This paper uses the complex regression analysis method to establish the customer’s load regression models, which consider economic indicators, temperature and rainfall. Furthermore, the proposed models are used to study the forecasting feasibility of the future energy sales and summer peak load demand. At first, this paper used least-squares techniques to derive regression models for considering economic indicators and temperature of 34 customer energy sales and total energy sales. Besides, the AMI high voltage customer demand data and system generating capacity for 24 hours were adopted to forecast summer peak load. The above-mentioned data analysis tool is used by EViews software to achieve, in order to verify the feasibility of the research framework. The study found that although its forecasting model accuracy is low only when mixed with temperature and high voltage demands. So, when mixed with high voltage demand data and system generating capacity for 24 hours to forecast peak load, the average error is ± 0.87% and in the majority of its energy sales forecasting model of average error is ±3%. This result can provide power company as future reference.

Based on collected power generation data associated with solar irradiance, PV system conversion efficiency and cell temperature, an hourly solar PV power estimation model by a parallel artificial neural network (ANN) and particle swarm optimization (PSO) algorithm is proposed. Weight matrices related to different seasons and geographic areas for estimation power generations have been trained by real measured operation data. The parallel PSO algorithm with heuristic global optimization technique assists the ANN training process to get near optimal solutions precisely. The accuracy and reliability of estimation is audited by actual details of photovoltaic power stations in various regions and scales. The results of estimation model can not only assist the electricity dispatcher to explicitly monitor the trend of solar power generation in different areas, but also coordinate with traditional power plants to meet load demand more accurately. The presented model will bring benefits to power dispatching for larger scales of intermittent and unstable solar power generation.

Radiotherapy is the primary treatment modality for non-metastatic nasopharyngeal carcinoma (NPC) across all TNM-stages. Locoregional control rates have been impressive even from the 2D-radiotherapy (RT) era, except when the ability to deliver optimal dose coverage to the tumor is compromised. However, short- and long-term complications following head and neck RT are potentially debilitating, and thus, there had been much research investigating technological advances in RT delivery over the past decades, with the primary goal of limiting normal tissue damage. On this note, with a plateau in gains of therapeutic ratio by modern RT techniques, future advances have to be focused on individualization of RT, both in terms of dose prescription and delineation of target volumes. In this review, we analyzed the guidelines and evidence related to contouring methods, and dose prescription for early and locoregionally-advanced (LA-) NPC. Next, with the preference for induction chemotherapy (IC) in patients with LA-NPC, we assessed the evidence concerning radiotherapy adaptations guided by IC response, as well as functional imaging and contour changes during treatment. Finally, we discussed on RT individualization that is guided by EBV DNA assessment, and its importance in the era of combinatorial immune checkpoint blockade therapy with RT.

In this study, a composite flame retardant was created by combining fish scale (FS), fishery waste, and ammonium polyphosphate (APP), a commercially available flame retardant. The composite flame retardant was added to epoxy resin (EP) to form an EP/APP/FS composite that prevents burns and is environmentally friendly. The use of FS conforms to the concept of circular economy and lowers costs by reducing the consumption of APP. Thermogravimetric analysis (TGA), integral procedural decomposition temperature (IPDT), pyrolysis kinetics, limiting oxygen index (LOI), the Underwriters Laboratories 94 (UL94) flammability test, scanning election microscopy, Raman spectroscopy, and energy-dispersive X-ray spectroscopy were used to determine the thermal properties, flame retardant properties, flame retardant mechanism, char morphology, and composition of the composites. The TGA results indicated that the addition of 40% flame retardant raised the char residue from 16.45 wt% (pure EP) to 36.07 wt%, IPDT from 685.6 °C (pure EP) to 1143.1°C, LOI from 21% (pure EP) to 30%, and UL94 classification from fail (pure EP) to V-0. These results suggest an increase in char residue, which indicates better protection of the polymer matrix material. The improvements in IPDT, LOI, and UL94 classification, which indicate greater thermal stability, lower flammability (from flammable to fireproof), and higher flammability rating (from fail to V-0), respectively, suggest that the composite material has favorable thermal properties and is less inflammable.

This study used the sol–gel method to synthesize a non-halogenated hyperbranched flame retardant containing nitrogen, phosphorus and silicon, HBNPSi, which was then added to a polyurethane (PU) matrix to form an organic–inorganic hybrid material. Using 29Si nuclear magnetic resonance, energy-dispersive X-ray spectroscopy of P- and Si-mapping, scanning electron microscopy, and X-ray photoelectron spectroscopy, this study determined the organic and inorganic dispersity, morphology, and flame retardance mechanism of the hybrid material. The condensation density of the hybrid material PU/HBNPSi was found to be 74.4%. High condensation density indicates a dense network structure of the material. The P- and Si-mapping showed that adding inorganic additives in quantities of either 20% or 40% results in homogeneous dispersion of the inorganic fillers in the polymer matrix without agglomeration, indicating that the organic and inorganic phases had excellent compatibility. In the burning test, adding HBNPSi to PU resulted in the material passing the UL-94 standard at the V2 level, unlike the pristine PU, which did not meet the standard. The results demonstrated that after non-halogenated flame retardant was added to PU, the material’s flammability and dripping were lower, thereby proving that flame retardants containing elements such as nitrogen, phosphorus, and silicon exert an excellent flame retardant synergistic effect.

Polydimethylsiloxane with hydroxy groups was functionalized to form functionalized polydimethylsiloxane, which subsequently underwent an addition reaction with isophorone diisocyanate to form the prepolymer. Next, 3-aminopropyltriethoxysilane (APTS) reacted with 3-glycidoxypropyltrimethoxysilane (GPTS) to produce bridged polysilsesquioxanes, and the sol–gel technology was employed to form hyperbranched polysiloxane nanoparticles with hydroxy groups, which was used as the additive. The hyperbranched polysiloxane and the prepolymer containing NCO functional groups then underwent an addition reaction to produce the hybrid materials. Fourier-transform infrared spectroscopy and 29Si nuclear magnetic resonance were used to characterize the structure of the polyurethane hybrid. Regarding thermal stability, after the hyperbranched polysiloxane nanoparticles was introduced, the integral procedural decomposition temperature increased from 348 ºC for polyurethane matrix to 859 ºC for the hybrid material. The results revealed that the thermal stability of the hybrid material substantially increased by approximately 247%.

Plant endophytic fungi and rhizosphere soil fungi are often reported as biocontrol agents against plant pathogens or with plant growth promotion potential. Four treatments were performed in field and greenhouse experiments where cucumber plants were inoculated with Trichoderma harzianum and Fusarium oxysporum in 2022. The roots, stems and leaves of cucumber plants and their rhizosphere soil were collected twice individually from the field and greenhouse for isolation of cucumber endophytic and rhizosphere soil fungi. All 74 genera and 1275 fungal strains were sequenced by rDNA ITS. The PDA media yielded the highest number of genera isolated from cucumber plants, rhizosphere soil and both compared to other media. There were no significant differences among the 4 media for the isolation of cucumber endophytic fungi. The best media for isolating fungi from roots and stems were MRBA and PDA, respectively. PDA and CZA had higher isolation efficiency for the rhizosphere soil fungi than MRBA. The 28 fungal genera had high isolation efficiency, and the endophytic Trichoderma strains were best isolated by MEA. It is suggested that PDA can be used as a basic medium, and different media can be considered for specific fungal genera.

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to obtain rapidly and accurately the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (Chll), and carotenoid (Caro) as indicators to study the variations in leaf position of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed by using competitive adaptive reweighted sampling (CARS) and iteratively retained informative variable (IRIV) algorithm to extract characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE), and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43, 2.38 for Chla, 0.8391, 0.53, 2.49 for Chlb, 0.7899, 2.24, 2.18 for Chll, and 0.7577, 0.27, 2.03 for Caro, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis, and fertilization decisions in plant growth management.

The 2019 novel SARS-like coronavirus (SARS-CoV-2) entry depends on the host membrane serine protease TMPRSS2, which can be blocked by some clinically-proven drugs. Here we analyzed spatial relevance between glycosylation sequons and antibody epitopes and found that, different from SARS-CoV S, most high-surface-accessible epitopes of SARS-CoV-2 S are blocked by the glycosylation, and the optimal epitope with the highest surface accessibility is covered by the S1 cap. TMPRSS2 inhibitor treatments may prevent unmasking of this epitope and therefore prolong virus clearance and may induce antibody-dependent enhancement. Interestingly, a heparin-binding sequence immediately upstream of the S1/S2 cleavage site has been found in SARS-CoV-2 S but not in SARS-CoV S. Binding of SARS-CoV-2 with heparins may lead to exposure of S686, which then facilitates the S1/S2 cleavage, induces exposure of the optimal epitope, and therefore increases the antibody titres. A combination of heparin and vaccine (or convalescent serum) treatments thus is recommended.