Evaluation of a low-cost portable NIRS device for monitoring muscle ischemia.

The main objective of this study is to evaluate the low-cost, open-source HEGduino device as a tissue oximetry monitor to advance the research of somatic NIRS monitoring. Specifically, this study analyzes the use of this portable functional NIRS system for detecting the cessation of blood flow due to vascular occlusion in an upper limb. 19 healthy patients aged between 25 and 50 were recruited and monitored using HEGduino device. Participants underwent a vascular occlusion test on one forearm. Raw values collected by HEGduino as well as the processed variables derived from the measurements were registered. Additional variables to characterize the signal noise during the tests were also recorded. The results of the data distribution curves for all the subjects in the study accurately detected the physiological events associated with transient tissue ischemia. The statistical analysis of the recorded data showed that the difference between the baseline values recorded by the red led (RED) and its normalized minimum variable was always different from zero (p < 0.014). Furthermore, the difference between the normalized baseline values recorded by the infrared led (IR) and the corresponding normalized minimum value was also different from zero (p < 0.001). The R-squared coefficient of determination for the noise variables considered in this study on the normalized RED and IR values was 0.08 and 0.105, respectively. The study confirms the potential of HEGduino system to detect an interruption of the blood flow by means of variations in regional tissue oxygen saturation. This study demonstrates the potential of the HEGduino device as a monitoring alternative to advance the study of the applicability of NIRS in muscle tissue oximetry.

A Comprehensive Review on Manufacturing and Characterization of Polyetheretherketone Polymers for Dental Implant Applications.

Aging, tooth trauma, and pathological infections cause partial or total tooth loss, leading to the usage of dental implants for restoration treatments. As such, mechanical and tribological properties play an important role in the osseointegration and durability of these implants. Metallic and ceramic implants are shown to have mechanical properties much higher than the natural teeth structure, leading to stress shielding-related failure of an implant. Stress shielding occurs due to the difference in the elastic modulus between the implant material and the surrounding teeth structure, leading to bone loss and implant failure. The implant's properties (i.e., mechanical) should be as close as human teeth components. To achieve this, various materials and coatings are being developed and investigated. This review is a comprehensive survey of materials, manufacturing, coating techniques, and mechanical and tribological characterizations of dental implants, with a particular focus on polyetheretherketone (PEEK) as a potential alternative dental implant material. PEEK has mechanical properties similar to natural teeth, which make it a promising material for dental implants. The findings of this review suggest that PEEK offers superior biocompatibility, osseointegration, and wear resistance for implant applications. With the help of bioactive coatings, bone growth on the implant surface can be promoted. In addition, PEEK dental implants made using three-dimensional (3D) printing technology can significantly reduce the cost of implants, making them more affordable and increasing access to dental care, which can improve oral health significantly. In summary, this review highlights the potential of PEEK as a promising alternative dental implant material, and provides an overview of various techniques, testing, and future directions for PEEK dental implants.

Energy-aware bio-inspired spiking reinforcement learning system architecture for real-time autonomous edge applications.

Mobile, low-cost, and energy-aware operation of Artificial Intelligence (AI) computations in smart circuits and autonomous robots will play an important role in the next industrial leap in intelligent automation and assistive devices. Neuromorphic hardware with spiking neural network (SNN) architecture utilizes insights from biological phenomena to offer encouraging solutions. Previous studies have proposed reinforcement learning (RL) models for SNN responses in the rat hippocampus to an environment where rewards depend on the context. The scale of these models matches the scope and capacity of small embedded systems in the framework of Internet-of-Bodies (IoB), autonomous sensor nodes, and other edge applications. Addressing energy-efficient artificial learning problems in such systems enables smart micro-systems with edge intelligence. A novel bio-inspired RL system architecture is presented in this work, leading to significant energy consumption benefits without foregoing real-time autonomous processing and accuracy requirements of the context-dependent task. The hardware architecture successfully models features analogous to synaptic tagging, changes in the exploration schemes, synapse saturation, and spatially localized task-based activation observed in the brain. The design has been synthesized, simulated, and tested on Intel MAX10 Field-Programmable Gate Array (FPGA). The problem-based bio-inspired approach to SNN edge architectural design results in 25X reduction in average power compared to the state-of-the-art for a test with real-time context learning and 30 trials. Furthermore, 940x lower energy consumption is achieved due to improvement in the execution time.

Low-Cost, Scalable Simulations in Obstetric Trauma and Resuscitative Hysterotomy for Emergency Medicine Residents.

Introduction: Simulation-based learning is essential for health care providers to prepare for rare obstetric emergencies, such as severe trauma and maternal cardiac arrest. These situations demand rapid and prompt actions, often testing the skill of emergency physicians. Resuscitative hysterotomy (RH), a critical procedure in maternal cardiac arrest, requires technical expertise, coordination, and anatomical knowledge. The high cost of commercial trainers and complex existing models restricts accessibility. This resource introduces a low-cost anatomically accurate RH task trainer and assesses its effectiveness in improving skills and confidence among trainee emergency physicians. Methods: A 20-minute-long case scenario depicted the resuscitation of a pregnant trauma patient with tension pneumothorax and uterine rupture, culminating in maternal cardiac arrest necessitating RH. Residents performed RH on the task trainer under faculty guidance. Feedback followed the Pendleton model, and an online questionnaire gauged the residents' experiences. Results: Thirty emergency medicine residents participated in the simulation. The questionnaire revealed positive responses, confirming the session's relevance and enhancement of clinical skills and confidence. Discussion: Our results underscore the RH task trainer's critical role in improving residents' skills and confidence during obstetric trauma simulations. Its realism and effectiveness were notably well received. Future refinements aim to augment fidelity while preserving affordability and integrating regular reinforcement sessions. This innovative educational approach equips health care professionals to respond adeptly to rare and challenging obstetric emergencies, ultimately elevating outcomes for mothers and infants during critical situations.

Highly stable scalable production of porous graphene-polydopamine nanocomposites for drug molecule sensing.

As atenolol overdosing can lead to severe health complications, the rapid detection of atenolol intake in point-of-care settings is highly desirable. The recent advancement of redox analytical methodologies has facilitated the efficacious quantification of these compounds for drug analysis, but their performance still presents challenges in practical applications. This study addresses these challenges by controlling the electropolymerization of polydopamine (PDA) on highly porous laser-induced graphene (LIG) electrodes with enhanced electrochemical redox activity for the detection of drug molecules such as atenolol, with minimized interference with the other active substances to induce variation of electrochemical behavior. The enhanced sensitivity of atenolol is attributed to the superhydrophilicity and increased number of active surface sites and -NH2 groups in the PDA polymer through a controlled polymerization process. Moreover, the simulation results further reveal that highly sensitive sensing of atenolol molecules relies on optimal adsorption of the atenolol molecule on dopamine or dopaminequinone structural units. The resulting sensors with high repeatability and reproducibility can achieve a low detection limit of 80 µM and a sensitivity of 0.020 ± 0.04 µA/µM within a linear range from 100 to 800 µM. The materials and surface chemistry in the electrode design based on highly porous LIG provide insights into the integration and application of future scalable and cost-effective electrochemical sensors for use in point-of-care or in-field applications.

Design of a low-cost force insoles to estimate ground reaction forces during human gait.

This paper proposes a low-cost electronic system for estimating ground reaction forces (GRF) during human gait. The device consists of one master node and two slave nodes. The master node sends instructions to slave nodes that sample and store data from two force insoles located at the participant's feet. These insoles are equipped with 14 piezo-resistive FlexiForce A301 sensors (FSR). The slave nodes are attached to the ankles and feet of each participant. Subsequently, the start command is transmitted through the master node, which is connected to the USB port of a personal computer (PC). Once the walking session is completed, the information obtained by the slave nodes can be downloaded by accessing the access point generated by these devices through Wi-Fi communication. The GRF estimation system was validated with force platforms (BTS Bioengineering P6000, Italy), giving on average a fit measure equal to 68 . 71 % ± 4 . 80 % in dynamic situations. Future versions of this device are expected to increase this fit by using machine learning models.

Low-cost fabrication methods of ZnO nanorods and their physical and photoelectrochemical properties for optoelectronic applications.

Zinc Oxide (ZnO) nanorods have great potential in several applications including gas sensors, light-emitting diodes, and solar cells because of their unique properties. Here, three low cost and ecofriendly techniques were used to produce ZnO nanorods on FTO substrates: hydrothermal, chemical bath deposition (CBD), and electrochemical deposition (ECD). This study explores the impact of such methods on the optical, structural, electrical, morphological, and photoelectrochemical properties of nanorods using various measurements. XRD analysis confirmed the hexagonal wurtzite structure of ZnO nanorods in all three methods, with hydrothermal showing a preferred orientation (002) and CBD and ECD samples showing multiple growth directions, with average particle sizes of 31 nm, 34 nm, and 33 nm, respectively. Raman spectra revealed hexagonal Wurtzite structure of ZnO, with hydrothermal method exhibiting higher E2 (high) peak at 438 cm-1 than CBD and ECD methods. SEM results revealed hexagonal ZnO nanorods became more regular and thicker for the hydrothermal method, while CBD and ECD led to less uniform with voids. UV-vis spectra showed absorption lines between 390 nm and 360 nm. Optical bandgap energies were calculated as 3.32 eV, 3.22 eV, and 3.23 eV for hydrothermal, CBD, and ECD samples, respectively. PL spectra revealed UV emission band with a small intensity peak around 389 nm and visible emission peaks at 580 nm. Temperature dependent PL measurements for ZnO nanorods indicated that the intensities ratio between bound exciton and free exciton decreases with temperature increases for the three methods. Photocurrent measurements revealed ZnO nanorod films as n-type semiconductors, with photocurrent values of 2.25 µA, 0.28 µA, and 0.3 µA for hydrothermal, CBD, and ECD samples, and photosensitivity values of 8.01, 2.79, and 3.56 respectively. Our results suggest that the hydrothermal method is the most effective approach for fabricating high-quality ZnO nanorods for optoelectronic applications.

Costs and benefits of allomaternal care to mothers and others in wild Phayre's leaf monkeys.

OBJECTIVES: Allomaternal care (AMC) is suggested to be energetically beneficial to mothers and costly to allomothers. However, among primates, AMC is a heterogeneous phenomenon and its implications are less clear especially in female dispersal species. Here, we investigated infant care in a female dispersal species, Phayre's leaf monkeys (Trachypithecus phayrei crepusculus), to evaluate whether mothers were constrained by infant care and benefitted energetically from AMC, whether AMC was energetically costly for allomothers and how maternal experience was associated with AMC. MATERIALS AND METHODS: Data were collected via instantaneous focal animal sampling between 2004 and 2005 for juvenile and adult females (N = 18) from two groups at the Phu Khieo Wildlife Sanctuary, Thailand (440 h). We used generalized linear mixed models to determine how infant care during the first month after birth affected the time mothers and allomothers spent feeding, socializing, resting, and locomoting and how AMC varied. RESULTS: In the first month, infants spent 26% of their time with an allomother. We found no differences in mothers' overall activity before versus after birth, although mothers fed significantly more and rested less when without their infant. Allomothers fed and rested less when with an infant. AMC varied between 0.0% and 20.5%, with immature females being most active. DISCUSSION: Mothers appear to benefit energetically from AMC such that their overall activity after birth remained unchanged. Costs and benefits for allomothers seem to be variable. Some very active immature females may be benefitting from learning-to-mother. The overall low cost of AMC may facilitate a reciprocal social network among unrelated females.

Low-Cost Classroom and Laboratory Exercises for Investigating Both Wave and Event-Related Electroencephalogram Potentials.

Electroencephalography (EEG) has given rise to a myriad of new discoveries over the last 90 years. EEG is a noninvasive technique that has revealed insights into the spatial and temporal processing of brain activity over many neuroscience disciplines, including sensory, motor, sleep, and memory formation. Most undergraduate students, however, lack laboratory access to EEG recording equipment or the skills to perform an experiment independently. Here, we provide easy-to-follow instructions to measure both wave and event-related EEG potentials using a portable, low-cost amplifier (Backyard Brains, Ann Arbor, MI) that connects to smartphones and PCs, independent of their operating system. Using open-source software (SpikeRecorder) and analysis tools (Python, Google Colaboratory), we demonstrate tractable and robust laboratory exercises for students to gain insights into the scientific method and discover multidisciplinary neuroscience research. We developed 2 laboratory exercises and ran them on participants within our research lab (N = 17, development group). In our first protocol, we analyzed power differences in the alpha band (8-13 Hz) when participants alternated between eyes open and eyes closed states (n = 137 transitions). We could robustly see an increase of over 50% in 59 (43%) of our sessions, suggesting this would make a reliable introductory experiment. Next, we describe an exercise that uses a SpikerBox to evoke an event-related potential (ERP) during an auditory oddball task. This experiment measures the average EEG potential elicited during an auditory presentation of either a highly predictable ("standard") or low-probability ("oddball") tone. Across all sessions in the development group (n=81), we found that 64% (n=52) showed a significant peak in the standard response window for P300 with an average peak latency of 442ms. Finally, we tested the auditory oddball task in a university classroom setting. In 66% of the sessions (n=30), a clear P300 was shown, and these signals were significantly above chance when compared to a Monte Carlo simulation. These laboratory exercises cover the two methods of analysis (frequency power and ERP), which are routinely used in neurology diagnostics, brain-machine interfaces, and neurofeedback therapy. Arming students with these methods and analysis techniques will enable them to investigate this laboratory exercise's variants or test their own hypotheses.

Subdural drain with Nelaton catheter and latex glove, a low-cost alternative and how to make it.

Background: One of the most commonly encountered surgical pathologies in neurosurgical practice worldwide is subdural hematoma. The use of prefabricated drains following surgical procedures is widely recommended. However, their availability can be inconsistent due to various issues. Methods: An intensive search was conducted regarding the availability and cost of subdural drains. The Medtronic subdural evacuating port system costs between 100 and 150 USD, the Blake drain costs between 35 and 40 USD, and the Jackson-Pratt drain costs between 25 and 35 USD. We present a low-cost alternative and describe how it can be implemented using materials available in almost every hospital. Results: A simple step-by-step guide for crafting handmade subdural drains has been created, aiming to make this affordable alternative accessible to every surgeon who may need one due to the unavailability of prefabricated drains in developing countries. Conclusion: The benefits associated with using a subdural drain during the evacuation of subdural hematomas are well-documented. In cases where prefabricated drains are not available, a handmade alternative can always be utilized. Materials are often readily available in every hospital, and the cost may not exceed 100 MXN (5 USD), making it at least 5 times cheaper than the cheapest prefabricated alternative. This solution is particularly beneficial for developing countries without access to prefabricated drains.

Simultaneous Regulation of Organic and Inorganic Components in Interphase by Fiber Separator for High-stable Sodium Metal Batteries.

Uncontrollable side reactions at the metal interface have been identified as the root cause of the formation of a fragile solid electrolyte interphase, leading to irreversible sodium loss in sodium metal batteries. Here, we proposed an interface engineering strategy that employed a carboxyl functionalized cellulose separator to provide strong dipole moments and induce the cleavage of P-F bond to construct a SEI rich in NaF. In addition, we employed nuclear magnetic resonance technology confirmed that the separator with strong dipole moments prevented the reduction of organic solvents by attracting electrons, thereby inhibiting the formation of organic oligomers. SEI with high NaF content and few oligomers is smooth and robust, obviously decreasing the interface impedance of the Na anode. The symmetric Na||Na cells, equipped with the functionalized separator, efficiently operated for 1400 hours with a stable 72 mV overpotential at 0.25 mA cm-2, exhibiting low energy barrier and fast ion transport kinetics. The Na||Na3V2(PO4)3 cell also showed stable cycling performance, with the capacity remaining at 94.83% of the initial capacity after 1000 cycles at 1C. The proposed separator could control the formation and composition of SEI, paving the way for the development of long-life sodium metal batteries.

First in-Lab Testing of a Cost-Effective Prototype for PM2.5 Monitoring: The P.ALP Assessment.

The goal of the present research was to assess, under controlled laboratory conditions, the accuracy and precision of a prototype device (named 'P.ALP': Ph.D. Air-quality Low-cost Project) developed for PM2.5 concentration level monitoring. Indeed, this study follows a complementary manuscript (previously published) focusing on the in-field evaluation of the device's performance. Four P.ALP prototypes were co-located with the reference instrument in a calm-air aerosol chamber at the NIOSH laboratories in Pittsburgh, PA (USA), used by the Center for Direct Reading and Sensor Technologies. The devices were tested for 10 monitoring days under several exposure conditions. To evaluate the performance of the prototypes, different approaches were employed. After the data from the devices were stored and prepared for analysis, to assess the accuracy (comparing the reference instrument with the prototypes) and the precision (comparing all the possible pairs of devices) of the P.ALPs, linear regression analysis was performed. Moreover, to find out the applicability field of this device, the US EPA's suggested criteria were adopted, and to assess error trends of the prototype in the process of data acquisition, Bland-Altman plots were built. The findings show that, by introducing ad hoc calibration factors, the P.ALP's performance needs to be further implemented, but the device can monitor the concentration trend variations with satisfying accuracy. Overall, the P.ALP can be involved in and adapted to a wide range of applications because of the inexpensive nature of the components, the small dimensions, and the high data storage capacity.

Wear Prediction of Functionally Graded Composites Using Machine Learning.

This study focuses on the production of functionally graded composites by utilizing magnesium matrix waste chips and cost-effective eggshell reinforcements through centrifugal casting. The wear behavior of the produced samples was thoroughly examined, considering a range of loads (5 N to 35 N), sliding speeds (0.5 m/s to 3.5 m/s), and sliding distances (500 m to 3500 m). The worn surfaces were carefully analyzed to gain insights into the underlying wear mechanisms. The results indicated successful eggshell particle integration in graded levels within the composite, enhancing hardness and wear resistance. In the outer zone, there was a 25.26% increase in hardness over the inner zone due to the particle gradient, with wear resistance improving by 19.8% compared to the inner zone. To predict the wear behavior, four distinct machine learning algorithms were employed, and their performance was compared using a limited dataset obtained from various test operations. The tree-based machine learning model surpassed the deep neural-based models in predicting the wear rate among the developed models. These models provide a fast and effective way to evaluate functionally graded magnesium composites reinforced with eggshell particles for specific applications, potentially decreasing the need for extensive additional tests. Notably, the LightGBM model exhibited the highest accuracy in predicting the testing set across the three zones. Finally, the study findings highlighted the viability of employing magnesium waste chips and eggshell particles in crafting functionally graded composites. This approach not only minimizes environmental impact through material repurposing but also offers a cost-effective means of utilizing these resources in creating functionally graded composites for automotive components that demand varying hardness and wear resistance properties across their surfaces, from outer to inner regions.

Recycling of Sewage Sludge: Synthesis and Application of Sludge-Based Activated Carbon in the Efficient Removal of Cadmium (II) and Lead (II) from Wastewater.

The limited supply of drinking water has aroused people's curiosity in recent decades. Adsorption is a popular method for removing hazardous substances from wastewater, especially heavy metals, as it is cheap, highly efficient, and easy to use. In this work, a new sludge-based activated carbon adsorbent (thickened samples SBAC1 and un-thickened samples SBAC2) was developed to remove hazardous metals such as cadmium (Cd+2) and lead (Pb+2) from an aqueous solution. The chemical structure and surface morphology of the produced SBAC1 and SBAC2 were investigated using a range of analytical tools such as CHNS, BET, FT-IR, XRD, XRF, SEM, TEM, N2 adsorption/desorption isothermal, and zeta potential. BET surface areas were examined and SBAC2 was found to have a larger BET surface area (498.386 m2/g) than SBAC1 (336.339 m2/g). While the average pore size was 10-100 nm for SBAC1 and 45-50 nm for SBAC2. SBAC1 and SBAC2 eliminated approximately 99.99% of Cd+2 and Pb+2 out the water under all conditions tested. The results of the adsorption of Cd+2 and Pb+2 were in good agreement with the pseudo-second-order equation (R2 = 1.00). Under the experimental conditions, the Cd+2 and Pb+2 adsorption equilibrium data were effectively linked to the Langmuir and Freundlich equations for SBAC1 and SBAC2, respectively. The regeneration showed a high recyclability for the fabricated SBAC1 and SBAC2 during five consecutive reuse cycles. As a result, the produced SBAC1 and SBAC2 are attractive adsorbents for the elimination of heavy metals from various environmental and industrial wastewater samples.

Assessment of Low-Cost and Higher-End Soil Moisture Sensors across Various Moisture Ranges and Soil Textures.

The accuracy and unit cost of sensors are important factors for a continuous soil moisture monitoring system. This study compares the accuracy of four soil moisture sensors differing in unit costs in coarse-, fine-, and medium-textured soils. The sensor outputs were recorded for the VWC, ranging from 0% to 50%. Low-cost capacitive and resistive sensors were evaluated with and without the external 16-bit analog-to-digital converter ADS1115 to improve their performances without adding much cost. Without ADS1115, using only Arduino's built-in analog-to-digital converter, the low-cost sensors had a maximum RMSE of 4.79% (v/v) for resistive sensors and 3.78% for capacitive sensors in medium-textured soil. The addition of ADS1115 showed improved performance of the low-cost sensors, with a maximum RMSE of 2.64% for resistive sensors and 1.87% for capacitive sensors. The higher-end sensors had an RMSE of up to 1.8% for VH400 and up to 0.95% for the 5TM sensor. The RMSE differences between higher-end and low-cost sensors with the use of ADS1115 were not statistically significant.

Gas Detection and Classification Using Multimodal Data Based on Federated Learning.

The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, early detection and gas type identification are necessary. The main focus of this paper is multimodal gas data that were obtained simultaneously by using multiple sensors for gas detection and a thermal imaging camera. As the reliability and sensitivity of low-cost sensors are less, they are not suitable for gas detection over long distances. In order to overcome the drawbacks of relying just on sensors to identify gases, a thermal camera capable of detecting temperature changes is also used in the collection of the current multimodal dataset The multimodal dataset comprises 6400 samples, including smoke, perfume, a combination of both, and neutral environments. In this paper, convolutional neural networks (CNNs) are trained on thermal image data, utilizing variants such as bidirectional long-short-term memory (Bi-LSTM), dense LSTM, and a fusion of both datasets to effectively classify comma separated value (CSV) data from gas sensors. The dataset can be used as a valuable source for research scholars and system developers to improvise their artificial intelligence (AI) models used for gas leakage detection. Furthermore, in order to ensure the privacy of the client's data, this paper explores the implementation of federated learning for privacy-protected gas leakage classification, demonstrating comparable accuracy to traditional deep learning approaches.

Laboratory Tests of Metrological Characteristics of a Non-Repetitive Low-Cost Mobile Handheld Laser Scanner.

The popularity of mobile laser scanning systems as a surveying tool is growing among construction contractors, architects, land surveyors, and urban planners. The user-friendliness and rapid capture of precise and complete data on places and objects make them serious competitors for traditional surveying approaches. Considering the low cost and constantly improving availability of Mobile Laser Scanning (MLS), mainly handheld surveying tools, the measurement possibilities seem unlimited. We conducted a comprehensive investigation into the quality and accuracy of a point cloud generated by a recently marketed low-cost mobile surveying system, the MandEye MLS. The purpose of the study is to conduct exhaustive laboratory tests to determine the actual metrological characteristics of the device. The test facility was the surveying laboratory of the University of Agriculture in Kraków. The results of the MLS measurements (dynamic and static) were juxtaposed with a reference base, a geometric system of reference points in the laboratory, and in relation to a reference point cloud from a higher-class laser scanner: Leica ScanStation P40 TLS. The Authors verified the geometry of the point cloud, technical parameters, and data structure, as well as whether it can be used for surveying and mapping objects by assessing the point cloud density, noise and measurement errors, and detectability of objects in the cloud.

Reliability of Real-Time Kinematic (RTK) Positioning for Low-Cost Drones' Navigation across Global Navigation Satellite System (GNSS) Critical Environments.

UAVs are nowadays used for several surveying activities, some of which imply flying close to tall walls, in and out of tunnels, under bridges, and so forth. In these applications, RTK GNSS positioning delivers results with very variable quality. It allows for centimetric-level kinematic navigation in real time in ideal conditions, but limitations in sky visibility or strong multipath effects negatively impact the positioning quality. This paper aims at assessing the RTK positioning limitations for lightweight and low-cost drones carrying cheap GNSS modules when used to fly in some meaningful critical operational conditions. Three demanding scenarios have been set up simulating the trajectories of drones in tasks such as infrastructure (i.e., building or bridges) inspection. Different outage durations, flight dynamics, and obstacle sizes have been considered in this work to have a complete overview of the positioning quality. The performed tests have allowed us to define practical recommendations to safely fly drones in potentially critical environments just by considering common software and standard GNSS parameters.

Environmental injustice in the air quality for digital platform delivery workers in Bogotá, Colombia, 2021 / Injusticia ambiental en la calidad del aire para repartidores de plataformas digitales de Bogotá, Colombia, 2021

Introduction: Air quality is a matter of interest for public health due to its rapid deterioration in low- and middle-income countries and the effects of polluted air on the health of populations. Objective: To explore the air quality conditions in which digital platform delivery workers carry out their work, evaluating the localities of Kennedy and Usaquén in Bogotá, 2021. Materials and methods: We developed a mixed parallel convergent study based on four sources of information: 1) Ethnographic observation in five commercial locations of the two localities; 2) Monitoring of PM10 and PM2.5 in 56 delivery routes using a low-cost sensor; 3) Daily logs of the routes to support the device data interpretation, and 4) A semi-structured interview applied to the drivers to explore their danger perception during the routes. Results: We identified elements causing environmental injustice among digital platform delivery workers between the two study locations. The routes made by the delivery drivers in the locality of Kennedy registered higher concentrations of PM10 and PM2.5, compared to the values observed in Usaquén. The sources of air pollution identified by the delivery drivers through ethnographic observation and the router logbook showed the worst parameters in Kennedy. Conclusions: We evidenced that air quality, urban equipment, road infrastructure, mobile sources, and geospatial location are elements that mark the presence of environmental injustice for the digital platform delivery drivers in the studied localities. To reduce this inequity, it is necessary for digital delivery platforms and the district government to implement strategies that reduce the exposure and emission of air pollutants to protect the health of digital platform delivery workers.

Introducción: La calidad del aire es un asunto de interés para la salud pública por su rápido deterioro en los países de bajos y medianos ingresos, y los efectos del aire contaminado en la salud de las poblaciones. Objetivo: Explorar las condiciones de la calidad del aire en las que los repartidores de plataformas digitales desarrollaron su trabajo en las localidades de Kennedy y Usaquén de Bogotá durante el 2021. Materiales y métodos: Se llevó a cabo un estudio mixto, paralelo y convergente, basado en cuatro fuentes de información: 1) observación etnográfica en cinco ubicaciones comerciales de las dos localidades; 2) monitoreo de PM10 y PM2.5 en 56 rutas de reparto, empleando un equipo de bajo costo; 3) bitácoras diarias de los recorridos que apoyaron la interpretación de los datos del equipo, y 4) entrevista semiestructurada con el rutero para explorar sus percepciones frente a los peligros durante los recorridos. Resultados: Se identificaron diferencias en las condiciones de trabajo, las percepciones y las exposiciones a material particulado de los repartidores entre las dos localidades de estudio que constituyeron fuentes de injusticia ambiental. Los recorridos que realizaron los repartidores en la localidad de Kennedy registraron mayores concentraciones de PM10 y PM2.5. Las fuentes de contaminación atmosférica identificadas por los repartidores mostraron los peores parámetros en Kennedy. Conclusiones: Se evidenció que la calidad del aire, el equipamiento urbano, la infraestructura vial, las fuentes móviles y la ubicación geoespacial son elementos que marcan la presencia de injusticia ambiental para los repartidores. Para disminuir esta inequidad, es necesario que las plataformas de reparto digital y el gobierno distrital implementen estrategias que reduzcan la exposición y la emisión de contaminantes del aire con el fin de proteger la salud de los repartidores de plataformas.

Insights into the environmental benefits of using apple pomace for biosorption of lead from contaminated water.

The apple processing industry generates large quantities of organic waste, presenting a major source of organic contamination. Consequently, finding an effective solution for valorizing this waste has become a pressing issue. This study aims to address two key concerns: (i) solving an agricultural problem by efficiently using agri-food residue, and (ii) removing lead, an extremely toxic element, from contaminated waters to mitigate environmental pollution. Two biosorbents were tested: raw apple waste (RA), obtained from a mixture of apple varieties, and the same material after extracting valuable bioactive and reusable components, extracted apple (EA). The study evaluated the influence of pH, initial biosorbent mass, adsorption kinetics, and equilibrium isotherms. The results are very promising, showing a lead removal efficiency of 82 % for RA and 100 % for EA at a low initial concentration of the solution of 20 mg Pb2⁺/L and an optimal pH of 5 ± 0.5. The Langmuir model predicted a maximum adsorption capacity of 44.6 mg/g for RA and 48.6 mg/g for EA. These findings demonstrate that apple waste, even after selective extraction of valuable bioactive components, can be effectively used for environmental remediation on a practical scale.