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The Internet of Things (IoT) is a global network of interconnected computing, sensing, and networking devices that can exchange data and information via various network protocols. It can connect numerous smart devices thanks to recent advances in wired, wireless, and hybrid technologies. Lightweight IoT protocols can compensate for IoT devices with restricted hardware characteristics in terms of storage, Central Processing Unit (CPU), energy, etc. Hence, it is critical to identify the optimal communication protocol for system architects. This necessitates an evaluation of next-generation networks with improved characteristics for connectivity. This paper highlights significant wireless and wired IoT technologies and their applications, offering a new categorization for conventional IoT network protocols. It provides an in-depth analysis of IoT communication protocols with detailed technical information about their stacks, limitations, and applications. The study further compares industrial IoT-compliant devices and software simulation tools. Finally, the study provides a summary of the current challenges, along with a broad overview of the future directions to tackle the challenges, in the next IoT generation. This study aims to provide a comprehensive primer on IoT concepts, protocols, and future insights that academics and professionals can use in various contexts.

Power Line Communication (PLC) facilitates the usage of power cables to transmit data. The issue is that sending data to unavailable nodes is time-consuming. Machine Learning has solved this by predicting a node having optimum readings. The more the machine learning models learn, the more accurate they become, as the model becomes always updated with the node’s continuous availability status, so self-training algorithms have been used. A dataset of 2000 instances of a node of a 500-node implemented PLC network has been collected. These instances consist of CINR(Carrier-to-Interference plus Noise Ratio), SNR(Signal-to-Noise Ratio), and RSSI(Received Signal Strength Indicator) as features for the label, which is a node is UP/Down. The data set has been split into 85% as a training set and 15% as a testing set. 15% of the training data are unlabeled. Self-training classifier has been used to allow Light Gradient Boosting Machine (LGBM) and Support Vector Machine (linear and non-linear kernel) to behave in a self-training manner as well as the training of label propagation and label spreading algorithms. Supervised Learning algorithms (Random Forest and logistic regression) have been trained on the dataset to compare the results. The best model is the Label Spreading, which resulted in accuracy equals 94.67%, f1-score equals 0.947, precision is 0.946, and recall equals 0.947 with training time equals 0.018 sec. and memory consumption equals 0.99 MB.

The aim of this study is to develop an artificial intelligence (AI)-driven pipeline for forecasting blood glucose levels to mitigate risks associated with hypoglycemia and hyperglycemia. The main research question focuses on the effectiveness of hybrid data preprocessing and feature engineering in enhancing glucose level predictions. The proposed approach employs a hybrid methodology for handling missing data and advanced feature engineering techniques. A recurrent neural network (RNN) model is developed to forecast glucose levels with a lead time of 30 minutes. The model is evaluated using metrics such as root mean square error (RMSE) and mean absolute error (MAE). Experimental results indicate that the proposed pipeline achieves an average RMSE of 19.64 ± 0.11 and an MAE of 13.54 ± 0.11 across all patients. The results demonstrate improved forecasting accuracy, enabling early detection of critical glucose fluctuations. The integration of hybrid preprocessing and RNN modeling effectively predicts glucose levels, providing valuable insights for diabetes management. This approach supports better prevention of glucose emergencies, ultimately enhancing the quality of life for individuals with diabetes.

Power Line Communication technology uses power cables to transmit data. Knowing whether a node is working in advance without testing saves time and resources, leading to the proposed model. The model has been trained on three dominant features, which are SNR (Signal to Noise Ratio), RSSI (Received Signal Strength Indicator), and CINR (Carrier to Interference plus Noise Ratio). The dataset consisted of 1000 readings, with 90% in the training set and 10% in the testing set. In addition, 50% of the dataset is for class 1, which indicates whether the node readings are optimum. The model is trained with multi-layer perception, K-Nearest Neighbors, Support Vector Machine with linear and non-linear kernels, Random Forest, and adaptive boosting (ADA) algorithms to compare between statistical, vector-based, regression, decision, and predictive algorithms. ADA boost has achieved the best accuracy, F-score, precision, and recall, which are 87%, 0.86613, 0.9, 0.8646, respectively.

Xanthomicrol, a trimethoxylated hydroxyflavone, is the main active component of Dracocephalum kotschyi Boiss leaf extract. Preliminary in vitro studies identified this compound as a potential antiangiogenic and anticancer agent. This study aimed to evaluate in vivo anticancer effect of xanthomicrol and investigate its molecular mechanism of action in a mouse melanoma (B16F10) model. Effect of xanthomicrol on B16F10 melanoma cell viability was determined using the MTT assay. For in vivo experiments, C57BL/6 mice were inoculated subcutaneously with B16F10 cells. After five days, once daily administration of xanthomicrol, thalidomide, or vehicle was commenced and continued for 21 consecutive days. On the 26th day, blood samples and tumor biopsies were taken for subsequent molecular analysis. Xanthomicrol showed inhibitory effect on viability of B16F10 melanoma cells (IC50 value: 3.433 μg/ml). Initial tumor growth, tumor volume and weight, and angiogenesis were significantly decreased in xanthomicrol-treated animals compared with those in vehicle group. Protein expression of phosphorylated Akt, mRNA expressions of HIF-1α and VEGF in tumor tissues, and serum VEGF were significantly decreased in xanthomicrol-treated animals compared with vehicle-treated animals. Thus, xanthomicrol inhibited cancer cell growth both in vitro and in vivo. This effect, at least in part, was exerted by interfering with PI3K/Akt signaling pathway and inhibiting VEGF secretion by tumor cells. Further studies are required to elucidate the exact molecular mechanisms of antitumor activity of xanthomicrol.

Wearable devices are a growing field of research that can have a wide range of applications. The energy harvester is the most common source of power for wearable devices as well as in wireless sensor networks that require a battery-free operation. However, their power is restricted; consequently, power saving is crucial for wearable devices. Finding the best schedule for the various tasks that run on the wearable device can help to reduce power consumption. This paper presents a task scheduler for wearable medical devices based on Gaining–Sharing Knowledge (GSK) algorithm. The purpose of this task scheduler is to handle the tasks of a heart rate sensor and a temperature sensor to optimize the energy consumption throughout wearable medical devices. The proposed GSK-based scheduling algorithm is assessed against the state-of-the-art technique. The data used in our experiments are collected from an in-lab prototype.

Background One of the major concerns in implantable optoelectronics is the heat generated by emitters such as light emitting diodes (LEDs). Such devices typically produce more heat than light, whereas medical regulations state that the surface temperature change of medical implants must stay below + 2 °C. The LED’s reverse current can be employed as a temperature-sensitive parameter to measure the temperature change at the implant’s surface, and thus, monitor temperature rises. The main challenge in this approach is to bias the LED with a robust voltage since the reverse current is strongly and nonlinearly sensitive to the bias voltage. Methods To overcome this challenge, we have developed an area-efficient LED-based temperature sensor using the LED as its own sensor and a CMOS electronic circuit interface to ensure stable bias and current measurement. The circuit utilizes a second-generation current conveyor (CCII) configuration to achieve this and has been implemented in 0.35 μm CMOS technology. Results The developed circuits have been experimentally characterized, and the temperature-sensing functionality has been tested by interfacing different mini-LEDs in saline models of tissue prior to in vivo operation. The experimental results show the functionality of the CMOS electronics and the efficiency of the CCII-based technique with an operational frequency up to 130 kHz in achieving a resolution of 0.2 °C for the surface temperature up to + 45 °C. Conclusions We developed a robust CMOS current-mode sensor interface which has a reliable CCII to accurately convey the LED’s reverse current. It is low power and robust against power supply ripple and transistor mismatch which makes it reliable for sensor interface. The achieved results from the circuit characterization and in vivo experiments show the feasibility of the whole sensor interface in monitoring the tissue surface temperature in optogenetics.

This study aims to evaluate the nutritive value of Ajuga iva (A. iva) harvested from three distinct altitude regions in Tunisia (Dougga, Mograne, and Nabeul). The chemical composition, phenolic concentration, gas production, and in vitro dry matter (DM) digestibility were determined. The highest concentrations of neutral detergent fiber (NDF) and acid detergent fiber (ADF) were for A. iva cultivated in Nabeul. In contrast, the highest crude protein (CP) concentration was observed in that cultivated in Mograne, and the lowest (p < 0.01) CP concentration was noted in that cultivated in Dougga. Additionally, the cultivation regions affected the concentrations of free-radical scavenging activity, total flavonoids, and total polyphenols (p < 0.01). The highest free-radical scavenging activity was observed with A. iva cultivated in Dougga and Mograne. The highest (p < 0.05) gas production rate and lag time were observed in A. iva cultivated in Mograne and Nabeul regions. DM digestibility differed between regions and methods of determination. The highest (p < 0.01) DM degradability, determined by the method of Tilley and Terry and the method of Van Soest et al., was for A. iva cultivated in Mograne and Dougga, while the lowest (p < 0.01) value was recorded for that cultivated in the Nabeul region. Likewise, metabolizable energy (ME) and protein digestibility values were higher for A. iva collected from Mograne region than that collected from the other sampling areas. In conclusion, the nutritive value of A. iva differed between regions. Therefore, care should be taken when developing recommendations for using A. iva in an entire region. Season- and region-specific feeding strategies for feeding A. iva are recommended.

Soda lime phosphate bioglass-ceramics with incorporation of small additions of TiO 2 were prepared in the metaphosphate and pyrophosphate region, using an appropriate two-step heat treatment of controlled crystallization defined by differential thermal analysis results. Identification and quantification of crystalline phases precipitated from the soda lime phosphate glasses were performed using X-ray diffraction analysis. Calcium pyrophosphate (β-Ca 2 P 2 O 7 ), sodium metaphosphate (NaPO 3 ), calcium metaphosphate (β-Ca(PO 3 ) 2 ), sodium pyrophosphate (Na 4 P 2 O 7 ), sodium calcium phosphate (Na 4 Ca(PO 3 ) 6 ) and sodium titanium phosphate (Na 5 Ti(PO 4 ) 3 ) phases were detected in the prepared glass-ceramics. The degradation of the prepared glass-ceramics were carried out for different periods of time in simulated body fluid at 37 °C using granules in the range of (0.300–0.600 mm). The released ions were estimated by atomic absorption spectroscopy and the surface textures were measured by scanning electron microscopy. Evaluation of in vivo bioactivity of the prepared glass-ceramics was carried through implanting the samples in the rabbit femurs. The results showed that the addition of 0.5 TiO 2 mol% enhanced the bioactivity while further increase of the TiO 2 content decreased the bioactivity. The effect of titanium dioxide on the bioactivity was interpreted on the basis of its action on the crystallization process of the glass-ceramics.

Effective water quality monitoring systems are essential for environmental management and public health; however, traditional monitoring methods can be inefficient, costly, and resource-intensive. This work examined India and Hong Kong as case studies, focusing on optimizing water quality parameters for improved system performance while reducing costs and power consumption. Through preprocessing techniques, including correlation matrix analysis, sensitivity analysis, and ANOVA, the initial set of seven water quality parameters was reduced to four: DO, pH, BOD, and Fecal Coliform. To overcome limitations in conventional assessment methods, deep learning models were employed to enhance water quality forecasting and classification. LSTM was used to forecast WQI values, achieving a RMSE of 0.2498. Meanwhile, CNN classified water with 97.20% accuracy, while also forecasting the selected water quality parameters. This work contributes to the field by demonstrating a lightweight, low-power deep learning approach that achieves comparable or superior performance using fewer parameters, making it suitable for real-time water monitoring in resource-limited environments.