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Ternary geopolymers synthesized with fly ash, granulated blast furnace slag, and steel slag offer a combination of improved mechanical properties, environmental sustainability, cost-effectiveness and flexibility in composition. However, the brittleness of the ternary geopolymer limits its wide employment. This problem can be addressed by integration of low-modulus polyethylene (PE) fibers and high-modulus steel (ST) fibers in ternary geopolymers. This study systematically investigates the mechanical properties and multi-scale strain hardening mechanisms of hybrid fiber-reinforced engineering geopolymer composites (HFEGC). The results reveal that the hybrid fiber with 1 vol% PE fiber and 1 vol% of ST fiber results in the highest compressive, tensile, and four-point bending strength of the ternary geopolymer, which were 32% (62 MPa), 123% (7.1 MPa), and 214% (16 MPa) higher than that of the geopolymer without fibers, respectively. Furthermore, the ultimate tensile strain of HFEGC is 7.4%, which is 155% higher than the geopolymer composite with single PE fiber. This study provides a viable solution for enhancing the ductility of ternary geopolymers, paving the way for their broader utilization in construction.

This research presents a new model for finding optimal conditions in the concrete technology area. To do that, results of a series of laboratory investigations on concrete samples were considered and used to design several artificial intelligence (AI) models. The data samples include 8 parameters i.e., silica fume replacement ratio, fly ash replacement ratio, fine aggregate, water content, high rate water reducing agent, coarse aggregate, total cementitious material, and age of samples, were used to predict and optimize the compressive strength of concrete samples. For optimization purposes, this study used a human learning optimization (HLO) algorithm to find the optimal results as well as optimizing the kernel coefficients of the support vector regression (SVR) models. Initially, to form the core of this research, various models were constructed and proposed to design the required relationship between the data using SVR. Since different SVR kernels have their own coefficients, using optimization theory, the probability of error in the models was reduced and the models were identified and executed with the highest accuracy. Finally, the polynomial model was selected as the model with the lowest computational error and the highest accuracy for evaluating the compressive strength of the concrete samples. The accuracy of the proposed SVR model for training and testing data was obtained as the coefficient of determination (R2) = 0.9694 and R2= 0.9470, respectively. This function was considered as a relation, to be developed by the HLO algorithm to find optimal options under different conditions. The results for 14 samples, which are the most important examples of this research, showed that the optimal states are obtained with a high level of accuracy. This confirms the proper use/develop of the SVR-HLO algorithm in designing the predictive model as well as finding optimal conditions in the concrete technology area.

Shear design of RC beams with and without stirrups using laboratory experiments is difficult or even impossible as a large number of variables need to be considered simultaneously, such as the span-to-depth ratio, web width and reinforcement ratio. In addition, due to the complex shear failure mechanism, empirical approaches for shear design are derived within the boundaries of their own testing regimes. Thus, the generalization ability and applicability of these approaches are limited. To address this issue, this study uses machine learning approaches for shear design. A random forest model is constructed to predict the shear strength of RC beams. The hyperparameters of RF are tuned using beetle antennae search algorithm modified by Levy flight and inertia weight. The developed model is trained on two data sets of RC beams with and without stirrups containing 194 and 1849 samples, respectively. The obtained model has high prediction accuracy with correlation coefficients of 0.9367 and 0.9424 on these two test data sets, respectively. The proposed method is powerful and efficient in shear design of RC beams with and without stirrups and therefore paves the way to intelligent construction.

Lymph node status is a key factor in determining stage, treatment, and prognosis in cancers. Small lymph nodes in fat-rich gastrointestinal and breast cancer specimens are easily missed in conventional sampling methods. This study examined the effectiveness of the degreasing pretreatment with dimethyl sulfoxide (DMSO) in lymph node detection and its impact on the analysis of clinical treatment–related proteins and molecules. Thirty-three cases of gastrointestinal cancer specimens from radical gastrectomy and 63 cases of breast cancer specimens from modified radical mastectomy were included. After routine sampling of lymph nodes, the specimens were immersed in DMSO for 30 minutes for defatting. We assessed changes in the number of detected lymph nodes and pN staging in 33 gastrointestinal cancer specimens and 37 breast cancer specimens. In addition, we analyzed histologic characteristics, Masson trichrome special staining, and immunohistochemistry (gastrointestinal cancer: MMR, HER2, and PD-L1; breast cancer: ER, PR, AR, HER2, Ki-67, and PD-L1). Molecular status was evaluated for colorectal cancer (KRAS, NRAS, BRAF, and microsatellite instability) and breast cancer (HER2) in gastrointestinal cancer specimens and the remaining 26 breast cancer specimens. Compared with conventional sampling, DMSO pretreatment increased the detection rate of small lymph nodes (gastrointestinal cancer: P< .001; breast cancer: P< .001) and improved pN staging in 1 case each of gastric cancer, colon cancer, and rectal cancer (3/33; 9.1%). No significant difference in the morphology, special staining, protein, and molecular status of cancer tissue after DMSO treatment was found. Based on these results and our institutional experience, we recommend incorporating DMSO degreasing pretreatment into clinical pathologic sampling practices.

In the loose and fractured coal seam with particularly low uniaxial compressive strength (UCS), driving a roadway is extremely difficult as roof falling and wall spalling occur frequently. To address this issue, the jet grouting (JG) technique (high-pressure grout mixed with coal particles) was first introduced in this study to improve the self-supporting ability of coal mass. To evaluate the strength of the jet-grouted coal-grout composite (JG composite), the UCS evolution patterns were analyzed by preparing 405 specimens combining the influential variables of grout types, curing time, and coal to grout (C/G) ratio. Furthermore, the relationships between UCS and these influencing variables were modeled using ensemble learning methods i.e. gradient boosted regression tree (GBRT) and random forest (RF) with their hyperparameters tuned by the particle swarm optimization (PSO). The results showed that the chemical grout composite has higher short-term strength, while the cement grout composite can achieve more stable strength in the long term. The PSO-GBRT and PSO-RF models can both achieve high prediction accuracy. Also, the variable importance analysis demonstrated that the grout type and curing time should be considered carefully. This study provides a robust intelligent model for predicting UCS of JG composites, which boosts JG design in the field.

A series of Sm3+-doped La3Si6N11phosphor materials were synthesized by a high temperature solid-state reaction method. The crystal structure, microstructure, photoluminescence properties, decay curves as well as thermal quenching properties of the as-prepared phosphors were investigated systematically. The excitation spectra contain a wide asymmetric band below 350 nm originating from the host absorption, several sharp excitation peaks in the range of 300–550 nm corresponding to f-f transition of Sm3+. Under the excitation of 369 and 414 nm light, the phosphors exhibit strong narrow-band orange-red emission peaked at 605 nm. The average decay time of La2.99Si6N11:0.01Sm3+sample is fitted to be 0.38 ms and the CIE coordinates were calculated to be (0.6105, 0.3833). For water resistance, La3Si6N11:Sm3+is better than K2SiF6:Mn4+phosphor. After soaking in deionized water for 300 min, the La3Si6N11:Sm3+sample retains approximately 80% of its initial relative emission intensity. When the temperature rises to 423 K (150 °C), the emission intensity of La2.99Si6N11:0.01Sm3+sample remains 85% in comparison to that of room temperature. The activation energy was calculated to be 0.63253 eV, which is higher than those of Sm3+-activated oxide phosphors, indicating that the phosphor has relatively good thermal stability.

To improve absorption efficiency (AE) and subsequently improve external quantum efficiency (EQE) remains one of the significant challenges for Mn4+-doped red-emitting fluoride phosphors. In this study, we propose to use Mn4+as a part of matrix to enhance the AE of fluoride phosphors. Red-emission phosphors Cs2MnF6, Cs2MnF6:Sc3+, and Cs2MnF6:Si4+were synthesized successfully by a coprecipitation method. The Rietveld refinement of X-ray diffraction reveals that this red phosphor exhibits a cubic structure in Fm3̅mspace group. Owing to Mn4+being a part of matrix, this kind of red phosphor possesses an extremely high AE, which can be promoted to 88%. The doping of Sc3+and Si4+ions into Cs2MnF6can effectively increase the luminescence intensity to 253 and 232%, respectively, relative to that of Cs2MnF6. The relative emission intensity of Cs2MnF6:5%Si4+red phosphor preserves about 115% when temperature rises to 175 °C. By employing Cs2MnF6:5%Si4+as a red-emitting component, high-performance LED-1 with Ra = 86.2, R9 = 82.1 and CCT = 3297 K, and LED-2 with an ultrawide color gamut (NTSC value of 122.3% and rec. 2020 value of 91.3%) are obtained. This work may provide a new idea to explore a new type of fluoride phosphor with high EQE for high-performance white-light-emitting diodes.

Mn4+ activated fluoride phosphors have been considered as highly promising red component for warm white light-emitting diodes (WLEDs) in recent years. In this work, a novel nonequivalent doping Cs3AlF6:Mn4+ red-emitting phosphor were synthesized via a co-precipitation method and its crystal structure, morphology, and luminescent properties were investigated in detail. The Cs3AlF6:Mn4+ phosphor with a sheet morphology have broad band absorption in blue light region ([?]467 nm) and narrow bandwidth emissions in red region ([?]634 nm). The WLED fabricated with Cs3AlF6:Mn4+ red phosphor, YAG:Ce3+ yellow phosphor and a blue InGaN LED chip demonstrates intense warm light with good luminescence performance of low related color temperature (CCT, Tc = 3659 K) and high color rendering index (CRI, Ra = 86.2). These results indicate the Cs3AlF6:Mn4+ red phosphor can be a potential candidate for warm WLEDs.

A number of rockburst accidents occurring in soft coal seams have shown that the rockburst mechanism involved in soft coal seams is significantly different from that involved in hard coal seams. Therefore, the method used to evaluate rockburst in hard coal seams is not applicable to soft coal seams. This paper established an energy integral model for the rockburst-inducing area and a friction work calculation model for the plastic area. If the remaining energy after the coal seam is broken in the rockburst-inducing area is greater than the friction work required for the coal to burst out, then a rockburst accident will occur. Mechanisms of “quaking without bursting” and “quaking and bursting” are clarified for soft coal seams and corresponding control measures are proposed as the optimization of roadway layouts and use of “three strong systems” (strong de-stressing, strong supporting, and strong monitoring).

Background. Malaria is a global public health concern, mainly occurring in sub-Saharan Africa. Children infected with malaria are more likely to develop severe disease, which can be fatal. During COVID-19 in 2020, diagnosing and treating malaria became difficult. We analyzed the clinical characteristics and laboratory indicators of children with severe malaria in Benin to provide important information for designing effective prevention and treatment strategies to manage pediatric cases. Methods. Clinical characteristics of pediatric patients with severe malaria admitted to two hospitals in Benin (Central Hospital of Lokossa and Regional Hospital of Natitingou, located ∼650 kilometers apart) were collected from January to December 2020. Patients were grouped according to age (group A: 4–12 months old, group B: 13–36 months old, and group C: 37–60 months old), and clinical and laboratory indicators were compared. The incidences of severe pediatric malaria in both hospitals in 2020 were calculated. Inclusion, exclusion, and blood transfusion criteria were identified. Results. We analyzed 236 pediatric cases. The main clinical symptoms among all patients were severe anemia, vomiting, prostration, poor appetite, dysphoria, and dyspnea. Over 50% of patients in group A experienced vomiting and severe anemia. Most patients in group B had severe anemia and prostration. Delirium affected significantly more patients in group C than in groups A and B. In group C, the hemoglobin and hematocrit levels were significantly higher p<0.05, and the leukocyte count was significantly lower p<0.01 than in groups A and B. Parasitemia was significantly higher in group C than in group A p<0.01. Twelve deaths occurred. Conclusions. Severe pediatric malaria is seasonal in Benin. The situation in children under 5 years old is poor. The main problems are severe disease conditions and high fatality rates. Effective approaches such as prevention and early and appropriate treatment are necessary to reduce the malaria burden in pediatric patients.

Quantitative damage identification of surrounding rock is important to assess the current condition and residual strength of underground tunnels. In this work, an underground tunnel model with marble-like cementitious materials was first fabricated using the three-dimensional (3D) printing technique and then loaded to simulate its failure mode in the laboratory. Lead zirconate titanate piezoelectric (PZT) transducers were embedded in the surrounding rock around the tunnel in the process of 3D printing. A 3D monitoring network was formed to locate damage areas and evaluate damage extent during loading. Results show that as the load increased, main cracks firstly appeared above the tunnel roof and below the floor, and then they coalesced into the tunnel boundary. Finally, the tunnel model was broken into several parts. The resonant frequency and the peak of the conductance signature firstly shifted rightwards with loading due to the sealing of microcracks, and then shifted backwards after new cracks appeared. An overall increase in the root-mean-square deviation (RMSD) calculated from conductance signatures of all the PZT transducers was observed as the load (damage) increased. Damage-dependent equivalent stiffness parameters (ESPs) were calculated from the real and imaginary signatures of each PZT at different damage states. Satisfactory agreement between equivalent and experimental ESP values was achieved. Also, the relationship between the change of the ESP and the residual strength was obtained. The method paves the way for damage identification and residual strength estimation of other 3D printed structures in civil engineering.

An investigation was conducted on the overall burst-instability of isolated coal pillars by means of the possibility index diagnosis method (PIDM). First, the abutment pressure calculation model of the gob in side direction was established to derive the abutment pressure distribution curve of the isolated coal pillar. Second, the overall burst-instability ratio of the isolated coal pillars was defined. Finally, the PIDM was utilized to judge the possibility of overall burst-instability and recoverability of isolated coal pillars. The results show that an overall burst-instability may occur due to a large gob width or a small pillar width. If the width of the isolated coal pillar is not large enough, the shallow coal seam will be damaged at first, and then the high abutment pressure will be transferred to the deep coal seam, which may cause an overall burst-instability accident. This approach can be adopted to design widths of gobs and isolated coal pillars and to evaluate whether an existing isolated coal pillar is recoverable in skip-mining mines.

In this paper, we study the quasi-stationarity and quasi-ergodicity of general Markov processes. We show, among other things, that if Xis a standard Markov process admitting a dual with respect to a finite measure mand if Xadmits a strictly positive continuous transition density p(t, x, y) (with respect to m) which is bounded in (x, y) for every t> 0, then Xhas a unique quasi-stationary distribution and a unique quasi-ergodic distribution. We also present several classes of Markov processes satisfying the above conditions.

Abstract: The problem of the hard roof hanging in deep mines may cause large deformations of roadways, which even jeopardizes the safety of personnel. Finding an effective and safe method to reduce the high stress caused by that is of significance. In this study, the stress relief technique was employed to 6305 working face in the Dongtan coal mine to maintain the roadway stability. The theoretical analysis, numerical modeling, and field practice were utilized to investigate the effect of the stress relief technique on the main hard roof. The range of pre-splitting cutting angles by stress relief technique was first analyzed theoretically. Furthermore, the stress evolution of the surrounding rock along the goaf, the best breaking position, and patterns of the main roof were obtained by numerical simulation. Also, the field test was carried out and the results showed that the total deformation of roadway is controlled within 30–60 mm, and the change rate of anchor cable stress is controlled within the range of 13 to 16%, ensuring the long-term stability of the roadway along the goaf. Successful practical engineering provides a guideline in similar conditions especially for the stability of deep underground space under high-stress environments. Article highlights:

A systematical study of the stress relief technique in deep mines from theoretical analysis to field application.

The best cutting angles along hard roof goaf is 75° based on the universal distinct element simulation.

The designed parameters achieved less roadway deformation by the field monitoring.

Mn4+activated fluoride phosphors have been considered as highly promising red component for warm white light-emitting diodes (WLEDs) in recent years. In this work, a novel nonequivalent doping Cs3AlF6:Mn4+red-emitting phosphor were synthesized via a co-precipitation method and its crystal structure, morphology, and luminescent properties were investigated in detail. The Cs3AlF6:Mn4+phosphor with a sheet morphology have broad band absorption in blue light region (∼467 nm) and narrow bandwidth emissions in red region (∼634 nm). The WLED fabricated with Cs3AlF6:Mn4+red phosphor, YAG:Ce3+yellow phosphor and a blue InGaN LED chip demonstrates intense warm light with good luminescence performance of low related color temperature (CCT, Tc = 3659 K) and high color rendering index (CRI, Ra = 86.2). These results indicate the Cs3AlF6:Mn4+red phosphor can be a potential candidate for warm WLEDs.