Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural properties of stereotypic knowledge and their influences on the construal of social situations.

This research focused on the role that higher order structural properties of stereotypic knowledge play in the processing of social information. It is argued that stereotypic assumptions about cause-effect relations provide important constraints for the causal structure underlying the perceiver's subjective representation of social information. Experiment 1 shows how, within the context of a jury decision experiment, the causal structure underlying stereotypic knowledge about African Americans influences the construal of causality in a situation involving a member of that group. Results from 2 additional experiments indicate that this construal effect is based in part on stereotypic knowledge affecting the encoding of the trial evidence instead of on biasing responses at the output stage. The implications of these findings are discussed, and a theoretical framework is offered according to which the application of category knowledge involves not only the matching of stereotypic attributes but also the alignment of structural relations in the environment.
Executive Overconﬁdence and Corporate Environmental, Social, and Governance Performance

: ESG (environmental, social, and governance) has gained widespread recognition as a fundamental investment approach on a global scale. Demonstrating strong ESG performance has evolved into a vital strategic imperative for fostering sustainable corporate growth and bolstering competitiveness. Given their critical roles within companies, it is crucial for decision-makers to investigate the impact of executive overconﬁdence on ESG performance. Through an examination of Chinese A-share listed companies spanning the years 2009 to 2020, this research reveals a signiﬁcant correlation between executive overconﬁdence and improved corporate ESG performance. Mechanism tests uncover that overconﬁdent executives exhibit robust risk-taking abilities and a heightened drive to garner attention, both of which contribute to the enhancement of ESG performance. Heterogeneity analysis demonstrates that, in companies characterized by lower-quality accounting information, lower institutional shareholding ratios, ample cash ﬂow, and increased government subsidies, the positive inﬂuence of executive overconﬁdence on ESG performance is even more pronounced. Furthermore, our investigation unveils that overconﬁdent executives exert a positive impact on corporate ESG performance through three primary pathways: assuming responsibility for environmental protection (E), embracing social responsibility (S), and fortifying corporate governance (G). It is worth noting that this boost in ESG performance, in turn, translates into an enhancement of corporate value. Ultimately, this research contributes to a deeper understanding of the economic ramiﬁcations of executive overconﬁdence and enriches the body of knowledge pertaining to the mechanisms for enhancing ESG performance.
Quality of High-strength Concrete depending on Various S/A and Unit Water Contents

Although water-to-cement ratio is the main factor of determining the compressive strength of the concrete, sand-to-aggregate ratio, and unit water content dominate segregation resistance and target slump of the concrete, respectively. Therefore, in this research, to suggest a fundamental data on sand-to-aggregate ratio and unit water content for mix design of high strength concrete, fundamental properties of the high strength concrete of 30 % water-to-cement ratio were evaluated depending on the various sand-to-aggregate ratio and unit water content. As a result of the experiment, for fresh state concrete properties, as the sand-to-aggregate ratio was increased, slump, slump flow, and air content were decreased, and as the unit water content was increased, slump, slump flow, and air content were increased. Based on the 180 mm of slump, each slump and slump flow measurements deduced favorable performances at lower and higher than this value, respectively. In segregation aspect, about 45 % of sand-to-aggregate ratio was good and under this conditions, it was good to control the superplasticizer dosage and unit water content. Furthermore, to achieve better compressive strength, under the 45 or 50 % of sand to aggregate ratio, low unit water content of showedmaximumvalueofcompressivestrength.
A Critical Review of the Common Ownership Literature

: The rapid growth in index funds and significant consolidation in the asset-management industry over the past few decades has led to higher levels of common ownership and increased attention on the topic by academic researchers. A consensus has yet to emerge from the literature regarding the consequences of increased common ownership on firm behavior and market outcomes. Given the potential implications for firms and investors alike
Hairpin DNA Functionalized Gold Nanorods for mRNA Detection

Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.
An examination of opportunities for small non-community drinking water systems to improve drinking water safety

AN EXAMINATION OF OPPORTUNITIES FOR SMALL NON-COMMUNITY DRINKING WATER SYSTEMS TO IMPROVE DRINKING WATER SAFETY Wendy Pons Advisor: University of Guelph, 2015 Professor A. Papadopoulos Waterborne diseases are among the world’s most significant yet largely preventable public health issues. A large number of waterborne disease outbreaks in developed countries are attributed to water from small, non-municipal water systems (SDWSs). This thesis is an investigation of the challenges and opportunities to improve the safety of the water supply in SDWSs, with a focus on systems located in Ontario. This thesis utilized data from a systematic review, SDWS data from Ontario, a cross sectional survey of SDWS operators and focus groups of public health inspectors. These data were used to (1) summarize factors contributing to SDWS outbreaks (2) investigate the relationships between certain key characteristics of SDWSs and the performance of these systems, and (3) explore the experience and future training needs of SWDS operators and public health inspectors in Ontario. This research found the leading causes of outbreaks in SDWSs were the failure of an existing water treatment system (22.7%) and lack of water treatment (20.2%). In Ontario, 66% of water operators were not trained and 16% had one year or less experience. Thirty four percent of systems utilized water treatment and 45% operated on a seasonal basis. The odds of having a positive E. coli test result were greater in systems using ground and surface water with treatment compared to ground water with no treatment (apparently indicating the failure of an existing water treatment system). Operators had a preference for online training courses or on-site training. SDWS inspectors reported needing support in the form of education initiatives and a community of practice to promote knowledge exchange. Main concerns to water safety were the technical ability of the water operator and having a long time period between inspections of water systems. The findings of this research help identify opportunities to target training to specific groups of operators. There is a need to further explore the effectiveness of water treatment and other water protection measures currently in place in SDWSs in Ontario. Future research is needed to explore the cost-benefit of increasing inspection frequency and to explore a variety of education initiatives for inspectors and operators of SDWSs.
Empirical evidence of an integrative knowledge competence framework for ERP systems implementation in UK industries

Enterprise Resource Planning (ERP) systems can greatly improve business productivity and better serve customers by creating values through integrating business processes and sharing current information. Knowledge management (KM) is crucial for ERP systems implementation, but is particularly demanding task. This paper discusses ERP systems implementation in UK manufacturing and service sector organisations, focusingon empirical evidence of an innovative KM approach for improving knowledge competence for ERP success. Qualitative research was conducted, using semi-structured interviews with ERP experts. Data analysis used a combination of thematic and comparative analysis. The findings suggest that the integrative knowledge competence framework can provide ERP practitioners with useful guidance on what the key knowledge determinants are and how the relationships between knowledge components should be best managed to achieve ERP systems implementation success in real life business situations.
Effect of corneal collagen cross linking on crystalline lens clarity graded by Pentacam Nucleus Staging Software

Background : Discuss the effect of ultraviolet rays (UVR) emitted during corneal collagen crosslinking (CXL) on cataract development in keratoconus patients in Cairo, Egypt. Methods : We performed a study that describes the effect of CXL on human lens clarity, 100 eyes were investigated by oculus pentacam in the Egyptian Armed Forces Hospital at Kobri el Kobba in collaboration with Al-Azhar university hospitals diagnosed in January 2021. Results : Our study reported that CXL has a highly signi ﬁ cant effect in developing lens opacities especially the older age and small anterior chamber (AC) depth and reported also its affection on visual acuity (VA). Conclusion : CXL results in signi ﬁ cant changes and an increase in the densitometry of the crystalline lens which is a problem that increases the risk of the development of cataracts.
Time optimizing in Economical Grid Using Adaptive Stochastic Petri Net Based on Learning Automata Intelligent approach in Economical Grid

in this paper, intelligent algorithm of ALATO based on Learning Automata using Adaptive Stochastic Petri Net presented to optimize the time in Economical Grid. Adaptive Stochastic Petri nets based on learning automata predicts the following optimized state through getting information from former system states and dynamic environmental reactions, makes the system current states appeared based on that, changes the probability of events occurrences during the time and causes the events to be activated based on probability of occurrence. Comparing suggested algorithm and the existed algorithm has been shown in which the mentioned algorithm acts a significant decline in considered time in 200 considered independent tasks. Keywords-Economical Grid; Adaptive Stochastic Petri Net(ASPN); Learning Automata; Time Optimization
Compositional non-blockingness veriﬁcation of ﬁnite automata with prioritised events

Thispaperaddressestheveriﬁcationofnon-blockingnessformodulardiscrete-eventsystems, i.e.,discrete-eventsystemsthatarecomposedfromcomponentmodels.Forsuchsystems, theexplicitconstructionofamonolithicrepresentationturnsoutintractableforrelevant applications,sincesuchaconstructioningeneralisofexponentialcostw.r. t.thenumber ofcomponents.Onewellestablishedapproachtocircumventtheneedforamonolithic representationfortheveriﬁcationtaskathandistoalternate(a)thesubstitutionofindividual componentsbyabstractionsand(b)thecompositionofonlyasmallnumberofstrategically chosencomponentsatatime.Whensuccessful,oneendsupwithasinglemoderatelysized automatonwhichdoesnotrepresenttheoverallbehaviourinanydetailbutwhichdoesblock ifandonlyiftheoriginalmodularsystemfailstobenon-conﬂicting. Thisapproachisreferred toas compositional veriﬁcation and originates from the ﬁeld of process algebra with more recent adaptations to ﬁnite automata models. The main contribution of the present study is the development of a number of abstraction rules valid for compositional veriﬁcation of non-conﬂictingness in the presence of global event priorities, i.e., where high priority events from one component possibly preempt events with lower priority of all components.
The Reﬂective Modeling Practitioner: Promoting Self-regulation and Self-conﬁdence in Computational Modeling and Simulation Practices*

This study investigated the eﬀects of a team-based modeling intervention that implemented reﬂective practices to support students’ self-regulated learning in the context of modeling assignments. We used a mixed method design to answer the three research questions: (1) What metacognitive strategies do students, organized in teams, implement when solving computational modeling assignments? (2) What are students’ levels of performance in solving computational modeling assignments in teams? (3) What are the relationships between teams’ level of conﬁdence and their implemented metacognitive strategies and level of performance in the computational modeling assignment? The learning intervention was guided by a reﬂective modeling practitioner model, bringing together modeling practices with elements of self-regulated learning. The results illustrate students’ levels of self-reported conﬁdence in three levels, showing that from the twelve teams studied, seven reported an increase in conﬁdence as the project progressed, three reported a decrease in their conﬁdence, and two reported an initial struggle, but their conﬁdence increased as they completed the assignment. The implications relate to the learning interventions in the team modeling activity that can inﬂuence the teams’ reported self-conﬁdence, which can impact the skills students acquire and the strategies they use when faced with challenges
Similarity Learning and Visualization with Siamese Topological Networks

In this study a model of Siamese topological neural networks, which consisted of a pair of hierarchical neural networks each with a low-dimensional internal layer, is proposed. Through similarity learning, the objective of the proposed siamese network is to learn low-dimensional topological representations of a given similarity between pairwise high-dimensional inputs. The low-dimensionality of the internal layer allows human to visualize the structure of the high-dimensional data in the context of their similarities, for example, label-similarity or rank-similarity. Different from many similarity learning techniques, dimensionality reduction is integrated in the proposed siamese networks allowing flexible context-oriented visualization analysis for high-dimensional data.
The Price of Journals in Geography

We assess the importance of publisher conduct and journal quality in determining the price and cost-effectiveness of journals in geography. Drawing on a database of 136 journals in which geographers publish, we examine the price of journals, publishers' market share, the determinants of journal prices, and the cost-effectiveness of journals. We find that commercial presses charge 2.3 times more for journals than society and university presses and 50 percent more for journals published on behalf of universities and societies. Journals in physical geography are almost twice as expensive as those in human geography and 43 percent of them can be considered “overpriced. ” Four commercial presses hold 72 percent of the titles and 93 percent of the value of subscriptions, enabling them to exercise oligopolistic market power over pricing. Our multivariate analysis shows that the price of journals, of articles, and of citations in geography is driven by publisher conduct rather than journal quality as measured by citations accrued to articles. Geographers can further the transition underway in scientific publishing by self-archiving their published work, disseminating their findings through social networks, and paying closer attention to journal cost-effectiveness in choosing where to publish and review.
INFORMS Journal on Computing

We introduce the problem of selecting patient-donor pairs in a kidney exchange program to undergo a crossmatch test, and we model this selection problem as a two-stage stochastic integer programming problem. The optimal solutions of this new formulation yield a larger expected number of realized transplants than previous approaches based on internal recourse or subset recourse. We settle the computational complexity of the selection problem by showing that it remains NP-hard even for maximum cycle length equal to two. Furthermore, we investigate to what extent different algorithmic approaches, including one based on Benders decomposition, are able to solve instances of the model. We empirically investigate the computational ef ﬁ ciency of this approach by solving randomly generated instances and study the corresponding running times as a function of maximum cycle length, and of the presence of nondirected donors. Summary of Contribution: This paper deals with an important and very complex issue linked to the optimization of transplant matchings in kidney exchange programs, namely, the inherent uncertainty in the assessment of compatibility between donors and recipients of transplants. Although this issue has previously received some attention in the optimization literature, most attempts to date have focused on applying recourse to solutions selected within restricted spaces. The present paper explicitly formulates the maximization of the expected number of transplants as a two-stage stochastic integer programming problem. The formulation turns out to be computationally dif ﬁ culty, both from a theoretical and from a numerical perspective. Different algorithmic approaches are proposed and tested experimentally for its solution. The quality of the kidney exchanges produced by these algorithms compares favorably with that of earlier models. History
Energy and Built Environment

Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy consumption and building performance. Modeling frameworks are usually built to accomplish a certain task, but the stochasticity of the occupant makes it diﬃcult to apply that experience to a similar but distinct environment. For complex and dynamic environments, the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable. It is expected that they will make a substantial contribution to reducing global energy consumption. Among these control techniques, the reinforcement learning (RL) method seems distinctive and applicable. The success of the reinforcement learning method in many artiﬁcial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control. Fruitful algorithms complement each other and guarantee the quality of the optimization. However, the examination of occupant behavior based on reinforcement learning methodologies is not well established. The way that occupant interacts with the RL agent is still unclear. This study brieﬂy reviews the empirical applications using reinforcement learning, how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.
Surface Water Quality Modeling in the Rio Grande

The City of Albuquerque (City) in New Mexico owns and operates the Southside Water Reclamation Plant (SWRP), which discharges an average of 200623 m 3 /d (53 mgd, 82 cfs) of secondary treated wastewater to Segment 2-105 of the Rio Grande. The City developed a surface water quality model to investigate the impact of the SWRP discharge on the Rio Grande (Seguin 1992). The model was used to assess instream compliance with the State and Pueblo water quality standards. The model will also be used as a wasteload allocation tool and to guide the City's future consideration of additional and/or alternative wastewater treatment to insure compliance with both the State and Pueblo standards. This paper discusses the system modeled, the model development, sampling programs established to calibrate and verify the model, the results of the model, and model applications.
THE MEDIATING ROLE OF LIBRARIANS’ COMPETENCIES IN THE APPLICATION OF METADATA PRACTICES FOR THE MANAGEMENT OF DIGITAL INFORMATION RESOURCES IN UNIVERSITY LIBRARIES IN NIGERIA

Metadata practice has evolved over the years with the developments in ICTs, as such it is very important to ascertain metadata librarians' competencies with ICT and metadata tools for the management of digital information resources. This study adopted survey research design. The secondary population comprised all federal universities in Nigeria that are having and managing digital information resources. The primary population comprised the 275 staffs from 20 federal universities that are involved in the application of metadata for the management of digital information resources. Due to the manageable size of the respondents, total enumeration was used. A structured questionnaire was used for data collection after being validated and its reliability tested. The research questions were analyzed with descriptive analysis while the hypothesis were tested with inferential statistics. The findings of this study shows that the respondents have the necessary competencies (skills) to support the management of digital information resources through the application of metadata practice. Furthermore, the analysis revealed that the respondents have more basic ICT skills than core metadata skills for the management of digital information resources. The study concluded that librarians’ competencies have a significant influence on the management of digital information resources in Federal University libraries in Nigeria . Therefore, it is recommended that the various university management understudied put metadata ICT skills of librarians and facilitating conditions into consideration when designing and implementing metadata projects for the management of digital information resources.
ELASTIC -ANELASTIC PROPERTIES OF THE AEGEAN LITHOSPHERE - ASTHENOSPHERE INFERRED FROM LONG PERIOD RAYLEIGH WAVES

The highest deformation rate along the Africa/Euras ia convergence zone is well documented in the Aegean area, being >4 cm/yr. However, it is still under question whether continental deformation is distributed along major faults which extend through the whole lithosphere or over large areas. Furthermore, our k nowledge concerning the implication of lithosphere-asthenosphere coupling in lithospheric plates driving forces is poor. These questions can not easily be answered as most of the available information is mainly located at or close to the surface (geodesy, tectonics, seismi city). The high rates and type of surface continental deformation within the Aegean constitut e this region particularly interesting in this perspective. This work is towards contributing to the better kno wledge of the physical properties of the Aegean lithosphere by introducing experimental elastic and anelastic parameters inferred from long period Rayleigh wave. For this scope path -average phase velocities and attenuation coefficients of fundamental Rayleigh wave crossing the Aegean were extracted over the period range 10-100 s. The wavetrains were recorded at the temporary broadband stations installed some years ago in the Aegean region withi n the framework of a large scale experiment (SEISFAULTGREECE project).