RESUMO Na agricultura irrigada, é comum a aplicação de água no solo cultivado sem critério, resultando em uso ineficiente ou não otimizado da água. Em muitos casos, a relação solo-água-planta, bem como as potencialidades e limitações dos sistemas de irrigação são ignoradas. A agricultura irrigada sustentável requer o desenvolvimento de um sistema de monitoramento da umidade do solo que evite a perda de água e melhore a eficiência do uso. O objetivo deste estudo foi desenvolver e avaliar a eficiência de um sistema inteligente de monitoramento da umidade do solo. O sistema é composto por duas estações: a primeira coleta os dados sobre parâmetros de umidade aparente do solo usando sensores, enquanto a segunda transmite esses dados para uma estação de processamento central. O sistema determina precisamente os valores atuais de umidade do solo, permitindo estimar o volume de água de irrigação necessário para atender à demanda hídrica da cultura com base na capacidade de campo. Os resultados da curva de calibração dos sensores indicam que o sistema pode medir com precisão a umidade do solo atual, auxiliando no manejo da irrigação. Para áreas irrigadas em condições de solo não saturado, recomenda-se o uso de tensiômetros devido à sua maior confiabilidade entre a capacidade de campo e o ponto de murcha permanente, garantindo práticas de irrigação mais precisas. critério casos soloáguaplanta, soloáguaplanta planta, planta solo-água-planta ignoradas estações central atual saturado recomendase recomenda se permanente precisas

ABSTRACT Water application to cultivated soil is often done without careful consideration in irrigated agriculture, leading to inefficient or suboptimal water usage. In many instances, the intricate relationship between soil, water, and plants, as well as the potential and limitations of irrigation systems, is overlooked. Sustainable irrigated agriculture necessitates the development of a soil moisture monitoring system that curtails water loss and enhances overall efficiency. This study aimed to develop and assess the efficiency of an intelligent system for monitoring soil moisture. The system comprises two stations: the first collects data on apparent soil moisture parameters using sensors, while the second transmits this data to a central processing station. The system precisely determines current soil moisture values, enabling estimation of the required irrigation water volume to meet the crop’s water demand based on field capacity. Results from the calibration curve of the sensors indicate that the system can measure current soil moisture precisely, aiding in irrigation management. For irrigated areas under unsaturated soil conditions, it is recommended to use tensiometers due to their higher reliability between field capacity and permanent wilting point, ensuring more accurate irrigation practices. usage instances plants systems overlooked stations station values crops crop s management conditions point practices

The concept of digital transformation (DT) has revolutionized different facets of society including institutions of learning, where it has continued to influence teaching, learning and research. Over time and with the continued technological advancements in biological science disciplines, it has become clear that the increased convergence between these disciplines and digital systems application will become more apparent. For example, the advent and application of next-generation sequencing (NGS), machine learning (ML), artificial intelligence (AI) and other automated biological processes have afforded unique huge data sets with high societal impact. As such, digitalization of the bioeconomy remains necessary. The digitalization of the biosciences has birthed emerging concepts like "digital biology", "engineered biology" and "bio-revolution" and involve computer-centered biosciences. These are built around the integration of scientific data, modeling at several levels, as well as networked science. Nonetheless, it is still plagued by information derivation policies and technicality issues. Although, digital biology has seen a major boost in recent years with the emergence of new and updated digital biology databases and platforms for advanced computational modelling, and connection and visualization of bio-data at a molecular level, more efforts are needed from governmental stakeholders (policy makers), industry and academia to enhance the impact of computational automation and digital transformation in bioscience activities. Hence, a steady upscale in digitalization of the biosciences would ensure bioprocess development, increase teaching, research efficiency and productivity, birth more innovative future applications for various industries, and thereby impact several aspects of life. This article presents how selected biological science disciplines have been digitally transformed through data analytics and how the concept could be further explored in advancing teaching, learning and research.

SUMMARY: The rapid advancement of the Internet of Things (IoT) has transformed our lives by connecting objects and creating an intelligent environment that drives automation and massive data collection. IoT has overcome challenges through remote monitoring, robotics, and sensors, with powerful wireless technologies opening up possibilities in various fields, including anatomical techniques. The objective of this study is to demonstrate the feasibility and benefits of integrating IoT technology into the body maceration process by developing a low-cost, accessible, automated maceration control device compatible with theArduino IDE programming environment. Using an open-source NodeMCU system based on the ESP-12E chip, which employs the LUA programming language and is compatible with Arduino IDE, we incorporated a microSD card reader module for data storage, a digital temperature sensor (DS18B20), a clock module (DS3231), a magnetic stirrer, and an aquarium heater controlled by a digital thermostat with relay (W1209). To validate the device, we conducted maceration tests on C57BL mice, comparing two groups: Control (n=5; weight=17g) subjected to maceration in 4 liters of water at 10°C without agitation; Experimental (n=5; weight=17g) exposed to maceration in 4 liters of water at 35°C, with agitation at 1500 r/min and water flow renewal at a rate of 900 ml/h. Specimens were evaluated for their degree of maceration, cleaned, weighed, and photographed. The maceration time for the control and experi- mental groups was 730 hours and 60 hours, respectively. In the experimental group, no foul odors, damage, or bone demineralization were detected. The device cost was 60 USD, and programming through the Arduino IDE environment was straightforward. The focus on hardware contributed to accessibility and time savings. The system operated with a conventional power supply and required a water supply, making it adaptable to various configurations with greater capacity. Under this configuration, we effectively controlled the variables (temperature, agitation, and water flow). The NodeMCU-based bone maceration system improved the performance and adaptability of existing systems and increased accessibility by reducing costs, complexity, and foul odors.

RESUMEN: El rápido avance del Internet de las Cosas (IoT) ha transformado nuestras vidas al conectar objetos y crear un entorno inteligente que impulsa la automatización y la recolección masiva de datos. El IoT ha superado desafíos a través del monitoreo remoto, la robótica y los sensores, con tecnologías inalámbricas potentes que abren posibilidades en diversos campos, incluidas la anatomía y las técnicas anatómicas. El objetivo de este estudio fue demostrar la viabilidad y los beneficios de integrar la tecnología IoT en el proceso de maceración cadavérica mediante el desarrollo de un dispositivo de control de maceración automatizado, accesible y de bajo costo, compatible con el entorno de programación Arduino IDE. Usando un sistema NodeMCU de código abierto basado en el chip ESP-12E, que emplea el lenguaje de programación LUA y es compatible con Arduino IDE, incorporamos un módulo lector de tarjetas microSD para el almacenamiento de datos, un sensor de temperatura digital (DS18B20), un módulo de reloj (DS3231), un agitador magnético y un calentador de acuario controlado por un termostato digital con relé (W1209). Para validar el dispositivo, realizamos pruebas de maceración en ratones C57BL, comparando dos grupos: control (n=5; peso=17g) sometido a maceración en 4 litros de agua a 10°C sin agitación; experimental (n=5; peso=17g) expuesto a maceración en 4 litros de agua a 35°C, con agitación a 1500 r/min y renovación del flujo de agua a una velocidad de 900 ml/h. Los especímenes fueron evaluados según su grado de maceración, limpiados, pesados y fotografiados. El tiempo de maceración para los grupos control y experimental fue de 730 horas y 60 horas, respectivamente. En el grupo experimental no se detectaron malos olores, daños ni desmineralización de los huesos. El costo del dispositivo fue de 60 USD y la programación a través del entorno Arduino IDE fue sencilla. El enfoque en el hardware contribuyó a la accesibilidad y al ahorro de tiempo. El sistema operaba con una fuente de alimentación convencional y requería un suministro de agua, lo que lo hace adaptable a diversas configuraciones con mayor capacidad. Bajo esta configuración, controlamos eficazmente las variables (temperatura, agitación y flujo de agua). El sistema de maceración ósea basado en NodeMCU mejoró el rendimiento y la adaptabilidad de los sistemas existentes, y aumentó la accesibilidad al reducir los costos, la complejidad y los malos olores.

Resumen La automatización de decisiones morales es un tema que suscita profundo interés en la sociedad contemporánea, caracterizada por un creciente uso de la tecnología. Esta investigación se propone analizar los factores cruciales que deben considerarse al establecer límites para la automatización de juicios y acciones con implicaciones éticas. Los investigadores se plantearon la siguiente pregunta central: ¿Qué tipos de decisiones éticas no deberían delegarse a sistemas automatizados y cómo se puede mantener la responsabilidad humana en procesos morales automatizados? El estudio reveló que existen categorías específicas de decisiones éticas que no deberían ser completamente automatizadas. Entre estas se encuentran aquellas que implican dilemas morales complejos, las que afectan directamente la vida humana, o las que requieren un alto grado de empatía y comprensión contextual. Además, los hallazgos subrayan la importancia de mantener la supervisión y responsabilidad humana, incluso en procesos que están parcialmente automatizados. Los investigadores concluyeron que es fundamental alcanzar un equilibrio entre aprovechar la eficiencia que ofrece la automatización y preservar el juicio ético humano. Esto implica el diseño de sistemas capaces de identificar situaciones que requieren intervención humana y el establecimiento de mecanismos claros de rendición de cuentas. En síntesis, el estudio determina que, si bien la automatización presenta ventajas en términos de eficiencia y consistencia, existen límites éticos que deben ser respetados. Las decisiones morales más complejas y con mayor impacto en la vida humana deben permanecer bajo control humano, mientras que la automatización puede aplicarse de forma limitada en procesos más rutinarios, siempre bajo supervisión humana y con mecanismos de responsabilidad claramente definidos.

Abstract The automation of moral decisions is a topic that arouses deep interest in contemporary society, characterized by an increasing use of technology. This research aims to analyze the crucial factors that must be considered when establishing limits for the automation of judgments and actions with ethical implications. The researchers posed the following central question: What types of ethical decisions should not be delegated to automated systems, and how can human responsibility be maintained in automated moral processes? The study revealed that there are specific categories of ethical decisions that should not be fully automated. These include those involving complex moral dilemmas, those directly affecting human life, or those requiring a high degree of empathy and contextual understanding. Furthermore, the findings underscore the importance of maintaining human oversight and responsibility, even in processes that are partially automated. The researchers concluded that it is essential to achieve a balance between leveraging the efficiency offered by automation and preserving human ethical judgment. This involves designing systems capable of identifying situations that require human intervention and establishing clear accountability mechanisms. In summary, the study determines that while automation presents advantages in terms of efficiency and consistency, there are ethical limits that must be respected. The most complex moral decisions with the greatest impact on human life should remain under human control, while automation can be applied in a limited way to more routine processes, always under human supervision and with clearly defined responsibility mechanisms.

Abstract This article explores the implications of incorporating milking systems in the labour practices of dairy workers in southern Chile. The research design was qualitative, exploratory and cross-sectional. The fieldwork consisted of field observations and semi-structured interviews with producers and workers located in the Los Ríos and Los Lagos regions. The main changes observed from the perspective of the producers are transformations in the distribution of time, place of work and administration of agricultural personnel. In the case of workers, changes observed in work routines, tasks and skills, which respectively intensify, change and/or are lost.

Resumen Este artículo explora las implicancias de la incorporación de sistemas de ordeña automatizados en las prácticas laborales de trabajadores de lecherías del sur de Chile, a través de una investigación cualitativa y transversal. El trabajo de campo consistió en observaciones en terreno y entrevistas semiestructuradas a productores y trabajadores ubicados en las regiones de Los Ríos y Los Lagos, de Chile. Los principales cambios observados desde la perspectiva de los productores son transformaciones en la distribución del tiempo, en el lugar de trabajo y en la administración del personal agrícola. En el caso de los trabajadores, se observaron cambios en las rutinas de trabajo, las labores y las habilidades, que respectivamente se intensifican, cambian y/o se pierden. En síntesis, las principales implicancias observadas, para productores y trabajadores, tienen relación con la organización del trabajo en las lecherías, las transformaciones en las habilidades laborales y el sentido y significado del trabajo agrícola luego de su inserción.

Abstract Weeds negatively affect crop yield and quality by competing with the crop for resources. Detecting them in time allows us to optimize control through the precise application of herbicides and reduce their environmental impact. However, its detection and classification are challenging due to the great diversity of species and similarities with crops. Deep learning methods have allowed the development of convolutional neural networks (CNN) for the classification of weed species. However, CNNs require large and varied data sets for training. This study proposes an innovative methodology using CNN, YOLOv8l and RetinaNet classifiers augmented with Stable Diffusion data, implemented artificial images of weeds. Three data sets were configured for training CNNs (real, artificial and mixed) in commercial tomato crops infested by Solanum nigrum L., Portulaca oleracea L. and Setaria verticillata L. The results showed a high degree of agreement with artificial images, reaching a maximum mean Average Precision (mAP) of 0.93 in both networks for the mixed data set. Stable Diffusion made it possible to generate large, high-quality data sets from a limited set of images, reducing the need to obtain a large number of images in the field and their subsequent manual labeling. Furthermore, the method can be adapted to other crops and weed species, thus contributing to the advancement of automated weed management systems.

Resumen Las malas hierbas afectan negativamente el rendimiento y calidad de las cosechas al competir con el cultivo por los recursos. Detectarlas a tiempo permite optimizar el control mediante la aplicación precisa de herbicidas y reducir su impacto ambiental. Sin embargo, su detección y clasificación es un desafío debido a la gran diversidad de especies y similitudes con los cultivos. Los métodos tradicionales de aprendizaje profundo (Deep Learning) han permitido el desarrollo de redes neuronales convolucionales (CNN) para la clasificación de especies de mala hierba. Sin embargo, las CNN requieren amplios y variados conjuntos de datos para su entrenamiento. Este estudio propone una metodología innovadora utilizando clasificadores de CNN, YOLOv8l y RetinaNet aumentados con datos de Stable Diffusion (SD), implementado imágenes de malas hierbas generadas artificialmente. Para ello, se configuraron tres conjuntos de datos para el entrenamiento de las CNNs (real, artificial y mixto) en cultivos de tomate comercial infestados por Solanum nigrum L., Portulaca oleracea L. y Setaria verticillata L. Los resultados mostraron un alto grado de acuerdo con imágenes artificiales, alcanzando un mean Average Precision (mAP) máximo de 0,93 en ambas redes para el conjunto de datos mixto. SD permitió generar grandes conjuntos de datos de alta calidad a partir de un conjunto limitado de imágenes, reduciendo la necesidad de obtener un gran número de imágenes en campo y su posterior etiquetado manual. Además, el método puede adaptarse a otros cultivos y especies de mala hierba, contribuyendo así al avance de los sistemas automatizados de gestión de malas hierbas.

RESUMEN La parálisis cerebral espástica es una enfermedad que abarca un gran porcentaje de la población infantil. Por lo tanto, el objetivo de este estudio es realizar un prototipo de silla de ruedas eléctrica con capacidad de control manual y electroencefalográfico. Con este fin, se abre la posibilidad de brindar autonomía y capacidad de desplazamiento a este grupo de pacientes. La simulación fue construida en el entorno de Webots y conectada con el GUI de Matlab mediante los Protocolos de Transmisión de Internet; usando los lenguajes de programación Python, Matlab y C# fue posible simular el desplazamiento, posibilidad de comunicación con luces y sonido. Además, fueron evaluados los materiales potenciales de construcción tanto en costos como el análisis estático.

ABSTRACT Spastic cerebral palsy is a disease that covers a wide range of the child population. Therefore, this study aims to make a prototype electric wheelchair with manual and electroencephalographic control capabilities. The possibility of providing autonomy and mobility to this group of patients is open. The simulation was built in the Webots environment and connected to the Matlab GUI through the Internet Transmission Protocols; using the programming languages Phyton, Matlab, and C#, it was possible to simulate the displacement and possibility of communication with lights and sound. In addition, potential construction materials were evaluated both in cost and static analysis.

BACKGROUND: The 2013-2016 West Africa Ebola outbreak highlighted the importance of laboratory capacity to outbreak response while also revealing its long-standing neglect. The outbreak prompted massive international investment into strengthening laboratory services across multiple healthcare settings OBJECTIVE: In this article, we explore hospital-based clinical laboratory workers' experiences and perceptions of their everyday working environment in Sierra Leone, and how recent investments in laboratory strengthening have shaped these METHODS: This qualitative study draws on in-depth interviews with eight laboratory workers and participant observation of laboratory practices at a tertiary referral hospital in Freetown between April 2019 and December 2019. Interview and observational data were coded and analysed using a reflexive thematic approach RESULTS: The Ebola outbreak prompted international investments in automated devices, biosafety training, and a new dedicated infectious diseases laboratory. However, little investment was made in the infrastructure and supply systems needed to sustain routine laboratory work or keep machines functioning. Laboratory workers perceived their work to be under-recognised and undervalued by the government, hospital managers and clinical staff, a perception compounded by under-use of the hospital's laboratory services by clinicians CONCLUSION: Understanding laboratory technicians' views, experiences, and priorities is essential to any sustainable laboratory-strengthening effort. Investments in personnel should match investments in technologies and infrastructure for outbreak response What this study adds: This study contributes to an understanding of how clinical laboratory personnel in Sierra Leone view and experience their work, and introduces the concept of social invisibility to explain these experiences.

ABSTRACT Purpose: Amid rising health awareness, natural products which has milder effects than medical drugs are becoming popular. However, only few systems can quantitatively assess their impact on living organisms. Therefore, we developed a deep-learning system to automate the counting of cells in a gerbil model, aiming to assess a natural product’s effectiveness against ischemia. Methods: The image acquired from paraffin blocks containing gerbil brains was analyzed by a deep-learning model (fine-tuned Detectron2). Results: The counting system achieved a 79%-positive predictive value and 85%-sensitivity when visual judgment by an expert was used as ground truth. Conclusions: Our system evaluated hydrogen water’s potential against ischemia and found it potentially useful, which is consistent with expert assessment. Due to natural product’s milder effects, large data sets are needed for evaluation, making manual measurement labor-intensive. Hence, our system offers a promising new approach for evaluating natural products. Purpose awareness popular However organisms Therefore deeplearning deep learning product s Methods finetuned fine tuned Detectron2. Detectron2 Detectron . Detectron2) Results 79%positive 79positive positive 79% 79 85%sensitivity 85sensitivity sensitivity 85% 85 truth Conclusions waters water useful assessment evaluation laborintensive. laborintensive labor intensive. intensive labor-intensive Hence 7 8

Abstract Under two detection schemes, this study analyzes one of the most destructive weather systems - the explosive cyclones - in the South Atlantic, from 2010 to 2020. Then, two methods are presented to study these systems: the Observational Method (OBSM) and the Automated Method (AUTM). The first uses visual analysis of the mean sea level pressure (mslp) fields and functions to identify the local minimums using the Grid Analysis and Display (GrADS) software. The second utilizes a function from OpenGrADS called mfhilo. It shows the local minimum in the grid using laplacian, magnitude, and percentile. Two shell algorithms for data manipulation are used for the AUTM: one to trace the cyclones’ trajectories according to a previously defined fixed area and the other to separate them into explosives. The OBSM methodology showed 271 cases averaging 25 yearly and revealed important characteristics regarding the intensities. According to AUTM’s methodology, from the 2705 ordinary cyclone cases identified, 299 are explosives. There is a clear seasonality pattern in the systems’ distribution along South America, similar to OBSM, but more highlighted. In summer, they concentrate at high latitudes, while in winter and spring, they are assembled near southern Brazilian and Uruguayan coasts. schemes Atlantic 201 2020 Then (OBSM AUTM. AUTM . (AUTM) mslp (mslp GrADS (GrADS software mfhilo laplacian magnitude percentile explosives 27 2 intensities AUTMs s 270 identified 29 America highlighted summer latitudes spring coasts 20 202 (AUTM

Abstract This study focused on the use of MRI imaging techniques and artificial ligaments in diagnosing and reconstructing Cruciate Ligament (CL) injuries of the knee joint. CL injuries are common in athletes and can cause significant pain and disability. MRI imaging is a powerful diagnostic tool that accurately identifies the extent and location of the injury. The use of Artificial ligaments has been increasing in the surgical reconstruction of the ligament. In this study, we offer a deep learning (DL)-a based method called ThreeFold CNN for arthroscopically detecting injuries to the CL in the knee during MRI. The CL on MRI was first separated using two deep convolutional neural networks (2DCNNs). Then structural problems within the isolated ligament were detected using a classification CNN to create a completely automated DL-based diagnostic method. We employ the Satin Bowerbird Optimizer (SBO) method for accelerating the performance of the model. The CL injury diagnosis system's specificity and sensitivity are 97 and 97, respectively, at the optimum threshold. In contrast, the clinical radiologists' specificity varied between 0.91 and 0.99, while their sensitivity was between 0.97 and 0.99. The diagnostic performance of the CL injury diagnosis system and CR did not differ in a statistically significant manner at P < .05. The CL injury diagnosis system's area under the ROC curve was 98.5, which indicates outstanding overall diagnostic accuracy. (CL joint disability DLa DL 2DCNNs. 2DCNNs DCNNs . (2DCNNs) DLbased SBO (SBO model systems s 9 respectively threshold contrast radiologists 091 0 91 0.9 099 99 0.99 097 05 .05 985 98 5 98.5 accuracy (2DCNNs 09 0. .0 98.

ABSTRACT Optimizing pilot-automation collaboration necessitates early safety assessment of the aircraft, which involves a rigorous examination of potential scenarios. This article seeks to establish a comprehensive framework for safety analysis, exploring various levels of automation (LoA) within high-technology projects such as electric vertical takeoff and landing (eVTOL) aircraft. The method is divided into three phases: the first involves defining the operations concept; the second involves applying the systems theoretic process analysis (STPA) method; and the third encompasses determining the safest LoA. Furthermore, this article scrutinizes the landing operations of eVTOL aircraft in urban centers. The identified control actions (CAs) include flight monitoring, landing verification, speed selection, and maneuvering. Ultimately, a LoA categorized as automated decision making emerged as the safest. This entails the concurrent monitoring and generation of alternatives by both the pilot and the autopilot, with the final selection of the optimal alternative and its subsequent implementation exclusively entrusted to the autopilot. This work contributes by presenting evidence that safety analysis should commence at the level of individual CAs, rather than at a higher level encompassing complete operations or the entire aircraft. This approach aims to generate comprehensive, practical, and effective safety requirements. pilotautomation scenarios (LoA hightechnology high technology (eVTOL phases concept STPA (STPA Furthermore centers CAs (CAs verification maneuvering Ultimately autopilot practical requirements

Abstract In the realm of modern cardiology, the integration of computer-interpreted electrocardiograms (CI-ECGs) has marked the beginning of a new era of diagnostic precision and efficiency. Contemporary electrocardiogram (ECG) integration systems, applying algorithms and artificial intelligence, have modernized the interpretation of heart rhythms and cardiac morphology. Due to their ability to rapidly analyze and interpret ECG recordings CI-ECGs have already profoundly impacted clinical practice. This review explores the evolution of computer interpreted ECG technology, evaluates the pros and cons of current automatic reporting systems, analyzes the growing role of artificial intelligence on ECG interpretation technologies, and discusses emerging applications that may have transformative effects on patient outcomes. Emphasis is placed on the role of ECGs in the automatic diagnosis of occlusion myocardial infarctions (OMI). AI models enhance accuracy and efficiency in ECG interpretation, offering insights into cardiac function and aiding timely detection of concerning patterns for accurate clinical diagnoses. The shift to AI-driven diagnostics has emphasized the importance of data in the realm of cardiology by improving patient care. The integration of novel AI models in ECG analysis has created a promising future for ECG diagnostics through a synergistic fusion of feature-based machine learning models, deep learning approaches, and clinical acumen. Overall, CI-ECGs have transformed cardiology practice, offering rapid, accurate, and standardized analyses. These systems reduce interpretation time significantly, allowing for quick identification of abnormalities. However, sole reliance on automated interpretations may overlook nuanced findings, risking diagnostic errors. Therefore, a balanced approach in integrating automated analysis with clinical judgment is necessary. computerinterpreted CIECGs CI (CI-ECGs (ECG morphology practice technology technologies outcomes OMI. OMI . (OMI) diagnoses AIdriven driven care featurebased feature based approaches acumen Overall rapid analyses significantly abnormalities However findings errors Therefore necessary (OMI

ABSTRACT In hydrological modeling and for the development of projects related to soil and water conservation and civil engineering, determination of water infiltration variables into the soil plays a key role. However, measuring infiltration with manual infiltrometers is work-intensive and requires several people, which casts doubts on the consistency of the process description. Our objective was to develop automated and compact systems for data acquisition by double-concentric-ring and Cornell infiltrometers. The systems are based on air differential pressure sensors and microcontrollers using open-source software and a simple construction. We developed ten sets of automatic infiltrometers for each method that were properly calibrated. The equipment was validated in the field, and the alignment of the automatically measured with the hand-measured infiltration data was considered satisfactory. The proposed systems make data records of infiltration and associated variables possible, with less operator dependence than manual measurement strategies. In addition, the enhanced resolution resulting from infiltrometer automation makes the infiltration curve more representative, especially in the initial infiltration stage. engineering role However workintensive work intensive people description doubleconcentricring double concentric ring opensource open source construction calibrated field handmeasured hand satisfactory possible strategies addition representative stage

BACKGROUND: The Library Management System (LMS) is crucial to the operations and performance of the library. Libraries around the world have automated their operations to provide relevant services to users and to move away from operating manually. OBJECTIVES: The purpose of the study was to examine the impact of LMS on the provision of information in libraries of the local municipality of Rustenburg. METHOD: This was a quantitative study that examined the impact of LMS on information provision in libraries of the Rustenburg local municipality. The survey research design was adopted and data was collected using questionnaires. RESULTS: The findings revealed that Rustenburg libraries depend highly on manual systems, which is a sign that they do not have adequate information and communication technologies. The few libraries that have online systems used them for circulation and are not maximising the full functions of the library. CONCLUSION: The study concludes that the local municipality of Rustenburg should provide funding to acquire information and communication technologies. Furthermore, it should adopt a library system in all libraries and train staff in all modules. CONTRIBUTION: The LMS's are crucial in the operations and performance of the library. Similar studies focused on the type of management systems available for use and how they can be managed. Having said that, most third world countries are still using manual systems, therefore this study takes the discussion further by looking at the impact that the lack of systems in the third world countries have on information provision in libraries.