HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

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.

Evaluation and Prediction of Groundwater Quality in the Municipality of Za-Kpota (South Benin) Using Machine Learning and Remote Sensing

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.

Assessment of vegetation cover changes and the contributing factors in the Al-Ahsa Oasis using Normalized Difference Vegetation Index(NDVI)

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.

Assessing Spatio-Temporal Land Cover Changes in Dhund River Basin, Eastern Rajasthan (India), Using Multi-Temporal Landsat Data

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.

Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

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.

Perspectives and Experiences of Education Stakeholders: A Quantitative Study on the Adoption of Artificial Intelligence in Executive Training Using Structural Equation Modeling

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).

Study of Urban Sprawl and Its Impact on Vegetation, Land Surface Temperature and Air Pollution Using Remote Sensing and GIS in Kathmandu Valley from 2015 to 2020

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.

Children’s Health, Parent-Child Activities, Using Digital Devices, and Social Competence: Serial Mediation

Background: Studies have pointed out the influence of different children’s activities and prolonged use of digital products on their social development. However, whether the parent-child activities and using digital devices were serial mediators of the relationship between children’s health and social development needs further verification. Purpose: This study explored how parent-child activities and children’s use of digital devices influence the relationship between children’s health and their social competence. Method: This study used data from Kids in Taiwan: National Longitudinal Study of Child Development and Care. A total sample of 2164 participants was used in this study. Serial mediation analyses were performed using model six of Hayes’ PROCESS (2012). Results: This study found that parent-child activities and the use of digital devices can serially mediate the relationship between children’s health and social competence. Children’s health could directly improve their social competence, but it could also serially mediate social competence by increasing parent-child activities and reducing the use of digital devices. Conclusion: Childcare policy planners and parenting educators should not only call on parents to reduce the use of electronic products for their children, but also encourage parents to spend more time interacting with their children, so that children can learn social skills by interacting with others in their daily lives.

Using Technologies for Creative-text Translation,edited by James Luke Hadley,Kristiina Taivalkoski-Shilov,Carlos S.C.Teixeira and Antonio Toral,New York,Routledge,2022,219 pp.,ISBN:9780367555818

Despite the COVID-19 pandemic,creative-text translation is less impacted by uncertainty than interpretation.Research on the effectiveness and viability of incorporating artificial intelligence into the translation of creative works that capture human aesthetic value is still in its infancy.The book Using Technologies for Creative-text Translation goes deep into this emerging field,covering significant findings of machine translation in creative text from the prevailing perspectives on the application of machine translation to the efficiency of machine-to-translation rhetoric.This review describes the background of the work,sorts out its logical relationships,identifies the research findings,and summarises the ingenious ideas.In summary,the book takes into account the perspectives of multiple disciplines,which helps scholars,translators and practitioners understand the application of machine translation in creative texts.

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.

Impacts of land use/cover change on water balance by using the SWAT model in a typical loess hilly watershed of China

Land use/cover change(LUCC)plays a key role in altering surface hydrology and water balance,finally affect-ing the security and availability of water resources.However,mechanisms underlying LUCC determination of water-balance processes at the basin scale remain unclear.In this study,the Soil and Water Assessment Tool(SWAT)model and partial least squares regression were used to detect the effects of LUCC on hydrology and water components in the Zuli River Basin(ZRB),a typical watershed of the Yellow River Basin.In general,three recommended coefficients(R^(2)and E ns greater than 0.5,and P bias less than 20%)indicated that the output results of the SWAT model were reliable and that the model was effective for the ZRB.Then,several key findings were obtained.First,LUCC in the ZRB was characterized by a significant increase in forest(21.61%)and settlement(23.52%)and a slight reduction in cropland(-1.35%),resulting in a 4.93%increase in evapotranspiration and a clear decline in surface runoffand water yield by 15.68%and 2.95%at the whole basin scale,respectively.Second,at the sub-basin scale,surface runoffand water yield increased by 14.26%-36.15%and 5.13%-15.55%,respectively,mainly due to settlement increases.Last,partial least squares regression indicated that urbanization was the most significant contributor to runoffchange,and evapotranspiration change was mainly driven by forest expansion.These conclusions are significant for understanding the relationship between LUCC and water balance,which can provide meaningful information for managing water resources and the long-term sustainability of such watersheds.

Spatiotemporal detection of land use/land cover changes and land surface temperature using Landsat and MODIS data across the coastal Kanyakumari district, India

This study assesses the changes in land use/land cover(LULC) and land surface temperature(LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques. Landsat images are used to estimate the LULC changes and the MODIS data for LST.The Maximum Likelihood Classification(MLC) method is used, and the LULC is classified into six categories: Agriculture Land, Barren Land, Salt Pan, Sandy Beach, Settlement, and Waterbody. Within the two decades of the present change detection study, upheave in the Settlement area of 49.89% is noticed, and the Agriculture Land is exploited by 20.09%. Salt Pan emits a high LST of 31.57°C, and the Waterbodies are noticed with a low LST of 28.9°C. However, the overall rate of LST decreased by 0.56°C during this period. This study will help policymakers make appropriate planning and management to overcome the impact of LULC and LST in the forthcoming years.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Addressing nonresponse bias in forest inventory change estimation using response homogeneity classifications

Estimating amounts of change in forest resources over time is a key function of most national forest inventories(NFI). As this information is used broadly for many management and policy purposes, it is imperative that accurate estimations are made from the survey sample. Robust sampling designs are often used to help ensure representation of the population, but often the full sample is unrealized due to hazardous conditions or possibly lack of land access permission. Potentially, bias may be imparted to the sample if the nonresponse is nonrandom with respect to forest characteristics, which becomes more difficult to assess for change estimation methods that require measurements of the same sample plots at two points in time, i.e., remeasurement. To examine potential nonresponse bias in change estimates, two synthetic populations were constructed: 1) a typical NFI population consisting of both forest and nonforest plots, and 2) a population that mimics a large catastrophic disturbance event within a forested population. Comparisons of estimates under various nonresponse scenarios were made using a standard implementation of post-stratified estimation as well as an alternative approach that groups plots having similar response probabilities(response homogeneity). When using the post-stratified estimators, the amount of change was overestimated for the NFI population and was underestimated for the disturbance population, whereas the response homogeneity approach produced nearly unbiased estimates under the assumption of equal response probability within groups. These outcomes suggest that formal strategies may be needed to obtain accurate change estimates in the presence of nonrandom nonresponse.

Genetic dissection of N use efficiency using maize inbred lines and testcrosses

Although the use of heterosis in maize breeding has increased crop productivity,the genetic causes underlying heterosis for nitrogen(N) use efficiency(NUE) have been insufficiently investigated.In this study,five N-response traits and five low-N-tolerance traits were investigated using two inbred line populations(ILs) consisting of recombinant inbred lines(RIL) and advanced backcross(ABL) populations,derived from crossing Ye478 with Wu312.Both populations were crossed with P178 to construct two testcross populations.IL populations,their testcross populations,and the midparent heterosis(MPH)for NUE were investigated.Kernel weight,kernel number,and kernel number per row were sensitive to N level and ILs showed higher N response than did the testcross populations.Based on a highdensity linkage map,138 quantitative trait loci(QTL) were mapped,each explaining 5.6%–38.8% of genetic variation.There were 52,34 and 52 QTL for IL populations,MPH,and testcross populations,respectively.The finding that 7.6% of QTL were common to the ILs and their testcross populations and that 11.7% were common to the MPH and testcross population indicated that heterosis for NUE traits was regulated by non-additive and non-dominant loci.A QTL on chromosome 5 explained 27% of genetic variation in all of the traits and Gln1-3 was identified as a candidate gene for this QTL.Genome-wide prediction of NUE traits in the testcross populations showed 14%–51% accuracy.Our results may be useful for clarifying the genetic basis of heterosis for NUE traits and the candidate gene may be used for genetic improvement of maize NUE.

Identifying eco-functional zones on the Chinese Loess Plateau using ecosystem service bundles

Optimizing the function of ecosystem services(ESs)is vital for implementing regional ecological management strategies.In this study,we used multi-source data and integrated modelling methods to assess the spatiotemporal variations in eight typical ESs on the Chinese Loess Plateau from 2000 to 2015,including grain production,raw material provision,water conservation,carbon storage service,soil conservation,oxygen production,recreation,and net primary productivity(NPP)services.Then,we divided the ecosystem service bundles(ESBs)according to relationships among the eight ESs,obtaining four types of eco-functional areas at the county(city or banner or district)level based on the spatial clustering of similarities in different ES types.We also identified and assessed the contributions of influencing factors to these eco-functional areas using principal component analysis(PCA)across spatiotemporal scales.We found that the spatiotemporal variations in different ESs were noticeable,with an overall increase in grain production and soil conservation services,no significant change in carbon storage service,and overall decreases in raw material provision,water conservation,oxygen production,recreation,and NPP services.From 2000 to 2015,the number of significant synergistic ES pairs decreased,while that of significant trade-off pairs increased.To the changes of ESBs in the eco-functional areas,the results indicated that the indirect loss of these ESs from forest and grassland due to urban expansion should be reduced in ecological development area(ESB 2)and multi ecological functional area(ESB 3).Meanwhile,crop planting structures and planting densities should be adjusted to reduce ES trade-offs associated with water conservation service in grain-producing area(ESB 4).Lastly,ESB-based ecofunctional zoning can be used to improve ecological restoration management strategies and optimize ecological compensation schemes in ecologically fragile area(ESB 1).

Comparison Study on Sedimentomorphological Characteristics Using Integrated Geo-Techniques: A Case Study of Two Representative Areas in Kuwait

Intensive aeolian processes occur due to the scarcity of rainfall and lack of vegetation cover in arid regions. The study of recent surface sediments in arid areas is important for environmental assessments, evaluation of natural resources, and land use planning. In this study, two areas were chosen as they show changes in lithology, environment and landforms. The two study areas are Al-Rawdatain in the northern part, and Al-Managish in the southern part of Kuwait. The current study aims to define the sedimentomorphic zones in these areas, with an emphasis on Quaternary geomorphological evolution by providing an integrated approach based on satellite images, topographic maps, field measurements, and laboratory analysis. Remote sensing data were spatially analyzed to classify and detect the temporal changes in the surface sediments and geomorphology based on the field measurements (n = 42) as ground truthing points for supervising the classification. Samples from both areas were collected and subjected to grain size (dry mechanical sieving) and X-Ray Diffraction (XRD) analysis. The resulting data were statistically analyzed for grain size distributions and mineralogy based on the US standard set of sieves. The study found that the Aeolian sand sheet deposits are the most frequent recent surface deposits in Kuwait and cover most of the other sediments. The direction of movement of the sand sheets is from NW towards SE. The mineralogical composition of the aeolian recent surface sediments revealed that they are mostly derived from the Dibdibba Formation and Tigris-Euphrates fluvial terrace deposits. Quartz is the most frequent component of the studied surface sediments in the study areas (66%). The calcite mineral is also found in subordinate amounts in the study areas (10%).

Identification of Groundwater Potential Zones in Samendeni Watershed in Sedimentary and Semi-Arid Contexts of Burkina Faso, Using Analytic Hierarchy Process (AHP) Method and GIS

Demand for water increases in Samendeni regarding the undertaken agricultural projects while pressure on surface water from global warming/evapotranspiration also increases. Thus, the need to evaluate the groundwater potential in the catchment is crucial as alternative supplier of water and resilience to climate hazards. The AHP was performed integrating ten influencing factors such as geomorphology, geology, soil, land use/land cover (lulc), slope, rainfall, drainage density, borehole rate & depth and piezometric level to generate groundwater potential zones (GWPZs) in Samendeni watershed (4420 km2). All the factors were processed and ranged into five (5) classes. Weight was assigned to each class of thematic layer. These thematic layers were then reclassified based on the normalized weight to be used in the calculation of groundwater potential zones (GWPZ). The final output, groundwater potential map, revealed a significant groundwater potential with very good (11%), good (31%), moderate (30%), poor (20%), and very poor (8%) of proportion. The interesting (very good, good) GWPZs in the study area are mostly in the central towards the east. The poor zones in term of groundwater potential are concentrated in the upper west region of the watershed. Besides the cross-validation with the relationship between different groundwater potential zones and the wells available in the study area, the overall accuracy was estimated to 88% provided from the result of the similarity analysis where 22 out of the 25 validation wells match with the expected yield classes of GWPZs. The statistics from that validation revealed the performance of AHP method to delineate groundwater potential zones at catchment level.

Detection of Environmental Toxins in Mixed Matrices of Tap Water, Soil, Food Waste, Serum and Milk Using Hememics Biosensor

Exposure to toxins can lead to a wide range of adverse health effects, including respiratory problems, neurological disorders, cancer, and reproductive issues. Toxins can come from various sources, such as industrial waste, agricultural runoff, and household chemicals. Therefore, detecting and monitoring toxins in the environment is crucial for protecting human health and the environment. This study aimed to evaluate the performance of Hememics biosensor system in detecting environmental toxins such as Ricin and Staphylococcal enterotoxin B (SEB) in mixed matrixes. When Ricin and SEB are spiked into soil, chopped lettuce, tap water, milk and serum, the biosensor was able to detect these toxins, without sample processing, at a level of detection comparable to lab testing with high sensitivity and specificity. Furthermore, Hememics biosensor system is designed to be network-enabled, which means that results can be transmitted to relevant agencies for quick decisions. This feature is crucial in cases where quick action is needed to prevent further contamination or exposure to harmful toxins.

Simulation of urban affordable housing land-use evolution based on CA-MAS model

In order to simulate the evolution of affordable housing land use a dynamic model that combines cellular automata CA and a multi-agent system MAS is established.This paper aims to utilize the approach of decision factors on site selection of affordable housing through a literature review to construct a hierarchy model of those factors identifying the weight of each factor by an analytic hierarchy process AHP .Based on those weight factors the CA-MAS model is designed. Nanjing city is taken as an example to verify the feasibility of the model.The results show that the CA-MAS model is pragmatic and effective in simulating evolution of affordable housing land use which also promotes the fundamental understanding and perception of the development of affordable housing and urbanization.