Introduction: Neoadjuvant chemotherapy in breast cancer offers the possibility to facilitate breast and axillary surgery, it is a test of chemosensibility in vivo with significant prognostic value and may tailor adjuvant treatment according to response. Material and Methods: a retrospective single institution of 482 stage II and III breast cancer patients treated with neoadjuvant chemotherapy based on anthracycline and taxans plus antiHEr2 in those Her2 positive cases. Survival was calculated at 5 and 10 years. Kaplan Meier with log rank test were calculated for difference according to TNM, pathological and molecular surrogated subtype, TILs cut-off 20%, response to chemotherapy and the presence of vascular invasion. Results: In the univariate those factors that influenced long-term patient outcome were TNM (p=0.000); pathologic subtype (p=0.003); Tils cut off 20%; achieving pCR (p=0.000), RCB (p=0.000) and vascular invasion (p=0.000). After a mean follow up of 126 months, Luminal tumors presented a substantial difference of survival rates calculated at 5 or 10 years (81.2% compared to 74.7%) whereas for TNBC was 75.3 and 73.5, respectively. The greatest difference was seen according to response those patients with pCR presented a 10yDDFS of 95.5 vs 72.4% for those patients without pCR. This difference was especially meaningful in TNBC were 10y DDFS according to the RCB 0 to 3 was 100%, 80.6%, 69% and 49.2% respectively. Patients with a special bad prognosis in our series were lobular carcinomas with 10y DDFS of 42.9% vs 79.7%, p= 0.001 for ductal carcinomas; and those patients with vascular invasion at the surgical specimen with a 10yDDFS of 59.2% vs 83.6, p=0.,000 for those patients without vascular invasion. Conclusion: long-term outcomes after neoadjuvant chemotherapy can help patient and clinician make well-informed decisions.

Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems and has enjoyed significant interest from the research community. This paper discusses the recent critical advancements in the PIML domain. Novel methods and applications of domain decomposition in physics-informed neural networks (PINN) in particular are highlighted. Additionally, we explore recent Works toward utilizing neural operator learning to intuit relationships in physics systems traditionally modeled by sets of complex governing equations and solved with expensive differentiation techniques. Finally, expansive applications of traditional physics-informed machine learning and potential limitations are discussed. In addition to summarizing recent work, we propose a novel taxonomic structure to catalog physics-informed machine learning based on how the physics-information is derived and injected into the machine learning process. The taxonomy assumes the explicit objectives of facilitating interdisciplinary collaboration in methodology, thereby promoting a wider characterization of what types of physics-problems are served by the physics-informed learning machines, and assisting in identifying apt targets for future work. To summarize, the major twofold goal of this work is to summarize recent advancements and introduce a taxonomic catalog for applications of physics-informed machine learning.

Parametric models have been widely used in pertinent literature for reconstructing, modifying and representing a wide range of airfoil and/or hydrofoil profile geometries. Design spaces corresponding to these models can be exploited for modeling and profile-shape optimization under various performance criteria. Accuracy requirements, along with the need for modeling local features, often lead to high-dimensional design spaces that hinder the process of shape optimization and design through analysis. In this work, we propose a shape-informed dimensional reduction approach that attempts to tackle this deficiency by producing low-dimensional latent design spaces that can be efficiently used in shape representation and optimization. Furthermore, geometric moments are introduced in an attempt to cost-effectively capture analysis-relevant information that is generally expensive to produce. Specifically, geometric integral properties, although intrinsic features of the shape, are quite commonly related to performance indicators employed in performance optimization and therefore provide a cost-effective physics-informed component in the description of the design in the latent space. To this end, we employ the generalized Karhunen-Loeve expansion to produce a shape- and physics-informed subspace retaining the highest possible geometric variance and robustness, that is, lack of invalid designs. At the same time, a series of shape discretizations, encoding the foil's shape profile, are examined with regards to their effect on the resulting latent space's quality and efficiency. Our results demonstrate a significant reduction in the dimensionality of the original design space while maintaining a high representational capacity and a large percentage of valid geometries that facilitate fast convergence to optimal solutions in performance-based optimization.

The Physics Informed Neural Networks framework is applied to the understanding of the dynamics of Coronavirus of 2019. To provide the governing system of equations used by the framework, the Susceptible-Infected-Recovered-Death mathematical model is used. The study focused on finding the patterns of the dynamics of the disease which involves predicting the infection rate, recovery rate and death rate; thus predicting the active infections, total recovered, susceptible and deceased at any required time. The study used data that was collected on the dynamics of COVID-19 from the Kingdom of Eswatini between March 2020 and September 2021. The obtained results showed less errors thus making highly accurate predictions.

High temperature (HT) geothermal wells can provide green power 24h a day 7 days a week. Harsh environmental and operational conditions, long-term durability requirements of such wells require special cementitious composites for well construction. This paper reports a comprehensive assessment of geothermal cement composites in cyclic pressure function laboratory tests, field exposures in an HT geothermal well (300-350oC) as well as a numerical model to complement the experimental results. Performances of calcium-aluminate-cement (CAC)-based composites and calcium free cement were compared against reference Ordinary Portland Cement (OPC)/silica blend. The stability and degradation of the tested materials was characterized by crystalline composition, thermo-gravimetric, and elemental analyses, morphological studies, water-fillable porosity, and mechanical properties measurements. All CAC-based formulations outperformed the reference blend both in the function and exposure tests. The reference OPC/silica lost its mechanical properties during the 9-month well exposure through extensive HT carbonation, while properties of the CAC-based blends improved over that period. The Modified Cam-Clay (MCC) plasticity parameters of several HT cement formulations were extracted from triaxial and Brazilian tests and verified against the experimental results of function cyclic tests. These parameters can be used in well integrity models to predict field-scale behavior of the cement sheath under geothermal well conditions.

Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, nonlinear constraints may result in outcomes that are either infeasible or suboptimal, such as non-convex optimization. This paper introduces a novel regularized physics-informed neural network (RPINN) framework as a new NOPT tool for both supervised and unsupervised data-driven scenarios. By using custom activation functions and regularization penalties in an artificial neural network (ANN), RPINN can handle data variability and noisy inputs. Besides, it uses physics principles to build the network architecture, computing the optimization variables based on network weights and learned features. In addition, it uses automatic differentiation training to make the system scalable and cut down on computation time through batch-based back-propagation. The test results for both supervised and unsupervised NOPT tasks show that our RPINN can provide solutions that are competitive compared to state-of-the-art solvers. In turn, RPINN’s robustness against noisy input measurements makes it particularly valuable in environments with fluctuating information. Additionally, RPINN’s ANN-based foundation offers significant flexibility and scalability.

Introduction: The aim of this study was to prospectively evaluate parents’/guardians’ late recall of surgical procedures and risks of elective surgery after pre-operative interview with surgeons. Materials and Methods: All parents/guardians of patients <18 years of age undergoing elective procedures from January 15th to September 1st, 2022, were prospectively enrolled in the study. Before discharge, parents/guardians were asked to complete a questionnaire regarding the duration of the consent procedure, duration of the interview, quality of the informative handouts, and their ability to recall the type of disease, type of surgical procedure, and surgical risks. Answers were graded from 1 to 4. Results: One hundred and two questionnaires were returned. In all cases, informed consent was obtained between 24 and 72 hours prior to surgery. The following responses were collected: 98/102 (96%) parents/guardians reported that the duration of the consent process was adequate; 95/102 (93%) reported that the handouts were fully informative, and 7/102 (7%) reported that they were partially informative regarding explanation of the disease and surgical procedure; regarding complications, 93/102 (91%) perceived the handouts to be fully/partially informative, while 4/102 (4%) perceived the handouts to be poorly/non-informative, and 5/102 (5%) did not provide a response; 94/102 (92%) stated that they remembered the pathology, but only 87/94 (93%) recalled it correctly; 90/102 (88%) stated that they remembered the type of procedure, but only 76/90 (84%) recalled it correctly; and 53/102 (52%) stated that they remembered the surgical risks, but only 20/53 (38%) could recall more than one complication. Conclusions: Late recall of surgical complications by parents was poor despite the high perceived quality of the surgical risk handouts and medical interview.

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis.

This article presents the development of a Faith-Informed Advocacy Model that integrates faith-based principles and values into advocacy efforts within Christian social work. Drawing from the intersection of Christian ethics and social work principles, this model provides a framework for Christian social workers to engage in advocacy work that is grounded in their faith and guided by ethical principles. The article includes case studies that illustrate the application of the model in real-world scenarios.

Real estate market analysis and place-based decision-making can both benefit from understanding house price development. Although considerable amounts of interest have been devoted to housing price modelling, the assessment of house price fluctuation still requires further comparing studies. Housing price prediction is challenging as contributing factors are quite dynamic and subject to a variety of regulating elements. The future understanding of the housing market trends not only provides sufficient customers’ investment trust potential but also enables the financial support to progress more realistic in advance. In this study, a comprehensive data-informed framework is developed to investigate and anticipate real estate house prices using historical data by combining explanatory features. We examined about 500 houses in the Boston area as a case study and discussed how the increase in housing prices could vary by each of the contributing components. Fourteen Machine Learning (ML) regressors imply to the dataset and lead to a comparative study of the accuracy of all the models. ML-based regressors forecast real estate home prices as a function of thirteen influencing factors. The most informative features were also selected by conducting the Permutation Feature Importance technique on all the features The study provides a comprehensive tool for evaluating the robustness and efficiency of ML models for housing price predictions. The results highlighted Random Forest as the best model has an R2 equals to 0.88 and Voting Regressor as the second highest rated model has R2 equals to 0.87. Results of multivariate exploratory data analysis also implied that the average number of rooms and percentage of the lower status of the population have the most significant impact on the price range predictions.

Leak of carbon dioxide (CO2) from conventional underground carbon storage reservoirs is an increasing concern. It is highly desirable to inject CO2 into low-temperature reservoirs so that CO2 can be locked inside the reservoir in solid state as CO2-hydrates. Marine gas hydrate reservoirs and surrounding water aquifers are attractive candidates for this purpose. However, the nature of low-permeability of these marine sediments hinders injection of CO2 in commercial scale due to the low-injectivity of wells with conventional completions. This study investigates injection of CO2 into the low-permeability marine reservoirs using a new type of well namely radial-lateral wells (RLW). An analytical model was developed for predicting CO2 injectivity of the RLW to assess well injectivity improvement. A case study and sensitivity analysis were performed with field data from the South China Sea. Result of analysis reveals that the injectivity of the RLW is nearly proportional to reservoir permeability, lateral wellbore length, and number of laterals. CO2 injection rate is predicted to be 19 tons/day to 250 tons/day, which is 3 to 15 times higher than the injectivity of frac-packed wells. It is feasible to inject CO2 into the low-permeability-low-temperature marine reservoirs at commercial flow rates. This work provides an analytical tool to predict CO2-injectivity of RLW in low-temperature marine reservoirs for leak-free CO2 storage.

In reservoir engineering, one of the main sources of information for the characterization of reservoir and well parameters is well testing. Among the unconventional well testing methodologies, Harmonic Well Testing (HPT) is appealing from an economic standpoint because it could provide well performance and reservoir behavior monitoring without having to interrupt field production. Recorded pressure analysis is performed in the frequency domain by adopting a derivative approach similar to conventional well testing. To this end, pressure and rate data must be decomposed into harmonic components. Test interpretability can be significantly improved if pressure data are detrended prior to interpretation, filtering out non periodic events such as discontinuous production from neighboring wells and flow regime variations which did not respect the designed test periodicity. Therefore, detrending offers the possibility of overcoming the limitation of HPT applicability due to the difficulty of imposing a regularly pulsing rate for the whole test duration (typically lasting several days). This makes HPT attractive for well performance monitoring, especially in gas storage fields. In this paper, the application of different detrending methodologies to synthetic HPT pressure data generated in different reservoir and operational scenarios is presented and discussed. Moreover, a real case application is also presented.

In this research, the application of the Physics-Informed Neural Network (PINN) model is explored to solve transport equation-based Partial Differential Equations (PDEs). The primary objective is to analyze the impact of different activation functions incorporated within the PINN model on its predictive performance, specifically assessing the Mean Squared Error (MSE) and Mean Absolute Error (MAE). The dataset used in the study consists of a varied set of input parameters related to strut diameter, unit cell size, and the corresponding yield stress values. Through this investigation, the aim is to understand the effectiveness of the PINN model and the significance of choosing appropriate activation functions for solving complex PDEs in real-world applications. The outcomes suggest that the choice of activation function may have minimal influence on the model's predictive accuracy for this particular problem. The PINN model showcases exceptional generalization capabilities, indicating its capacity to avoid overfitting with the provided dataset. The research underscores the importance of striking a balance between performance and computational efficiency while selecting an activation function for specific real-world applications. These valuable findings contribute to advancing the understanding and potential adoption of PINN as an effective tool for solving challenging PDEs in diverse scientific and engineering domains.

Proactivity is a particularly important attribute of knowledge-intensive organizations, where the work required to increase the potential of knowledge-intensive employees is crucial. A priority challenge for these firms is also to understand the increasing importance of the functioning of cognitive mechanisms leading to increased knowledge workers' well-being following the implementation of the psychological contract. The aim of this article is to identify the relationship between inclusive leadership, the fulfillment of the psychological contract, two dimensions of well-being: workplace and life well-being and knowledge workers' proactivity. Based on survey data collected through the CAWI method from 1.000 knowledge workers employed in Polish companies in the business services sector, the research hypotheses were tested using a stepwise equation modelling (SEM) technique, leading to a model containing all the main constructs. The results obtained indicate that inclusive leadership positively impacts the fulfillment of the psychological contract. Furthermore, the fulfillment of the psychological contract positively affects proactive working behavior with and the wellbeing of knowledge workers. Along with proactive work behavior, two dimensions of well-being, were examined as outcome variables. The analysis also shows that knowledge-intensive organizations, intending to build the proactivity of their employees and nurture a high level of well-being in their lives and in the workplace, should take care to fulfil the expectations and obligations of the psychological contract. However, one way to achieve this is for managers to use an inclusive management style, which supports an atmosphere of a safe working environment in a diverse setting and leads to employees feeling comfortable sharing their opinions and ideas. The study of inclusive leadership in the context of knowledge-intensive organizations provides human resource professionals and employee man-agers with important insights into how inclusive leadership can effectively contribute to the psychological contract, which will consequently lead to proactive work behavior and also improve employees' workplace and life well-being.

Optimism and self-efficacy have been associated with psychological health. Empathy has also been found to promote positive functioning and to have a unique role in community health volunteering. This study investigated whether self-efficacy and optimism were associated with psychological and subjective well-being in a group of healthcare volunteers and whether empathy added incrementally to these associations. A sample of 160 Italian clown doctors volunteering in various hospitals completed self-report measures of self-efficacy, optimism, empathy, psychological well-being, and subjective well-being. Results indicated that self-efficacy and optimism were associated with both outcomes and that aspects of empathy, such as others’ perspective-taking and personal distress for others’ difficulties, incrementally added to these associations, although with opposite effects. The present study adds to previous research on the role of self-efficacy, optimism, and empathy for community health volunteers’ psychological health and offers suggestions regarding the training of this type of volunteer.

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

This article presents a detailed examination of the methodology and modeling tools utilized to analyze gas flow in pipelines, rooted in the fundamental principles of gas dynamics. The methodology integrates numerical simulations with modern neural network techniques, particularly focusing on the Physics-Informed Neural Network (PINN) method. This innovative approach combines artificial neural networks (ANNs) with physical equations, offering a more efficient and accurate way to model various complex processes and phenomena. The proposed mathematical model, based on the Euler equation, has been meticulously implemented using the Python language. Verification with analytical solutions ensures the accuracy and reliability of the computations. In the research, a comprehensive comparative analysis was conducted between results obtained using the PINN method and those from conventional Computational Fluid Dynamics (CFD) approaches. The analysis highlighted the advantages of the PINN method, which produced smoother pressure and velocity fluctuation profiles while reducing computation time, demonstrating its potential as a transformative modeling tool. The data derived from this study are of paramount importance for ensuring ongoing energy supply reliability and can also be used to create predictive models related to gas behavior in pipelines. The application of modeling techniques for gas flow simulation has the potential to revolutionize the integrity of our energy infrastructure and utilization of gas resources. However, it is crucial to emphasize that the effectiveness of such models relies on continuous monitoring and frequent updates to ensure alignment with real-world conditions. This research not only contributes to a deeper understanding of compressible gas flows but also underscores the crucial role of advanced modeling methodologies in the sustainable management of gas resources for both current and future generations.

This study aimed at exploring what role artificial intelligence techniques could play in the futural numerical analysis. In this paper, a convolutional neural network techniques based on modified loss function is proposed as a surrogate of finite element method(FEM). Several surrogate-based physics-informed neural networks(PINNs) are developed to solve representative boundary value problems based on elliptic partial differential equations (PDEs). Results from the proposed surrogate-based approach are in good agreement with ones from conventional FEM. It is found that modification of the loss function could improve the prediction accuracy of the neural network. It is indicated that to some extent the artificial intelligence technique could replace conventional numerical analysis as a great surrogate model.

Aim: The overall aim of the study was to assess the reasons and experiences of participants involved in Antibody Mediated Prevention (AMP) HIV prevention clinical trial at University of North Carolina (UNC) Project, Lilongwe, Malawi. We determined the participants’ reasons for participating in HIV Prevention clinical trials; and the experiences of participants in HIV Prevention clinical trials. Methods: We adopted the qualitative cross-sectional study method. Data were collected using in-depth interviews (IDIs). Purposive sampling was used to select 12 study participants who consented to take part in the study. All participants were the ones taking part in the AMP HIV prevention study at the UNC Project. Data analysis was done concurrently with data collection using content analysis. Results: Individuals were motivated to participate in HIV research due to a range of perceived benefits. These included personal, health, and financial benefits. Participants' research experiences and their continued participation in HIV research were influenced by the research clinic context and the nature of their interactions with research staff. Conclusion: When the clinical trial study participants’ expectations are met through what they experience in the study, the chances of them adhering to the study visits and procedures are high. Even for those who did not have any expectations prior to the study, feeling welcomed and being able to open up to the study staff encouraged their continued participation. In the end, this outweighed the negative comments made by the people in their communities or their friends

{This study investigates the coordinated behaviors and the underlying collective intelligence in biological groups, particularly those led by informed leaders. By establishing new convergence criteria based on real biological group experiments, this research introduces the concept of a volitional term and heterogeneous networks, constructing a coupled force Cucker-Smale model with informed leaders. The inclusion of the informed leader aspect distinctively characterizes the dynamics of leaders and followers, enabling a more accurate representation of biological group behavior. The paper then proposes the Flock Leadership Hierarchy Network (FLH), a model reflecting real biological interactions. By employing time slicing and rolling time windows, the study methodically analyses group behavior stages, using volatility and convergence time as metrics to examine the relationship between group consistency and interactions. Comparative experiments show the FLH network's superior performance. The Kolmogorov-Smirnov test indicates that the FLH network follows a power-law distribution, highlighting the evolutionary intelligence of biological groups. This research offers new perspectives on the evolution of biological groups, contributing to our understanding of both natural and artificial systems, such as animal migration and autonomous drone operations.

Partial differential equations (PDEs) are used to describe a wide range of phenomena, such as heat transfer, fluid dynamics, and quantum mechanics. By solving PDEs, we can ob- tain insights into the behavior of the system and make predictions about its future evolution. Conventional numerical methods for obtaining the approximate solutions of PDEs may re- quire extensive computational resources and time, especially for complex PDEs and large- scale problems. The recently developed physics-informed neural network (PINN) has shown promise in a variety of scientific and engineering fields by incorporating physical laws into the loss functions of the neural network (NN). In addition, the training of PINN does not require ground truth data, but it has poor generalization ability to unseen domains. On the other hand, a convolutional neural network (CNN) has fast inference and better generalization ability, but it requires a large amount of training data. Taking the advantages of PINN and CNN by using Legendre multiwavelets (LMWs) as basis functions, we introduce a new method to approach the PDEs in this paper, namely Physics-Informed Legendre Multiwavelets CNN (PiLMWs-CNN), in order to continuously approximate a grid-based state representation that can be handled by a CNN. PiLMWs-CNN enable us to train our models using only physics-informed loss functions without any pre- computed training data, simultaneously providing fast and continuous solutions that gener- alize to previously unknown domains. In particular, the LMWs can simultaneously possess compact support, orthogonality, symmetry, high smoothness, and high approximation order. Compared to orthonormal polynomial (OP) bases, the approximation accuracy can be greatly increased and computation costs can be significantly reduced by using LMWs. We applied PiLMWs-CNN to approximate the damped wave equation, incompressible Navier-Stokes (N- S) equation, and two-dimensional heat conduction equation. The experimental results show that this method provides more accurate, efficient, and fast convergence with better stability when approximating the solution of PDEs.

Despite the recognition of the importance of stakeholder inclusion into decisions about new solutions offered to society, responsible innovation (RI) has stalled at the point of articulating a process of governance with a strongly normative loading, without clear practical guidelines toward implementation practices. The principles of RI direct us to involve the user early in the innovation process. However, it lacks direction of how to involve users and stakeholders into this process. In this article, we try to understand how to empower users to become a part of innovation process though empirical cases. Based on 11 cases of firms innovating in digital health and welfare services, we look on firm practices for user integration into their innovation process, as well as how user’s behavior is changing due to new trends such as availability of information and digitalization of services. We try to explore this question through lenses of responsible innovation in the emerging field of digital healthcare.

The tambaqui Colossoma macropomum is the main species of edible fish originating from fish farming in the Amazon region. This study aimed to determine and compare the hematological and biochemical parameters of tambaqui from fish farms in municipalities in the Metropolitan Region of Manaus, Amazonas, Brazil. Two fish farms were sampled in a semi-excavated pond-type breeding system in each of the five towns investigated (Presidente Figueiredo, Manaus, Iranduba, Manacapuru and Novo Airão), totaling ten breeders. On each farm, ten specimens were captured, totaling 100 animals. Blood was collected by caudal puncture, and hematological and biochemical parameters were determined using the methodology described for teleosts fish. For the erythrogram in the variables of hematocrit, hemoglobin concentration and erythrocytes, significant differences were observed in Iranduba and Novo Airão, demonstrating indications of anemic processes and dehydration. For plasma biochemistry, low levels were observed in fish from Iranduba and Novo Airão. High values in lipid compounds were found in tambaqui from Manacapuru. High intraspecific variation was found in the leukogram and thrombogram, which determined similarities between tambaqui from different farms and municipalities. On the other hand, fish from Manacapuru have an inappropriate diet, indicated by the high levels of cholesterol and triglycerides found in the blood.

As mental health problems tend to increase during adolescence and is a serious public health issue in the Republic of Kazakhstan. Early detection is necessary and monitoring at the population level can be used to evaluate the progress of national programmes promoting positive well-being. Physical activity (PA) can be protective whereas increased screen time behaviours (STB) can be a risk for low levels of well-being. A national representative sample (n=4,731) of young adolescents aged 11y, 13y, and 15y from the Republic of Kazakhstan took part in the WHO collaborative Health Behaviour in School-aged Children (HBSC) study. Respondents completed the WHO-5 Well-being scale, and items in on PA and STB. Internationally recognised, recommended cut-offs were used for analyses. Two models of binary logistic regressions were performed to examine the associations with PA (Model 1) and PA with STB (Model 2) after stratification by gender and controlling for age, locality and family affluence. Three quarters of young adolescents in the Republic of Kazakhstan have good overall well-being, despite the proportion reduces as adolescents age from 11y to 15y (boys, OR=0.66 CI=0.49-0.80; girls, OR=0.55, CI=0.43-0.71). The odds ratio for positive well-being were more than twice for boys and more than 3.5 for girls who reported daily PA than not being active daily. Spending less time on STB for girls was associated with positive well-being than spending more STB time (OR=1.28, CI=1.04-1.59). Well-being among young adolescents drops dramatically between the ages of 11y and 15y and is higher among rural schools attendees than in urban schools. The recommended amounts of PA can be protective of low well-being for both boys and girls. However, meeting reporting STB recommendations was only protective for girls and not boys. Designing and implementing positive well-being programmes require consideration of locality and amounts of PA and STB

Using data from the China Family Panel Studies, this study examines the socioeconomic characteristics of Internet users, as well as the relationships between the dynamics of different forms of online activities and the subjective well-being of urbanites and rural migrants in urban China. The study finds that online behaviour may clearly reflect differences in individuals’ personal traits and socioeconomic positions. Patterns of the association between online activities and subjective well-being tend to differ among rural migrants and urbanites, especially in terms of depression. A difference-in-differences model is employed to estimate the impact of intensified engagement in online activities on depression and life satisfaction from 2010 to 2016. The results show that increased frequency of online entertainment exhibits a comparatively positive effect on depression and life satisfaction. Spending more time on online social networking has a similar impact on rural migrants, but not on urbanites. These findings suggest that the rapid development of urban China’s online community has important implications for residents’ subjective well-being.

We use Matlab 3D finite element fluid flow/transport modelling to simulate localized wellbore temperature events of order 0.05-0.1oC logged in Fennoscandia basement rock at ~ 1.5km depths. The temperature events are approximated as steady-state heat transport due to fluid draining from the crust into the wellbore via naturally occurring fracture-connectivity structures. Flow simulation is based on the empirics of spatially-correlated fracture-connectivity fluid flow widely attested by well-log, well-core, and well-production data. Matching model wellbore-centric radial temperature profiles to a 2D analytic expression for steady-state radial heat transport with Peclet number Pe ≡ r0φv0/D (r0 = wellbore radius, v0 = Darcy velocity at r0, φ = ambient porosity, D = rock-water thermal diffusivity), gives Pe ~ 10-15 for fracture-connectivity flow intersecting the well, and Pe ~ 0 for ambient crust. Darcy flow for model Pe ~ 10 at radius ~ 10 meters from the wellbore gives permeability estimate κ ~ 0.02Darcy for flow driven by differential fluid pressure between least principal crustal stress pore pressure and hydrostatic wellbore pressure. Model temperature event flow permeability κm ~ 0.02Darcy is related to well-core ambient permeability κ ~ 1µDarcy by empirical poroperm relation κm ~ κ exp(αmφ) for φ ~ 0.01 and αm ~ 1000. Our modelling of wellbore temperature events calibrates the concept of reactivating fossilized fracture-connectivity flow for EGS permeability stimulation of basement rock.

From 2008 to 2024, the Newborn Screening Translational Research Network (NBSTRN), part of the National Institute of Child Health and Human Development (NICHD) Hunter Kelly Newborn Screening Program, served as a robust infrastructure to facilitate groundbreaking research in newborn screening (NBS), rare disease, and genomics. Over fifteen years, NBSTRN developed into a significant international network, supporting innovative research on novel technologies to screen, diagnose, treat, manage, and understand the natural history of more than 280 rare diseases. A key component of these efforts has been the development of resources and expertise for the newborn screening community in ethical, legal, and social issues (ELSI). This paper presents findings from a survey conducted by the NBSTRN team with input from one of theNBSTRN's expert workgroups, the Bioethics and Legal Workgroup. With responses from 88 members of the NBS community including researchers and state NBS programs, the survey found that individuals rely on internal and collegial ELSI resources and highlighted a substantial demand for structured, accessible training and resources. The survey’s insights emphasize the importance of privacy and informed consent in NBS research, and suggested a need for improved collaborative tools and educational programs focused on ELSI for the NBS community. The survey results may inform future efforts in ELSI and NBS research including the development of expanded ELSI tools that leverage digital platforms and consultative services.

The deep energy method (DEM), a type of physics-informed neural network, is evolving as an alternative to finite element analysis. This method employs the principle of minimum potential energy to predict deformations under static loading conditions. However, the model’s accuracy is contingent upon choosing the appropriate architecture for the model, which can be challenging due to the high interactions between hyperparameters, large search space, difficulty in identifying objective functions, and non-convex relationships with the objective functions. To improve DEM’s accuracy, we first introduce random Fourier feature (RFF) mapping. RFF mapping helps with the training of the model by reducing bias towards high frequencies. The effects of six hyperparameters are then studied under compression, tension, and bending loads in planar linear elasticity. Based on this study, a systematic automated hyperparameter optimization approach is proposed. Due to the high interaction between hyperparameters and the non-convex nature of the optimization problem, Bayesian optimization algorithms are used. The models trained using optimized hyperparameters and having Fourier feature mapping can accurately predict deflections compared to finite element analysis. Additionally, the deflections obtained for tension and compression load cases are more sensitive to variations in hyperparameters than bending.

Public trust in science was tested and relied on during the SARS-CoV-2 pandemic, which has shaped global events since the WHO declaration in March 11, 2020. Public trust has been impacted through the government recommendations and mandates informed by public health guidance, including non-pharmaceutical and pharmaceutical interventions. The free-flow of ideas and in-formation so essential to the functioning of science has faced unprecedented challenge from widespread censorship in both the media and in scientific journals. This has created a poisoned environment for the building of trust between science and society. Scientific norms and ac-countability must be restored in order to rebuild the vital relationship between scientists and the public they serve.

Effective teamwork is essential to the success of sport and athletic organizations. Interprofessional Collaboration (IPC) and team-based care has also been identified as a priority for health and social care organizations.(World Health Organization, 2010) The Interprofessional Education Collaborative (IPEC) Core Competencies of (1) Values and Ethics; (2) Roles and Responsibilities; (3) Communication; and (4) Teams and Teamwork provide a guiding framework for IPC. (Interprofessional Education Collaborative, 2023). Student-athlete well-being is a key objective for individuals working with or for university athletic departments, with high levels of well-being being correlated with sport, academic, and personal improvements (Navarro et al., 2020). Due to the multifaceted factors that influence student-athlete well-being (e.g., cultural factors, coaches, academic pressure, family stress, etc.), there is an increasing need for an interdisciplinary/interprofessional approach to well-being (Lemelin et al., 2022, Navarro et al., 2020; Ulrich et al., 2022) However, significant barriers exist in implementing IPC into university athletic departments and little research exists on how to overcome roadblocks to integrating IPC in university athletic departments for enhancing student-athlete wellness. This paper will describe how a university athletic department used a team approach to enhance student-athlete health and well-being. The Interprofessional Wellness Team (IWT) included a clinical sport psychology doctoral student, a licensed mental health professional, athletic trainer, and sport dietitian. A case vignette is used to demonstrate how IPEC core competencies are operationalized by the team to address athlete health and well-being through IPC. Recommendations on the further implementation of IPC centered around student-athlete well-being will be provided.

Adolescents experience rapid physical, cognitive, and psychosocial growth with different factors that contribute to health and well-being. In this view, an important role is played by body weight and its perception. The aim of this study was to determine, in a sample of Italian late adolescents, whether health related quality of life (HRQoL) is associated with the different weight status categories (underweight, normal weight, overweight, obese), even considering sex differences. Data were collected from 1826 adolescents (n=735 males). HRQOL was analyzed using the Italian version of KIDSCREEN-52. Overweight adolescents showed a reduced psychological well-being (p<0.05) and self-perception (p<0.05), then other BMI categories. Subjects with obesity reported an increased bullying victimisation (p<0.05), reduced self-perception and eating disorders (p<0.001), while underweight were characterised by an altered adherence to the Mediterranean diet (p<0.001), eating disorders (p<0.001), and problematic use of social media (p<0.05). No sex differences were found, except for socio-economic status perception, where underweight girls reported a higher economic well-being than boys (p<0.05). Our results may suggest that there is an association between weight status categories and HRQoL, more pronounced in underweight and overweight adolescents. The association between BMI categories and psychosocial dimensions opens the need to define specific domains on which such preventive interventions should focus, always through a personalized perspective.

Shear velocity logs are crucial in the oil and gas industry for assessing subsurface mechanical properties, including rock stiffness, shear strength, and seismic wave propagation, essential for optimizing hydrocarbon exploration and production strategies. However, obtaining shear velocity logs conventionally is expensive and time-consuming, especially when drilling additional wells solely for this purpose. With the recent boom in machine learning algorithms adoption across various scientific domains, it proved to be an extremely valuable tool for numerous applications in the oil and gas industry. It makes use of the readily available large datasets collected over decades and leverages this data to train powerful, data-driven models, reducing the reliance on empirical relationships that usually have poor generalization. This study follows this approach and presents the use and comparison of machine learning algorithms for predicting shear velocity logs from conventional and readily available logs in the Ahnet field, Algeria. Ultimately, this study aims to enhance reservoir assessment and optimize hydrocarbon recovery processes, potentially reducing exploration costs and improving oil and gas production decision-making in the region.

Children from rural areas face numerous possibilities for neurodevelopmental conditions that may compromise their well-being and optimal development. Neuropsychology and electroencephalography (EEG) have shown strong agreement in detecting correlations between these two variables and suggest an association with specific environmental and social risk factors. The present meta-analysis aims to integrate the qualitative and quantitative EEG findings, their relationship with cognitive impairment in children living in vulnerable or rural environments, and the main risk factors influencing EEG abnormalities. The method for this purpose was based on a systematic string-based review from Ebscohost and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Qualitative and quantitative analyses were conducted from the outcomes that complied with the selected criteria. A total of 92 records were identified; however, only 20 were eligible, considering 11 for qualitative and 9 for quantitative analysis. The findings highlight a significant amount of literature on EEG and its relation with cognitive impairment from studies in children with epilepsy and malnutrition. In general, there is evidence about the advantages of implementing EEG diagnosis and research techniques in children living under risk conditions. Further research is needed to better describe and integrate the state of the art regarding EEG features extraction.

Evolutionary perspectives have generated many questions and some answers in the study of human health and disease. The field of evolutionary medicine, and related analytics of evolutionary psychiatry and evolutionary psychology have extended and expanded the way health disorders are viewed by searching for why humans, as a species, are vulnerable to certain pathological conditions. The search is organized into four domains that apply proximate and evolutionary explanations to human traits and developmental sequences. This framework opens inquiry to the ontogeny, phylogeny, mechanism, and adaptive significance of human health conditions. In this paper I argue that evolutionary medicine seems to parallel biomedicine in its primarily pathogenic focus. That is, conditions of pain, suffering, and disorder have received the most attention. Some work has used the architecture of evolutionary medicine to take a salutogenic approach, evaluating the proximate and evolutionary explanations of human well-being. I propose that an evolutionary understanding of human well-being requires a survey of emotions and their relationship with neurobiology, language, and culture. My anthropology based, multidisciplinary review of biopsychosocial processes reveals the way evolution has shaped modern human understanding of well-being through sociolinguistic learning processes and thereby our individual experiences of well-being. These insights have the power to contextualize human suffering and flourishing as we progress toward the goal of attenuating the former and expanding the latter.

Background: Bonsai integrates relaxation, leisure and art therapy into one medium that can have personal, emotional and commercial value and have benefits beyond the therapist’s couch. The art of bonsai, when used as a therapy tool may affect the capacity of the bonsai practitioner to adjust to negative and stressful life events. Objective: The study was initiated to evaluate the benefit of bonsai on the wellbeing of practitioners of the art. Method: 255 bonsai artists participated in a study that examined the effect of bonsai on their well-being. Semi-structured interviews were conducted with bonsai artists. Results: The majority of participants agreed that being able to work on their bonsais affected their moods positively. Discussion: Although bonsai artists did not report better mental health, participants reported an improvement in their mood when working on a bonsai tree. These findings are significant in that it can be especially useful in settings such as psychiatric hospitals, old age homes, orphanages, prisons, etc., as a preventative measure as well as restorative measures in the well-being of patients.

The work shows a single case intervention based on the motivational coaching model the “Cantón’s Giraffe”, theoretically and experimentally proven in a 20-years-old dancesport practitioner, who considered improving her execution with her dance partner. Methodological triangulation was used [34], combining a test-retest quantitative measure (Using Ryff Psychological Wellbeing Scales; the Rosenberg Self-Esteem Questionnaire and an ad-hoc instrument for the motivational force assessment) and subjective analysis of the responses from the interviews conducted. A three-phase intervention protocol was used [33]: identification, intervention, and follow-up. The results show an improvement in reevaluation scores, an increase in the subjective evaluation, and participant satisfaction and perceived resources raised at the end of the intervention. These results are in line with previous similar studies.

In metal additive manufacturing (AM), precise temperature field prediction is crucial for process monitoring, automation, control, and optimization. Traditional methods, primarily offline and data-driven, struggle with adapting to real-time changes and new process scenarios, which limits their applicability for effective AM process control. To address these challenges, this paper introduces the first physics-informed (PI) online learning framework specifically designed for temperature prediction in metal AM. Utilizing a physics-informed neural network (PINN), this framework integrates a neural network architecture with physics-informed inputs and loss functions. Pretrained on a known process to establish a baseline, the PINN transitions to an online learning phase, dynamically updating its weights in response to new, unseen data. This adaptation allows the model to continuously refine its predictions in real-time. By integrating physics-informed components, the PINN leverages prior knowledge about the manufacturing processes, enabling rapid adjustments to process parameters, geometries, deposition patterns, and materials. Empirical results confirm the robust performance of this PI online learning framework in accurately predicting temperature fields for unseen processes across various conditions. It notably surpasses traditional data-driven models, especially in critical areas like the Heat Affected Zone (HAZ) and melt pool. The PINN’s use of physical laws and prior knowledge not only provides a significant advantage over conventional models but also ensures more accurate predictions under diverse conditions. Furthermore, our analysis of key hyper-parameters—learning rate and batch size of the online learning phase—highlights their roles in optimizing the learning process and enhancing the framework’s overall effectiveness. This approach demonstrates significant potential to improve the online control and optimization of metal AM processes.

The Physics-Informed Neural Networks (PINNs) improve the efficiency of data utilization by combining physical principles with neural network algorithms and ensure that the predictions are consistent and stable with the physical laws. PINNs opens up a new approach to address inverse problems in fluid mechanics. Based on the single-relaxation-time lattice Boltzmann method (SRT-LBM) with the Bhatnagar-Gross-Krook (BGK) collision operator, the PINN-SRT-LBM model is proposed in this paper for solving the inverse problem in fluid mechanics. The PINN-SRT-LBM model consists of three components. The first component involves a deep neural network that predicts the equilibrium control equations in different discrete velocity directions within SRT-LBM. The second component employs another deep neural network to predict non-equilibrium control equations, enabling inference of the fluid's non-equilibrium characteristics. The third component, a physics informed function translates the outputs of the first two networks into physical infor-mation. By minimizing the residuals of the physical partial differential equations (PDEs), the physics informed function infers relevant macroscopic quantities of the flow. The model evolves two sub-models applicable to different dimensions, named PINN-SRT-LBM-I and PINN-SRT-LBM-II models according to the construction of the physical informed function. The innovation of this work is the introduction of SRT-LBM and discrete velocity models as physical drivers into the neural network through the interpretation function. Therefore, PINN-SRT-LBM allows the neural network to handle inverse problems of various dimensions and focus on problem-specific solving. Experimental results confirm the accurate prediction of flow infor-mation at different Reynolds numbers within the computational domain. Relying on the PINN-SRT-LBM models, inverse problems in fluid mechanics can be solved efficiently.

This study examines the effectiveness of a comprehensive psychomotor development program combined with adapted football training for adolescents diagnosed with intellectual disabilities in Romania. Twelve participants, with a mean age of 17.42 years underwent a carefully designed intervention program spanning 36 weeks. The program aimed to enhance fundamental movement skills, integrate these skills into specialized football training, and culminate in a Unified 7-a-side Football Match. Results indicate significant improvements in psychomotor skills, including static and dynamic balance, pulse processing speed, and identified stimuli. Moreover, the intervention positively impacted social interaction among participants. Statistical analysis, employing Pearson Correlation Analysis and Paired Samples t-Test (One-Sample), compared assessments. Results revealed significant differences between pre- and post-intervention measurements, providing robust evidence of the program's efficacy. The positive correlations between physical parameters and social interaction highlight the interconnectedness between physical fitness and social engagement. Our findings suggest that improvements in physical abilities may contribute to increased social interaction among children with intellectual disabilities, underscoring the holistic nature of our intervention, which addresses multiple dimensions of development simultaneously. The findings underscore the holistic benefits of integrating psychomotor development and adapted football programs for adolescents with intellectual disabilities, emphasizing the importance of promoting inclusive physical activities for their overall well-being.

The conventional well placement was done manually by using a numerical reservoir simulator, and it required a lengthy trial-and-error process. It required great experience and expertise to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. In addition, the traditional gradient-based methods such as Line-search and Trust-region were not viable in terms of maximum results obtained. Gradient-based methods were too dependent on the surface gradient of the solution and may only converge to local optima instead of global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. Thus, genetic algorithms were utilized to automate the manual trial-and-error process and to overcome the limitations of the traditional gradient-based method. The objectives of this study were to analyse the effect of different initial well placement distributions, the number of random solution sizes, and the crossover rate on cumulative oil production. A synthetic reservoir model built using CMG Builder was used as the testing platform for the optimization problems. Well-placement parameterization and optimization set-up were carried out using the CMG CMOST optimization tool. The integration of CMG IMEX and CMOST optimized cumulative oil production based on the objectives of the study. The results obtained showed that the higher number of random solutions used resulted in higher cumulative oil production, with more generations needed to reach the optimum solution. It can be concluded that the larger number of random solutions used increased the probability of reaching the optimum solution, but will take more generations.

(1) Background: Psychological well-being (PWB) and female sexual health are two important areas for women’s quality of life and research, and they are closely related. The aim of this study was to conduct a systematic review of the existing literature to explore the association between PWB and sexual health in women. (2) Methods: This review was carried out following PRISMA. The inclusion criteria were studies with samples of adult women, that evaluated and associated sexual functioning and mental health. Scientific articles were identified on Web of Science, Scopus, EBSCO (PsycInfo, PsycArticles, and Psicodoc), ProQuest, and PubMed. The search was limited to years between 2010 and 2023. (3) Results: 14 selected articles were analyzed, in which population samples and variables related to mental and sexual health were examined. 42.9% of the studies included clinical samples, 71.4% focused on anxiety and depression as main mental health variables, and 50% examined female sexual functioning as a sexual health variable. (4) Conclusions: This review provides more up-to-date information about valuable insights into the possible determinants of female sexual health. The association between PWB and female sexual health has been demonstrated.

The relationship between workplace well-being, seniority, workplace bullying, and employee performance was explored in the context of the Mangkutana district office. Data was collected through direct surveys and questionnaires distributed to respondents. The impact of these variables on employee performance was assessed using multiple linear regression analysis. The results unveiled significant insights. Firstly, a positive correlation was observed between favorable workplace well-being factors and employee performance. Feeling valued, supported, and satisfied in the work environment was linked to higher motivation and productivity. Interestingly, seniority exhibited an unexpected negative impact on employee performance. Longer-tenured employees might face complacency or adaptability challenges, leading to decreased performance. Furthermore, the research emphasized the detrimental impacts of workplace bullying on employee performance. Instances of bullying led to the emergence of stress, anxiety, and decreased job satisfaction, collectively leading to a decline in overall performance and well-being. In summation, this study accentuates the significance of fostering a positive work environment that places a premium on employee well-being and concurrently deals with matters related to seniority and workplace bullying. By recognizing and mitigating these factors, organizations can enhance employee performance, productivity, and job satisfaction, fostering a healthier and more successful work environment.

Loneliness, as a well-established risk factor of mental health, has been strongly associated with low subjective well-being (SWB). However, less is known about potential boundary conditions that may ameliorate this ‘dark side’ of loneliness. Social connections are critical to well-being from the standpoint that the lack of belonging was detrimental to human survival in the evolutionary past. We thus hypothesized that loneliness would exert a more pronounced influence on people’s SWB when the need for others (social resource) is perceived to be high–the presence of environmental harshness. With a particular focus on urban (i.e., Seoul) residents who are presumed to be more vulnerable to loneliness, two studies examined whether feeling lonely matters less to SWB under favorable environmental conditions. As expected, loneliness was less harmful to the SWB of individuals who perceived their surroundings as relatively secure and favorable (Study 1). We then experimentally replicated the results by exposing people to cues of either a harsh (e.g., images of scarcity) or a favorable environment (Study 2).

The differential distribution, size, and quality of urban green spaces (UGS) among localities generate a differential distribution of benefits provided to users. We analyzed the spatial distribution of five size categories of UGS among 15 municipalities of Mexico City, compared their total surface per capita and associated them with the social marginality index. We found 1,353 UGSs accessible for public use with a total area of 2,643 ha. Seventy-four percent of them had &lt;1 ha of surface area, and 51% were located in only three municipalities that were mostly middle- and high-income. These municipalities concentrated a higher area of green spaces per capita. We found a negative correlation between the marginality index and the area of UGS per municipality; the lower the marginality index, the higher the area of green spaces. We consider this a situation of environmental injustice since urban environmental services are distributed unequally with respect to marginalized populations.

Background: Depressive symptoms often occurred during the adolescence period. This situation affected their psychological well-being, which is an important element for a positive youth development. Thus, there is an urgent need to prevent such mental health problems and promote psychological health outcomes among adolescents. Mobile health has been created as a platform to promote mental health to adolescents to ensure ongoing safety and effectiveness. Despite the positive evidence to support the mobile health, tailored mobile health to promote mental health and prevent psychological symptoms among adolescents in Thailand is lacking.This study aimed to assess the feasibility and acceptability of delivering digital technology intervention prototype called “MU-My-Mind Mobile Application (MU-My-mind App)” among Thai adolescents and evaluate the efficacy of this application in improving psychological health outcomes. Materials and Methods: This quasi-experimental research was conducted in secondary school students in Thailand. A convenience sample was recruited and participants were asked to download the MU-My-Mind App on their smart phone. Health outcomes were also evaluated by self-administered questions and focus group interview at baseline and post-intervention. Feasibility and acceptability were assessed. Quantitative data were analyzed by descriptive statistics and pair t-test. Qualitative data were analyzed by thematic analysis. Results: Thirty students completed the MU-My-Mind application. Quantitative data suggested significant improvements on mindfulness, psychological well-being, depression, anxiety and stress. Qualitative data derived into six themes: a) overall feedback of the program, b) learning to manage stress and challenges, c) elevating relaxation responses and calmness, d) heightened mindfulness and concentration, e) increased awareness and management of emotions, and f) altering perspectives and managing thoughts. Overall, data suggested the acceptability and feasibility of the intervention. Conclusion: The “MU My Mind Mobile Application” prototype was demonstrated feasible and well acceptable among Thai adolescents to promote their psychological health outcome. However, there are some limitation and suggestion emerged during this pilot testing. Future research should be explored in a larger sample size with comparison control group and over a longer duration with a range of diverse samples to ensure the efficacy of this application.

Purpose: Using an identical experimental structure with both Thai and Singaporean undergraduates, we investigated relationships (interactions) among social support, stress, resilience, mindfulness, and self-efficacy on psychological well-being (PWB). Stress indicated a negative influence on PWB, but mindfulness, resilience, self-efficacy, and social support indicated positive influences. Methods: A cross-sectional predictive design was used with 966 Thai and 673 Singaporean university students. After calculating an adequate sample size and performing convenience sampling, we administered the following six standard scales: the Perceived Stress Scale, the Connor–Davidson Resilience Scale, the Mindfulness Awareness Scale, the General Self-Efficacy Scale, the Multi-dimensional Scale of Perceived Social Support, and the Psychological Well-being Scale—along with a demographic questionnaire. Descriptive statistics, correlation analysis, and structural equation modeling were performed for participants’ PWB. Results: Mindfulness had significant effects for both factors of PWB, including autonomy and growth, and cognitive triad, across two samples. In the Thai sample, resilience most strongly predicted autonomy and growth and perceived stress did so the cognitive triad, whereas in the Singaporean sample, perceived control most strongly predicted autonomy and growth and support from friends did so the cognitive triad. Conclusion: These findings provide specific knowledge toward enhancing psychosocial interventions and toward promoting PWB to strengthen mindfulness, resilience, perceived control of stress, and social support. stress, resilience, mindfulness, psychological well-being, university students

Introduction: The application of the concept of self-regulation that has an impact on the psychological well-being of adolescent social media users as self-regulation in a core aspect of human functioning that helps facilitate the successful pursuit of personal goals and adaptability to changes in the environment. This literature aims to determine the study of the psychological well-being philosophy of adolescent social media users based on self-regulation.Methods: This literature study was conducted through a search for scientific publications ranging from 2018-2022. The databases used are PubMed, Science Direct, and ProQuest. The literature search was carried out according to topics with four groups of keywords based on Medical Subject Heading (MeSH) and combined with Boolean operators AND, OR and NOT, keywords Self-regulation AND mental wellbeing AND social media AND adolescence.Results: A study of the psychological well-being of adolescent social media users based on self-regulation based on ontology, epistemology, and axiology studies. Self-regulation is a philosophy that emphasizes the dynamic process that directs the behavior of a teenager towards the desired final state and adapts to changes that occur in the surrounding environment. The adaptation process is carried out as a form of self-regulation of the positive and negative impacts of using social media on adolescents with a motivational approach and avoidance system. The approach motivation system is carried out if it provides positive aspects in social media. While avoidance motivation is carried out if it has a negative impact in the form of low self-esteem, depression, and body image disturbances as a result of social comparisons made by teenagers when they see other people's posts.Conclusion: The concept of psychological well-being of adolescent social media users based on self-regulation is beneficial for parents to improve the parenting pattern of adolescent children. Good parenting is needed in understanding the positive and negative impacts of using social media and reducing externalization problems in adolescents.

Scientific literature shows increased interest in the aged and the aging phenomenon. The Aging Attitudes Questionnaire - AAQ was validated for the Portuguese population to understand the importance of attitudes towards old age and their impact on the subjective well-being of the elderly. A sample of 400 subjects (from 18 to 93 years) answered a socio-demographic questionnaire, and the AAQ was composed of three subscales (psychosocial losses, physical change, and psychological growth). The CFA confirmed the tri-factorial structure with very good adjustment of the model to the data with the Cronbach alpha of the total scale scoring .84 and ranging from .65 to .77 for each factor. A total of 9 items were omitted both for poor factor loadings (<0.50. Notwithstanding, 3 items below the criteria were maintained, as they conceptually fit into the factor. Of the final 15 AAQp items, 5 belong to the Psychosocial Loss factor, 6 to Physical Change, and 4 to Psychosocial Growth. This tree factor model explained 50.1 % of the total variance. In conclusion, this study supports that AAQ has acceptable validity, confirming the composite reliability and the discriminant validity, but not the convergent validity. Through multi-group analysis, the invariance of the scale was confirmed. This validation is of pivotal importance once it allows measuring the attitudes towards aging, thus facilitating the promotion of wellbeing across the lifespan.

The goal is to reduce poverty, the method used is a literature study to see the effect of using Financial Technology (Fintech) on financial inclusion, method to see the effect of financial inclusion on poverty. From the results of the tests, the use of Fintech can increase financial inclusion, thereby encouraging poverty alleviation. It can be seen that the Financial Inclusion variable has a significant and negative effect on the poverty variable. This shows that countries that have high levels of financial inclusion are generally estimated to have low poverty rates. Increasing a country's financial inclusion can reduce poverty.

The mean flow and turbulence statistics of the flow through a simplified urban environment, which is an active research area in order to improve the knowledge of turbulent flow in cities, is investigated. This is useful for civil engineering, pedestrian comfort and for health concerns caused by pollutant spreading. In this work, we provide analysis of the turbulence statistics obtained from well-resolved large-eddy simulations (LES). A detailed analysis of this database reveals the impact of the geometry of the urban array on the flow characteristics and provides for a good description of the turbulent features of the flow within a simplified urban environment. The most prominent features of this complex flow include coherent vortical structures such as the so-called arch vortex, the horseshoe vortex and the roof vortex. These structures of the flow have been identified by an analysis of the turbulence statistics. The influence of the geometry of the urban environment (and particularly the street width and the building height) on the overall flow behavior have also been studied. Finally, the well-resolved LES results were compared with the experimental database from Monnier et al. to discuss differences and similarities between the respective urban configurations.

Social and behavioral rhythms (SBRs) are related to circadian rhythms well known as central points in the pathophysiology of bipolar disorders. The purpose is to verify in elderly people if having functional SBRs correlated with a positive perception of the quality of life (QoL) and health parameters and if there was an association between dysfunctionality of SBRs and Major Depressive Disorder (MDD). Sample: 141 people aged 65 years (58,9%Female). Each subject was submitted to SBRs Scale (in which higher scores show more dysfunctional SRB); SF-12 for QoL; PHQ-9 for depressive symptoms and underwent blood levels essays including cholesterol and triglyceride. The medical documented lifetime diagnosis including Major Depressive Disorder was taken into account. SBRs Scale score correlated inversely with SF-12 score (r=-0.360, p&lt;0.0001) and positively with PHQ9 (r=0.396, p&lt;0.0001). Lifetime MDD medical diagnosis was associated with a higher score at scale on social rhythms (24.811.9 vs 20.18.0, F=15.848, p&lt;0.0001).The study highlighted, for the first time, that SBRs such as sleep, and nutrition have a role in well-being and that dysregulation of SBRs is related to MDD. Further longitudinal studies with a sufficient number of individuals will have to confirm these data and clarify causal links of the association between QoL and SBRs.

This study examines the relationship between teleworking, gender roles and happiness of couples using data from the British Household Panel Survey (BHPS) and the Understanding Society Survey (USS) during the period 1991-2012. Various approaches are followed, including Probit-adapted fixed effects, multinomial Logit and Instrumental variables (IV). The results support that both men and women who are teleworkers spend more time on housework, while teleworking increases the probability that the household chores examined in this study, such as cooking, cleaning ironing and childcare, will be shared relatively to those who are non-teleworkers. In addition, women are happier when they or their spouse is teleworker, as well as, both men and women are happier when they state that the specific household chores are shared. Thus, women teleworkers may be happier because they can face the family demands and share the household chores with their spouse, increasing their fairness belief about the household division allocation and improving their well-being, expressed by happiness.

Unsupervised physics-informed deep learning can be used to solve computational physics problems by training neural networks to satisfy the underlying equations and boundary conditions without labeled data. Parameters such as network architecture and training method determine the training success. However, the best choice is unknown a priori as it is case specific. Here, we investigated network shapes, sizes, and types for unsupervised physics-informed deep learning of the two-dimensional Reynolds-averaged flow around cylinders. We trained mixed-variable networks and compared them to traditional models. Several network architectures with different shape factors and sizes were evaluated. The models were trained to solve the Reynolds-averaged Navier-Stokes equations incorporating Prandtl's mixing length turbulence model. No training data were deployed to train the models. The superiority of the mixed-variable approach was confirmed for the investigated high Reynolds number flow. The mixed-variable models were sensitive to the network shape. For the two cylinders, differently deep networks showed superior performance. The best fitting models were able to capture important flow phenomena such as stagnation regions, boundary layers, flow separation, and recirculation. We also encountered difficulties when predicting high Reynolds number flows without training data

This study addresses a gap in literature by comprehensively analyzing the emotional well-being of healthcare workers. Methods: The study employed an analytical cross-sectional design utilizing both qualitative and quantitative methodologies among 84 healthcare workers. Quantitative data was analyzed using STATA version 17 while qualitative data was analyzed using thematic analysis. Ethical approval was sought from the University of Livingstonia Research Ethics Committee (UNILIA-REC). Results: 88% of the participants had experienced verbal abuse; stress and anger, 87% emotional tiredness, 22% depression, and 4% physical abuse. Emotional tiredness was associated with department (p=0.004) and work demand (p=0.0047), stress and anger were associated with experience (p=0.033), department (p=0.002) and work demand (p=0.002), depression was associated with age (p=0.035), experience (p=0.025), cadre (p=0.046), staffing (p=0.005) and work demand(p=0.044), verbal abuse was associated with department (p=0.035) and physical abuse was associated with knowledge of policies (p=0.002). Being aware of emotions and controlling them was the common coping strategy used by most (26%) of the respondents. The study established that most (78%) of healthcare workers did not receive formal training on coping skills for emotional disturbances. Conclusions: Healthcare workers experience some emotional disturbances within their professional roles. There is a need for healthcare workers to be trained on how to cope with emotional disturbances.

International research has highlighted the relationship between the physical environment of schools and the perceived well-being of different actors in the educational community. Although these studies pointed out a relationship between these variables, there is little research that delves into the nature and quality of this relationship. The present study aimed to analyse the mediating effect of the perception of the physical environment of the classroom (considering aspects such as lighting, acoustics, visibility, size, order, and aesthetic features) on the perception of school climate and subjective well-being of students in state-funded schools in Chile. A total of 19.567 students (48,77% female) between fifth and eighth grade from 425 vulnerable schools in Chile participated. Through a simple statistical mediation model, it was identified that the perception of the physical environment mediates about 50% of the total effect on subjective well-being and school climate (c' = 0.09 (0.01); p < 0.001). There is an urgent need to incorporate indicators and evaluations addressing the perceptions of the infrastructure and physical environment in schools, especially when this has been expressed as an explicit and historical demand of the Chilean student movements. This type of approach, including multiple factors, could favour a more articulated vision of the quality of the educational environment, contributing to the academic and emotional development of students.

In the realm of digital transformation, effective leadership and motivation are pivotal for organisations navigating the complexities of today’s systems. This study explores the intersection of intrinsic motivation and mental well-being among mature students—an analogy that sheds light on strategies applicable to organisational contexts. In developed nations like the UK, mental health for mature students is increasingly recognised as a crucial component of their educational journey. Mature students, who typically enrol in higher education after an educational gap and upon turning 21, often face specific challenges that can impact their mental well-being while pursuing academic goals. The primary objective of our study was to assess the relationship between intrinsic motivation and the mental well-being of mature students. Our research revealed a significant positive correlation between these two variables. Confirmatory factor analysis (CFA) results supported a one-factor solution for the sample data from mature students. Additionally, we found that intrinsic motivation is a significant predictor of the mental well-being of mature students. Recognising and addressing the unique difficulties individuals encounter and providing appropriate support can enhance their well-being and contribute to the overall success of the higher education community.

In the contemporary accelerated pace of life, emphasizing the importance of sleep quality is essential for enhancing overall well-being and health. This study investigates the link between poor sleep quality and key factors affecting wellness, such as mental health and eating disorders. Through a cross-sectional analysis involving 407 participants, utilizing established measures including the Depression Anxiety Stress Scale (DASS-21), the Eating Disorder Examination Questionnaire Short (EDE-QS), and the single-item Sleep Quality Scale (SQS), data was collected and analyzed using SPSS v28 and R-Statistics. The findings reveal a significant correlation (p&lt;0.05) between DASS-21, EDE-QS, and SQS, indicating that individuals experiencing poor sleep quality exhibit higher levels of depression, anxiety, and stress. Furthermore, multinomial logistic regression analysis highlights low sleep quality as a risk factor for both mental health (OR: 1.071, 95% CI: 1.042, 1.102, p&lt;0.05, low vs. high sleep quality), and eating disorders (OR: 1.047, 95% CI: 1.004, 1.092, p&lt;0.05, low vs. high sleep quality). Overall, these results underscore the critical role of sleep quality in mental health and suggest insomnia as a predictive factor for both poor mental well-being and disordered eating habits. Further research through randomized controlled trials is warranted to validate the findings of this cross-sectional study.

The study explores how different dimensions of perfectionism influence psychological well-being (PWB) in emerging adults. Literature has deepened the relationships between maladaptive perfectionism (e.g., excessive self-criticism, perceived discrepancy from the standards) and low PWB. Less is known about whether and how adaptive perfectionism (e.g., pursuing personal standards) relates to PWB. Secondly, the study has investigated whether self-compassion (i.e., self-benevolence, seeing personal imperfections as a common condition) may mediate the relationships between adaptive/maladaptive perfectionism and PWB. Participants (N=217, 18-35 y. o.) completed self-report questionnaires measuring: adaptive/maladaptive perfectionism (Almost Perfect Scale-Revised, APS-R: high standards and order/discrepancy, respectively), PWB, and self-compassion (SCS). Adaptive perfectionism resulted associated with PWB, particularly higher presence of purpose in life, environmental mastery, self-acceptance, and personal growth. Conversely, discrepancy resulted the most robust predictor of low PWB (β=-.68), followed by high standards with a positive direction (β=.23; Rc²=.514, p&lt;.001). A strong negative association emerged between discrepancy and SCS (r=-.67, p&lt;.001). A mediation analysis shows that self-compassion has an indirect effect reducing the strength of the relationship between discrepancy and low PWB. Results suggest focusing on self-compassion as a buffer factor that reduces the negative impact of maladaptive perfectionism on psychological well-being. Implications for education and health psychology are discussed.

Alkali-activated materials, sometimes called geopolymers, can be used as alternative cementitious materials to conventional Portland cement. There is a significant current interest in these materials due to their low CO2 footprint. Typical applications of alkali-activated materials are within civil engineering, however, potential applications as well cementing material within oil & gas are also being studied with emerging interest. This paper presents a systematic study of compressive strength development from 1 to 28 days for metakaolin-based alkali-activated cement. The results show that compressive strength is highly dependent upon the initial Si/Al ratio in the mix design, as well as the concentration of the activator solution. Furthermore, due to the relatively low initial reactivity of the used metakaolin material, different types of co-binders were included in the slurry composition to improve early strength development. The two different co-binders tested were another, more reactive metakaolin material and Blast Furnace Slag (GGBFS). It was found that both these co-binders performed as intended, by ensuring early strength development via precipitation of K-A-S-H and C-A-S-H gels, respectively, and also by enabling subsequent strength development due to improved dissolution of the low-reactive metakaolin.

Considering the importance of assessing the impacts resulting from a sport career, this study aimed at developing and validating a semi-structure interview guide suitable for quantifying the socio-demographic and epidemiological profile of former professional football players. Based on the theoretical frameworks and several methodological procedures, an interview guide was de-veloped, consisting of 3 areas of conceptual organization (A1. Biographical data; A2. Career data; and A3. Post-Career Transition data) and 8 data collection categories (C1. Personal data; C2. Pro-fessional data; C3. Socio-demographic pathway in the post-career; epidemiological pathway; C4. Epidemiological pathway; C5. Moment of leaving the career; C6. Socio-demographic pathway in post-career; C7. Epidemiological pathway in the post-career; and C8. Perceptions on post-career planning). The construction and validation of the interview guide following 4 steps: Ad hoc con-struction of the interview guide (step 1); Review of the interview guide by a panel of experts (step 2); Definition of procedures and protocol for the application of the interview (step 3); Application of the pilot study for face validation of the interview guide (step 4). Thus, in procedural terms, four stages were considered for the construction and validation of the interview guide, namely the Ad hoc construction of the interview guide (i), the review of the in-terview guide by a panel of experts (ii), definition of procedures and protocol for the application of the interview (iii), and the application of the pilot study for the face validation of the interview guide (iv). The results showed that the interview guide for the study of the impacts of the sports careers on the quality of life of Portuguese former football players shows reliability for the collec-tion of qualitative and quantitative information from the respective content analysis. The use of the interview guide characterizes the path of former player, providing information and knowledge on the socio-demographic and epidemiological impact factors resulting from their post-career (sports retirement).

In conclusion, this qualitative exploration has unveiled the profound and essential impact of music on psychological well-being, especially in the post-pandemic world. Through in-depth literature reviews, informal interviews, and observations with thirty CAS participants from SMC, the researcher uncovered ten salient themes that collectively illustrate music's multifaceted role as a potent remedy. These findings showcase how music serves as an emotional outlet, a cognitive enhancer, and a source of inspiration. The emotional depth and spiritual resonance participants associate with music underscore its capacity to address diverse human needs, making it an indispensable tool in navigating the complexities of our post-pandemic reality. Moreover, the cognitive benefits observed, such as improved concentration and focus, are particularly pertinent in the context of remote work and education, highlighting music's necessity in our daily lives. Additionally, this study emphasizes the vital role of virtual music communities in fostering social connections, even amidst physical isolation, further underscoring the enduring relevance of music in these challenging times. This research contributes to the expanding knowledge of music's impact on well-being, offering a holistic perspective encompassing spiritual, emotional, social, and cognitive dimensions. It enriches existing music psychology theories and invites further exploration into the mechanisms underpinning music's diverse effects. These insights have practical implications for fields such as mental health, education, and remote work, where music can be utilized as an essential tool for stress reduction and emotional regulation. This is highlighted further in the Suggested songs that can help sought the mind, body, spirit and soul. Looking ahead, future research could delve deeper into the long-term impact of music engagement on well-being and explore the universality of music's benefits through cross-cultural studies. Overall, music, as illuminated by this study, stands as a timeless and indispensable companion, offering solace, inspiration, vitality, and connection in the face of any contemporary challenges.

The development of cost-effective methods for estimating hydraulic conductivity profiles has been an ongoing effort in the field of engineering practice, which can be used to increase availability to clarify the hydrogeological complexity of fractured rock aquifers for the aid of solving groundwater-related problems. A methodology is presented, which combines electrical well logs, fluid conductivity logs, double packer hydraulic tests, Archie’s law, and the Kozeny-Carman-Bear equation to investigate relations between formation factor and hydraulic conductivity. This method was applied to develop hydraulic conductivity profiles based on the data collected from 88 boreholes in Taiwan's mountainous areas. The investigation results include: (1) Well logging signals were suggested to be categorized by rock types to establish effective relationships with hydraulic conductivity. (2) Removing the mud-bearing section data with two proposed data clustering techniques could effectively enhance the correlation between the formation factor and hydraulic conductivity. (3) The predictive models for estimating hydraulic conductivity have been developed for sandstone, schist, and slate. (4) The prevalence of clay content in most of Taiwan's mountainous rock formations has been found, which implies that careful consideration of clay-related issues in complex geologic formations is essential while applying Archie's law theory.

We compare the well known Rayleigh-Ritz variational method (RRVM) with a recently proposed approach based on supersymmetric quantum mechanics and the Gram-Schmidt orthogonalization method (SSQMGS). We apply both procedures to a particular class of double-well harmonic oscillators that had been conveniently chosen for the application of the latter approach. The RRVM eigenvalues converge smoothly from above providing much more accurate results with less computational effort. Present results show that the unproved SSQMGS upper bounds do not hold.

The United Nations Organization in its agenda for 2023 approved Health and Well-being as one of the objectives of sustainable development, in this sense, Higher Education Insti-tutions contribute with interventions to improve each human process that generates a healthy life. In Latin America as well as in European countries, studies have been con-ducted to observe psychological well-being at this stage of life. The present study analyzes in a longitudinal way the psychological well-being in three times in people who attend group dynamics programs. The sample is 668 participants, with an age range between 65 and 80 years. The sample has been distributed in two analysis groups controlling for gender and age: group 1 (with group dynamics) and group 2 (without group dynamics). The Spanish adaptation of Ryff's (2006) Psychological Well-being scale α= 0.87 was ap-plied to both groups. The scale is composed of six dimensions of psychological well-being (self-acceptance, positive relationships, autonomy, mastery of the environment, purpose in life and personal growth). Statistical analyses showed significant differences between the two groups: greater psychological well-being and differences in the dimensions of positive relationships and personal growth in group 1. In addition, logistic regression analysis was carried out to test the predictive capacity of psychological well-being by the existence or not of group dynamics, with significant results in several dimensions. With the results obtained, we can conclude that group dynamics have a relevant role in con-tributing to the development of better levels of psychological well-being of the elderly with longitudinal increase in time. Future studies should be aimed at evaluating the value of group dynamics in the way of successful aging.

In the increasingly complex environment of downhole working conditions such as frequent opening and closing of oil and gas wells, acidification/multi-stage fracturing, steam huff and puff, and strong injection and production, the traditional casing string design method standards based on strength design have gradually highlighted their limitations. It is necessary to formulate string design method standards that consider the full life cycle of wells such as drilling and completion, fracturing, and production operations. Starting from the analysis of the advantages and disadvantages of traditional casing string design methods, this paper presents the characteristics of the strain design methods and seal design methods that have been formulated for the life cycle of the string. The strain design method breaks through the traditional design concept and allows the design concept of controllable deformation of the pipe string. The sealing design method is currently the only standard method for the design of tubing strings. At the same time, it further proposes to consider the trend of pipe strength deterioration and establish a time dimension-based life cycle pipe string design method standard, which effectively solves the safety problem of pipe string design in production and operation.

A The aim of this article is to present the results of studies investigating the relationship between trust in supervisor and in co-workers and trust in the organization as an institution on the one hand, and employee well-being on the other hand. The presented research project was carried out from the perspective of building a positive organization with employee well-being as one of the major goals. The research covered 1,113 individuals, employed with various organizations across Poland. The sample consisted of 48% women and 52% men. Trust was measured using the Trust in Supervisor Scale, Affect- and Cognition-Based Trust, concerning trust in co-workers, and the original Trust in the Workplace Questionnaire. The Eudemonic Well-Being in the Workplace Questionnaire was used to measure well-being. Canonical correlation analysis was used to assess simultaneous associations between sets of measured variables. The results prove that trust, including trust in co-workers, in supervisor and in the organization as an institution, promotes employee well-being in all its dimensions.

Based on ICT, specifically smartphones and their mobile apps, this exploratory study questions the impact of EMIs on employees’ perceived stress during work days. A sample of 15 workers, working at least 3 days a week - divided into one control groups (n=5) and one experimental group (n=10) - have used an EMI application “Mon Sherpa” for one-week length. Participants responded to two questionnaires at the beginning of the study: a sociodemographic questionnaire and the PSM-9 (Psychological Stress Measure). They completed the PSM-9 once again in the middle and at the end of the experiment, to compare the score’s evolution depending on the formed groups. Additionally, semi-structured interviews have been conducted with participants of the experimental group (n=9) to identify their application’s perception. Statistics results indicate no effects of the EMIs. However, interviews indicated somatic, behavioral, and cognitive evolutions throughout the experiment in the field of stress, anxiety, and invasive thoughts. These conflicting results might be explained by an immediate but not lasting effect of EMI’s on work-related stress. It may also be partly explained by some limitations of the study. More cross-disciplinary and larger research is required.

This current research follows up on Greenleaf’s oft-quoted best test of servant leadership that calls for employees to be better off financially, emotionally, physically, psychologically, etc. because of the time spent with the servant leader. While oft-quoted, little empirical work exists to see if this is true. In this study, 170 participants provided their perception of their supervisors’ level of servant leadership, their perception of the organization’s support, and their self-report of their general well-being. Gender and age bracket information described the participants, and there were no significant differences between gender or age brackets for participants’ perception of their supervisors’ servant leadership. The analysis showed that there was a moderate positive correlation between servant leadership, perceived organizational support, and general well-being. A modification of an existing general well-being instrument provided a new eight-item general well-being scale with a Cronbach’s alpha of 0.956.

Memphis aquifer is the primary drinking water source in Shelby County (Tennessee, USA) and supplies industrial, commercial, and residential water. Memphis aquifer is separated from the Shallow aquifer by a clayey layer known as Upper Claiborne Confining Unit (UCCU). All of the production wells in the Memphis area are screened in the Memphis aquifer or even deeper in the Fort Pillow aquifer. Traditionally, it was assumed that the UCCU could fully protect the Memphis aquifer from the contaminated Shallow aquifer groundwater. However, recent studies show that at some locations the UCCU is thin or absent which possibly leads to the contribution of Shallow aquifer to the Memphis aquifer. Accurately locating the breaches demands expensive and difficult geological or geochemical investigations, especially within an urban area. Hence, a pre-field investigation to identify the locations where the presence of breaches is likely can significantly reduce the cost of field investigations and improve the their results. In this study, to identify the locations where the presence of breaches in the UCCU is likely we use three different analyses: (1) pilot point calibration (PPC), (2) velocity and flow budget (VFB), and (3) particle tracking (PT) to post-process the developed groundwater results. These pre-field numerical investigations provide relevant and defensible explanations for groundwater flow anomalies in an aquifer system for informed decision-making and future field investigations. In this study, we identify five specific zones within the broad study area which are reasonable candidates for the future field investigations. Finally, we test the results of each analysis against other evidence for breaches to demonstrate that the results of the numerical analyses are reliable and supported by previous studies.

ABSTRACT: Background: Virtual Reality (VR) emerges as a promising non-pharmacological intervention for managing symptoms and providing distraction during chemotherapy. This study aims to assess VR's effectiveness on cancer-related symptoms, vital signs, and patients' perception of the chemotherapy in lung cancer patients. Methods: A quasi-experimental study was conducted on 100 patients. Participants were allocated into an intervention group (n = 55), which experienced immersive VR, and a comparison group (n = 45), which received usual care. Data were collected through questionnaires and checklists, including feedback on the VR experience, pain, vital signs, and common cancer symptoms, assessed through the Edmonton Symptom Assessment Scale. Results: VR had a significant impact on reducing the perception of the chemotherapy length. Patients reported high levels of satisfaction and tolerability. No adverse events were observed. VR did not have significant influence on pain intensity and vital signs. The only exceptions were oxygen saturation, where a significant difference (p = 0.02) was reported, and perception of chemotherapy duration. Conclusions: As a non-pharmacological intervention, VR proves beneficial in minimizing the perceived length of chemotherapy session for lung cancer patients, enhancing their overall treatment experience. The intervention showed to be a safe, feasible, and well-accepted distraction technique. Future research should explore VR's potential effects on a wider range of symptoms and evaluate its impact on long-term outcomes.

Background: The accelerated ageing of the world's population will lead to an increase in the number of older persons in the workforce. Computerized Cognitive Training (CCT) is effective in improving cognitive outcomes, but its benefits for older workers remain controversial. We investigate the real-world efficacy of CCT in the workplace, focusing on employees aged 50+ years from a public sector agency. Methods: Case managers (N = 82) were randomized to either Intervention group (24 40-minute CCT sessions two times per week) or waiting list passive Control group. Cognitive ability, wellbeing, job satisfaction, and productivity outcome measures were collected assessed before and after CCT or comparable Control wait time. Results: Participants undergoing CCT improved on a task of executive functioning (p = 0.04). There was a trend toward change in work productivity after CCT (p = 0.09) with the Control group showing a significant decrease (p = 0.02), while the Intervention group remained stable. Conclusions: CCT during office hours has a positive effect on cognition and well-being without affecting the productivity among white-collar office workers. CCT could be considered as an intervention to support the older workforce in managing the cognitive and behavioral challenges of changing workplace demands.

Abstract Objectives: Aging may challenge life and even affect individuals' well-being and flourishing. This includes challenges of shrinking social relations and being by yourself in old age while using your personal strengths. The current study examines two important personal resources, i.e., the skill of positive solitude and the 24 character-strengths, that may be associated with flourishing in old age. Methods: A convenience sample of 1085 community-dwelling adults (M = 57.20, SD = 6.24, range = 50-87), completed an online self-report survey with demographic measures and scales measuring personal strengths, the skill of positive solitude, and flourishing. Results: As hypothesized, the 24 character-strengths and positive solitude, were each associated with flourishing, and positive solitude moderated the relationships between the 24 character-strengths and flourishing. The 24 character-strengths and flourishing relationship was stronger among participants with lower levels of positive solitude. Conclusion: These findings highlight that despite the decrease in social relations which often characterizes old age, the skill of positive solitude serves as a valuable resource for flourishing in the later stages of life.

Students studying dentistry and nursing experience anxiety daily, which affects both their general well-being and academic achievement. Anxiety is exacerbated by several circumstances, such as stressors in one's personal life, clinical environment exposure, and academic expectations. To effectively regulate anxiety and support students' achievement, treatments that take these elements into account must be designed. In this study, 271 students from the National and Kapodistrian University of Athens, Greece's departments of dentistry and nursing were polled. Being the following resources, participants filled out an electronic questionnaire that was uploaded to Google Forms and assessed their levels of stress, anxiety, depression, resilience, hope, and spiritual well-being: a) the Depression, Anxiety, Stress Scale (DASS-21), b) the Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-12), c) the Adult Hope Scale (AHS), d) the Resilience Assessment Questionnaire (RAQ8), and e) the Brief Resilience Scale (BRS). Additionally, demographic data was gathered, such as gender, educational attainment, family income, and country of origin. Statistical analyses were performed to investigate the relationships between the variables, including hierarchical multiple linear regression and t-tests. The sample consisted of 271 students from the Department of Nursing (n=126, 46.5%) and Dentistry (n=145, 53.5%), with 68.60% of them being female, 80.10% being undergraduate students, and 48.30% being from Athens, the country's capital. Comparatively speaking, 74.50 percent of nursing students were female, while 62.80% of dental students were. Compared to dental students (2.10%), nurses (40.50%) participated in postgraduate studies at a higher rate. Compared to dental students (44.80%), nursing students reported a household income of just 13.90%, which is much less than €35,000. Anxiety was reported by 51.30% of participants overall, with dental students reporting higher anxiety than nursing students. Higher family wealth was associated with reduced stress levels, while female undergraduate students reported higher levels of anxiety than their male counterparts. Hope was a strong predictor of resilience, but stress and worry had a negative correlation. Promoting students' well-being and academic success requires effective stress-reduction and resilience-building techniques aimed to not only improve student performance but also support future healthcare professionals' personal sustainability and holistic growth.

The aging populations observed across numerous countries worldwide necessitate a thorough exploration of interventions aimed at promoting healthy aging. As individuals age, they undergo significant physiological changes that impact their functional capacity and often necessitate increased reliance on external support systems. Enhancing the autonomy and well-being of elderly individuals emerges as a critical imperative, serving not only individual interests but also broader societal goals. This review investigates various interventions designed to optimize the health and independence of aging populations, offering insights into effective strategies for promoting healthy aging and mitigating the societal burdens associated with an aging demographic.

Most recent university campuses follow the North American model, built on city limits or countryside, with large separate buildings in open green spaces. Studies suggest that prevalence and severity of mental health issues among university students are increasing over the past decade in most countries. University services were created to face this growing problem, however individual based interventions have limited effects on mental health and well-being of a large population. Our aim was to verify if and how the natural environment in Campuses is focused on programs to cope with the issue of mental health and well-being among students. Systematic review of literature was undertaken with search in Scopus and LILACS with the keywords “green areas” AND “well-being” AND “Campus”, following PRISMA guidelines. As a result, 32 articles were selected. Research on the topic is recent, mostly in the USA, Bulgaria, and China. Most studies used objective information on campuses’ greenness and/or university students’ perception. Mental health was usually measured by validated scores. Findings of all the studies indicated positive association between campus greenery and well-being of students. We conclude that there is a large potential for use of university campuses in programs and sites for students´ restoration and stress relief.

Most of the educational interventions proposed so far in the field of mental well-being in education ‎usually address just students. In this study, we focused on teachers as the Covid-19 pandemic reiterated ‎the importance of their mental well-being during demanding times. We investigated whether ‎neurofeedback (NF) training can increase optimism, self-esteem, life satisfaction, and the ability to ‎cope with stressful events, reflecting on the research literature that emphasizes these four factors, ‎which affect the ability to be confident in dealing with uncertainty and distress. ‎ Forty teachers aged 35-55 participated in the study. The test group took part in 12 NF training sessions, ‎and the control group was not treated. Our findings indicate that compared to the control group, ‎measures of optimism, self-esteem, life satisfaction, and coping with stressful events improved ‎significantly in the test group.‎ To the best of our knowledge, this is the first attempt to test a mental welfare intervention program for ‎teachers through NF. This study responded to a call to develop teacher intervention programs using ‎physiological measurement and technological innovations to improve their mental health and allow ‎them to cope better with stressful teaching conditions.

The eating behaviors of young people underwent substantial changes due to the COVID-9 19 pandemic. This study addresses the understanding of pandemic impacts on perceived psycho-10 logical and physical well-being exploring the connection between self-perceived eating behavior 11 quality and chronic stress. A sample of Portuguese young people aged 15-26 years (N=951), com-12 pleted a self-report questionnaire assessing socio-demographic data, perception of eating quality 13 behavior, appetite, stress, well-being and optimism. The results reveal gender differences, with 14 males scoring higher in well-being and optimism, while females exhibit elevated appetite and 15 chronic stress scores, and also reveals negative links between age group and eating quality and 16 chronic stress, and positive links between eating quality and well-being. Well-being emerges as a 17 significant predictor of stress in middle and oldest young adults that maintain or increase quality of 18 eating behaviors, while optimism plays a paradoxical role in predicting higher stress in younger 19 adults with reduced eating quality. These results emphasize the need to consider eating behaviors 20 and stress among young adults as an investment in global public health, given the significant rep-21 resentation of young adults in the population, to mitigate long-term health risks. Effectively reduc-22 ing detrimental eating behaviors and stress in these demographic groups can enhance overall well-23 being and longevity.

Online transportation services, initially presented as an innovative employment approach by categorizing workers as 'independent contractors', have come to be recognized for their exploitative nature. The notion of 'flexibility' suggests that workers have the autonomy to tailor their hours and location according to their income goals. However, this disguises the true situation for informal workers who lack rights and protections. This study delves into these issues from a psychological standpoint to uncover the mental and emotional interplay between the motivations and exploitation experienced by online drivers affiliated with multiple platforms. The study adopts a qualitative methodology with a phenomenological lens. Data was gathered through thorough interviews, observations, and document analysis, with Interpretative Phenomenological Analysis (IPA) employed as the data analysis technique. The study's participants were five male multi-platform online drivers from the Special Region of Yogyakarta, ranging in age from 18 to 64, and had been associated with at least two platforms for more than a year. The findings highlight two primary themes: firstly, the decision to join multiple platforms is chiefly driven by economic reasons, including a decline in orders, daily earnings, bonus incomes, and the conclusion of the initial profitable phase. Secondly, the exploitative conditions are linked to the platforms' algorithms, which exert control and influence the psychological state of the drivers. Nonetheless, the participants have developed effective strategies to manage fatigue, anxiety, and stress.

The well-being in the people is a key aspect of the field of psychology. Hence, it is important to an-alyse the variables that are related to life satisfaction and happiness as perceived by individuals and that, therefore, increase their overall well-being. The main objective of this study was to ana-lyse the predictive capacity of emotional intelligence and perceived social support on both the level of life satisfaction and perceived happiness. A total of 380 psychology students completed the Trait Meta Mood Scale, the Multidimensional Scale of Perceived Social Support, the Satisfaction With Life Scale, and the Subjective Happiness Scale. The results show that both emotional intelligence and social support are related to and predictive of subjective happiness and life satisfaction. The importance of developing the components of emotional intelligence and promoting an adequate social network in young people is highlighted.

Social networks are experiencing great popularity, with Instagram currently being the most intensively used network. On these platforms, users are continuously exposed to self-relevant information that fosters social comparisons. A distinction is made between ability-based and opinion-based comparison dimensions. To experimentally investigate the influence of these comparison dimensions on users' subjective well-being, an online exposition experiment (N = 409) was conducted. In a preliminary study (N = 107), adequate exposition stimulus material was selected in advance. The results of the main study indicated that exposition of ability-related social comparisons in the context of social media elicited lower well-being than exposition of opinion-related social comparisons. Theoretical and practical implications of the findings were discussed and suggestions for future work were outlined.

CBCT significantly impacts dental procedures and has brought significant reforms in our approach to diagnosis & treatment planning despite its limitations in differentiating soft tissues. It is an excellent imaging modality and quickly identifies sinus opacification and provides valuable in-sight into paranasal sinus pathologies with considerably lower radiation exposure. The present study aimed to investigate the occurrence of maxillary sinus abnormalities in CBCT scans, identify the frequency, type, and location of these findings, and find the correlation between the distance of periapical lesions and radiographic changes in the maxillary sinus. Two examiners independently evaluated 117 patients to diagnose and classify the cases into different abnormality subtypes. The periapical lesions most closely related to the sinus were recorded. The diameters of the left and right maxillary sinus ostium and the distance of the ostium's lower border to the sinus's osseous floor were recorded. The findings were correlated with the age and gender of these patients. The present study revealed that sixty-one patients were diagnosed with mucosal thickening (52.1%). The sinus wall most affected by mucosal thickening was the maxillary sinus floor, followed by the medial and lateral walls. Of 19 patients with periapical lesions, 15 had maxillary sinus mucosal thickening and was statistically significant (p=0.004). The high occurrence of abnormalities in the maxillary sinus emphasizes the importance for the radiologist to comprehensively interpret the whole volume acquired in CBCT images, including the entire sinus. Incidental findings may be considered in the individual clinical context of signs and symptoms, reducing the risk of overes-timating the real impact of radiographic findings.

In this study, we investigated, for the first time, the effects of the spatially varying effective mass, asymmetry parameter, and well width on the electronic and optical properties of the quantum well which has an improved Rosen-Morse potential. Calculations are made within the framework of the effective mass and parabolic band approximations. We have used the diagonalization method by choosing a wave function based on the trigonometric orthonormal functions to find eigenvalues and eigenfunctions of the electron confined within the improved Rosen-Morse potential. Our results show that the position-dependence mass, asymmetry, and confinement parameters cause significant changes in the electronic and optical properties of the structure we focus on since these effects create a significant increase in electron energies and a blue shift in the absorption spectrum. The increase in energy levels enables the development of optoelectronic devices that can operate at wider wavelengths and absorb higher energy photons. This offers potential advancements in fields such as optical communication, imaging technology, and solar cells.

Introduction: The relation between physical well-being and chronic pain is complex and involves several subjective and objective covariates. We aimed to assess the role of media-tor, confounder, or interactor played by covariates, including sleep quality, physical activity, perceived stress, smoking, and alcohol drinking in the relation between physical well-being and chronic pain. Method: We used Poisson regression to obtain incidence rate ratios (IRR) of the associa-tion between physical well-being and chronic pain in a cohort study carried out among uni-versity students. We applied General Structural Equation Modeling (GSEM) to assess me-diation, and stratum-specific analysis to distinguish confounding from interaction. We computed Relative Excess Risks due to Interaction (RERI), Attributable Proportion (AP), and Synergy index (S) to measure additive interaction. Results: High physical well-being is related to a large decrease in the risk of chronic pain (IRRTotal Effect= 0.58; 95%CI 0.50-0.81). Perceived stress mediates 12.5% of the total effect of physical well-being on chronic pain. The stratum-specific IRRs of current smokers and non-current smokers were different from each other and were larger than the crude IRR (IRR=1.49; 95% CI: 1.24-1.80), which indicates that smoking could be both confounder and interactor. Interaction analyses showed that physical activity could act as a potential interactor (RERI=0.25; 95% CI: 0.13, 0.60). Conclusion: Perceived stress is an important mediator of the relation between physical well-being and chronic pain, while smoking is both a confounder and an interactor. Our findings may prove useful in distinguishing high-risk from low-risk groups, in the interven-tions aimed at reducing chronic pain.

The COVID-19 pandemic has had a profound impact on the mental health, adjustment, and psychological well-being of young adults. Despite extensive research conducted in this area, eudaimonic well-being, one specific form of psychological well-being focusing on self-knowledge and self-realization within existential challenges, has received comparatively less attention. The current study aimed to investigate the potential linkages between two key psychological factors, psychological inflexibility and fear of death, with eudaimonic well-being among young adults in the context of the Covid-19 pandemic. One year after the outbreak, 317 young Italian adults (18-34 yrs.) participated in an online survey, including measures of psychological inflexibility (AAQ-II), fear of death (CL-FODS), and eudaimonic well-being (PWB). Results showed that psychological inflexibility was negatively associated with all the dimensions of Ryff’s model of eudaimonic well-being, while fear of death of others was found to be associated with three of these dimensions (autonomy, environmental mastery, and self-acceptance). Results also indicated a mediation role of psychological inflexibility in the association between fear of death and eudaimonic well-being. These findings expand our knowledge of the potential factors that may influence eudaimonic well-being and provide insights for clinical work with young adults challenged by uncertain times.

At the level of the 50 U.S. states, an interconnected nexus of well-being variables exists. These variables have been shown to strongly correlate with estimates of state IQ in interesting ways. But the state IQ estimates (McDaniel 2006) are now more than 16 years old, and the state well-being estimates (Pesta et al., 2010) are over 12 years old. Updated state IQ and well-being estimates are therefore needed. I thus first created new state IQ estimates by analyzing scores from both the Program for the International Assessment of Adult Competency (for adults), and the National Assessment of Educational Progress (for fourth and eighth grade children) exams. I also created new global well-being scores by analyzing state variables from the following four well-being subdomains: crime, income, health, and education. When validating the nexus, several interesting correlations existed among the variables. For example, state IQ most strongly predicted FICO credit scores, alcohol consumption (directly), income inequality, and state temperature. Interestingly, state IQ derived here also correlated .58 with state IQ estimates from over 100 years ago. Global well-being likewise correlated with many old and new variables in the nexus, including a correlation of .80 with IQ. In sum, at the level of the U.S. state, a nexus of important, strongly correlated variables exists. These variables comprise well-being, and state IQ is a central node in this network.

Moral identity can promote people’s well-being, but existing research has paid little attention to the mechanism of the link between the two. The current study proposed that the eudaimonic identity function is a critical mechanism that links moral identity and well-being. Specifically, the quality of identity commitment mediates the link between subjective well-being and the two dimensions of moral identity, namely, internalization and symbolization. We examined these hypotheses in 419 participants, who completed the Self-importance of Moral Identity Questionnaire, Satisfaction with Life Scale, Scale of Positive and Negative Experience, and Questionnaire for Eudaimonic Well-being. Results from the obtained data confirmed our hypotheses: There is a significant correlation between moral identity and subjective well-being. Both the internalization and symbolization dimensions of moral identity promote subjective well-being through the mediating role of identity commitment quality. We discussed these findings with respect to implications and proposed research suggestions for future studies.

We investigate the dynamics and stationary states of a semiconductor exciton-polariton condensate in a double well potential. We find that upon the population build up of the polaritons by above-threshold laser pumping, coherence relaxation due to the phase fluctuations of the polaritons drives the system into a stable fixed point corresponding to a self-organized PT-symmetric phase.

Despite gender equality being present in the social and political sphere, we still encounter aspects that are characteristic of sexism. Such aspects impact upon gender inequality and different types of violence towards women. The present article aims to examine the behaviour of adolescents from Huelva with regards to ambivalent sexism towards women on social networks and their influence on health. Further, we seek to uncover adolescent’s perceptions with regards to gender differences in the use of social networks, the relationship between sexism and women's emotional well-being was observed. The study sample was formed by young people aged between 14 and 16 years who were residing in rural and urban zones in the south of Spain. A mixed methods approach was taken. At a quantitative level, a sample of 400 young people was recruited. These were administered a questionnaire about sexism which was composed of two scales and has been validated at a national and international level. At a qualitative level, the study counted on 33 young people who participated in in-depth discussions via interviews and discussion groups. The results showed that sexism emerges in adolescence in the analysed sample from the south of Spain. This favoured a digital gender gap and was reinforced through social networks such as Instagram and Snapchat. Rising awareness and a critical view of the aforementioned sexism was shown on the behalf of females, particularly those from urban backgrounds.

Since the 1950s sugarcane production has grown rapidly from less than 0.5 billion tonnes in the late 50s to reach over 1.9 billion tonnes in 2012 on about 27 million hectares of agricultural land. This expansion has been boosted by the high demand for bioethanol promoted as a sustainable bioenergy source which accounted in 2010 for the biggest share of the global biofuel market. Despite its benefits, the scientific debate on sugar is growing especially that counterarguments are so many, including negative impacts on different interacting ecosystems and human well-being, e.g. bigger stress on land and water resources, environmental externalities on air, a harmful impact on the biodiversity and endemic species, negative environmental externalities, health, and socio-economic aspects. This paper provides a narrative systematic review (SR) of the impacts of sugarcane production on these different ecosystems employing the ecosystem services framework for its acceptance by policy-makers. The references included for the SR were 163 and results showed that the majority of the studies are from Brazil, Australia, South Africa and the USA (≈ 75% of the literature), most of them were from peer-reviewed journals (85%), and most of the case studies adopted a quantitative research approach (93%). The literature assessed showed that sugarcane, like all agro-systems, depends on the practices and techniques to transform negative impacts into positive externalities on ecosystems and human well-being. However, the literature studied failed to include the inter-linkage in sugarcane production impacts’ and therefore to evaluate the related ecosystem services with respect to the Millennium Ecosystem Assessment (MA) framework to account for existing trade-offs. Therefore, the findings are addressed to the scientific community and decision-maker for an intensification of interdisciplinary and integrated research based on the MA framework to cover all ecosystem services, for sustainable development of the sugarcane sector.

This work analyzes the severity and risk associated with Automatic Dependent Surveillance-Broadcast (ADS-B) message drop out in Detect and Avoid (DAA) function of Unmanned Aircraft System (UAS). Performance assessment of the Universal Access Transceiver (UAT) ADS-B message implies that in some cases ADS-B fails to update within a specified update interval, which is referred to as drop out in this work. ADS-B is a fundamental surveillance sensor for both class 1 and class 2 DAA systems. Message loss or drop out has been found as one of the common limitations of the ADS-B system. The key feature of this study is incorporating the update rate of real ADS-B data transmitted from the manned aircraft. The data were received from the Grand Forks International Airport, North Dakota. Monte Carlo method has been adopted to resolve encounter scenarios in the presence of drop out. The change in the alert triggered by the UAS DAA in the presence of ADS-B drop out has been investigated. Furthermore, the risk matrices are created to quantify the associated risk with drop out affected alerts. Simulation results depict that both the duration of drop out and DAA look-ahead time affect the alert-triggering function of UAS. With a small look- ahead window and longer duration of drop out, the number of warning alerts increases. Also, alerts are affected more during an overtaking encounter than that of a head-to-head encounter. A system-level analysis is also carried out to recognize the potential reasons behind the ADS-B drop out.

Classical methods for inverse problems are mainly based on regularization theory. In particular those which are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond, respectively, to the likelihood and prior probability models.

Views of aging include peoples’ assessment of their own aging process and their subjective age. Positive aging views relate to good psychological well-being which predicts better physical and mental health. While these were substantially studied, the moderating roles of self-aging attitudes and psychological well-being in the subjective age-age discrimination connection have been much less explored. The current study used a convenience sample of 568 participants (mean = 66.21y, SD = 11.95, age range 50-95), 55.8% women, 67.1% employed. In line with the hypotheses, young subjective age and psychological well-being were connected to less age discrimination in the workplace and higher psychological well-being mitigated the subjective age-age discrimination at work connection. When the perception of old age as a period of loss was added to the model, adults who perceived old age as a period of loss and reported lower levels of psychological well-being, demonstrated the strongest relationship between an increase in subjective age and an increase in age-related discrimination at work. The findings emphasize the importance of the psychological well-being of older employees as a resource for improving their attitudes to their last years at work.

Infill well location optimization poses significant challenges due to its complexity and time-consuming nature. Currently, determining the scope of infill wells relies heavily on field engineers' experience, often using single indices such as remaining oil saturation or abundance of remaining oil reserves to evaluate the potential of remaining oil. However, this approach lacks effectiveness in guiding the precise tapping of remaining oil in ultra-high water cut reservoirs. To address this, our study comprehensively considers factors influencing the recoverable potential of remaining oil in such reservoirs. We characterize differences in reservoir heterogeneity, the scale of recoverable remaining oil reserves, water flooding conditions, and oil-water flow capacity to construct a quantitative evaluation index system for the recoverable potential of remaining oil. Recognizing the varying degrees of influence of different indices on the recoverable potential of remaining oil, we determine the objective weight of each evaluation index by combining an accelerated genetic algorithm with the projection pursuit model. This approach enables the construction of a recoverable potential index for remaining oil and forms a quantitative evaluation method for the recoverable potential of remaining oil in ultra-high water cut reservoirs. Subsequently, we establish a mathematical model for infill well location optimization, integrating and optimizing infill well location coordinates, well length, well inclination angle, and azimuth angle. Using the main layer sand body of an oilfield in Bohai as a case study, we conducted evaluations of remaining oil potential and infill well location optimization. Results demonstrate that the assessment of remaining oil potential comprehensively characterizes the influence of reservoir physical properties and oil-water diversion capacity on remaining oil potential across different regional positions. This evaluation can effectively guide the determination of infill well location ranges based on the evaluation results. Furthermore, further optimization of infill well locations can effectively enhance reservoir development outcomes.

(1) Workplace nutrition interventions have garnered attention as a pivotal component of employee well-being and organizational productivity. However, the effectiveness of various intervention types remains inconclusive. This review aims to systematically evaluate the efficacy of cognitive, behavioral, and mixed nutrition interventions in the workplace, considering the nuances of intervention design, setting, and target demographics. (2) A comprehensive umbrella review was conducted, categorizing existing literature into person-oriented and environmental strategies. This review was prepared in line with the Joanna Briggs Institute methodology for umbrella reviews and the preferred reporting items for systematic reviews and meta-analyses reporting standard. (3) The analysis revealed a lack of definitive evidence supporting the universal effectiveness of any single intervention type. Nonetheless, behavioral and mixed interventions demonstrated more favorable outcomes compared to purely cognitive strategies. Factors such as intervention design, workplace setting, and target group characteristics were identified as significant determinants of intervention success. (4) The review emphasizes the imperative for additional investigations that utilize evidence-based approaches to formulate sound guidelines for efficacious nutrition interventions in occupational settings. This review functions as a foundational framework for guiding both scholarly research and the pragmatic execution of nutrition programs in the workplace.

There are few studies of the psychological effects of language brokering (speaking on behalf of) among minors migrated to Europe, even though language brokering is becoming more current in many migrant families. As the psychological implications of language brokering remain controversial, further investigation is needed. Earlier studies have documented that language brokering is sometimes linked with emotional maladjustment and developmental drawbacks, but other studies have shown faster acquisition of social and Language skills. The present study assessed the relation of language brokering with psychological well-being in a sample of Latino migrant adolescents living in Italy; the dependent variables were depression, anxiety, and emotions. Statistically significant differences between boys and girls confirmed the sex-specificity of language brokering and the lack of statistically significant negative association between language brokering and psychological well-being.

The syntheses of four novel syn-type tricyclic laddersiloxanes bearing eight or six alkenyl groups are presented. These compounds possess reactive alkenyl groups on both the bridged and side silicon atoms and their structures were determined by characterization using multinuclear NMR spectroscopy, mass spectrometry, and elemental analysis techniques. To investigate their reactivity, compounds were subjected to hydrosilylation using two different silanes, and the resulting fully hydrosilylated compounds were analyzed thoroughly. Remarkably, all the synthesized laddersiloxanes displayed high thermal stability, suggesting their potential as promising precursors for the development of new hybrid materials. Additionally, preliminary findings indicate the possibility of exploiting the reactivity difference between the alkenyl groups attached to the D- and T-unit silicon atoms for the synthesis of Janus molecules. These findings highlight the potential of the reported compounds as valuable building blocks in the construction of innovative materials.

As development progresses, when the bottom hole flowing pressure or formation pressure is less than the saturation pressure of crude oil in the reservoir, oil and gas two-phase seepage occurs in the reservoir. Due to the characteristics of oil and gas two-phase seepage, after the oil and gas two-phase seepage occurs in the reservoir, the well production will be reduced, or even greatly reduced. Therefore, how to predict the horizontal well capacity better in this case is an important problem that needs to be solved urgently. In this paper, the method and process of establishing the transient calculation model of two-phase flow in horizontal wells are introduced in detail from three aspects: fluid physical properties, reservoir oil-gas two-phase seepage, and the coupling model of Inflow Performance and Flow in Wellbore. The model is more reliable through the verification of production data from five wells in two oilfields.

Background: The importance of community health psychology in providing complex bio-psycho-social care is well-documented. We present a mixed-method study monitoring the efficacy of health psychology services in the Public Health Focused Model Program for Primary Care Development (2012-2017) in four disadvantaged micro-regions in North-East Hungary. Methods: Study 1 assessed the availability of the services using a sample of 17003 respondents. Study 2 applied a follow-up design to measure the efficacy of the health psychology services in a sample of 132 clients. In Study 3, we conducted focus group interviews to assess clients’ lived experiences. Results: More mental health issues and higher education predicted a higher probability of service use. Follow-up showed that individual and group-based psychological interventions resulted in lower depression and (marginally) higher well-being. Thematic analysis of the focus group interviews indicated that participants deemed topics like psychoeducation, a greater acceptance of psychological support, and heightened awareness of individual and community support important. Conclusions: The results of the monitoring studies demonstrate the important role health psychology services can play in primary health care in disadvantaged regions. Community health psychology can improve well-being, reduce inequality, support the population’s health awareness, and address unmet social needs in disadvantaged regions in Hungary.

Background: Community pharmacies are well-placed to deliver well-being interventions; however, to date, nothing has been produced specifically for this setting. The aim of this study was to develop a positive psychology intervention suitable for a community pharmacy setting with the goal of increasing the well-being of community members. Methods: Intervention development consisted of three steps: Step 1- identify the evidence-base and well-being model to underpin the basis of the intervention (Version 1); Step 2 - model the intervention and gather user feedback to produce Version 2, and Step 3 - revisit the evidence-base and refine the intervention to produce Version 3. Results: Findings from nine studies (seven RCTs, one cross-sectional, one N-1 design plus user feedback were applied to model a 6-week ‘Prescribing Happiness (P-Hap)’ intervention, underpinned by the PERMA model plus four other components from the positive psychology literature (Three Good Things, Utilising Your Signature Strengths in New Ways, Best Possible Selves and Character Strengths). A PERMA-based diary was designed to be completed 3-days a week as part of the intervention. Conclusions: This work is an important development which will direct the future implementation of interventions to support well-being in this novel setting. The next stage is to gain the perspectives of external stakeholders on the feasibility of delivering the P-Hap for its adoption into community pharmacy services in the future.

Teacher burnout has been shown to be one of the most common negative consequences of the COVID-19 pandemic. This study aimed to identify distinct psychological resources and burnout risk profiles of teachers and examine their association with Kolb’s educator roles and their professional experience. Methods: The survey data were collected from 330 preschool and primary school (84 males, Mage = 38.3, SD = 9.14) teachers using a convenience sampling method. Results: The two-step cluster analysis revealed two distinct profiles. The first profile, “high psychological resources, no burnout risk,” was characterized by absent symptoms of burnout and increased levels of well-being, self-control, and positive emotionality. The second profile, “moderate psychological resources, mild burnout,” was associated with medium levels of well-being, self-control, and positive emotionality accompanied by mild burnout. Our findings highlighted that cluster one had a significantly higher score for the facilitator role and cluster two for the expert and coach roles. Additionally, teachers with less professional experience were more likely to belong to cluster one, considering their adequate skills in digital literacy. Conclusions: These findings provide new insights into the explanation of teacher burnout and the design of intervention programs.