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Long-range (LoRa) communication technology, distinguished by its low power consumption and long communication range, is widely used in the Internet of Things. Nevertheless, the LoRa MAC layer adopts pure ALOHA for medium access control, which may suffer from severe packet collisions as the network scale expands, consequently reducing the system energy efficiency (EE). To address this issue, it is critical to carefully allocate transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) to each end device (ED). Owing to the low duty cycle and sporadic traffic of LoRa networks, evaluating the system EE under various parameter settings proves to be time-consuming. Consequently, we propose an analytical model aimed at calculating the system EE while fully considering the impact of multiple gateways, duty cycling, quasi-orthogonal SFs and capture effects. On this basis, we investigate a joint CH, SF and TP allocation problem, with the objective of optimizing the system EE for uplink transmissions. Due to the NP-hard complexity of the problem, the optimization problem is decomposed into two subproblems: CH assignment and SF/TP assignment. First, a matching-based algorithm is introduced to address the CH assignment subproblem. Then, an attention-based multiagent reinforcement learning technique is employed to address the SF/TP assignment subproblem for EDs allocated to the same CH, which reduces the number of learning agents to achieve fast convergence. The simulation outcomes indicate that the proposed approach converges quickly under various parameter settings and obtains significantly better system EE than baseline algorithms.

In mobile edge computing systems, base stations (BSs) equipped with edge servers can provide computing services to users to reduce their task execution time. However, there is always a conflict of interest between the BS and users. The BS prices the service programs based on user demand to maximize its own profit, while the users determine their offloading strategies based on the prices to minimize their costs. Moreover, service programs need to be pre-cached to meet immediate computing needs. Due to the limited caching capacity and variations in service program popularity, the BS must dynamically select which service programs to cache. Since service caching and pricing have different needs for adjustment time granularities, we propose a two-time scale framework to jointly optimize service caching, pricing and task offloading. For the large time scale, we propose a game-nested deep reinforcement learning algorithm to dynamically adjust service caching according to the estimated popularity information. For the small time scale, by modeling the interaction between the BS and users as a two-stage game, we prove the existence of the equilibrium under incomplete information and then derive the optimal pricing and offloading strategies. Extensive simulations based on a real-world dataset demonstrate the efficiency of the proposed approach.

Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a complex and challenging problem. To address this challenge, an advanced method is required to detect low-quality sensing data and identify malicious MUs that may disrupt the normal operations of an MCS system. Therefore, this article proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which can separate low-quality data from high-quality data in sensing tasks. First, we apply a correlation-focused spatial-temporal transformer network to predict the ground truth of the input sensing data. Then, we extract the sensing errors of the data as features based on the prediction results to calculate the implications among the data. Finally, we design a reputation-based truth discovery (TD) module for identifying low-quality data with their implications. Given sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy. Extensive experimental results demonstrate that PRBTD outperforms the existing methods in terms of identification accuracy and data quality enhancement.

A recent experiment has reported a nitrogen-doped lutetium hydride acheving a remarkable Tc of 294 K at just 1 GPa, significantly reducing the required pressure for obtaining room temperature superconductivity. However, subsequent experimental and theoretical investigations have encountered difficulties in replicating these results, leaving the structure of this Lu-H-N compound shrouded in uncertainty. Here, we propose a stable structure for Lu$_4$H$_7$N employing first-principles calculations. Our calculations reveal that Lu$_4$H$_7$N has a Tc of 1.044 K, which can be substantially enhanced to 11.721 K at 150 GPa, due to the increasing electron-phonon coupling (EPC). Notably, we delve into the nontrivial Z2 band topology of Lu$_4$H$_7$N, featuring discernible surface states near the Fermi level, and we explore its spin Hall conductivity characteristics. Furthermore, we find that the electron doping can enhance the EPC strength and Tc of Lu$_4$H$_7$N, such as the Lu$_4$H$_7$O structure we predict simulating electron doping for Lu$_4$H$_7$N with an impressive Tc of 3.837 K. This work demonstrates the coexistence of superconducting and topological properties in a Lu-H-N system compound, which holds the promise of guiding the search for novel topological superconducting materials.

This paper considers multiple unmanned aerial vehicles (UAVs) to assist sensing data transmissions from the ground users (GUs) to a remote base station (BS). Each UAV collects sensing data from the GUs and then forwards the sensing data to the remote BS. The GUs first backscatter their data to the UAVs and then all UAVs forward data to the BS by the nonorthogonal multiple access (NOMA) transmissions. We formulate a multi-stage stochastic optimization problem to minimize the long-term time-averaged age-of-information (AoI) by jointly optimizing the GUs' access control, the UAVs' beamforming, and trajectory planning strategies. To solve this problem, we first model the dynamics of the GUs' AoI statuses by virtual queueing systems, and then propose the AoI-aware sensing scheduling and trajectory optimization (AoI-STO) algorithm. This allows us to transform the multi-stage AoI minimization problem into a series of per-slot control problems by using the Lyapunov optimization framework. In each time slot, the GUs' access control, the UAVs' beamforming, and mobility control strategies are updated by using the block coordinate descent (BCD) method according to the instant GUs' AoI statuses. Simulation results reveal that the proposed AoI-STO algorithm can reduce the overall AoI by more than 50%. The GUs' scheduling fairness is also improved greatly by adapting the GUs' access control compared with typical baseline schemes., Comment: This paper has been accepted by IEEE TVT

La$_{3}$Ni$_{2}$O$_{7}$ has garnered widespread interest recently due to its high-temperature superconductivity under pressure, accompanied by charge density wave (CDW) ordering and metal-insulator (MI) transitions in the phase diagram. Here, we reveal with comprehensive calculations that La$_{3}$Ni$_{2}$O$_{7}$ possesses an antiferromagnetic ground state under both low and high pressures, with the strong Fermi surface nesting contributed by the flat band that leads to phonon softening and electronic instabilities. Several stable CDW orders with oxygen octahedral distortions are identified, which can trigger the MI transitions. The estimated CDW transition temperature ($\approx$120 K) at ambient pressure agrees nicely with experimental results. In the presence of apical oxygen vacancies, we identify two different phases, say, half distortion and full distortion phases, respectively, and their competition can lead to a pressure-induced MI transition, in good agreement with experimental observations. In addition, we find that the electron-phonon coupling is too small to contribute to superconductivity. These results appear to indicate an unconventional superconducting pairing mechanism mediated by antiferromagnetic fluctuations. A phase diagram that is consistent with the experimental results is given. The present results not only explain the origins of experimentally observed CDW and MI transitions, but also provide insight for deeply understanding the properties like superconductivity, CDW and the role of oxygen vacancies in pressurized La$_{3}$Ni$_{2}$O$_{7}$.

To realize room temperature ferromagnetic semiconductors is still a challenge in spintronics. Recent experiments have obtained two-dimensional (2D) room temperature ferromagnetic metals, such as monolayer MnSe2. In this paper, we proposed a way to obtain room temperature ferromagnetic semiconductors through metal-semiconductor transition. By the density functional theory calculations, a room temperature ferromagnetic semiconductor is obtained in monolayer MnSe2 with a few percent tensile strains, where a metal-semiconductor transition occurs with 2.2% tensile stain. The tensile stains raise the energy of d orbitals of Mn atoms and p orbitals of Se atoms near the Fermi level, making the Fermi level sets in the energy gap of bonding and antibonding states of these p and d orbitals, and opening a small band gap. The room temperature ferromagnetic semiconductors are also obtained in the heterostructures MnSe2/X (X = Al2Se3, GaSe, SiH, and GaP), where metal-semiconductor transition happens due to the tensile strains by interface of heterostructures. In addition, a large magneto-optical Kerr effect (MOKE) is obtained in monolayer MnSe2 with tensile strain and MnSe2-based heterostructures. Our theoretical results pave a way to obtain room temperature magnetic semiconductors from experimentally obtained 2D room temperature ferromagnetic metals through metal-semiconductor transitions.

Doping nonmagnetic semiconductors with magnetic impurities is a feasible way to obtain diluted magnetic semiconductors (DMSs). It is generally accepted that for the most extensively studied DMS, (Ga, Mn)As, its highest Curie temperature T$_{\text{C}}$ was achieved at 200 K with a Mn concentration of approximately 16% in experiments. A recent experiment reported record-breaking high electron and hole mobilities in the semiconductor BAs [Science 377, 437 (2022)]. Since BAs shares the same zinc-blende structure with GaAs, here we predict four DMSs (B, Mn)X (X = N, P, As, Sb) by density functional theory calculations. Our results indicate that a significantly higher T$_{\text{C}}$ in the range of 254 K to 300 K for (B, Mn)As with a Mn concentration of around 15.6%, and even higher T$_{\text{C}}$ values above the room temperature for (B, Mn)N and (B, Mn)P with a Mn concentration exceeding 12.5%. Furthermore, we have predicted a large hole mobility of 1561 cm$^{\text{2}}$V$^{\text{-1}}$s$^{\text{-1}}$ at 300 K for (B, Mn)As with a Mn concentration of about 3.7%, which is three orders of magnitude larger than the hole mobility of 4 cm$^{\text{2}}$V$^{\text{-1}}$s$^{\text{-1}}$ at 300 K observed in the experiment for (Ga, Mn)As. Our findings predict the emergence of a new family of DMS, (B, Mn)X, and are expected to stimulate both experimental and theoretical studies of the DMS with high T$_{\text{C}}$ and high mobilities.

To realize room temperature ferromagnetic (FM) semiconductors is still a challenge in spintronics. Many antiferromagnetic (AFM) insulators and semiconductors with high Neel temperature $T_N$ are obtained in experiments, such as LaFeO$_3$, BiFeO$_3$, etc. High concentrations of magnetic impurities can be doped into these AFM materials, but AFM state with very tiny net magnetic moments was obtained in experiments, because the magnetic impurities were equally doped into the spin up and down sublattices of the AFM materials. Here, we propose that the effective magnetic field provided by a FM substrate could guarantee the spin-dependent doping in AFM materials, where the doped magnetic impurities prefer one sublattice of spins, and the ferrimagnetic (FIM) materials are obtained. To demonstrate this proposal, we study the Mn-doped AFM insulator LaFeO$_3$ with FM substrate of Fe metal by the density functional theory (DFT) calculations. It is shown that the doped magnetic Mn impurities prefer to occupy one sublattice of AFM insulator, and introduce large magnetic moments in La(Fe,Mn)O$_3$. For the AFM insulator LaFeO$_3$ with high $T_N$ = 740 K, several FIM semiconductors with high Curie temperature $T_C >$ 300 K and the band gap less than 2 eV are obtained by DFT calculations, when 1/8 or 1/4 Fe atoms in LaFeO$_3$ are replaced by the other 3d, 4d transition metal elements. The large magneto-optical Kerr effect (MOKE) is obtained in these LaFeO$_3$-based FIM semiconductors. In addition, the FIM semiconductors with high $T_C$ are also obtained by spin-dependent doping in some other AFM materials with high $T_N$, including BiFeO$_3$, SrTcO$_3$, CaTcO$_3$, etc. Our theoretical results propose a way to obtain high $T_C$ FIM semiconductors by spin-dependent doping in high $T_N$ AFM insulators and semiconductors.

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much ''attention'' should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR)., Comment: 6 pages, 3 figures, This paper has been accepted for publication in IEEE Global Communications Conference (GLOBECOM) 2023

The recently discovered ATi$_3$Bi$_5$ (A=Cs, Rb) exhibit intriguing quantum phenomena including superconductivity, electronic nematicity, and abundant topological states, which provide promising platforms for studying kagome superconductivity, band topology, and charge orders. In this work, we comprehensively study various properties of ATi$_3$Bi$_5$ including superconductivity under pressure and doping, band topology under pressure, thermal conductivity, heat capacity, electrical resistance, and spin Hall conductivity (SHC) using first-principles calculations. Calculated superconducting transition temperature ($\mathrm{ T_{c}}$) of CsTi$_3$Bi$_5$ and RbTi$_3$Bi$_5$ at ambient pressure are about 1.85 and 1.92K. When subject to pressure, $\mathrm{ T_{c}}$ of CsTi$_3$Bi$_5$ exhibits a special valley and dome shape, which arises from quasi-two-dimensional to three-dimensional isotropic compression within the context of an overall decreasing trend. Furthermore, $\mathrm{ T_{c}}$ of RbTi$_3$Bi$_5$ can be effectively enhanced up to 3.09K by tuning the kagome van Hove singularities (VHSs) and flat band through doping. Pressure can also induce abundant topological surface states at the Fermi energy ($\mathrm{E}_{\mathrm{F}}$) and tune VHSs across $\mathrm{E}_{\mathrm{F}}$. Additionally, our transport calculations are in excellent agreement with recent experiments, confirming the absence of charge density wave. Notably, SHC of CsTi$_3$Bi$_5$ can reach as large as 226$ \hbar\cdot (e\cdot \Omega \cdot cm) ^{-1} $ at $\mathrm{E}_{\mathrm{F}}$. Our work provides a timely and detailed analysis of the rich physical properties for ATi$_3$Bi$_5$, offering valuable insights for further explorations and understandings on these intriguing superconducting materials., Comment: 11 pages, 5 figures

The two-dimensional (2D) van der Waals ferromagnetic semiconductors, such as CrI$_3$ and Cr$_2$Ge$_2$Te$_6$, and the 2D ferromagnetic metals, such as Fe$_3$GeTe$_2$ and MnSe$_2$, have been obtained in recent experiments and attracted a lot of attentions. The superexchange interaction has been suggested to dominate the magnetic interactions in these 2D magnetic systems. In the usual theoretical studies, the expression of the 2D Heisenberg models were fixed by hand due to experiences. Here, we propose a method to determine the expression of the 2D Heisenberg models by counting the possible superexchange paths with the density functional theory (DFT) and Wannier function calculations. With this method, we obtain a 2D Heisenberg model with six different nearest-neighbor exchange coupling constants for the 2D ferromagnetic metal Cr$_3$Te$_6$, which is very different for the crystal structure of Cr atoms in Cr$_3$Te$_6$. The calculated Curie temperature Tc = 328 K is close to the Tc = 344 K of 2D Cr$_3$Te$_6$ reported in recent experiment. In addition, we predict two stable 2D ferromagnetic semiconductors Cr$_3$O$_6$ and Mn$_3$O$_6$ sharing the same crystal structure of Cr$_3$Te$_6$. The similar Heisenberg models are obtained for 2D Cr$_3$O$_6$ and Mn$_3$O$_6$, where the calculated Tc is 218 K and 208 K, respectively. Our method offers a general approach to determine the expression of Heisenberg models for these 2D magnetic semiconductors and metals, and builds up a solid basis for further studies.

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance., Comment: 15 pages, 10 figures, 2 algorithms

In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP's signal beamforming. We aim to minimize the average age-of-information (AoI) by adapting the nodes' transmission scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP's beamforming vector and the IRS's phase shifting matrix. Considering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework to for AoI minimization in two steps. The users' transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the inner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes., Comment: 31 pages, 6 figures, 2 tables, 3 algorithms

A kagome lattice of 3d transition metals hosts flat bands, Dirac fermions and saddle points. It provides a versatile platform for achieving topological superconductivity, anomalous Hall effect, unconventional density wave order and quantum spin liquid when the strong correlation, spin-orbit coupling or magnetic order are involved in such a lattice. Here, using laser-based angle-resolved photoemission spectroscopy in combination with density functional theory calculations, we investigate the electronic structure of the newly discovered kagome superconductor CsTi$_3$Bi$_5$, which is isostructural to the AV$_3$Sb$_5$ (A=K, Rb or Cs) kagome superconductors and possesses a perfect two-dimensional kagome network of Titanium. We directly observed a strikingly flat band derived from the local destructive interferences of Bloch wave functions within the kagome lattices. We also identify the type-II Dirac nodal loops around the Brillouin zone center, the type-III Dirac nodal loops around the zone corners and type-III Dirac nodal lines along the k$_z$ direction. In addition, around the Brillouin zone center, Z2 nontrivial topological surface states are also observed which is formed from the band inversion due to strong spin orbital coupling. The simultaneous existence of such multi-sets of nontrivial band structures in one kagome superconductor not only provides good opportunities to study related physics in the kagome lattice but also makes CsTi$_3$Bi$_5$ an ideal system to realize noval quantum phenomena by manipulating its chemical potential with chemical doping or pressure.

Topological superconductors (TSC) become a focus of research due to the accompanying Majorana fermions. However, the experimentally reported TSC are extremely rare. The recent experiments reported the kagome TSC AV$_{3}$Sb$_{5}$ (A=K, Rb, Cs), which exhibit unique superconductivity, topological surface states (TSS), and Majorana bound states. More recently, the first titanium-based kagome superconductor CsTi$ _{3} $Bi$ _{5} $ with nontrivial topology was successfully synthesized as a perspective TSC. Given that Cs contributes little to the electronic structures of CsTi$ _{3} $Bi$ _{5} $ and binary compounds may be easier to be synthesized, here, by density functional theory calculations, we predict five stable non-magnetic kagome compounds Ti$ _{6} $X$ _{4} $ (X = Bi, Sb, Pb, Tl, In) which exhibit superconductivity with critical temperature Tc = 3.8$ - $5.1 K, nontrivial $\mathbb{Z}$$_2$ band topology, and TSS close to the Fermi level. In addition, the large intrinsic spin Hall effect is obtained in Ti$ _{6} $X$ _{4} $, which is caused by gapped Dirac nodes and nodal lines due to a strong spin-orbit coupling. This work offers new platforms for TSC and spintronic devices.

Superconductivity in transition metal nitrides (TMNs) has been investigated for a long time, such as zirconium nitride (ZrN) with a superconducting transition temperature Tc of 10 K. Recently, a phase diagram has been revealed in ZrNx with different nitrogen concentrations, which is very similar to that of high-temperature copper oxide superconductors. Here, we study the TMNs with face-centered cubic lattice, where ZrN and HfN have been experimentally obtained, and predict eight new stable superconductors by the first-principle calculations. We find that CuN has a high Tc of 30 K with a very strong electron-phonon coupling (EPC) strength. In contrast to ZrN, CuN has softening acoustic phonons at the high symmetry point L, which accounts for its much stronger EPC. In addition, the highly symmetrical structure leads to topological protected nodal points and lines, such as the hourglass Weyl loop in kx/y/z = 0 plane and Weyl points in kx/y/z = 2{\pi}/a plane, as well as quadratic band touch at {\Gamma} point. CuN could be a topological superconductor. Our results expand the transition metal nitrides superconductor family and would be helpful to guide the search for high temperature topological superconductors., Comment: 5 pages, 4 figures, 2 tables

The magnetic properties and the electronic excitations of the new diluted magnetic semiconductor Ba$_{1-x}$K$_{x}$(Zn$_{1-y}$Mn$_{y}$)$_{2}$As$_{2}$ have been studied by x-ray magnetic circular dichroism (XMCD) and resonant inelastic x-ray scattering (RIXS) at the Mn $L_{2,3}$ edge. The sum rule analysis of the XMCD spectra yields the net spin moment of $0.45\mu_{\text{B}}$/Mn and the small orbital moment of $0.05\mu_{\text{B}}$/Mn. This indicates that the Mn atoms are in the high-spin configurations of $d^{5}$, whereas the presence of competing ferromagnetic and antiferromagnetic interactions between the Mn ions reduces the net spin moment. RIXS spectra show broad peaks from 1 to 6 eV energy loss, which originate from the $d$-$d$ crystal field excitations of the Mn ions. Based on a comparison of the RIXS line shapes with those of Ga$_{1-x}$Mn$_{x}$As, we conclude that the ground state of Mn in Ba$_{1-x}$K$_{x}$(Zn$_{1-y}$Mn$_{y}$)$_{2}$As$_{2}$ consists not only of the charge-transferred $3d^{5}\underline{L}$ electron configuration ($\underline{L}$: ligand hole) with weakly bound holes as in Ga$_{1-x}$Mn$_{x}$As, but also of the pure $3d^{5}$ configuration with free holes.

Recently, three different magnetic states were observed experimentally in trilayer CrI3 under pressure,including ferromagnetic (FM)-upupup, FM-downupdown and FM-upupdown. To reveal the nature of the observed three magnetic states, we studied the magnetic properties of four possible stacking structures in trilayer CrI3: I (rhombohedral), II (monoclinic), III (hexagonal) and IV (triclinic). We find that all four stacking structures possess the FM-upupup ground state. After applying a few strains, the FM-downupdown becomes the ground state in II and III structures, and the FM-upupdown is preferred in IV structure, while the FM-upupup persists in I structure. Our results unveil that the three magnetic states observed in trilayer CrI3 may correspond to different stacking structures with small tensile strains, which can well interpret the experimentally obtained pressure dependent interlayer coupling and Curie temperature. Our present study paves a way to design the magnetic multilayers with required magnetic states by tuning stacking and strain., Comment: 8 pages, 4 figures

Recently, the superconducting transition temperature Tc = 287 K has been experimentally obtained in the material composed of carbon, sulfur, and hydrogen under the high pressure of 267 GPa. The material structure is unknown yet, where the carbon and sulfur were added at a molar ratio of 1:1. Here, fixing the molar ratio of C : S = 1:1, we studied several possible C-S-H structures, and found a new stable structure C2S2H4 using the first-principles calculations. The C2S2H4 shows an insulator-to-metal transition and the superconducting ground state at the pressure of 64 GPa, and its Tc can reach 16.5 K at 300 GPa. In addition, we found another stable structure of C2S3H4, whose Tc is 47.4 K at 300 GPa. The calculations show that the added S atom in C2S3H4 breaks part of C-H bonds in C2S2H4, makes the vibration of H atom at a lower frequency, and thus enhances the electro-phonon coupling and Tc. Our results suggest that the molar ratio of C:S lower than 1:1 in the C-S-H systems may be favorable to enhance Tc. This can be useful to figure out the structure of the C-S-H material with room temperature Tc in the recent experiment.