Terms Of Input Research Articles

CFD-Based Sensitivity-Analysis and Performance Investigation of a Hydronic Road-Heating System

To minimize the impact of snowfall and ice formation on safety of transportation, salt is sprinkled on the asphalt every winter. However, the use of salt has economical as well as ecological disadvantages. To resolve these problems, road heating systems are used in the northern regions of Europe and America. Despite their widespread usage, considerable potential of the operational optimization is evident. The current systems are controlled under predefined weather conditions such as start of operation at 5 °C air temperature, even when snowfall is absent. Consequently, loss of energy input to heat the system is caused. To avoid unnecessary financial and energetic expense, this study presents CFD-based performance investigation as a basis for a novel predictive controller to increase the operational efficiency of hydronic road heating systems (HRS). The simulation model was developed based on a real operational HRS located in Ingolstadt and composed of bridges and ramps for a total surface of 1989 m2. Climate data of the years 2019–2020 from local weather stations were implemented in the simulation model for performance prediction on extreme climate conditions. This investigation identified that up to 70% of operational hours in terms of energy input can be saved by using a hypothetical predictive controller, thus making the HRS a more economically efficient and environmentally attractive alternate to conventional de-icing techniques.

Composition of Chinook salmon faecal wastes with implications for environmental management

Intensive salmon aquaculture is now a major industry globally, with Chinook salmon (Oncorhynchus tshawytscha) the dominant culture species in New Zealand. Understanding the composition of Chinook salmon wastes is important for assessing environmental risks of coastal open-cage aquaculture, both in terms of nutrient inputs, and for evaluating potential characteristics that could be used as fish waste markers in the environment. The aim of our study was to describe the biochemical composition of faecal waste from Chinook salmon with a view to describing the characteristics most important for assessing waste inputs in the environment. A broad suite of biochemical characteristics, including nutrient, mineral and trace element concentrations, bulk carbon and nitrogen stable isotope and compound specific Ᵹ13C fatty acid and amino acid values, were measured during an experiment on fish kept in a recirculating aquaculture system (RAS) and fed a standard commercial pellet diet. We found that all biochemical characteristics of feed were conserved in faecal material, but some were conserved better than others. Fatty acid composition was similar in feed and faeces, but element profiles differed and showed high variability in faeces. Carbon and nitrogen bulk stable isotope values of faecal samples were generally lower than mean feed signatures, particularly for Ᵹ15N. For compound specific stable isotopes, the Ᵹ13C values in most fatty acids were similar to that of feed, however, Ᵹ13C values in amino acids were lower (with the exception of phenylalanine and glutamic acid). Based on our findings, we suggest that it may be possible to predict biochemical composition of faeces for well-conserved, or predictably-altered indicators using composition of feed samples alone. The differences between feed and faeces for some markers (e.g., elements, nutrient ratios, Ᵹ15N and Ᵹ13C in amino acids) may be useful for distinguishing between the presence of these two farm-derived wastes in the environment. Our results also suggest that the elemental composition of Chinook salmon waste is generally similar to that of other salmonid species, and supports the transfer of element composition data from other salmonids when setting mass balance model parameters for Chinook salmon.

Framework Convention on Tobacco Control 2030-A Program to Accelerate the Implementation of World Health Organization Framework Convention for Tobacco Control in Low- and Middle-Income Countries: A Mixed-Methods Evaluation.

Framework Convention on Tobacco Control (FCTC) 2030 Program (2017-2021) was launched to accelerate World Health Organization (WHO) FCTC implementation in 15 low- and middle-income countries (LMICs). We evaluated the Program in six domains: Governance; Smoke-Free Policies; Taxation; Packaging and Health Warnings; Tobacco Advertising, Promotion, and Sponsorship (TAPS) bans; and International and Regional Cooperation. Following a mixed-methods design, we surveyed (June-September 2020) FCTC focal persons in 14 of the 15 countries, to understand the Program's financial and technical inputs and progress made in each of the six domains. The data were coded in terms of inputs (financial = 1, technical = 1, or both = 2) and progress (none = 1, some = 2, partial = 3, or strong = 4) and a correlation was computed between the inputs and progress scores for each domain. We conducted semi-structured interviews with key stakeholders in five countries. We triangulated between the survey and interview findings. FCTC 2030 offered substantial financial and technical inputs, responsive to country needs, across all six domains. There was a high positive correlation between technical inputs and progress in five of the six domains, ranging from r = 0.61 for taxation (p < .05) to r = 0.91 and for smoke-free policies (p < .001). The interviews indicated that the Program provided timely and relevant evidence and created opportunities for influencing tobacco control debates. The FCTC 2030 Program might have led to variable, but significant progress in advancing FCTC implementation in the 15 countries. As expected, much of the progress was in augmenting existing structures and resources for FCTC implementation. The resulting advances are likely to lead to further progress in FCTC policy implementation. What this study adds: In many LMICs, WHO FCTC policies are not in place; and even when enshrined in law, they are poorly enforced. It is not clear how financial and technical assistance to high tobacco-burden LMICs can most effectively accelerate the implementation of WHO FCTC policies and offer value for money. Bespoke and responsive assistance, both financial and technical, to LMICs aimed at accelerating the implementation of WHO FCTC policies are likely to lead to progress in tobacco control.

Capacity of the Ethiopian primary health care system to achieve universal health coverage: a primary health care progression approach.

Comprehensive and globally comparable evidence about primary health care (PHC) capacity is needed to inform policies and decisions. We carried out a study to assess the Ethiopian PHC capacity in terms of governance, inputs, and population health and facility management domains. The PHC capacity of all the regions, city administrations and the Ministry of Health was assessed using the PHC progression model. The model has 33 measures categorized into three domains. Data were collected and synthesized from all relevant national and regional documents, datasets and key informants. A team of trained evaluation experts conducted external assessments at national and regional levels followed by an internal assessment and a validation workshop. All 33 measures were scored from 1 (lowest) to 4 (highest). The inter-rater reliability test indicated that the overall agreement between internal and external scores was 65%. We found the highest consistency in the internal assessment with a score of 0.84. The findings of this study indicated that the governance domain score was 2.8 out of 4, showing varying scores in quality management, priority setting as well as innovation and learning. The inputs domain score was 2.3 for drugs, supplies and facility infrastructure. The score for the population health and facility management domain was 2. A comparison of federal and national average scores for all measures indicated no significant difference between the two (P-value = 0.69). There are relevant PHC policies and leadership structures at the federal and regional levels. However, the capacity to effectively implement these policies and strategies at sub-national levels is sub-optimal. The challenges related to major inputs coupled with data-quality problems reduced the capacity of the PHC system at the local level. Periodic assessment of the PHC system and closely working with subnational units will potentially improve the capacity of PHC in Ethiopia.

Understanding the Influencing Factors of Enterprise Transformation and Upgrading Capability: A Case Study of the National Innovation Demonstration Zones, China

Being at the forefront of China’s regional innovation and development, enterprises in the National Innovation Demonstration Zones must take the lead in completing high-quality transformation and upgrading. In this study, we use data from 1770 manufacturing companies from China’s 21 National Innovation Demonstration Zones. Based on the multi-factor influence model, we quantitatively study a series of factors that affect enterprise transformation and upgrading capability in terms of innovation-driven input, economically beneficial output, regional high-quality development, and the enterprise’s basic environment. The research results show a good trend in enterprise transformation and upgrading capability, and 45% of the enterprises have higher capabilities than the average of these zones. There are significant differences in these capabilities across the different National Innovation Demonstration Zones. Economically beneficial output has a relatively large impact on enterprise transformation and upgrading capability, while innovation-driven input, regional high-quality development, and the enterprise’s basic environment all have a relatively small impact. We suggest that the National Innovation Demonstration Zones and relevant departments should provide policy guarantees for enterprise transformation and upgrading in terms of regional systems, industrial chain layout, and soft environment optimization, so as to fully enable policy benefits from top-level design.

Output Feedback Control of Hydraulic Active Suspensions With Experimental Validation

This brief presents a novel control method for half-vehicle active suspension systems driven by hydraulic actuators. First, a coordinate transform approach is introduced to reformulate the strict-feedback system into a canonical form, by which the widely used backstepping method is not required. Then, a modified high-gain observer (HGO) is studied to rebuild the unknown system states of the nonlinear active suspension system. To eliminate the effect of unknown nonlinearities, a simple robust unknown system dynamics estimator (USDE) with only one tuning parameter is developed in terms of the system input and output only. Finally, the observer and estimator are integrated to design an output feedback controller to regulate the vehicle motion. Comparative experiments are carried out to illustrate the effectiveness of the proposed method.

An experimental approach to colloquiality perception in L1 and L2 Spanish

Abstract Most studies on register have only approached it from the point of view of production rather than perception. However, features associated with less formal registers could vary in production and perception, particularly in the case of L2. Here an experimental methodology is used to look at five morphosyntactic linguistic features commonly considered more colloquial in Spanish and compare their effect in perceiving a sentence as more or less formal in two different groups of Spanish speakers, L1 and L2. Results show differences between the two groups, in the effect and weight of some of the experimental conditions and their interactions. These differences can be explained in terms of register input received in the classroom and perceptibility and processing impact of some of the features. The paper concludes with a discussion of the methodological challenges of this type of study as well as how it can complement register production studies.

Morphological resilience at the confluence of a very low discharge creek and a large river (upper Parana, Brazil)

Confluences are important environments within the drainage system for exchanges in energy, sediments, water, and the control of local ecology. Despite the significant advances in the understanding of confluences, the study of the junction of small rivers with very large rivers is still incipient. We present a case study of a confluence bar with an extremely low discharge ratio (Qr = 0.0002) and a high obtuse confluence angle (145°) just downstream of the Porto Primavera Dam, in the Upper Paraná River, Brazil. Our analysis includes bathymetric and sedimentological data from surveys prior to dam closure (1997), and after dam closure (2006, 2018), spanning a total of 21 years. Dam closure reduced the mean water level and suspended sediment load in the Paraná River, as well as controlling the occurrence of natural floods. Although the São Pedro Creek confluence bar has reduced, in terms of spatial area and sediment input, its morphology, sedimentation zones, and hydrodynamics has been preserved during this time frame (1997–2018). The stability of the confluence bar is probably mainly due to the high confluence angle (structural control) that causes high resistance in the Paraná River flow, allowing the generation of a large stagnation flow zone (depositional area). The Upper Paraná River, which is one of the most dammed watersheds in the world, has the São Pedro Creek and other small tributaries as important sediment sources for suspended load, mainly after the Porto Primavera dam construction. The results indicate that the stability of the São Pedro confluence bar is due to the association of the systems involved in the supply of sandy sediment, but mainly due to the obtuse angle, that imposes local conditions of flow and sedimentary circulation necessary for its formation and morphological maintenance.

EVALUASI PENGELOLAAN ALOKASI DANA DESA DALAM UPAYA MENINGKATKAN PEMBANGUNAN DAN PEMBERDAYAAN MASYARAKAT

metropolis, and village improvement to gain stability and fairness in development. Allocation of Vilthe large price range is the right of the village as district/metropolis governments have the proper to reap trendy Allocation funds (DAU) and unique Allocation price range (DAK). The expected effects of the Village Fund Allocation are the advent beta the village budget to finance village government packages, both village operations, and village community empowerment to improve village authorities services, improvement, governance, and network empowerment. Goal to determine the effectiveness of the Village Fund Allocation policy in Marga Sungsang Village, Banyuasin II District, Banyuasin Regency, South Sumatra visible from the context, input, manner, and product and evaluation of the extent of Village Fund Allocation in the control category technique: this is quantitative research with a cross-sectional method drawing near. Result: typical, this size has a big move-sectional scale, particularly: the overall context is 79.05 the general input is 76.10, the overall process is eighty one.45 and the overall product is seventy six.88. limitations: based on the researcher's direct theel in the research process, there are numerous barriers, along with the range of respondents is simplest 33 people, direction, twhichiswhichetheless not enough to describe the state of aff, airs and the item of studies which specializes in the effectiveness of the add program in terms of context, input, method, and product and level of control of Village Allocation within the class of Sungsang Marga Village, Bnayausin II District, Banyuasin Regency, South Sumatra. Contribution: For Sungsang Marga Village, Banyuasin II place, South Sumatra Banyuasin, to take advantage of extra village subsidies and increase similarly development of network development and empowerment. F, or the aTher this research to boom knowledge and expand peproperlyon andregardingal inregardinghe problem of coping with the allocation of village budget to solidarity improvement and empowerment. For the Alma mater, the outcomes of this lexpecteddexpectedato dd new references from the analysis of add management in improving network improvement and empowerment.

Merdeka Belajar Activity Unit at Madrasah Aliyah: Program Evaluation Study Using CIPP Method

Evaluation is an activity to find out the teaching and learning process has achieved the goals that have been set. Meanwhile, the Independent Learning Activity Unit program is a small unit of lessons arranged sequentially from easy to difficult with respect to knowledge and skills. This study aims to determine the existence of the Independent Learning Activity Unit program in terms of context, input, process and product at MAN 3 Medan. This research is a quantitative research with CIPP model evaluation. Data was collected by means of questionnaires or questionnaires, interviews and documentation. The population is 329 students with a sample of 82 students consisting of class XI at MAN 3 Medan. Data analysis techniques with editing, tabulating, analyzing and concluding. The results showed that the Independent Learning Activity Unit program at MAN 3 Medan was effective as seen from the results of the average value of the process in the category 51.02. When viewed from the results of the context, input and product, the Independent Learning Activity Unit has complied with the guidelines and is equipped with supporting facilities.

A new design of parity preserving reversible Vedic multiplier targeting emerging quantum circuits

AbstractReversible logic is used increasingly to design digital circuits with lower power consumption. The parity preserving (PP) property contributes to detect permanent and transient faults in reversible circuits by comparing the input and output parity. Multiplication is also considered one of the primary operations in both digital and analog circuits due to its wide applications in digital signal processing and computer arithmetic operations. Accordingly, Vedic mathematics, as a set of techniques sutras, has become popular and is extensively used to solve mathematical problems more efficiently and faster. This work proposes three PP reversible blocks, N1, N2, and N3, which are used to develop a novel effective 2‐bit PP reversible Vedic multiplier and 4‐bit ripples carry adders (RCAs). Moreover, 2‐bit Vedic multiplier and RCA are used to develop the 4‐bit PP reversible Vedic multiplier. The proposed designs outperform the most relevant state‐of‐the‐art structures in terms of garbage output (GO), constant input (CI), gate count (GC), and quantum cost (QC). Average savings of 22.37%, 35.44%, 35.44%, and 34.76%, and 17.76%, 26.60%, 24.52%, and 27.27% respectively, are observed for two‐bit and four‐bit PP reversible Vedic multipliers in terms of QC, GO, CI and GC as compared to previous works.

Explainable artificial intelligence model to predict brain states from fNIRS signals.

Objective: Most Deep Learning (DL) methods for the classification of functional Near-Infrared Spectroscopy (fNIRS) signals do so without explaining which features contribute to the classification of a task or imagery. An explainable artificial intelligence (xAI) system that can decompose the Deep Learning mode's output onto the input variables for fNIRS signals is described here. Approach: We propose an xAI-fNIRS system that consists of a classification module and an explanation module. The classification module consists of two separately trained sliding window-based classifiers, namely, (i) 1-D Convolutional Neural Network (CNN); and (ii) Long Short-Term Memory (LSTM). The explanation module uses SHAP (SHapley Additive exPlanations) to explain the CNN model's output in terms of the model's input. Main results: We observed that the classification module was able to classify two types of datasets: (a) Motor task (MT), acquired from three subjects; and (b) Motor imagery (MI), acquired from 29 subjects, with an accuracy of over 96% for both CNN and LSTM models. The explanation module was able to identify the channels contributing the most to the classification of MI or MT and therefore identify the channel locations and whether they correspond to oxy- or deoxy-hemoglobin levels in those locations. Significance: The xAI-fNIRS system can distinguish between the brain states related to overt and covert motor imagery from fNIRS signals with high classification accuracy and is able to explain the signal features that discriminate between the brain states of interest.

Wind-sea and swell separation of 1D wave spectrum by deep learning

In this study, three existing methods to separate wind-sea and swell from 1D wave spectra, namely the Pierson–Moskowitz (PM) method, the steepness method (HP method), and the overshooting method (JP method), were evaluated statistically using data from 38 meteorological buoys from National Data Buoy Center (NDBC) over the period 2010–2020. Among the three methods, the PM method shows the best agreement with the 2D separation method, because it uses the wind speed information as an input term. Using these buoy data, a new 1D wind-sea-swell separation method based on deep learning is proposed. This new method can directly compute the wind-sea or swell significant wave height (SWH) from a 1D wave spectrum with or without wind speed data as input. When the wind speed is used as an input term, the overall root-mean-square error (RMSE) of the method for wind-sea/swell SWH is 0.27/0.36 m compared to the 2D separation method, which outperforms all existing 1D separation methods. When there is no wind speed data, the RMSE of wind-sea/swell SWH can still reach 0.36/0.41 m, which is similar to the accuracy of the PM method uses wind speed as input. Both methods are robust and have no significant geographical dependence.

Time-varying LQ terminal control for compliant morphing structure using acceleration-based objective function

Flexible structures integrated with smart materials, like piezoelectric actuators, can constitute an active compliant morphing system with the advantages of lightweight, hinge-less, high precision, and adaptability. Such a system is desired to realize rapid, smooth point-to-point shape control without transient and residual vibrations. A time-varying linear quadratic terminal (TVLQT) scheme can be designed to satisfy such finite time control requirements with time-varying feedforward signal and feedback gains. However, the regular formulation of the objective function, with the quadratic expression of state and input, cannot ensure the control performance of the terminal point and especially residual responses after it. The sharp change of the input at the terminal time will induce a high level of residual structural vibration of the compliant structures. To solve such an issue, the key contribution of this study is presenting a new formulation of the objective function utilizing the acceleration of the system. This new objective function can be transformed in terms of state and input, but with the coupling term xTNu, and fortunately can still be solved via differential Riccati equations (DREs). Two compliant structures actuated by piezoelectric materials, a piezocompsite-actuated plate, and a smart space truss, are used to demonstrate the dynamic shape control problem and verify the proposed control scheme. The numerical simulations show that the sharp change phenomenon of the input has been canceled out, and the associated input at the terminal time is much closer to the analytical steady-state value so that the residual vibrations can be avoided. Finally, an experimental setup for shape control of the MFC-actuated plate is established and the effectiveness of the proposed control scheme is experimentally verified. The results imply that improved, smooth dynamic shape control performance of the compliant morphing structure can be realized both during morphing processes and after the terminal point.

An optimal robust fuzzy adaptive integral sliding mode controller based upon a multi-objective grey wolf optimization algorithm for a nonlinear uncertain chaotic system

This paper presents a novel robust fuzzy adaptive integral sliding mode controller designed via a multi-objective grey wolf optimization algorithm for a nonlinear uncertain chaotic system. To this end, proper integral surfaces are defined and a sliding mode stabilizer is designed to converge the system errors to zero. Next, the gradient descent approach is employed to tune the design gains of the integral sliding mode controller. Then, fuzzy rules are employed to regulate the coefficients of the proposed robust control approach. In order to acquire the proper parameters of the proposed controller and avoid trial-and-error processes, a multi-objective grey wolf optimization algorithm is employed to enhance the performance of the proposed controller. The challenging case study of a Duffing-Holmes oscillator, as a nonlinear autonomous uncertain chaotic system without stable equilibrium points, is considered to assess the behavior of the suggests optimal robust fuzzy adaptive integral sliding mode controller. The results of this study are compared with the outcomes of a distinguished work in the literature. Lastly, the discussions elucidate the efficiency of the proposed controller with respect to uncertain chaotic nonlinear systems in terms of optimal control inputs and minimum tracking error.

Long-term C and N sequestration under no-till is governed by biomass production of cover crops rather than differences in grass vs. legume biomass quality

Agricultural activities through conventional and intensive practices contribute to climate change by increasing emission of reactive carbon (C) and nitrogen (N) from soils to the atmosphere. Minimum soil disturbance, appropriate residue management and cover cropping as conservation practices are perceived as key strategies to limit GHGs emissions by increasing soil C and N sequestration as soil organic matter (SOM), thus promoting soil aggregation and enhancing soil fertility. Yet, the actual contribution of conservation practices to C and N sequestration, as well as mechanisms behind chemical and biochemical stabilization of SOM in the long-term are still controversial. In the present 9-year field study on a wheat-maize-soybean rotation we investigated the effect of no-till (NT) coupled with grass vs. legume cover crop (i.e., rye [Secale cereale L., NT-R] or hairy vetch [Vicia villosa Roth, NT-V]) on main crop yields, C and N input by cover and main crops, soil aggregation and C and N sequestration rates, in comparison with conventional tillage (CT). We hypothesized that NT-R may lead to higher biomass input, C sequestration and comparable yield to CT, while NT-V may increase N input, N sequestration and lead to comparable yield to CT. We found that yield of winter wheat, maize, and soybean were never reduced under both NT treatments, neither during the transition phase, nor afterwards. Rye and hairy vetch provided the same amount of biomass and C input, although vetch doubled N input compared with rye. Moreover NT-V increased cumulative biomass and C input from main crop residues compared with NT-R. Both NT-R and NT-V promoted C (+0.4 Mg ha−1 y−1 and +0.6 Mg ha−1 y−1, respectively) and N (+88 kg ha−1 y−1 and +145 kg ha−1 y−1, respectively) soil sequestration, mainly due to the increase of macroaggregate-associated C and N, thus corroborating a major role of NT for macroaggregates formation and SOM stabilization within macroaggregates. Since no difference was found between cover crops in terms of biomass input, and C and N sequestration potential, we concluded that cover crop biomass production (rather than biomass quality) and retention onto the soil as residue were the main drivers of soil C and N sequestration. Therefore, both rye and hairy vetch may be combined with NT and promise significant potential as effective C farming practices.

Influence of Enterprise’s Factor Inputs and Co-Opetition Relationships to Its Innovation Output

In the context of economic globalization, innovation has become a major drive for the sustainable development of enterprises, which emphasizes the importance of studying the influencing factors of enterprise innovation output. The purpose of this study is to clarify the influence mechanism of different indicators on enterprise innovation output, and then provide relevant suggestions for improving enterprise innovation ability. This paper takes 562 enterprises in Chaoyang Sub-park and Fengtai Sub-park of Zhongguancun in Beijing within the time span between 2015 and 2016 as the research objects, and constructs a comprehensive indicator system of influencing factors for enterprise innovation output from the perspective of enterprise co-opetition relationship, factor input and environmental factors. A quantitative model of innovation output and influencing factors was built and then solved, via spike-and-slab sparse function and stepwise regression, aiming at analyzing the influence of different indicators on enterprise innovation output. In addition, this paper also classifies enterprises according to their innovation level and explores the influence of indicators on different types of enterprises. The innovation of this study lies in the modeling of competition and cooperation between enterprises and the establishment of a relatively comprehensive indicator system of influencing factors for enterprise innovation output. The results show that the degree of technological collaboration between enterprises, the level of financing and the degree of capital and labor input in innovation activities have significant positive effects on enterprise innovation output. On the contrary, product and service competition, as well as capital competition, tends to have a negative impact, which urges enterprises to pay heed to the intensity of competition faced by products and services as well as the diversity of financing sources and investment targets to reduce the negative impacts. In addition, enterprises with different levels of innovation should take customized measures in terms of factor input and co-opetition relationships, in that some indicators such as network structure indicators negatively influence the innovation output of enterprises with lower levels of innovation, but has positive impacts on those with higher levels of innovation.

An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research. The safety criteria for medical imaging are highly stringent, and models are required for an explanation. However, existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs. Thus, the interpretability of CNNs has come into the spotlight. Since medical imaging data are limited, many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public ImageNet datasets by the transfer learning method. Unfortunately, this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models. In this study, we trained from scratch rather than relying on transfer learning, creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI. Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning. The deep learning tasks were simplified. Simplification included data management, neural network architecture, and training. Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model. Our results demonstrated that the proposed method was feasible and efficient: the Dice coefficient reached 94.48%, and the accuracy reached 99.7%. It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures. This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

Zastosowanie metody DEA w badaniach empirycznych oceny efektywności podmiotów handlowych

Przedmiot badań: Artykuł zawiera przegląd badań opisanych w literaturze, dotyczących wykorzystania metody DEA (Data Envelopment Analysis) w różnych obszarach i sektorach gospodarki ze szczególnym uwzględnieniem sektora handlu detalicznego. Kwerenda literatury ma potwierdzić brak zastosowania narzędzia pomiaru efektywności, jakim jest metoda granicznej obwiedni danych, w odniesieniu do polskich jednostek handlu detalicznego przy jednoczesnym potwierdzeniu użycia tejże metody do oceny efektywności podmiotów handlowych w innych krajach.&#x0D; Cel badawczy: Celem pracy jest identyfikacja i ocena badań empirycznych odnoszących się do efektywności podmiotów handlowych z wykorzystaniem metody DEA wykonanych w Polsce i na świecie. Cele szczegółowe dotyczą rozpoznania przyjętych w badaniach modeli DEA oraz ich analizy pod kątem między innymi użytych grup nakładów i efektów.&#x0D; Metoda badawcza: W pierwszym etapie zidentyfikowano i zaprezentowano przykłady wykorzystania nieparametrycznej metody granicznej analizy danych do pomiaru efektywności jednostek gospodarczych różnych obszarów na podstawie publikacji krajowych. Następnie przeanalizowano badania zagraniczne pod kątem użycia metody w sektorze handlu detalicznego, w szczególności: (1) rodzaju zastosowanego modelu DEA, (2) przyjętych w modelach danych i zmiennych do badania.&#x0D; Wyniki: Przeprowadzone analizy potwierdziły możliwość zastosowania metody DEA do oceny efektywności podmiotów sektora handlu detalicznego oraz wykazały istniejącą lukę badawczą na gruncie polskim w tym zakresie. Stwierdzono, iż dostępne źródła krajowe nie zawierają badań, w których metoda DEA służyłaby jako narzędzie oceny efektywności funkcjonowania podmiotów handlu detalicznego.

Two Parallel Roads Impacting the Functional Preparedness of Accredited Hospitals: The Indian Healthcare Quality Standards and Disaster Management Act 2005.

Quality management of healthcare service providers and disaster preparedness are two important aspects that affect patient safety and the overall outcome of care delivery. Accreditation standards and legal framework for disaster management are intertwined and pave the way for achieving the optimum level of safety in healthcare system. The aim of the study is to evaluate the functional preparedness of hospital staff for managing disaster situations. It was a quantitative, cross-sectional, ex-post-facto study to understand the functional elements by deeply studying the Knowledge, Attitude and Practice (KAP) scores of various level hospital staff. Knowledge scores of the participants showed positive correlation with attitude scores (r = 0.424, P < 0.001) and practice scores (r = 0.446, P < 0.001). The study showed a significant difference between mean KAP score of males (14.96 ± 3.5) and female (16.38 ± 2.6). Similarly, the staff who obtained excellent scores in the KAP study belonged to a higher age group while those who obtained poor were in the lowest age group (F (2, 715) =10.739; P < 0.001). The performance of staff significantly increased (P < 0.01) with increase in age as number of years of experience and on-the-job training enriched their understanding. Inspite, of the same level of inputs in terms of training and sensitization inputs, the staff of Administration departments showed higher knowledge than other staff (χ2 =97.37, P < 0.001). Analysis of educational qualification of participants elicits that it has a statistically significant effect on their performance in KAP study (F (5, 709) = 12.82; P < 0.001). The KAP scores are direct indicator of effectiveness of quality standards in preparing the hospital for disaster management. The study shows that variables like age, gender, department, educational qualification are correlated with performance and affect the attitude and practice during a disaster event.