External Variables Research Articles

EMERGING PATHWAYS TO GREEN PURCHASE INTENTION: DECODING THE INFLUENCE OF ENVIRONMENTAL AWARENESS, GREEN PERCEIVED VALUE AND TECHNOLOGY ADOPTION MODEL ANTECEDENTS ON CONSUMERS’ PURCHASE INTENTION

Understanding the buying behaviour of consumers for green products has become an emerging issue. The purpose of present study is to assess purchase intention of consumers with respect to green products (environment friendly) shopping in India. Further, this study examines the role of environmental awareness, green perceived value, perceived usefulness and attitude towards using in building consumer intentions to purchase green products. Further, the study extends the TAM (Technology Adoption Model) by Davis (1989) by incorporating external variables like environmental awareness and green perceived value.This study examines the information gathered from 260 clients via a Google form survey. Structural equation modeling (SEM), validity and reliability testing, and exploratory factor analysis were conducted using the gathered data. The result of the present study shows that environmental awareness and green perceived value exert positive and differential effects on attitude towards using green products. The study also found that attitude and perceived usefulness are equally important for the purchase intention of customers towards green products. Implications of the present work are useful for academicians, marketers, customers, and policy makers.

Determinants of Bank’s Financial Stability in Pakistan

The purpose of this study is to investigate the role of both internal and external factors in achieving better stability in context of Pakistani banking industry. The internal determinants that this research study have used are bank size, funding risk, liquidity risk, credit risk and return on assets while external variables utilized includes money supply and unemployment rate. The study contains 10 years secondary data over the period 2013-2022 of 24 commercial banks of Pakistan. This study conducted correlation analysis and GMM model estimation for analysis. The findings show that liquidity risk, bank profitability, funding risk and bank size have positive and significant impact on bank stability. On the other hand, credit risk effects negatively bank stability. The result showed that money supply positively affects the stability of banks in Pakistan. On the contrary, unemployment is negatively affecting banks stability. The findings of this study have important implications for policymakers, regulators, banks, investors, and other stakeholders in the Pakistani banking sector. By enhancing the understanding of the factors influencing bank stability, the research contributes to identifying the importance of development of evidence-based policies, risk management strategies, and regulatory reforms aimed at development of a stable and resilient banking system in Pakistan.

Work Adaptation and Employee Well-Being: Uncovering the Significance of Work Pattern Choices of Higher Education Employees During the Pandemic

ABSTRACT Background Maintaining a pleasant work environment and striking a healthy work-life balance are critical for improving employee well-being and performance. The Job Stress and Health model sheds light on acute challenges that employees confront as a result of numerous work-related or external variables. The COVID-19 epidemic has resulted in abrupt changes in work habits and lifestyles, resulting in severe stress and disruptions to people’s physical and mental health around the world. Purpose During the pandemic, this study was undertaken to investigate employee work pattern choices, such as work from home, blended work, or work from the office. Methods This study examined how work pattern decisions affected HEI employees’ well-being during the COVID-19 epidemic using the Job Stress and Health model and an online survey. To investigate work model preferences and challenges, demographics, work patterns, and well-being indicators including sleep and performance were collected and evaluated using descriptive and inferential statistics. The study questionnaire was close-ended and prepared in English. Results Even after the pandemic, a significant proportion of employees preferred the blended work paradigm, showing the long-term impact of the shift in work habits. Furthermore, the shift in work schedules has had an impact on employee well-being, particularly changes in sleep patterns. Discussion Even though the employees of higher education are adaptable and accept a blended work model, it is important to note that work-related stress and inefficiency can arise depending on the resource requirements. Also, physical and mental health aspects while working remotely or in a blended model need consideration, especially when implemented in the long term. Translation to Health Education Practice Understanding the effects of these work pattern choices on employee well-being is critical for organizations to adopt policies that foster a healthy work environment and assist employees in reaching optimal work-life balance. A AJHE Self-Study quiz is online for this article via the SHAPE America Online Institute (SAOI) http://portal.shapeamerica.org/trn-Webinars.

LA TRADUZIONE COME CATALIZZATORE DI CHIAREZZA NEI TESTI NORMATIVI A PARTIRE DA UN’ANALISI COMPARATIVA DELLA SINTASSI IN VARIETÀ DI ITALIANO LEGISLATIVO

Translation is often considered as an ally of plain legal language. This corpus-based study sets out to provide empirical support for this hypothesis by comparing different varieties of legislative Italian used in a monolingual context (Italy) and in two multilingual settings (Switzerland and the European Union). The investigation relies primarily on a quantitative analysis of syntax informed by natural language processing (NLP) methods. The results suggest that translated legislation features shorter sentences, fewer nominalizations, an underuse of the passive voice, fewer non-finite clauses, less deep syntactic trees, shorter dependency links, and a preference for the SVO order with an explicit subject. Among the two multilingual contexts, Swiss legislation shows a slightly higher level of accessibility compared to EU directives. A complementary analysis using readability metrics confirms these trends. Nevertheless, in addition to the translation process in multilingual contexts, other latent external variables may have a (hardly quantifiable) impact on the level of accessibility, such as institutional language policies, legal traditions, drafting guidelines, and training programs for translators and language experts.

Investigating User-Centric Factors Influencing Smartwatch Adoption and User Experience in the Philippines

Smartwatches enable users to easily monitor their health, self-quantify, and track various activities. However, manufacturers and researchers in the field of smartwatches must explore and improve perceived usability to enhance the user experience of consumers and increase the device’s adoption rate. Therefore, this study investigates the factors influencing the adoption of smartwatches among Filipinos, focusing on usability and demographic influences. This is performed by utilizing the UTAUT2 model to examine key factors. External variables are explored, including perceived usability and privacy. To analyze the data acquired, partial least squares structural equation modeling (PLS-SEM) was conducted. The results indicated that performance expectancy, effort expectancy, social influence, hedonic motivation, price value, habit, and behavioral intention significantly influence smartwatch adoption. Habit emerged as positively affecting intention to use and usage behavior. However, facilitating conditions were found not to be significant in influencing intention to use and usage behavior, and privacy was perceived as having an insignificant relationship with the intention to use smartwatches. These findings offer theoretical and practical implications for enhancing smartwatch design and usability, addressing the diverse needs of users, and expanding inclusivity in the market.

Research on the efficient process-oriented structural optimization method of the large-scale vacuum cryostat for fusion reactors

An efficient optimization design for the large and complex components of fusion reactors is crucial to address the engineering design requirements and further promote technical standardization. Based on research status, current engineering designs for fusion reactors have some deficiencies, such as time and energy wastage, inefficiency, and difficulties in covering the typical ‘multi-variable multi-objective’ design requirements. These are pressing and common problems that urgently need to be overcome. To deal with the aforementioned technical challenges, it is vitally important to design an efficient, precise, and normalized approach that is tailored for the development of future fusion reactors. Therefore, this paper proposes a process-oriented optimization design method, which involves Coupled external parameterized modeling, Experimental points design, Response surface optimization, and Structural integrity validation (CERS), to improve the currently inefficient design methods. And the vacuum cryostat, the largest and complex component of a tokamak, is taken as an example to present the basic procedures of CERS. Firstly, the functions, basic structures, load types, analysis methods, and verification criteria of the cryostat are presented in detail. Then, real-time data interaction between external global parametric variables and ANSYS via coupling is established by CERS, which achieves parametric modeling of the cryostat and efficient experimental point design and optimization analysis with multi-variables and multi-objectives in an automatic way. Subsequently, this study demonstrates the significance and sensitivity of various structural parameters of the cryostat from such objectives as maximum deformation, maximum equivalent stress, and total mass. And the optimal set of its structural parameters is obtained by establishing a mathematical optimization model. Finally, the structural integrity is verified. The results indicate that the optimized cryostat maintains a minimum safety margin of 23% and will not suffer fatigue damage under various load events during its service. Moreover, the nonlinear buckling load multiplier ∅ is 5.4, obtained by analyzing the load-displacement curve of the cryostat according to the zero-curvature criterion. This shows that the designed cryostat is stable enough. The proposed method is simple, efficient, and reliable, and can be applied to both the cryostat and other complex components of fusion reactors in engineering design fields. It has great value of practical technical reference and can further promote the standardization of engineering design technology for future fusion reactors.

Mobile traffic prediction with attention-based hybrid deep learning

Mobile data consumption is ever-increasing due to the continuous emergence of bandwidth-hungry mobile applications as well as the drastic increase in number of mobile terminals resulted from the pervasive adoption of Internet of Things. In the 5G+ era, network operators have to deal with new technical challenges on network management, resource optimization and mobility scheduling, which require the ability to perform efficient mobile traffic prediction. Traditional prediction methods using convex optimization, Markov model and linear equations cannot describe the various nonlinearity changes of network traffic. In this paper, we propose a novel traffic prediction algorithm for mobile networks by introducing a hybrid deep learning method equipped with the attention mechanism, which analyzes the traffic in cellular zones to determine the spatiotemporal characteristics in the residential community. The hybrid deep learning model includes autoregressive integrated moving average mode (ARIMA) preprocessing with decomposition ability for time series, external spatial feature variables, sequence-to-sequence (seq2seq; attention-based CNN-LSTM) pretraining and extreme gradient boosting (XGBoost) optimization processing for integrating nonlinear relationships. The CNN method shows good nonlinear generalization ability to mine deep features of community traffic and capture the nonlinear trend. Our proposed approach first decomposes the community traffic data into linear and residual parts through ARIMA and then seizes the deep features of community traffic through the constructed pretraining seq2seq framework. Finally, the pretraining outcomes are further adjusted by the XGBoost method. Simulation results show that the proposed solution improves the accuracy of prediction apparently.

Using mobile technology acceptance model to explore the satisfaction and continuous use of live streaming for training

ABSTRACT During the digital era and especially the COVID-19 pandemic, live streaming emerged as a vital component of mobile learning and training. This study sought to determine whether the social presence and interactivity experienced by learners using live digital platforms could enhance their learning outcomes, satisfaction, and propensity for ongoing use.Leveraging the mobile technology acceptance model as its research framework, this study integrated interactivity and social presence as external variables. The aim was to uncover how these variables impact user satisfaction and continued use, while also exploring the mediating role of learning effectiveness in driving continued use. A live streaming system designed for mobile training was tested among 67 employees in a pilot study. The results demonstrated that:(1)Ease of use positively impacted perceptions of mobile usefulness.(2)Mobile usefulness, interactivity, and social presence had a positive effect on satisfaction, yet ease of use didn't significantly influence satisfaction.(3)Interactivity enhanced social presence.(4)Satisfaction was a key driver of continued use, with learning effectiveness acting as a partial mediator between them. The findings from this research can guide businesses looking to utilize live streaming for training purposes and serve as a reference for academic research in similar areas.

Numerical investigation of two-dimensional fuzzy fractional heat problem with an external source variable.

This study suggests a strategy for calculating the fuzzy analytical solutions to a two-dimensional fuzzy fractional-order heat problem including a diffusion variable connected externally. We propose Sawi residual power series scheme (SRPSS) which is the amalgamation of Sawi transform and residual power series scheme under the Caputo fractional differential operator. We demonstrate three different examples to derive the fuzzy fractional series solution which is characterized by its rapid convergence and easy finding of the unknown coefficients using the concept of limit at infinity. The most significant aspect of this scheme is that it derives the results without time effort compared with the traditional residual power series approach. Our findings confirm that the SRPSS is a robust and valuable method for approximating the solution of fuzzy fractional problems. Furthermore, we provide 2D and 3D symbolic representations to present the physical behavior of fuzzy fractional problems under the lower and upper bounded solutions.

A novel methodology for map-based model fitting: A case study with a Dual Source Heat Pump experimental dataset

This paper presents a new methodology to adjust map-based models to experimental data and reports the main results of a comprehensive experimental campaign of a Dual Source Heat Pump (DSHP) prototype. The prototype tested incorporates variable speed components (compressor, circulation pumps, and fan). The novelty of this prototype lies in its ability to select two possible heat sources: air or ground. Thus, it can operate as a geothermal or aerothermal heat pump, as well as a chiller, thanks to the additional capacity to reverse the cycle. Thanks to this hybrid approach, several advantages can be obtained compared to conventional equipment, such as higher efficiency, the requirement of smaller borehole heat exchangers, or the absence of defrost cycles. In a prior study, polynomial models were developed to accurately characterize the DSHP’s performance (i.e., condenser and evaporator capacities and electrical energy consumption). These models were obtained considering the external variables to the unit as independent variables to facilitate their applicability using variables commonly measured in real installations. Due to the complexity of heat pump performance, which in current equipment can be influenced by up to 5 or 6 independent variables, the search for suitable polynomial models required the availability of a complete working map including more than 3000 working points. Thus, this previous work developed these models based only on simulation results. In this sense, this paper concludes the development of these models by focusing on two critical issues concerning empirical model development. The first aspect involves determining the minimum number and location of testing points needed to define the experimental sample for the model adjustment. The reported experimental data were obtained by analyzing the most suitable experimental design methodology to create the experimental matrices in each operating mode of the DSHP. The second aspect focuses on the final adjustment of models using experimental data. A novel fitting approach for empirical models is introduced in the last part of this study. The developed methodology enables the integration of simulation and experimental results for the final fitting of empirical models through a two-step adjustment. The first step involves analyzing and defining polynomial functionals from the complete working maps generated by simulation. Subsequently, in a second step, the polynomial models are refitted to a suitable experimental sample using the methodology presented in this work. The latter allows for the increase of the accuracy of the models and the minimization of experimental costs. This novel approach ensures a robust characterization of systems with many independent variables using a minimum amount of experimental data. Significant benefits can be obtained from its application, such as the reduction of experimental cost and an increase in the model’s accuracy through an effective combination of both experimental and simulated information. Furthermore, it can be considered of general applicability to other engineering problems where the characterization of physical systems influenced by a high number of independent variables is required.

Classification of autonomous vehicle crash severity: Solving the problems of imbalanced datasets and small sample size

Only a few researchers have shown how environmental factors and road features relate to Autonomous Vehicle (AV) crash severity levels, and none have focused on the data limitation problems, such as small sample sizes, imbalanced datasets, and high dimensional features. To address these problems, we analyzed an AV crash dataset (2019 to 2021) from the California Department of Motor Vehicles (CA DMV), which included 266 collision reports (51 of those causing injuries). We included external environmental variables by collecting various points of interest (POIs) and roadway features from Open Street Map (OSM) and Data San Francisco (SF). Random Over-Sampling Examples (ROSE) and the Synthetic Minority Over-Sampling Technique (SMOTE) methods were used to balance the dataset and increase the sample size. These two balancing methods were used to expand the dataset and solve the small sample size problem simultaneously. Mutual information, random forest, and XGboost were utilized to address the high dimensional feature and the selection problem caused by including a variety of types of POIs as predictive variables. Because existing studies do not use consistent procedures, we compared the effectiveness of using the feature-selection preprocessing method as the first process to employing the data-balance technique as the first process.Our results showed that AV crash severity levels are related to vehicle manufacturers, vehicle damage level, collision type, vehicle movement, the parties involved in the crash, speed limit, and some types of POIs (areas near transportation, entertainment venues, public places, schools, and medical facilities). Both resampling methods and three data preprocessing methods improved model performance, and the model that used SMOTE and data-balancing first was the best. The results suggest that over-sampling and the feature selection method can improve model prediction performance and define new factors related to AV crash severity levels.

The Digital Insurance Paradigm: A Theoretical Exploration

The advent of technology has significant impact on the economy as a whole. Technology is attributed to the transition such as from traditional mode to online mode. Financial services industries such as banking, insurance are also witnessed huge growth in their businesses. Several technologies such as artificial intelligence, cloud computing, internet of things, block chain technology etc are gaining momentum these days. Insurance companies are using electronic mode as a alternate channels for communication, sales and distribution. The study focuses on the acceptance of electronic channels for availing digitalized insurance services by policyholders. The main aim of the study is to develop conceptual model for explaining user’s behavior intention towards adoption of digital life insurance services. The study has used unified theory of acceptance and use of technology model while incorporating some external variables such as trust, self efficacy etc. Attitude has also been used as a mediator for explaining the relation between other constructs and behavior intention. Keywords: Online Insurance, Trust, Attitude, Technology, Financial Services, Unified Theory of Acceptance and Use of Technology.

Efektivitas Program Ketahanan Pangan dalam Menangani Kasus Stunting di Kabupaten Asahan Tahun 2024

Food is one of the basic human needs. If these needs are not met, both in quantity and quality at the individual and household levels, it will interfere with the achievement of a healthy, active and sustainable quality of life and can lead to various health and nutrition problems. Stunting is one of the chronic nutritional problems caused by low access and affordability to food. The purpose of this study was to analyze the relationship between household food security and the incidence of stunting in toddlers aged 6-23 months in Asahan Regency in 2024. This type of research is observational with a case-control design. The research sample was under-fives aged 6-23 months, totaling 126 for each case and control group with a ratio of 1:1. The sampling technique used the total sampling method. Data were analyzed in stages, namely univariate, bivariate and multivariate analysis. Bivariate statistical test using chi-square and multivariate using logistic regression. The results of the bivariate analysis showed that household food security was associated with the incidence of stunting in children aged 6-23 months (p=0.04, OR=2.70, 95% CI: 0.94-8.77). External variables that were significantly associated with the incidence of stunting in children aged 6-23 months were maternal height (p=0.00, OR=2.03, 95% CI: 1.14-3.65) and history of LBW (p=0.03, OR=3.02, 95% CI: 0.98-11.04). Multivariate analysis results

Insights into nanostructured silica particle formation from sodium silicate using spray drying: An experimental and theoretical study

Spray drying is a widely used operational procedure utilized in diverse sectors. The current knowledge of droplet mobility processesinside the drying chamber is not fully comprehensive due to the complex nature of droplet evaporation issues. The present study used a comprehensive methodology that integrated numerical models and actual testing to investigate the drying process of droplets in the context of spray drying. The precursor material utilized in this investigation was colloidal silica. The influence of external hydrodynamic variables, such as drying temperature (473, 673, and 873 K) and carrier gas flow rate (2, 4, 6 L/min), was thoroughly investigated in connection to the dynamics of droplets and the subsequent creation of silica particles. Distinct alterations in the size and shape of the silica particles were observed due to variations in drying temperature and carrier gas flow rate. The validity of this investigation was established by the generation of two discrete particle morphologies, namely spherical particles and doughnut-shaped particles. Spherical particles were produced under reduced carrier gas flow rates of 2 L/min and lower drying chamber temperatures of 473 K. In the quantitative evaluation, the computational fluid dynamics (CFD) computations properly established the evaporation rate constant (K) and the temperature difference (ΔT) inside the droplets. Furthermore, qualitative calculations provide insight into the complex dynamics of droplet motion inside the spray chamber. The results of this study have the potential to open up new possibilities for regulating nanoparticle creation via the use of the spray drying process.

Analysis of external load in professional English Football League Two players

Previous research has utilised Global Positioning Systems (GPS) based technology to provide detailed insights on activity profiles and external demands of professional football. However, there remains minimal research exploring this in the English Football League (EFL), in particular, League Two. Considering differences such as a unique fixture frequency and congestion, and its impact of training occurrence, a study on the external physical loading of EFL League Two players is necessary. Furthermore, analysis of positional differences due to the varied activity profiles is warranted. Therefore, the aims of the current study were to explore the difference in the external demands across training mesocycles, and differences between four-, three-, or two-day training weeks. Additionally, positional differences in external demands in competitive fixtures were explored. Twenty, male, professional soccer players from the EFL League Two (27 ± 6 years, mass 81 ± 8 kg, stature 181 ± 8 cm) were monitored across 41 competitive matches during the 2023-24 season. Players were grouped into the following positional categories central defender (n=3); wide defender (n=4); central midfielder (n=5); wide midfielder (n=5) and striker (n=3). The ethics committee of the sport department at University Academy 92 approved this study. A GPS system (Apex, STATSports, Northern Ireland) was used to quantify external load data from training and match day (MD) across 5 mesocycles of the season. Total distance ranged from 92.1 ± 6.5 m·min-1 to 106.2 ± 8.7 m·min-1 and 44.0 ± 2.0 m·min-1 to 56.8 ± 4.9 m·min-1 across mesocycles in matches and training, respectively. Likewise, High-Speed Running also varied across mesocycles, ranging from 5.6 ± 1.9 m·min-1 to 6.7 ± 1.0 m·min-1 and 0.9 ± 0.3 m·min-1 to 1.3 ± 0.4 m·min-1 in matches and training, respectively. Significant differences (p < 0.05) in several external load variables were evident between four-, three-, or two-day training weeks. When analysing positional differences in matches, the CM achieved the greatest total distance (106.3 ± 17.6 m·min-1), with WM exhibiting the greatest amount of distance at high speed (7.9 ± 2.1 m·min-1) and total accelerations and decelerations (1.9 ± 0.3 ·min2; 1.0 ± 0.3 ·min2). Consequently, results from this research should allow practitioners to physically prepare players optimally in preparation for MD. In conclusion, this study highlighted that external match load performance was influenced by players' positions. Moreover, the consideration of training days in preparation for a fixture, might allow players to be in optimum physical capacity for MD.

Weekly accumulative external load based on participation in football matches

The aim of this study was to compare the weekly accumulative external load according to their participation in the previous match and the following match in three teams of different categories belonging to a football club. Sixty football players participated, belonging to the under-23 (U23, n: 20), U21 (n: 22) and U19 (n: 18) teams. Players were monitored by global positioning system devices during all training sessions and official matches. For the accumulative weekly load (from Monday to Sunday), the following external load variables were used: total distance covered (TD, m), distance covered at different speed thresholds (>14.0, >18.0, >21.0 and >24.0 km·h−1), the number of moderate-high intensity accelerations (ACC: >2 m·s−2, n) and decelerations (DEC: <−2 m·s−2, n), the player load (PL, arbitrary units, au) and the acceleration load (Aload, au). Players were grouped according to their participation in the previous and next matches: starter and starter (SS, when the player was a starter in both the previous and the following matches), starter and non-starter (SN), non-starter and starter (NS), and non-starter and non-starter (NN). This study revealed that NS and SS groups had a higher absolute and relative accumulative weekly load for the global indicators (Aload, PL and TD) in the three teams than NN and SN (range of ES: 0.1–3.4). This information would allow coaches to refine the required stimulation regarding players’ role in the team based on the player's participation in the previous and next match.

External Market and Internal Market Efficiency in Increasing Hospital Visit Numbers

Hospital health institutions have experienced developments that have led to professional services, in line with improvements in various factors, both internal and external, including the increasing awareness of hospital managers implementing professional management and the increasing demands of the public for the quality of hospital services. This study aims to determine External Market and Internal Market Efficiency in Increasing Hospital Visit Numbers. This type of research is quantitative research. The population in this study was the entire community of Malang district. The sampling technique used purposive sampling technique. Determining the sample in this study used the Slovin formula. The research instrument uses a questionnaire and data analysis uses multiple linear regression. The results of the analysis show that the R value shows a close relationship between the independent variables (X1, X2) and the dependent variable (Y) of 0.823. The adjusted R square value is 0.467, meaning that 46.7% of the community's decision variables in returning visits can be explained by external marketing variables. This can be seen from the significance level being smaller than α (0.000<0.05), so that H0 is accepted and H1 is rejected. Conclusions in this research include external marketing variables, internal marketing have a significant effect on the variable intention to revisit at Hospital X in Malang district, both simultaneously and partially dan the variable that more dominantly influences patient satisfaction is the internal marketing variable.

Practical application of a heuristic model for swim coaches to optimize training

Swim coaches routinely monitor the performance and performance determining variables of their athletes to optimize training programs in an individual-specific manner with the ultimate aim to swim faster and win races. To interpret the collected data, they require a suitable, and practically useful, conceptual framework, which can be found in the power balance of swimming. For coaches a heuristic model for training and performance optimization based on the power balance has been proposed. We build on this model and illustrate how it can be employed in the training practice using data of an exemplary sub-elite swimmer (700 FINA points), which was collected continuously during two training sessions. Variables that are used in daily swim training practice, such as heart rate (HR), stroke count (SC), stroke rate (SR), and lap time (LT), were measured. By combining external load variables (e.g. LT) and internal load variables (e.g. HR) with technical variables (e.g. SR), the degree to which the swimmer complies with the training program can be determined. The measured values of these variables are presented using a traffic light feedback system indicating the degree of compliance. The traffic light system enables coaches to adjust the program if deemed necessary. It is thus shown how the model and commonly measured variables can assist swim coaches in the design and evaluation of training sessions in their pursuit of personal performance improvement leading to greater athletic success.

Measurement and evaluation of agricultural technological innovation efficiency in the Yellow River Basin of China under water resource constraints

Improving the efficiency of agricultural technology innovation is an inevitable requirement for ecological protection and sustainable agricultural development in the Yellow River Basin. Under the background of prominent water resources constraints in the Yellow River Basin, the actual level and regional differences of agricultural technology innovation efficiency in the basin are analyzed, which will provide theoretical basis and data reference for the development plan of agricultural modernization in the Yellow River Basin. The construction of evaluation index system and the choice of method are the main factors that affect the measurement results. Therefore, this paper first selects input indicators such as water-saving technology level and output indicators such as total agricultural output value to construct an innovative evaluation index system of agricultural technology innovation efficiency in the Yellow River Basin under the consideration of water resources constraints. Secondly, the three-stage DEA model was used to eliminate the interference of external environmental variables, and the overall and regional agricultural technology innovation efficiency of the Yellow River Basin during 2011–2020 was measured with the data of prefecture-level cities under the constraint of water resources. Finally, from the perspective of the whole and the region, the spatio-temporal dynamic evolution law is analyzed and evaluated. The results show that: on the whole, the agricultural technology innovation efficiency in the Yellow River Basin is good and rising in a wave trend, the adjusted pure technology efficiency increases, while the scale efficiency decreases. From the regional perspective, although there is regional heterogeneity, the overall development trend is good, and the lower Yellow River has the highest efficiency, followed by the upper reaches and the middle reaches. From the perspective of the change law, the spatio-temporal evolution distribution shows a multi-polarization trend, and the different regions in the upper, middle and lower reaches show great differences. Therefore, promoting the innovation of water-saving technology and the integration of agricultural science and technology, promoting the integration of production, study and research, and strengthening the regional linkage mechanism are helpful to improve the competitiveness of agricultural science and technology and achieve sustainable agricultural development in the Yellow River Basin.

Combined EORTC FDG-PET/CT and RECIST 1.1 response criteria and prediction of pathological response to neoadjuvant nivolumab and ipilimumab for clinical stage III melanoma.

9590 Background: Accurate identification of pathological response to neoadjuvant therapy for clinical stage III melanoma before surgery may allow us to better tailor the treatment. RECIST 1.1 has shown poor concordance rates with pathological responses and EORTC FDG-PET/CT response criteria using SUV max is susceptible to internal and external variables leading to difficulty in determining accuracy appropriately. Methods: We conducted a multicenter retrospective analysis of patients with clinical stage III melanoma treated with neoadjuvant nivolumab 3mg/kg + ipilimumab 1mg/kg(N3+I1) for two cycles who did baseline and pre-operative 18F-FDG-PET/CT. RECIST 1.1 and EORTC PETCT criteria were used to classify patients in terms of response. The pathological results were correlated to the image findings. Results: Between Jan 2019 and Jan 2024, 37 patients with clinical stage III melanoma treated with neoadjuvant N3+I1 were identified. Gender: 25 males (67%), median age (years): 58 (range: 31-78), BRAFV600 mut: 25(67%), positive nodes on baseline exam:1=30 (81%), 2=4(11%), and 3 or more =3(8%). Stage AJCC 8th edition: IIIB=23 (62%), IIIC=12 (32%), IIID=2(6%). Among them, 34 had baseline and post-neoadjuvant therapy PET-CT. Twenty-one (62%) had major pathological response (MPR) and 13(38%) had non-MPR. Using objective response and non-objective criteria to capture MPR and non-MPR, respectively, we found the following results: RECIST 1.1 (sensitivity= 48%, specificity=100%, accuracy=68%), EORTC-PET (sensitivity=90%, specificity=69%, accuracy=82%). Combining RECIST 1.1 and EORTC-PET/CT analysis, we found that: double objective responders (CR/PR and CMR/PMR, n=10) had 100% specificity to MPR and double progressors (PD/PMD, n=9) had 95% specificity to non-MPR. The only patient classified as double progressor that did not have non-MPR presented an intense inflammatory response with fever, pain and erythema leading to an increase in the size and number of lymph-nodes. No viable cells were found in the pathological report. Conclusions: Using combined RECIST 1.1 and EORTC-PET/CT response criteria may help to predict pathological response in patients with clinical stage III melanoma who undergo to neoadjuvant nivolumab and ipilimumab.