Hantaviruses are enveloped viruses that possess a tri-segmented, negative-sense RNA genome. The viral S-segment encodes the multifunctional nucleocapsid protein (N), which is involved in genome packaging, intracellular protein transport, immunoregulation and several other crucial processes during hantavirus infection. In this study we have generated fluorescently tagged N protein constructs derived from Puumalavirus, the dominant hantavirus species in Central, Northern and Eastern Europe. We have comprehensively characterized this protein in the rodent cell line CHO-K1, monitoring the dynamics of N protein complex formation and investigating co-localization with host proteins as well as the viral glycoproteins Gc and Gn. We found a significant spatial correlation of N with vimentin, actin and P-bodies, but not with microtubules. N constructs also co-localized with Gn and Gc, albeit not as strong as the glycoproteins associated with each other. Moreover, we as-sessed oligomerization of N constructs, observing efficient and concentration-dependent multi-merization, with complexes comprising more than 10 individual proteins.

Following legalization, cannabis has quickly become an important horticultural crop in Canada and increasingly so in other parts of the world. However, due to previous legal restrictions on cannabis research there are limited scientific data on the relationship between nitrogen (N), phosphorus (P), and potassium (K) supply (collectively: NPK) and the crop yield and quality. This study examined the response of a high delta-9-tetrahydrocannabinol (THC) Cannabis sativa cultivar grown in deep-water culture with different nutrient solution treatments varying in their concentrations (mg L^-1) of N (70, 120, 180, 250, 290), P (20, 40, 60, 80, 100) and K (60, 120, 200, 280, 340) according to a central composite design. Results demonstrated that inflorescence yield responded quadratically to N and P, with the optimal concentrations predicted to be 194 and 59 mg L^-1, respectively. Inflorescence yield did not respond to K in the tested range. These results can provide guidance to cultivators when formulating nutrient solutions for soilless cannabis production and demonstrates the utility of surface response design for efficient multi-nutrient optimization.

Vachellia nilotica (L.) Willd. Ex Del. is multipurpose leguminous tree which is common in grass-land and savanna ecosystems in southern and eastern Africa. These ecosystem soils are reported to be acidic and nutrient limited, specifically with regards to nitrogen (N) and phosphorus (P). The presence of this plant in these terrestrial ecosystems improves soil fertility benefiting the sur-rounding vegetation due to its ability fix atmospheric N. This study seeks to understand the mi-crobial symbiosis and physiological adaptations of V. nilotica in these acidic and nutrient defi-cient KZN soils. The soils used for this study were collected from the Ukulinga Grassland Nutri-ent Experiment located at the Ukulinga research farm of the University of KwaZulu-Natal, Pie-termaritzburg, South Africa. Due to long-term soil nutrient addition treatments, these soils of-fered a diverse nutrient variation for better understanding the effects of acidity and nutrient var-iation on microbial symbiosis, plant nutrition and biomass accumulation of V. nilotica. V. nilotica was able to maintain growth by relying on both atmospheric and soil derived N across all treat-ments decreasing carbon (C) growth costs. There was an increased reliance on atmospheric de-rived N of un-nodulated high N treated plants. The plants grown in high N+P soils were able to nodulate with various species from the Mesorhizobium genus which resulted in an increased bio-mass compared to other plants. The results of this study show that V. nilotica can alter N sources to reduce C growth costs. Also, free-living N2 fixing bacteria play an important role under P deficient conditions.

Neurokinin-1 receptor (NK-1R) antagonists exert an antitumor action, are safe and do not cause serious side-effects. These antagonists (via the NK-1R) exerted multiple actions against cancer: antiproliferative and anti-Warburg effects, apoptosis, anti-angiogenic and antimetastatic. These multiple effects have been shown in a broad-spectrum of cancers. The drug aprepitant (an NK-1R antagonist) is currently used in clinical practice as antiemetic. In in vivo and in vitro studies, aprepitant also showed the aforementioned multiple antitumor actions against many types of cancer. A successful combination therapy (aprepitant and radiotherapy) has recently been reported in a patient suffering from lung carcinoma: the tumor mass disappeared and side-effects were not observed. Aprepitant could be considered as an intelligent bullet against cancer. The administration of aprepitant in cancer patients to prevent recurrence and metastasis after surgical procedures, thrombosis and thromboembolism is discussed as well as the possible link, through the SP/NK-1R system, between cancer and depression. Our main aim is to review the multiple antitumor actions exerted by aprepitant and the use of this drug is suggested in cancer patients. Altogether, the data support the reprofiling of aprepitant for a new therapeutic use as an antitumor agent.

This study aimed to assess the ecotoxicological effects of the interaction of fullerene (C₆₀) and benzo[a]pyrene (B[a]P) on the marine mussel, Mytilus galloprovincialis. The uptake of nC₆₀, B[a]P and mixtures of nC₆₀ and B[a]P into tissues was confirmed by GC-MS, LC-HRMS and ICP-MS. Biomarkers of DNA damage as well as proteomics analysis were applied to unravel the toxic effect of B[a]P and C₆₀. Antagonistic responses were observed at the genotoxic and proteomic level. Differentially expressed proteins (DEPs) were only identified in the B[a]P single exposure and the B[a]P mixture exposure groups containing 1 mg/L of C₆₀, the majority of which were down-regulated (~52%). No DEPs were identified at any of the concentrations of nC₆₀ (p < 0.05, 1% FDR). Using DEPs identified at a threshold of (p < 0.05; B[a]P and B[a]P mixture with nC₆₀), gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis indicated that these proteins were enriched with a broad spectrum of biological processes and pathways, including those broadly associated with protein processing, cellular processes and environmental information processing. Among those significantly enriched pathways, the ribosome was consistently the top enriched term irrespective of treatment or concentration and plays an important role as the site of biological protein synthesis and translation. Our results demonstrate the complex multi-modal response to environmental stressors in M. galloprovincialis.

A Pogorelov polytope is a combinatorial simple 3-polytope realizable in the Lobachevsky (hyperbolic) space as a bounded right-angled polytope. It has no 3- and 4-gons and may have any prescribed numbers of k-gons, k ≥ 7. Any polytope with only 5-, 6- and at most one 7-gon is Pogorelov. For any other prescribed numbers of k-gons, k ≥ 7, we give an explicit construction of a Pogorelov and a non-Pogorelov polytopes. Any Pogorelov polytope different from Löbel polytopes can be constructed from the 5- or the 6-barrel by cuttings off pairs of adjacent edges and connected sums with the 5-barrel along a 5-gon with the intermediate polytopes being Pogorelov. For fullerenes there is a stronger result. Any fullerene different from the 5-barrel and the (5, 0)-nanotubes can be constructed by only cuttings off adjacent edges from the 6-barrel with all the intermediate polytopes having 5-, 6- and at most one additional 7-gon adjacent to a 5-gon. This result can not be literally extended to the latter class of polytopes. We prove that it becomes valid if we additionally allow connected sums with the 5-barrel and 3 new operations, which are compositions of cuttings off adjacent edges. We generalize this result to the case when the 7-gon may be isolated from 5-gons.

Some studies have demonstrated that in vivo degradation processes are influenced by the material’s physico-chemical properties. The present study compares two hydroxyapatites manufactured on an industrial scale, deproteinized at low and high temperatures, and how physico-chemical properties can influence the mineral degradation process of material performance in bone biopsies retrieved 6 months after maxillary sinus augmentation. Residual biomaterial particles were examined by field scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) to determine the composition and degree of degradation of the bone graft substitute material. According to the EDX analysis, the Ca/P ratio significantly lowered in the residual biomaterial (1.08±0.32) compared to the initial composition (2.22±0.08) for the low-temperature sintered group, which also presented high porosity, low crystallinity, low density, a large surface area, and poor stability and a high resorption rate compared to the high-temperature sintered material. This demonstrates that variations in the physico-chemical properties of bone substitute material clearly influence the degradation process. Further studies are needed to determine whether the resorption of deproteinized bone particles proceeds slowly enough to allow sufficient time for bone maturation to occur.

Monitoring seaweed growth rates and biomass is crucial for optimizing harvest strategies in aquaculture systems. While such a task can be performed manually on a small farm, such as the Integrated Multi-Trophic Aquaculture (IMTA) system at Harbor Branch Oceanographic Institute at Florida Atlantic University (HBOI), a commercial farm will have to rely on the automated sensor to perform such a task. This study introduces an advanced LSTM-based approach for forecasting seaweed growth and biomass. Utilizing a combination of real and synthetic data, LSTM models are trained and evaluated for their predictive performance. Synthetic sensor data was generated using mathematical equations that simulate realistic aquaculture conditions, with added noise to reflect sensor variability. Building on the foundation of the Pseudorandom Encoded Light for Evaluating Biomass (PEEB) sensor deployed at the seaweed tank in the HBOI IMTA system, we refine the process of biomass estimation by introducing non-linear regression models for predicting seaweed growth and biomass. The results showed that the LSTM model trained with a loss function under physics constraint, combining MSE and physical laws, outperformed models trained with MSE alone, achieving a significantly lower error in predicting seaweed growth. The variation trend of the predicted biomass from the network matched well with the sensor measurement after moving average preprocessing, demonstrating the robustness of the proposed technique in handling noisy sensor data. This study highlights the potential of integrating machine learning with physical models to optimize seaweed cultivation and support sustainable aquaculture practices.

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different type: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using continuum removal methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account change of support. Secondly, the two-sensor data were integrated with soil granulometric properties by using the multivariate geostatistical techniques of multi-collocated cokriging and factor cokriging, in order to achieve a more exhaustive and finer-scale soil characterisation. The paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalised factors at two different scales (two per each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterised by different granulometry and clay composition. The applied method is sufficiently flexible and could be applied to any number and type of sensors.

Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with quantitative traits controlled by multiple minor effect alleles. Therefore, genomic selection (GS) was proposed to estimate all markers simultaneously, thereby capturing all their effects. However, breeding programs are still struggling to identify the best strategy to implement it into their programs. Traditional breeding programs need to be optimized to implement GS effectively. This review explores the optimization of breeding programs for variety release based on aspects of the breeder’s equation. Optimizations include reorganizing field designs, training populations, increasing the number of lines evaluated, and leveraging the large amount of genomic and phenotypic data collected across different growing seasons and environments to increase heritability estimates, selection intensity, and selection accuracy. Breeding programs can leverage their phenotypic and genotypic data to maximize genetic gain and selection accuracy through GS methods utilizing multi-trait and, multi-environment models, high-throughput phenotyping, and deep learning approaches. Overall, this review describes various methods that plant breeders can utilize to increase genetic gains and effectively implement GS in breeding .

Many complex behaviours in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans multiple length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modelling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modelling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviours offers a means to take synthetic biology beyond single molecules or cells and towards the creation of systems with functions that can only emerge from collectives at multiple scales.

Protecting sensitive data, such as data collected from sensors, is crucial for ensuring the accurate assessment of sensing devices and preventing unauthorized access. In this regard, Internet of Things (IoT) devices offer a promising alternative for in-situ monitoring. However, IoT sensing devices are often constrained by limited processing power and memory. Therefore, lightweight and efficient security algorithms are essential. This review paper assesses the implementation of lightweight cryptographic algorithms for power-constrained microcontrollers in IoT systems using symmetric cryptography. The implemented algorithms analyze the resource constraints of the IoT devices and compare their performance for efficient implementation of secure monitoring systems. Experimental results demonstrate the performance of various lightweight encryption algorithms on low-power microcontrollers. The analysis reveals the comparative performance of these algorithms in terms of average power and energy consumption, memory usage, latency, and throughput.

Detection of volatile organic compounds (VOCs) is a rapidly growing research area due to importance of VOCs in environmental pollution, human health assessment, food quality control, and homeland security. Optical sensing materials based on porphyrins are particularly appealing for VOCs detection, owing to availability of porphyrins, their exceptional optical and binding properties, as well as their photo- and chemical stability. As research and technology continue to advance, optical sensors involving these materials are expected to play an increasing role in various applications. This article presents a comprehensive overview of porphyrin-based sensing materials developed for use as optical sensors for VOCs. First, sensing films composed exclusively of porphyrin molecules are discussed followed by materials obtained by grafting or incorporation of porphyrins into organic and inorganic polymer matrices. Considering growing interest to multianalyte analysis with porphyrin-based sensor arrays, special section is devoted to this area.

Biomolecular detections of Helicobacter pylori(HP) were compared using specially modified sensors and fluorine immobilized on a carbon nanotube (HFCNT) electrode, which yielded sensitive results. Handheld voltammetric circuits were used for optimization. An anodic voltammogram of HP molecular oxidation was obtained at 0.0 V (versus the Ag/AgCl/KCl) in a 0.1M NH4H2PO4 electrolyte solution. Under optimized conditions, the analytical working range was 2.98x103-22.127x103 CFU/ml HP using square wave (SW) stripping voltammetry, precision of R2 =0.9857 (SWSV), the detection limit approached to 2.5x102 CFU/ml HP (S/N=3).The developed techniques have been applied to diagnosis of early-stage HP infections using stomach tissue from healthy humans and gastric patients.

In this review, recent advances in the combination of CRISPR-Cas systems with graphene-based electrolyte-gated transistors are discussed in detail. In a first part, the functioning of CRISPR-Cas systems is briefly explained, as well as the most common ways to convert their molecular activity into measurable signals. Besides optical means, conventional electrochemical transducers are also developed. However, it seems that the incorporation of CRISPR/Cas systems into transistor devices could be extremely powerful, as the former provides molecular amplification, while the latter provides electrical amplification; combined, the two could help to advance in terms of sensitivity and compete with conventional PCR assays. Today, organic transistors suffer from poor stability in biological media, where graphene materials perform better by being extremely sensitive to their chemical environment while being stable. The need for fast and inexpensive sensors to detect viral RNA arose on the occasion of the COVID-19 crisis, but many other RNA viruses are of interest, such as dengue, hepatitis C, hepatitis E, West Nile fever, Ebola, polio, for which detection means are needed.

High-precision, ultra-thin angular detectable imaging upon a single pixel holds significant promise for light field detection and reconstruction, thereby catalyzing advancements in machine vision and interaction technology. Traditional light-direction angle sensors relying on optical components like gratings and lenses face inherent constraints from diffraction limits in achieving device miniaturization. Recently, angle sensors via coupled double nanowires have demonstrated prowess in attaining high-precision angle perception of incident light at sub-wavelength device scales, which may herald a novel design paradigm for ultra-compact angle sensors. However, the current approach to measure the three-dimensional (3D) incident light direction is unstable. In this paper, we propose a flexible segmented concentric nanorings structure capable of discerning the 3D light-direction based on its sensitivity character to both elevation (θ) and azimuth (ϕ) angles at a micrometer device scale. Through deep learning (DL) analysis and prediction, our simulations reveal that for angle scanning with a step size of 1o, the device can still achieve a detection range of 0∼360o for azimuth and 45o∼90o for elevation, with an average accuracy of 0.19o, and DL can further solve some data aliasing problems to expand the sensing range. Our design broadens the angle sensing dimension based on mutual resonance coupling among nanorings segments, and through waveguide implementation or sensor array arrangements, the detection range can be flexibly adjusted to accommodate diverse application scenarios.

The 6TiSCH protocol (IEEE 802.15.4e) is crucial for the Industrial Internet of Things (IIoT), utilizing a time-slotted channel hopping (TSCH) mode based on node distribution. In our experiment, we explored node position clustering and adjusted the capacity of existing cells in congested areas to understand traffic patterns and optimize cell selection for TSCH resource allocation, particularly in slot frames. Efficient cell allocation prevents packet loss, manages congestion, and enhances data transmission reliability. Node clustering increases cell usage efficiency and reduces communication latency between nodes, with each cell allocation tailored to its traffic needs through the sink node from the clustering result. We evaluated the clustering method using the K-means algorithm through experiments using the 6TiSCH simulator. Additionally, we considered using the routing protocol for low power and lossy networks (RPL) as a comparison, such as OF0 and MRHOF, as the objective function to assess clustering results and their impact on throughput and packet delivery. Our method significantly improved average performance metrics: under the OF0 routing protocol, we achieved a 30.01% latency reduction, 15.95% faster joining time, 8% higher packet delivery ratio, and 13.82% throughput increase. Similarly, with the MRHOF routing protocol, we observed a 12.34% improvement in packet delivery ratio, 21.06% latency reduction, 12.68% faster joining time, and 25.97% higher throughput speed.

The use of low cost sensors has dramatically increased in recent years in all the engineering sectors. In the field of buildings and automotive, low cost sensors open very interesting perspectives, because they allow to monitor temperature and humidity distributions together with air quality, in a widespread and punctual way and allow control of all energy parameters. The main issue still open remains the validation of the measurements. In this work, we propose an innovative approach to verify the measurements given by some low-cost systems built ad hoc for automotive applications. Two independent low-cost measurement systems have been set to measure Particulate air Matter (PM) concentration, TVOC concentration, CO2 concentration, formaldehyde concentration, air temperature, relative humidity, pressure, air flow velocity and GPS position. These systems have been calibrated by the comparison with standard and certified sensors used by the regional authority of Emilia-Romagna region (ARPAE, Italy) for characterising the air-quality. The innovation of this approach lies in both the in-field comparison of low-cost and high quality sensors and the use of proper conversion approaches for mass concentration measurements. A quantitative analysis of the sensors performance is given, with a focus on effects of time-granularity, relative humidity, mass conversion from particle counts and size detection response. The results show that the low-cost sensors measurements of air temperature, relative humidity and particle number concentration are in a good agreement with high quality sensors measurements, with a strong impact of relative humidity on performance indicators. Overall, a good quality and consistency of the data among the sensors has been achieved.

A Partial discharge (PD) occurrence in high-voltage (HV) and medium-voltage (MV) power assets is a warning sign of insulation system degradation or defective manufacturing/assembly, that may cause catastrophic damage and eventually lead to the breakdown of the power equipment. Ultra-high frequency (UHF) PD measurement technology has extensively been used to monitor the insulation health of the HV or MV equipment due to its high immunity against ambient noise and conducted disturbances. This paper presents experimental validation of a new design of a UHF planar rectangular microstrip antenna that can be used for PD monitoring of HV and/or MV power equipment, such as power transformers. The designed planar rectangular microstrip antenna has a compact size of 80mmx70mmx1.6mm. Laboratory experiments were performed to validate the antenna sensitivity to PD activity caused by insulation defects created inside an oil-filled transformer tank model. PD measurement results confirm that the proposed rectangular microstrip antenna has high sensitivity and high signal-to-noise ratio (SNR), thanks to its capability to suppress external noise disturbances, especially during online PD monitoring. Thus, the proposed UHF antenna seems to be a promising and alternative PD sensor that can be employed in UHF PD monitoring systems of HV or MV power equipment.

A single-track magnetic code tape based absolute position sensor system is demonstrated. Unlike traditional dual-track systems, our method simplifies manufacturing and avoids crosstalk between tracks, offering higher tolerance to alignment errors. The sensing system employs an array of magnetic field sensing elements that recognize the bit sequence encoded on the tape. This approach allows for accurate position determination even when the number of sensing elements is fewer than the number of bits covered, and without the need for specific spacing between sensing elements and bit length. We demonstrate the system's ability to learn and adapt to various magnetic code patterns, including those that are irregular or have been altered. Our method can identify and localize the sensed magnetic field pattern directly within a self-learned magnetic field map, providing robust performance in diverse conditions. This self-adaptive capability enhances operational safety and reliability, as the system can continue functioning even when the magnetic tape is misaligned or has undergone changes.

Non-invasive continuous health monitoring has become feasible with the advancement of biosensors. While monitoring certain biomarkers such as heart rate or skin temperature are now at certain maturity, monitoring molecular biomarkers is still challenging. Progress has been shown in sampling, measurement and interpretation of data towards non-invasive molecular sensors that can be integrated in daily wearable items. Towards this goal, this paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. In controlled experiments, we have shown the variation of glucose concentration in water can be wirelessly detected by tracking the resonant frequency of the embroidered sensors. The current sensors operate at 1.8GHz–2GHz and respond to the change of glucose concentration with a sensitivity of 0.17 MHz/(mg/dl). The embroidered IDT-based sensors with wireless sensing will be a new measurement modality for molecular wearable sensors. The establishment of a wireless sensing mechanism for embroidered IDT-based sensors will be followed by investigation of sweat for molecular detection. This will require adding functionalities for sampling and interpretation of acquired data. We envisage the embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.

This study explores the potential of a deep-water hydroponic system integrated with Internet of Things (IoT) technology to enhance the production of Chinese celery (Apium graveolens), a popular leafy vegetable with culinary and medicinal properties. Traditional soil cultivation of Chinese celery can take 75-90 days and is susceptible to fungal diseases. To address these challenges, a 2 x 6 meter hydroponic greenhouse was designed and constructed to accommodate 1,000 plants. The study investigated the effects of two key factors on celery growth and yield: daily light exposure (660 nm wavelength, 6:00-11:00 p.m.) and internal temperature regulation using a fog sprayer when temperatures surpassed 35°C. The experiment was conducted in four separate hydroponic houses, each with different factor controls: House 1 (light and temperature), House 2 (temperature only), House 3 (light only), and House 4 (natural conditions). The IoT-based system maintained optimal water conductivity and pH levels, monitored through the Blynk application and regulated by an ESP32 microcontroller. Sensor data, including water conductivity, pH, internal temperature, humidity, and water temperature, were collected throughout the study. Results revealed significant differences in weight and final yield among the houses, with House 1 achieving the highest yield, 13.91% higher than House 4. A comparison between Houses 2 and 3 indicated that temperature played a more critical role than light in celery cultivation under similar conditions. An economic analysis demonstrated the cost-effectiveness of investing in the automated control system, with House 1 being 14% more profitable than House 4 and a breakeven point achieved at 27 months. This research highlights the benefits of IoT-controlled hydroponic systems in elevating Chinese celery production towards sustainable and efficient practices, offering valuable insights for modern agriculture.

Glucose is a simple sugar molecule. The sugar molecule is chemically symbolised as C6H12O6. This means that the glucose molecule contains 6 carbon atoms (C), 12 hydrogen atoms (H) and 6 oxygen atoms (O). In human blood, the molecule glucose circulates as blood sugar. Normally after eating or drinking, our body breaks down the sugars in food and uses them to obtain energy for our cells. To do this, our pancreas produces insulin. Insulin "pulls" sugar from the blood and puts it into the cells for use. If someone has diabetes, their pancreas can't produce enough insulin. As a result, the level of glucose in the blood rises. This can lead one to many potential complications, including blindness, disease, nerve damage, amputation, stroke, heart attack and damage to blood vessels etc. In this study, a non-invasive method for monitoring blood glucose has been developed and is therefore easily usable. The device developed can measure the glucose level continuously and eliminates the disadvantages of the invasive system. Near-IR sensors (optical sensors) were used to estimate the concentration of glucose in the blood; these sensors have a wavelength of 940 nm. The sensor is placed on the tip of the finger and the output of the optical sensor is then connected to the microcontroller at the analogue input. Another sensor used, but only to provide more medical information was the heartbeat sensor. After processing and regression analysis, the glucose level is predicted, and data is sent via the Bluetooth network to a developed APP. The results of the implemented device were compared with available invasive methods. The study consists of a hardware platform inserted into an armband and then a small black parallelepiped-shaped box in which the optical sensors are inserted. The hardware consists of a microcontroller, a Near-IR optical sensor, a heartbeat sensor and a Bluetooth module.

Turbidity stands as a crucial indicator for assessing water quality, and while turbidity sensors exist their high cost prohibit their extensive use. In this paper, we introduce an innovative turbidity sensor, and it is the first low-cost turbidity sensor which is designed specifically for long-term stormwater in-field monitoring. Its low cost ($23.50 USD), enables the implementation of high spatial resolution monitoring schemes. The sensor design is available under open hardware and open-source licences, and the 3D-printed sensor housing is free to modify based on different monitoring purposes and ambient conditions. The sensor was tested both in the laboratory and in the field. The laboratory results show a strong linear relationship (R2 > 0.99) between the sensor readings and the commercial hand-held turbidimeter (Thermo Scientific AQ4500) results of the solution. In the field, the low-cost sensor measurements were statistically significantly correlated (p < 0.01) to a standard high-cost commercial turbidity sensor (GreenSpan TS-1000). Biofouling and drifting issues were also analysed after the sensors were deployed in the field for more than 6 months, showing both biofouling and drift occur during monitoring. Nonetheless, in terms of maintenance requirements, the low-cost sensor exhibited similar needs compared to the Green-Span sensor.

This article describes an approach for optimising sensor-controlled systems through minimal intervention, utilising Fuzzy Linear Systems of Equations (FLSE). Staring with a generalised model of the system behaviour, incorporating an array of control units, environmental sensors, and an expert knowledge base. The described problems of detecting the level of intervention needed to change the system state to another is handled with the help of developed methods for solving the inverse problem for FLSE. By achieving minimal intervention, we ensure that system adjustments effective, economically optimal and non-intrusive. A MATLAB-based implementation is presented.

The rapid growth of the global wearable technologies market is projected to reach $61.30 billion by 2030 which reflects a shift towards personalised health monitoring embedded in everyday activities. This paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. Experimental results demonstrate the sensor’s capability to detect water droplets and glucose concentrations, showcasing promising applications for health monitoring. The embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

This study introduces a Fiber Specklegram Sensor (FSS) design for temperature measurement utilizing a single-multi-single mode optical fiber configuration. FSS technology offers unique advantages for potential development into portable sensing systems. This investigation explores an FSS design compatible with miniaturization by optimizing the interrogation system based on the relationship between speckle grain size and filtering fiber area. Temperature variations are detected through fluctuations in optical power at the sensor output, offering a simple and reduced interrogation scheme. The performance of the FSS is assessed through post-calibration measurements compared to a commercial Fiber Bragg Grating (FBG) sensor, demonstrating comparable results. These findings pave the way for compact and portable fiber optic temperature sensing systems leveraging FSS advantages while maintaining compatibility with established fiber optic measurements.

Machine learning (ML) systems that use sensors obtain observations from sensors and use them to recognize and interpret the current situation. These systems are susceptible to sensor-based adversarial example attacks (AEs); if some sensors are vulnerable and can be compromised by an attacker, the attacker can change the output of the system by changing the values of the sensors. The detection of compromised sensors is important to defend the system against sensor-based AEs, because we can check the sensors and replace them by detecting the sensors used by the attacker. In this paper, we propose a method to detect the sensors used in sensor-based AEs by utilizing the features of the attack that cannot be avoided. In this method, we introduced a model called the feature-removable model (FRM), which allows us to select the features used as inputs into the model. Our method detects the sensors used in sensor-based AEs by finding the inconsistencies between the outputs of the FRM obtained by changing the selected features. We evaluated our method using a human activity recognition model with sensors attached to the user’s chest, wrist, and ankle. We demonstrate that our method can accurately detect sensors used by the attacker and achieves an average Recall of Detection of 0.92, and the average Precision of Detection is 0.72.

This paper describes the optimal design of a miniature fiber-optic linear displacement sensor. It is characterized by its ability to measure displacements along a millimetric range with sub-micrometric resolution. The sensor consists of a triangular reflective grating and two fiber-optic probes. The measurement principle of the sensor is presented. The design of the sensor's triangular grating has been geometrically optimized by considering the step angle of the grating to enhance the sensor's resolution. The optimization method revealed a global optimum at which the highest resolution is obtained

The presence of high concentrations of flammable gases and volatile organic compounds in the atmosphere has been widely reported to be detrimental to human survival. A lot of research effort has been put towards finding an efficient means of quick detection of these gases below their ‘immediately dangerous to life or health’ concentrations. Detecting these gases in an oxygen-deficient environment is a crucial task to consider and has been overlooked. In this research, double substitution spinel with chemical formula Co1-2xNixMnxFe2-yCeyO4, where 0 ≤ x = y ≤ 0.3, were prepared via the glycol-thermal technique. The final products following the appropriate substitution are CoFe2O4 (dried naturally), CoFe2O4 (dried with Infrared lamp), Co0.8Ni0.1Mn0.1Fe1.9Ce0.1O4, Co0.6Ni0.2Mn0.2Fe1.8Ce0.2O4, and Co0.4Ni0.3Mn0.3Fe1.7Ce0.3O4 spinel ferrites. The X-ray diffractometry (XRD), High resolution transmission electron micrographs (HRTEM), and X-ray photoelectron spectroscopy (XPS) of the samples confirm the formation of the spinel. The gas sensing performance of this samples were tested at operating temperature of 225 ºC towards liquefied petroleum gas (LPG), ammonia, ethanol, and propanol. The Co0.8Ni0.1Mn0.1Fe1.9Ce0.1O4-based sensor was selective to LPG, with a high response of 116.43 toward 6 000 ppm of LPG when helium was used as carrier gas, 3.35 when dry-air was the carrier gas, 4.4 when nitrogen was the carrier gas, but was not sensitive when argon was used as a carrier gas.

In the field of clinical medicine, the real-time monitoring of carbon dioxide gas exhaled by the human body is of great significance. At present, the detection devices in the market are mainly detected by sucking a small amount of gas in the nasal cavity to the detection device, and there are some problems such as too long sampling tubes, easy blockage or distortion, and abnormal gas dispersion. In this paper, a micro-nano optical fiber sensor that can directly detect the concentration of end-tidal carbon dioxide is proposed. The measurement is achieved by using the principle of high evanescent field absorption, and the operating band is 2.004 μm. The sensor uses micro-nano optical fiber as the sensing area, and then detects the presence of carbon dioxide gas exhaled by human body through optical power attenuation. The function of micro-nano fiber is to realize the transmission of signal light and also serve as the absorption medium of the gas to be measured. In addition, the variation of light power also reflects the respiratory cycle of the human body. The sensor can realize rapid real-time response to carbon dioxide gas detection, with small size, low cost, and easy to replace. It has great application potential in clinical scenarios such as Gastrointestinal Endoscopes that require real-time monitoring of human respiration.

A significant replacement for numerous fossil fuels is solar energy as the main type of renewable energy resource. However, solar cells need battery energy storage units to handle the intermittent and uncontrollable nature of solar energy. In this light, there is a significant market for small, portable battery charge controllers that automate battery charging. However, the issue of slow charging and premature battery life loss plagues current industry-standard or innovative battery technology. This study proposes a quick pulse charging technique that stops battery deterioration and minimizes the overall charging period. The primary goal of this study is to develop, construct, and execution of a practical, versatile, and compact photovoltaic charge controller at cut rates. The suggested charge controller can disconnect and reconnect the battery during battery overcharging and deep discharging conditions using sensors with relays. The laboratory model is tested using a less expensive PV panel, battery, and DSP controller. The charging behavior of the solar-powered PWM charge controller is studied compared to that of the Constant Voltage - Constant Current (CV-CC) method. The proposed method is pertinent for minimizing energy issues in impoverished placesat a reasonable price.

Recently, there has been remarkable progress in the development of smart textiles, especially knitted strain sensors, to achieve reliable sensor signals. Stable and reliable electro-mechanical properties of sensors are essential for using textile-based sensors in medical applications. How-ever, challenges associated with significant hysteresis and low gauge factor (GF) values remain for using strain sensors for motion capture. To evaluate these issues, a comprehensive investiga-tion of the cyclic electro-mechanical properties of weft-knitted strain sensors was conducted in the present study to develop a drift-free elastic strain sensor with a robust sensor signal for mo-tion capture for medical devices. Several variables were considered in the study, including the variation of the basic knit pattern, the incorporation of the electrically conductive yarn, and the size of the strain sensor. The effectiveness and feasibility of the developed knitted strain sensors are demonstrated through experimental evaluation, by determining the gauge factor, its non-linearity, hysteresis and drift. The developed knitted piezoresistive strain sensors have a GF of 2.4, a calculated drift of 50 %, 12,5 % hysteresis, and 0.3 % nonlinearity in parts.

In this paper, a sensitivity sensor of dual-core photonic crystal fiber (DC-PCF) filled with magnetic liquid is introduced and investigated by the finite element method (FEM). To regulate the energy coupling involving the two cores, the magnetic fluid is filled into the pore between the two cores. To adjust the coupling between the supermodes in the DC-PCF, the refractive index of the magnetic fluid-filled air hole may change due to the external magnetic field. This specifically created a magnetic fluid-filled DC-PCF, the magnetic fluid-filled hole is not used as the core for energy transmission, thus avoiding transmission loss. The dip wavelength and the magnetic field displayed an excellent linear connection between 80 and 260 Oe, depending on the numerical data. The detection sensitivity of the magnetic field reached 515.75 pm/Oe at a short fiber length of 482 m. The designed magnetic fluid-filled DC-PCF has high sensitivity and small volume and has great application prospects in medical and industrial field magnetic field detection.

The outer membrane of Gram-negative bacteria contains a variety of pore-forming structures collectively referred to as porins. Some of these are voltage-dependent, but weakly so, closing at high voltages. Triplin, a novel bacterial pore-former, is a three-pore structure, highly voltage dependent, with a complex gating process. The three pores close sequentially: pore 1 at positive potentials, 2 at negative and 3 at positive. A positive domain containing 14 positive charges (the voltage sensor) translocates through the membrane during the closing process and the translocation is proposed to take place by the domain entering the pore and thus blocking it resulting in the closed conformation. This mechanism of pore closure is supported by kinetic measurements that show that in the closing process the voltage sensor travels through most of the transmembrane voltage before reaching the energy barrier. Voltage-dependent blockage of the pores by polyarginine but not by a 500 fold higher concentrations of polylysine, is consistent with the model of pore closure, with the sensor consisting mainly of arginine residues, and with the presence, in each pore, of a complementary surface that serves as a binding site for the sensor.

The versatility of metal complexes of corroles raised the interest in the use of these molecules as element of chemical sensors. The tuning of the macrocycle properties by synthetic modification of the different components of the corrole ring, such as functional groups, molecular skeleton, and coordinated metal, allows the creation of a vast library of corrole-based sensors. However, the scarce conductivity of most of aggregates of corroles limits the development of simple conductometric sensors and requires the use of optical or mass transducers that are rather more cumbersome and less prone to be integrated in microelectronics systems. To compensate the scarce conductivity, corroles are often used to functionalize the surface of conductive materials such as graphene oxide, carbon nanotubes, or conductive polymers. Alternatively, they can be incorporated in heterojunction devices where they are interfaced with a conductive material such as a phthalocyanine. Herewith, we introduce two heterojunction sensors made of junctions of lutetium bisphthalocyanine (LuPc2) with either 5,10,15-tris(pentafluorophenyl) corrolato Cu (1) or 5,10,15-tris(4-methoxyphenyl)corrolato Cu (2). Optical spectra show that after forming the heterojunction, corroles maintain their original structure. The conductivity of the devices reveals an energy barrier for interfacial charge transport, which is larger in the 1/LuPc2 device. The different interfacial barriers is also manifested by the opposite response respect to ammonia: with a 1/LuPc2 behaving as a n-type conductor and 2/LuPC2 as a p-type conductor. Furthermore, the sensors show a high sensitivity respect to relative humidity with a reversible and fast response in the range 30-60%.

The use of intelligent sensor-based packaging in food products provide quick evaluation of the quaility of the food. This study was done to develop bio-based color sensor from surimi-based color sensor incorporated with sappan wood extract (SWE) for smart food packaging. The SWE with different concentration (0.15%, 0.25%, and 0.35%) was incorporated to the myofibrillar protein-based edible film. The sappan wood-surimi edible film (SSEF) was subjected to physical properties analysis and the color changes at different pH values and soaking time at different condition were evaluated. Based on the results, different concentration of the SWE significantly (p&lt;0.05) affected the physical properties of the film. With the increasing of pH values, the darkness, redness, and blueness of the film was increased. Based on the evaluation of the SSEF with different soaking condition, the color changes of the film in acidic condition were more stable than in neutral and alkaline condition. The results from this study showed that SSEF have the possibilities to be used as a smart food packaging possessing the capabilities to act as color sensor due to its sensitivity to the changes in pH condition of the product.

Road infrastructure is a key public asset because it benefits the social and economic development of any country. It plays an important role for the development of the industrial complex, the production sector and the road surfaces of transport roads should be of high quality, have a long service life. Road infrastructure, as well as all infrastructure, requires preservation, maintenance and repair. There are special requirements for the roadway that must be observed during construction or repair. This article is devoted to a detailed study of the use of fiber optics sensors (FOS) based on the fiber Bragg grating (FBG) for road surface monitoring. Such a fiber sensor consisting of a fiber Bragg grid and a pair of grids can offer the possibility of simultaneous measurement of deformation and temperature for monitoring the pavement. Temperature and deformation measurements were carried out by installing a sensor on the surface of a made asphalt sample. The built-in fiber sensor based on FBG provides important information about how the pavement structure can withstand the load, subsidence of soil, timely implement road safety and stability measures evaluate and predict the service life of the pavement. The results of the study showed that the synchronicity, repeatability and linearity of the characteristics of the fiber sensor are excellent. The difference between the experimental and theoretical results is about 7%. Thus, based on the results of the obtained data, the fiber sensor on the FBG can be used for monitoring, designing road surfaces and in general transport infrastructure.

In contemporary science and technology, photonic sensors are essential. They may be made to be extremely resistant to some physical parameters while also being extremely sensitive to other physical variables. Most photonic sensors may be incorporated on chips and operate with CMOS technology, making them suitable for use as extremely sensitive, compact, and affordable sensors. Photonic sensors can detect electromagnetic (EM) wave changes and convert them into an electric signal due to the photoelectric effect. Depending on the requirements, scientists have found ways to develop photonic sensors based on several interesting platforms. In this work, we extensively reviewed the most generally utilized photonic sensors for detecting vital environmental parameters and personal healthcare. These sensing systems include optical waveguides, optical fibers, plasmonics, metasurfaces, and photonic crystals. Various aspects of light are used to investigate photonic sensors' transmission or reflection spectra. In general, resonant cavity or grating-based sensor configurations that work on wavelength interrogation methods are preferred, so these sensor types are mostly presented. We believe this paper will provide insight into the novel types of available photonic sensors.

Existence of carcinogenic nitrite in food has attracted much attention, however, it is still a challenge to develop nitrite sensor with higher sensitivity and selectivity to enlarge its applications in daily life. Herein, one-dimensional honeycomb-like carbon nanofibers (HCNFs) were synthesized with an electrospun technology, and its specific structure enables the controlling growth and highly dispersed bismuth nanoparticles (Bi NPs) on its surface, which endows the obtained Bi/HCNFs with excellent electrocatalytic activity toward nitrite oxidation. By modifying Bi/HCNFs on screen-printed electrode, the constructed Bi/HCNFs electrode (Bi/HCNFs-SPE) can be used for nitrite detection in one drop of solution, and exhibits higher sensitivity (1269.9 μA mM-1 cm-2) in a wide range of 0.1~800 μM with lower detection limit (19 nM). Impressively, the Bi/HCNFs-SPE has been successfully used for nitrite detection in food and environment samples, the satisfactory property and recovery indicates its feasibility in further practical applications.

Current generation witnesses a huge interest in optical waveguide due to their salient feature such as low cost, immune to electromagnetic interference, easy to multiplex, compact size, etc. These features of the optical fibers makes it an useful tool for various sensing applications including medicine, automotive, biotechnology, food quality control, aerospace, physical and chemical monitoring etc. Among all the reported application, the device has been widely exploited to measure the physical and chemical variation in surrounding environment. Optical fiber based temperature sensor plays a crucial role in this decade to detect high fever and tackle COVID like pandemic. Recognizing the major developments in the field of the optical fibers, this article aims to provide recent progress in temperature sensor utilizing several sensing configuration including standard fiber, photonic crystal fiber, and Bragg grating fibers. Additionally, the article also highlights the advantages, limitations, and future possibilities in this area.

A highly sensitive strain sensor based on tunable cascaded Fabry-Perot interferometers (FPIs) is proposed and experimentally demonstrated. Cascaded FPIs consist of a sensing FPI and a reference FPI which effectively generate the Vernier Effect (VE). The sensing FPI comprises a hollow core fiber (HCF) segment sandwiched between single-mode fibers (SMFs), and the reference FPI consists of a tunable air reflector, which is constituted by a computer programable fiber holding block to adjust the desired cavity length. Simulation results predict the dispersion characteristics of modes carried by HCF. The sensor’s parameters are designed corresponding to a narrow bandwidth range, i.e., 1530 nm to 1610 nm. Experimental results demonstrate that the proposed sensor exhibits optimum strain sensitivity of 23.9 pm/με in the range of 0 to 3000 με which is 13.73 times higher than the single sensing FPI strain sensitivity of 1.74 pm/με. The strain sensitivity of the sensor can be further enhanced by extending the source bandwidth. The proposed sensor exhibits ultra-low temperature sensitivity of 0.49 pm/°C in the wider temperature range of 25 °C to 135 °C, providing good isolation for eliminating cross-talk between strain and temperature. The sensor is very robust, cost-effective, easy to manufacture, repeatable, and shows a highly linear and stable response in the wider range of axial strain. Based on the sensor’s performance, it may suit plenty of practical applications in the real sensing world

The integration of metal nanoparticles and solid carriers can achieve ideal stability, high load and good conductivity. In this work, copper nanoparticles (Cu NPs) were sequentially deposited on a cobalt metal-organic framework (Co-MOF) by bonding with exposed imino groups, followed by a reduction reaction to prepare a new Cu@Co-MOF composite. Cu@Co-MOF acts as a non-enzymatic electrochemical sensor to detect glucose (Glu) in an alkaline medium. The composite working electrode of Cu@Co-MOF/GCE (GCE = glassy carbon electrode) improves the electrocatalytic activity for Glu oxidation. Cu@Co-MOF/GCE shows excellent electrocatalytic performances in Glu concentration ranging 0.005~1.8 mmol∙L⁻¹ (mM): the sensitivities are 282.89 μA∙mM⁻¹∙cm⁻² in 0.005-0.4 mM Glu and 113.15 μA∙mM⁻¹∙cm⁻² in 0.4-1.8 mM Glu respectively with low detection limit of 1.6 μM (S/N = 3) and high selectivity and stability.

The article presents the results of tests related to failure analysis and finding ways to diagnose used semiconductor elements, among other, in power electronics converter systems on vessels and offshore facilities (drilling and production rigs, wind turbines). Diagnostic relationships were found between the temperature change in the above systems and the signals generated in form of elastic waves of acoustic emission.

Seismic monitoring in areas where induced earthquakes could occur is a challenging topic for seismologists due to generally very low signal to noise ratio. Therefore, the seismological com-munity is devoting several efforts to the development of high-quality networks around the areas where fluid injection and storage and geothermal activities take place, also following the national induced seismicity monitoring guidelines. The use of advanced data-mining strategy, such as template matching filters, auto-similarity search and deep-learning approaches is recently further fostering such a monitoring enhancing the seismic catalogues and lowering the magnitude of completeness of these areas. In this framework, we carried out an experiment where a small-aperture seismic array was installed around the gas reservoir of Collalto, in North Italy. The continuous velocimetric data, acquired for 25 days, were analysed through the application of the optimized auto-similarity search technique FAST. The array was conceived as a cost-effective network, aimed at integrating, right above the gas storage site, the permanent high-resolution Collalto Seismic Network. The analysis allowed to detect micro-events down to magnitude Ml=-0.4 within a distance of ~15km from the array. Our results confirmed that the system based on the array installation and the FAST data-analysis might contribute to lower the magnitude of completeness around the site of about 0.7.

Using two metal insulated metal waveguides (MIM) and five resonator (a quadrilateral cavity and three rings with different dimensions), we design a plasmonic refractive index sensor. Using the time domain finite difference method, we examine and analyze the sensor performance (numerical analysis) and using the transmission line model method, we theoretically examine the performance of the sensor By summarizing these two methods and calculating the sensitivity coefficient S, quality coefficient Q and figure of merit (FOM), a plasmonic sensor with good performance is obtained. Using the supermods of the resonators, we will achieve a stable wavelength spectrum and a suitable and stable sensitivity coefficient spectrum. Challenges and changes that occur in different modes of this sensor will lead to a balanced and relatively good structure. This designed sensor can be useful in the development of integrated optical circuits.

In this paper, a plasmonic refractive index sensor based on metal insulated metal waveguide (MIM) with three rings and a resonant cavity is first proposed and numerically evaluated. Next, we add four teeth to the sensor structure. We study and simulate the resonant wavelengths and refractive index of resonators using the time difference finite difference method. Given that the sensor and the conduction characteristics of plasmonic waves are influenced by the structure parameters,Therefore, by changing the refractive index and changing the dimensions and coordinates of the cavity and rims, we can change the passage coefficient in the resonant modes and measure the sensor performance. As a result, we obtain the sensitivity coefficient, the figure of merit (FOM) and the quality factor Q of the sensor. We modulate the resonance wavelength FWHM using the generated modes and reach a sensitivity of 2166 nm / RIU. These plasmonic sensors with a simple framework and balanced performance and high optical resolution can be used to measure the refractive index in the medical, chemical and food industries.

Light-addressable potentiometric sensor (LAPS) is an electrochemical sensor based on the field-effect principle of semiconductor. It is able to sense the change of Nernst potential on the sensor surface, and the measuring area can be controlled by the illumination. Due to the unique light-addressable ability of LAPS, the chemical imaging system constructed with LAPS can realize the two-dimensional image distribution detection of chemical/biomass. In this paper, the advantages of LAPS as sensing unit of microelectrochemical analysis system are summarized. Then, the greatest development of LAPS analysis system is explained and discussed. Especially, this paper focused on the research of ion diffusion, enzymatic reaction, microbial metabolism and droplet microfluidics by using LAPS analysis system. Finally, the development trends and prospects of LAPS analysis system are illustrated.

In this study, we seek to analyze and numerically evaluate a plasmonic sensor. To form the sensor structure, we use several amplifiers, such as two rings attached to each other and a cavity, as well as two metal insulating metal waveguides (MIM). At the beginning of this simulation, we must examine the resonant wavelengths and the refractive index of the resonators using the finite difference time domain method. By changing the refractive index and changing the dimensions of the cavity and the rims, we seek to investigate the sensor performance and the conduction characteristics of the plasmonics and to obtain the effect of these parameters. To evaluate the sensor performance, we calculate the three factors of sensitivity coefficient S, quality factor Q and figure of merit (FOM), here we reach the sensitivity of 987.6 nm / RIU. Such a plasmonic sensor with a simple framework and high optical resolution can be very useful for sensor systems on optical circuits.

Climate risk is one of the confronting factors in Indian agriculture. To overcome this distrust, a large number of sensors can be installed in the fields,The extensive IoT platform can process the data sent by these sensors.The Data stream can be processed in real-timeusing Fuzzylogic, to offer smart solution. Network coding can enhance throughput and security. Thus reducing human interaction and improve efficiency.

Climate risk is one of the confronting factors in Indian agriculture. To overcome this distrust, a large number of sensors can be installed in the fields,The extensive IoT platform can process the data sent by these sensors.The Data stream can be processed in real-timeusing Fuzzylogic, to offer smart solution. Network coding can enhance throughput and security. Thus reducing human interaction and improve efficiency.

Ag+ pollution is great of harm to the human body and the biology. Therefore, it is an urgent need to develop inexpensive and accurate detection methods. Herein, lignin-derived structural memory carbon nanodots (CSM-dots) with outstanding fluorescence property were fabricated via a green method, which reserve functional and structural units of the precursor molecules. The CSM-dots could specifically bind Ag+, accompanied with a remarkable fluorescence quenching response. This “turn-off” fluorescence behavior was used for Ag+ determination in a linear range of 5-290 μM with the detection limit as low as 500 nM. Furthermore, the finding showed that this sensing nano-platform was successfully used for Ag+ determination in real samples and intracellular imaging, showing great potential in biological and environmental monitoring applications.

It was recently demonstrated that a co-planar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, Finite Element (FE) modelling was employed to simulate the electric field distribution from a co-planar capacitive sensor and the way it interacts with a non-conducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with sub-surface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.

Dermal water content is an important biophysical parameter in preserving skin integrity and preventing skin damage. Traditional electrical-based and open-chamber evaporimeters have several well-known limitations. In particular, such devices are costly, sizeable, and only provide arbitrary outputs. They also do not permit continuous and non-invasive monitoring of dermal water content, which can be beneficial for various consumer, clinical and cosmetic purposes. We report here on the design and development of a digital multi-wavelength optical sensor that performs continuous and non-invasive measurement of dermal water content. In-silico investigation on porcine skin was carried out using the Monte Carlo modelling strategy to evaluate the feasibility and characterise the sensor. Subsequently, an in-vitro experiment was carried out to evaluate the performance of the sensor and benchmark its accuracy against a high-end, broad band spectrophotometer. Reference measurements were made against gravimetric analysis. The results demonstrate that the developed sensor can deliver accurate, continuous, and non-invasive measurement of skin hydration through measurement of dermal water content. Remarkably, the novel design of the sensor exceeded the performance of the high-end spectrophotometer due to the important denoising effects of temporal averaging. The authors believe, in addition to wellbeing and skin health monitoring, the designed sensor can particularly facilitate disease management in patients presenting diabetes mellitus, hypothyroidism, malnutrition, and atopic dermatitis.

The present paper describes the development of a prosthetic hand based on the human hand anatomy. The hand phalanges are printed by using 3D printed with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables to create personalized joins that allow the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand grasping start. Their use may provide the prosthetic hand the possibility of classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate with more naturalness by adding conditions and classifications to the Electromyogram sensor.

Landslides are a frequent and recurrent problem in hilly regions of India and predicting them is always a challenging task. In this paper, an attempt was made to deal with this problem using advanced physical and numerical modeling methods. Detailed understanding of the initial slope failures is very interesting, and challenging at the same time, in the design and development of wireless sensor network based on early warning of landslide monitoring. A small scale physical model was developed to assess the instability through a sensor network with variable rain fall intensity. This was achieved by increasing the simulated rain water flow intensity in different time spans (dry condition, at t=0 to t= 30 min, 0.5 mm/min at t=30 to t= 60 min, 0.75 mm/min at t=60 to t=91 min and 1 mm/min at t=91 to t= 120 min). The water level and movement in the slope was recorded by rainfall sensor, vibration sensor, soil moisture sensor and a digital camera. The following changes were observed during the slope failure: a) movement of small particles at top of the slope; b) initial failure of medium size soil particle; c) scouring of soil mass; d) whole slope collapse. The obtained results clearly indicated the superiority and effectiveness of the proposed system in providing a factor of safety for the progressive slope.

The incorporation of nanomaterials on (bio)sensors based on composite materials has led to important advances in analytical chemistry field due to the extraordinary properties that these materials offer. Nanodiamonds (NDs) are a novel type of material that has raised much attention, as they have the possibility of being produced on large scale by using relatively inexpensive synthetic methodologies. Moreover, NDs present some other interesting features as suitability for fluorescence due to surface functionalization and a proved biocompatibility, which makes them well suited for biomedical applications. In addition, NDs can be modified with metallic nanoparticles (NP), such as silver or gold, in order to combine the special features of both. The aim of this research work is the nanostructuration of novel sensing devices using NDs combined with silver (Ag@NDs) and gold (Au@NDs) nanoparticles. A complete morphological and electrochemical characterization as function of the prepared nanocomposite composition have been performed in order to improve the electroanalytical properties of the developed (bio)sensors.

The measurement range of a conventional current sensor is narrow because it is used with signals relative to the rated values of the measurement range from a voltage-type device. Consequently, multiple current sensors must be used in accordance with the measurement range. To address this problem, this paper proposes a new current sensor with a clamp-shaped part for low current measurement and a simple straight structure for high current measurement. The output signals of the proposed current sensor are amplified with a Hall element using the magnetic force of a rectangular air gap inside the clamp. To verify the characteristics of the proposed current sensor, a current was applied to an external load, and the value determined by the current sensor noted. Then, electromagnetic field analysis was performed through current sensor modeling and the results obtained compared to the actual sensor results. The proposed sensor had a 1% linearity in the output signals and exhibited dynamic characteristics over a wide current range.

Fluxgate magnetometers require calibration methods appropriate to their application levels and particularities; however the development of fully controlled calibration procedures presents a particular challenge regarding the inevitable influence of the local geomagnetic field and other external interferences when a laboratory with magnetically shielded walls is not available. In that context, we discussed the development of an automated calibration method for fluxgate magnetometers, considering those limitations in time and space, and avoiding some of the problems commonly found in other proposed solutions for the same challenge. For this task, we designed and built a new set of high level procedures, electronic systems and software, which perform active testing and automated calibration of fluxgate magnetometers, considering some resource constraints and employing instruments commonly found in electronic calibration laboratories.

We present the design, fabrication, and response of a polymer-based Laterally Amplified Chemo-Mechanical (LACM) humidity sensor based on mechanical leveraging and parametric amplification. The device consists of a sense cantilever asymmetrically patterned with a polymer and flanked by two stationary electrodes on the sides. When exposed to a humidity change, the polymer swells after absorbing the analyte and causes the central cantilever to bend laterally towards one side, causing a change in the measured capacitance. The device features an intrinsic gain due to parametric amplification resulting in an enhanced signal-to-noise ratio (SNR). 11-fold magnification in sensor response was observed via voltage biasing of the side electrodes without the use of conventional electronic amplifiers. The sensor showed a repeatable and recoverable capacitance change of 11% when exposed to a change in relative humidity from 25-85%. The dynamic characterization of the device also revealed a response time ~1s and demonstrated a competitive response with respect to a commercially available reference chip.

Over 55% of stoma patients suffer complications such as dehydration. Outcomes may be improved through communicating stoma output data to the patient and their clinical teams. Past artificial neural networks to improve accuracy in fluid level sensing were designed to account for ‘slosh’ caused by variable acceleration in one or two axes of movement. This paper describes the development of a novel sensor platform for non-invasive monitoring of stoma output in real time through incorporating a volumetric array consisting of thermistors and capacitive sensors into an ostomy appliance. Stoma output which exits the body at core temperature passes into a stoma appliance in a pattern which is dictated by water content, existing effluent within the bag and distortion of the usual bag shape. By using thermistors, a thermal boundary demarcates the accumulated level of fecal material as the effluent settles. A capacitive array allows the measurement of volume of output. The sensing components communicates via near field communication (NFC) and transmits data to a smartphone application by Bluetooth low energy (BLE). Testing of the device on 11 existing ileostomy patients with 51.6 bag hours of data found a correlation between measured volume and predictive value, supporting its use in this population.

The paper presents construction, laboratory tests as well as the first field application of a new fiber-optic rotational seismograph. The system based on fiber-optic gyroscope (FOG) with determined Angle Random Walk of the order of 10-8 rad/Sqrt(s) and a few rad/s maximum detectable amplitude of rotation in the frequency range from DC to 328.12 Hz. It has been designed for rotational seismology area of interest. This work also presents exemplary relevant measurements which were conducted using a set of two devices installed in the geophysical observatory in Książ, Poland.

Two 2,2':6',2''-terpyridine-based Schiff bases (TPySSB and TPySB) have been synthesized. The TPySSB shows remarkable selective ‘off-on’ fluorescence for Al³⁺ by photoinduced electron transfer (PET) mechanism of sensing. Chemosensor TPySSB binds Al³⁺ in a 1:2 ratio with an association constant 6.8×10⁵ (R²=0.98) and this 1:2 stoichiometric model is established on Job’s plot and ¹H NMR. Compared TPySSB and TPySB, it is of great importance of the existence of salicylidene unit due to its strong binding abilities of both phenol and C=N structure to the Al³⁺.

This paper presents a device for measuring and maintaining the constant water level in a tank. The device uses a capacity sensor to measure the water level. The sensor has a DC voltage output proportional to the water level in the tank. This voltage can be used in other automation too. The water level, in percentages, is displayed on a vu meter with 8 LEDs. The circuit for maintaining the constant level consists in a comparator with hysteresis. The level of the water is adjustable, using a reference voltage from a potentiometer (marked in percentages). The level of the water is compared with the prescribed level and the difference between them commands the start/stop of the water pump which supplies the tank through a relay. The device is powered by 220 V and is isolated from the environment.

Functional mobility assessments (i.e., Timed Up and Go) are commonly used clinical tools for mobility and fall risk screening in the aging population. In this work, we proposed a new Mixed Reality (MR)-based assessment that utilized a Microsoft HoloLens^TM headset to automatically lead and track the performance of functional mobility tests, and subsequently evaluated its validity in comparison with reference inertial sensors. Twenty-two healthy adults (10 older, 12 young) participated in this study. An automated functional mobility assessment app was developed based on the HoloLens platform. Mobility performance was recorded with the headset built-in sensor and validated with reference inertial sensor (Opal, APDM) taped on the headset and lower back. Results indicate vertical kinematic measures by HoloLens was in good agreement with the reference sensor (Normalized RMSE ~ 10%). Additionally, the HoloLens-based test completion time was in perfect agreement with clinical standard stopwatch measure. Overall, our preliminary investigation indicates that it is possible to use an MR headset to automatically guide users to complete common mobility tests with good measurement accuracy, thus it has great potential to provide objective and efficient sensor-based mobility assessment.

Sparse recovery of fluid flows using data-driven proper orthogonal decomposition (POD) basis is systematically explored in this work. Fluid flows are manifestations of nonlinear multiscale PDE dynamical systems with inherent scale separation that impact the system dimensionality. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches require the knowledge of the underlying basis space spanning the manifold in which the system resides. In this study, we employ an approach that learns basis from singular value decomposition (SVD) of training data to reconstruct sparsely sensed information. This results in a set of four control parameters for sparse recovery, namely, the choice of basis, system dimension required for sufficiently accurate reconstruction, sensor budget and their placement. The choice of control parameters implicitly determines the choice of algorithm as either $l_2$ minimization reconstruction or sparsity promoting $l_1$ norm minimization reconstruction. In this work, we systematically explore the implications of these control parameters on reconstruction accuracy so that practical recommendations can be identified. We observe that greedy-smart sensor placement provides the best balance of computational complexity and robust reconstruction for marginally oversampled cases which happens to be the most challenging regime in the explored parameter design space.

A nanostructured electrochemical bi-sensor system for analysis of milks has been developed using the Layer by Layer technique. The non-enzymatic sensor [CHI+IL/CuPcS]2, is a layered material containing a negative film of the anionic sulfonated copper phthalocyanine (CuPcS) acting as electrocatalytic material, and a cationic layer containing a mixture of an ionic liquid (IL) (1-butyl-3-methylimidazolium tetrafluoroborate) that enhances the conductivity and chitosan (CHI) that facilitates the enzyme immobilization. The biosensor ([CHI+IL/CuPcS]2-GAO) results from the immobilization of galactose oxidase on the top of the LbL layers. FTIR, UV-vis and AFM have confirmed the proposed structure and cyclic voltammetry has demonstrated the amplification caused by the combination of materials in the film. Sensors have been combined to form an electronic tongue for milk analysis. Principal Component Analysis has revealed the ability of the sensor system to discriminate between milk samples with different lactose content. Using PLS-1 calibration models, correlations have been found between the voltammetric signals and chemical parameters measured by classical methods. PLS-1 models provide excellent correlations with lactose content. Additional information about other components such as fats, proteins and acidity can also be obtained. The method developed is simple and the short response time permits its use in assaying milk samples on-line.

The present article is dealing with the state of question about building-energy monitoring systems used for data collection to estimate the Heat Lost Coefficient (HLC) with existing methods and so determinate buildings’ Envelope Energy Performance (EEP). In addition, the data requirements of HLC estimation methods are related with commonly used methods for fault detection, calibration and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests since 1978, a qualitative and quantitative analysis of Monitoring and Controlling System (MCS) specifications has been carried out. Although most actual Buildings Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply the HLC estimation methods.

A plasmonic refractive index (RI) sensor based on metal-insulator-metal (MIM) waveguide coupled with concentric double rings resonator (CDRR) is proposed and investigated numerically. Utilizing the novel supermodes of the CDRR, the FWHM of the resonant wavelength can be modulated, and a sensitivity of 1060 nm/RIU with high figure of merit (FOM) 203.8 is realized in the near-infrared region. The unordinary modes as well as the influence of structure parameters on the sensing performance are also discussed. Such plasmonic sensor with simple framework and high optical resolution could be applied to on-chip sensing systems and integrated optical circuits. Besides, the special cases of bio- sensing and triple rings are also discussed.

This work focuses on the design improvement of a tri-axial piezoresistive accelerometer specifically designed for head injuries monitoring where medium-G impacts are common, for example in sports such as racing cars or American Football. The device requires the highest sensitivity achievable with a single proof mass approach, and a very low error (<1%) as the accuracy for these types of applications is paramount. The optimization method differs from previous work as it is based on the progressive increment of the sensor mass moment of inertia (MMI) in all three axes. The work numerically demonstrates that an increment of MMI determines an increment of device sensitivity with a simultaneous reduction of cross-talk in the particular axis under study. The final device shows a sensitivity increase of about 80% in the Z-axis and a reduction of cross-talk of 18% respect to state-of-art sensors available in the literature. Sensor design, modelling and optimization are presented, concluding the work with results, discussion and conclusion.

This paper address the development of a new technic in the sleep analysis domain. Sleep is defined as a periodic physiological state during which vigilance is suspended and reactivity to external stimulations diminished. We sleep on average between six and nine hours per night and our sleep is composed of four to six cycles of about 90-minutes each. Each of these cycles is composed of a succession of several stages of sleep, more or less deep. The analysis of sleep is usually done using a polysomnography. This examination consists of recording, among other things, electrical cerebral activity by electroencephalography (EEG), ocular movements by electrooculography (EOG) and chin muscle tone by electromyography (EMG). The recording is done mostly in a hospital, more specifically in a service for monitoring the pathologies related to sleep. The readings are then interpreted manually by an expert to generate a hypnogram, a curve showing the succession of sleep stages during the night in 30-second epochs. The proposed method is based on the follow-up of the thermal signature that makes it possible to classify the activity into three classes: "awakening", "calm sleep" and "agitated sleep". The contribution of this non-invasive method is part of the screening of sleep disorders, to be validated by a more complete analysis of the sleep. The measure provided by this new system, based on temperature monitoring (patient and ambient), aims to be integrated into the tele-medicine platform developed within the framework of the Smart-EEG project by the SYEL - SYstèmes ELectroniques team. Analysis of the data collected during the first surveys carried out with this method showed a correlation between thermal signature and activity during sleep. The advantage of this method lies in its simplicity and the possibility of carrying out measurements of activity during sleep and without direct contact with the patient at home or hospitals.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

Reactive NO_x is one of the major air pollutants, which also plays a key role as greenhouse gas. Many research efforts have been devoted to not only detection of NO_x in air but also abatement of NO_x emission. The aim of this mini review is to provide a panoramic snapshot of the electrochemical analysis methods for the emission and detection of NO_x in atmosphere, with special emphasis on NO_x sensor. The electrochemical detecting mechanism and materials for fabricating electrochemical gas sensors are discussed and the prospects and challenges in this area are also evaluated. This work will serve as a useful source to inform the interested audience of the latest developments and applications in the field of NO_x emission and electrochemical detection.

Wearable technology has been proposed as a potential tool to change the way of human life, such as the smart bracelet and the Google Glass. In the wearable technology, the inertial sensor has great significance in tracking the object movements. The paper focused on detecting the movements of user’s finger based on the inertial sensor to give the control signals. Firstly, the attitude matrix, which represented the transformation relation of carrier coordinate system and the navigation coordinate system, was obtained. Secondly, the attitude matrix was expressed based on the quaternions. Thirdly, the finger gesture was processed by the attitude matrix to get the attitude angle. Finally, the robot was controlled by attitude angle to make the moving action. The experimental results showed the detection of the finger movement is effective.

Aiming to address the issue that the existing multi-view stereo reconstruction methods have insufficient adaptability to the repetitive patterns and weak textures in the multi-view images, this paper proposes a three-dimensional (3D) reconstruction algorithm based on feature enhancement and weight optimization MVSNet (Abbreviated as FEWO-MVSNet). To obtain accurate and detailed global and local features, we first develop an adaptive feature enhancement approach to obtain multi-scale information from the images. Second, we introduce an attention mechanism and a spatial feature capture module to enable high-sensitivity detection for weak texture features. Third, based on the 3D convolutional neural network, the fine depth map for multi-view images can be predicted and the complete 3D model is subsequently reconstructed. Last, we evaluated the proposed FEWO-MVSNet through training and testing on the DTU, BlendedMVS, and Tanks&amp;Temples datasets. The results demonstrate significant superiorities of our method for 3D reconstruction from multi-view images, with our method ranking first in accuracy and second in completeness when compared to the existing representative methods.

This paper presents the experimental characterization and numerical optimization of the parametric design of inductors used for the induction heating process of metallic samples. Here, the variation of the geometric parameters of the inductor (the distance between the coil and the material, as well as the number of turns and separation between them), was analyzed. In addition, changes in the power input values of the heating system variables were also investigated; and finally, the heating time of the materials was taken into consideration. For the multi-objective numerical optimization process, a full factorial 2K experimental design was devised. Afterward, from preliminary experiments, a response surface was performed, from which the optimal candidates for the heating process were obtained. In the experiments, a methodology was designed for the acquisition and processing of heating data on ASTM E8 tensile test specimens. The main objective of this paper is to determine both the geometric parameters of the coil as well as the power input values of the circuit, which allow for generating uniform heating in the specimens. The objectives of the optimization process are to maximize the heating temperature minimizing the consumption of electrical current in the induction circuit. The data generated in the heating of the specimens were acquired using a Fluke® TI10 thermographic camera, while a BK Precision® regulated power supply was used for the induction circuit. The parameters obtained in the optimization process for the optimal candidates were corroborated through experimental tests. The paper shows the initial (non-optimal) and final (optimal) results obtained from the experimental tests. The heating station created with the optimal coil will be used for mechanical characterization tests on metallic materials at temperatures above room temperature.

Understanding plant species and community status is the most fundamental step in assessing biodiversity and considering environmental conservation, and these depend on the identification of plant species. Plant identification is usually done manually using botanical illustrations, but due to the diversity of plant species, identifying a single species from among many candidates listed in illustrated books is a labor-intensive and time-consuming task, even for experts. Against this background, systems for automatic plant species recognition are in demand by experts and amateurs alike. In this study, we proposed a method for automatic plant identification by applying multifractal analysis to images of leaf veins, including veinlets. In the experiment, we conducted two experiments: one to see if there is a change in the number of dimensions depending on the degree of leaf growth, and the other to see how the number of dimensions changes depending on the taxonomic group. As a result, there was almost no change depending on the degree of growth, and the number of dimensions varied depending on the taxonomic group, suggesting that different taxonomic groups can be classified into different taxonomic groups, suggesting that multifractal analysis can be applied to leaf vein images, including veinlets, to enable automatic plant identification.

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. Addressing critical challenges in EMS solutions such as data overhead, single-point failures, nonstationary environments, and scalability, the proposed LSD-MADDPG effectively harmonizes individual building needs with entire community energy management goals. By leveraging and sharing only strategic information among agents, the proposed approach demonstrates to optimize the EMS decision-making processes, while enhancing training efficiency and safeguarding data privacy - a critical concern in the community setting. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings, aligning individual buildings’ energy management actions with overall community goals in a fair manner, highlighting its potential for future smart community energy management advancements.

Bipolar disorder (BD) is a complex mental health condition characterized by alternating periods of mania and depression. This review provides a comprehensive overview of BD, focusing on its genetics, causal hypotheses, clinical signs and evolution, potential biomarkers with an emphasis on electroencephalography (EEG), and treatment options. Genetic studies have identified several susceptibility genes and polygenic risk scores, highlighting the heritable nature of BD. Various causal hypotheses, including monoamine dysregulation, kindling, circadian rhythm disruptions, and neuroinflammation, have been proposed to explain its pathophysiology. The clinical course of BD is characterized by recurrent episodes of mania/hypomania and depression, with significant heterogeneity among individuals. EEG and other biomarkers hold promise for improving diagnosis, predicting treatment response, and elucidating the neurobiological underpinnings of BD. Current treatment options include pharmacotherapy, psychotherapy, and neuromodulation techniques, which can be tailored to individual needs based on clinical presentation and response to treatment. Future research should focus on identifying more specific biomarkers, developing novel treatments, and optimizing existing therapies to improve outcomes for individuals with BD.

Inherited cardiomyopathies are a diverse group of heart muscle diseases caused by genetic mutations that result in structural and functional abnormalities of the myocardium. Understanding genotype-phenotype correlations in these conditions is vital for personalized cardiac medicine, enabling targeted therapeutic strategies and predictive diagnostics. This review explores the major types of inherited cardiomyopathies—hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and restrictive cardiomyopathy (RCM)—and provides detailed insights into how different genetic mutations manifest as clinical features. The integration of multi-omics approaches and advanced disease modeling techniques has enhanced our ability to dissect these correlations. The review also discusses the implications of these findings for personalized medicine, including tailored therapeutic strategies, predictive diagnostics, and future research directions.

The utilization of Artificial Intelligence (AI) in biotechnology is widespread, addressing challenges such as elucidating disease pathophysiology and novel therapeutic approaches. AI plays a critical role in tasks such as drug manufacturing, chemical analysis, RNA and DNA sequencing, and enzyme studies by automating processes and expediting data collection and analysis. AI has particularly transformed the analysis of omics data for researchers, providing tools to handle complex and multifaceted datasets effectively. Through AI, integrative analysis of multi-omics data can be conducted in efficient ways. This article discusses the application of multi-omics and AI technologies in studying various diseases related to infection (COVID-19), mutation (cancer), and senescence (alopecia) to understand their pathophysiology. The applications of AI were performed as the aspect of the diagnosis, prognosis, and treatment for each disease. We introduce AI-driven analysis of pathophysiology can overcome traditional limitations in pathology, with diverse features showing potential in defining the causes of diseases, and potentially enhancing primary omics data in the distinguish the subtype of diseases. In this context, integrated approaches using multi-omics data and AI technology could provide fresh biological insights by comprehending the molecular mechanism of diseases and is instrumental in advancing precision medicine. We anticipate that the rapidly advancing AI-based multi-omics data analysis will significantly contribute to promoting healthy longevity.

We propose an innovative approach called SMPTC3, designed specifically to enhance security and privacy in cross-chain transaction verification. This approach addresses multi-chain, multi-participant information exchange and large-scale cross-chain transfers, resisting various types of malicious attacks. We leverage the verifiability of cross-chain transactions based on smart contracts and innovatively transform transaction information into confidential sets, organizing them into quadratic secret polynomials. By utilizing secret sharing and random distribution techniques, we construct a secure multiparty computation method, tailored for cross-chain transactions. To enhance the efficiency of cross-chain transactions, we introduce cross-chain batch processing technology, grouping inter-chain transactions into cross-chain transaction sets. Unlike traditional distributed notary technologies, SMPTC3 designates honest participants as a cross-chain notary group, reducing the time required for redundant signature confirmations and significantly lowering the possibility of malicious notaries. Theoretical analysis and empirical experiments demonstrate that SMPTC3 is highly efficient in addressing cross-chain transaction security issues.

This paper presents an enhanced Fully Connected Neural Network (FCNN) framework that incorporates manifold learning and Renormalization Group (RG) methods. By integrating Christoffel symbols, the model adapts to the curvature and topology of the data manifold, improving learning from geometrically complex data. Additionally, RG methods enable effective multi-scale data processing. We demonstrate the enhanced model’s performance on synthetic and real-world datasets, showing improved accuracy compared to standard FCNNs, particularly in complex systems simulation.

Unmanned delivery technology using unmanned aerial vehicles (UAVs) has the potential to solve issues such as traffic congestion and labor shortages. We investigated a method to construct a route between the source and destination nodes in a wireless multi-hop network by installing a radio terminal in each house. If the relay functions of all nodes in the network are in operation, the number of control messages for route construction increases, and the message relay forwarding load of each node increases. Keeping all node pairs multi-hop connectivity, we establish a method to operate the relay function with as few nodes as possible. End nodes that have not activated the relay function connect to the relay node with the closest physical distance. In this paper, we propose methods for selecting nodes to operate the relay function. Specifically, one method is based on random selection, another method is based on the number of adjacent nodes, and a third method is based on the Multipoint Relay (MPR) of the Optimized Link State Routing Protocol (OLSR). For each method, we evaluate whether the route construction is available, the number of relay nodes, the total distance of constructed routes, and the distance in population-dense regions. We clarify the characteristics of each method, and the method based on MPR is the best for constructing a network suitable for UAV navigation routes. In addition, the smaller the number of relay nodes, the more unexpected collisions on the links due to route conflicts. We clarify the minimum node density required to apply the MPR method which reduces the number of relay nodes suppressing route conflicts.

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by distilling knowledge from foundation models. Specifically, we first employ ChatGPT and DALL-E to synthesize reference images of unseen categories from text prompts. Then, the test image is aligned with text and reference images using CLIP and DINO. Finally, the predicted logits are aggregated according to their confidence to produce the final prediction.Experiments are conducted on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet. The results demonstrate that our model can significantly improve classification accuracy compared to previous approaches, achieving AUROC scores above 96\% across all test datasets. Our code is available at https://github.com/1134112149/MICW-ZIC.

Although the field of geomatics has seen multiple technological advances in the recent years which enabled new applications and simplified the consolidated ones, some tasks remain challenging, inefficient, and time- and cost-consuming. This is the case of accurate tridimensional surveys of narrow spaces. Static laser scanning is an accurate and reliable approach but impractical for extensive tunnel environments; portable laser scanning is on the other hand time-effective and efficient but not very reliable without ground control constraints. This paper describes the development process of a novel image-based multi-camera system meant to solve this specific problem: delivering accurate, reliable, and efficient results. The development is illustrated from the system conceptualization and initial investigations to the design choices and requirements for accuracy. The resulted working prototype has been put to the test to verify effectiveness of the proposed approach.

Nowadays, Multi-needle electrospinning has used to be an efficient method for producing nanofiber membranes. But in the process, the fluctuation errors in electrospinning fluid flow rate have directly influence the membrane quality, which bringing instability and still have not been solved yet. For finding out the solution to the inaccurate flow control in multi-needle, it is necessary to develop a multi-stage flow runner spinneret for large-scale production of nanofiber membrane. At first, the paper uses the finite element software COMSOL to numerically simulate the polymer flow in the runner of spinneret. Based on it, the velocity field distribution curve was draw and the velocity instability coefficient of the polymer flow was attained, which are providing theoretical guidance for the optimal design of spinneret. Next, the response surface analysis method (RSM) was used to establish a regression model between the average diameter of the nanofiber membrane and the process parameters. Then, above regression model used to perform variance data processing between the average diameter and the process parameter configuration. After that, the 2D and 3D response surface profiles and the significance analysis were completed. Finally, the stability of the nanofibers production was improved, which attained the optimal adjustment of process configuration in multi-needle electrospinning.

Low carbon supply chain management (LCSCM) strategies are required in the Indian manufacturing industries due to rapidly growing energy usage for financial and environmental concerns. LCSCM technologies face a number of challenges that limit their adoption. Eleven hurdles were selected from the literature as well as the recommendations of business and academic experts. These barriers are defined by a number of factors, including a lack of information, financial worries, environmental concerns, and governmental limits, among others. In addition, efforts are being made to quantify the most optimal relevant barriers using Multi criteria decision making (MCDM) techniques such as interpretive structural modeling (ISM) and decision-making trail and evaluation laboratory (DEMATEL). MICMAC analysis is used to estimate the sensitivity and priority of barriers based on dependency power and driving power. One of these barriers is a needed factor; three are driving elements; four barriers are linked factors; and three are self-sufficient factors. The findings of this study can help academic specialists, industry representatives, and practitioners in their ongoing attempts to adopt 'LCSCM techniques' in Indian manufacturing industries. The model is appropriately explained and applies the link between the barriers.The objective of this paper is the Evaluation and selection of most influential of Barriers to Adopt Low Carbon Supply Chain Management (LCSCM) in the Indian Manufacturing Sector is interrelated.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

With much more challenging scenarios encountered in engineering, traditional quadrotors couldn’t accomplish all desired missions perfectly. Task-driven based reconfigurable quadrotors have more latent capacity in environment with dense obstacles and narrow corridors. The translational reconfigurable quadrotors and rotational reconfigurable quadrotors are proposed in this presentation, assumptions, mathematical models and related motion control laws are given subsequently, in order to cope with hazard missions, model reference adaptive control laws are proposed, sufficient numerical simulations demonstrate the validity of the proposed control laws.

This paper addresses the critical issue of trust in Artificial Intelligence systems, especially when users might find it challenging to comprehend the internal decision-making processes of such systems. A relevant topic of research in this respect is Theory of Mind, which involves attempting to understand these systems as if they possessed beliefs, desires, and intentions. We focus on the latter: intentions, and we examine how producing explanations based on them can improve \emph{understandability} while allowing for a better interpretation of how they align with human values. We also review some existing methods for identifying intentions in AI systems and we conclude with a discussion on possible future directions in this line of research.

Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, by using the multi-scale cascade encoder architecture as the backbone, we propose a novel Multi-scale Encoder Vulnerability Detection (MEVD) approach to detect well-known high-risk vulnerabilities in smart contracts. Firstly, we use the gating mechanism to design a unique Surface Feature Encoder (SFE) to enrich the semantic information of code features. Then, by combining a Base Transformer Encoder (BTE) and a Detail CNN Encoder (DCE), we introduce a dual-branch encoder to capture the global structure and local detail features of the smart contract code, respectively. Finally, to focus the model's attention on vulnerability-related characteristics, we employ the Deep Residual Shrinkage Network (DRSN). Experimental results on three types of high-risk vulnerability datasets demonstrate performance compared to state-of-the-art methods, and our method achieves an average detection accuracy of 90%.

It is necessary to manage the power consumption and use fault-tolerance techniques to meet high reliability in Real-Time Embedded Systems, . Hence, in recent years, this is the main reason for providing an ideal approach to decrease power consumption through the worst-case behavior of instantaneous power management (peak-power management), while observing the Thermal Design Power (TDP). Although the multi-core chips in real-time embedded systems provide great opportunity for implementation of Two-phase Triple modular redundancy to meet high reliability, the energy overhead management of concurrently executing tasks is one of the challenges facing designers. In this article, we propose a scheme named Permanent Fault Aware, Two-Phase Peak-Power Management (PFA-TP3M), for scheduling real-time tasks in multi-core systems to eliminate peak power overlap from concurrent running tasks to maintain peak power consumption at the chip TDP. and through mapping different parts of each task on separate cores, prevents consecutive conflicts on cores, which have faced permanent faults to design the fault-tolerant system. Our simulation results show that our proposed scheme provides up to 700x fault tolerance increase (630x on average) compared to the state-of-the-art power management algorithm, an achievement costs up to 14%. (average 11%) increase the length of the task graph compared to it.

Chromatographic separation is a critical technique in the manufacturing processes of both chemical products and biopharmaceuticals. Its principle relies on exploiting the differences in distribution between the stationary and mobile phases to achieve the separation of mixtures. The precision of substance concentration during separation directly impacts the quality and usability of the final product. Therefore, the development of an effective and precise separation control technique has long been a vital concern in the field of chromatographic separation control. Currently, the employment of Simulated Moving Bed (SMB) technology for chromatographic separation, which allows for continuous feed, has been recognized as a cutting-edge technique. SMB is a continuous process technology that enhances the efficiency of adsorbents within the bed. This sequential multi-column SMB technology not only increases production capacity but also reduces the consumption of solvents and water. It is acknowledged as one of the cleanest manufacturing technologies in the biopharmaceutical industry. However, multi-column SMB involves various variables such as flow rates in multiple sections and valve switching times, rendering its system control highly complex. Unlike traditional control objectives that aim to minimize control output errors, the control objective of SMB is to achieve a specific proportion of substance concentration. Consequently, designing and implementing traditional control theories for SMB is challenging, leading to SMB systems largely being controlled based on simple PLC controls. Moreover, these controls are often applicable only to single or a few-column SMB setups, limiting the effectiveness of high-capacity applications. To achieve effective control of SMB, this study employs an adjustable intelligent fuzzy controller with a structure like an approximate neural network (NN) for SMB control research. Simulation results demonstrate that the intelligent controller effectively achieves desirable control outcomes for the SMB system.

This work tackles the problem of image restoration, a crucial task in many fields of applied sciences, focusing on removing degradation caused by blur and noise during the acquisition process. Drawing inspiration from the multi-penalty approach based on the Uniform Penalty principle introduced in [Bortolotti et al. arXiv.math.NA/2309.14163], we develop here a new image restoration model and an iterative algorithm for its effective solution. The model incorporates pixel-wise regularization terms and establishes a rule for parameters selection, aiming to restore images through the solution of a sequence of constrained optimization problems. To achieve this, we present a modified version of the Newton Projection method, adapted to multi-penalty scenarios, and prove its convergence. Numerical experiments demonstrate the efficacy of the method in eliminating noise and blur while preserving the image edges.

Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex and nonlinear nature of this problem, we introduce a novel multi-agent DQN framework. This framework defines a large action space, state space, and reward functions, enabling us to learn a near-optimal policy. Simulation results demonstrate the superiority of our Parameterized Deep DQN (PD-DQN) approach when compared to traditional DQN and RL methods. Specifically, we show that our approach outperforms traditional DQN methods in terms of convergence speed and final performance. Additionally, our approach shows 72.2 % and 108.5 % improvement over DQN methods and RL method respectively in handling large-scale multi-agent problems in M-MIMO networks.