Search Results (7,170)

Analyzing accident reports to absorb past experiences is crucial for construction site safety. Current methods of processing textual accident reports are time-consuming and labor-intensive. This research applied the LDA topic model to analyze construction accident reports, successfully identifying five main types of accidents: Falls from Height (23.5%), Struck-by and Contact Injuries (22.4%), Slips, Trips, and Falls (21.8%), Hot Work & Vehicle Hazards (18.1%), and Lifting and Machinery Accidents (14.2%). By mining the rich contextual details within unstructured textual descriptions, this research revealed that environmental factors constituted the most prevalent category of contributing causes, followed by human factors. Further analysis traced the root causes to deficiencies in management systems, particularly poor task planning and inadequate training. The LDA model demonstrated superior effectiveness in extracting interpretable topics directly mappable to engineering knowledge and uncovering these latent factors from large-scale, decade-spanning textual data at low computational cost. The findings offer transformative perspectives for improving construction site safety by prioritizing environmental control and management system enhancement. The main theoretical contributions of this research are threefold. First, it demonstrates the efficacy of LDA topic modeling as a powerful tool for extracting interpretable and actionable knowledge from large-scale, unstructured textual safety data, aligning with the growing interest in data-driven safety management in the construction sector. Second, it provides large-scale, empirical evidence that challenges the traditional dogma of “human factor dominance” by systematically quantifying the critical role of environmental and managerial root causes. Third, it presents a transparent, data-driven protocol for transitioning from topic identification to causal analysis, moving from assertion to evidence. Future work should focus on integrating multi-dimensional data for comprehensive accident analysis. Full article

Computer-assisted surgery (CAS) and virtual surgical planning (VSP) have transformed jaw reconstruction, allowing immediate insertion of dental implants during surgery for better rehabilitation of occlusal function. However, traditional planning for optimal location and angulation of dental implants and fibula relies on experience and can be time-consuming. This study aimed to propose a function-driven workflow and develop an automatic computer program for optimal positioning of simultaneous dental implants and fibula segments. A customized computer program was developed using MATLAB. Computed tomography (CT) of the lower limbs of ninety-one Southern Chinese individuals was retrieved and cross-sections of three-dimensional (3D) fibula models were comprehensively investigated for implant installation. Our research proves that the accuracy of the program in identifying the anatomical orientation of the fibula was 92%. The ideal location, angulation and length of implant could be automatically generated based on any selected implant diameter, with a surgical feasibility of 94%. To the best of our knowledge, this is the first study to develop and validate a customized automatic computer program for osseointegrated implant design in fibula flap surgery. This program can be incorporated into the current workflow of CAS to further the development of reliable and efficient surgical planning for function-driven jaw reconstruction. Full article

Lodging is one of the key limiting factors in achieving high wheat yield. The application of plant growth retardants (PGRts) is regarded as an effective practice to prevent lodging. For accurate PGRt selection and the establishment of stable, high-yield production plans, it is essential to make clear the regulation strategies for lodging resistance and yield in PGRts. Field experiments were conducted at two test sites. At the initial jointing stage of wheat, Chlormequat Chloride (CCC) or Uniconazole (S₃₃₀₇) was sprayed. Compared with the control (CK), spraying CCC or S₃₃₀₇ significantly reduced the culm lodging index (CLI) and decreased the lodging rate from 7.1% to 15.6%. CCC was more capable of adjusting plant morphology (reducing plant height and second internode length and increasing stem diameter), while S₃₃₀₇ was more effective in enhancing breaking strength. The contents of GA, IAA, and zeatin nucleoside (ZR) and the activities of lignin-related enzymes (TAL and CAD) were significantly correlated with different stem indicators and CLI. Compared with CK, the yield after spraying CCC or S₃₃₀₇ increased by 6.5% and 6.0%, respectively. CCC mainly enhanced the yield by increasing grain weight per spike and the SPAD value of leaves, while S₃₃₀₇ mainly did so by increasing the number of spikes and the effective leaf area. Moreover, carbon metabolism-related enzymes (Rubisco, SS, and SPS) were significantly positively correlated with the yield. The enzyme activity of CCC was higher at the heading stage, while that of S₃₃₀₇ was higher at the filling stage. Hence, spraying CCC or S₃₃₀₇ can significantly enhance lodging resistance and yield. The optimal PGRts should be selected based on the climate and the growth stage of the wheat. Full article

Background/Objectives: Head and neck cancers significantly affect patients’ functional and psychosocial well-being. Multidisciplinary tumor boards have a central role in optimizing treatment strategies, but the relationship between tumor characteristics, comorbidities, and quality of life (QoL) remains insufficiently explored. Methods: We conducted a retrospective study of 94 patients with head and neck cancers evaluated by the oncology committee of Coltea Clinical Hospital in 2024. QoL was assessed post-surgery using the EORTC QLQ-C30 and H&N35 questionnaires. Descriptive statistics, non-parametric tests, correlations, and multivariate regression analyses were performed to examine associations between clinical variables and QoL outcomes. Results: The cohort comprised 82 men (87.2%) and 12 women (12.8%), with a mean age of 61.5 ± 9.8 years. The most common tumor site was the larynx (43.6%). Global QoL was low (mean = 42.3, SD = 11.7), and fatigue scores were high (mean = 61.5, SD = 13.5). All EORTC domains showed non-normal distributions (Shapiro–Wilk, p < 0.05). Kruskal–Wallis analysis revealed significantly lower QoL scores in patients with metastatic adenopathy with aunknown primary (p = 0.03). Spearman’s correlation indicated a moderate negative association between Charlson Comorbidity Index and QoL (r = −0.38, p = 0.01). Multivariate regression confirmed comorbidities (β = −2.5, p = 0.02) and tumor type (metastatic adenopathy, β = −8.0, p = 0.04) as independent predictors of reduced QoL. Conclusions: Patients with advanced disease and higher comorbidity burden experience significantly poorer QoL after head and neck cancer surgery. Tumor board decisions facilitate individualized treatment planning; however, systematic integration of QoL metrics is essential to optimize both oncological and functional outcomes. Full article

Cross-docking has emerged as a critical logistics strategy to reduce lead times, lower inventory levels, and enhance supply chain responsiveness. However, in high-throughput terminals, efficient coordination of inbound and outbound trailers remains a complex task, especially under uncertain and dynamically changing conditions. We propose a practical framework that helps logistics terminals assign trailers to docks in real time. It links live sensor data with a mathematical optimization model, so that the system can quickly adjust trailer plans when traffic or workload changes. Real-time data from IoT sensors, GPS, and operational records are preprocessed, enriched with predictive analytics, and used as input for a Mixed-Integer Linear Programming (MILP) model solved in rolling horizons. This enables the continuous reallocation of inbound and outbound trailers, ensuring synchronized flows and balanced dock utilization. Numerical experiments compare the adaptive approach with conventional first-come-first-served scheduling. Results show that average inbound dock utilization improves from 68% to 71%, while the share of periods with full utilization increases from 33.3% to 41.4%. Outbound utilization also rises from 57% to 62%. Moreover, trailer delays are significantly reduced, and the overall makespan shortens from 45 to 40 time slots. These findings confirm that adaptive, real-time trailer assignment can enhance efficiency, reliability, and resilience in cross-docking operations. The proposed framework thus bridges the gap between static optimization models and the operational requirements of modern, high-throughput logistics hubs. Full article

To address path deviation and efficiency reduction issues in traditional interpolation optimization algorithms for complex path machining, this paper proposes a chord error-priority bilevel interpolation optimization method (CPBI). First, arc length parametric modeling of the machining path is performed within the Frenet–Serret framework, yielding curvature and torsion information. After introducing geometric-based multi-machining constraints in the outer layer, the velocity upper limit is established by controlling chord error to dynamically adjust regions with curvature mutation. In the inner layer, combining the velocity limit with bidirectional scanning achieves adaptive optimization of interpolation step size and optimal velocity planning that balances precision and smoothness. Simulation results demonstrate that CPBI effectively reduces the number of interpolation points by 30–50% while ensuring the chord error. Compared with the reference method, the CPBI improved efficiency by 14.31% and 34.72% in machining experiments on S-shaped and wave-shaped paths, respectively. The results validated the CPBI’s high precision and efficiency advantages in complex path machining, providing an effective solution for CNC path optimization in high-end manufacturing. Full article

Background: Childhood obesity is a growing public health challenge in Poland and worldwide, associated with serious long-term health consequences. Effective prevention requires family-centered, evidence-based interventions that actively engage both children and their caregivers. This study presents the Finnish Smart Family practice—an evidence-based lifestyle counseling method developed by the Finnish Heart Association—and describes its adaptation and implementation in Poland as part of the EU Health4EUkids project. The study emphasizes the method’s practical utility for professionals working with families of children with obesity. Methods: The Smart Family approach is a structured lifestyle counseling method based on findings from the Special Turku Coronary Risk Factor Intervention Project (STRIP) that is grounded in health psychology and strength-based counseling principles. Unlike traditional counseling, which focuses mainly on information transfer, Smart Family promotes motivation, families’ active participation, and recognition of their strengths in areas such as nutrition, physical activity, sleep, and oral hygiene. The method uses practical tools including the Smart Family card, other supporting materials, and dedicated online platforms for both families and healthcare providers. These tools enable families to self-assess their lifestyle, select discussion topics during visits, and set achievable goals while supporting professionals in initiating non-judgmental, collaborative conversations. In Poland, the program was adapted using culturally appropriate materials and professional training, followed by pilot implementation in primary healthcare and educational settings that included pre-implementation planning, practical training sessions, the application of intervention tools, and outcome evaluation. Results: Pilot implementation demonstrated high usability and effectiveness. The approach enabled non-judgmental, supportive engagement with families, facilitated active participation in setting health goals, and promoted sustainable lifestyle changes in nutrition, physical activity, sleep, and other health behaviors. Evaluation highlighted the importance of supporting program objectives at the national level, standardizing child healthcare practices, and engaging media and local authorities to create a supportive ecosystem. Conclusions: The Polish experience confirms that Smart Family is an evidence-based intervention that strengthens professional competence, provides practical tools for family-centered care, and supports the long-term prevention of child-hood obesity and related non-communicable diseases. Its integration into healthcare and educational settings offers a promising strategy for improving public health outcomes. Full article

Accurate forecasts of the U.S. renewable energy consumption mix are essential for planning transmission upgrades, sizing storage, and setting balancing market rules. We introduce a Bayesian Dirichlet ARMA model (BDARMA) tailored to monthly shares of hydro, geothermal, solar, wind, wood, municipal waste, and biofuels from January 2010 through January 2025. The mean vector is modeled with a parsimonious VAR(2) in additive log ratio space, while the Dirichlet concentration parameter follows an intercept plus five Fourier harmonics, allowing for seasonal widening and narrowing of predictive dispersion. Forecast performance is assessed with a 61-split rolling origin experiment that issues twelve month density forecasts from January 2019 to January 2024. Compared with three alternatives (a Gaussian VAR(2) fitted in transform space, a seasonal naive approach that repeats last year’s proportions, and a drift-free ALR random walk), BDARMA lowers the mean continuous ranked probability score by 15 to 60 percent, achieves componentwise 90 percent interval coverage near nominal, and maintains point accuracy (Aitchison RMSE) on par with the Gaussian VAR through eight months and within 0.02 units afterward. These results highlight BDARMA’s ability to deliver sharp and well-calibrated probabilistic forecasts for multivariate renewable energy shares without sacrificing point precision. Full article

In the context of rapid development within the logistics sector and widespread advocacy for sustainable development, this paper proposes enhancements to the task scheduling and path planning components of four-way shuttle systems. The focus lies on refining and innovating modeling approaches and algorithms to address issues in complex environments such as uneven task distribution, poor adaptability to dynamic conditions, and high rates of idle vehicle operation. These improvements aim to enhance system performance, reduce energy consumption, and achieve sustainable development. Therefore, this paper presents an energy-saving and efficiency-enhancing optimization study for a four-way shuttle-based high-density automated warehouse system, utilizing deep reinforcement learning. In terms of task scheduling, a collaborative scheduling algorithm based on an Improved Genetic Algorithm (IGA) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) has been designed. In terms of path planning, this paper provides the A*-DQN method, which integrates the A* algorithm(A*) with Deep Q-Networks (DQN). Through combining multiple layout scenarios and adjusting various parameters, simulation experiments verified that the system error is within 5% or less. Compared to existing methods, the total task duration, path planning length, and energy consumption per order decreased by approximately 12.84%, 9.05%, and 16.68%, respectively. The four-way shuttle vehicle can complete order tasks with virtually no conflicts. The conclusions of this paper have been validated through simulation experiments. Full article

Rural livelihood transformation is increasingly vital for achieving agricultural modernization, reducing poverty, and promoting sustainable development in developing countries. Despite growing attention to land consolidation as a tool for improving agricultural resource allocation and productivity, its role in shaping rural livelihoods remains insufficiently understood. Addressing this gap, this study investigates the impacts of China’s High-Standard Farmland Construction (HFC), the country’s flagship land consolidation policy, on farmers’ livelihoods, focusing on both income level and income structure. Using provincial panel data from 30 regions, we adopt a continuous difference-in-differences design and mediation effect model to identify the causal effects of HFC. The results indicate that HFC significantly promotes total household income. Specifically, HFC facilitates mechanized agricultural production by consolidating fragmented plots, reducing production costs, and improving crop yields, thereby increasing agricultural income. Simultaneously, mechanization substitutes for labor and releases surplus workers, who often move to off-farm employment, diversifying income sources and stabilizing household livelihoods. Heterogeneity analysis reveals that the benefits of HFC are unevenly distributed. Low-income households, central provinces, and major grain-producing areas experience the greatest gains, and moderate-scale implementation proves more effective than either small- or excessively large-scale projects. This study highlights mechanization as a key mechanism linking land consolidation to rural livelihood transformation. The findings demonstrate that well-planned and efficiently implemented HFC policies can not only enhance agricultural productivity but also foster diversified and inclusive rural livelihoods. Full article

The use of high biomass production plants in studies of metal phytoremediation is an established practice. This strategy aims to identify plants that tolerate unusual amounts of metals such as nickel (Ni). When we compare the biomass production capacity of a Ni hyperaccumulator, for example Alyssum bertolonii, this rate is 4 to 5 kg/ha per crop cycle; on the other hand, species with a high biomass production capacity, for example Canavalia ensiformis, can produce 20 t ha⁻¹ to 25 t ha⁻¹ of green phytomass, 5 t ha⁻¹ to 8 t ha⁻¹ of dry phytomass and 1000 kg ha⁻¹ to 1800 kg ha⁻¹ of seeds. In this context, we planned an experiment to verify the tolerance and Ni accumulation capacity in Canavalia ensiformis. Our hypothesis was that increasing Ni concentration in the soil would not hinder the plant’s biomass production. We conducted a completely randomized experiment with five concentrations of Ni added to the soil and five replicates in a greenhouse during the vegetative stage. We evaluated the plant’s development, biomass production, and Ni accumulation in its organs. Our results demonstrated high tolerance to the metal, maintaining a biomass accumulation capacity of 68% of the dry mass in the soil with 277.8 mg kg⁻¹ of Ni at the highest concentration tested, compared to plants in the control soil. Considering that under these conditions the plants obtained a biomass of 10 g of leaves and 15 g of roots, and a nickel accumulation capacity of 75.05 mg kg⁻¹ in leaves and 102 mg kg⁻¹ in roots, the total Ni accumulation in the plants reached 2.37 mg Ni/plant in the soil with 277.8 mg kg⁻¹ of Ni. This soil Ni concentration would be lethal for most plants, and the metal concentration in the tissue exceeds the established limits for non-tolerant crops. With these results, this study aims to provide a foundation for improving the use of Canavalia ensiformis in phytoremediation. Full article

Background/Aim: Photodynamic therapy uses a photosensitizer and light to generate reactive oxygen species that kill tumor cells and can shift inflammatory signaling. Hypericin is a potent photosensitizer, but its immunomodulatory impact in breast cancer needs clarification. We evaluated the phototoxic and cytokine-modulating effects of hypericin-mediated photodynamic therapy in MCF-7 human breast adenocarcinoma cells. This study examines how HYP-PDT affects MCF-7 breast cancer cells by assessing viability and cytokine secretion to guide the development of targeted, immune-enhancing PDT protocols. Methods: MCF-7 cells were incubated with hypericin at 0, 0.125, 0.25, 0.5, or 1 μM, then exposed to light doses of 0, 1, 2, or 5 J/cm². Viability was measured 24 h later by MTT; selected conditions were also assessed by Trypan Blue. Cell supernatants collected after sublethal treatment were analyzed for IL-6, IL-8, IL-10, and TNF-α using a multiplex immunoassay. Experiments were repeated four times. Statistical analyses followed the study’s plan for group comparisons. Results: At 1 J/cm², MTT values did not differ from matched dark controls across hypericin concentrations. At 2 and 5 J/cm², some conditions showed increased MTT signal relative to controls, indicating higher metabolic activity; Trypan Blue performed at 0 J/cm² showed a concentration-dependent reduction in viability with hypericin. Hypericin-PDT decreased IL-6 and IL-8 concentrations and increased TNF-α in MCF-7 supernatants. No statistically significant changes were detected for IL-10. Conclusions: Hypericin-PDT altered inflammatory readouts in MCF-7 cells, with reductions in IL-6 and IL-8 and an increase in TNF-α, consistent with a pro-inflammatory shift. Viability results suggest condition-dependent changes in metabolic activity or survival effects that warrant confirmation with matched cell counts across all light doses. These findings support further standardized dosimetry and multi-line validation of hypericin-PDT in breast cancer models. Full article

The readiness of tourists to accept innovative food is investigated in this research through the prism of the Protection Motivation Theory and the Theory of Planned Behavior, combining two previously developed yet seldom researched psychological dimensions, namely, food neophobia as a restraining force and food involvement as a motivating force. The quantitative approach and the generation-by-generation analysis using partial least squares (PLS-SEM) and multiple group analysis were used to conduct the study on a sample of 985 domestic tourists in Serbia. The results suggest that food involvement eases openness toward gastronomic innovations and mitigates the negative impact of neophobia, whereas the generational differences reveal that younger tourists are more willing to be experimental, and older generations tend to be conservative in their food consumption. The study is relevant to the academic literature because it puts motivational and barrier factors into context within the PMT and TPB paradigms and provides operational implications for the design of tourism propositions that can be used to promote innovative and sustainable gastronomic experiences. The novelty of the present study is that it uses the hybrid model of food neophobia and food involvement in the generational context of a post-transition society, i.e., Serbia. Full article

Wet markets serve as critical infrastructure for access to fresh food for urban residents in China, playing a vital role in daily life and public well-being. However, their accessibility is often shaped by disparities between urban cores and rapidly expanding peripheral districts, raising concerns over spatial equity in the urban food environment. This study investigates these disparities in Shanghai by comparing wet market accessibility in Putuo district (urban core) and Minhang district (periphery). Accessibility is measured using the Gaussian-enhanced two-step floating catchment area (2SFCA) method, incorporating travel time data from the Baidu Map API for multiple transportation modes. The Gini coefficient is further employed to evaluate the equity of accessibility distribution. The results reveal a notable disparity: residents in the periphery (Minhang) experience a higher average level of accessibility, but their access is distributed significantly less equitably compared to those in the traditional urban core (Putuo). These findings underscore a critical trade-off between development efficiency and spatial equity, highlighting the need for targeted planning strategies and policies to address spatial inequalities in fresh food access in rapidly transforming cities. Full article

Climate change is reshaping plant distribution and ecological adaptation worldwide. Ficus tikoua Bur., a perennial resource plant native to Southwest and South China, has not been systematically assessed for its future cultivation potential. In this study, we used the Biomod2 ensemble modeling framework, integrating 12 algorithms with 469 occurrence records and 16 environmental variables, to predict the potential distribution and niche dynamics of F. tikoua under current and future climate scenarios (SSP126, SSP370, and SSP585). The ensemble model achieved high predictive accuracy based on multiple algorithms and cross-validation. The minimum temperature of the coldest month (bio6, 43.5%), maximum temperature of the warmest month (bio5, 25.0%), and annual precipitation (bio12, 10.3%) were identified as the dominant factors shaping its distribution. Model projections suggest that suitable habitats will generally expand northwestward, while contracting in the southeast. Core areas, such as the Yunnan–Guizhou Plateau and the Sichuan Basin, are predicted to remain highly stable. In contrast, southeastern marginal regions are likely to experience a decline in suitability due to intensified heat stress. Niche analyses further revealed strong niche conservatism (overlap D = 0.83–0.94), suggesting that the species maintains stable climatic tolerance and adapts primarily through range shifts rather than evolutionary change. This finding suggests limited adaptive flexibility in response to rapid warming. Overall, climate warming may enhance cultivation opportunities for F. tikoua at higher latitudes and elevations, while emphasizing the importance of protecting stable core habitats, planning climate adaptation corridors, and integrating this species into climate-resilient agroforestry strategies. These findings provide practical guidance for biodiversity conservation and land-use planning, offering a scientific basis for regional policy formulation under future climate change. Full article