Despite public and private investments in the senior housing sector,an alternative to retirement homes is not yet truly present in Italy,except for a few rare cases.The spots in residential facilities for the elderly ...Despite public and private investments in the senior housing sector,an alternative to retirement homes is not yet truly present in Italy,except for a few rare cases.The spots in residential facilities for the elderly are limited and not enough to fill a demand for spaces that is continuously increasing.Another underlying problem is that the type of user that senior housing is aimed at is not currently considered by the Italian market;the impact of factors that can decrease the quality of life in elderly people,such as loneliness,lack of physical activity or loss of routine is underestimated.This set of negative factors promotes the opposite of what is considered active aging.In recent years senior houses,intended as a residential typology for self-sufficient elderly people,have undergone a significant evolution,reflecting social,demographic and technological changes;this reflects a paradigm shift in the way society approaches care to the elderly,focusing increasingly on autonomy,personalization and well-being.From 2010 to 2024,there has been greater attention towards customization of programs and spaces dedicated to the elderly,with the aim of offering services that meet everyone’s specific needs.Senior houses are becoming more oriented towards a wellbeing-based approach and are starting to focus on social inclusion as well,promoting recreational and cultural activities to improve the quality of life of elderly vips.A strategy used for social inclusion is to dedicate part of the project to functions open to the public(kindergartens,community centers,spaces for associations,etc.)so that the project fits into the urban level of the city by interacting with it.The proposal is to integrate cultural spaces with senior housing in a way that the elderly residents can become the keepers and narrators of local heritage,creating intergenerational communities.展开更多
This study examines the spatial and temporal patterns of wetland degradation in Delhi from 1991 to 2021 using remote sensing and GIS techniques.The Automated Water Extraction Index(AWEI)was applied to pre-monsoon Land...This study examines the spatial and temporal patterns of wetland degradation in Delhi from 1991 to 2021 using remote sensing and GIS techniques.The Automated Water Extraction Index(AWEI)was applied to pre-monsoon Landsat imagery to delineate surface water bodies over the past 30 years accurately.Supervised classification was employed to generate land use maps,while census data was utilized to analyze urbanization trends across the region.Classification accuracy was assessed using Google Earth reference data through a confusion matrix,ensuring the reliability of the land cover analysis.Results reveal a significant decline in wetland extent,especially in densely populated and rapidly urbanizing districts such as North West,South,and East Delhi.During this time,the urban population increased from 52.7% to 97.4%,accompanied by a 70.2% expansion of built-up areas,while wetlands contracted from 32.9 km^(2) to 30.2 km^(2).South Delhi experienced the most severe wetland loss,with water body coverage dropping from 0.800% to 0.025%,whereas North East and Central Delhi maintained higher wetland coverage due to the influence of the Yamuna River and targeted conservation efforts.The study highlights the strong linkage between urban growth and wetland decline,which threatens biodiversity,groundwater recharge,and ecological stability.These findings emphasize the urgent need for integrated urban planning and conservation policies to safeguard wetlands,thereby promoting sustainability and water security in the National Capital Region.展开更多
Objective: To assess antiretroviral therapy (ART) adherence rates and associated factors among people living with HIV in Vietnam. Methods: A cross-sectional study was conducted at the Hospital for Tropical Diseases, H...Objective: To assess antiretroviral therapy (ART) adherence rates and associated factors among people living with HIV in Vietnam. Methods: A cross-sectional study was conducted at the Hospital for Tropical Diseases, Ho Chi Minh City from June to August 2022. Data were collected from 347 people living with HIV using structured questionnaires assessing sociodemographics, substance use, drug side effects, treatment beliefs, treatment satisfaction, and depression. ART adherence was evaluated using a multi-method tool, including self-report, pill count, the Provider Interview Tool, and the Visual Analog Scale. Participants were classified as having high adherence only if they met all four criteria across these methods. Multivariable logistic regression was used to identify factors influencing adherence, with significance set at P<0.05. Results: High ART adherence was observed in 69.5% of the participants. Adherence was significantly lower among tobacco users (OR 0.49, 95% CI 0.30-0.83, P=0.007), those with higher depression scores (per 1-point increase) (OR 0.89, 95% CI 0.84-0.95, P<0.001), frequent substance users (OR 0.50, 95% CI 0.30-0.83, P=0.007), and those experiencing more severe drug side effects (per level increase) (OR 0.64, 95% CI 0.45-0.92, P=0.016). Participants able to afford treatment had nearly three times higher odds of adherence than those unable to pay (OR 2.85, 95% CI 1.48-5.47, P=0.002). Conclusions: ART adherence among people living with HIV in Vietnam remains suboptimal. Interventions should target substance use, drug side effects, financial barriers, and depression screening to improve adherence.展开更多
Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-spe...Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.展开更多
In L2 content-based classrooms,code-switching or translanguaging seem to be a common practice adopted by teachers.There has been growing research discussing the potentials of L1 in these classrooms.Most of the current...In L2 content-based classrooms,code-switching or translanguaging seem to be a common practice adopted by teachers.There has been growing research discussing the potentials of L1 in these classrooms.Most of the current studies have focused on the analysis of lesson interactions and yet the perception of the content teachers has remained underexplored.This case study investigated the introspective views of a group of content teachers at a secondary school using questionnaires and written accounts.Data analyses showed that these teachers were generally aware of the interpersonal and ideational functions achieved by the use of L1 and they also seemed to have a positive view towards their practices of using L1 in English-medium classrooms.Based on the findings,practical implications for content teachers in relation to making medium of instruction decisions and suggestions for further research are discussed.展开更多
Introduction:Having a primary care usual source of care(USC)is associated with better population health outcomes.However,the percent of adults in the United States(US)with a usual primary care provider is declining.We...Introduction:Having a primary care usual source of care(USC)is associated with better population health outcomes.However,the percent of adults in the United States(US)with a usual primary care provider is declining.We sought to identify factors associated with establishing a USC at an urgent care clinic or emergency department as opposed to primary care.Methods:We analyzed data from 57,152 participants in the All of Us study who reported having a USC.We used the Andersen Behavioral Model of Health Services Use framework and multivariable logistic regression to examine associations among predisposing,enabling,and need factors,according to the source of usual care.Results:An urgent care clinic,minute clinic,or emergency department was the source of usual care for 6.3%of our sample.The odds of seeking care at this type of facility increased with younger age,lower educational attainment,and better health status.Black and Hispanic individuals,as well as those who reported experiencing discrimination in medical settings or that their provider was of a different race and ethnicity,were also less likely to have a primary care USC.Financial concerns,being anxious about seeing a provider,and the inability to take time off from work also increased the likelihood of having a non‐primary care USC.Conclusions:Improving the rates of having a primary care USC among younger and healthy adults may be achievable through policies that can improve access to convenient,affordable primary care.Efforts to improve diversity among primary care providers and reduce discrimination experienced by patients may also improve the USC rates for racial and ethnic minority groups.展开更多
The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competi-tion for land use.However,low policy implementation effectiveness has led to construction beyond designated ...The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competi-tion for land use.However,low policy implementation effectiveness has led to construction beyond designated spatial plan.The study used a quantitative survey using Landsat images in 2016,2019,and 2022.The data analysis techniques used geographic information systems integrated with Artificial Neural Network(ANN)and Cellular Automata(CA)models.This study aims to predict land-use change in 2031,evaluate its alignment with spatial planning,and provide guidance for controlling land-use change.The results showed that there has been an increase in land use.In 2019,built-up land reached 7,069.65 Ha.The model shows its ability to predict land simulation and transformation,where it is predicted that built-up land in 2031 will experience an increase of up to 40.10%,so development and change cannot be avoided every year.This study also suggests that decision-makers and local governments should reconsider spatial planning strategies.This study shows that there have been many land use changes from 2016 to 2022.The model shows its ability to predict simulation and land transformation.When using the model,there are many changes in the land use area in 2031.This is due to wet agricultural land turning into built-up land by almost 70%.This study shows that road network influence land-use change.The cellular automata model managed to capture the complexity with simple rules.Predictions for future research should focus on conserving wetlands and primary forests.展开更多
Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different cro...Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different crop types are less concerned.The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience strategies.Despite increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region.Using Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral features.The random forest model was applied to classify LULC,providing insights into both historical and future trends.Results indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by 2034.The study found that paddy crops exhibited the highest values,while common bean and maize performed poorly.Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate change.The study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area.These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.展开更多
Marburg virus disease(MVD)is a highly fatal illness,with a case fatality rate of up to 88%,though this rate can be significantly reduced with prompt and effective patient care.The disease was first identified in 1967 ...Marburg virus disease(MVD)is a highly fatal illness,with a case fatality rate of up to 88%,though this rate can be significantly reduced with prompt and effective patient care.The disease was first identified in 1967 during concurrent outbreaks in Marburg and Frankfurt,Germany,and in Belgrade,Serbia,linked to laboratory use of African green monkeys imported from Uganda.Subsequent outbreaks and isolated cases have been reported in various African countries,including Angola,the Democratic Republic of the Congo,Equatorial Guinea,Ghana,Guinea,Kenya,Rwanda,South Africa(in an individual with recent travel to Zimbabwe),Tanzania,and Uganda.Initial human MVD infections typically occur due to prolonged exposure to mines or caves inhabited by Rousettus aegyptiacus fruit bats,the natural hosts of the virus.展开更多
The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle co...The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.展开更多
With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and ...With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.展开更多
Ammonia(NH_(3)) volatilization from rice fields contributes to poor air quality and indicates low nitrogen use efficiency. Although organic fertilizers can meet the nitrogen requirement for rice growth, the simultaneo...Ammonia(NH_(3)) volatilization from rice fields contributes to poor air quality and indicates low nitrogen use efficiency. Although organic fertilizers can meet the nitrogen requirement for rice growth, the simultaneous effects of organic fertilizers on NH_(3) volatilization and rice yield in paddy fields are poorly understood and quantified. To address this gap in our knowledge, experimental field plots were established in a conventional double-cropping paddy field in the Pearl River Delta region, southern China. Five fertilizer treatments were used besides the control with no fertilizer: fresh organic fertilizer, successively composted organic fertilizer, chemically composted organic fertilizer, mixture of chemically composted organic fertilizer with inorganic fertilizer, and chemical fertilizer. Ammonia volatilization was measured using a batch-type airflow enclosure method. No significant differences in grain yield were observed among organic and chemical fertilizer treatments. However, compared with chemical fertilizer, chemically composted organic fertilizer and successively composted organic fertilizer significantly decreased total NH_(3) volatilization by 70% and 68%, respectively. The ammonium-nitrogen concentration in field surface water correlated strongly(P < 0.01) and positively with NH_(3) volatilization across fertilization treatments. Our findings demonstrate that chemically composted organic fertilizer can sustain rice yield while reducing NH_(3) volatilization. An important future step is to promote these field measurements to similar rice cultivation areas to quantify the regional-and national-scale impact on air quality and nitrogen deposition in sensitive areas, and to design and implement better fertilizer management practices.展开更多
As the global population continues to expand,the demand for natural resources increases.Unfortunately,human activities account for 23%of greenhouse gas emissions.On a positive note,remote sensing technologies have eme...As the global population continues to expand,the demand for natural resources increases.Unfortunately,human activities account for 23%of greenhouse gas emissions.On a positive note,remote sensing technologies have emerged as a valuable tool in managing our environment.These technologies allow us to monitor land use,plan urban areas,and drive advancements in areas such as agriculture,climate changemitigation,disaster recovery,and environmentalmonitoring.Recent advances in Artificial Intelligence(AI),computer vision,and earth observation data have enabled unprecedented accuracy in land use mapping.By using transfer learning and fine-tuning with red-green-blue(RGB)bands,we achieved an impressive 99.19%accuracy in land use analysis.Such findings can be used to inform conservation and urban planning policies.展开更多
Accessing drinking water is a global issue. This study aims to contribute to the assessment of groundwater quality in the municipality of Za-Kpota (southern Benin) using remote sensing and Machine Learning. The method...Accessing drinking water is a global issue. This study aims to contribute to the assessment of groundwater quality in the municipality of Za-Kpota (southern Benin) using remote sensing and Machine Learning. The methodological approach used consisted in linking groundwater physico-chemical parameter data collected in the field and in the laboratory using AFNOR 1994 standardized methods to satellite data (Landsat) in order to sketch out a groundwater quality prediction model. The data was processed using QGis (Semi-Automatic Plugin: SCP) and Python (Jupyter Netebook: Prediction) softwares. The results of water analysis from the sampled wells and boreholes indicated that most of the water is acidic (pH varying between 5.59 and 7.83). The water was moderately mineralized, with conductivity values of less than 1500 μs/cm overall (59 µS/cm to 1344 µS/cm), with high concentrations of nitrates and phosphates in places. The dynamics of groundwater quality in the municipality of Za-Kpota between 2008 and 2022 are also marked by a regression in land use units (a regression in vegetation and marshland formation in favor of built-up areas, bare soil, crops and fallow land) revealed by the diachronic analysis of satellite images from 2008, 2013, 2018 and 2022. Surveys of local residents revealed the use of herbicides and pesticides in agricultural fields, which are the main drivers contributing to the groundwater quality deterioration observed in the study area. Field surveys revealed the use of herbicides and pesticides in agricultural fields, which are factors contributing to the deterioration in groundwater quality observed in the study area. The results of the groundwater quality prediction models (ANN, RF and LR) developed led to the conclusion that the model based on Artificial Neural Networks (ANN: R2 = 0.97 and RMSE = 0) is the best for groundwater quality changes modelling in the Za-Kpota municipality.展开更多
The abandonment of date palm grove of the former Al-Ahsa Oasis in the eastern region of Saudi Arabia has resulted in the conversion of delicate agricultural area into urban area.The current state of the oasis is influ...The abandonment of date palm grove of the former Al-Ahsa Oasis in the eastern region of Saudi Arabia has resulted in the conversion of delicate agricultural area into urban area.The current state of the oasis is influenced by both expansion and degradation factors.Therefore,it is important to study the spatiotemporal variation of vegetation cover for the sustainable management of oasis resources.This study used Landsat satellite images in 1987,2002,and 2021 to monitor the spatiotemporal variation of vegetation cover in the Al-Ahsa Oasis,applied multi-temporal Normalized Difference Vegetation Index(NDVI)data spanning from 1987 to 2021 to assess environmental and spatiotemporal variations that have occurred in the Al-Ahsa Oasis,and investigated the factors influencing these variation.This study reveals that there is a significant improvement in the ecological environment of the oasis during 1987–2021,with increase of NDVI values being higher than 0.10.In 2021,the highest NDVI value is generally above 0.70,while the lowest value remains largely unchanged.However,there is a remarkable increase in NDVI values between 0.20 and 0.30.The area of low NDVI values(0.00–0.20)has remained almost stable,but the region with high NDVI values(above 0.70)expands during 1987–2021.Furthermore,this study finds that in 1987–2002,the increase of vegetation cover is most notable in the northern region of the study area,whereas from 2002 to 2021,the increase of vegetation cover is mainly concentrated in the northern and southern regions of the study area.From 1987 to 2021,NDVI values exhibit the most pronounced variation,with a significant increase in the“green”zone(characterized by NDVI values exceeding 0.40),indicating a substantial enhancement in the ecological environment of the oasis.The NDVI classification is validated through 50 ground validation points in the study area,demonstrating a mean accuracy of 92.00%in the detection of vegetation cover.In general,both the user’s and producer’s accuracies of NDVI classification are extremely high in 1987,2002,and 2021.Finally,this study suggests that environmental authorities should strengthen their overall forestry project arrangements to combat sand encroachment and enhance the ecological environment of the Al-Ahsa Oasis.展开更多
Land cover is an impression of natural cover on surface of earth such as bare soil, river, grass etc. and utilization of these natural covers for various human needs and purposes by mankind is defined as land use. Lan...Land cover is an impression of natural cover on surface of earth such as bare soil, river, grass etc. and utilization of these natural covers for various human needs and purposes by mankind is defined as land use. Land cover identification, delineation and mapping is important for planning activities, resource management and global monitoring studies while baseline mapping and subsequent monitoring is done by application of land use to get timely information about quantity of land that has been used. The present study has been carried out in Dhund river watershed of Jaipur, Rajasthan which covers an area of about 1828 sq∙km. The minimum and maximum elevation of the area is found to be 214 m and 603 m respectively. Land use and land cover changes of three decades from 1991 to 2021 have been interpreted by using remotes sensing and GIS techniques. ArcGIS software (Arc map 10.2), SOI topographic map, Cartosat-1 DEM and satellite data of Landsat 5 and Landsat 8 have been used for interpretation of eleven classes. The study shows an increase in cultivated land, settlement, waterbody, open forest, plantation and mining due to urbanization because of increasing demands of food, shelter and water while a decrease in dense forest, river, open scrub, wasteland and uncultivated land has also been marked due to destruction of aforementioned by anthropogenic activities such as industrialization resulting in environmental degradation that leads to air, soil and water pollution.展开更多
The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the la...The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.展开更多
The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in ...The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).展开更多
The Kathmandu Valley has seen substantial urbanization over the past decades while being the nation’s economic centre. Built-up areas have expanded quickly along with the population, having a significantly negative i...The Kathmandu Valley has seen substantial urbanization over the past decades while being the nation’s economic centre. Built-up areas have expanded quickly along with the population, having a significantly negative influence on the environment. Recently, Kathmandu was named as the most polluted city in Asia. Urban sprawl has had a negative influence on Kathmandu’s residents in several ways. The state of urban sprawl and the effects it has had on the Kathmandu Valley have been examined using land sat imagery. In this study, IDW was used in GIS to analyze the pollution status using data of PM 2.5 and PM 10 obtained from various monitoring sites. A supervised classification was used to create a LULC map of Kathmandu for the years 2015, 2018, and 2020. To assess the state of the vegetation and determine whether the Kathmandu Valley is being affected by urban heat, NDVI and Land sat temperature calculations were also made. The study’s results were obtained using remote sensing and GIS technology. The built-up area in Kathmandu Valley has grown by 20% over the past five years, impacting land use patterns and deteriorating vegetation cover. Due to the rise of built-up area, which is a good heat absorber, the temperature in the Kathmandu Valley is rising along with the degradation of the vegetation cover. The pollution in the Kathmandu Valley is at its worst, and residents are compelled to breathe air that is significantly more polluted than the prescribed limit.展开更多
Background:The sustainability of rural surgical and obstetrical facilities depends on their efficacy and quality of care,which are difficult to measure in a rural context.In an evaluation of rural practice,it is often...Background:The sustainability of rural surgical and obstetrical facilities depends on their efficacy and quality of care,which are difficult to measure in a rural context.In an evaluation of rural practice,it is often the case that the only comparators are larger referral facilities,for which facility‐level comparisons are difficult due to differences in population demographics,acuity of patients,and services offered.This publication outlines these limitations and highlights a best‐practice approach to making facility‐level comparisons using population‐level data,risk stratification,tests of noninfer-iority,and Firth logistic regression analysis.This includes an investigation of minimum sample‐size requirements through Monte Carlo power analysis in the context of low‐acuity rural surgical care.Methods:Monte Carlo power analysis was used to estimate the minimum sample size required to achieve a power of 0.8 for both logistic regression and Firth logistic regression models that compare the proportion of surgical adverse events against facility type,among other confounders.We provide guidelines for the implementation of a recommended methodology that uses risk stratification,Firth penalized logistic regression,and tests of noninferiority.Results:We illustrate limitations in facility‐level comparison of surgical quality among patients undergoing one of four index procedures including hernia repair,colonoscopy,appendectomy,and cesarean delivery.We identified minimum sample sizes for comparison of each index procedure that fluctuate depending on the level of risk stratification used.Conclusion:The availability of administrative data can provide an adequate sample size to allow for facility‐level comparisons in surgical quality,at the rural level and elsewhere.When they are made appropriately,these comparisons can be used to evaluate the efficacy of general practitioners and nurse practitioners in performing low‐acuity procedures.展开更多
文摘Despite public and private investments in the senior housing sector,an alternative to retirement homes is not yet truly present in Italy,except for a few rare cases.The spots in residential facilities for the elderly are limited and not enough to fill a demand for spaces that is continuously increasing.Another underlying problem is that the type of user that senior housing is aimed at is not currently considered by the Italian market;the impact of factors that can decrease the quality of life in elderly people,such as loneliness,lack of physical activity or loss of routine is underestimated.This set of negative factors promotes the opposite of what is considered active aging.In recent years senior houses,intended as a residential typology for self-sufficient elderly people,have undergone a significant evolution,reflecting social,demographic and technological changes;this reflects a paradigm shift in the way society approaches care to the elderly,focusing increasingly on autonomy,personalization and well-being.From 2010 to 2024,there has been greater attention towards customization of programs and spaces dedicated to the elderly,with the aim of offering services that meet everyone’s specific needs.Senior houses are becoming more oriented towards a wellbeing-based approach and are starting to focus on social inclusion as well,promoting recreational and cultural activities to improve the quality of life of elderly vips.A strategy used for social inclusion is to dedicate part of the project to functions open to the public(kindergartens,community centers,spaces for associations,etc.)so that the project fits into the urban level of the city by interacting with it.The proposal is to integrate cultural spaces with senior housing in a way that the elderly residents can become the keepers and narrators of local heritage,creating intergenerational communities.
文摘This study examines the spatial and temporal patterns of wetland degradation in Delhi from 1991 to 2021 using remote sensing and GIS techniques.The Automated Water Extraction Index(AWEI)was applied to pre-monsoon Landsat imagery to delineate surface water bodies over the past 30 years accurately.Supervised classification was employed to generate land use maps,while census data was utilized to analyze urbanization trends across the region.Classification accuracy was assessed using Google Earth reference data through a confusion matrix,ensuring the reliability of the land cover analysis.Results reveal a significant decline in wetland extent,especially in densely populated and rapidly urbanizing districts such as North West,South,and East Delhi.During this time,the urban population increased from 52.7% to 97.4%,accompanied by a 70.2% expansion of built-up areas,while wetlands contracted from 32.9 km^(2) to 30.2 km^(2).South Delhi experienced the most severe wetland loss,with water body coverage dropping from 0.800% to 0.025%,whereas North East and Central Delhi maintained higher wetland coverage due to the influence of the Yamuna River and targeted conservation efforts.The study highlights the strong linkage between urban growth and wetland decline,which threatens biodiversity,groundwater recharge,and ecological stability.These findings emphasize the urgent need for integrated urban planning and conservation policies to safeguard wetlands,thereby promoting sustainability and water security in the National Capital Region.
文摘Objective: To assess antiretroviral therapy (ART) adherence rates and associated factors among people living with HIV in Vietnam. Methods: A cross-sectional study was conducted at the Hospital for Tropical Diseases, Ho Chi Minh City from June to August 2022. Data were collected from 347 people living with HIV using structured questionnaires assessing sociodemographics, substance use, drug side effects, treatment beliefs, treatment satisfaction, and depression. ART adherence was evaluated using a multi-method tool, including self-report, pill count, the Provider Interview Tool, and the Visual Analog Scale. Participants were classified as having high adherence only if they met all four criteria across these methods. Multivariable logistic regression was used to identify factors influencing adherence, with significance set at P<0.05. Results: High ART adherence was observed in 69.5% of the participants. Adherence was significantly lower among tobacco users (OR 0.49, 95% CI 0.30-0.83, P=0.007), those with higher depression scores (per 1-point increase) (OR 0.89, 95% CI 0.84-0.95, P<0.001), frequent substance users (OR 0.50, 95% CI 0.30-0.83, P=0.007), and those experiencing more severe drug side effects (per level increase) (OR 0.64, 95% CI 0.45-0.92, P=0.016). Participants able to afford treatment had nearly three times higher odds of adherence than those unable to pay (OR 2.85, 95% CI 1.48-5.47, P=0.002). Conclusions: ART adherence among people living with HIV in Vietnam remains suboptimal. Interventions should target substance use, drug side effects, financial barriers, and depression screening to improve adherence.
基金Funded by the Spanish Government and FEDER funds(AEI/FEDER,UE)under grant PID2021-124502OB-C42(PRESECREL)the predoctoral program“Concepción Arenal del Programa de Personal Investigador en formación Predoctoral”funded by Universidad de Cantabria and Cantabria’s Government(BOC 18-10-2021).
文摘Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.
文摘In L2 content-based classrooms,code-switching or translanguaging seem to be a common practice adopted by teachers.There has been growing research discussing the potentials of L1 in these classrooms.Most of the current studies have focused on the analysis of lesson interactions and yet the perception of the content teachers has remained underexplored.This case study investigated the introspective views of a group of content teachers at a secondary school using questionnaires and written accounts.Data analyses showed that these teachers were generally aware of the interpersonal and ideational functions achieved by the use of L1 and they also seemed to have a positive view towards their practices of using L1 in English-medium classrooms.Based on the findings,practical implications for content teachers in relation to making medium of instruction decisions and suggestions for further research are discussed.
文摘Introduction:Having a primary care usual source of care(USC)is associated with better population health outcomes.However,the percent of adults in the United States(US)with a usual primary care provider is declining.We sought to identify factors associated with establishing a USC at an urgent care clinic or emergency department as opposed to primary care.Methods:We analyzed data from 57,152 participants in the All of Us study who reported having a USC.We used the Andersen Behavioral Model of Health Services Use framework and multivariable logistic regression to examine associations among predisposing,enabling,and need factors,according to the source of usual care.Results:An urgent care clinic,minute clinic,or emergency department was the source of usual care for 6.3%of our sample.The odds of seeking care at this type of facility increased with younger age,lower educational attainment,and better health status.Black and Hispanic individuals,as well as those who reported experiencing discrimination in medical settings or that their provider was of a different race and ethnicity,were also less likely to have a primary care USC.Financial concerns,being anxious about seeing a provider,and the inability to take time off from work also increased the likelihood of having a non‐primary care USC.Conclusions:Improving the rates of having a primary care USC among younger and healthy adults may be achievable through policies that can improve access to convenient,affordable primary care.Efforts to improve diversity among primary care providers and reduce discrimination experienced by patients may also improve the USC rates for racial and ethnic minority groups.
基金supported by the Ministry of Education,Culture,Research,and Technology Directorate General of Higher Education,Research,and Technology grant number[2147/UN2621/PN/2022].
文摘The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competi-tion for land use.However,low policy implementation effectiveness has led to construction beyond designated spatial plan.The study used a quantitative survey using Landsat images in 2016,2019,and 2022.The data analysis techniques used geographic information systems integrated with Artificial Neural Network(ANN)and Cellular Automata(CA)models.This study aims to predict land-use change in 2031,evaluate its alignment with spatial planning,and provide guidance for controlling land-use change.The results showed that there has been an increase in land use.In 2019,built-up land reached 7,069.65 Ha.The model shows its ability to predict land simulation and transformation,where it is predicted that built-up land in 2031 will experience an increase of up to 40.10%,so development and change cannot be avoided every year.This study also suggests that decision-makers and local governments should reconsider spatial planning strategies.This study shows that there have been many land use changes from 2016 to 2022.The model shows its ability to predict simulation and land transformation.When using the model,there are many changes in the land use area in 2031.This is due to wet agricultural land turning into built-up land by almost 70%.This study shows that road network influence land-use change.The cellular automata model managed to capture the complexity with simple rules.Predictions for future research should focus on conserving wetlands and primary forests.
基金supported by the Deanship of Research and Graduate Studies at the King Khalid University(RGP2/287/46)the Princess Nourah bint Abdulrahman University Researchers Supporting Project(PNURSP2025R733)+1 种基金the Princess Nourah bint Abdulrahman University Research Supporting Project(RSPD2025R787)the King Saud University,Saudi Arabia.
文摘Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different crop types are less concerned.The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience strategies.Despite increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region.Using Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral features.The random forest model was applied to classify LULC,providing insights into both historical and future trends.Results indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by 2034.The study found that paddy crops exhibited the highest values,while common bean and maize performed poorly.Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate change.The study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area.These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.
文摘Marburg virus disease(MVD)is a highly fatal illness,with a case fatality rate of up to 88%,though this rate can be significantly reduced with prompt and effective patient care.The disease was first identified in 1967 during concurrent outbreaks in Marburg and Frankfurt,Germany,and in Belgrade,Serbia,linked to laboratory use of African green monkeys imported from Uganda.Subsequent outbreaks and isolated cases have been reported in various African countries,including Angola,the Democratic Republic of the Congo,Equatorial Guinea,Ghana,Guinea,Kenya,Rwanda,South Africa(in an individual with recent travel to Zimbabwe),Tanzania,and Uganda.Initial human MVD infections typically occur due to prolonged exposure to mines or caves inhabited by Rousettus aegyptiacus fruit bats,the natural hosts of the virus.
基金co-supported by the National Natural Science Foundation of China(Nos.52272403,52402506)Natural Science Basic Research Program of Shaanxi,China(Nos.2022JC-27,2023-JC-QN-0599)。
文摘The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.
基金supported by Major Science and Technology Projects in Henan Province,China,Grant No.221100210600.
文摘With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.
基金funded by the National Natural Science Foundation of China(No.41771291)the Agricultural Science and Technology Innovation Fund of Jiangsu,China(No.CX(21)3183)+2 种基金the Specially-Appointed Professor Program of Jiangsu,Chinathe Six Talent Peaks Project in Jiangsu Province,China(No.NY-083)the Startup Foundation for Introducing Talent of NUIST,China。
文摘Ammonia(NH_(3)) volatilization from rice fields contributes to poor air quality and indicates low nitrogen use efficiency. Although organic fertilizers can meet the nitrogen requirement for rice growth, the simultaneous effects of organic fertilizers on NH_(3) volatilization and rice yield in paddy fields are poorly understood and quantified. To address this gap in our knowledge, experimental field plots were established in a conventional double-cropping paddy field in the Pearl River Delta region, southern China. Five fertilizer treatments were used besides the control with no fertilizer: fresh organic fertilizer, successively composted organic fertilizer, chemically composted organic fertilizer, mixture of chemically composted organic fertilizer with inorganic fertilizer, and chemical fertilizer. Ammonia volatilization was measured using a batch-type airflow enclosure method. No significant differences in grain yield were observed among organic and chemical fertilizer treatments. However, compared with chemical fertilizer, chemically composted organic fertilizer and successively composted organic fertilizer significantly decreased total NH_(3) volatilization by 70% and 68%, respectively. The ammonium-nitrogen concentration in field surface water correlated strongly(P < 0.01) and positively with NH_(3) volatilization across fertilization treatments. Our findings demonstrate that chemically composted organic fertilizer can sustain rice yield while reducing NH_(3) volatilization. An important future step is to promote these field measurements to similar rice cultivation areas to quantify the regional-and national-scale impact on air quality and nitrogen deposition in sensitive areas, and to design and implement better fertilizer management practices.
文摘As the global population continues to expand,the demand for natural resources increases.Unfortunately,human activities account for 23%of greenhouse gas emissions.On a positive note,remote sensing technologies have emerged as a valuable tool in managing our environment.These technologies allow us to monitor land use,plan urban areas,and drive advancements in areas such as agriculture,climate changemitigation,disaster recovery,and environmentalmonitoring.Recent advances in Artificial Intelligence(AI),computer vision,and earth observation data have enabled unprecedented accuracy in land use mapping.By using transfer learning and fine-tuning with red-green-blue(RGB)bands,we achieved an impressive 99.19%accuracy in land use analysis.Such findings can be used to inform conservation and urban planning policies.
文摘Accessing drinking water is a global issue. This study aims to contribute to the assessment of groundwater quality in the municipality of Za-Kpota (southern Benin) using remote sensing and Machine Learning. The methodological approach used consisted in linking groundwater physico-chemical parameter data collected in the field and in the laboratory using AFNOR 1994 standardized methods to satellite data (Landsat) in order to sketch out a groundwater quality prediction model. The data was processed using QGis (Semi-Automatic Plugin: SCP) and Python (Jupyter Netebook: Prediction) softwares. The results of water analysis from the sampled wells and boreholes indicated that most of the water is acidic (pH varying between 5.59 and 7.83). The water was moderately mineralized, with conductivity values of less than 1500 μs/cm overall (59 µS/cm to 1344 µS/cm), with high concentrations of nitrates and phosphates in places. The dynamics of groundwater quality in the municipality of Za-Kpota between 2008 and 2022 are also marked by a regression in land use units (a regression in vegetation and marshland formation in favor of built-up areas, bare soil, crops and fallow land) revealed by the diachronic analysis of satellite images from 2008, 2013, 2018 and 2022. Surveys of local residents revealed the use of herbicides and pesticides in agricultural fields, which are the main drivers contributing to the groundwater quality deterioration observed in the study area. Field surveys revealed the use of herbicides and pesticides in agricultural fields, which are factors contributing to the deterioration in groundwater quality observed in the study area. The results of the groundwater quality prediction models (ANN, RF and LR) developed led to the conclusion that the model based on Artificial Neural Networks (ANN: R2 = 0.97 and RMSE = 0) is the best for groundwater quality changes modelling in the Za-Kpota municipality.
文摘The abandonment of date palm grove of the former Al-Ahsa Oasis in the eastern region of Saudi Arabia has resulted in the conversion of delicate agricultural area into urban area.The current state of the oasis is influenced by both expansion and degradation factors.Therefore,it is important to study the spatiotemporal variation of vegetation cover for the sustainable management of oasis resources.This study used Landsat satellite images in 1987,2002,and 2021 to monitor the spatiotemporal variation of vegetation cover in the Al-Ahsa Oasis,applied multi-temporal Normalized Difference Vegetation Index(NDVI)data spanning from 1987 to 2021 to assess environmental and spatiotemporal variations that have occurred in the Al-Ahsa Oasis,and investigated the factors influencing these variation.This study reveals that there is a significant improvement in the ecological environment of the oasis during 1987–2021,with increase of NDVI values being higher than 0.10.In 2021,the highest NDVI value is generally above 0.70,while the lowest value remains largely unchanged.However,there is a remarkable increase in NDVI values between 0.20 and 0.30.The area of low NDVI values(0.00–0.20)has remained almost stable,but the region with high NDVI values(above 0.70)expands during 1987–2021.Furthermore,this study finds that in 1987–2002,the increase of vegetation cover is most notable in the northern region of the study area,whereas from 2002 to 2021,the increase of vegetation cover is mainly concentrated in the northern and southern regions of the study area.From 1987 to 2021,NDVI values exhibit the most pronounced variation,with a significant increase in the“green”zone(characterized by NDVI values exceeding 0.40),indicating a substantial enhancement in the ecological environment of the oasis.The NDVI classification is validated through 50 ground validation points in the study area,demonstrating a mean accuracy of 92.00%in the detection of vegetation cover.In general,both the user’s and producer’s accuracies of NDVI classification are extremely high in 1987,2002,and 2021.Finally,this study suggests that environmental authorities should strengthen their overall forestry project arrangements to combat sand encroachment and enhance the ecological environment of the Al-Ahsa Oasis.
文摘Land cover is an impression of natural cover on surface of earth such as bare soil, river, grass etc. and utilization of these natural covers for various human needs and purposes by mankind is defined as land use. Land cover identification, delineation and mapping is important for planning activities, resource management and global monitoring studies while baseline mapping and subsequent monitoring is done by application of land use to get timely information about quantity of land that has been used. The present study has been carried out in Dhund river watershed of Jaipur, Rajasthan which covers an area of about 1828 sq∙km. The minimum and maximum elevation of the area is found to be 214 m and 603 m respectively. Land use and land cover changes of three decades from 1991 to 2021 have been interpreted by using remotes sensing and GIS techniques. ArcGIS software (Arc map 10.2), SOI topographic map, Cartosat-1 DEM and satellite data of Landsat 5 and Landsat 8 have been used for interpretation of eleven classes. The study shows an increase in cultivated land, settlement, waterbody, open forest, plantation and mining due to urbanization because of increasing demands of food, shelter and water while a decrease in dense forest, river, open scrub, wasteland and uncultivated land has also been marked due to destruction of aforementioned by anthropogenic activities such as industrialization resulting in environmental degradation that leads to air, soil and water pollution.
文摘The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.
文摘The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).
文摘The Kathmandu Valley has seen substantial urbanization over the past decades while being the nation’s economic centre. Built-up areas have expanded quickly along with the population, having a significantly negative influence on the environment. Recently, Kathmandu was named as the most polluted city in Asia. Urban sprawl has had a negative influence on Kathmandu’s residents in several ways. The state of urban sprawl and the effects it has had on the Kathmandu Valley have been examined using land sat imagery. In this study, IDW was used in GIS to analyze the pollution status using data of PM 2.5 and PM 10 obtained from various monitoring sites. A supervised classification was used to create a LULC map of Kathmandu for the years 2015, 2018, and 2020. To assess the state of the vegetation and determine whether the Kathmandu Valley is being affected by urban heat, NDVI and Land sat temperature calculations were also made. The study’s results were obtained using remote sensing and GIS technology. The built-up area in Kathmandu Valley has grown by 20% over the past five years, impacting land use patterns and deteriorating vegetation cover. Due to the rise of built-up area, which is a good heat absorber, the temperature in the Kathmandu Valley is rising along with the degradation of the vegetation cover. The pollution in the Kathmandu Valley is at its worst, and residents are compelled to breathe air that is significantly more polluted than the prescribed limit.
基金Doctors of BC and the British Columbia Ministry of Health,Grant/Award Number:GR005415。
文摘Background:The sustainability of rural surgical and obstetrical facilities depends on their efficacy and quality of care,which are difficult to measure in a rural context.In an evaluation of rural practice,it is often the case that the only comparators are larger referral facilities,for which facility‐level comparisons are difficult due to differences in population demographics,acuity of patients,and services offered.This publication outlines these limitations and highlights a best‐practice approach to making facility‐level comparisons using population‐level data,risk stratification,tests of noninfer-iority,and Firth logistic regression analysis.This includes an investigation of minimum sample‐size requirements through Monte Carlo power analysis in the context of low‐acuity rural surgical care.Methods:Monte Carlo power analysis was used to estimate the minimum sample size required to achieve a power of 0.8 for both logistic regression and Firth logistic regression models that compare the proportion of surgical adverse events against facility type,among other confounders.We provide guidelines for the implementation of a recommended methodology that uses risk stratification,Firth penalized logistic regression,and tests of noninferiority.Results:We illustrate limitations in facility‐level comparison of surgical quality among patients undergoing one of four index procedures including hernia repair,colonoscopy,appendectomy,and cesarean delivery.We identified minimum sample sizes for comparison of each index procedure that fluctuate depending on the level of risk stratification used.Conclusion:The availability of administrative data can provide an adequate sample size to allow for facility‐level comparisons in surgical quality,at the rural level and elsewhere.When they are made appropriately,these comparisons can be used to evaluate the efficacy of general practitioners and nurse practitioners in performing low‐acuity procedures.
