Search Results (17,147)

There is a compelling literature base in the field of education that highlights how school leaders are crucial to improving academic processes and outcomes, including instruction and raising student achievement. Research has also demonstrated that effective school leaders exhibit behaviors aligned with promoting the core issues of teaching, learning, and school improvement. Less well-known is what science says about the effectiveness of professional development and leadership preparation programs for developing the competencies needed for addressing the diverse mental health needs of students. Better understanding the science is important as school leaders are increasingly tasked with connecting leadership behaviors to students’ social and emotional outcomes, and these leaders play a large role in whether, and to what extent, mental health promotion and prevention are effectively implemented in school settings. Drawing from diverse literature bases of education and psychology, the primary objective of this narrative literature review is to determine and define effective leadership behaviors, skills, and competencies in the context of implementing school mental health programs and strategies. The secondary objective is to summarize the existing research examining leadership preparation and professional development programs that promote effective leadership practices and highlight examples of leadership programs focused on enhancing school mental health. Full article

The shallow-sea hydrothermal vent at Guishan Islet, located off the coast of Taiwan, serves as a remarkable natural site for studying microbial ecology in extreme environments. In April 2019, we investigated the composition of prokaryotic picoplankton communities, their gene expression profiles, and the dissolved inorganic carbon uptake efficiency. Our results revealed that the chemolithotrophs Thiomicrorhabdus spp. contributed to the majority of primary production in the waters affected by the hydrothermal vent plume. The metatranscriptomic analysis aligned with the primary productivity measurements, indicating the significant gene upregulations associated with carboxysome-mediated carbon fixation in Thiomicrorhabdus. Synechococcus and Prochlorococcus served as the prokaryotic photoautotrophs for primary productivity in the waters with lower influence from hydrothermal vent emissions. Thiomicrorhabdus and picocyanobacteria jointly provided organic carbon for sustaining the shallow-sea hydrothermal vent ecosystem. In addition to the carbon fixation, the upregulation of genes involved in the SOX (sulfur-oxidizing) pathway, and the dissimilatory sulfate reduction indicated that energy generation and detoxification co-occurred in Thiomicrorhabdus. This study improved our understanding of the impacts of shallow-sea hydrothermal vents on the operation of marine ecosystems and biogeochemical cycles. Full article

This study explores the relationship between forgiveness and interpersonal problem-solving skills among university students using a correlational design. The sample includes 443 students aged 18–26 from Mersin and Selçuk Universities, selected through convenience sampling. Data were collected using the Heartland Forgiveness Scale, the Interpersonal Problem-Solving Inventory, and a personal information form. Pearson correlation and multiple regression analyses were conducted. Results indicate significant relationships between forgiveness and problem-solving skills. Forgiveness positively predicts constructive problem-solving (r = 0.45, p < 0.01) and negatively correlates with negative approaches to problems (r = −0.37, p < 0.01), lack of self-confidence (r = −0.29, p < 0.01), and unwillingness to take responsibility (r = −0.31, p < 0.01). Forgiveness explains 25.2% of the variance in negative approaches, 8% in constructive problem-solving, 13.4% in self-confidence, and 10.3% in responsibility avoidance. Self-forgiveness, forgiveness of others, and situational forgiveness are significant predictors across these dimensions. Findings suggest that forgiving students manage interpersonal conflicts more positively, with reduced negative emotions and avoidance behaviors. Promoting forgiveness and problem-solving skills through educational programs may enhance students’ social harmony, adjustment, and life satisfaction, benefiting both individuals and society. This aligns with existing literature highlighting the emotional and relational benefits of forgiveness. Full article

In the medical field, it is extremely important to use deep learning technology to automatically generate diagnostic reports for chest X-ray images. This technology provides an effective solution to the challenges faced by the medical field in processing large numbers of chest X-ray images. Especially during large-scale outbreaks of epidemics such as the new COVID-19, rapid and accurate screening and diagnosis of cases become important tasks. This study uses deep learning technology to automatically generate diagnostic reports for chest X-ray images, which significantly reduces the workload of doctors, reduces the risk of misdiagnosis and missed diagnosis, and provides technical support for improving public health emergency response capabilities. In this study, we propose an innovative network architecture to address the limitations of traditional image description networks in generating chest X-ray diagnostic reports, especially the large area deviation between abnormal and normal areas, and the lack of effective alignment of the two modalities of image and text. The convolutional block attention module (CBAM) is adopted to effectively alleviate the data bias problem through a sophisticated feature attention mechanism and improve the model’s ability to recognize abnormal image areas. The cross-attention mechanism is adopted to optimize the alignment process between images and texts, ensuring the accuracy and reliability of the diagnosis report. Full article

The design of industrial installations using digital design techniques (digital twin), aligned with the concept of Industry 4.0, provides a tool to optimize maintenance costs, process gas emissions, energy consumption and to reduce the risks associated with production testing. Modern manufacturing plants conduct chemical processes by combining production experience with model-based research. Analyzing processes using advanced digital techniques can replace traditional methods of technological process balancing. The methodology based on the digital twin already serves as a holistic system of process connections, supporting production, research and development, production planning, and quality control. This paper presents the digital design, optimization, and comparison of process data obtained through simulations for two different types of ammonia recovery units in soda ash production using the ammonia–soda process. Using specialized modeling software and relying on historical data, engineering assumptions, and new concepts, virtual models were created in which the material and thermal balances of the process were simulated. This research is divided into two stages. In the first stage, a model-based approach and model optimization techniques are presented, while in the second stage, the preparation of models of the distillation installation is presented, and the influence of various structural parameters of the equipment on the temperature profile and gas flow rate in the ammonia recovery section is discussed. The process of the research method, based on simulations in a virtual environment, allows for evaluating the implementation potential of the proposed concepts, optimizing process parameters, and redefining the approach to conducting chemical processes. A series of simulations conducted in studies on ammonia recovery indicated a potential increase in gaseous ammonia recovery by up to 14.09%, taking into account the type of distillation apparatus or the height of the packing section. Full article

A hybrid attention transformer (HAT) was developed for accurate daily PM_2.5 predictions in Seoul. The performance of the HAT was evaluated through a comparative analysis of its predictions against ground-based observations and those from a three-dimensional chemical transport model (3-D CTM). The results demonstrated that the HAT outperformed the 3-D CTM, achieving a 4.60% higher index of agreement (IOA). Additionally, the HAT exhibited 22.09% fewer errors and 82.59% lower bias compared to the 3-D CTM. Diurnal variations in PM_2.5 predictions from both models were also analyzed to explore the characteristics of the proposed model further. The HAT predictions closely aligned with observed PM_2.5 throughout the day, whereas the 3-D CTM exhibited significant diurnal variability. The importance of the input features was evaluated using the permutation method, which revealed that the previous day’s PM_2.5 was the most influential feature. The robustness of the HAT was further validated through a comparison with the long short-term memory (LSTM) model, which showed 18.50% lower errors and 95.91% smaller biases, even during El Niño events. These promising findings highlight the significant potential of the HAT as a cost-effective and highly accurate tool for air quality prediction. Full article

This paper presents an experimental study on the elastic support in a discrete rail fastening system used in a ballastless tram track structure. The study focuses on the elastic support of the anchor element, specifically the Pm49 baseplate. These elements significantly influence environmental pollution along tram routes, such as vibration (at low frequencies) or noise (at high frequencies), as well as static and dynamic rail deflections. The authors outline a methodology for identifying the static and dynamic characteristics of the discrete elastic support in laboratory conditions. The procedure follows the European standard EN 13146-9 for track category A (tramway), as classified according to the European standard EN 13481-5. The study analyzes how the thickness and density of the tested materials affect stiffness. Additionally, it examines the correlation between parameters identified easily on-site (thickness, Shore hardness and density) and laboratory-determined parameters (static and dynamic stiffness), which are costly and time-consuming to measure. The research confirms that prototype prefabricated vibration isolation baseplate pads made of styrene butadiene rubber (SBR) granules, recycled from end-of-life car tires, can achieve equivalent basic static and dynamic parameters, compared to underlays made of two-component polyurethane (PU) resin. This aligns with the strategy of promoting sustainable materials in construction. The innovative and prefabricated SBR rubber baseplate pads can also be used in repair and maintenance works (regardless of weather conditions), as they enable the quick launch of tram traffic. The results of the research included in this article can be used by other scientists, recycled rubber producers, tram track designers or construction site engineers. Full article

This paper proposes a physics-constrained 3D Swin Transformer (ST) for gravity inversion. By leveraging the self-attention mechanism in 3D ST, the method effectively models global dependencies within gravity data, enabling the network to reweight features globally and focus on critical anomalous regions. Additionally, prior gradient information is integrated into the loss function, and a hierarchical weight allocation strategy is adopted to guide the model in learning boundary information of density structures and deep-seated features more effectively. Synthetic experiments demonstrate that the proposed method achieves lower model errors, better boundary alignment, and higher inversion accuracy. The approach is further validated using gravity anomaly observations from the Gonghe Basin in Qinghai, yielding reliable and precise inversion results. Full article

Introduction: Radiation therapy plays an important role in the treatment of localized breast cancer. Hypofractionated (HF) radiation therapy has emerged as a promising alternative to conventional fractionation (CF) schedules, offering comparable efficacy with reduced treatment duration and costs. However, concerns remain regarding its safety and rate of toxicity, particularly in patients undergoing mastectomy with breast reconstruction. This study aimed to assess the implant-related complications in breast cancer patients receiving HF post-mastectomy radiation therapy (PMRT) and reconstruction compared to CF PMRT. Methods: A retrospective study was conducted on 59 breast cancer patients who underwent mastectomy and breast reconstruction between 2013 and 2021 and received adjuvant PMRT. Patient demographics, treatment characteristics, and implant-related complications were analyzed. Statistical tests including chi-square, Fischer’s exact test, and multivariable Cox regression were employed for analysis. Results: Of the 59 patients, 29 received HF PMRT and 30 received CF PMRT. At a median follow-up of 23.4 months, there was no significant difference in major implant-related complications between the two groups (24.1% in HF vs. 33.3% in CF, p = 0.436). Most complications in the HF group occurred within the first two years post-radiation. Age over 40 was identified as a significant predictor for higher implant-related complications (p = 0.029). Conclusions: Our findings indicate that HF PMRT and reconstruction does not increase the risk of major implant-related complications compared to CF PMRT. These results align with the existing literature, supporting the safety of HF radiation in breast cancer patients who underwent mastectomy with reconstruction. Full article

This study designs and develops a wearable exoskeleton piano assistance system for individuals recovering from neurological injuries, aiming to help users regain the ability to perform complex tasks such as playing the piano. While soft robotic exoskeletons have proven effective in rehabilitation therapy and daily activity assistance, challenges remain in performing highly dexterous tasks due to structural complexity and insufficient motion accuracy. To address these issues, we developed a modular division method based on multi-domain mapping and a top-down process model. This method integrates the functional domain, structural domain, and user needs domain, and explores the principles and methods for creating functional construction modules, overcoming the limitations of traditional top-down approaches in design flexibility. By closely combining layout constraints with the design model, this method significantly improves the accuracy and efficiency of module configuration, offering a new path for the development of piano practice assistance devices. The results demonstrate that this device innovatively combines piano practice with rehabilitation training and through the introduction of ontological modeling methods, resolves the challenges of multidimensional needs mapping. Based on five user requirements (P), we calculated the corresponding demand weight (K), making the design more aligned with user needs. The device excels in enhancing motion accuracy, interactivity, and comfort, filling the gap in traditional piano assistance devices in terms of multi-functionality and high adaptability, and offering new ideas for the design and promotion of intelligent assistive devices. Simulation analysis, combined with the motion trajectory of the finger’s proximal joint, calculates that 60° is the maximum bending angle for the aforementioned joint. Physical validation confirms the device’s superior performance in terms of reliability and high-precision motion reproduction, meeting the requirements for piano-assisted training. Through multi-domain mapping, the top-down process model, and modular design, this research effectively breaks through the design flexibility and functional adaptability bottleneck of traditional piano assistance devices while integrating neurological rehabilitation with music education, opening up a new application path for intelligent assistive devices in the fields of rehabilitation medicine and arts education, and providing a solution for cross-disciplinary technology fusion and innovative development. Full article

The enhancement of mechanisms to protect the rights of migrants and refugees within the European Union represents a critical area for human-centered artificial intelligence (HCAI). Traditionally, the focus on algorithms alone has shifted toward a more comprehensive understanding of AI’s potential to shape technology in ways which better serve human needs, particularly for disadvantaged groups. Large language models (LLMs) and retrieval-augmented generation (RAG) offer significant potential to bridging gaps for vulnerable populations, including immigrants, refugees, and individuals with disabilities. Implementing solutions based on these technologies involves critical factors which influence the pursuit of approaches aligning with humanitarian interests. This study presents a proof of concept utilizing the open LLM model LLAMA 3 and a linguistic corpus comprising legislative, regulatory, and assistance information from various European Union agencies concerning migrants. We evaluate generative metrics, energy efficiency metrics, and metrics for assessing contextually appropriate and non-discriminatory responses. Our proposal involves the optimal tuning of key hyperparameters for LLMs and RAG through multi-criteria decision-making (MCDM) methods to ensure the solutions are fair, equitable, and non-discriminatory. The optimal configurations resulted in a 20.1% reduction in carbon emissions, along with an 11.3% decrease in the metrics associated with bias. The findings suggest that by employing the appropriate methodologies and techniques, it is feasible to implement HCAI systems based on LLMs and RAG without undermining the social integration of vulnerable populations. Full article

This article analyses how energy sufficiency can be applied in tourism destinations. It begins by highlighting the importance of decarbonizing tourism for climate action, given the sector’s high CO₂ emissions. Energy sufficiency, a key pillar of the energy transition, is defined as the voluntary reduction in energy demand within climate and CO₂ emission constraints. The study investigates how stakeholders interpret and frame this concept, by focusing on strategies that align with the public image of the resort to reduce its energy requirements. The methodology includes semi-structured interviews with key players in Verbier—Val de Bagnes, focus groups with cooperation partners, participant observations at local meetings and events, and analysis of local documentation. Research was undertaken using living lab methods. The results reveal five main categories and eleven subcategories in which local stakeholders frame sufficiency, including oppositional framings, off-framings, selective framings, institutional framings, and disempowering framings. The article concludes with recommendations to reframe sufficiency in order to align with the destination’s strategy and representations. While energy sufficiency is often overshadowed by narratives of economic growth, technological innovation, and material abundance, it appears crucial to reframe these narratives and integrate post-growth strategies that prioritize sustainability in tourism planning. Full article

Smart cities embody a transformative approach to modernizing urban infrastructure and harness the power of deep learning (DL) and Vehicle-to-Home (V2H) technology to redefine home energy management. Neural network-based Q-learning algorithms optimize the scheduling of household appliances and the management of energy storage systems, including batteries, to maximize energy efficiency. Data preprocessing techniques, such as normalization, standardization, and missing value imputation, are applied to ensure that the data used for decision making are accurate and reliable. V2H technology allows for efficient energy exchange between electric vehicles (EVs) and homes, enabling EVs to act as both energy storage and supply sources, thus improving overall energy consumption and reducing reliance on the grid. Real-time data from photovoltaic (PV) systems are integrated, providing valuable inputs that further refine energy management decisions and align them with current solar energy availability. The system also incorporates battery storage (BS), which is critical in optimizing energy usage during peak demand periods and providing backup power during grid outages, enhancing energy reliability and sustainability. By utilizing data from a Tunisian weather database, smart cities significantly reduce electricity costs compared to traditional energy management methods, such as Dynamic Programming (DP), Rule-Based Systems, and Genetic Algorithms. The system’s performance is validated through robust AI models, performance metrics, and simulation scenarios, which test the system’s effectiveness under various energy demand patterns and changing weather conditions. These simulations demonstrate the system’s ability to adapt to different operational environments. Full article

Polyethylene mulch film (PEM) is widely utilized in garlic cultivation, significantly enhancing garlic yield. However, the prolonged use of this material leads to serious environmental issues that adversely affect soil health and plant growth. To promote the adoption of biodegradable mulch film (BDM) in garlic cultivation, we investigate the effects of BDMs with thicknesses of 0.006 mm, 0.008 mm, and 0.010 mm on garlic growth and soil properties, comparing them with the commonly used PEM 0.008 mm. The results indicated that the agronomic traits of garlic were significantly improved under both PEM and BDMs compared to no mulching, with yield increases ranging from 75.42% to 90.39%. The highest garlic yield was observed with the BDM 0.008 mm. Most above-ground agronomic traits of garlic did not exhibit significant differences between PEM and BDMs, although a few traits showed slight increases under the BDMs 0.008 mm and 0.010 mm. The quality characteristics of garlic bulbs, including the contents of soluble sugar, vitamin C, and allicin, did not differ significantly between PEM and BDMs. Soil temperature was significantly higher under both PEM and BDMs compared to no mulching. In comparison to PEM 0.008 mm, the application of BDMs 0.006 mm and 0.008 mm significantly enhanced potassium availability in the soil. Furthermore, the activities of catalase, phosphatase, and invertase were notably increased under the BDM 0.008 mm, suggesting that this type of mulch could improve the physicochemical properties of the soil. Additionally, the BDM 0.008 mm remained intact throughout the low-temperature overwintering period, began to partially degrade as temperatures rose in March and April, and exhibited considerable fragmentation during the maturity and harvest periods of garlic. Its degradation rate was well aligned with the growth requirements of garlic. Taken together, these findings suggested that the BDM 0.008 mm is particularly effective, resulting in significant yield increase and an appropriate degradation rate. These results provided a valuable reference for the selection and application of BDM in garlic cultivation. Full article

Previous data show that the knockdown of the MYBL1 gene in the MDA-MB-231 cell line leads to the downregulation of VCPIP1 gene expression. In addition, MYBL1 and VCPIP1 genes are co-expressed and dysregulated in some of the same triple negative breast cancer patient samples. We propose that the co-expression of the two genes is attributed to the MYBL1 transcription factor regulation of the VCPIP1 gene. We identify the MYBL1 transcription factor binding site upstream of the VCPIP1 start site and show that the MYBL1 protein can bind to the sequence identified in the VCPIP1 promoter region. Combined with the results from the knockdown study, these data support the ability of MYBL1 to regulate the VCPIP1 gene. The VCPIP1 gene functions as a deubiquitinating enzyme involved in DNA repair, protein positioning, and the assembly of the Golgi apparatus during mitotic signaling. The transcriptional regulation of VCPIP1 by the MYBL1 gene could implicate MYBL1 in these processes, which might contribute to tumor processes in TNBC. Although both genes are involved in cell cycle regulatory mechanisms, converging signaling mechanisms have not been identified. In a separate study, we performed sequence alignment of the MYBL1 transcript variants and identified an exon unique to the canonical variant. Probes that specifically target the unique MYBL1 exon show that the exon is overexpressed in tumor cell lines compared to non-tumor breast cells. We are classifying this unique MYBL1 exon as a tumor-associated exon. Full article