Selection Decisions Research Articles

AI-Powered Location Intelligence: Revolutionizing Site Selection and Investment Decisions

This comprehensive article explores the transformative impact of artificial intelligence on location intelligence and site selection in the commercial real estate industry. The article examines how AI-powered systems are revolutionizing traditional approaches to property analysis, investment decisions, and operational efficiency across various real estate sectors. Through an examination of current implementations, benefits, and future trends, this analysis demonstrates how machine learning models, advanced analytics, and real-time data processing capabilities are enabling more accurate predictions, improved decision-making, and enhanced operational performance in the real estate industry. The article highlights the significant advancements in areas such as retail site selection, office space development, and industrial property optimization, while also addressing the quantifiable benefits and return on investment of AI implementation.

Ultimate load bearing of helical piles prediction and evaluation using machine learning-based algorithms

ABSTRACT This study aims to predict the Ultimate Load-Bearing (ULB) capacity of Helical Piles (HP) using six Machine Learning Algorithms (MLA) on an in situ-based 110 pile load tests including a wide range of pile properties: shaft diameters (73–406 mm), helix diameters (254–762 mm), helix spacing (300–1000 mm), number of helices (1–6), pile lengths (2.4–16 m), and helix thicknesses (6–12 mm). The measured axial ULB is analysed using the Brinch-Hansen 80% criterion and 5% criterion of the average diameter of the helices. Load-displacement curves were fitted using the Hyperbolic Function. MLA including Multi Linear Regression (MLR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest, eXtreme Gradient Boosting, and Support Vector Regression were optimised to grid search for hyperparameters like neighbour count, tree depth, learning rate, and kernel type. Input parameters were categorised into Geometric and Soil Properties packages. Results indicate that the DT algorithm excelled in pullout loading, KNN in compression loading, and MLR for Brinch-Hansen 80% criterion estimations. The input parameters related to the soil surrounding the pile helices have the most impact on the ULB prediction of HP. This study enhances HP foundation design by enabling data-driven decisions for optimal pile selection and configuration.

Research on Low-Carbon Site Selection Decision Model and Algorithm for Green Logistics

This work proposes a low-carbon site selection decision model and algorithm for green logistics. The focus is to minimize carbon emissions while optimizing the distribution network. With the development of smart shared logistics and increasing environmental awareness, there is an urgent need to tackle issues in urban logistics, including pollution, resource inefficiency, high carbon energy usage, and greenhouse gas emissions. By integrating geographic, environmental, and operational data, the proposed site selection model incorporates multi-objective optimization to balance carbon efficiency with cost-effectiveness in logistics site selection. We employ a hybrid algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to solve the complex decision-making process. The model is tested using real-world logistics data, and the results demonstrate significant reductions in carbon emissions and transportation costs when compared to conventional logistics site selection methods. Further sensitivity analysis reveals that the model is robust under varying transportation demands and geographic constraints. Also, we highlight the role of green logistics in achieving corporate sustainability goals aligning with global carbon neutrality initiatives. We believe this model offers a practical approach to fostering environmentally responsible supply chains and will contribute to the advancement of sustainable logistics operations, helping organizations make informed, eco-friendly decisions in site selection.

Development of an Algorithm and Software for Optimal Route Selection

The paper deals with developing an optimal route selection algorithm and software, which, taking into account various criteria, ensures the adoption of efficient and economical route selection decisions. The algorithm is based on permutations for defining routes. It utilizes multi-criteria analysis methods such as TOPSIS and CRITIC, which enable the selection of optimal routes based on various factors, including cost optimization, time savings, safety measures, etc. The paper also presents the proposed algorithm's software, which helps to solve real-world problems related to route selection.

The Evolution and Role of Molecular Tools in Measuring Diversity and Genomic Selection in Livestock Populations (Traditional and Up-to-Date Insights): A Comprehensive Exploration

Distinctive molecular approaches and tools, particularly high-throughput SNP genotyping, have been applied to determine and discover SNPs, potential genes of interest, indicators of evolutionary selection, genetic abnormalities, molecular indicators, and loci associated with quantitative traits (QTLs) in various livestock species. These methods have also been used to obtain whole-genome sequencing (WGS) data, enabling the implementation of genomic selection. Genomic selection allows for selection decisions based on genomic-estimated breeding values (GEBV). The estimation of GEBV relies on the calculation of SNP effects using prediction equations derived from a subset of individuals in the reference population who possess both SNP genotypes and phenotypes for target traits. Compared to traditional methods, modern genomic selection methods offer advantages for sex-limited traits, low heritability traits, late-measured traits, and the potential to increase genetic gain by reducing generation intervals. The current availability of high-density genotyping and next-generation sequencing data allow for genome-wide scans for selection. This investigation provides an overview of the essential role of advanced molecular tools in studying genetic diversity and implementing genomic selection. It also highlights the significance of adaptive selection in light of new high-throughput genomic technologies and the establishment of selective comparisons between different genomes. Moreover, this investigation presents candidate genes and QTLs associated with various traits in different livestock species, such as body conformation, meat production and quality, carcass characteristics and composition, milk yield and composition, fertility, fiber production and characteristics, and disease resistance.

Leveraging group model building to operationalize implementation strategies across implementation phases: an exemplar related to child maltreatment intervention selection

BackgroundImplementation strategies can help support the adoption and implementation of health interventions that are appropriate for a local context and acceptable to decision makers and community members. Implementation strategies should be designed to handle the complexity of the multi-level, dynamic contexts in which interventions are implemented. Systems science theories and methods explicitly attend to complexity and can be valuable for specifying implementation strategies. Group Model Building (GMB) combines research partner engagement strategies with systems science to support researchers’ and partners’ learning about complex problems and to identify solutions through consensus. This paper specifies how GMB can operationalize implementation strategies — methods for supporting evidence implementation in real-world practice — and describes how GMB can aid in selecting and tailoring both health interventions and implementation strategies. A case study in child maltreatment prevention planning is provided to illustrate how GMB was used to specify the “actions” — strategy activities — for three implementation strategies (conduct local consensus discussions; build a coalition; model and simulate change) during the earliest implementation phases, with the goal of supporting intervention selection decisions. Examples are provided of generalizable research products that can be produced concurrently through GMB, in addition to contextually-driven implementation support.MethodsParticipants (n = 8) were engaged over four sessions using tailored GMB activities. Participants generated a qualitative system dynamics model that described their theory of change for how to prevent child maltreatment in their communities. This theory of change reflected a dynamic understanding of the interconnected determinants of child maltreatment.ResultsGMB was acceptable to participants and resulted in products that could be used for implementation planning (e.g., to model and simulate change) and future research. GMB fostered trust and idea sharing between participants.ConclusionGMB can facilitate learning about which outcomes are (or are not) impacted by interventions, which resources and approaches are required for quality implementation (e.g., implementation strategies), and tradeoffs in outcomes and resources between interventions. GMB also provides a structured, effective process to generate a shared implementation vision amongst participants. Lessons learned include methods for developing trust with and between participants, and the need for researchers to tailor GMB actions for participant and project needs.

Ultrasound Utilization in Hospitalized Kidney Transplant Recipients: Useful or Overused?

Kidney transplant ultrasonography is an important diagnostic tool in the care of transplant recipients. This modality of nonradiation-based imaging allows for precise and expedient reporting of allograft architecture, which can inform clinical decision-making. However, as with any diagnostic tool, overuse may lead to unnecessary interventions and costs on the healthcare system. To better understand the use of ultrasonography in hospitalized kidney transplant recipients and outcomes of subsequent interventions, we conducted a single-center retrospective study at a large transplant program in Ontario, Canada. We noted that over 30% of admissions resulted in a ultrasonographic survey within the first 24 h of presentation; however, most of these did not change clinical management or lead to a subsequent procedural intervention. Using multivariable logistic regression, we identified predictors for receiving an ultrasound, including time from transplantation, elevated serum creatinine and infectious diagnosis. Procedural interventions (e.g., drain or biopsy) resulted from less than 20% of all ultrasound investigations, with patients closer to the time of index transplant or with elevated serum creatinine values more likely to receive an intervention. In conducting a cost analysis, we estimated that approximately $80000 CAD per year could be saved with more selective decisions on ultrasound requisitions. Overall, our results indicate that despite being an informative tool, the broad use of ultrasonography in the kidney transplant population may not yield significant changes to transplant care.

Assessment of phenotypic diversity and morphometry of indigenous chickens in Kabwe District, Zambia

This study characterized Zambian indigenous chickens' phenotypes and morphometric traits in Kabwe District, Zambia, to create a base for breeding program development. To achieve this, 206 hens and 228 cocks we sampled in Kabwe district. Each sampled chicken was phenotyped for 4 qualitative traits: ear lobe color, shank color, comb type, and eye color. Individual linear body measurements were recorded from each chicken using a tailor's tape in centimeters, which included body length (BL), chest circumference (CC), corpus length (CL), keel length (KL), shank circumference (SC), shank length (SL), thigh circumference (TC), and thigh length (TL). The findings showed that the predominant phenotypes were single comb type (98.91%), orange eyes (80.44%), white shanks (33.70%), and red-white earlobe color (50%). The average body weight was 1698.24 g, which showed a significant positive correlation (p<0.05) with all linear body measurements. For both sexes, body weight correlated positively with all linear measurements, except in males where body length showed a nonsignificant negative correlation. A stepwise regression analysis indicated that chest circumference, keel length, body length, shank circumference, thigh circumference, and corpus length (CL) are significant predictors of body weight (R² = 76.90%, P < 0.05). Additionally, the CHAID data mining algorithm identified keel length as the primary predictor of body weight, with corpus length and thigh length also contributing, achieving an R² of 61.20%. These findings provide valuable insights for instituting chicken breeding programs aimed at enhancing productivity and selection decisions.

Genetic evaluation of productive longevity in a multibreed beef cattle population.

Genetic selection for traits that have direct impact on profitability, such as productive longevity (PL), which blends cow longevity with regular reproductive performance, is fundamental for the economic success of beef cow-calf operations. The purpose of this study was to develop data screening strategy and a statistical model to predict genetic merit for PL in a multibreed beef cattle population. Pedigree (n = 1,352,765) and phenotype (n = 978,382) information were provided by Leachman Cattle of Colorado and genotypes (n = 26,342) were provided by the Zoetis commercial genotyping laboratory. A repeatability model (REP) including the systematic effects of age at first calving, year-season of progeny birth, pedigree-based retained heterosis, and parity number, as well as the random effects of additive genetic, permanent environment, contemporary group, and residual were fitted to adjust PL. In addition, a random regression model (RRM) was fitted to investigate PL considering the same effects, with the difference that random effects were regressed on parity. Estimated breeding value (EBV) were obtained by single-step GBLUP (ssGBLUP) and transformed to predict differences in number of calves through linear regression. Predictive performance was assessed in a group of 7,268 cows born in 2010. Heritability estimates for PL were relatively low, with values of 0.109 for REP and a decreasing trend for RRM with values ranging from 0.16 to 0.04. Repeatability for PL was of moderate magnitude, with values of 0.415 for REP and from 0.29 to 0.57 for RRM. Heritability estimates suggest that most of phenotypic variation was accounted for by environmental factors, but long-term genetic selection could still be effective. REP was more efficient than RRM, showing lower number of iterations and time to reach convergence with comparable solutions to RRM. Validation results showed that correlations between EBV and phenotypes (observed/pre-corrected) increased over the years ranging from 0.04 to 0.92. Repeatability values and the validation approach suggested that using a cow's first record (second parity success or failure) is a reasonably good indicator of posterior performance for PL. Therefore, the inclusion of PL in a multibreed genetic evaluation program, incorporation into selection indexes with existing economic traits, can enable more profitable selection and breeding decisions in beef cattle herds.

Evaluation of a goalkeeper-specific motor coordination assessment in youth football

ABSTRACT Talent identification and development (TID) in football can be enhanced through objective assessments of talent predictors. Yet, available instruments rarely consider the unique demands of goalkeepers (GKs). During early phases of talent development, considering a GK’s giftedness relating to, for example, different abilities (e.g. motor coordination), can complement views on highly specialized GK-specific attributes (e.g. technical skills). Therefore, this study aimed to evaluate a GK-specific motor coordination assessment to support TID in football at early developmental phases. Six tests were designed to assess ball control relevant to GKs. Their content and face validity were confirmed by independent experts (N = 8). The assessment was evaluated with GKs selected for the German Football Association’s TID program (U12-U15; N = 120). This study examined the assessment’s test–retest reliability and agreement, the structural validity, and the concurrent validity. The overall test score demonstrated good test–retest reliability, although some individual tests showed lower coefficients. Additionally, limited agreement between repeated measurements due to considerable measurement error as well as issues with the structural validity of the test battery were identified. Nevertheless, the findings support the assessment’s concurrent validity as higher-rated (i.e. more talented) GKs outperformed lower-rated individuals. Yet, the test battery’s diagnostic accuracy is not high enough to justify individual decisions for talent selection. Overall, these findings support the consideration of motor coordination as a talent predictor in youth GKs and emphasize the assessment’s potential to enhance coaches’ evaluations. The identified psychometric weaknesses in some individual tests provide impetus to further optimize the test battery.

Human limits in machine learning: prediction of potato yield and disease using soil microbiome data

BackgroundThe preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network.ResultsPrediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power.ConclusionsOur study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.

Service selection and switching decisions: user behavior in high-interoperability environments

Current legislation and market dynamics require and encourage systems to become more open and interoperable. Given this shift, the challenge arises as to how service providers can navigate such environments and whether and how known platform economics are affected by that change. In this context, our work investigates behavior when users interact with services in highly interoperable environments, examining the influence of service attributes and platform economics on service selection and switching decisions, with a focus on the role of transaction cost and time, onboarding time, privacy, ownership, and community. For this purpose, we designed and conducted an extensive survey study with more than 500 respondents that combined a conjoint study with an experimental part on switching scenarios. Our findings suggest that transaction features such as cost, time, and privacy are the main factors in service selection with part-worth utilities being 2.1 to 14.7 times higher than non-transactional features. Additionally, building a strong community and offering ownership opportunities to users are effective strategies for customer retention. Further, we observe that rational choice theory does not explain switching decisions in many cases. Our study has important implications for both industry practitioners and policymakers. Practitioners can use our results to carefully manage effective customer retention strategies, while policymakers can use them to better regulate digital markets.

Cost Performance Comparison of Road Construction Projects Considering Bidding Condition and Project Characteristics

Developing road infrastructure facilities is important because it directly affects national competitiveness and has significant socio-economic ripple effects throughout the country. However, road construction projects are vulnerable to various risks and prone to cost overruns because they are funded by large-scale national budgets and conducted over long time periods. Therefore, tracking the changes in construction costs from planning to completion and providing insights for similar future projects is essential for successful project planning and management. Considering the bidding conditions and project characteristics of road construction projects, this study comparatively analyzed the construction cost growth of 170 road construction projects based on the bid award rate and facility-specific project characteristics, such as construction type, contract method, facility capacity, and construction cost components. This study analyzed the differences in cost performance between various sample groups using non-parametric tests (Mann–Whitney U test and Kruskal–Wallis test), considering the non-normality of the collected sample. In addition, this study conducted multiple regression analysis to examine the causal relationship between all variables used in the analysis and cost performance and to identify critical factors. The results indicated that the construction cost growth of the group with a bid award rate of less than 70% was higher compared to that of other groups. Furthermore, the differences in construction cost growth based on project characteristics were more pronounced in the following groups: construction types (expansion/renovation and complex), contract method (long-term continuous), road length (≥7.5 km), % of bridge/tunnel length (<10%), construction cost (≤USD 100 mil.), % of direct construction cost (≥70%), and % of bridge/tunnel cost (both less and more than 50%). Furthermore, the regression model showed that % of direct construction cost, bid award rate, total road length, and contract method were shown to be critical factors in construction cost growth, which implies the importance of indirect cost management, PDS selection decisions that affect bid award rates, and risk management for complex projects from the perspective of construction cost growth management. These results empirically demonstrate that bidding conditions and project characteristics are key to understanding the pattern of construction cost growth in road construction projects. Hence, this study provides significant references that can be used by policy makers and clients to estimate cost buffers for future road projects.

Thermophysical modeling of niobium alloys informs materials selection and design for high-temperature applications

There is renewed interest in refractory alloys that possess higher service temperatures than incumbent Ni-based superalloys (⪆1100 °C). Thermophysical property data for six Nb-alloys are gathered from the literature and reviewed, and new data are provided for two Hf-containing Nb-alloys; elastic modulus, thermal expansion, thermal conductivity, and heat capacity are presented for C103, and new thermal conductivity data are provided for a higher strength alloy, WC-3009. Comparisons with Ni-superalloys and other refractory-metal based alloys provide context. Physics-based models are provided that describe the temperature dependencies of the Young’s modulus, coefficient of thermal expansion and density, and thermal conductivity; such that fair comparisons can be made across alloys for any given condition. The results suggest a need for improved understanding of the temperature dependence of the elastic modulus. A performance index is introduced for making informed materials selection decisions in the context of lightweight, panel-shaped applications subjected to sharp thermal transients or steep thermal gradients, and the significant strain rate sensitivity of Nb-alloys is highlighted. Ultimately, the relative value of current commercial alloy, C103, as well as the promise of specific Nb-W-Zr alloys are highlighted.

Planting time and variety effects on biomass, harvest index, and yield of irrigated soybean in mid‐Southern United States

AbstractSoybean [Glycine max (L.) Merr.] biomass and grain yield has increased over the past several decades in the mid‐southern United States. This is attributable to technological advances and improved management strategies. However, a better understanding of biomass accumulation and partitioning is needed to improve our knowledge base of varietal growth habits relative to yield, planting date, and harvest index (HI). Field experiments within a split plot arrangement in a randomized complete block design were established in 2017 and 2018 in Stoneville, MS. The study aimed to evaluate the effect of early (late‐April or mid‐May) and late (late‐May) planting on biomass, HI, and yield amongst eight soybean varieties. Soybean total biomass accumulation was collected at multiple development stages, including V4, R2, mid R5, mid R6, and R8, and partitioned into senesced leaves, pods, and seeds. Overall, the planting date had no effect on yield, HI, and biomass accumulation at any of the growth stages. Yet, the interaction between planting date and variety significantly affected biomass accumulation at the mid R5 stage. Contrarily, the variety selection significantly affected yield, HI, and biomass accumulation at all growth stages except mid R6. The total biomass accumulation at R8 was greatest for Asgrow 46X6, Asgrow 4632, Terral 4857X, Terral 48A76, and Credenz 4748, when pooled over planting dates. Averaged across two planting dates, the greatest yield was produced by Terral 48A76, Asgrow 4632, and Asgrow 46X6. Furthermore, averaged across site‐years, HI was greatest for Asgrow 4632 and Terral 48A76. Based on the results of this study, evaluating soybean HI rather than overall biomass accumulation may be more beneficial for variety selection decisions.

Selection indexes in terms of functional features in modern dairy cattle breeding in Europe

Abstract The increase in demand for dairy products requires continued progress in dairy farming for a sustainable supply. Europe, known as the world's leading milk producer, plays a key role in meeting this growing demand. Modern dairy farming has moved beyond its historical focus on milk yield and now focuses on functional traits such as udder health, fertility and calf survival. As a result, selection indicators have become essential tools, combining multiple attributes to support selective decisions. However, these rates show considerable variability across countries, reflecting their distinct breeding goals. Poland's production and functionality (PF) Index emphasizes production and functional traits to enhance dairy cattle. Portugal uses the total economic merit (M€T) and total performance index (IPT) for a broader assessment covering a wider range of traits. Ukraine is transitioning towards a more comprehensive breeding system incorporating stress tolerance and longevity. Factors such as climate change and the need for sustainable practices drive this evolution, underscoring the economic importance of traits beyond mere production. Future trends may include features such as feed efficiency, methane emissions reduction and stress resistance. Diverse breeding objectives across countries lead to different selection index constructions, essential for effective selection, ranking and breeding of superior individuals. This comprehensive review offers insight into constantly evolving dairy farming strategies in Europe, with a focus on Poland, Portugal and Ukraine, while highlighting the key role of functional traits in shaping the future of dairy farming.

Situational and Dispositional Achievement Goals and Measures of Sport Performance: A Systematic Review with a Meta-Analysis.

The purposes of this systematic review (PROSPERO ID: CRD42024510614, no funding source) were to quantify relationships between situational and dispositional dichotomous achievement goals and sport performance and explore potential relationship moderators. Published studies that reported at least one situational or dispositional achievement goal and a performance score were included. Studies without performance scores or based in a non-sport context were excluded. Information sources consisted of studies found in relevant published meta-analyses and EBSCOhost databases (finalized September 2024). The following statistics were conducted to assess the risk of bias: class-fail-safe n, Orwin's fail-safe n, and funnel plots with trim and fill estimates. The summary statistics were r and d. Thirty studies from 1994 to 2024 met all inclusion criteria with 8708 participants from Europe, Asia, North America, and Oceania. The majority of samples were non-elite male youths and adolescents. The random-effects relationships (r) between task climate, 0.20 [0.14, 0.25], task orientation, 0.17 [0.12, 0.23], ego orientation, 0.09 [0.03, 0.16], and sport performance were small and significantly different (p < 0.05) from zero, while the ego motivational climate relationship was not, -0.00 [-0.48, 0.05]. The random-effects standard differences in means (d) for both the task orientation, 0.08 [0.02, 0.14], and ego orientation, 0.11 [-0.05, 0.26] were minimal in meaningfulness. Mixed-effects moderator analyses resulted in the following significant (p < 0.05) sub-group differences: subjective compared to objective performance measures (task orientation), elite compared to non-elite samples (task climate), and athlete-completed compared to coach-completed performance measures and performance records (task orientation). Finding only 30 studies meeting the inclusion criteria, which limited sub-group samples for moderation analyses, was the main limitation. Despite this limitation, AGT provides athletes and practitioners performance enhancement strategies. However, caution is warranted regarding relationship expectations given the small mean effect size values and the true prediction interval ranging from negative to positive, perhaps as a result of the heterogeneous samples and performance measures. A clear line of future research, considering the reviewed studies, with elite athletes is needed to verify the performance benefits of the task climate and ego orientation as well as the use of the ego goal orientation in selection decisions.

Development of a material selection decision support system for an automotive application

This study presents the development of a material selection decision support system for an automotive rooftop tent. The system uses five parameters of material cost, formability, corrosion resistance, tensile strength and hardness to optimize material choice from a list of aluminium alloy options. This was done before production via the deep drawing process can commence. The parameters were quantified using the average market prices for each alloy, product requirements obtained from the case study and the performance data that was obtained from the uniaxial tensile and hardness tests. These inputs were combined with the developed material selection framework and then programmed into a web-based decision support system for automated, data-driven material selection. The output presents the recommendation for the optimum material that balances cost-effectiveness and performance. The decision support system was successfully used to select the optimum alloy for the rooftop tent. Results show that AA1050 is the optimum material, providing weight reduction at the minimum achievable cost without compromising the product requirements for the rooftop tent. The decision support system is also scalable, and this allows it to be used with larger datasets for different products and processes in the future.

A deep neural network model for classifying pharmacy practice publications into research domains

BackgroundPharmacy practice faculty research profiles extend beyond the clinical and social domains, which are core elements of pharmacy practice. But as highlighted by journal editors in the Granada Statements, there is no consensus on these terms. Four domains (clinical, education, social & administrative, and basic & translational) of pharmacy practice faculty research are proposed. ObjectivesTo develop a classifier for categorizing pharmacy practice faculty publications into four proposed domains, and to compare the model with zero-shot performances of state-of-the-art, general purpose large language models (gpLLMs). MethodsOne thousand abstracts from 2018 to 2021 documents published by pharmacy practice faculty were reviewed, labelled and used to screen and finetune several Bidirectional Encoders Representations from Transformers (BERT) models. The selected model was compared with zero-shot performances of 7 state-of-the-art gpLLMs including ChatGPT-4o, Gemini-1.5-Pro, Claude-3.5, LLAMA-3.1 and Mistral Large, using 80 randomly selected abstracts from 2023 publications labelled with ≥80% consensus by all authors. Classification metrics included F1, recall, precision and accuracy, and reproducibility was measured with Cohen's kappa. A use case was demonstrated by testing the null hypothesis that the research domain distribution of faculty publications was independent of the pandemic. ResultThe model – Pharmacy Practice Research Domain Classifier (PPRDC) produced a 5-fold stratified cross-validation metrics of 89.4 ± 1.7, 90.2 ± 2.2, 89.0 ± 1.7, and 95.5 ± 0.6, for F1, recall, precision and accuracy, respectively. PPRDC produced perfectly reproducible classifications (Cohen's kappa = 1.0) and outperformed zero-shot performances of all gpLLMs. F1 scores were 96.2 ± 1.6, 92.7 ± 1.2, 85.8 ± 3.2, and 83.1 ± 9.8 for education, clinical, social, and translational domains, respectively. ConclusionsPPRDC (https://sadeosun-pprdc.streamlit.app) performed better than gpLLMs in this abstract classification task. Among several other impacts, PPRDC opens a new frontier in bibliometric studies; it will also advance the goals of the Grenada Statements by aiding authors and journal editors in journal selection and article prioritization decisions, respectively.

Comparative analysis of environmental footprints: conventional versus high-strength concrete in construction

Abstract This paper presents a comparative analysis of the environmental footprints associated with conventional and high-strength concrete in the construction industry. As sustainability becomes increasingly critical in construction practices, understanding the environmental impacts of various concrete types is essential. The study employs a comprehensive methodology to assess key environmental factors, including carbon footprint, energy consumption, water usage, and waste generation, for both conventional and high-strength concrete. Data collection and analysis reveal significant differences between the two concrete types in terms of their environmental impacts. Findings indicate that while high-strength concrete offers enhanced structural performance, it tends to have higher environmental costs compared to conventional concrete. The paper discusses the implications of these findings for sustainable construction practices and emphasizes the importance of informed material selection decisions in reducing the environmental footprint of construction projects.