Search Results (629)

Disbudding is a common practice on dairy farms, with the hot iron method (HID) widely used, though it causes considerable pain if no analgesia is provided. This study included two experiments. In Experiment 1, an alternative method using policresulen (POD) was evaluated in 24 Holstein calves randomly assigned to either POD or HID at 21 ± 2 days of age. Calves in the POD group received 0.2 mL of 36% policresulen per horn bud, while those in the HID group were fully cauterized. The cornual nerve was blocked with 5 mL of 2% lidocaine in both treatments, and all calves received meloxicam (0.5 mg/kg body weight) for three days post-procedure. Calves treated with POD exhibited fewer pain-related behaviors, such as scratching the horn buds, rubbing against objects, and head shaking, and showed faster horn bud regression. However, 12-month observations revealed that 9 of 12 POD-treated calves showed horn regrowth, indicating limited long-term effectiveness. Experiment 2 assessed consumer perceptions through a questionnaire and video with 236 participants. Participants with farming experience were more familiar with disbudding and preferred HID. In contrast, individuals with less agricultural contact demonstrated a greater willingness to pay for products from farms implementing animal welfare practices, with 76% favoring POD. Overall, participants experienced in agribusiness prioritized technical knowledge and practicality, while others valued animal welfare and were willing to pay higher prices for welfare-friendly practices. Full article

In low-permeability reservoirs, studying reservoir sensitivity is crucial for optimizing water flooding, as it identifies detrimental mineral-fluid interactions that can cause formation damage and reduce injection efficiency. However, existing diagnostic methods for sensitivity-induced damage rely on post-facto pressure monitoring and lack a quantitative relationship between sensitivity factors and water injectivity impairment. Furthermore, correlating microscale interactions with macroscopic injectivity parameters remains challenging, causing current models to inadequately represent actual injection behavior. This study combines microscopic techniques (e.g., SEM, XRD, NMR) with macroscopic core flooding experiments under various sensitivity-inducing conditions to analyze the influence of reservoir mineral composition on flow capacity, evaluate formation sensitivity, and assess the dynamic impact on water injectivity. The quantitative relationship between clay minerals and injectivity impairment in low-permeability reservoirs is also investigated. The results indicate that flow capacity is predominantly governed by the type and content of sensitive minerals. In water-sensitive reservoirs, water injection induces clay swelling and migration, leading to flow path reconfiguration and water-blocking effects. In salt-sensitive formations, high-salinity water promotes salt precipitation within pore throats, reducing permeability. In velocity-sensitive formations, fine particle migration causes flow resistance to initially increase slightly and then gradually decline with continued injection. Acidizing generally enhances pore connectivity but induces pore-throat plugging in chlorite-rich reservoirs. Alkaline fluids can exacerbate heterogeneity and generate precipitates, though appropriate concentrations may improve connectivity. Under low effective stress, rock dilation increases porosity and permeability, while elevated stress causes compaction, increasing flow impedance. Full article

Unidirectional carbon fiber-reinforced polymer (UD-CFRP) composites demonstrate superior tensile creep strain and stress relaxation behavior along fiber orientation. However, prolonged transverse compressive loading in structural connection zones induces significant interfacial stress relaxation and creep deformation, primarily driven by resin matrix degradation and interfacial slippage under thermal-mechanical interactions, and remains poorly understood. This study systematically investigates the transverse stress relaxation characteristics of UD-CFRP through controlled experiments under varying thermal conditions (20–80 °C) and compressive stress levels (30–80% ultimate strength). Post-relaxation mechanical properties were quantitatively evaluated, followed by the development of a temperature-stress-time-dependent predictive model aligned with industry standards. The experimental results reveal bi-stage relaxation behavior under elevated temperatures and compressive stresses, characterized by a rapid primary phase and stabilized secondary phase progression. Notably, residual transverse compressive strength remained almost unchanged, while post-relaxation elastic modulus increased by around 10% compared to baseline specimens. Predictive modeling indicates that million-hour relaxation rates escalate with temperature elevation, reaching 51% at 60 °C/60% stress level—about 1.8 times higher than equivalent 20 °C conditions. These findings provide crucial design insights and predictive tools for ensuring the long-term integrity of CFRP-based structures subjected to transverse compression in various thermal environments. Full article

Proppant crushing seriously affects the efficiency and effectiveness of oil and gas production. In conventional studies, multi-particle crushing research often adopts the particle replacement method; however, this method results in a relatively rough and discontinuous crushing simulation process, making energy conservation difficult to maintain before and after crushing, neglects complex mechanical behaviors such as internal stress distribution and crack propagation of particles, and thus lacks mechanical authenticity. Thus, this study employs the bonded crushing method and establishes a calibration method for mesoscopic parameters. By constructing a particle flow numerical model, the force and crushing processes of proppants under different mesoscopic parameter conditions for both single-particle clusters and multi-particle clusters are simulated, enabling comprehensive monitoring of internal crack propagation within particle clusters. The study systematically analyzes and investigates the influence of key mesoscopic parameters including the tensile strength of parallel bonds (pb-ten), cohesion of parallel bonds (pb-coh), effective modulus (emod), and stiffness ratio (kratio) on the maximum force required for particle crushing. Additionally, orthogonal experiment analysis is used to study the influence of different mesoscopic parameters on the proppant crushing rate. The results show that the larger the pb-ten and pb-coh, the less likely the proppant particle clusters are to crush; conversely, the higher the emod, the more likely the particle clusters are to crush. Within a certain range, pb-ten has the most significant impact on the proppant crushing rate, followed by pb-coh and emod, while kratio has a smaller impact. Based on the research results regarding the influence of laws of different mesoscopic parameters on proppant crushing performance, the mesoscopic parameters of the proppant were calibrated using the post-experiment proppant crushing rate as the fitting index. The simulation results were then compared with the experimental results, verifying the accuracy of the model. The findings of this study clarify the influence of laws of mesoscopic parameters on proppant crushing performance, providing a basis for the subsequent calibration of mesoscopic parameters for numerical proppants and helping to accurately characterize the macroscopic crushing performance of numerical proppants. Full article

Understanding how trust in artificial intelligence evolves is crucial for predicting human behavior in AI-enabled environments. While existing research focuses on initial acceptance factors, the temporal dynamics of AI trust remain poorly understood. This study develops a temporal trust dynamics framework proposing three phases: formation through accuracy cues, single-error shock, and post-error repair through explanations. Two experiments in financial advisory contexts tested this framework. Study 1 (N = 189) compared human versus algorithmic advisors, while Study 2 (N = 294) traced trust trajectories across three rounds, manipulating accuracy and post-error explanations. Results demonstrate three temporal patterns. First, participants initially favored algorithmic advisors, supporting “algorithmic appreciation.” Second, single advisory errors resulted in substantial trust decline (η² = 0.141), demonstrating acute sensitivity to performance failures. Third, post-error explanations significantly facilitated trust recovery, with evidence of enhancement beyond baseline. Financial literacy moderated these patterns, with higher-expertise users showing sharper decline after errors and stronger recovery following explanations. These findings reveal that AI trust follows predictable temporal patterns distinct from interpersonal trust, exhibiting heightened error sensitivity yet remaining amenable to repair through well-designed explanatory interventions. They offer theoretical integration of appreciation and aversion phenomena and practical guidance for designing inclusive AI systems. Full article

This study examines the influence of steel fiber reinforcement on the mechanical properties of geopolymer concrete incorporating different slag to fly ash binder ratios (75:25, 50:50, and 25:75). Three fiber contents (0%, 1%, and 2%) by volume were used to assess their impact on compressive strength, flexural strength, initial stiffness, and toughness. Compressive tests were conducted at 1, 7, and 28 days, while flexural behavior was evaluated through a four-point bending test at 28 days. The results showed that geopolymer concrete with 75% slag and 25% fly ash experienced the highest compressive strength and modulus of elasticity, regardless of the steel fiber content. The addition of 1% and 2% steel fiber content enhanced the compressive strength by 17.49% and 28.8%, respectively, compared to the control sample. The binder composition of geopolymer concrete plays a crucial role in determining its compressive strength. Reducing the slag content from 75% to 50% and then to 25% resulted in a 15.1% and 33% decrease in compressive strength, respectively. The load–displacement curves of the 2% fiber-reinforced beams display strain-hardening behavior. On the other hand, after the initial crack, a constant increase in load causes the specimen to experience progressive strain until it reaches its maximum load capacity. When the peak load is attained, the curve gradually drops due to a loss in load-carrying capacity known as post-peak softening. This behavior is attributed to steel’s ductility and is evident in specimens 75S25FA2 and 50S50FA2. Concrete with 75% slag and 25% fly ash demonstrated the highest peak load but the lowest ultimate displacement, indicating high strength but brittle behavior. In contrast, concrete with 75% fly ash and 25% slag showed the lowest peak load but the highest displacement. Across all binder ratios, the addition of steel fibers enhanced the flexural strength, initial stiffness, and toughness. This is attributed to the bridging action of steel fibers in concrete. Additionally, steel fiber-reinforced beams exhibited a ductile failure mode, characterized by multiple fine cracks throughout the midspan, whereas the control beams displayed a single vertical crack in the midspan, indicating a brittle failure mode. Full article

The Sohodol Gorges has become a location of interest for tourists seeking ecological experiences and outdoor activities. The main purpose of the present study is to evaluate the attitudes of Romanian tourists toward the development of geotourism in this region following the COVID-19 pandemic. In conjunction with the research questions, hypotheses, variables, and research methodology, the following research objectives were emphasized in this study of the Oltenia region: (1) investigate how certain socio-demographic variables, such as age, gender, level of education, and occupation, influence tourists’ perceptions of the various aspects of geotourism development in the Sohodol Gorges; (2) analyze the different dimensions of geotourism, including its economic, ecological, and socio-cultural impacts, thus contributing to a deeper understanding of how geotourism is perceived in the study area in the post-pandemic context. For a qualitative evaluation of the information presented in this study, the authors used a qualitative survey with open questions and closed questions as a data collection method. For data processing and analysis, the EViews version 12.0 software package was used, enabling complex statistical analyses such as multiple regressions and correlation coefficient determination. These techniques were essential for identifying and interpreting the relationships between demographic variables and tourist perceptions. The research results provide a detailed picture of the influence that demographic and behavioral factors have on tourists’ perceptions in the context of post-COVID-19 geotourism development in the Sohodol Gorges of Romania. Education level and age play a significant role in shaping economic and environmental perceptions, indicating that tourists with higher education levels are more aware of the economic and ecological impact of tourism. Full article

As sustainable tourism practices gain traction globally, immersive technologies such as augmented reality (AR) and virtual reality (VR) have emerged as effective tools to enrich visitor experiences while supporting heritage site preservation. Particularly within built cultural environments, these technologies facilitate non-invasive interpretation of architectural spaces, enabling sustainable interaction with fragile historical structures. Despite growing scholarly attention, existing research has primarily focused on the pre-adoption phase or the technical affordances of AR/VR, with limited understanding of user behavior in the post-adoption phase. To address this gap, this study integrates the Expectation Confirmation Model (ECM) with the experiential attributes of AR/VR-enabled heritage applications, proposing an integrated theoretical model to identify key determinants of tourists’ continuance intention. Based on 434 valid survey responses collected at the Terracotta Warriors Museum, a UNESCO World Heritage Site, and analyzed using structural equation modeling (SEM), the results reveal that perceived usefulness, perceived ease of use, satisfaction, and confirmation directly influence continuance intention, while visual appeal, entertainment, enjoyment, interactivity and confirmation exert indirect effects through mediating mechanisms. The findings contribute theoretically by extending ECM to the heritage tourism domain and empirically by providing robust evidence from a high-profile non-Western site. Practically, this study offers actionable implications for designing immersive experiences that enhance post-visit continuance intention and align with broader sustainability objectives. Full article

Additively manufactured thermoplastic polyurethane (TPU 95A) is widely used in engineering, yet its machining behavior remains insufficiently explored. This study investigates the post-processing machinability of FDM-fabricated TPU 95A using CNC milling, with a particular focus on material removal rate (MRR) and surface roughness (Ra). A full factorial design of experiments (81 runs) is conducted, considering four input parameters such as spindle speed (N; 2000, 4000, 6000 rpm) and feed rate (F; 100, 200, 300 mm/min) on the CNC vertical machining center, together with infill density (ϕ; 33%, 66%, 100%) and layer thickness (LT; 1.0, 1.5, 2.0 mm). MRR is modeled and optimized across all densities, achieving strong fit (R² = 0.94; Adj-R² = 0.93). The optimum conditions are found to be MRR ≈ 1251 mm³/min at F = 300 mm/min, ϕ = 100%, N ≈ 3500 rpm and LT ≈ 1.05 mm. Ra can only be measured for 100% infill specimens, as lower infill surfaces violate profile measurement requirements. Its regression model shows weak explanatory power (R² = 0.14; Adj-R² = 0.03) and is excluded from optimization. Instead, Ra is reported descriptively: milling reduced roughness from ≈25–30 μm (as-printed) to ≈13.8 μm under favorable conditions. Overall, the study highlights machining’s role in the hybrid manufacturing practice. Full article

This study investigated the tensile behaviors of 12.70 mm and 15.20 mm diameter anchor cable specimens with ultimate tensile strengths of 1860 MPa and their material specimens through experiments and finite element (FE) simulations. Material specimens and anchor cable specimen tensile samples were prepared, and the complete engineering stress–strain curves were obtained via uniaxial tensile tests. FE analysis was used to simulate the uniaxial tensile tests, and the applicability of different constitutive models for describing the true stress–strain relationships was evaluated by comparing the simulated and experimental engineering stress–strain curves. The results showed that the Ludwik, Hollomon, and Swift models, fitted using the pre-necking hardening stage, overestimated the post-necking true stress, while the Voce model underestimated it. In contrast, the Ling and Swift + Voce models provided accurate post-necking true stress predictions. Based on the Ling model and the Rice and Tracey fracture criterion, the load–displacement relationship and fracture behavior of the 12.7 mm anchor cable specimen were best described with W = −0.1 and a = 2, whereas W = −0.1 and a = 3 yielded optimal predictions for the 15.2 mm anchor cable specimen. Full article

Background: A point prevalence survey conducted at Singapore General Hospital in 2021 showed 48% of inpatients on antibiotics. We hypothesize that a mobile application, transforming complex antibiotic prescribing information into a succinct and individualized resource, will empower healthcare professionals and improve antibiotic prescriptions. Hence, we developed ABxSG using the behavioral design thinking approach (BDTA) to ensure positive user experience and sustained engagement. We aim to evaluate the impact of ABxSG on the proportion of inpatients on antibiotics, antibiotic appropriateness, and the number of antibiotic-related interventions by pharmacists. Methods: ABxSG was launched in March 2023. An interrupted time series analysis was conducted to evaluate its impact on the above outcomes measured using data collected from October 2021 to September 2024. There were 18 data points pre- and post-ABxSG. Results: Following the ABxSG launch, there was an immediate reduction in the proportion of inpatients on antibiotics by 1.66% (p < 0.01), followed by a sustained reduction of 3.12% at 18 months (p < 0.01). Piperacillin-tazobactam appropriateness increased by 2.76% at 1 month (p = 0.11) and further increased by 7.09% at 18 months (p < 0.05). Similarly, ceftriaxone appropriateness increased by 5.74% (p < 0.05) at 1 month and remained above pre-ABxSG levels. There was a significant increase in the number of pharmacist-led interventions for dosing optimization, with 96 more interventions/month at 18 months (p = 0.14). Conclusion: Antimicrobial stewardship teams must remain agile, embrace innovations, and adopt digital technologies to engage and empower clinicians. ABxSG reduced the proportion of inpatients on antibiotics and improved antibiotic prescriptions. The incorporation of BDTA in ABxSG, strong hospital leader support, and strategic planning to promote adoption led to its success. Full article

The integration of Artificial Intelligence into education is transforming how abstract and complex concepts are delivered, especially through embodied tools like social robots. This study examines the impact of AlphaMini, a Slovenian-speaking social robot supported by model ChatGPT 4o and trained on structured book-based content, on student engagement during knowledge management lessons. A case study approach was used, including student questionnaires, classroom observations, and post-session discussions, with 70 university students from diverse academic fields. Engagement was assessed across behavioral, emotional, and cognitive dimensions, with comparisons based on prior robot experience. Results show AlphaMini significantly enhanced emotional and behavioral engagement, with moderate cognitive gains. Students familiar with social robots demonstrated higher engagement, interacting more naturally and actively. Informal feedback highlighted positive attitudes toward AlphaMini, especially among students who regularly use generative AI tools like ChatGPT or Copilot. Participants appreciated its human-like gestures, Slovenian language use, and emotionally supportive presence. Many suggested its potential use in primary and inclusive education, where emotional safety and playful interaction are crucial. This study contributes to the growing evidence on AI in education, showing that combining generative AI with social robotics can foster motivation, participation, and emotionally rich learning experiences. Full article

Background: Cochlear implants (CIs) serve diverse populations with hearing loss, but patients with single-sided deafness (SSD) often show lower post-implantation usage and satisfaction than bilateral CI users. This disparity may stem from their normal contralateral ear providing sufficient auditory input for many daily situations, reducing the perceived need for consistent CI use. Consequently, uniform screening and evaluations, typically designed for bilateral hearing loss, often fail to address SSD’s unique needs. Methods: This narrative review synthesizes the current literature to explore patient and device factors shaping CI integration, outcomes, and experience in SSD. It highlights implications for developing personalized care strategies distinct from those used in bilateral hearing loss. Results: SSD patients face unique challenges: reliance on compensatory behaviors and significant auditory processing difficulties like acoustic–electric mismatch and place–pitch discrepancy. Anatomical factors and deafness of duration also impact outcomes. Traditional measures are often insufficient due to ceiling effects. Music perception offers a sensitive metric and rehabilitation tool, while big data and machine learning show promise for predicting outcomes and tailoring interventions. Conclusions: Optimizing CI care for SSD necessitates a personalized approach across candidacy, counseling, and rehabilitation. Tailored strategies, including individualized frequency mapping, adaptive auditory training, advanced outcome metrics like music perception, and leveraging big data for precise, data-driven predictions, are crucial for improving consistent CI usage and overall patient satisfaction. Full article

Accelerated digital transformations and the evolution of consumer behavior in recent years underscore the need for a systemic perspective in marketing analytics to better comprehend the complex interplay between technology, data, and the profound changes triggered by global events, such as the COVID-19 pandemic. The COVID-19 pandemic has catalyzed a massive shift toward digitalization and transformed e-commerce from an option to a necessity for both businesses and consumers. This paper analyzes the total store and non-store sales, as well as total e-commerce sales, of the US retail trade across six main business categories and nine subcategories from the first quarter of 2018 to the first quarter of 2024. The data was divided into three time spans, corresponding to pre-, during, and post-COVID-19 pandemic periods, to examine the changing behavior of US consumers over time for different business categories. The statistical and econometric methods employed are the partial autocorrelation function (PACF), autocorrelation function, autoregressive integrated moving average model, inferential statistics, and regression model. The results indicate that the pandemic significantly increased non-store retailer sales compared to the pre-pandemic period, underscoring the importance of e-commerce. When physical stores reopened, e-commerce sales did not decline to pre-pandemic levels. The PACF analysis showed seasonality and lagged correlations. Thus, the pandemic-induced buying behaviors of US consumers continue to influence current sales patterns. The pandemic was more than just a temporary disruption, which permanently changed the retail sector. Retailers that quickly adapted to online models gained a competitive edge, whereas US consumers became accustomed to the convenience and flexibility of e-commerce. The behavior of US consumers adapted not only in response to immediate needs during the pandemic but also led to longer-term shifts in spending patterns, with each category reacting uniquely based on product type and perceived necessity. The analysis of how the COVID-19 pandemic transformed consumer behavior in the US reveals several important implications for both consumers and trade policymakers. First, the long-lasting and structural shift toward e-commerce is confirmed, representing a fundamental change in the dynamics of demand and supply. For consumers, the convenience, flexibility, and accessibility of digital channels have moved beyond mere situational advantages to become a behavioral norm. This shift has empowered consumers by giving them greater access to price comparisons, more diverse options, and increased informational transparency. Additionally, the data shows the emergence of hybrid consumption models: essential goods are mainly purchased online, while purchases of branded clothing, electronics, furniture, luxury items, and similar products continue to favor the traditional retail experience. Full article

The stability of open-pit mines under low-temperature conditions is critical for safe and efficient coal extraction. However, the mechanisms of rock damage and fracture under combined temperature and stress effects remain unclear, particularly regarding the evolution of mechanical properties under repeated freeze–thaw cycles and varying peripheral pressures. This study investigates the damage and rupture behavior of coal-bearing sandstone in cold-region open-pit mines through experimental testing and theoretical modeling. The research was conducted in three stages: (1) freeze–thaw and peripheral pressure experiments to evaluate mechanical property evolution; (2) acoustic emission monitoring to analyze internal fracture initiation, propagation, and coalescence under temperature–stress coupling; (3) development of a local deterioration model to quantify post-damage strength decay considering low-temperature erosion and freeze–thaw effects. Results show that increasing freeze–thaw cycles leads to a transition from brittle to ductile behavior, while higher peripheral pressures significantly enhance ductility. Mechanical parameters are highly sensitive to peripheral pressure but largely independent of freeze–thaw cycle count. Acoustic emission signals respond strongly to temperature, and temperature–stress coupling governs the three-stage evolution of fracture germination, extension, and penetration. The local deterioration model effectively captures post-peak residual strength and damage evolution. These findings indicate that in regions with higher microcrack density, fault propagation is driven by rapid coalescence under stress concentration, whereas in lower-density regions, it is dominated by gradual fracture growth and temperature-induced expansion. The results provide theoretical guidance for stability assessment and support design in open-pit coal mines in cold environments. Full article