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OBJECTIVE: Real-time continuous glucose monitoring (CGM) is effective for diabetes management in cases of type 1 diabetes and adults with type 2 diabetes (T2D) but has not been assessed in adolescents and young adults (AYAs) with T2D. The objective of this pilot interventional study was to assess the feasibility and acceptability of real-time CGM use in AYAs with T2D. METHODS: Adolescents and young adults (13-21 years old) with T2D for six months or more and hemoglobin A1c (A1c) greater than 7%, on any Food and Drug Administration-approved treatment regimen, were included. After a blinded run-in period, participants were given access to a real-time CGM system for 12 weeks. The use and acceptability of the real-time CGM were evaluated by sensor usage, surveys, and focus group qualitative data. RESULTS: Participants (n = 9) median age was 19.1 (interquartile range [IQR] 16.8-20.5) years, 78% were female, 100% were people of color, and 67% were publicly insured. Baseline A1c was 11.9% (standard deviation ±2.8%), with median diabetes duration of 2.5 (IQR 1.4-6) years, and 67% were using insulin. Seven participants completed the study and demonstrated statistically significant improvement in diabetes-related quality of life, with the mean Pediatric Quality of Life inventory (PedsQL) diabetes score increasing from 70 to 75 after using CGM (P = .026). Focus group results supported survey results that CGM use among AYAs with T2D is feasible, can improve quality of life, and has the potential to modify behavior. CONCLUSION: Real-time CGM is feasible and acceptable for AYAs with T2D and may improve the quality of life of patients with diabetes. Larger randomized controlled trials are needed to assess the effects on glycemic control and healthy lifestyle changes.

BackgroundPeople with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy's effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named "Sleep Catcher," that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i.MethodsThe application development process will be described-writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input.ResultsThe mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI.ConclusionThere is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

BACKGROUND: The chicken body louse is an obligate ectoparasite of domestic chickens. Chicken body lice feed on feathers, and infestation with this louse is linked to decreases in egg production, hen weight, and feed conversion efficiency. However, it is unknown how chicken body lice impact egg-laying chickens in cage-free environments. Welfare and behavior metrics were collected from flocks of egg-laying chickens either infested with chicken body lice or left uninfested. METHODS: In two trials, two flocks of cage-free commercial egg-laying chickens were infested with chicken body lice or maintained as uninfested controls. At three timepoints, behavior and welfare of all chickens was measured. On-animal sensors were used to quantify pecking, preening, and dustbathing behavior. Other animal-based welfare metrics included recording comb wounds and skin lesions. RESULTS: Birds infested with chicken body lice exhibited significantly more preening behaviors than uninfested birds, even at low louse levels. Moderate or severe skin lesions were detected on birds that were moderately infested with chicken body lice while skin lesions were never detected on uninfested birds. CONCLUSIONS: The welfare of chickens was impacted by the chicken body louse, a chewing louse that primarily feather feeds. Evidence of skin lesions on infested birds suggests that lice may cause more damage to birds than previously thought, and further evaluation of louse economic damage is necessary.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

The supply of macroalgal feed materials for abalone aquaculture is threatened by increasing costs, limited availability. and the allocation of seaweed resources for other purposes. The use of biofouling Sargassum horneri (SH) as additional macroalgal biomass for abalone has been studied, albeit full inclusion is discouraged due to low utilization. This study applied heat treatment and fermentation to increase the nutritive value of SH meals for the aquaculture feed industry. Untreated (UN) as control, heat treated (HT, 121[degrees]C for 20 min), and fermented (FE, 10' CFU/mL each of Bacillus sp. SJ-10 and Bacillus sp. KRF-7) SH meal was fed to the abalone (11.60 [+ or -] 0.02 g body weight and 42.98 [+ or -] 0.53 mm shell length) for 8-wk growth trial. The results showed that there is no apparent difference in the growth performance (body weight and shell length morphometries) and survival of abalone given experimental diets (P > 0.05). The crude protein, lipid, and ash composition of edible abalone flesh were similarly unaffected (P > 0.05). The gut microbiota profile of abalone was analyzed through next-generation sequencing. The [alpha]-diversity indicators (microbial richness and abundance) were statistically similar (P > 0.05). and the [beta]-diversity of operational laxonomic units revealed short distances within samples and clustering among diet groups. The core phyla (Fusobacteria, Proteobaeteria. Tenericutes) and genera (Psychrilyohacter, Vibrio, Mycoplasma) known to inhabit the gut of abalone predominantly were likewise identilied irrespective of diets; however, there is a difference in their relative abundance. Genus Psychrilyohacter was relatively abundant in UN and FE compared with HT, whereas Vibrio and Mycoplasma had higher abundance in FE and HT-fed abalone, respectively. Taste sensory attributes were discriminated in the abalone meat using an electronic tongue. Principal component analysis of sensor scores revealed several overlapping points, resulting in a negative discrimination index (-184). which means no apparent difference in the general taste profile was detected. Furthermore, the taste screening matrix through radar mapping revealed slight intensity differences in the umami. saltiness, and sourness flavor. Overall, the methods used for heat treatment and fermentation procedures herein were ineffective in improving the nutritive value of seaweed meal for abalone. KEY WORDS: abalone, gulfweed. heat treatment, fermentation. Haliotis discus harmed, Sargassum horneri, INTRODUCTION The abalone (Haliotis spp.) is an ecologically and economically important marine gastropod mollusc worldwide. They are found in tropical and temperate regions, particularly in the low intertidaj zone of [...]

The light field (LF) imaging technique can capture 3D scene information in 4D by recording both 2D intensity and 2D direction of incoming light rays. Due to this capability, LF has shown a great interest in virtual reality (VR) and augmented reality (AR) for enhanced immersion, improved depth perception and reconstruction of realistic 3D environments. This paper presents a comprehensive review of LF imaging technology and other approaches used for VR content creation. The applications of LF technology beyond VR and AR are also discussed. The challenges and limitations of other approaches for VR content creation are examined. State‐of‐the‐art research has focused on how VR experiences benefit from LF technology and identified the challenges to creating comfortable, immersive and realistic VR content such as (1) image size and resolution, (2) processing speed, (3) precise calibration and (4) depth reconstruction. Recommendations that can be considered for creating immersive VR content are provided to enhance user experience. These recommendations aim to contribute to developing more comfortable and realistic VR content, extending the potential applications of LF imaging technology in diverse fields. [ABSTRACT FROM AUTHOR]

Real-time monitoring of structural health status is very important for supersonic aircraft, since the long-term operation in harsh service environments, such as high temperatures, can cause potential damage. Structural health monitoring (SHM) based on lamb wave has been approved to be a critical technique for the reliable operation of supersonic aircraft, but is limited by the operating temperature of conventional piezoelectric sensors, which makes it difficult to meet the high-temperature monitoring requirements. The BiFeO 3 –BaTiO 3 ceramics exhibit excellent piezoelectric properties and high Curie temperature (T c), making them promising for high-temperature SHM of supersonic aircraft. Here, we fabricated a piezoelectric sensor using 0.67BiFeO 3 -0.33BaTiO 3 -0.25%molMnO 2 (BF33BTMN-025) ceramic, which exhibits a piezoelectric charge constant (d 33) of 135 pC/N at room temperature and a T c of 448 °C. The BF33BTMN-025 ceramic sensor operates effectively at temperatures up to 200 °C and maintains stable damage monitoring sensitivity as well. Compared with Pb(Zr, Ti)O 3 (PZT), BaTiO 3 (BTO) and other piezoelectric sensors commonly used in SHM field, the BF33BTMN-025 ceramic sensor has a higher operating temperature and a comparable sensitivity. Our work demonstrates that the BF33BTMN-025 ceramic sensor can be employed as piezoelectric sensor for high-temperature SHM, which has a wide application potential in the field of real-time monitoring of supersonic aircraft. [ABSTRACT FROM AUTHOR]

In the era of Internet of Things (IoT) and Artificial Intelligence (AI), sensors have become an integral part of intelligent systems. Although the traditional sensing technology is very mature in long‐term development, there are remaining defects and limitations that make it difficult to meet the growing demands of current applications, such as high‐sensitivity detection and self‐supplied sensing. As a new type of sensor, array triboelectric nanogenerators (TENG)‐based tactile sensors can respond to wide dynamic range of mechanical stimuli in the surrounding environment and converting them into quantifiable electrical signals, thus realizing real‐time self‐supplied tactile sensing. The array structure allows for fine delineation of the sensing area and improved spatial resolution, resulting in accurate localization and quantification of the detected tactile signals, and have been widely used in wearable devices, smart interaction, medical and health detection, and other fields. In this paper, the latest research progress of functional tactile sensors based on arrayed triboelectric nanogenerators is systematically reviewed from the aspects of working mechanism, material selection, material processing, structural design, functional integration, and application. Finally, the challenges faced by arrayed triboelectric tactile sensors are summarized with a view to providing inspiration and guidance for the future development of tactile sensors. [ABSTRACT FROM AUTHOR]

Spermidine is an essential biomarker related to antiaging. Although the detection of spermidine levels is in high demand in life science fields, easy‐to‐use analytical tools without sample purification have not yet been fully established. Herein, we propose an organic field‐effect transistor‐based chemical sensor for quantifying the spermidine concentration in commercial cosmetics. An extended‐gate structure was employed for organic field‐effect transistor (OFET)‐based chemical sensing in aqueous media. A coordination‐bond‐based sensing system was introduced into the OFET device to visualize the spermidine detection information through changes in the transistor characteristics. The extended‐gate‐type OFET has shown quantitative responses to spermidine, which indicates sufficient detectability (i. e., the limit of detection for spermidine: 2.3 μM) considering actual concentrations in cosmetics. The applicability of the OFET‐based chemical sensor for cosmetic analysis was validated by instrumental analysis using high‐performance liquid chromatography. The estimated recovery rates for spermidine in cosmetic ingredient products (108–111 %) suggest the feasibility of cosmetic analysis based on the OFET‐based chemical sensor. [ABSTRACT FROM AUTHOR]

Dormancy has been suggested to be used for metal oxide gas sensors to specifically refer to their response decrease or disappearance after long periods of inactivity at room temperature. In this paper, 1 wt% Pt–SnO 2 composite nanoceramics with especially strong responses to hydrogen–containing atmospheres at room temperature, e.g. a response of 11170 to 1 % H 2 –20 % O 2 –N 2 , were successfully prepared through pressing and sintering. A clear dormancy has been observed for them as their responses decreased seriously with time when they were stored in ambient air at room temperature. Commercial ceramic heating plates (Metal Ceramics Heater, MCH) of 10 mm × 10 mm were attached to bars of 1 wt% Pt–SnO 2 composite nanoceramics. Dormant bars after 12 months of storage were completely regenerated through a heat–treatment of 200 °C for 10 min by attached MCH ceramic heating plates. According to X-ray photoelectron spectroscopy analyses, the formation of hydroxyl groups on SnO 2 in Pt–SnO 2 composite nanoceramics and deposition of impurity gases like H 2 S were proposed responsible for the dormancy, and their removal through mild heat–treatments result in the regeneration. It is suggested that heaters like commercial MCH ceramic heating plates should be included in future room–temperature gas sensors to provide a practicable regeneration from dormancy when needed. [ABSTRACT FROM AUTHOR]

The global population is rapidly increasing, so there is a critical requirement to satisfy the food production demand. Conventional methods of agriculture are inadequate to meet building demand which leads to declining farming sector and adaptable to other industries. Most of the farming activities are highly dependent on the labor which leads to increase in cost and time of operation. The rapid growth of mechanization for all farm activities cannot completely reduce the human involvement. As a result, agricultural automation is critically important. In terms of automation, this study emphasizes the crucial role of UAVs in precision and smart agriculture. The adoption of drones for various farm operations has the possibility to minimize labor requirements as well as operational time. This review provides overview of conceptual design, command flow operation, Micro-controller boards, remote-control systems and attachments like sensors, cameras, motors in UAVs for the purpose of automation in farm activities. The Internet of Things (IoT) employed in UAVs with image processing and machine learning algorithms provides accurate and precision results in farm activities. Furthermore, this study discusses future advancements, limitations and challenges for farmers in adapting to UAVs. Highlights: The recent literature from 2014 to 2024 on UAVs in agriculture were examined by PRISMA Methodology The overview of IOT system (sensors, cameras, micro-controllers and connectivity gateways) in UAVs were provided. The applications of UAVs in smart farming were compared and summarized by emphatical data and case studies. The limitations and future views of UAVs in farming are provided. [ABSTRACT FROM AUTHOR]

The data collected at Weigh-in-Motion (WIM) stations have many applications. Primarily, the data are used for pavement design, bridge design, freight studies, transportation planning, and enforcement. The WIM data are also used in mechanistic-empirical pavement design guide (Pavement-ME) for predicting performance. Highway agencies and the state department of transportation (DOTs) are the main users of WIM data. With a number of important applications, all WIM users desire to collect accurate loading data. This paper addresses three core issues related to WIM systems accuracy and calibration procedures, i.e., how to; (1) perform successful calibration of a WIM system by quantifying the effect of sample size (truck runs), speed, temperature, and truck type on measurement errors, (2) model gross vehicle weight (GVW) WIM errors as a function of individual axle errors [(single axle (SA) and two tandem axles (TA), (drive and trailer)], and (3) estimate WIM measurement errors using the long-term pavement performance (LTPP) and the ASTM protocols. The research objectives were accomplished by synthesizing and analyzing the WIM error data available in the LTPP database for bending plate (BP) and quartz piezo (QP) sensors. Successful WIM equipment calibration can eliminate systematic weights, speed, and axle spacing errors. The ASTM and the LTPP accuracy estimation methods agree; however, the methods should be compared when the sample size is small (10 or fewer truck runs). The representative pre- and post-calibration data can be collected accurately using one test truck with 12 or more runs at multiple speed points for QP and BP sensors. The developed models showed that the GVW errors could be accurately predicted using SA and two TA. The results also show that if GVW errors are within ASTM Type I tolerance, the SA and TA errors will likely be within acceptable limits. Therefore, calibrating a WIM site using GVW errors should continue. The suggested changes in current WIM procedures can significantly reduce time and resources for successful equipment calibration. [ABSTRACT FROM AUTHOR]

A joystick set for controlling different degrees of freedom was designed, materialized and characterized. The joystick was designed for 3D printing, improved through several iterations, producing multiple samples using multi-material additive manufacturing. Two different materials (one structural and another conductive) were used in the filament material extrusion process to obtain resistive-based sensing unit elements. The sensor was put into operation via a voltage divider circuit. Experimentation was implemented in a testing bed (analogic sensor), in a real context (binary mode) and in a simulation environment (multiple-state signal) for the command of a robotized wheelchair. The final design is capable to meet the requirements of reducing the material used and the printing times. The results reveal the possibility of using 3D printed resistive-based sensing units to implement binary signals as well as multiple state signals and the sensor set has been evaluated by controlling a robotized wheelchair. [ABSTRACT FROM AUTHOR]

A series of europium doped lanthanum oxychloride samples were prepared through solid state method which were utilized as an electrode material and also as photocatalyst. The compounds form tetragonal phase having P4/nmm space group (No. 129). The sample's energy band-gap vary in between 4.14 and 4.35 eV. With a carbon paste electrode in a 0.1 M KOH electrolyte, Cyclic Voltammetry (CV) and Electrochemical (AC) Impedance Spectroscopy are performed. Results reveal that the sample synthesized can be an electrode material for sensing tin in alkaline medium in the concentration range of 1 to 5 mM. Also, the samples are considered to be photocatalyst for the decolourization of Rhodamine.B (Rh.B) dye in aqueous medium concentration of 10 ppm using UV light irradiation. LaOCl:Eu3+ sample as catalyst be able to degrade the Rh.B dye to an extent of 90% in 60 mins. These results demonstrated the relevance of synthesized compound for sensor and photocatalytic applications. Novelty: In this article, the La1-xEuxOCl (0.00 ≤ x ≤ 0.09) synthesised compounds were characterized for morphological nature, structural feature, and functional group analysis with band gap energies. Very particularly for the first time the synthesized samples have undergone preliminary research into their possible usage as electrodes for sensor applicability involving the detection of Tin in alkaline media. Moreover, the samples offered improved photocatalytic effectiveness for the decolourization of Rhodamine.B dye under the UV light irradiation. [ABSTRACT FROM AUTHOR]

Vehicular control systems are required to be both extremely reliable and robust to different environmental conditions, e.g. load or tyre–road friction. In this paper, we extend a new paradigm for state estimation, called Twin-in-the-Loop filtering (TiL-F), to the estimation of the unknown parameters describing the vehicle operating conditions. In such an approach, a digital-twin of the vehicle (usually already available to the car manufacturer) is employed on-board as a plant replica within a closed-loop scheme, and the observer gains are tuned purely from experimental data. The proposed approach is validated against experimental data, showing to significantly outperform the state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

In this research study, the ability of a boron nitride nanocluster (hereafter B24N24 NC) to detect osmium tetroxide (hereafter OT) after the decoration of Ni into the B24N24 is investigated through the density functionals B3LYP, M06-2X and B97D. The results revealed that the interaction of the pure B24N24 was a physical adhesion or adsorption and the sensing response (SR) of B24N24 is approximately 5.2. The adsorption energy of OT becomes more negative by changing from −5.0 to −20.9 kcal mol−1 after the decoration of the Ni metal on the surface of B24N24, which also leads to a noticeable increase in the corresponding SR to 76.2, indicating that the sensitivity of Ni-decorated B24N24 (Ni@B24N24) is increased. Based on energy decomposition analysis, the nature of the interaction between OT and Ni@B24N24 is mostly an electrostatic cation–lone pair interaction. The theoretically obtained results further confirmed that Ni@BN-based nanostructures can be used for practical purposes. [ABSTRACT FROM AUTHOR]

The burgeoning intersection of machine learning (ML) with electrochemical sensing heralds a transformative era in analytical science, pushing the boundaries of what's possible in detecting and quantifying chemical substances with unprecedented precision and efficiency. This convergence has accelerated a number of discoveries, improving electrochemical sensors' sensitivity, selectivity, and ability to comprehend complicated data streams in real-time. Such advancements are crucial across various applications, from monitoring health biomarkers to detecting environmental pollutants and ensuring industrial safety. Yet, this integration is not without its challenges; it necessitates navigating intricate ethical considerations around data use, ensuring robust data privacy measures, and developing specialized software tools that balance accessibility and security. As the field progresses, addressing these challenges head-on is essential for harnessing the full potential of ML-enhanced electrochemical sensing. This review briefly explores these dimensions, spotlighting the significant technological strides, the ethical landscape, and the dynamic interplay between open-source and proprietary software solutions while also casting a forward gaze at the promising future directions of this interdisciplinary venture. [ABSTRACT FROM AUTHOR]

We designed a bi-metallic Co-Ni/BTC/4,4′-BiPy metal organic frameworks (MOFs) as an electrode material for the electrochemical detection of epinephrine and nor-epinephrine. The bi-metallic MOFs were synthesized by a solvothermal method. Following this, the bimetallic MOFs were modified with BTC and amine rich 4,4′-BiPy to improve charge transfer kinetics through non-covalent π – π interaction. This modified electrode was employed as a sensing platform for the simultaneous electrochemical detection of epinephrine and nor-epinephrine. The MOFs modified platform exhibited a 10–50 μM linear range with a limit of detection of 0.724 μM ± 0.128 (N = 3) and 0.815 μM ± 0.124 (N = 3), a sensitivity of 0.583 and 0.505 μA μM−1 cm−2 corresponding to epinephrine and nor-epinephrine detection. Finally, the electrochemical sensor was tested in blood and urine samples spiked with known concentrations of epinephrine and nor-epinephrine. Results confirmed the usefulness of the proposed platform for the detection of epinephrine and nor-epinephrine in clinical samples. [ABSTRACT FROM AUTHOR]

A growing trend in the utilization of compliant micro-motion stages, which offer exceptional precision and repeatability in positioning. These stages enable the creation of dual-range positioning systems, allowing for precise positioning at the nanoscale within a centimeter-scale working area when combined with conventional stages. However, such systems often come with a high price tag and require substantial physical space. This research presents an alternative solution in the form of a compact, cost-effective XY micro-motion stage with dual-range manipulation to address these limitations. The primary objective is to maintain workspace efficiency while improving positioning accuracy. This is achieved by integrating a long-range, low-resolution linear encoder with short-range, high-resolution capacitive sensors. The linear encoder determines the stage's position and provides coarse positioning data, while the capacitive sensors step in to correct any positional errors, enabling precise fine positioning. By adopting this approach, an impressive positioning precision of approximately 1.5 μm is attained within a 3 mm × 3 mm workspace. The compliant stage is constructed using aluminum, and wire electric discharge machining is employed. This material is well-suited for this application due to its high reversible strain and compatibility with compliant systems. [ABSTRACT FROM AUTHOR]

Recent studies affirm the potential of autonomous vehicles (AVs) in reducing traffic accidents and fatalities. This article presents an overview of on-road AV testing and analyzes crash data involving AVs. The study emphasizes fatalities and errors, comparing AVs to conventional human-driven vehicles or nonautonomous vehicles (non-AVs). Statistical analysis indicates that in most cases, human errors predominantly cause accidents, with responsibility falling on non-AV entities, such as bicyclists and motorcyclists, as well as adverse weather and lighting conditions. Notably, rear-end collisions are prevalent. AVs display superior sensing and processing capabilities, except in adverse weather and automation failure scenarios. Surprisingly, human drivers are accountable for most accidents, overshadowing the significance of AV error rates. Common collision causes between AVs and non-AVs encompass overspeeding, inadequate following distances, and decision-making deficiencies in non-AVs. The study shows the potential of AVs to enhance road safety while shedding light on areas requiring continued improvement. [ABSTRACT FROM AUTHOR]

Wearable triboelectric nanogenerators (TENGs) have attracted attention owing to their ability to harvest energy from the surrounding environment without maintenance. Herein, polyetherimide–Al2O3 (PAl) and polyvinylidene fluoride‐co‐hexafluoropropylene (PVDF‐HFP, PH) nanofiber membranes were used as tribo‐positive and tribo‐negative materials, respectively. Phytic acid‐doped polyaniline (PANI)/cotton fabric (PPCF) and ethylenediamine (EDA)‐crosslinked PAl (EPAl) nanofiber membranes were used as triboelectrode and triboencapsulation materials, respectively. The result showed that when the PAl–PH‐based TENG was shaped as a circle with a radius of 1 cm, under the pressure of 50 N, and the frequency of 0.5 Hz, the open‐circuit voltage (Voc) and short‐circuit current (Isc) reached the highest value of 66.6 V and −93.4 to 110.1 nA, respectively. Moreover, the PH‐based TENG could be used as a fabric sensor to detect fabric composition and as a sensor‐inductive switch for light bulbs or beeping warning devices. When the PAl–PH‐based TENG was shaped as a 5 × 5 cm2 rectangle, a 33 μF capacitor could be charged to 15 V in 28 s. Interestingly, compared to PAl nanofiber membranes, EPAl nanofiber membranes exhibited good dyeing properties and excellent solvent resistance. The PPCF exhibited <5% resistance change after washing, bending, and stretching. [ABSTRACT FROM AUTHOR]

This research identifies the urgent need for adaptive and real-time urban weather visualization models to support city planning and decision-making. The proposed solution involves the use of digital twin technology integrated with the Fuzzy Sugeno method to handle dynamic and complex environmental data. The novelty of this approach lies in its ability to provide real-time, adaptive visualizations through a combination of IoT (Internet of Things) data and advanced fuzzy logic processing, enhancing the accuracy and relevance of urban weather monitoring. This method enables the system to process real-time weather data collected from IoT sensors distributed throughout the city, resulting in accurate and relevant visualizations. The weather data, including temperature, humidity, and wind speed, is sent to a data centre where the Fuzzy Sugeno method is used to transform this data into visualizable weather information. The weather visualizations include conditions such as heavy rain, regular rain, drizzle, overcast, cloudy, sunny cloudy, somewhat cloudy, and sunny. The results of this study demonstrate that the system can display weather conditions adaptively and in real-time, significantly contributing to the understanding and management of urban heat islands. In ten experiments with temperature ranges from 21°C to 34°C, humidity from 55% to 87%, and wind speeds from 12 km/h to 32 km/h, the dominant defuzzification value was 0.6, corresponding to sunny cloudy weather conditions. The implementation of this system also shows improvements in the quality and accuracy of weather information presented, thereby aiding in more responsive and sustainable urban planning. [ABSTRACT FROM AUTHOR]

Electric vehicles need continuous monitoring of the energy supply all the time to provide the user with accurate information about the available energy. However, an increase in the power consumed by the monitoring system may cause a significant decrease in the vehicle’s autonomy. This letter presents a monitoring and estimation system for an electric car with a hybrid energy supply based on supercapacitors and batteries based on an embedded system capable of monitoring the general current, the current from the battery system, the voltage in the battery system, the voltage in the supercapacitor system, estimating the value of the state of charge from batteries, and showing the measurements to the user with an OLED display. The proposed system performs similarly in the estimation but decreases the energy consumption by 99.9% concerning the laboratory systems. [ABSTRACT FROM AUTHOR]

Hydroponic farming is a promising alternative to soil-based farming. However, it requires a precise control of the growth environment, which is hard to achieve with energy-constrained embedded systems. This letter presents an energy optimization technique for the continuous operation of energy harvesting-based hydroponics sensor nodes. The proposed technique is based on the self-tuning model, that dynamically adjust the duty cycle of the node, ensuring the autonomous operation of the Internet of Things system. The model can be programmed in a low-power microcontroller, allowing the decision-making process to reside entirely on the sensor node. Experimental results show that in the same time period, the self-tuning model allows $3.5\times $ more data transmissions than a uniform 5-min duty cycle, while ensuring a minimum voltage level in the storage device. This balance allows the stored energy to be enough for continuous monitoring, providing a clean and cost-effective alternative to perpetually power the hydroponic system. [ABSTRACT FROM AUTHOR]

The growth of both operational satellites and orbital debris is creating the requirement for more robust Space Surveillance and Tracking (SST)-related applications. These systems necessarily must leverage ground-based sensors (optical and radar) to realise higher performance solutions. In this context, the European Union Space Surveillance and Tracking (EUSST) consortium groups European national agencies and institutions, and is in charge of carrying out the following services: conjunction analysis, fragmentation analysis and re-entry prediction, and the Italian Air Force is in charge of the latter two. In this framework, the Italian SST Operational Centre (ISOC) has recently upgraded its system to the ISOC Suite, an integrated platform providing multiple functions and services in the SST domain. This paper presents the orbit determination functions provided by the novel ISOC Suite. First, a statistical index is computed to assess the measurements correlation to a catalogued object. If it is successful, the object predicted orbit is refined through measurements according either to batch or sequential filters; otherwise these are used to refine a first estimate of the target orbital state computed according to dedicated methodologies. After the presentation of the prototypal software architecture, the ISOC Suite performance are assessed and discussed both in terms of synthetic and real data. [ABSTRACT FROM AUTHOR]

Worldwide, heavy metal pollution is a major problem that has an impact on human health and the environment. The identification and removal of heavy metals from the environment is necessary to safeguard both human health and the ecology. An emerging family of nanomaterials called nanoribbons has shown significant promise in the monitoring and eradication of heavy metals (HMs) in various industries. We first go over how nanoribbons are made and their structural characteristics before talking about how their special surface chemistry and reactivity make it possible for them to highly efficiently and specifically adsorb and collect heavy metals. Recently, progress has been made in the use of nanoribbons as heavy metal sensors, and these sensors have shown great promise for real-time heavy metal contamination monitoring due to their hyper sensitivity, rapid reaction times, and inexpensive production. They are a feasible alternative to traditional remediation procedures because of their greater adsorption capacity and quick separation. In this study, the current state of the art and possible uses of nanoribbons for the removal and detection of heavy metals are briefly discussed. Additionally, it highlights their significant potential for alleviating the global issue of heavy metal contamination on a worldwide scale. [ABSTRACT FROM AUTHOR]

In contemporary research literature, inconsistencies regarding the validity and reliability of different brands of global positioning systems (GPSs) have been reported regarding the positioning of GPS units on athletes. For this reason, the present work investigates the validity and reliability of the measurements of GPS units placed at different locations. Thirty-two amateur soccer players (age: 21.18 ± 3.16 years; height: 175 ± 8.03 cm; body mass: 74.21 ± 4.85 kg) voluntarily participated in the current study. Participants were asked to complete a team sport simulation cycle (TSSC). During the tests, two GPS units were placed between the shoulder blades and on the chest of each athlete. Data from Polar Team Pro GPS units at both locations on the body, previously measured test area distance, and a radar gun (the gold standard) were compared to determine validity. The reliability of GPS units in measuring maximum speed is moderate (CV = 9.77–9.08, ICC = 0.23). The reliability of GPS units in measuring total distance is good (CV = 4.43–9.39, ICC = 0.15). The reliability of GPS units in measuring distance covered is poor (CV = 17.51–35.37). The measurements of total distance covered, and maximum speed recorded by the chest and back GPS units are not valid (Max speed = GPS chest-radar gun; R 2 = 0.075, GPS back-radar gun; R 2 = 0.106, total distance = GPS chest-1200 m; R 2 = 0.003, GPS back-1200 m; R 2 = 0.097). Consequently, it can be said that 10-Hz Polar GPS sensors have good reliability for measuring total distance when placed on the chest and moderate reliability for measuring peak speed when placed on the chest and back; however, they have poor reliability at both positions for evaluating distances at different running speeds. [ABSTRACT FROM AUTHOR]

In order to realize the automatic monitoring of physical fitness index in athletic training, a new method of automatic monitoring and identification is put forward in this paper. In this paper, a network structure model is designed to automatically monitor the physical fitness index in sports training based on IOT and WSN. The collected a number of physical parameters in sports training, including heart rate, maximal oxygen absorption, respiration entropy, and load parameters. Then, by monitoring heart and lung function data, Cooper's method was used to measure the maximal oxygen intake, and EQO2 was used as the training index. Then, the dynamic parameters of physical fitness index were extracted, and the change of gas metabolism and critical threshold were analyzed. This paper builds a system structure model to monitor the physical fitness index of physical training, and realizes the modular design of the system. The experiment results indicate that the system is not very different from the real one, and it can be used to automatically monitor the physical performance index in sports training. Practice has proved that the system has good stability and reliability, and is suitable for physical monitoring in the process of sports training. [ABSTRACT FROM AUTHOR]

The state of the driver of being extremely tired or sleepy through the operation of the vehicle is called driver drowsiness. Different factors caused this state such as alcohol, lack of sleep, and the side effect of some medication. The drowsiness of drivers is a serious safety lead to accidents or fatalities on external and internal roads. The increased number of road accidents resulted from drowsy driving. A special smart, reliable, and accurate system, Using Python language 3.6 for Windows, was designed to build an alert system for drivers in detecting drowsiness driver. This system is crucial in reducing accidents road by the ability to concentrate, react quickly, and produce sound decisions through driving. This system implements a real-time detector that can monitor the states of drivers through driving. Smart cameras with 16-megapixel were used to ensure that capturing photos have a high quality. These cameras were used in gathering the driver's dataset in different alertness states, including both alert states and drowsy. The collected dataset is processed by extracting all relevant features such as head movement, yawning, and eye closure, which were used in identifying the driver's drowsiness. Python's libraries such as TensorFlow, OpenCV, Keras, and Pygame are used for extracting all the above features. Viola-Jones algorithm is used in face eye region detecting and extracting from the image of the face in the proposed system. A Support Vector Machine (SVM) algorithm was used in classifying between drowsy and non-drowsy drivers. The system is tested and evaluated in the real world, to ensure that the system is reliable and robust; it has high performance and accuracy, and the accuracy is about 99.1%. This system can be used in manufacturing vehicles. [ABSTRACT FROM AUTHOR]

Fires represent an important risk to an entire planet, destroying everything from huge cities to impenetrable forests. This can be prevented using fire detection systems, but have been slow to be implemented due to concerns about the high cost, specialized connection, false alarms, and unreliability of existing facility-based detection systems. This take a first step towards utilizing DL to detect fire in images in this work. A Forest Fires dataset, obtained by an UCI ML Repository, is utilized for both training and testing purposes in this study. The components of preprocessing methods involve defining the paths for training and testing data, converting images to pixel representations, normalizing of the data and target variable selection. The model is applied in place due to its ability to highlight intricate details and patterns that are the main elements of precise fire detection. This research presents new methods for detecting forest fires through the use of a carefully selected dataset, transfer learning using the Hybrid (ResNet152V2 and InceptionV3) model and also deep learning based ConvNext model, and innovative preprocessing procedures. The novelty of this study arises from the effective incorporation of Hybrid (ResNet152V2, InceptionV3) model and ConvNext model into the field of fire detection, demonstrating its capability to attain exceptional levels of accuracy and precision. Techniques of visualization and comprehensive evaluation metrics raise the study's novelty. By utilizing the Hybrid (ResNet152V2 and InceptionV3) model, which attains an astounding accuracy, recall, f1-score, and precision of 99.47%, exceptional performance is achieved. While also ConvNext model get 95.53% accuracy. This study makes a valuable contribution to the field of fire detection systems by utilizing innovative deep neural network architectures to enhance performance and dependability. [ABSTRACT FROM AUTHOR]

In today's society, keeping our gardens attractive is complex, especially if you need more time to care for them. This can cause the plant to wilt if it is not watered occasionally to keep the soil moist. In summer, this problem tends to get worse because the temperature tends to rise and reach high degrees. The objective is to design an automatic and manual irrigation system with a humidity detector through hardware programming and free software to solve this. The necessary components will be identified and selected, humidity thresholds will be established, and the adoption of technologies such as internet of things (IoT), Arduino, and humidity sensors will be promoted to solve the problem in automated irrigation systems. The technical specifications of the components are described, and the circuit design is presented. A programming algorithm will be developed to control the frequency and duration of irrigation, as well as the state of the water pump. Implementing the automated system will allow precise water supply control, contributing to the healthy growth of plants and crops in green areas. [ABSTRACT FROM AUTHOR]

Remote sensing technology is a vital component of disaster management, poised to revolutionize how we safeguard lives and property through enhanced prediction, mitigation, and recovery efforts. Disaster management hinges on continuous monitoring of various environments, from urban areas to forests and farms. Data from these observations are relayed to servers, where sophisticated processing algorithms forecast impending disasters. Remote sensing technology operates through a layered framework. The sensing layer acquires raw data, the network layer facilitates data transmission, and the data processing layer extracts meaningful insights. The application layer then leverages these insights to make informed decisions. Elevating the intelligence of remote sensing technology necessitates advancements across these layers. This paper delves into disaster management concepts and highlights the pivotal role played by remote sensing technology. It offers a comprehensive exploration of each layer within the remote sensing technology framework, detailing foundational principles, tools, and methodologies for enhancing intelligence. Addressing challenges inherent to this technology, the paper also presents future-oriented solutions. Furthermore, it examines the influence of wireless network infrastructure, alongside emerging technologies like the Internet of Things, cloud computing, virtual machines, and low-power wireless networks, in nurturing the evolution and sustainability of remote sensing technology. [ABSTRACT FROM AUTHOR]

AbstractFood adulteration, whether intentional or accidental, poses a significant public health risk. Traditional detection methods often lack the precision required to detect subtle adulterants that can be harmful. Although chromatographic and spectrometric techniques are effective, their high cost and complexity have limited their widespread use. To explore and validate the application of nanoparticle-based sensors for enhancing the detection of food adulteration, focusing on their specificity, sensitivity, and practical utility in the development of resilient food safety systems. This study integrates forensic principles with advanced nanomaterials to create a robust detection framework. Techniques include the development of nanoparticle-based assays designed to improve the detection specificity and sensitivity. In addition, sensor-based technologies, including electronic noses and tongues, have been assessed for their capacity to mimic and enhance human sensory detection, offering objective and reliable results. The use of nanomaterials, including functionalized nanoparticles, has significantly improved the detection of trace amounts of adulterants. Nanoparticle-based sensors demonstrate superior performance in terms of speed, sensitivity, and selectivity compared with traditional methods. Moreover, the integration of these sensors into food safety protocols shows promise for real-time and onsite detection of adulteration. Nanoparticle-based sensors represent a cutting-edge approach for detecting food adulteration, and offer enhanced sensitivity, specificity, and scalability. By integrating forensic principles and nanotechnology, this framework advances the development of more resilient food-safety systems. Future research should focus on optimizing these technologies for widespread application and adapting them to address emerging adulteration threats. [ABSTRACT FROM AUTHOR]

As manufacturing looks to employ more smart and autonomous processes to improve how items are made, reducing scrappage rates and with it the associated waste of time and energy, new ultra‐compact sensors are needed that can be deployed where existing instrumentation cannot. The use of traditional methods when constructing optical sensors limits the progress that can be made in reducing their size and weight, however, emerging technologies such as metasurfaces offer a platform by which these barriers can be overcome to develop the sensors needed to underpin this manufacturing transition. Here it is demonstrated that how a single metasurface can be used to deliver all the optical manipulations required to create a metasurface‐based confocal sensor with only the addition of a point source and point detector. By combining the optical functionality of both the illumination and the collection optics in this way the system is simplified and reduced in size significantly. While here how a metasurface can be used to reduce the number of elements needed to produce an ultra‐compact confocal sensor is demonstrated, this approach can be used to simplify a far wider range of instrumentation to greatly reduce their size and weight. [ABSTRACT FROM AUTHOR]

Nature's diversity offers an abundance of promising solutions for novel bioinspired functional materials and systems. In particular, single‐celled organisms exhibit solutions and material properties that are realized at the nano‐ and micro‐scales. Ciliates are ubiquitous unicellular eukaryotes that are well‐adapted to a wide range of environmental conditions. They have developed a large variety of interesting and highly specialized characteristics with unique properties and design. In this review, the background of selected ciliate characteristics is highlighted with respect to material properties and structure‐function relationships. Hierarchically complex mineralized structures, highly efficient sensors for movement and protection, shape‐memory structures, as well as survival and detoxification strategies are to be emphasized. Proposed future bioinspired applications of these properties of ciliates expand the possibilities to more sustainable materials and development processes. [ABSTRACT FROM AUTHOR]

Sensing technology is gaining attention and continuously advancing, making it a recommended element of individualized healthcare management. This is due to the powers exhibited by organometallic compounds, which are further enhanced by the field of bioengineering. Organometallic compounds have a wide range of biological activity and find uses in industrial and material science fields. Their unique ability to specifically target and overcome constraints faced by traditional counterparts makes them potential contenders for sensor technology. These compounds are highly sensitive to changes in their environment, allowing them to be utilized as sensors for detecting various chemicals or conditions. Additionally, the versatility of organometallic compounds enables their integration into different sensor platforms, making them suitable for environmental monitoring, medical diagnostics, and industrial quality control. This article provides a comprehensive summary of recent advancements in the design and synthesis of organometallic compounds, with a specific emphasis on their potential use as sensors. It also discusses the changes made to the structure, the processes used for functionalization, the incorporation of microfluidics, and the resulting impact on the materials’ sensing capabilities. These biologically derived methods align with sustainability goals and enhance the affordability, applicability, and effectiveness of sensing. [ABSTRACT FROM AUTHOR]

Summary In food technology, the term ‘fermentation’ encompasses a spectrum of microbial‐driven bioprocesses that preserve and improve the quality of raw materials, transforming them into new fermented products with minimal resource consumption. Furthermore, fermentation plays a crucial role in driving the eco‐friendly transformation of the agricultural industry. Within this domain, the fermented beverage sector stands out due to its consumer‐friendly appeal. To promote innovation in this field, there is a need for cost‐effective, versatile and sustainable techniques that can accelerate research and development activities. In the bioprocess sector, innovation management requires consideration of an array of variables, including diverse raw materials, various microorganisms and different fermentation parameters. Direct injection mass spectrometry (DIMS) technologies offer solutions for bioprocess monitoring, particularly in the analysis of volatile organic compounds (VOCs) and are constantly improving their performance in terms of sensitivity and specificity. Among the DIMS methods, proton transfer reaction‐mass spectrometry (PTR‐MS), when coupled with autosampling and customised data handling and analysis, has demonstrated its efficiency in studying VOCs associated with fermentation. This approach brings automation to data production and management, offers exceptional versatility akin to a sensor and aligns closely with the principles of green chemistry. In this perspective paper, after reviewing key aspects of modern fermentation practice, we showcase the application of PTR‐MS as a model to demonstrate its potential as sensor‐like approach to drive innovation within the fermented beverage sector. This approach enables swift, large‐scale assessments of multiple variables while providing comprehensive insights into the quality and safety of the final products. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are a pivotal technology in today's intelligent transportation systems, improving traffic conditions by enhancing road safety, reducing congestion, conserving energy, decreasing emissions, and boosting travel efficiency and comfort. The key to achieving these objectives is to enable autonomous vehicles to perceive their surroundings in real time, with accuracy and stability, and to make appropriate decisions and controls on the basis of this perception. This perception relies on onboard sensors such as cameras, radars, lidars, and ultrasonic sensors. Radar sensors, one of the most frequently used perception devices in autonomous vehicles, are known for their strong anti-interference ability, effectiveness under occlusion, accurate range measurement, and wide coverage. However, radar sensors are limited by low resolution, high cost, large data volume, and difficulty in identifying object types and colors. Therefore, compensating for the inadequacies of radar sensing technology and enhancing the perceptual abilities of autonomous vehicles are ongoing challenges. In this paper, we introduce a novel image signal-based perception and transmission technology, aimed at overcoming the limitations of radar sensing and improving the perception efficiency and quality of autonomous vehicles. This technology employs image signals as the information medium, transmitting road information from the perception end to the processing end via wireless communication, thus enabling road condition perception. Compared with radar sensing technology, this image-based perception transmission technology is more cost-effective, as it only requires standard cameras and eliminates the need for expensive radar equipment. It captures a richer array of information, including object shape, size, position, direction, color, and texture, rather than just distance and velocity. The processing is more flexible, utilizing existing image processing and machine learning technologies for image signal compression, encoding, decoding, recognition, and analysis, thereby enhancing the accuracy and real-time capabilities of perception. [ABSTRACT FROM AUTHOR]

Palladium morphologies are prominently used in Hydrogen gas sensing applications owing to their unique characteristics and properties. In this review article, Palladium nanoparticles, thin films, and alloys were designated as the scope of Palladium morphologies. The aim of this review article is to explore Hydrogen sensing using Palladium, focusing on the recent advancements in the field.. The principles underlying Hydrogen sensing mechanisms with Palladium are discussed initially, highlighting the unique properties of Palladium that make it a promising material for this purpose. Special attention is given to the surface interactions and structural modifications that influence the sensitivity and selectivity of Palladium‐based sensors The study also addresses key challenges and recent innovations in the field which contribute to the enhancement of Palladium‐based Hydrogen sensing capabilities. The current state of research is critically examined to identify gaps in knowledge and future research directions are highlighted. The prospects and challenges associated with the use of Palladium for Hydrogen sensing, emphasizing its pivotal role in advancing sensor technologies for Hydrogen detection are also discussed. [ABSTRACT FROM AUTHOR]

Background: Mycoplasma (M.) hyopneumoniae is associated with respiratory disease in pigs and is the primary agent of enzootic pneumonia. Quantification of M. hyopneumoniae-related outcome parameters can be difficult, expensive, and time-consuming, in both research and field settings. In addition to well-established methods, technological tools are becoming available to monitor various aspects of relevant animal- and environment-related features, often in real-time. Therefore, this study aimed to assess whether certain parameters, such as animal movement and body temperature using microchips (IMT), correlate with established parameters and whether the currently used parameters can be rationalized. Results: The percentage of movement was significantly reduced by M. hyopneumoniae infection in pigs (p < 0.05), where the M. hyopneumoniae-infected group showed a lower percentage of movement (1.9%) when compared to the negative control group (6.9%). On the other hand, macroscopic (MLCL) and microscopic (MLL) lung lesions, respiratory disease score (RDS), M. hyopneumoniae-DNA load, and anti-M. hyopneumoniae antibody levels increased significantly in the M. hyopneumoniae-infected group 28 days post-inoculation (p < 0.05). Moderate (r > 0.30) to very strong correlations (> 0.80) were observed between the abovementioned parameters (p < 0.05), except for IMT. A significant and moderate correlation was reported between IMT and rectal temperature (r = 0.49; p < 0.05). Last, the average daily weight gain and the percentage of air in the lung were not affected by M. hyopneumoniae infection (p > 0.05). Conclusions: M. hyopneumoniae infection significantly reduced the movement of piglets and increased lung lesions, M. hyopneumoniae-DNA load, and anti-M. hyopneumoniae antibody levels; and, good correlations were observed between most parameters, indicating a direct relationship between them. Thus, we suggest that changes in movement might be a reliable indicator of M. hyopneumoniae infection in pigs, and that a selected group of parameters—specifically RDS, MLCL, MLL, M. hyopneumoniae-DNA load, anti-M. hyopneumoniae antibody levels, and movement—are optimal to assess M. hyopneumoniae infection under experimental conditions. [ABSTRACT FROM AUTHOR]

To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in information and communication technology, precision agriculture and data analytics, are creating a perfect opportunity for the creation of smart connected farms (SCFs) and networked farmers. A network and coordinated farmer network provides unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. The aim of this article is to provide a comprehensive overview of the state of the art in SCF including the advances in engineering, computer sciences, data sciences, social sciences and economics including data privacy, sharing and technology adoption. More specifically, we provide a comprehensive review of key components of SCFs and crucial elements necessary for its success. It includes, high-speed connections, sensors for data collection, and edge, fog and cloud computing along with innovative wireless technologies to enable cyber agricultural system. We also cover the topic of adoption of these technologies that involves important considerations around data analysis, privacy, and the sharing of data on platforms. From a social science and economics perspective, we examine the net-benefits and potential barriers to data-sharing within agricultural communities, and the behavioral factors influencing the adoption of SCF technologies. The focus of this review is to cover the state-of-the-art in smart connected farms with sufficient technological infrastructure; however, the information included herein can be utilized in geographies and farming systems that are witnessing digital technologies and want to develop SCF. Overall, taking a holistic view that spans technical, social and economic dimensions is key to understanding the impacts and future trajectory of Smart and Connected Farms. [ABSTRACT FROM AUTHOR]

ABSTRACT The coordination polymer {[Cu6(CN)6]·HPD}n, CP1; (HPD = heptane‐1,7‐diamine) was prepared and characterized by single‐crystal and powder X‐ray diffraction, UV–Vis spectroscopy, electron microscopy (TEM and SEM), and elemental analyses. The copper atoms are coordinated by C ≡ N ligands, and both exhibit tetrahedral geometry. Cu1 and Cu2 create the pretty unique cluster structure of two quadruple [Cu2(μ3‐CN)2] units exhibiting coprophilic interaction. The building blocks [Cu3(CN)3]2 form fused distorted rectangular rings surrounded with the [Cu2(μ3‐CN)2] motifs exhibiting wide voids to accommodate the guest HPD. The structure of 1 indicates the formation of channels with dimensions 5.121–8.469 Ǻ which extend three‐dimensionally creating fused cavity morphology with different shapes. CP1 was utilized as an efficient sensor for selective quantitative estimation of metal ions (Co2+) and explosives (NB). Advanced oxidation process (AOP) was used as an efficient photocatalyst for the removal of the antibiotic meloxicam (MEL) utilizing AOP in the presence of H2O2 as an oxidant. Scavenge experiments, reusability of catalyst 1, and mechanism of oxidation were also discussed. [ABSTRACT FROM AUTHOR]

This study presents a cost-effective IoT-based Supervisory Control and Data Acquisition system for the real-time monitoring and control of photovoltaic systems in a rural Pakistani community. The system utilizes the Blynk platform with Arduino Nano, GSM SIM800L, and ESP-32 microcontrollers. The key components include a ZMPT101B voltage sensor, ACS712 current sensors, and a Maximum Power Point Tracking module for optimizing power output. The system operates over both Global System for Mobile Communications and Wi-Fi networks, employing universal asynchronous receiver–transmitter serial communication and using the transmission control protocol/Internet protocol and hypertext transfer protocol for data exchange. Testing showed that the system consumes only 3.462 W of power, making it highly efficient. With an implementation cost of CAD 35.52, it offers an affordable solution for rural areas. The system achieved an average data transmission latency of less than 2 s over Wi-Fi and less than 5 s over GSM, ensuring timely data updates and control. The Blynk 2.0 app provides data retention capabilities, allowing users to access historical data for performance analysis and optimization. This open-source SCADA system demonstrates significant potential for improving efficiency and user engagement in renewable energy management, offering a scalable solution for global applications. [ABSTRACT FROM AUTHOR]