Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Landscape Recovery Scheme in the UK: a Q methodology study in Yorkshire, UK

Embedded within the European Union’s Green Deal is a re-enforced scope to encourage farmers’ participation in primarily voluntary agri-environmental schemes. Although outside of the European Union, the newly announced agri-environment schemes in England mirror such a policy shift towards incentivising participation in order to delivery more and better climate public goods. Farmers’ viewpoints regarding such schemes and contracts are therefore important to examine as they should be main determinants of current and future enrolment. In this paper, upland Yorkshire farmers are asked to express their opinions for the landscape Recovery scheme that aims to encourage collaboration and achieve landscape-wide interventions to ensure lasting delivery of climate public goods. Viewpoints show a dichotomy between pragmatic and environmentally-conscious farmers with preferences leaning towards ‘broad and shallow’ schemes that would have simple contract requirements and only achieve marginal gainsin the delivery of agri-environmental climate public goods.
“I Thought About Solutions”: How Students’ Narratives of Relational Conflict, Grit, and Agency Predict Academic Performance within Community College

Objective. Theorists have posited that community college students’ socio-academic integrative relationships with other students, college staff, and instructors, as well as their sense of college-related aspiration, mindset, grit, and agency, may impact their academic success. When community college students narrate (i.e., create stories that help them understand, investigate, and communicate) about higher education, they may reflect upon such relational and internal experiences in ways that signal later academic performance. This study reviews how students narrate to reflect upon their relational and internal experiences within a community college. It then connects students’ narrative explorations to their year-end grade point average (GPA). Method. Script analysis was used to explore how 104 ethnically, culturally, and linguistically diverse community college students reflected on their best and worst college experiences using different scripts, which are collective and shared ways of knowing that people use to organize their understandings of recounted experiences. Multiple regression models linked students’ narrative scripts to their year-end GPA. Results. Students narrated on their college lives using a variety of different scripts. When narrating about their worst experiences, students’ focus on socio-academic relational conflicts and on their grit and agency while coping with college-related difficulties predicted their having a higher year-end GPA. Conclusion. In a partial confirmation of Wang’s and Deil-Amen’s theoretical expectations, students’ narrative expressions of grit and agency, as well as their relational experiences and conflicts with other students, instructors, and staff at college, predicted their academic success.
The Confounding Question of Confounding Causes in Randomized Trials

It is sometimes thought that randomized study group allocation is uniquely proficient at producing comparison groups that are evenly balanced for all confounding causes. Philosophers have argued that in real randomized controlled trials this balance assumption typically fails. But is the balance assumption an important ideal? I run a thought experiment, the CONFOUND study, to answer this question. I then suggest a new account of causal inference in ideal and real comparative group studies that helps clarify the roles of confounding variables and randomization. 1. Confounders and Causes2. The Balance Assumption3. The CONFOUND Study 3.1. CONFOUND 13.2. CONFOUND 24. Disjunction C and the Ideal Study 4.1. The ultimate ‘other cause’: C4.2. The ideal comparative group study4.3. Required conditions for causal inference5. Confounders as Causes, Confounders as Correlates6. Summary Confounders and Causes The Balance Assumption The CONFOUND Study 3.1. CONFOUND 13.2. CONFOUND 2 CONFOUND 1 CONFOUND 2 Disjunction C and the Ideal Study 4.1. The ultimate ‘other cause’: C4.2. The ideal comparative group study4.3. Required conditions for causal inference The ultimate ‘other cause’: C The ideal comparative group study Required conditions for causal inference Confounders as Causes, Confounders as Correlates Summary
Constraints on dolomite formation in a Late Palaeozoic saline alkaline lake deposit, Junggar Basin, north‐west China

Alkaline lakes (pH > 9) are among the few modern sedimentary environments that are hydrochemically favourable for low‐temperature dolomite formation. While Mg‐clays and Mg‐evaporites also form more easily in alkaline environments, few studies have focused on how the kinetically inhibited dolomite wins the competition for Mg2+. Here, a basin‐wide survey of the distribution, paragenesis and stable C, O and Mg isotopes of main Mg‐rich minerals in the Late Palaeozoic saline alkaline lake deposit of the north‐west Junggar Basin, north‐west China, is conducted to study the influence of the formation and diagenesis of eitelite, northupite and Mg‐clays on dolomite formation. Large, isolated dolomite crystals (20 to 70 μm in diameter), show positive δ13C values (ranging from +1 to +7‰) and a restricted distribution in the mudstones of the lake‐transitional zone. These crystals have been interpreted as organogenic dolomite driven by methanogenesis via fermentation of organic substrates. The δ18O values of dolomitic mudstones (from −7.4 to +3.4‰), calcitic mudstones (from −15.1 to −3.3‰) and bedded Na‐carbonate evaporites (from +0.08 to +3.7‰), together with their Mg isotopic compositions, suggest that dolomite was not enriched in the most concentrated environments or during stages with most Mg sources, but in the organic‐rich deposits containing few other authigenic Mg‐rich minerals. Dolomite is at a competitive disadvantage for Mg2+ ions compared to Mg‐evaporite and Mg‐clay minerals due to its slow crystallization rates and the deficiency of micritic calcium carbonate precursors. However, it can nucleate and progressively grow into large crystals (>20 μm) if bacterial methanogenesis could effectively lower porewater pH (<8.5) and induce the dissolution of generated eitelite, northupite or Mg‐clays. These findings suggest that high salinity and/or high alkalinity are not always favourable conditions for dolomite formation and highlight the active role of pH fluctuations in inducing low‐temperature dolomite formation.
The Quantum Space Phase Transitions for Particles and Force Fields

We introduce a phenomenological formalism in which the space structure is treated in terms of attachment space and detachment space. Attachment space attaches to an object, while detachment space detaches from the object. The combination of these spaces results in three quantum space phases: binary partition space, miscible space and binary lattice space. Binary lattice space consists of repetitive units of alternative attachment space and detachment space. In miscible space, attachment space is miscible to detachment space, and there is no separation between attachment space and detachment spaces. In binary partition space, detachment space and attachment space are in two separate continuous regions. The transition from wavefunction to the collapse of wavefuction under interference becomes the quantum space phase transition from binary lattice space to miscible space. At extremely conditions, the gauge boson force field undergoes a quantum space phase transition to a “hedge boson force field”, consisting of a “vacuum” core surrounded by a hedge boson shell, like a bubble with boundary.
A systematic review of empirical studies on computer-assisted language learning

The aim of this study was to examine the current trends and main findings of the empirical computer-assisted foreign language learning studies. The database, Web of Science, was reviewed and a total of 36 studies between 2000 and 2020 were analysed. The systematic analysis was performed with the content analysis method. The content of the reviewed articles was analysed in the following categories: The technological trends, the technological context used, variables, duration of the studies, data collection tools, data analysis strategies, skills/factors analysed, underlying theories, and countries of the studies, target languages, participants, and years of the studies. Along with other insights this review found, the most common technological trend was educational software, while the technological contexts used varied greatly. Key variables from mostly small studies (less than 100 participants generally tertiary students), focused on technological contexts, language learning skills (typically vocabulary and speaking) and participants’ attitudes. Underlying theories of these studies varied greatly, Technology Acceptance Model and Cognitive Load Theory being common. This review study can serve as a guide for practitioners who plan designing computer-assisted language learning and teaching activities.
Artificial Intelligence-Based Hazard Detection in Robotic-Assisted Single-Incision Oncologic Surgery

Simple Summary SILS and U-VATS procedures present many advantages, but also a series of limitations, such as the narrow operating space and poor surgeon ergonomics. To enhance the surgeon’s performance, this study aims to develop an AI and AR-based hazard detection system. The focus is on detecting and locating hemorrhages, a common occurrence especially in cancer patients. The system utilizes the YOLO v5 algorithm, trained to accurately identify bleeding in real-time. When bleeding is detected, it is displayed on a Hololens 2 device, alerting the doctor to its occurrence and origin. The system demonstrated its capability to distinguish between instances of bleeding and intraoperative irrigation, reducing the risk of false-negative and false-positive results. The current advancements in AI and AR technologies present potential of real-time hazard detection systems, which can serve as effective tools to assist surgeons, particularly in high-risk surgeries. Abstract The problem: Single-incision surgery is a complex procedure in which any additional information automatically collected from the operating field can be of significance. While the use of robotic devices has greatly improved surgical outcomes, there are still many unresolved issues. One of the major surgical complications, with higher occurrence in cancer patients, is intraoperative hemorrhages, which if detected early, can be more efficiently controlled. Aim: This paper proposes a hazard detection system which incorporates the advantages of both Artificial Intelligence (AI) and Augmented Reality (AR) agents, capable of identifying, in real-time, intraoperative bleedings, which are subsequently displayed on a Hololens 2 device. Methods: The authors explored the different techniques for real-time processing and determined, based on a critical analysis, that YOLOv5 is one of the most promising solutions. An innovative, real-time, bleeding detection system, developed using the YOLOv5 algorithm and the Hololens 2 device, was evaluated on different surgical procedures and tested in multiple configurations to obtain the optimal prediction time and accuracy. Results: The detection system was able to identify the bleeding occurrence in multiple surgical procedures with a high rate of accuracy. Once detected, the area of interest was marked with a bounding box and displayed on the Hololens 2 device. During the tests, the system was able to differentiate between bleeding occurrence and intraoperative irrigation; thus, reducing the risk of false-negative and false-positive results. Conclusion: The current level of AI and AR technologies enables the development of real-time hazard detection systems as efficient assistance tools for surgeons, especially in high-risk interventions.
Νanomaterial-Loaded Polymer Coating Prevents the In Vitro Growth of Candida albicans Biofilms on Silicone Biomaterials

Early failure of silicone voice prostheses resulting from fungal colonization and biofilm formation poses a major concern in modern ear nose throat surgery. Therefore, developing new infection prevention techniques to prolong those implants’ survivorship is crucial. We designed an in vitro laboratory study to include nanomaterial-enhanced polymer coating with a plasma spraying technique against Candida albicans growth to address this issue. The anti-biofilm effects of high- and low-dose Al2O3 nanowire and TiO2 nanoparticle coatings were studied either alone or in conjunction with each other using checkerboard testing. It was demonstrated that both nanomaterials were capable of preventing fungal biofilm formation regardless of the anti-fungal agent concentration (median absorbance for high-dose Al2O3-enhanced polymer coating was 0.176 [IQR = 0.207] versus control absorbance of 0.805 [IQR = 0.381], p = 0.003 [98% biofilm reduction]; median absorbance for high-dose TiO2-enhanced polymer coating was 0.186 [IQR = 0.024] versus control absorbance of 0.766 [IQR = 0.458], p < 0.001 [93% biofilm reduction]). Furthermore, synergy was revealed when the Bliss model was applied. According to the findings of this work, it seems that simultaneous consideration of Al2O3 and TiO2 could further increase the existing antibiofilm potential of these nanomaterials and decrease the likelihood of localized toxicity.
Interplay between turbulence, neoclassical and zonal flows during the transition from low to high confinement mode at ASDEX Upgrade

The spatio-temporal interplay between turbulence, mean and zonal flows has been investigated at the L–H and H–L transitions in the edge region of the ASDEX Upgrade tokamak. Close to both transitions, an intermediate phase (I-phase) characterized by ‘limit cycle like oscillations’ (LCOs) is observed in which periodic bursts of turbulence correlate with E×B flow reduction and relaxation of gradients. During the I-phase, the E×B velocity is dominated by the mean flows indicating that turbulence driven flows are small. Periodic dithers between L-mode and the phases with LCOs are also observed just before the H-mode onset where the edge density and temperature profile gradients evolve on similar timescale as the flows. Thus connection between mean and E×B flows holds during the all evolution from L-mode to H-mode demonstrating the fundamental role of the neoclassical flows in the L–H transition physics.
Effect of Psychological Meaningfulness on Job Involvement, Proactive Behavior, and Performance: Focusing on the Mediating Effect of Self-Efficacy

Employee well-being and productivity are crucial factors for organizational success, and understanding the relationships between psychological meaningfulness, job involvement, proactive behavior, and performance can help organizations to create more supportive and engaging work environments. This empirical study aimed to examine these relationships, with a particular focus on the mediating role of self-efficacy. Research models and hypotheses were derived from a literature review and tested using structural equation analysis on data collected from a questionnaire survey of 398 employees. Results indicated that psychological meaningfulness was significantly related to self-efficacy, job involvement, proactive behavior, and performance. Self-efficacy was found to play a significant mediating role in the relationship between psychological meaningfulness and job involvement, between psychological meaningfulness and proactive behavior, and between psychological meaningfulness and performance. These findings suggest that fostering employees’ psychological meaningfulness can improve their self-efficacy, job involvement, proactive behavior, and, ultimately, their performance. These results have important implications for organizations seeking to enhance their employees’ well-being and productivity.
Local ergodicity in coupled harmonic vibrators: classical and quantal treatments

Ensemble-time ergodicity is proven under some restrictive assumptions for a classical system, comprising interacting harmonic oscillators. An atom in a monatomic chain or lattice is shown to behave ergodically, in the sense that the time average behavior of a lattice point is identical to the ensemble average of the behavior of a lattice point at any long time (in large excess of the inverse vibrational frequencies). This equivalence (for ‘local observables’) differs from the Fermi–Pasta–Ulam result for mode energies (which are non-local). Then, the analogous quantal result is derived, with extensions to wider instances. Relationships to canonical typicality and to the eigenstate thermalization hypothesis are discussed and possibilities of experimental verifications of the results are indicated.
A Sequential Block-Structured Dictionary Learning Algorithm for Block Sparse Representations

Dictionary learning algorithms have been successfully applied to a number of signal and image processing problems. In some applications, however, the observed signals may further have a multisubspace structure that enables block-sparse signal representations. For this case, a new algorithm for learning block-structured dictionaries for block-sparse signal representations is proposed in this paper. It is obtained by solving a sequence of penalized low rank matrix approximation problems where the $\ell _{1,2}$-norm is introduced as a penalty promoting block sparsity and then using a block coordinate descent approach to estimate the unknowns. The proposed algorithm has the advantage of involving simple closed form solutions for both, the sparse coding and dictionary update stages. In particular, the sparse coding stage involves a simple shrinkage operation related to soft thresholding and it is related to the uniformly most powerful invariant testing procedure. Experimental results showing the improved efficacy and significant gain in computational time, offered by the proposed algorithm over the usual K singular value decomposition block extension are presented.
On a Shared Property of Deontic and Epistemic Modals

Epistemic modals encode an evidential restriction, requiring that the speaker have inferential evidence for the prejacent (Karttunen, 1972). Stone (1994) and von Fintel and Gillies (2010) encode this restriction lexically in e.g. must, which (given a unified treatments of modals, Kratzer 1991) raises the question: what happens to this restriction when must receives a deontic interpretation? I claim that both deontic and epistemic modals have in common a requirement that their prejacent be inferred from some premises (Glass, 2013). I argue, following Lance and Little 2006, that this is a property of moral reasoning quite generally; in epistemic modal bases, it amounts to an inferential evidence requirement. Deontic and epistemic modals form a natural class with respect to this property to the exclusion of other modal bases; I argue that it is what prevents their acceptability in certain exclamatives (cf. *Wow, must Sue be the murderer! ). It also offers insight into why languages like English sometimes lexicalize these two modal bases to the exclusion of others.
Bioprospecting of Five Ocimum sp. Cultivars from Croatia: New Potential for Dietary and Dermatological Application with Embryotoxicity Tests

Ocimum basilicum L. is the most common Ocimum species, and it is used as an ornamental plant and in food condiments. This unique study examined the chemical composition and biological activities of six extracts from five basil cultivars, including their antimicrobial, antidiabetic, antilipidemic, neuroprotective, and anticollagenase activity. Moreover, their toxicological effects were studied using the zebrafish Danio rerio. Volatile components were determined using HS-SPME and GC-MS, while total polyphenols were detected using HPLC and the spectrophotometric Folin–Ciocalteu method. Spectrophotometric assays (DPPH, ABTS, ORAC, FRAP) were performed to determine antioxidant activity, collagenase inhibition, acetylcholinesterase inhibition, and pancreatic lipase inhibition. Antimicrobial activity was determined using the broth microdilution test. The study found that the biological activities of different basil cultivars varied depending on the proportion of active compounds, as determined by chemical analyses. All six basil extracts significantly inhibited α-amylase, while Purple basil extract most significantly inhibited the activity of collagenase, acetylcholinesterase, and pancreatic lipase. Purple basil and Dark Opal basil I extracts exhibited the highest antimicrobial activity, while the Dark Opal basil II extract had the most significant antioxidant potential. The findings in this study suggest that ethanolic basil extracts have the potential to be used as dietary drugs and implemented in antiaging products. This study is unique in its aims to compare the chemical composition and biological activities of basil cultivars from Croatia and to evaluate potential toxicological effects through embryotoxicity tests on zebrafish Danio rerio embryos, and it reports the first evidence of anticollagenase, antidiabetic, and antilipidemic activities for these cultivars.
Quantitative Analysis for Measuring and Suppressing Bullwhip Effect

Increasing competition in the market generally leads to affect the fluctuation in the product’s demand. Such fluctuations pose a serious concern for the decision maker at each stage of the supply chain. Moreover, the capacity constraint at any level of the supply chain would make the situation more critical by elevating the Bullwhip Effect. The present article introduces a new algorithm i.e. “Iterative Proportional Algorithm (IPA)” under above mentioned situation, which instead of elevating, discourages the bullwhip effect. A comparative analysis of the proposed algorithm with the policies defined in [6] has been provided to explain the bottlenecks of existing policies. It has been established numerically, that application of IPA is beneficial for both retailers as well as supplier, as the combined profit (loss) of all the retailers increases (decreases) and subsequently minimizes the bull whip effect of the supplier. We have incorporated the concept of Product Fill Rate (PFR) through which it is shown that IPA gives better results as compared to other allocation mechanisms.
The Influence of Firm Characteristics and Capital Structure on Sustainable Growth Rate

This study examines the impact of firm characteristics and capital structure on the sustainable growth rate of technology and consumer goods companies listed on JII, considering the moderating influence of industrial sector variables. The research utilizes secondary panel data sourced from annual financial reports. The population consists of JII-listed companies from 2018 to 2022, with a purposive sampling approach resulting in a sample size of six firms. The Structural Equation Modeling - Partial Least Square analysis findings reveal that firm size, characteristics, and capital structure significantly influence the sustainable growth rate, with firm size further enhanced through industrial sector moderation. The study contributes theoretically by emphasizing the significance of firm characteristics and capital structure while offering practical insights by showcasing how firms can stimulate sustainable growth through enhanced profitability and capital structure, thereby facilitating reinvestment and development.