Human Activities Research Articles

Pollution of a tropical watershed with antibiotic residues and resistance genes: impact of human activities

Pollution of a tropical watershed with antibiotic residues and resistance genes: impact of human activities

Comparative morphology of blood cells of Ambassis nalua, A. vachelli, and Inegocia japonica

Hematological investigation is an essential tool for monitoring fish health. This research compared morphometric traits and blood cell characteristics between two representative pelagic fish species (Ambassis nalua and A. vachelli) and one benthic species (Inegocia japonica) collected from seagrass meadows off Libong Island in Thailand, where they coexist with human activity. Blood samples were collected and prepared using the smear technique. The erythrocytes of all observed specimens had elliptical or oval shapes. The benthic fish I. japonica significantly outperformed both pelagic fish in terms of the mean size of many erythrocyte features. However, more erythrocyte nuclear abnormalities were found in I. japonica, along with A. nalua. Lymphocytes made up the highest proportion of leukocytes in I. japonica, followed by neutrophils. The morphometric erythrocyte data of I. japonica possibly indicated the greater oxygen requirement of fish living in demersal habitats. The baseline parameters from the hematological data of the sampled fish will be used to monitor environmental quality.

Detection and tracking of barchan dunes using artificial intelligence

Barchans are crescent-shape dunes ubiquitous on Earth and other celestial bodies, which are organized in barchan fields where they interact with each other. Over the last decades, satellite images have been largely employed to detect barchans on Earth and on the surface of Mars, with AI (Artificial Intelligence) becoming an important tool for monitoring those bedforms. However, automatic detection reported in previous works is limited to isolated dunes and does not identify successfully groups of interacting barchans. In this paper, we inquire into the automatic detection and tracking of barchans by carrying out experiments and exploring the acquired images using AI. After training a neural network with images from controlled experiments where complex interactions took place between dunes, we did the same for satellite images from Earth and Mars. We show, for the first time, that a neural network trained properly can identify and track barchans interacting with each other in different environments, using different image types (contrasts, colors, points of view, resolutions, etc.), with confidence scores (accuracy) above 70%. Our results represent a step further for automatically monitoring barchans, with important applications for human activities on Earth, Mars and other celestial bodies.

Evaluation of beach nourishment in Mar del plata, Argentina: An application of the CoastSat toolkit

Sandy coasts play a crucial role in various human activities and support the economies of many coastal states. Due to their importance, coastal erosion mitigation techniques are often implemented. Among these techniques, beach nourishment is considered an environmentally acceptable method for coastal protection and restoration. In this work, a methodology for the evaluation of such projects is proposed and applied to the biggest beach nourishment project in the city of Mar del Plata, Argentina. The nourishment was carried out between 1998 and 1999 in Playa Grande (PG), Varese (V) and Bristol (B) bays. The CoastSat toolkit was implemented to obtain shoreline positions from satellite imagery and to derive a 36-year monthly time series of beach width in the region. For the analysis, three distinctives time lapses are identified: pre-intervention, response, and new equilibrium. An exponential decay function was fitted to determine the response lapse and its characteristics. Metrics such as mean beach width and trends were computed and used to compare the condition of beaches before and after the project. Results show that the region experienced a net growth after nourishment, with beach width doubling in Bristol and Playa Grande, and increasing tenfold in Varase. Nourishment did not alter the erosive trends of Playa Grande and Bristol, nor the growth trend of Varese. Regarding renourishment planning, according to our findings, for Playa Grande and Bristol beaches to achieve a net growth of 1 m, it would require the deposition of 17 m³ of sediment per meter of coastline. This study highlights the suitability of the CoastSat toolkit for assessing the effectiveness of beach regeneration.

Understanding the Evolution and Transmission Dynamics of Antibiotic Resistance Genes: A Comprehensive Review.

Antibiotic resistance poses a formidable challenge to global public health, necessitating comprehensive understanding and strategic interventions. This review explores the evolution and transmission dynamics of antibiotic resistance genes, with a focus on Bangladesh. The indiscriminate use of antibiotics, compounded by substandard formulations and clinical misdiagnosis, fuels the emergence and spread of resistance in the country. Studies reveal high resistance rates among common pathogens, emphasizing the urgent need for targeted interventions and rational antibiotic use. Molecular assessments uncover a diverse array of antibiotic resistance genes in environmental reservoirs, highlighting the complex interplay between human activities and resistance dissemination. Horizontal gene transfer mechanisms, particularly plasmid-mediated conjugation, facilitate the exchange of resistance determinants among bacterial populations, driving the evolution of multidrug-resistant strains. The review discusses clinical implications, emphasizing the interconnectedness of environmental and clinical settings in resistance dynamics. Furthermore, bioinformatic and experimental evidence elucidates novel mechanisms of resistance gene transfer, underscoring the dynamic nature of resistance evolution. In conclusion, combating antibiotic resistance requires a multifaceted approach, integrating surveillance, stewardship, and innovative research to preserve the efficacy of antimicrobial agents and safeguard public health.

Effects of a supplemented diet containing 7 probiotic strains (Honeybeeotic) on honeybee physiology and immune response: analysis of hemolymph cytology, phenoloxidase activity, and gut microbiome

BackgroundIn this study, a probiotic mixture (Honeybeeotic) consisting of seven bacterial strains isolated from a unique population of honeybees (Apis mellifera ligustica) was used. That honeybee population was located in the Roti Abbey locality of the Marche Region in Italy, an area isolated from human activities, and genetic contamination from other honeybee populations. The aim was to investigate the effects of this probiotic mixture on the innate immunity and intestinal microbiome of healthy common honeybees in two hives of the same apiary. Hive A received a diet of 50% glucose syrup, while hive B received the same syrup supplemented with the probiotics, both administered daily for 1 month. To determine whether the probiotic altered the immune response, phenoloxidase activity and hemolymph cellular subtype count were investigated. Additionally, metagenomic approaches were used to analyze the effects on gut microbiota composition and function, considering the critical role the gut microbiota plays in modulating host physiology.ResultsThe results revealed differences in hemocyte populations between the two hives, as hive A exhibited higher counts of oenocytoids and granulocytes. These findings indicated that the dietary supplementation with the probiotic mixture was safe and well-tolerated. Furthermore, phenoloxidase activity significantly decreased in hive B (1.75 ± 0.19 U/mg) compared to hive A (3.62 ± 0.44 U/mg, p < 0.005), suggesting an improved state of well-being in the honeybees, as they did not require activation of immune defense mechanisms. Regarding the microbiome composition, the probiotic modulated the gut microbiota in hive B compared to the control, retaining core microbiota components while causing both positive and negative variations. Notably, several genes, particularly KEGG genes involved in amino acid metabolism, carbohydrate metabolism, and branched-chain amino acid (BCAA) transport, were more abundant in the probiotic-fed group, suggesting an effective nutritional supplement for the host.ConclusionsThis study advocated that feeding with this probiotic mixture induces beneficial immunological effects and promoted a balanced gut microbiota with enhanced metabolic activities related to digestion. The use of highly selected probiotics was shown to contribute to the overall well-being of the honeybees, improving their immune response and gut health.

Comprehensive review of industrial wastewater treatment techniques.

Water is an indispensable resource for human activity and the environment. Industrial activities generate vast quantities of wastewater that may be heavily polluted or contain toxic contaminants, posing environmental and public health challenges. Different industries generate wastewater with widely varying characteristics, such as the quantity generated, concentration, and pollutant type. It is essential to understand these characteristics to select available treatment techniques for implementation in wastewater treatment facilities to promote sustainable water usage. This review article provides an overview of wastewaters generated by various industries and commonly applied treatment techniques. The characteristics, advantages, and disadvantages of physical, chemical, and biological treatment methods are presented.

Constructing a Bayesian belief network to provide insights into the dynamic drivers of ecosystem service relationships

Vegetation restoration has induced complex spatiotemporal changes in ecosystem service (ES) relationships. However, dynamic analysis on the driving mechanisms underlying ES relationships remains insufficient, constraining the comprehension on sustainable vegetation restoration. In this study, a comprehensive index (ESI) representing the coordination among four ESs (carbon sequestration, water yield, baseflow regulation, and soil conservation) was quantified in Yan’an City, a typical vegetation restoration area. Using the Bayesian belief network, a social-ecological framework was constructed to explore changes in driving factors of ESI during 1990 to 2020. The results revealed that: (1) the coordination of ES relationships showed a significant enhancement over the past 30 years, which primarily attributed to areas with the highest values of water yield. Areas with low values of baseflow regulation posed a risk to the ESI. (2) The combined contribution of FVC and precipitation was consistently close to 70 %, emerging as the dominant driving factors for the ESI. But in the last decade, contributions from human activities have increased significantly. (3) Scenario reasoning highlighted that expanding vegetation cover alone would exhibit threshold effects on the ESI improvement. Additionally, the results revealed a promising prospect of enhancing baseflow and maintaining the ESI through human actions. This study provides an example of using Bayesian belief network to analyze social-ecological interactions, enhancing the understanding of changes in ES relationships under vegetation restoration.

Path analysis reveals combined winter climate and pollution effects on the survival of a marine top predator.

Marine ecosystems are experiencing growing pressure from multiple threats caused by human activities, with far-reaching consequences for marine food webs. Determining the effects of multiple stressors is complex, in part, as they can affect different aspects of biological organisation (behaviour, individual traits and demographic rates). Determining the combined effects of stressors, through different biological pathways, is key to predict the consequences for the viability of populations threatened by global change. Due to their position in the food chain, top predators such as seabirds are considered more sensitive to environmental changes. Climate change is affecting the prey resources available for seabirds, through bottom-up effects, while organic pollutants can bioaccumulate in food chains with the greatest impacts on top predators. However, knowledge of their combined effects on population dynamics is scarce. Using a path analysis, we quantify the effects of climate change and pollution on the survival of adult great black-backed gulls, both directly and through effects of individuals' body mass. Warmer ocean temperatures in gulls' winter foraging areas in the North Sea were correlated with higher survival, potentially explained by shifts in prey availability associated with global climate change. We also found support for indirect negative effects of organochlorines, highly toxic pollutants to seabirds, on survival, which acted, in part, through a negative effect on body mass. The results from this path analysis highlight how, even for such long-lived species where variance in survival tends to be limited, two stressors still have had a marked influence on adult survival and illustrate the potential of path models to improve predictions of population variability under multiple stressors.

Holocene ecosystem and temperature development inferred from invertebrate remains in Zminje Jezero (Dinaric Alps, Montenegro)

AbstractLake Zminje Jezero (1535 m a.s.l.) in Montenegro was studied for chironomid and other aquatic invertebrate remains in a sediment sequence dating back to 12,000 calibrated 14C years before present (cal yr BP), providing, to our knowledge, the first lake-sediment record studied for chironomids and other associated chitinous aquatic invertebrate remains in the Dinaric Alps. Changes in chironomid and other invertebrate remains along the record make it possible to constrain changes of relevant environmental variables for aquatic invertebrates in the lake including temperature, oxygen availability, trophic status and water depth. The results suggest moderate changes in trophic conditions with chironomid assemblages indicating meso- to eutrophic conditions during the analysed interval. Invertebrate assemblages were typical for lakes with relatively high oxygen availability with a minor trend during the middle to late Holocene to conditions observed in lakes that are more hypoxic. A change in temperature is suggested in the earlier part of the record when the share of chironomid taxa adapted to warmer conditions increased, at the Younger Dryas to Holocene transition, whereas later no particularly pronounced shifts in temperature-sensitive taxa were observed. July air temperatures were estimated based on chironomid assemblages using a transfer function based on calibration data consisting of 117 lakes sampled in the Swiss Alps and northern Switzerland. The results suggest that temperatures rapidly increased by 5 °C at the onset of the Holocene (11,500 cal yr BP) leading to a relatively warm early to mid-Holocene and thereafter slightly decreased during the late Holocene. Reconstructed temperatures are discussed together with a previously published pollen record for our study site and are broadly consistent with other chironomid-based temperature reconstructions from Central, Eastern and Southern Europe. From ca. 3000 cal yr BP sedimentation rates increased and from ca. 500 cal yr BP onwards pollen data suggest that the vegetation and lake catchment were affected by human activities, possibly influencing chironomid and other invertebrate assemblages in the lake and thereby also reconstructed temperatures. Overall, our results show that combining analyses of chironomid and other invertebrate assemblages can provide valuable insights into long-term environmental changes and can provide temperature reconstructions for small mountain lakes in the Balkans, such as Zminje Jezero.

Spatial and seasonal assessment of surface water quality for domestic use in a semi-arid area of the Upper Kebir Sub-basin, NE Algeria

ABSTRACT The present study was conducted out in the Upper Kebir Sub-basin [North East (NE) Algeria] aiming to evaluate the status and spatial–seasonal variability of water quality for domestic purposes using hydrogeochemical parameters and water quality indices. Surface water samples were collected from selected 27 sites, in the period 2020–2021 during both the wet and dry seasons and were analysed for 22 parameters. The findings were compared with standards and the water quality indices were calculated. The analysis revealed that the basin was mainly polluted and showed significant seasonal and spatial variations in water quality, considerably influenced by climatic conditions (surface runoff) and human activities (urban sewage and agricultural activities). The results reveal significant spatio-temporal fluctuations and highlight areas likely to be affected by anthropogenic activities.

Modeling of water level trends and characterizing potential influencing factors in Lake Baringo in Kenya

Water plays a significant role in every sector of the ecosystem. The fluctuation of the water levels in lakes is influenced by natural and man-made factors within the water catchment. Lake Baringo, which has no visible outlet, has been rising drastically, causing panic among native communities and businesses on the shores of the lake. Using GIS and Remote Sensing, this study intends to analyze the changes in water lakes using the Automatic water extraction index (AWEI), determine the causes of the fluctuation using Land use Land cover, land surface temperature, soil erosion, and siltation in the lake basin and the lake respectively, precipitation, and later predict of the water level for the year 2030 using the MOLUSCE tool. The tool utilizes an artificial neural network and cellular automata to analyze land use and land cover conveniently. It was found that the lake’s water level has been increasing drastically over the years, and the leading causes of the fluctuations were increased rainfall and human activities within the water basin. There are visible increased human activities within the water basin, such as agriculture, deforestation, settlement, and urbanization. It was also found that there will be a further increase in water level in 2030. With all the above results, it is recommended that better policies be made to conserve the water basin effectively, and a plan should be drawn to re-delineate the new riparian buffer.

Identification of landscape features structuring movement connectivity for Namibian elephants

ContextHuman modification of landscapes poses a significant threat to wildlife, particularly in Africa where population growth and land conversion are expected to increase. Habitat loss and fragmentation have led to declines in wildlife populations, highlighting the need to identify and preserve critical habitats, including core use areas and connectivity between them. Most recently, the identification of habitat corridors has become a key objective.ObjectivesOur study objectives are to (1) empirically quantify connectivity across the Kunene—Etosha landscape in Northwestern Namibia using GPS tracking data on wild African elephants, and (2) assess the landscape features (i.e., geologic, biotic, and human-made) influencing connectivity and corridor types (e.g., fast movement corridors versus slow multi-use movement corridors).MethodsWe used GPS telemetry data from 66 elephants collared in Northwestern Namibia to empirically quantify connectivity using a graph theoretic approach and assess landscape features influencing connectivity. Based on the ‘movescape’ approach, we identify different types of corridors and examined how landscape features differed across these corridors using multiple regression models on locations classified into different types of use categories by machine learning algorithms.ResultsOur results revealed strong variation in connectivity across the landscape, with paths of high connectivity near water sources between the study areas. We found that factors related to water sources and human presence primarily influenced connectivity. Water holes serve as hubs across the ecosystem for both male and female elephants with lower use areas peripheral to areas with water. Connectivity between Kunene and Etosha National Park was relatively rare among the collared elephants, but we highlight the key areas used to move between the two regions.ConclusionWater was the key feature structuring space use, and human presence influenced connectivity between water points, highlighting the importance of landscape planning in relation to limited water sources and human activities. Our results suggest that focusing management efforts on areas where water is limited for both elephants and humans will be important to reduce conflict and maintain ecosystem connectivity.

Recognizing salat activity using deep learning models via smartwatch sensors

AbstractIn this study, we focus on human activity recognition, particularly aiming to distinguish the activity of praying (salat) from other daily activities. To achieve this goal, we have created a new dataset named HAR-P (Human activity recognition for Praying), which includes eight different activities: walking, running, sitting, standing, walking upstairs, walking downstairs, typing with a keyboard, and praying (salat). The HAR-P dataset was collected from 50 male individuals, who wore smartwatches on their dominant wrists. We compare the activity classification performance using three state-of-the-art algorithms from the literature: Long Short-Term Memory, Convolutional Long Short-Term Memory, and Convolutional Neural Network—Long Short-Term Memory. To assess the influence of sensors, data from accelerometer, gyroscope, linear acceleration sensor, and magnetic field sensor were utilized. The impact of individual sensor data as well as combinations thereof was investigated. The highest classification accuracy within single sensor groups, reaching 95.7%, was achieved using the accelerometer data with the Convolutional Long Short-Term Memory method. Combining two sensor groups resulted in an increase in accuracy of up to 9%. The highest accuracy of 96.4% was obtained by utilizing three sensor groups together with the Convolutional Neural Network—Long Short-Term Memory method. Furthermore, the evaluation of sensor and model performance was conducted using the stratified k-fold cross-validation method with 5-folds. These findings contribute significantly to evaluating the performance of sensor combinations and different algorithms in activity classification. This study may provide an effective foundation for the automatic recognition and tracking of human activities and offer an applicable model, particularly for the recognition of religious practices such as praying.

Driving factors and trend prediction for annual runoff in the upper and middle reaches of the Yellow River from 1990 to 2020

Abstract The Yellow River Basin (YRB) plays a pivotal role in the water resources management of its region, significantly influenced by the interplay between climate change and human activities, particularly in its upper and middle reaches (UMRYR). This study aims to elucidate the evolving patterns and determinants of runoff within the UMRYR, a matter of considerable importance for the basin's water resource management, strategy, and distribution. Utilizing the Google Earth Engine (GEE) platform, this research accessed comprehensive datasets including precipitation, drought index, and terrace area, among others, to examine their effects on runoff variations at five gauge stations across the YRB. Terrace data was extracted from Landsat imagery via the Random Forest Model, while annual runoff figures from 1990 to 2020 were sourced from the Sediment Bulletin of China River. Employing the Mann-Kendall test, we assessed the temporal changes in runoff over three decades. Further, runoff determinants were analyzed through stepwise regression and redundancy analysis, leading to the construction of a linear regression model. This model, alongside a multiple linear regression approach, forecasted annual runoff, with its accuracy validated through cross-validation and comparison against the ARIMA time series model. Our findings reveal the efficacy of the random forest algorithm in classifying terraces, achieving an accuracy rate exceeding 0.8. The period from 1990 to 2020 saw a general increase in annual runoff across the five gauging stations in the UMRYR, albeit with variations in the pattern, particularly at the Tangnaihai gauge station which presented the most complex changes. Crucially, three main drivers—summer precipitation (SP), terrace area (TR), and drought index (DI)—were identified as significant predictors in the regression models. The multiple linear regression model outperformed the ARIMA model in forecasting accuracy, underlining the significance of integrating these drivers into runoff prediction models for the UMRYR.&amp;#xD;

Spatial–temporal variation and correlation analysis of ecosystem service values and ecological risks in winter city Shenyang, China

Ecosystems in winter cities are complex and fragile, experiencing significant changes due to climate variations and human construction activities. Previous studies on the assessment of overall ecosystem service value (ESV) and ecological risk index (ERI) in winter cities are scarce. In this study, we constructed ESV and ERI measurement models using land use data in 2000, 2010, and 2020 using the improved value per unit area factor method and the landscape pattern index method, respectively, to reveal their spatial and temporal change characteristics. Geographic detectors were used to explore the driving roles of natural and artificial factors on the changes of ESV and ERI. The combination in ESV and ERI can then provide a more quantitative and accurate basis for policy decisions, identify priority areas for urban ecological restoration, and reduce the risk to ecosystems. The results of the study show that the total ESV of Shenyang city decreased from 273.97 × 108 CNY to 270.38 × 108 CNY during 2000–2020. Although the decrease is not large, the ESV changes structurally with the advancement of urbanization. During the 20 years, the construction land with the lowest ecological service function continues to expand, increasing by 354 km2, the grassland decreased by 215.9 km2, and the arable land decreased by 196.6 km2. The ecological service function of the water area is the strongest, with an increase of 51.3 km2 in the water area, ensuring that there is no significant decline in ESV. The size of the ERI is Very high, High, and Medium value zones remained relatively stable, while the size of the Very Low-value zone decreased by 12.78% and the size of the Low-value zone increased by 13.21%. The interaction factors that contributed most to the changes in ESV and ERI were annual evapotranspiration (EVP)/ Normalized Difference Vegetation Index (NDVI) and Annual sunshine hours (SSD)/ Digital Elevation Model (DEM) , respectively. There was a spatial correlation between ESV and ERI. The areas with the highest ESV supply capacity and at the same time facing severe ecological risks to the landscape pattern are distributed in the northeastern hilly lands. This area should be prioritized to develop planning and control measures to prevent further erosion of forest lands and grasslands and reduce ecological risks. These results provide a theoretical basis for ensuring ecological security and sustainable development in winter cities.

An Improved Buffer-Surfactant System for Human Red Blood Cell Acetylcholinesterase Activity Testing.

An Improved Buffer-Surfactant System for Human Red Blood Cell Acetylcholinesterase Activity Testing.

Biological analysis method based on the relationship between athlete’s PSI rhythm and sports injury

Biorhythm is the law of cyclical changes in individual life activity characteristics as time changes. Biorhythm is an internal law of a biological organism, which comes from the changes that a biological organism is affected by changes in the external environment during natural activities, so its change law is similar to the cycle of the natural environment. Biological rhythms play an important role in mediating human body functions, vital activities, body temperature and other physiological reactions. The balance and stability of biological rhythms can ensure the healthy life activities of the human body. Disrupting the balance of biological rhythms may cause various kinds of human body. In this paper, the research of biological analysis method based on the relationship between athlete’s PSI (Physical, Sensitive, Intellectual) rhythm and sports injury is mainly introduced, and ideas and directions are intended to be provided for analyzing the relationship between athlete’s physical strength, emotion, intelligence and sports injury. This paper proposes a research strategy based on the biological analysis method of athlete’s PSI rhythm and sports injury, including measurement of biological rhythm, literature retrieval method, expert interview method, questionnaire survey method, clustering algorithm-based biological analysis method. A research experiment on the biological analysis method of the relationship between PSI rhythm and sports injury is conducted. The experimental results of this paper indicated that most sports injuries of athletes occur in the critical period of PSI rhythm, with an average incidence of 70.14%. Athletes’ training and competition can be avoided in this critical period as much as possible.

Employing of machine learning and wearable devices in healthcare system: tasks and challenges

AbstractDisease outbreaks are nowadays a critical issue despite the development and rapid growth of technology. One of the major challenges facing healthcare professionals and healthcare industries is disease prevention and control by technology. Due to busy work schedules, maintaining a healthy lifestyle has become impossible, so the solution to these previous challenges is the intelligent health monitoring system. Over the past few years, a lot of research has been done on the use of Machine learning (ML) techniques in healthcare applications. With wearable devices, ML greatly helps in tracking human activities and vital signs as well as helping to monitor and diagnose patients’ health so it plays a huge role in elderly care. Research and development of more applications for wearable devices has been underway in the past five years, due to significant technological advances in medical sensors. Despite the widespread use of wearable devices, there is little research on machine learning applications of these devices. This paper presents a review of the different areas of recent ML research for healthcare wearable devices. It also discusses the different challenges facing ML applications on wearable devices.

Tackling Environmental Problems from the Root: An Ibuanyidanda Perspective

There are series of reports in the media, books, conferences, symposia, et cetera of the deplorable condition of our natural environment. This crisis afflicting the environment occasioned by increasing human activities has reached the level that, the survival of the human species itself is threatened. Urgent remedies, therefore, need to be sought for. Though, many attempts have been made to remedy the situation, unfortunately environmental crisis continues to deepen, because the root problem (defective mindset) has not yet been fully tackled. This article presents an ethic from Asouzu’s Ibuanyidanda philosophy, which sees realities as missing links and argues that this ethic can guide human actions toward environmental sustainability. What could be garnered from Asouzu’s Ibuanynidanda philosophy is that environmental problems could be traced to defective mindsets that human beings are imbued with. Thus, unlike most other environmental ethical theories that aim at controlling human actions towards the environment, Ibuanyidanda theory believes the present environmental crisis could only be resolved if the root cause (the mind) is tackled. This is because, it is better to tackle the root cause of our negative actions (mind) than to concentrate on the actions themselves. When a change of our present divisive and hegemonic mindset, is thoroughly affected as prescribed by Ibuanyidanda philosophy, environmental sustainability instead of degradation would become the byproduct of our development.