Input Levels Research Articles

Green innovation in Norwegian firms: Navigating the complexity of productivity and performance

In an era where sustainability demands are increasing, green innovation (GI) is garnering widespread acclaim for its profound social and environmental benefits. This study explores how GI affects productivity and efficiency in Norwegian service and manufacturing firms, using a state-of-the-art stochastic frontier model (semi-parametric smooth coefficient) tailored to analyze firm-specific impacts essential for competitiveness in a green economy. We identify two main pathways through which GI affects productivity: technological heterogeneity and shifts in inefficiency. The impact on technology is split into neutral (direct) and non-neutral (indirect) effects, with the neutral effect being independent of factor inputs, and the non-neutral effect being influenced by both technology heterogeneity and input levels. We evaluate the marginal effects of GI on productivity through technology and inefficiency. Our GI construct includes innovations such as reduced material/energy use, lower CO2 emissions, noise reduction, hazardous material substitution, and adoption of renewable energy. Analyzing data from 3,130 Norwegian firms, our findings highlight the crucial role of GI in sustainability, underscoring its strategic importance across sectors. We find a positive neutral pathway effect but negatively impacts productivity through non-neutral pathways. GI also positively influences inefficiency, particularly in manufacturing.

Pixel Distillation: Cost-Flexible Distillation Across Image Sizes and Heterogeneous Networks.

Previous knowledge distillation (KD) methods mostly focus on compressing network architectures, which is not thorough enough in deployment as some costs like transmission bandwidth and imaging equipment are related to the image size. Therefore, we propose Pixel Distillation that extends knowledge distillation into the input level while simultaneously breaking architecture constraints. Such a scheme can achieve flexible cost control for deployment, as it allows the system to adjust both network architecture and image quality according to the overall requirement of resources. Specifically, we first propose an input spatial representation distillation (ISRD) mechanism to transfer spatial knowledge from large images to student's input module, which can facilitate stable knowledge transfer between CNN and ViT. Then, a Teacher-Assistant-Student (TAS) framework is further established to disentangle pixel distillation into the model compression stage and input compression stage, which significantly reduces the overall complexity of pixel distillation and the difficulty of distilling intermediate knowledge. Finally, we adapt pixel distillation to object detection via an aligned feature for preservation (AFP) strategy for TAS, which aligns output dimensions of detectors at each stage by manipulating features and anchors of the assistant. Comprehensive experiments on image classification and object detection demonstrate the effectiveness of our method.

A deep learning model for prediction of autism status using whole-exome sequencing data.

Autism is a developmental disability. Research demonstrated that children with autism benefit from early diagnosis and early intervention. Genetic factors are considered major contributors to the development of autism. Machine learning (ML), including deep learning (DL), has been evaluated in phenotype prediction, but this method has been limited in its application to autism. We developed a DL model, the Separate Translated Autism Research Neural Network (STAR-NN) model to predict autism status. The model was trained and tested using whole exome sequencing data from 43,203 individuals (16,809 individuals with autism and 26,394 non-autistic controls). Polygenic scores from common variants and the aggregated count of rare variants on genes were used as input. In STAR-NN, protein truncating variants, possibly damaging missense variants and mild effect missense variants on the same gene were separated at the input level and merged to one gene node. In this way, rare variants with different level of pathogenic effects were treated separately. We further validated the performance of STAR-NN using an independent dataset, including 13,827 individuals with autism and 14,052 non-autistic controls. STAR-NN achieved a modest ROC-AUC of 0.7319 on the testing dataset and 0.7302 on the independent dataset. STAR-NN outperformed other traditional ML models. Gene Ontology analysis on the selected gene features showed an enrichment for potentially informative pathways including potassium ion transport.

The Effectiveness of Remote Exercise Rehabilitation Based on the "SCeiP" Model in Homebound Patients With Coronary Heart Disease: Randomized Controlled Trial.

While exercise rehabilitation is recognized as safe and effective, medium- to long-term compliance among patients with coronary heart disease (CHD) remains low. Therefore, promoting long-term adherence to exercise rehabilitation for these patients warrants significant attention. This study aims to investigate the impact of remote exercise rehabilitation on time investment and related cognitive levels in homebound patients with CHD. This study utilizes the SCeiP (Self-Evaluation/Condition of Exercise-Effect Perception-Internal Drive-Persistence Behavior) model, alongside WeChat and exercise bracelets. A total of 147 patients who underwent percutaneous coronary intervention in the cardiovascular department of a grade III hospital in Jiangsu Province from June 2022 to March 2023 were selected as study participants through convenience sampling. The patients were randomly divided into an experimental group and a control group. The experimental group received an exercise rehabilitation promotion strategy based on the "SCeiP" model through WeChat and exercise bracelets, while the control group followed rehabilitation training according to a standard exercise rehabilitation guide. The days and duration of exercise, levels of cardiac rehabilitation cognition, exercise planning, and exercise input were analyzed before the intervention and at 1 month and 3 months after the intervention. A total of 81 men (55.1%) and 66 women (44.9%) were recruited for the study. The completion rate of exercise days was significantly higher in the experimental group compared with the control group at both 1 month (t145=5.429, P<.001) and 3 months (t145=9.113, P<.001) after the intervention. Similarly, the completion rate of exercise duration was significantly greater in the experimental group (t145=3.471, P=.001) than in the control group (t145=5.574, P<.001). The levels of autonomy, exercise planning, and exercise input in the experimental group were significantly higher than those in the control group at both 1 month and 3 months after the intervention (P<.001). Additionally, the experimental group exhibited a significant reduction in both process anxiety and outcome anxiety scores (P<.001). Repeated measures ANOVA revealed significant differences in the trends of cognitive function related to cardiac rehabilitation between the 2 patient groups over time: autonomy, F1,145(time×group)=9.055 (P<.001); process anxiety, F1,145(time×group)=30.790 (P<.001); and outcome anxiety, F1,145(time×group)=28.186 (P<.001). As expected, the scores for exercise planning (t145=2.490, P=.01 and t145=3.379, P<.001, respectively) and exercise input (t145=2.255, P=.03 and t145=3.817, P<.001, respectively) consistently demonstrated superiority in the experimental group compared with the control group at both 1 and 3 months after the intervention. Interestingly, we observed that the levels of exercise planning and exercise input in both groups initially increased and then slightly decreased over time, although both remained higher than the preintervention levels (P<.001). The remote health intervention based on the "SCeiP" model effectively enhances exercise compliance, exercise planning, exercise input, and cognitive levels during cardiac rehabilitation in patients with CHD. Chinese Clinical Trial Registry ChiCTR2300069463; https://www.chictr.org.cn/showproj.html?proj=192461.

Beyond symbolism: The roles of action planning and case-making in immigrant integration policymaking

Abstract How do city-level policymakers build support for substantive action in policy domains characterized by low levels of national salience and limited local capacity, and which evidentiary resources support as well as reflect these uses? Despite much attention to policymakers’ engagement with evidence, existing work tends to focus on domains where the issues at stake attract high levels of input and influence from central governments. This limits empirical and theoretical understanding of how local efforts to implement potentially contentious policies arise, and through which means. In response, we examine how municipal actors in 12 cities and regions across the UK have devised and communicated policies on immigrant integration—an area that lacks national policy inputs yet is locally consequential—through the mechanism of “action planning.” Drawing on 6 years’ worth of documentary evidence generated through a university-initiated collaboration with these municipalities, we show how action plans gather attention for objectives and propagate examples of practice to other cities—what we call “case-making.” This serves as a micro-foundation for the action planning mechanism, which links symbolic statements about immigrant integration with substantive intended actions.

Drivers of greenhouse gas emissions in agricultural soils: the effect of residue management and soil type

Developing successful mitigation strategies for greenhouse gases (GHGs) from crop residue returned to the soil can be difficult due to an incomplete understanding of factors controlling their magnitude and direction. Therefore, this study investigates the effects of varying levels of wheat residue (WR) and nutrient management on GHGs emissions (CO2, N2O, and CH4) across three soil types: Alfisol, Vertisol, and Inceptisol. A combination of laboratory-based measurements and a variety of data analysis techniques was used to assess the GHG responses under four levels of WR inputs (0, 5, 10, and 15 Mg/ha; WR0, WR5, WR10, and WR15) and three levels of nutrient (NP0: no nutrient, NP1: nutrients (N and P) were added to balance the residue C/nutrient stoichiometry of C/N/P= 100: 8.3: 2.0 to achieve 30% stabilization of added residue C input at 5 Mg/ha (R5), and NP2: 3 × NP1). The results of this study clearly showed that averaged across residue and nutrient input, Inceptisol showed negative N2O flux, suggesting consumption which was supported by its high legacy phosphorus (19.7 mg kg⁻1), elevated pH (8.49), and lower clay content (13%), which reduced microbial activity, as indicated by lower microbial biomass carbon (MBC) and alkaline phosphatase (Alk-P) levels. N2O emissions were more responsive to nutrient inputs, particularly in Vertisol under high WR (15 Mg/ha) input, while CH4 fluxes were significantly reduced under high residue inputs, especially in Vertisol and Inceptisol. Alfisol exhibited the highest total carbon mineralization and GWP, with cumulative GWP being 1.2 times higher than Vertisol and 1.4 times higher than Inceptisol across residue and nutrient input. The partial least square (PLS) regression revealed that anthropogenic factors significantly influenced CO2 and N2O fluxes more than CH4. The anthropogenic drivers contributed 62% and 44% of the variance explained for N2O and CH4 responses. Our study proves that different biogeochemical mechanisms operate simultaneously depending on the stoichiometry of residue C and nutrients influencing soil GHG responses. Our findings provide insight into the relative contribution of anthropogenic and natural drivers to agricultural GHG emissions, which are relevant for developing process-based models and addressing the broader challenge of climate change mitigation through crop residue management.

Multi‐Response Optimization of an Orthotropic Steel Deck Section with Thermo‐mechanical Tensioning

In this investigation, Taguchi's grey relational analysis (GRA) is utilized to optimize parameters in an orthotropic steel deck section subjected to thermo‐mechanical tensioning. In this study, it is aimed to determine optimal values for deck thickness, welding sequence, and welding speed, which are evaluated through finite‐element analysis based on outputs such as longitudinal stress, transverse stress, and deflection. In this study, a previously validated numerical simulation model is extended by conducting a parametric analysis to investigate the influence of varying parameters on the model's behavior. An analysis of variance is conducted to identify the most significant variable affecting the input parameters. Additionally, a confirmatory test is also performed to validate the characteristics of the Grey Relational Grade. In the results, it is indicated that faster welding speeds result in increased deflection of the deck plate. Furthermore, the transition from compressive to tensile longitudinal stress is more abrupt in thinner decks (9–10 mm) compared to thicker decks (11–12 mm). This redistribution of residual stresses is attributed to the increasing thickness of the deck plate. Optimal input levels (A2–B1–C1: 10 mm deck thickness, welding sequence case 1, and 0.005 m s−1 welding speed) are determined using GRA. Welding speed contributes the maximum to the optimization results among the evaluated variables.

Penerapan Jaringan Syaraf Tiruan untuk Pengenalan Pola Huruf Menggunakan Metode Bidirectional Associative Memory (BAM)

The process in artificial intelligence is similar to the process that occurs in the human brain. The pattern recognition process is to determine whether the input and output values ​​are the same so that the system can detect the pattern. A reciprocal relationship between the input and output levels is made possible by the two interconnected layers of the Bidirectional Associative Memory (BAM) technique. As a result, this technique can serve as an associative memory, meaning that some of the data stored in it can be called upon.. The load signal is transmitted from the input layer X to the output layer Y in this procedure. The weight value will be modified by computing the outcomes between the ranks [1,0]. In this work, the active sigmoid function is employed. Three input characters—the letters S, O, and X—are used in this study's 5x5 order matrix. A pattern [27 4] that matches the target is produced by the target letter S [1,-1], a pattern [27 -3] that does not match the target is produced by the target letter O [1.1], and a pattern [-37 21] that matches the target is produced by the target letter X [-1,1]. Consequently, not all patterns are able to meet the established objectives.

Bottom-up effects of nutrient solutions on grape-grape phylloxera (Daktulosphaira vitifoliae) interaction.

Fertilizers generally influence the nutritional quality or defense ability of the plants, which can indirectly cause an increase in populations of herbivorous insect pests such as grape phylloxera, Daktulosphaira vitifoliae (Homoptera: Phylloxeridae, Fitch). The effects of nutrient solutions on grape-grape phylloxera interactions were analyzed using five concentrations (nitrogen content: 15, 30, 60, 120, and 180mg/L) of Hoagland's nutrient solution to irrigate Vitis vinifera L. × Vitis labrusca L. (Kyoho). Life table variables and life history variables showed a hump-shaped or an inverted hump-shaped curve as nutrient input level increased. Sixty microgram per liter treatment resulted in the greatest longevity and egg incubation, shortened the nymph duration and significantly increased the adult lifespan. Overall, both excessive and insufficient nutrient inputs curbed growth and multiplication of D. vitifoliae. These results provide a theoretical basis for vineyards for amending fertilizer inputs to prioritize the prevention and control of this pest.

FORMATION OF PROFESSIONAL COMPETENCE OF FOREIGN STUDENTS AT THE DEPARTMENT OF CHILDREN INFECTIOUS DISEASES

The article highlights the experience of professional competence formation of foreign medical students with an English-language form of education at the Department of Children Infectious Diseases of the Ivano-Frankivsk National Medical University. Traditional and interactive teaching methods used during classroom work with students are described. Professional competence involves the ability to use knowledge, skills and abilities in practice effectively. For future doctors, it includes mastering diagnostic methods, emergency situations management, and using modern technics of diagnosis and treatment. Aim. The aim is to describe and analyze methods of professional competence formation of foreign students at the Department of Children Infectious Diseases during classroom work. Research results. At the Department of Children Infectious Diseases, foreign students study children infectious diseases and elective disciplines - immunoprophylaxis of infectious diseases, clinical parasitology and tropical medicine. When teaching medical disciplines to foreign students, it is necessary to take into account their insufficient level of Ukrainian language proficiency, which is an obstacle to communicating with patients and fully mastering the method of taking anamnesis when working in the hospital near the patient’s bedside. Therefore, the teacher also plays the role of an interpreter. During practical classes control of the input level of knowledge is carried out with the use of multiple choice questions. The final level of knowledge is monitored using situational tasks. Work with the theoretical material takes place in the form of a discussion, the "round table" method with "brainstorming". The main part of the practical class is conducted at the patient's bedside in the departments of the infectious disease hospital. The teacher translates during direct communication between students and patients, corrects the techics of the examination. During classes, students receive structured mini-cases with a description of a clinical case and work in small groups. During case discussions, the teacher is liberal, ready to listen to various opinions and ways of solving a problem situation. Students of the fifth year visit lectures, during which teachers demonstrate multimedia presentations with graphs, schemes of pathogenesis, photos of patients. Combining the lecturer's comments with video information and animation greatly enhances the attention of the audience. Demonstration of clinical cases during the lecture, followed by a discussion in a dialog mode, is valuable. During the lectures, information about the epidemiological situation regarding a particular disease in the countries from which the students came is presented and discussed. Conclusion. For professional competence formation of future doctors, a combination of traditional and interactive teaching methods is effective. In the process of the professional competence formation, all its components should be taken into account: cognitive, motivational and personal. The use of the case method increases cognitive interest, promotes the development of communication skills and clinical thinking. A competent, highly qualified teacher who is fluent in English and uses modern information sources, is an active organizer and participant in the educational process.

Weed community differences in row crops with varying input levels in Ghana

Abstract Peanut (Arachis hypogaea L.) and maize (Zea mays L.) are essential crops for Ghana’s economy and food security, but weed infestation poses a significant threat to their cultivation. Crop rotations influence weed communities, but little is known about these processes in peanut-cropping systems in West Africa. This study investigated the impact of different crop rotations and input levels on weed communities in Ghana over 3 yr. Results showed that low inputs (absence of herbicide and fertilization) favored species richness, while higher input levels (weed control with herbicides and fertilizer use) reduced it. Diversity and evenness were also affected by inputs, with varying patterns across locations and seasons. Weed population growth rates (λ) varied significantly by location and treatment; all management programs resulted in increasing weed populations. Principal component analysis revealed distinct associations between weed species and crop management. The majority of weed species exhibited a generalist behavior and did not associate with a particular management. However, billygoat weed (Ageratum conyzoides L.) and Benghal dayflower (Commelina benghalensis L.) were positively associated with high-input systems, while purple nutsedge (Cyperus rotundus L.) exhibited strong associations with low and medium inputs. The weed–crop rotation dynamics described here demonstrate how management drives the selection of weed species that are more pervasive and interfere with important food crops in Ghanaian agriculture.

Pulsed Signal Duration Shaper with Adaptive Response Thresholds

His paper represents a shaper designed to restore the width of rectangular pulses transmitted over an open-optic (wireless) communication channel and having shape distortions in the form of amplitude modulation, pedestal and tightening of the front and decay. The principle of pulse width recovery is based on the formation of voltage comparator thresholds adaptive to changes in high and low pulse signal levels. Due to the use of sample-and-hold circuit (UVH - S/H circuit) in the circuit, the values of the input signal levels are stored in the previous half-periods. The threshold voltage of the comparator before its operation is formed as the output voltage of a resistive voltage divider into two inputs, one of which is supplied with the current voltage of the input signal, and the other with the voltage level of the signal recorded in the S/H circuit at the previous half-cycle of information pulses. The study presents the method for estimated error in pulse width formation from comparator bias voltages, S/H circuit and modulation changes in the shape of the information signal. The example provides quantitative estimates of the error. In the Micro Cap circuit modeling environment, a model experiment was performed, diagrams of the signal source and shaper models on ideal components and time diagrams of the device operation are presented. The operability of the device during the passage of a pulse signal with the above-mentioned shape distortions has been confirmed. A large depth of modulation of the input signal amplitude is allowed. The formative properties of the circuit are provided. The speed of the shaper is determined by the speed of the comparator and the S/H circuit. To reduce the error of the duration forming of information pulse signals, it is necessary to choose a voltage comparator and sample-and-hold circuit devices with minimal static errors.

Comprehensive evaluation of the implementation of close-type county medical alliance in Shandong Province using entropy weight TOPSIS method and non-integer rank sum ratio method

BackgroundTo improve the capacity of county medical and health services, China encourages all localities to pilot the close-type county medical alliance. In Shandong Province, medical alliances have been piloted in 47 counties, ranking first in the country. The objective of this study is to comprehensively evaluate the implementation of close-type county medical alliance in Shandong Province, identify the differences between different county regions, and analyze the reasons, so as to provide a reference for the construction of a new county medical and health service system with clear goals, powers and responsibilities, and division of labor.MethodsThe implementation of the close-type county medical alliance was comprehensively evaluated in 47 national pilot counties in Shandong Province using entropy weight TOPSIS method and non-integer rank sum ratio method. Variance analysis was used for comparison of the comprehensive evaluation results.ResultsThe weight coefficient of evaluation indicators was highest for information interconnection, at 18.06%, and lowest for orderly referral of patients, at 3.64%. There was no difference in results of the comprehensive evaluation of entropy weight TOPSIS method and non-integer rank sum ratio method. Comprehensively order the implementation status of each pilot county according to the relative paste progress, 13 counties Y5, Y11, Y14, Y16, Y20, Y25, Y26, Y27, Y28, Y32, Y33, Y40 and Y42 were ranked highest, whereas county Y37 was ranked lowest. Non-integer rank sum ratio method graded counties into three grades: excellent, good and average. Kruskal-Wallis nonparametric test showed that the difference between the grades was statistically significant (H\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\:=\\:$$\\end{document}37.099, p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:<\\:$$\\end{document}0.001). Variance analysis based on comprehensive evaluation results showed that implementation status was not correlated with the county economic development level, the level of health resources input and the medical service ability of the lead hospital.ConclusionsOur findings indicated that the implementation of the close-type county medical alliance is significantly different between pilot counties, with a marked differentiation within the same urban area. Therefore, effective measures are recommended to reduce this gap, including promoting informatization empowerment of the county medical community, strengthening government responsibility and improving policy effectiveness.

An explainable hybrid framework for estimating daily reference evapotranspiration: Combining extreme gradient boosting with Nelder-Mead method

Accurate estimation of reference evapotranspiration (ETo) is essential for effective water resources management, irrigation system design, and various hydrological and agricultural applications. This study employed extreme gradient boosting (XGBoost) model, signal decomposition techniques, and XGBoost coupled with Nelder–Mead (NM) method to enhance ETo prediction across two meteorological stations in Iran. This study proposed a novel framework which lies in its comprehensive integration of advanced techniques to create a model that is both interpretable and highly accurate for ETo estimation. For this aim, sixty meteorological variables were categorized into solar and cloud-based, temperature-based, wind and humidity-based, and pressure-based groups to analyze their effect on ETo estimation. Feature selection methods, including the Gradient Boosting Machine, Kendall’s Tau, and Relief Algorithm, were employed to identify the most influential predictors. Variables with normalized weights equal to or greater than 0.9 were selected for model input, resulting in the top 10% of variables being utilized. The XGBoost models were then developed using these selected inputs (level 1), a wavelet-based hybrid was developed according to the most effective features (level 2), and the XGBoost model was coupled by NM algorithm (level 3) for ETo estimation. Results of estimated ETo by levels 1 to 3 were compared with four common empirical approaches. The results indicated that Kendall’s Tau-based feature selection provided the most accurate predictions in Shiraz, achieving an RMSE of 0.721 (mm/day) for solar and cloud-based variables during the test phase. Additionally, the application of wavelet analysis further refined the model inputs, which enhanced ETo estimation in most variable groups. Integrating the NM algorithm with XGBoost demonstrated significant improvements in ETo estimation, where it could improve RMSE to 0.091 and 0.155 (mm/day) for testing section in Fasa and Shiraz, respectively.

The impact of boundary conditions on the acoustical behavior of lightweight granular particle stacks

The acoustical behavior of air-saturated granular stacks can differ significantly from that of more traditional sound absorbing materials such as fibrous webs and foams when tested in a standing wave tube. The latter materials can often be modeled as equivalent fluids by using one-dimensional theory, with the further assumption that their behavior does not depend on the input signal level. In contrast, level dependence of the response of lightweight granular materials such as glass bubbles has been consistently observed in previous studies. At low input levels, the absorption coefficient of glass bubble stacks shows solid-like behavior with multiple peaks associated with radial modes of the solid phase of the edge-constrained stack: i.e., the response is two-dimensional. In contrast, at high input levels, glass bubble stacks show one-dimensional fluid-like behavior, with the quarter wave depth resonance dominating the response. It is hypothesized here that at high input levels the particles slip axially at the circumferential boundary, thus showing a one-dimensional response, while at low levels, the particles are constrained to have zero axial velocity, thus creating a two-dimensional response. It is shown here that a two-dimensional finite difference implementation of the poro-elastic Biot theory can accurately reproduce the observed level-dependent behavior.

B-231 Evaluation of the KingFisher Flex for DNA Isolation From FFPE Tissue Utilizing Autolys M Tubes and the MagMAX FFPE DNA/RNA Ultra Kit

Abstract Background Our institution is facing increasing demand for oncology testing that requires high quality DNA extracted from formalin-fixed paraffin-embedded (FFPE) tissue. The current DNA extraction workflow involves a time and labor-intensive manual process that limits our ability to accommodate increasing downstream clinical testing volumes. We evaluated the KingFisher (KF) Flex utilizing Autolys M Tubes and the MagMAX FFPE DNA/RNA Ultra Kit as a possible high-throughput, automated DNA isolation workflow option that would decrease hands-on time and be conducive to volume growth without the need for additional staff. Methods DNA from 52 FFPE tissue samples (n=24 unique cases previously extracted by the current “manual method”) were extracted using Autolys M tubes, the MagMAX FFPE DNA/RNA Ultra Kit, and the KF Flex (Thermo Fisher Scientific Waltham, MA) over 4 “automated method” runs. Method modifications to the published procedure were introduced over the runs to determine any effects on DNA quality/quantity. Timings were taken during each run to compare hands-on and total run time against that of the manual method. Precision experiments were performed by extracting 4 replicates of sample on a single run (intra-assay precision) and 2 samples from 2 cases over 2 runs (inter-assay precision). Three cases were processed using varying tissue input levels to assess input-yield correlation. DNA yield was measured using HS dsDNA Qubit (Thermo Fisher Scientific, Waltham, MA). A subset of samples was tested by digital droplet PCR (ddPCR) (n=10), chromosomal microsomal microarray (CMA) (n=1), and next-generation sequencing (NGS) (n=3). Variants and QC metrics of the KF DNA were compared to those of the manual method DNA. Results The average total DNA yield was 3% lower from samples extracted following the manufacturer recommended KF procedure compared to matched samples extracted using the manual method (n=28 samples from 23 unique cases). A CV of 2.8% was observed in an intra-assay experiment (n=4 replicates from 1 case) and 3.0% in an average inter-assay experiment (n=2 replicates from 2 cases). There was a direct correlation between the tissue input amount (4 amounts of varied tissue input) and the total DNA yield (n=3 samples). We observed concordant fractional abundance values for ddPCR samples and concordant variant detection for NGS and CMA samples. We did not see a significant difference in the quality metrics tracked for any of the downstream testing assays. Conclusions This evaluation demonstrated that, as compared to our manual method, the automated method resulted in comparable DNA yields and downstream assay results. The incorporation of this automation would significantly increase our FFPE DNA extraction capacity without requiring additional staff while reducing hands-on time per run by about six hours. We would also be able to reduce ergonomic hazards related to tube cap manipulation, eliminate use of hazardous reagents, and leverage the plate format to better integrate extracted DNA into our clinical sample preparation workflows. These operational benefits highlight the value of automating high-throughput workflows.

Analyzing the spatial scale effects of urban elements on urban flooding based on multiscale geographically weighted regression

In the context of rapid urbanization and the frequent occurrence of extreme rainfall in cities, the risk of flooding in the future will further increase, and the problem of urban flooding cannot be ignored. Urban elements exhibit significant spatial heterogeneity, which largely determines the spatial distribution differences in urban flooding. Therefore, it is important to clarify the scale of influence of different urban elements and explore their scale effects on urban flooding to accurately assess the risk of urban flooding. Taking Zhengzhou City, China, as the study area, this study analyzed the urban elements associated with urban flooding, quantified the scale of the influence of urban elements on flooding using multiscale geographically weighted regression (MGWR), and further explored the spatial scale effects of urban elements on urban flooding. The results showed that MGWR can better fit the spatially non-uniform distribution of urban flooding and that the scale of the influence of urban elements on urban flooding can be reflected by the bandwidth of MGWR. The results of MGWR indicated that the bandwidths of elevation, number of drainage outfalls (NDO1Abbreviations: RRP, Rainfall return period; DDR, Distance to river; NDO, Number of drainage outfalls; ILMFCP, Input level of materials for flood control projects; PRL, Proportion of residential land; PCL, Proportion of commercial land; PIL, Proportion of industrial land; PPSL, Proportion of public service land; PRA, Proportion of road area; PGL, Proportion of green land; PD, Population density; MI, Manpower input; GDP, Gross domestic product; ILER, Investment level in education and research.1), distance to river (DR), Gross domestic product (GDP), proportion of residential land (PRL), and proportion of commercial land (PCL) were small, and their influence scales were localized. In contrast, the influence of rainfall return period (RRP), slope, proportion of industrial land (PIL), proportion of public service land (PPSL), proportion of road area (PRA), proportion of green land (PGL), input level of materials for flood control projects (ILMFCP), population density (PD), manpower input (MI), and investment level in education and research (ILER) were at global scales. The most influential factors for urban flooding were RRP, PD, and MI. Slope, DR, and PRA had less influence on urban flooding. This study helps improve the effectiveness of urban flood prevention and mitigation efforts.

Cross-domain autonomous driving visual segmentation based on enhanced target data learning

Within the broader context of Information and Communications Technology (ICT), the quest for reliable and scalable visual segmentation methods poses significant challenges, particularly in autonomous driving, where real-world scene complexity requires advanced solutions. To address data scarcity and improve segmentation performance, we propose a novel unsupervised domain adaptation (UDA) approach that enhances target domain learning. Our method introduces multiple perturbations consistency, leveraging spatial context within the target domain to improve recognition. By applying perturbations at input and feature levels and using a consistency loss, we enhance contextual learning. Additionally, a weight mapping technique reduces the impact of detrimental source domain information. Experimental results demonstrate that our approach outperforms baseline methods on the GTAV→Cityscapes and SYNTHIA→Cityscapes datasets.

Economic Feasibility of Ratoon Pigeon Pea under Different Levels of Irrigation, Fertilizers and Mulching

Background: Ratooning in pigeon pea is one of the unexplored options for increasing the production and income through more number of crops per year under adequate supply of inputs. Dearth of knowledge on the ratooning, its yield and economics in pigeon pea on which the current study aimed at providing the basic idea of its suitability for cultivation. Methods: The field experiment investigated during October 2020- February 2021 at University of Agricultural Sciences, GKVK, Bengaluru. The experiment was conducted with 2 levels of irrigation, 3 levels of fertigation and 2 levels of mulching under split-split plot design. Result: The experimentation of ratoon pigeon pea with varied levels of inputs revealed that ratooning was found to be an economically feasible option with higher returns and lower cost of cultivation. The study comprehensively covers and states the adaptability of the practice in terms of yield and economics.

Work of ART: Profiling the First 100 Patients Discharged Home From an Acute Hospital with the Active Recovery Team (ART)

Abstract Background The Active Recovery Team (ART) is a new therapy lead rehabilitation team that offers supported discharge and admission avoidance from a large acute hospital. This interdisciplinary team consists of a physiotherapist, occupational therapist, medical social worker and two therapy assistants. ART accepts referrals from all services within the hospital including the emergency department. Methods As ART is a new service, a retrospective audit of the first 100 patients who discharged on the pathway was completed. Demographics included age, sex, referring specialty, presenting complaint, frailty (measured by the Clinical Frailty Score) and level of input received. ART use the coding of low, medium and high intensity to describe the intervention provided based on dosage (visits) and complexity. Results Most (67%) of the group were female and mean age was 77 years. Only 8% were pre-frail (CFS 0-3), 86% were living with mild to moderate frailty (CFS 4-6) and 6% considered severely frail (CFS 7-9). The type of intervention patients received was 31% low, 33% medium and 36% high intensity. Referrals came from a range of specialties, but the highest percentage of referrals were from emergency medicine (29%), medicine (20%), orthopaedics (18%) and geriatric medicine (16%). Most (53%) initially presented post a fall at home. Most frequent reason for ART referral (78%) was that the patient was discharging home not at their functional baseline but had potential to an achieve a specific goal(s) with further input (most frequently within domains of mobility indoors/outdoors, transfers, stairs and activities of daily living). Conclusion This is an effective pathway that improves the transition from hospital to home. ART offer patients an opportunity for further rehabilitation at home with continuity of care from the acute hospital. Future work will examine the impact of ART on length of stay, readmission rate and explore patient satisfaction with this service.