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Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.

In collinear factorization, light-cone distribution amplitudes (LCDAs) are key ingredients to calculate the production rate of a hadron in high energy exclusive processes. For a doubly-heavy meson system (such as Bc, J/ψ, Y etc), the LCDAs contain perturbative scales that can be integrated out and then are re-factorized into products of perturbatively calculable distribution parts and non-relativistic QCD matrix elements. In this re-factorization scheme, the LCDAs are known at next-to-leading order in the strong coupling constant αs and at leading order in the velocity expansion. In this work, we calculate the Ov2 corrections to twist-2 LCDAs of S-wave Bc mesons. These results are applicable to heavy quarkonia like ηc,b, J/ψ and Y by setting mb = mc. We apply these relativistically corrected LCDAs to study their inverse moments and a few Gegenbauer moments which are important for phenomenological study. We point out that the relativistic corrections are sizable, and comparable with the next-to-leading order radiative corrections. These results for LCDAs are useful in future theoretical analyses of the productions of heavy quarkonia and Bc mesons.

To study the seismic performance of wood frame structures filled with light wood shear walls, three full-size single-layer single span wooden frame structures with infill walls were designed and manufactured. The beams and columns were connected by mortise-tenon joints, and quasi-static tests were conducted on the specimens under reversed cyclic load. The failure modes and load-displacement hysteresis performance of structures with door opening infill wall, window opening infill wall, and solid infill wall were investigated. The seismic performance was analyzed using indicators such as strength, ductility, and equivalent viscous damping ratio. The failure modes of light wood frame filling walls were the tearing of sheathing panel and the failure of nail connections. The filled wall with the opening initially exhibited inclined cracks at the corner of the opening, and then they extended to the periphery. Compared with the solid filled wall, the positive and negative bearing capacity of the structure with door opening decreased, and that of the structure with window opening decreased also. Because the specimens with opening in the filled wall were more conducive to the deformation of the structure when the bearing capacity was not significantly reduced, the ductility of the specimen with door opening was the highest.

The aerial–terrestrial integrated Internet of Things (ATI-IoT) utilizes both aerial platforms (e.g., drones and high-altitude platform stations) and terrestrial networks to establish comprehensive and seamless connectivity across diverse geographical regions. The integration offers significant advantages, including expanded coverage in remote and underserved areas, enhanced reliability of data transmission, and support for various applications such as emergency communications, vehicular ad hoc networks, and intelligent agriculture. However, due to the inherent openness of wireless channels, ATI-IoT faces potential network threats and attacks, and its security issues cannot be ignored. In this regard, incorporating physical layer security techniques into ATI-IoT is essential to ensure data integrity and confidentiality. Motivated by the aforementioned factors, this review presents the latest advancements in ATI-IoT that facilitate physical layer security. Specifically, we elucidate the endogenous safety and security of wireless communications, upon which we illustrate the current status of aerial–terrestrial integrated architectures along with the functions of their components. Subsequently, various emerging techniques (e.g., intelligent reflective surfaces-assisted networks, device-to-device communications, covert communications, and cooperative transmissions) for ATI-IoT enabling physical layer security are demonstrated and categorized based on their technical principles. Furthermore, given that aerial platforms offer flexible deployment and high re-positioning capabilities, comprehensive discussions on practical applications of ATI-IoT are provided. Finally, several significant unresolved issues pertaining to technical challenges as well as security and sustainability concerns in ATI-IoT enabling physical layer security are outlined.

In 5G-and-beyond (B5G) Internet of Things (IoT) networks, the integration of intelligent reflecting surfaces (IRSs) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) techniques can significantly improve signal quality and increase network capacity. However, a single fixed IRS lacks the dynamic adjustment capability to flexibly adapt to complex environmental changes and diverse user demands, while mmWave MIMO is constrained by limited coverage. Motivated by these challenges, we investigate the application of drone-mounted IRS-assisted MIMO communications in B5G IoT networks, where multiple IRS-equipped drones are deployed to provide real-time communication support. To fully exploit the advantages of the proposed MIMO-enabled air-to-ground integrated information transmission framework, we formulate a joint optimization problem involving beamforming, phase shift design, and drone deployment, with the objective of maximizing the sum of achievable weighted data rates (AWDRs). Given the NP-hard nature of the problem, we develop an iterative optimization algorithm to solve it, where the optimization variables are tackled in turn. By employing the quadratic transformation technique and the Lagrangian multiplier method, we derive closed-form solutions for the optimal beamforming and phase shift design strategies. Additionally, we optimize drone deployment by using a distributed discrete-time convex optimization approach. Finally, the simulation results show that the proposed scheme can improve the sum of AWDRs in comparison with the state-of-the-art schemes.

This study describes applying the visual target color and spectra of light sources to calculate the perceived luminance in a tunnel interior lighting environment. The proposed approach aims to identify the combined effects of the light source, target surface color, and human eye on the perception of luminance in a tunnel interior lighting environment. The new method was tested in DIALux software using three light-emitting diodes (LEDs) with correlated color temperatures (CCTs) of 3000 K, 4000 K, and 6000 K, as well as four observed targets with red, yellow, blue, and green colors. Overall findings demonstrated that the yellow surface target’s mesopic luminance for the specified light source is greater than that of the other three-color surface targets. Additionally, it can be concluded that the mesopic luminance under a low CCT LED is greater than under a high CCT LED in the case of the specific color surface target.

Background To simplify the adaptive treatment planning workflow while achieving the optimal tumor-dose coverage in pancreatic cancer patients undergoing daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT). Methods In daily adaptive MR-IGRT, the plan objective function constructed during simulation is used for plan re-optimization throughout the course of treatment. In this study, we have constructed the initial objective functions using two methods for 16 pancreatic cancer patients treated with the ViewRay™ MR-IGRT system: 1) the conventional method that handles the stomach, duodenum, small bowel, and large bowel as separate organs at risk (OARs) and 2) the OAR grouping method. Using OAR grouping, a combined OAR structure that encompasses the portions of these four primary OARs within 3 cm of the planning target volume (PTV) is created. OAR grouping simulation plans were optimized such that the target coverage was comparable to the clinical simulation plan constructed in the conventional manner. In both cases, the initial objective function was then applied to each successive treatment fraction and the plan was re-optimized based on the patient’s daily anatomy. OAR grouping plans were compared to conventional plans at each fraction in terms of coverage of the PTV and the optimized PTV (PTV OPT), which is the result of the subtraction of overlapping OAR volumes with an additional margin from the PTV. Results Plan performance was enhanced across a majority of fractions using OAR grouping. The percentage of the volume of the PTV covered by 95% of the prescribed dose (D 95 ) was improved by an average of 3.87 ± 4.29% while D 95 coverage of the PTV OPT increased by 3.98 ± 4.97%. Finally, D 100 coverage of the PTV demonstrated an average increase of 6.47 ± 7.16% and a maximum improvement of 20.19%. Conclusions In this study, our proposed OAR grouping plans generally outperformed conventional plans, especially when the conventional simulation plan favored or disregarded an OAR through the assignment of distinct weighting parameters relative to the other critical structures. OAR grouping simplifies the MR-IGRT adaptive treatment planning workflow at simulation while demonstrating improved coverage compared to delivered pancreatic cancer treatment plans in daily adaptive radiation therapy.

Experimental methods are commonly used for patient‐specific IMRT delivery verification. There are a variety of IMRT QA techniques which have been proposed and clinically used with a common understanding that not one single method can detect all possible errors. The aim of this work was to compare the efficiency and effectiveness of independent dose calculation followed by machine log file analysis to conventional measurement‐based methods in detecting errors in IMRT delivery. Sixteen IMRT treatment plans (5 head‐and‐neck, 3 rectum, 3 breast, and 5 prostate plans) created with a commercial treatment planning system (TPS) were recalculated on a QA phantom. All treatment plans underwent ion chamber (IC) and 2D diode array measurements. The same set of plans was also recomputed with another commercial treatment planning system and the two sets of calculations were compared. The deviations between dosimetric measurements and independent dose calculation were evaluated. The comparisons included evaluations of DVHs and point doses calculated by the two TPS systems. Machine log files were captured during pretreatment composite point dose measurements and analyzed to verify data transfer and performance of the delivery machine. Average deviation between IC measurements and point dose calculations with the two TPSs for head‐and‐neck plans were 1.2±1.3% and 1.4±1.6%, respectively. For 2D diode array measurements, the mean gamma value with 3% dose difference and 3 mm distance‐to‐agreement was within 1.5% for 13 of 16 plans. The mean 3D dose differences calculated from two TPSs were within 3% for head‐and‐neck cases and within 2% for other plans. The machine log file analysis showed that the gantry angle, jaw position, collimator angle, and MUs were consistent as planned, and maximal MLC position error was less than 0.5 mm. The independent dose calculation followed by the machine log analysis takes an average 47±6 minutes, while the experimental approach (using IC and 2D diode array measurements) takes an average about 2 hours in our clinic. Independent dose calculation followed by machine log file analysis can be a reliable tool to verify IMRT treatments. Additionally, independent dose calculations have the potential to identify several problems (heterogeneity calculations, data corruptions, system failures) with the primary TPS, which generally are not identifiable with a measurement‐based approach. Additionally, machine log file analysis can identify many problems (gantry, collimator, jaw setting) which also may not be detected with a measurement‐based approach. Machine log file analysis could also detect performance problems for individual MLC leaves which could be masked in the analysis of a measured fluence. PACS numbers: 87.53.Bn, 87.55.Qr, 87.55.km, 87.57.Uq

We revisit χ QJ → ℓ + ℓ − (with J =0,1,2 and Q = b , c ) within the Standard Model (SM). The electromagnetic contributions are given in the color-singlet model with non-vanishing lepton masses at the leading order of v . Numerically, the branching ratios of χ QJ → ℓ + ℓ − predicted within the SM are so small that such decays are barely possible to be detected at future BESIII and SuperB experiments, but may be possible to be observed at the LHC. We investigate χ b 0 → ℓ + ℓ − in Type-II 2HDM with large tan β , and χ b 2 → ℓ + ℓ − in the Randall–Sundrum model, to see their chance to be observed in future experiments.

A bstract The light-cone distribution amplitudes (LCDAs) serve as important nonperturbative inputs for the study of hard exclusive processes. In this paper, we calculate ten LCDAs at twist-2 for the S-wave and P-wave B c mesons up to the next-to-leading order (NLO) of the strong coupling α s and leading order of the velocity expansion. Each one of these ten LCDAs is expressed as a product of a perturbatively calculable distribution and a universal NRQCD matrix-element. By use of the spin symmetry, only two NRQCD matrix-elements will be involved. The reduction of the number of non-perturbative inputs will improve the predictive power of collinear factorization.