Search Results (20)

The dual-active-bridge (DAB) converter has emerged as a promising topology for renewable energy applications and microgrid systems due to its high power density and bidirectional energy-transfer capability. Enhancing the overall efficiency and reliability of DAB converters requires the simultaneous realization of zero-voltage switching (ZVS) across all switches and the minimization of current stress over wide load and voltage ranges—two objectives that are often in conflict. Conventional modulation strategies with limited degrees of freedom fail to meet these dual goals effectively. To address this challenge, this paper introduces an enhanced integrated optimization strategy based on triple phase shift (EIOS-TPS). This approach formulates the power transmission requirement as an equality constraint and incorporates ZVS and mode boundary conditions as inequalities, resulting in a comprehensive optimization framework. Optimal phase-shift parameters are obtained using the Karush–Kuhn–Tucker (KKT) conditions. To mitigate zero-current switching (ZCS) under a light load and achieve full-range ZVS with reduced current stress, a modulation factor λ is introduced, enabling a globally optimized control trajectory. An experimental 1176 W prototype is developed to validate the proposed method, which achieves full-range ZVS while maintaining low current stress. In the low-power region, it improves efficiency by up to 2.2% in buck mode and 2.0% in boost mode compared with traditional control strategies, reaching a peak efficiency of 96.5%. Full article

Superparamagnetic iron oxide nanoparticles (SPIONs) have gained significant attention for Magnetic Fluid Hyperthermia (MFH)-based cancer therapy. However, achieving high heating efficiency under a biologically safe Alternating Magnetic Field (AMF) remains a challenge. This study investigates the synthesis and optimization of SPIONs encapsulated in TPGS-stabilized PLGA nanoparticles (TPS-NPs) using a modified single emulsion solvent evaporation (M-SESE) method. The aim was to achieve efficient magnetic heating under biologically safe AMF conditions while maintaining biocompatibility and colloidal stability, making these magnetic nanoplatforms suitable for MFH-based cancer treatment. TPS-NPs were characterized using various techniques, including Dynamic Light Scattering (DLS), Atomic Force Microscopy (AFM), Transmission Electron Microscopy (TEM), and Superconducting Quantum Interference Device (SQUID) magnetometry, to evaluate their hydrodynamic size (Dh), zeta potential (ζ), encapsulation efficiency, and superparamagnetic properties. Calorimetric MFH studies demonstrated superior heating efficiency, with Specific Absorption Rate (SAR) and Intrinsic Loss Power (ILP) values optimized at an AMF of 4.1 GAm⁻¹s⁻¹, remaining within Hergt’s biological safety limit (~5 GAm⁻¹s⁻¹). These findings suggest that SPION-encapsulated TPS-NPs exhibit enhanced heat induction, making them promising candidates for MFH-based cancer therapy. The study highlights their potential as multifunctional nanoplatforms for magnetic hyperthermia therapy, paving the way for clinical translation in oncology for advanced cancer treatment. Full article

The interference of human activities in water bodies has contributed to a deterioration in water quality. With the advancement of the Internet of Things (IoT), aided by transmission technologies such as LoRa (Long Range), low-cost solutions have emerged for long-distance environment monitoring scenarios. One key challenge in such IoT-based systems is selecting LoRa transmission parameters to ensure efficient data exchange among nodes, adapting to varying network conditions. Well-known strategies adapt transmission parameters according to network context through information exchange among nodes and LoRa gateway(s). In this work, we introduce a novel LoRa parameter selection algorithm by incorporating three major LoRa metrics (RSSI, SNR, and PDR) and conducting a comprehensive characterization and validation in the forest environment to build a set of reference values of transmission quality, which are employed in a binary search methodology, utilizing the R-array, representing the transmission quality according to LoRa parameters. The experimental results indicate that the proposed algorithm achieves a 16.20% reduction in Time on Air (ToA). Furthermore, our algorithm optimized the transmission power (TP) selection, achieving at least 38% lower energy consumption than ADR TP parameters. These results highlight that our proposed algorithm can enhance the transmissions in a rainforest environment. Full article

Wireless communication technologies (WSN) are pivotal for the successful deployment of the Internet of Things (IoT). Among them, long-range (LoRa) and long-range wide-area network (LoRaWAN) technologies have been widely adopted due to their ability to provide long-distance communication, low energy consumption (EC), and cost-effectiveness. One of the critical issues in the implementation of wireless networks is the selection of optimal transmission parameters to minimize EC while maximizing the packet delivery ratio (PDR). This study introduces a reinforcement learning (RL) algorithm, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER), designed to optimize network transmission parameter selection, particularly the spreading factor (SF) and transmission power (TP). This research explores a variety of network scenarios, characterized by different device numbers and simulation times. The proposed approach demonstrates the best performance, achieving a 17.2% increase in the packet delivery ratio compared to the traditional Adaptive Data Rate (ADR) algorithm. The proposed DDQN-PER algorithm showed PDR improvement in the range of 6.2–8.11% compared to other existing RL and machine-learning-based works. Full article

Currently, a variety of Low-Power Wide-Area Network (LPWAN) technologies offer diverse solutions for long-distance communication. Among these, Long-Range Wide-Area Network (LoRaWAN) has garnered considerable attention for its widespread applications in the Internet of Things (IoT). Nevertheless, LoRaWAN still faces the challenge of channel collisions when managing dense node communications, a significant bottleneck to its performance. Addressing this issue, this study has developed a novel “time allocation adaptive Data Rate” (TA-ADR) algorithm for network servers. This algorithm dynamically adjusts the spreading factor (SF) and transmission power (TP) of LoRa (Long Range) nodes and intelligently schedules transmission times, effectively reducing the risk of data collisions on the same frequency channel and significantly enhancing data transmission efficiency. Simulations in a dense LoRaWAN network environment, encompassing 1000 nodes within a 480 m × 480 m range, demonstrate that compared to the ADR+ algorithm, our proposed algorithm achieves substantial improvements of approximately 30.35% in data transmission rate, 24.57% in energy consumption, and 31.25% in average network throughput. Full article

Quantum computing allows the implementation of powerful algorithms with enormous computing capabilities and promises a secure quantum Internet. Despite the advantages brought by quantum communication, certain communication paradigms are impossible or cannot be completely implemented due to the no-cloning theorem. Qubit retransmission for reliable communications and point-to-multipoint quantum communication (QP2MP) are among them. In this paper, we investigate whether a Universal Quantum Copying Machine (UQCM) generating imperfect copies of qubits can help. Specifically, we propose the Quantum Automatic Repeat Request (QARQ) protocol, which is based on its classical variant, as well as to perform QP2MP communication using imperfect clones. Note that the availability of these protocols might foster the development of new distributed quantum computing applications. As current quantum devices are noisy and they decohere qubits, we analyze these two protocols under the presence of various sources of noise. Three major quantum technologies are studied for these protocols: direct transmission (DT), teleportation (TP), and telecloning (TC). The Nitrogen-Vacancy (NV) center platform is used to create simulation models. Results show that TC outperforms TP and DT in terms of fidelity in both QARQ and QP2MP, although it is the most complex one in terms of quantum cost. A numerical study shows that the QARQ protocol significantly improves qubit recovery and that creating more clones does not always improve qubit recovery. Full article

The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network. Full article

Here we report the synthesis and investigation of bulk and nano-sized La_0.7Ba_0.3−xCa_xMnO₃ (x = 0, 0.15, 0.2 and 0.25) compounds that are promising candidates for magnetic refrigeration applications. We compare the structural and magnetic properties of bulk and nano-scale polycrystalline La_0.7Ba_0.3−xCa_xMnO₃ for potential use in magnetic cooling systems. Solid-state reactions were implemented for bulk materials, while the sol–gel method was used for nano-sized particles. Structurally and morphologically, the samples were investigated by X-ray diffraction (XRD), optical microscopy and transmission electron microscopy (TEM). Oxygen stoichiometry was investigated by iodometry. Bulk compounds exhibit oxygen deficiency, while nano-sized particles show excess oxygen. Critical magnetic behavior was revealed for all samples using the modified Arrott plot (MAP) method and confirmed by the Kouvel–Fisher (KF) method. The bulk polycrystalline compound behavior was better described by the tricritical field model, while the nanocrystalline samples were governed by the mean-field model. Resistivity in bulk material showed a peak at a temperature T_p1 attributed to grain boundary conditions and at T_p2 associated with a Curie temperature of T_c. Parent polycrystalline sample La_0.7Ba_0.3MnO₃ has T_c at 340 K. Substitution of x = 0.15 of Ca brings T_c to 308 K, and x = 0.2 brings it to 279 K. Nanocrystalline samples exhibit a very wide effective temperature range in the magnetocaloric effect, up to 100 K. Bulk compounds exhibit a high and sharp peak in magnetic entropy change, up to 7 J/kgK at 4 T at T_c for x = 0.25. To compare the magnetocaloric performances of the studied compounds, both relative cooling power (RCP) and temperature-averaged entropy change (TEC) figures of merit were used. RCP is comparable for bulk polycrystalline and nano-sized samples of the same substitution level, while TEC shows a large difference between the two systems. The combination of bulk and nanocrystalline materials can contribute to the effectiveness and improvement of magnetocaloric materials. Full article

This work concentrates on the problem of optimizing the cost of a passive wavelength division multiplexing (WDM) optical network used as a transport network for carrying the xHaul packet traffic between a set of remote radio sites and a central hub in a 5G radio access network (RAN). In this scope, we investigate the flexible use of optical add-drop multiplexers (OADMs) for the aggregation of traffic from a number of remote sites, where the type/capacity of optical devices—OADMs and optical multiplexers (MUXs)—is selected in accordance with the traffic demand. The approach is referred to as Flex-O. To this end, we formulate the xHaul network planning problem consisting in the joint provisioning of transmission paths (TPs) between the remote sites and the hub with optimized selection and placement of OADMs on the paths and proper selection of MUXs at the ends of the TPs. The problem formulation takes into accounts the optical power budget that limits the maximum transmission distance in a function of the amount and type of optical devices installed on the TPs. The network planning problem is modeled and solved as a mixed-integer linear programming (MILP) optimization problem. Several network scenarios are analyzed to evaluate the cost savings from the flexible (optimized) use of OADMs. The scenarios differ in terms of the availability of OADMs and the capacity of the WDM devices applied on the TPs. The numerical experiments performed in three mesh networks of different size show that the cost savings of up to between 35 and 45% can be achieved if the selection of OADMs is optimized comparing to the networks in which either single-type OADMs are used or the OADMs are not applied. Full article

The spatial and temporal distributions of dust aerosol and its radiative heating effect over Taklimakan Desert (TD) and Tibetan Plateau (TP) were analyzed using the CALIPSO aerosol products and the SBDART model during 2007–2020. The annual dust aerosol optical depths (DAOD at 532 nm) ranged from 0.266 to 0.318 over TD and 0.086 to 0.108 over TP, with means of 0.286 ± 0.015 and 0.097 ± 0.006, respectively. The regional mean DAODs of TD (TP) from spring to winter were 0.375 ± 0.020 (0.107 ± 0.010), 0.334 ± 0.028 (0.110 ± 0.010), 0.235 ± 0.026 (0.071 ± 0.008), and 0.212 ± 0.045 (0.083 ± 0.011), respectively. The maximal (minimal) seasonal DAOD of TD appeared in spring (winter), while that of TP appeared in summer (autumn). Although neither the annual nor the seasonal DAODs showed a statistically significant trend over both TD and TP, their yearly fluctuations were apparent, showing coefficients of variation of 0.053 and 0.065 over TD and TP, respectively. The profile of dust extinction coefficient (σ_D) showed the maximum in spring and summer over TD and TP, respectively. It showed a weak increasing trend of σ_D over both TD and TP in spring, but a decreasing trend in autumn. The dust of TD is concentrated within 1–4 km, where the annual averaged shortwave (SW) dust heating rates (DHRs) were larger than 2 K·day⁻¹ from March to September. Over TP, the dust heating layer with SW DHR > 2 K·day⁻¹ ranged from 3 to 4 km during March to June. The SW DHR was much larger in spring and summer than in the other two seasons over both regions, with the maximum in spring. A relatively strong dust heating layer with top >5 km appeared along the north slope of the TP, indicating an important energy transport channel from TD to TP, especially in spring and summer. It showed an increasing trend of the SW DHR over both TD and TP in spring and winter, but a decreasing trend in summer and autumn. Over TD, the most powerful heating appeared within 2–4 km, but the strength and the area of high-value DHR reduced from spring to winter. The highest SW DHR of TP appeared over the Qaidam Basin, acting as an important transmission channel of dust and its heating. For the columnar mean of lower than 10 km, the annual mean DHRs of TD and TP were 0.93 and 0.48 K⋅day⁻¹, respectively. Although the DAOD and DHR of TP were both lower, its shortwave dust heating efficiency (DHE) was 1.7 times that of TD, which suggested that the same amount of dust imported to TP could generate a stronger heating effect than it did at the source. Full article

A microgrid generates a large amount of power data during daily operation, which needs to be safely transferred, stored, and deleted. In this paper, we propose a secure storage and deletion verification scheme that combines blockchain and edge computing for the problems of limited storage capacity of blockchain and unverifiable data deletion. Firstly, edge computing is used to preprocess power data to reduce the amount of data and to improve the quality of data. Secondly, a hybrid encryption method that combines the improved ElGamal algorithm and the AES-256 algorithm is used to encrypt outsourcing data, and a secure storage chain is built based on the K-Raft consensus protocol to ensure the security of data in the transmission process. Finally, after initiating a data deletion request and successfully deleting the data, a deletion proof is generated and stored in the chain built, based on the Streamlet consensus protocol. The experimental results illustrate that the basic computing cost, block generation time, and communication delay of this scheme are the most efficient; the efficiency of the improved ElGamal algorithm is three times that of the traditional algorithm; the transaction throughput of the the double-layer blockchain can reach 13,000 tps at most. This scheme can realize the safe storage of microgrid data, and can also realize the efficient deletion and verification of outsourcing data. Full article

Power line communication (PLC) is an important interconnection technology for the smart grid, but the robustness of PLC transmission is faced with a great challenge due to strong non-Gaussian noise and interference. In this paper, a narrowband interference (NBI) resistant preamble is designed, and an effective timing and frequency synchronization method is proposed for OFDM-based PLC systems in the smart grid, which is capable of simultaneously conveying some bits of transmission parameter signaling (TPS) as well. In the time domain, the cyclic extension of the training OFDM symbol is scrambled, which makes it feasible to combat against NBI contamination. More accurate timing detection and sharper correlation peak can be implemented under the power line channel and the AWGN channel in the presence of NBI, compared with the conventional Schmidl’s and Minn’s methods with the same preamble length. Furthermore, the TPS transmitted using the proposed method is also immune from the NBI. The proposed method is capable of improving the synchronization performance of the PLC transmission significantly, which is verified by theoretical analysis and computer simulations. Full article

LoRaWAN is renowned and a mostly supported technology for the Internet of Things, using an energy-efficient Adaptive Data Rate (ADR) to allocate resources (e.g., Spreading Factor (SF)) and Transmit Power (TP) to a large number of End Devices (EDs). When these EDs are mobile, the fixed SF allocation is not efficient owing to the sudden changes caused in the link conditions between the ED and the gateway. As a result of this situation, significant packet loss occurs, increasing the retransmissions from EDs. Therefore, we propose a Resource Management ADR (RM-ADR) at both ED and Network Sides (NS) by considering the packet transmission information and received power to address this issue. Through simulation results, RM-ADR showed improved performance compared to the state-of-the-art ADR techniques. The findings indicate a faster convergence time by minimizing packet loss ratio and retransmission in a mobile LoRaWAN network environment. Full article

Kefir, a fermented probiotic drink was tested for its potential anti-oxidative, anti-apoptotic, and neuroprotective effects to attenuate cellular oxidative stress on human SH-SY5Y neuroblastoma cells. Here, the antioxidant potentials of the six different kefir water samples were analysed by total phenolic content (TPC), total flavonoid content (TFC), ferric reducing antioxidant power (FRAP), and 2,2′-diphenyl-1-picrylhydrazyl radical (DPPH) assays, whereas the anti-apoptotic activity on hydrogen peroxide (H₂O₂) induced SH-SY5Y cells was examined using MTT, AO/PI double staining, and PI/Annexin V-FITC assays. The surface and internal morphological features of SH-SY5Y cells were studied using scanning and transmission electron microscopy. The results indicate that Kefir B showed the higher TPC (1.96 ± 0.54 µg GAE/µL), TFC (1.09 ± 0.02 µg CAT eq/µL), FRAP (19.68 ± 0.11 mM FRAP eq/50 µL), and DPPH (0.45 ± 0.06 mg/mL) activities compared to the other kefir samples. The MTT and PI/Annexin V-FITC assays showed that Kefir B pre-treatment at 10 mg/mL for 48 h resulted in greater cytoprotection (97.04%), and a significantly lower percentage of necrotic cells (7.79%), respectively. The Kefir B pre-treatment also resulted in greater protection to cytoplasmic and cytoskeleton inclusion, along with the conservation of the surface morphological features and the overall integrity of SH-SY5Y cells. Our findings indicate that the anti-oxidative, anti-apoptosis, and neuroprotective effects of kefir were mediated via the upregulation of SOD and catalase, as well as the modulation of apoptotic genes (Tp73, Bax, and Bcl-2). Full article

Most Internet of Things (IoT) systems are based on the wireless sensor network (WSN) due to the reduction of the cable layout cost. However, the battery life of nodes is a key issue when the node is powered by a battery. A Low-Power WSN Protocol with ADR and TP Hybrid Control is proposed in this paper to improve battery life significantly. Besides, techniques including the Sub-1GHz star topology network with Time Division Multiple Access (TDMA), adaptive data rate (ADR), and transmission power control (TPC) are also used. The long-term testing results show that the nodes with the proposed algorithm can balance the communication quality and low power consumption simultaneously. The experimental results also show that the power consumption of the node with the algorithm was reduced by 38.46-54.44% compared with the control group. If using AAA battery with 1200 mAh, the node could run approximately 4.2 years with the proposed hybrid control algorithm with an acquisition period of under 5 s. Full article