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AI chatbots have emerged as powerful tools for providing text-based solutions to a wide range of everyday challenges. Selecting the appropriate chatbot is crucial for optimising outcomes. This paper presents a comprehensive comparative analysis of five leading chatbots: ChatGPT, Bard, Llama, Ernie, and Grok. The analysis is based on a systematic review of 28 scholarly articles. The review indicates that ChatGPT, developed by OpenAI, excels in educational, medical, humanities, and writing applications but struggles with real-time data accuracy and lacks open-source flexibility. Bard, powered by Google, leverages real-time internet data for problem solving and shows potential in competitive quiz environments, albeit with performance variability and inconsistencies in responses. Llama, an open-source model from Meta, demonstrates significant promise in medical contexts, natural language processing, and personalised educational tools, yet it requires substantial computational resources. Ernie, developed by Baidu, specialises in Chinese language tasks, thus providing localised advantages that may not extend globally due to restrictive policies. Grok, developed by Xai and still in its early stages, shows promise in providing engaging, real-time interactions, humour, and mathematical reasoning capabilities, but its full potential remains to be evaluated through further development and empirical testing. The findings underscore the context-dependent utility of each model and the absence of a singularly superior chatbot. Future research should expand to include a wider range of fields, explore practical applications, and address concerns related to data privacy, ethics, security, and the responsible deployment of these technologies. [ABSTRACT FROM AUTHOR]

The primary objective of this research paper is to conduct a comprehensive investigation into the process of developing briefs for Public–Private Partnership (PPP) projects. The study aims to outline the fundamental stages, standard processes, and critical decision points involved in this process. This is achieved through a comparative analysis of briefing frameworks used in three countries that are among the top in the PPP Market Maturity chart: The United Kingdom, Australia, and Canada. The paper discusses the stages and decision points within these countries' briefing processes, highlighting their challenges, similarities, and differences. It emphasizes the fundamental role of effective communication and coordination in PPP projects, which involve multiple stakeholders. By examining these interconnected activities and analyzing the phases, stages, and key processes constituting the briefing process, the study provides valuable insights into the PPP briefing processes in these nations. The paper concludes by proposing a conceptual process framework for brief development in PPP projects, which will be further tailored for adaptation in the PPP market in the United Arab Emirates. [ABSTRACT FROM AUTHOR]

This paper presents an experimental comparison of two types of Li-ion battery stacks for low-voltage energy storage in small urban Electric or Hybrid Electric Vehicles (EVs/HEVs). These systems are a combination of lithium battery cells, a battery management system (BMS), and a central control circuit—a lithium energy storage and management system (LESMS). Li-Ion cells are assembled with two different active cathode materials, nickel–cobalt–aluminum (NCA) and lithium iron phosphate (LFP), both with an integrated decentralized BMS. Based on experiments conducted on the two assembled LESMSs, this paper suggests that although LFP batteries have inferior characteristics in terms of energy and power density, they have great capacity for improvement. [ABSTRACT FROM AUTHOR]

This paper introduces a rigorous and comprehensive approach to the assessment of marine shafting systems through the utilization of an advanced π-Theorem-based scaling methodology. Integrating journal-bearing similarity assessment and shaft-line scaling methodology with advanced dimensional analysis, the study aims to provide a methodology foundation for systematic replication and analysis of marine shafting systems through scaled models. The proposed scaling methodology ensures geometric and mechanical similarity in terms of shaft-line deflection, considering key scaling parameters such as shaft length, diameter, weight, loads, rotational speed, material properties, bearing locations, and offsets. The advanced dimensional analysis computes specific non-dimensional ratios to guarantee a close resemblance between a real-size system and a scaled lab model. The methodology is analytically derived and validated with numerical simulations for a case study, conducting comparative analysis, evaluating discrepancies, and utilizing the integrated framework for experimentation. [ABSTRACT FROM AUTHOR]

The objective of this study is to perform a comparative analysis of the impact of incorporating alkaline earth metal carbonates (MCO3, where M–Mg, Ca, Sr, Ba) into low-calcium fly ash (FA) on the geopolymerization processes and the resultant properties of composite geopolymers. Mechanical activation was employed to enhance the reactivity of the mixtures. The reactivity of the mechanically activated (FA + alkaline earth carbonate) blends towards NaOH solution was experimentally studied using XRD analysis and FTIR spectroscopy. In agreement with thermodynamic calculations, MgCO3 demonstrated the most active interaction with the alkaline solution, whereas strontium and barium carbonates exhibited little to no chemical interaction, and calcite was situated in the transition region. As the calcite content in the mixture with FA increased, the compressive strength of the geopolymers continuously improved. The addition of Mg, Sr, and Ba carbonates to the FA did not enhance the strength of geopolymers. However, the strength of geopolymers based on these blends was comparable with that of geopolymers based on 100% FA. The strength of geopolymers synthesized from the 100% FA and from the (90% FA + 10% MCO3) blends, mechanically activated for 180 s, at the age of 180 days was 11.0 MPa (0% carbonate), 11.1 MPa (10% MgCO3), 36.5 MPa (10% CaCO3), 13.6 MPa (10% SrCO3), and 12.4 MPa (10% BaCO3) MPa, respectively. The influence of carbonate additives on the properties of the composite geopolymers was examined, highlighting filler, dilution, and chemical effects. The latter determined the unique position of calcite among the carbonates of alkaline earth metals. [ABSTRACT FROM AUTHOR]

As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of XAI techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset of 1205 instances, we employed several machine learning algorithms with a particular focus on Random Forest, which demonstrated highest accuracy at 91%. The models' precision, recall and F1-score were also evaluated, with Random Forest achieving a precision of 0.93, a recall of 0.94, and an F1-score of 0.94. Our study utilizes SHAP, LIME, Anchors, ALE, and Counterfactual explanations to reveal the specific contributions of various features impacting adaptability predictions. SHAP values highlighted 'Class Duration' significance (mean SHAP value: 0.175); LIME explained socio-economic and institutional factors' intricate influence. Anchors provided high-confidence rule-based explanations (confidence: 97.32%), emphasizing demographic characteristics. ALE analysis underscored the importance of 'Financial Condition' with a positive slope, while Counterfactual scenarios highlighted the impact of slight feature variations of 0.5 change in 'Class Duration'. Consistently, 'Class Duration' and 'Financial Condition' emerge as key factors, while the study also underscores the subtle effects of 'Institution Type' and 'Load-shedding'. This multi-faceted interpretability approach bridges the gap between machine learning performance and educational relevance, presenting a model that not only predicts but also explains the dynamic factors influencing student adaptability. The synthesized insights advocate for educational policies accommodating socioeconomic factors, instructional time, and infrastructure stability to enhance student adaptability. The implications extend to informed and personalized educational interventions, fostering an adaptable learning environment. This methodical research contributes to responsible AI application in education, promoting predictive and interpretable models for equitable and effective educational strategies. [ABSTRACT FROM AUTHOR]

Transitional streets serve as intermediary spaces between the Central Business Districts (CBDs) and surrounding residential areas, offering diverse functions and activities within urban interiors. However, a practical methodology for accurately classifying these streets has been lacking, due primarily to transitional areas' spatial constraints and functional complexities. This study leverages Point of Interest (PoI) data from 2023 to develop an innovative methodological framework that addresses these challenges. This framework analyses transitional streets' functional distribution and typology, employing PoI frequency density and functional type ratios to identify and classify functional zones. It generally delineates eight main types of transitional streets in the CBD of Chongqing, a prototypical hillside city. Utilising advanced data technology from internet maps, this research pioneers new approaches for identifying and analysing the functionality of transitional streets. The findings underscore the effectiveness of PoI data in precisely recognising the functional types of transitional streets, thereby providing a robust theoretical and practical foundation for the in-depth study of transitional streets. Moreover, the results enhance urban spatial planning in hillside cities of China, effectively demonstrating the advantages of PoI data in defining street typology compared to traditional methods. This approach provides a more detailed understanding of urban functional dynamics by allowing for a more nuanced data analysis of street functions. [ABSTRACT FROM AUTHOR]

As escalating environmental pollution results from economic development, the green supply chain is vital in enhancing ecological quality. In light of the rapid growth of online shopping, the dual-channel supply chain has gained significant popularity. Governments have implemented policies to achieve carbon peaks and neutrality worldwide. Considering green quality and channel preferences, this study constructs a Stackelberg model led by the manufacturer within a dual-channel supply chain to compare the effects under two government subsidy strategies. A Stackelberg game-theoretic approach is applied to analyze the model. The findings indicate the following: (1) Increases in the subsidy coefficient can increase product greenness and the overall profit in both subsidy strategies, which makes sense; (2) When direct sales channels become dominant, subsidizing the manufacturer proves superior to subsidizing the retailer in terms of promoting green quality and overall profit, which extends existing studies of government subsidy decisions. Furthermore, subsidizing the retailer may negatively affect the total supply chain profit; (3) Consistent with previous literature, intense competition between manufacturers and retailers is expected to enhance the overall profit of the supply chain; (4) Interestingly, customer preference for online channels does not influence product green quality under manufacturer subsidization strategies but exhibits a negative impact under retailer subsidization strategies. Finally, this research provides managerial implications for decision-makers and potential issues for future research. [ABSTRACT FROM AUTHOR]

This study aims to comprehensively analyze the operational status and international competitiveness of global container shipping enterprises in the era of sustainability and digitalization within the field of international container transportation. Utilizing the entropy method, this study quantitatively evaluates the direct operational strength of 14 leading container shipping enterprises while considering multiple factors including environmental protection, service quality, company scale, customer satisfaction, research and development level, and brand influence. The contributions of this study to the existing knowledge are primarily manifested in several aspects: firstly, by constructing a comprehensive evaluation framework, it offers a new perspective for assessing the international competitiveness of container shipping enterprises, facilitating a more comprehensive understanding of their strengths and weaknesses. Secondly, this study emphasizes the crucial roles of environmental protection and customer service in the competitiveness of shipping enterprises, providing new strategic directions for the industry's sustainable development and digital transformation. Lastly, through detailed analysis of the operational performance of different companies, this study provides specific improvement suggestions for shipping enterprises, aiding them in achieving more precise management and more efficient development. The research findings demonstrate that companies exhibit varied performance in different aspects, showcasing their respective strengths and challenges. Particularly, this study identifies leading enterprises that have made significant progress in environmental technology innovation and customer service, while also highlighting deficiencies in some companies regarding scale expansion and brand building. These findings not only offer valuable insights for the development of the shipping industry but also serve as a window for policymakers, investors, and consumers to gain a deeper understanding of the shipping market. Through the thorough analysis conducted in this study, we aim to contribute to the sustainable development and digital transformation of the global container shipping industry. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has underscored the critical need for the advancement of diagnostic and therapeutic platforms. These platforms rely on the rapid development of molecular binders that should facilitate surveillance and swift intervention against viral infections. In this study, we have evaluated by three independent research groups the binding characteristics of various published RNA and DNA aptamers targeting the spike protein of the SARS-CoV-2 virus. For this comparative analysis, we have employed different techniques such as biolayer interferometry (BLI), enzyme-linked oligonucleotide assay (ELONA), and flow cytometry. Our data show discrepancies in the reported specificity and affinity among several of the published aptamers and underline the importance of standardized methods, the impact of biophysical techniques, and the controls used for aptamer characterization. We expect our results to contribute to the selection and application of suitable aptamers for the detection of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

In recent years, heavy rainfall events have occurred frequently in the Qinba region. Forecasting and predicting heavy rainfall in the Qinba region is difficult due to the unique underlying terrain and complicated mechanisms involved. One significant weather system that might bring significant rainfall to the region is the southwest vortex (SWV); however, its different positions, intensities, and interaction with other weather systems might result in precipitation with different intensities and distributions. In this study, ERA-5 reanalysis data, FY-4A satellite data, and conventional observation data were used to examine heavy rainstorms that occurred in the Qinba region in the periods of 3–4 September 2021 (referred to as Stage I) and 4–5 September 2021 (referred to as Stage II), while the SWV was in effect. During Stage I, the northwest vortex (NWV) and SWV generated a mesoscale shear line and mesoscale convective complex (MCC) in the Qinba region. This led to a considerable area of heavy rainfall, with a maximum hourly precipitation of 129 mm and heavy precipitation at 15 stations. During Stage II, a mesoscale convective system (MCS) influenced by the SWV was initiated by a low-level jet, resulting in a localized heavy downpour with a maximum hourly precipitation of 72 mm. Significant topography-forced uplift was found in both Stages I and II in the high-altitude Qinba region. Furthermore, the rainfall was stronger during Stage I due to the secondary circulation that developed in the middle and lower levels. These findings will improve our capability to predict rainstorms and prevent disasters in the Qinba region. [ABSTRACT FROM AUTHOR]

Lindera aggregata is a species of the Lauraceae family, which has important medicinal, economic and ornamental values. In this study, we sequenced, assembled and annotated the chloroplast genome of L. aggregata and reannotated and corrected eight unverified annotations in the same genus. The chloroplast genomes taxa from Lindera and from different genera of Lauraceae were compared and analyzed, and their phylogenetic relationship and divergence time were speculated. All the 36 chloroplast genomes had typical quadripartite structures that ranged from 150,749 to 154,736 bp in total length. These genomes encoded 111–112 unique genes, including 78–79 protein-coding genes, 29–30 tRNA and 4 rRNA. Furthermore, there were 78–97 SSRs loci in these genomes, in which mononucleotide repeats were the most abundant; there were 24–49 interspersed repeats, and forward repeat types were the most frequent. The codon bias patterns of all species tended to use codons ending with A or U. Five and six highly variable regions were identified within genus and between genera, respectively, and three common regions (ycf1, ndhF-rpl32 and rpl32-trnL) were identified, which can be used as important DNA markers for phylogeny and species identification. According to the evaluation of the Ka/Ks ratio, most of the genes were under purifying selection, and only 10 genes were under positive selection. Finally, through the construction of the evolutionary tree of 39 chloroplast genomes, the phylogenetic relationship of Lauraceae was clarified and the evolutionary relationship of Lindera was revealed. The species of genus Lindera experienced rapid adaptive radiation from Miocene to Pleistocene. The results provided valuable insights for the study of chloroplast genomes in the Lauraceae family, especially in the genus Lindera. [ABSTRACT FROM AUTHOR]

The Tripterospermum, comprising 34 species, is a genus of Gentianaceae. Members of Tripterospermum are mostly perennial, entwined herbs with high medicinal value and rich in iridoids, xanthones, flavonoids, and triterpenes. However, our inadequate understanding of the differences in the plastid genome sequences of Tripterospermum species has severely hindered the study of their evolution and phylogeny. Therefore, we first analyzed the 86 Gentianae plastid genomes to explore the phylogenetic relationships within the Gentianae subfamily where Tripterospermum is located. Then, we analyzed six plastid genomes of Tripterospermum, including two newly sequenced plastid genomes and four previously published plastid genomes, to explore the plastid genomes' evolution and phylogenetic relationships in the genus Tripterospermum. The Tripterospermum plastomes have a quadripartite structure and are between 150,929 and 151,350 bp in size. The plastomes of Tripterospermum encoding 134 genes were detected, including 86 protein-coding genes (CDS), 37 transfer RNA (tRNA) genes, eight ribosomal RNA (rRNA) genes, and three pseudogenes (infA, rps19, and ycf1). The result of the comparison shows that the Tripterospermum plastomes are very conserved, with the total plastome GC content ranging from 37.70% to 37.79%. In repeat sequence analysis, the number of single nucleotide repeats (A/T) varies among the six Tripterospermum species, and the identified main long repeat types are forward and palindromic repeats. The degree of conservation is higher at the SC/IR boundary. The regions with the highest divergence in the CDS and the intergenic region (IGS) are psaI and rrn4.5-rrn5, respectively. The average pi of the CDS and the IGS are only 0.071% and 0.232%, respectively, indicating that the Tripterospermum plastomes are highly conserved. Phylogenetic analysis indicated that Gentianinae is divided into two clades, with Tripterospermum as a sister to Sinogeniana. Phylogenetic trees based on CDS and CDS + IGS combined matrices have strong support in Tripterospermum. These findings contribute to the elucidation of the plastid genome evolution of Tripterospermum and provide a foundation for further exploration and resource utilization within this genus. [ABSTRACT FROM AUTHOR]

Background: This research conducts a contextual comparative analysis between Dar es Salaam and Mombasa ports, employing a hybrid data envelopment analysis (DEA) model that integrates the contextual value-added approach (CVA). The assessment incorporates various inputs (quay length, number of cranes, and storage area) and outputs (number of ship calls and cargo throughput) to compute efficiency scores, offering nuanced insights into the strengths, weaknesses, and areas for improvement of both ports. Methods: The hybrid DEA model with CVA is applied to calculate efficiency scores, considering the diverse inputs and outputs. This approach allows for a comprehensive evaluation of the relative performance of Dar es Salaam and Mombasa ports. The study also explores the influence of trade-related externalities on port efficiency, providing a holistic understanding of the factors shaping the ports' operational effectiveness. Results: The efficiency scores depict distinctive performance trends between Dar es Salaam and Mombasa ports. Notably, Dar es Salaam exhibits maximum efficiency (efficiency value of 1) in 2018 and 2021, while Mombasa attains optimal performance (efficiency value of 1) in 2021. However, efficiency values fluctuate for both ports in other years, ranging between 0.895 and 0.985 for Mombasa and 0.924 and 0.960 for Dar es Salaam. Conclusions: This study highlights the dynamic efficiency levels of Dar es Salaam and Mombasa ports over multiple years and identifies critical factors influencing their performance. The findings contribute valuable insights to the field of port analysis, offering guidance to port management and policymakers in optimizing the efficiency and competitiveness of these vital maritime hubs. [ABSTRACT FROM AUTHOR]

Few data are available on antibody response for some SARS-CoV-2 vaccines, and there is a lack of ability to compare vaccine responses in the same population. This cross-sectional study conducted in Nicaragua examines the SARS-CoV-2 antibody responses in individuals, previously exposed to high infection rates who have received various vaccines. The vaccines under comparison include well-known ones like Pfizer (BNT162b2) and AstraZeneca (ChAdOx1-S), alongside less-studied vaccines including Soberana (Soberana 02), Abdala (CIGB-66), and Sputnik V/Sputnik Light. Overall, 3195 individuals participated, with 2862 vaccinated and 333 unvaccinated. We found that 95% of the unvaccinated were seropositive, with much lower titers than the vaccinated. Among the vaccinated, we found that Soberana recipients mounted the highest anti-spike response (mean difference (MD) = 36,498.8 [20,312.2, 52,685.5]), followed by Abdala (MD = 25,889.9 [10,884.1, 40,895.7]), BNT162b2 (MD = 12,967.2 [7543.7, 18,390.8]) and Sputnik with AstraZeneca as the reference group, adjusting for age, sex, vaccine status, days after last dose, and self-reported COVID-19. In addition, we found that subjects with complete vaccination series had higher antibody magnitude than those with incomplete series. Overall, we found no evidence of waning in the antibody magnitude across vaccines. Our study supports the conclusion that populations with high infection rates still benefit substantially from vaccination. [ABSTRACT FROM AUTHOR]

This study critically examines existing frameworks aimed at establishing restorative practices in community development by conducting a comparative analysis of a meso-level development framework called the Five Pillars of Development and a micro-level framework implemented by a grassroots initiative. Both frameworks were implemented in Washington, DC, and both seek to address the enduring negative externalities disinvested communities face and suggest the need for restorative practices in community development. Restorative development practices trace their roots to the field of restorative economics, which is a subfield of sustainable development. It argues that sustainability must also address the sins of the past and restore systems and systems components that have been negatively impacted by past development practices. This study aims to discern the adaptability of the more broadly applicable meso-level Five Pillars framework in capturing nuanced micro-level frameworks like the one devised by the grassroots organization in Ward 8. By identifying potential gaps, this study proposes strategies to enhance the utility of the more transferable meso-level framework, particularly for communities lacking resources to formulate their own micro-level framework. This study, therefore, contributes valuable insights and recommendations for bridging potential disparities between the meso-level and micro-level frameworks, as demonstrated in the Ward 8 case. The overarching objective is to enrich the community development field by presenting a restorative framework that refines existing meso-level approaches and facilitates opportunities for micro-level applications. [ABSTRACT FROM AUTHOR]

Traditional news outlets, such as newspapers and television, are no longer major sources of news. These media channels have been replaced by social platforms, which have increased in value as information distributors. This change in communication is an underlying reason for the election fraud controversies that occurred in the United States and South Korea, which hold high standards of democracy, during similar periods. This study investigates a model for sharing political disputes over social networks, especially Twitter, and illustrates the influence of political polarization. This study examines Twitter content around the presidential elections in the United States and South Korea in 2020 and 2022, respectively. It applies semantic network analysis and structural topic modeling to describe and compare the dynamics of online discourse on the issue of election fraud. The results show that online spaces such as Twitter serve as public spheres for discussion among active political participants. Social networks are key settings for forming and spreading election fraud controversies in the United States and South Korea, with differences in content. In addition, the study applies large-volume text data and new analytical methods such as the structural topic model to examine the in-depth relationships among political issues in cyberspace. [ABSTRACT FROM AUTHOR]

Safety and prevention of injuries should always be considered in a firefighting environment due to the hazardous conditions experienced on the fireground. These hazardous environmental conditions lead to an increased risk of contracting job-related injuries and illnesses. This review article focuses on evaluating from a statistical perspective the potential solutions found in the literature and how they decrease the likelihood and impact of occupational firefighting injuries. Investigating, identifying, and prioritizing the most common activities leading to injury, the nature of injury, and the body parts affected is a vital step in the implementation of preventive solutions. The scientific community has conducted various studies to evaluate the main injuries and injury profiles commonly suffered by firefighters. Researchers have conducted many independent studies on firefighter communities in the United States, while others have referenced national databases from sources such as the National Fire Protection Association, the Bureau of Labor Statistics, and the National Electronic Injury Surveillance System. Unfortunately, the results of these independent studies lacked standardization in survey categories and terminology, impairing the ability to obtain a clear consensus among studies on the primary nature of injuries, the body parts injured, and the activities contributing to these injuries. Consequently, this review article performed a comparative statistical analysis of published data between 1992 and 2020 to define and rank the most common work scenarios where firefighters were likely to be injured, the most common types of injuries, the parts of the body affected, and the activities that most contribute to United States firefighter injuries as documented in both national databases and independent research surveys. The statistical analysis consisted of determining the mean, standard deviation, confidence intervals (95%), and coefficients of variation for the reported data. The present study identified that despite the preventative measures taken by many organizations in the firefighting community, strains and sprains were still the leading type of injury reported from all the databases under this analysis. [ABSTRACT FROM AUTHOR]