.The results indicate that the K=3 model achieves a lower DBI value (0.214) compared to K=5 (0.224), indicating better cluster separation. Although K=5 produces more compact clusters, it does not improve overall clustering quality due to weaker separation. Therefore, the K=3 configuration is identified as the optimal model, as it provides a better balance between cluster separation and interpretability. These findings demonstrate that a multi-attribute clustering approach can effectively support data-driven decision-making in evaluating and improving food quality in the MBG program.

The rapid increase in hypertension and diabetes cases has created a strong demand for advanced healthcare systems that support early diagnosis and effective clinical decision-making. Managing these chronic conditions requires continuous evaluation of multiple patient attributes, which becomes difficult when performed manually. Traditional approaches rely heavily on basic statistical techniques and human interpretation, making them inefficient for handling large-scale data, identifying complex patterns, and providing real-time predictions. As a result, such systems often lack accuracy, scalability, and reliability, particularly in distributed healthcare environments. A key challenge lies in their inability to handle imbalanced datasets, perform multi-condition prediction, and enable seamless remote communication between systems. To address these limitations, this work proposes a real-time decision support system powered by Artificial Intelligence (AI) using a dual client–server architecture. The server handles data preprocessing, model training, and prediction using Machine Learning (ML) algorithms such as Complement Naive Bayes (CNB), Multinomial Naive Bayes (MNB), Perceptron, and a Tao Tree Classifier (TTC). Preprocessing methods include Label Encoding and K-Means Synthetic Minority Oversampling Technique (KMeans-SMOTE) to manage categorical data and class imbalance. A Flask-based Application Programming Interface (API) using Hypertext Transfer Protocol (HTTP) enables efficient communication between the client and server. The client system allows users to upload datasets, which are processed remotely to predict blood pressure categories and diabetes status. Lightning Memory-Mapped Database (LMDB) is used for secure and efficient data management. The proposed system ensures accurate multi-target prediction, real-time accessibility, and seamless device communication, ultimately improving healthcare services, reducing manual effort, and supporting better clinical decisions.

The study was conducted collaboratively by the Indian Institute of Horticultural Research (ICAR-CHES) and the Department of Vegetable Science at Odisha University of Agriculture and Technology (OUAT), Bhubaneswar, with the objective of enhancing yield in lablab bean (Lablab purpureus L.), an important legume vegetable. A total of 32 biparental progenies were evaluated in randomized block design with 3 replications for a comprehensive set of traits directly influencing pod and seed production. The results revealed significant variation among the progeny lines across all measured characteristics. Key growth phases, such as days to first germination (2–5 days) and days to first harvest (64–112 days), varied widely, identifying lines suitable for shorter cropping cycles. Important structural components like stem diameter, branch number and inflorescence count also showed considerable variation, indicating differences in plant architecture. Crucially, yield-related traits exhibited a broad range: pod number per plant (36–185), individual pod weight and seed characteristics like size and 100-seed weight (19.36–43.31 g). This directly translated to a wide spectrum of pod yield per plant, with the top-performing line, BP-12, yielding 1120.00 g. The extensive genetic variability observed confirms that the breeding process successfully generated a valuable pool of diverse genetic material. Promising lines, including BP-1, BP-12, BP-21 and BP-18, which excelled in multiple yield attributes, have been identified for further evaluation. The selection of these superior biparental progenies is expected to produce individuals with yields in the subsequent generations.

First and last names are frequently used in experimental research to signal group memberships such as gender, ethnicity, or social status. In other cases, names are included to enhance the realism of experiments, under the assumption that they do not convey any attributes beyond those manipulated by the researcher. Regardless of their intended function, the use of names requires careful attention to construct validity. Additionally, names may unintentionally evoke perceptions of characteristics outside the researcher's control, potentially confounding the intended experimental treatments. This contribution presents findings from a cross-national survey experiment specifically designed to validate the gender and social status signaled by first and last names, further exploring perceptions of age and ethnicity in three countries: Italy, Germany, and the United States. Based on nearly 18,000 evaluations (of around 120 first names and 120 last names in each country) from 900 respondents, we find substantial variation in the perception of the intended attributes related to names, as well as evidence of intersectional perceptions, with names signaling multiple attributes simultaneously. We provide descriptive statistics on the perceptions associated with our large sample of names across the three countries. Accompanied by replication packages, this contribution aims to offer a practical resource for researchers seeking to incorporate names into their studies.

ABSTRACT A critical challenge for diverse teams is ensuring that members coordinate their work effectively. While research has examined how diversity triggers social categorization that harms coordination, we know little about how the social status attached to these differences influences coordination, especially when members hold different ranks across multiple attributes simultaneously. We introduce a construct called dyadic status incongruence , which occurs when two team members hold conflicting ranks across different status hierarchies (e.g., one has higher education but shorter organizational tenure than the other). Drawing on status inconsistency theory, we argue that this incongruence generates ambiguity and disagreement over who has higher status, which in turn reduces interpersonal liking and ultimately hinders dyadic coordination. We further propose that team specialization mitigates these adverse effects by clarifying task roles and redirecting members' attention from status‐based comparisons to respective expertise for task execution. We tested our hypotheses using round‐robin data from 743 dyads among 221 members in 57 teams at a technology firm, employing polynomial regression, response surface analysis, and social relations modeling. These results were supplemented by a qualitative study using semi‐structured interviews with 15 employees from this firm to provide contextual evidence for the observed effects. Our findings support the proposed model, advancing a more precise, status‐based account of why coordination breaks down in diverse teams and how these effects can be mitigated.

Effective architectural design requires integrating multiple attributes to achieve optimal outcomes, yet students often struggle to balance these technical and conceptual demands. This study assesses the design preferences of undergraduate architecture students at Bingham University Karu to understand how these priorities impact their design outcomes and professional readiness. A quantitative research methodology was employed, utilizing a structured online questionnaire distributed to 189 students from the 200 to 400 levels. A total of 111 responses (58.7% response rate) were analyzed using descriptive statistics to determine mean scores and preference rankings across four key attributes: Interior Space Planning, Building Form and Appearance, Sustainability, and Site Planning. The findings revealed that students significantly prioritized Interior Space Planning, which achieved the highest mean score (4.44) and frequency of preference. Sustainability and Site Planning followed, while Building Form and Appearance received the least attention, ranking last with a mean score of 3.79. Data indicated that while students acknowledge site integration as important, it is often viewed as a supervisory requirement rather than a personal design passion. The results suggest a shift in architectural education away from pure formalism toward a functionalist, performance-based approach. However, the disparity in attribute prioritization highlights a gap in achieving a comprehensive skill set required for professional practice. The study recommends that architectural educators emphasize the integration of diverse attributes, particularly site responsiveness and incorporate building performance simulation tools to bridge the gap between functional necessity and technical validation

Seed treatment technologies: Effects on physical, functional, and physiological seed quality.

Urban forests generate a wide range of ecological and social benefits, yet they may also impose disservices when poorly designed, improperly located, or insufficiently maintained. Although a substantial body of literature has examined the economic value of urban forest services, relatively limited attention has been given to quantifying their disservices and understanding how residents jointly evaluate both aspects. Addressing this gap, this study investigates residents' preferences for key services and disservices of urban forests in the 29 districts of Taichung City, Taiwan, using the best-worst scaling approach. A structured survey was administered to capture trade-offs among multiple forest attributes, including tree shade, tree health, biodiversity-related components, property value effects, and maintenance-related disservices. The results show that high tree shade is the most preferred attribute, followed by healthy tree conditions and property value enhancement. Residents were willing to pay NT$118.79 per person per month for high shade coverage and NT$81.83 per month for a property value increase of NT$61,315. In contrast, poor tree conditions emerged as the most significant disservice, for which residents required compensation of NT$66.49 per person per month. These findings underscore the importance of designing urban forests that provide substantial shading benefits while minimizing risks and maintenance burdens associated with deteriorating tree conditions. Overall, the findings highlight the importance of evaluating urban forests through a balanced lens that considers both services and disservices. Incorporating such preference-based indicators can help cities better prioritize management efforts and support evidence-based decision-making for sustainable urban forestry planning. • Assess Taichung residents' preferences for urban forest services and disservices. • Preferred attributes and attribute levels are assessed by best-worst choice method. • Preference rank: high tree shade > good tree condition > increasing property value. • Willing-to-pay rank: tree shade (NT$118.79) > increasing property value (NT$81.83). • Each resident required monthly compensation of NT$66.49 for poor tree conditions.

Reservoir porosity inversion from multiple seismic attributes based on hybrid deep learning and ensemble methods

ASRL: Correlation-robust pedestrian attribute recognition via fixed orthogonal classifier.

Therapeutics and vaccines involving mRNA have shown significant progress over the last five years. These lipid nanoparticle-based formulations introduce substantial analytical complexity, as multiple Quality Attributes (QAs) must be monitored to ensure product efficacy, stability, and safety. Encapsulation efficiency (EE) and mRNA integrity are particularly critical, yet current analytical approaches often require separate assays, challenging sample handling, and limited selectivity toward free versus encapsulated species. Therefore, new chromatographic methods capable of resolving these species and supporting multi-payload formulations remain a major unmet need. To rapidly obtain multiple QAs for mRNA-LNP formulations, we present an optimized two-dimensional liquid chromatography (2D-LC) workflow combining anion-exchange chromatography (AEX) in the first dimension (1D) and ion-pair reversed-phase liquid chromatography (IP-RPLC) in the second dimension (2D). The 1D-AEX evaluated encapsulation efficiency of mRNA within lipid nanoparticles based on the mRNA ratio of intact and disrupted drug products, while 2D-IP-RPLC assessed integrity profiles and mRNA-lipid adducts. Attention was paid to the 2D optimization to eliminate solvent incompatibilities and ensure efficient transfer between dimensions. This approach enables simultaneous determination of EE, mRNA integrity, mRNA-lipid adducts, and transcript ratios in multi-payload mRNA-LNP formulations. It also provides chromatographic assessment of free mRNA integrity within formulations. This method furthermore enables the characterization of additional species and confirms the presence of surface-associated mRNA. The workflow demonstrates good selectivity and applicability to both mono- and multi-payload mRNA-LNP products. Overall, the developed 2D-LC platform is a powerful analytical tool for comprehensive mRNA-LNP characterization. By enabling simultaneous assessment of several critical QAs, including EE, integrity, transcript ratios, and mRNA-lipid adducts, it streamlines analytical workflows and reduces reliance on multiple independent assays. This approach provides mechanistic insight into LNP structure, including detection of surface-associated RNA species, and establishes an innovative tool to support formulation development, process optimization, drug products release, and stability studies for next-generation mRNA vaccines and therapeutics.

Secondary succession is a widespread phenomenon in the Anthropocene due to global land-use and climate change. Our ability to predict successional trajectories remains limited due to key knowledge gaps related to early secondary succession and how successional trajectories vary across socio-ecological systems and multiple forest attributes. Therefore, we analyzed the first 5 years of secondary forest succession across six tropical landscapes (i.e., socio-ecological systems) in three countries (Australia, Ghana, and Mexico) that differ in land-use intensity and two main forest types (dry and wet). We established 122 permanent plots in recently abandoned agricultural fields, monitored them annually for up to 5 years, and quantified 12 forest attributes related to structure, diversity, functional composition, and biotic interactions. We found that a large variation in successional trajectories was explained by the six landscapes (average r2 across 12 attributes is 54%; range: 18%-78%), indicating that succession is the result of a socio-ecological system. An additional 39% of the variation (range: 19%-70%) was explained by plots occuring within landscapes, which reflects variation in landscape context and local land use intensity. Countries had a stronger impact on succession than forest type, indicating that the social component is more important early in succession, whereas the ecological component may become more important later in succession. Countries with lower land use intensity (e.g., subsistence agriculture, shorter duration of use, no mechanization) showed a higher start and speed of succession, as vegetation legacies can kickstart succession. Forest attributes followed distinct successional trajectories: forest structure and diversity increased over time, reflecting a deterministic component of succession, whereas functional composition and biotic interactions varied more with forest type, reflecting environmental filtering. These findings highlight the importance of integrating early succession, socio-ecological systems, and multiple dimensions of forest recovery to better understand and predict forest succession in human-modified tropical landscapes.

Context-Dependent Choice and Retail Decisions Traditional assortment models assume that consumers evaluate products independently of the alternatives available (i.e., the “context”). In “Assortment and Price Optimization Under a Multiattribute (Contextual) Choice Model,” the authors challenge this assumption by analyzing assortment and pricing decisions under a context-dependent choice framework known as the contextual concavity (CC) model. The CC model incorporates reference dependence across multiple attributes, such as price and quality, and captures well-documented context effects, including compromise and decoy effects. The study makes several contributions. It characterizes the structure of optimal assortments under multiattribute loss aversion, develops a polynomial-size mixed-integer linear programming formulation for solving the general problem, and analyzes the joint assortment and pricing decision. Numerical experiments show that ignoring context effects, by relying on standard context-independent models such as the multinomial logit, can lead to substantial profit losses, with gaps ranging from 3% to 63%. These findings highlight the strategic importance of incorporating contextual effects into retail decisions.

Abstract Customer opinions, preferences, and decision-makers’ insights play a vital role in marketing by influencing product development, strategy, and customer experience. Computing with Words (CWW) offers an effective method for processing this qualitative information using linguistic terms instead of numerical values. This work aims to explore the application of one of the most widely used methodologies in CWW—the 2-tuple linguistic model—in marketing. It employs a combination of bibliometric analysis and a systematic literature review for a comprehensive analysis. Articles published between 2000 and 2024 on the Web of Science database are analyzed, incorporating Scopus´s Field-Weighted Citation Impact (FWCI) metrics to assess the impact of individual studies and the average influence of research in marketing-related areas. From an initial sample of 165 peer-reviewed articles, 90 were selected for analysis. The findings indicate that the 2-tuple linguistic model is primarily applied to decision-making problems, with Multiple Attribute Group Decision Making emerging as the most prominent theme due to its high centrality and high density in the strategic diagram, as well as its strong connections to various marketing-related areas. The 2-tuple linguistic model is widely applied in areas such as supplier management and product development and innovation, with digital transformation and consumer behavior representing potential directions for applying the model to address their respective marketing challenges. Using the FWCI, this work also identifies marketing-related areas within decision-making themes where the 2-tuple linguistic model is already applied but has limited influence, revealing opportunities for development and future research.