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This paper proposes a strategy for synergistic improvement of physical fitness and skills of basketball players with the content of big data analysis of decision-making of physical fitness training programs and estimation of the human posture of basketball players. The data related to physical training and testing are processed using neural network methods, and a priori algorithms with continuous attributes discretize the data to realize data fragmentation and prevent data distortion caused by intrinsic correlation so as to build up a technical and methodological platform for physical training programs. At the same time, the basketball action posture estimation algorithm based on multi-scale spatiotemporal correlation features is proposed, and the human body temporal sequence feature capture module based on Transformer is constructed to improve the accuracy of basketball action posture estimation to meet the needs of skill training. The men’s basketball team of Shandong Agricultural University in China was used as a research object to carry out basketball physical fitness and skill training practices. The experimental group’s basketball players’ performance in barbell bench press and weighted squat was 11.06kg and 10.05kg more than that of the control group. Their performance in 3/4-court sprint running, and return running. Restriction-area footwork was 0.52s, 2.16s, and 1.97s faster than that of the control group, with significant differences in basketball skills, such as 14-meter round-trip straight-line dribbling around the pole and 30-second in situ spotting of two-handed chest pass (the first time in the experimental group). Basketball skills showed significant differences (P

Dioscorea cirrhosa L. (D. cirrhosa) tuber is a traditional medicinal plant that is abundant in various pharmacological substances. Although diosgenin is commonly found in many Dioscoreaceae plants, its presence in D. cirrhosa remained uncertain. To address this, HPLC-MS/MS analysis was conducted and 13 diosgenin metabolites were identified in D. cirrhosa tuber. Furthermore, we utilized transcriptome data to identify 21 key enzymes and 43 unigenes that are involved in diosgenin biosynthesis, leading to a proposed pathway for diosgenin biosynthesis in D. cirrhosa. A total of 3,365 unigenes belonging to 82 transcription factor (TF) families were annotated, including MYB, AP2/ERF, bZIP, bHLH, WRKY, NAC, C2H2, C3H, SNF2 and Aux/IAA. Correlation analysis revealed that 22 TFs are strongly associated with diosgenin biosynthesis genes (—r2— > 0.9, P

Conventional Li-ion battery intercalation cathodes leverage charge compensation that is formally associated with redox on the transition metal. Employing the anions in the charge compensation mechanism, so-called anion redox, can yield higher capacities beyond the traditional limitations of intercalation chemistry. Here, we aim to understand the structural considerations that enable anion oxidation and focus on processes that result in structural changes, such as the formation of persulfide bonds. Using a Li-rich metal sulfide as a model system, we present both first-principles simulations and experimental data that show that cation vacancies are required for anion oxidation. First-principles simulations show that the oxidation of sulfide to persulfide only occurs when a neighboring vacancy is present. To experimentally probe the role of vacancies in anion redox processes, we introduce vacancies into the Li2TiS3 phase while maintaining a high valency of Ti. When the cation sublattice is fully occupied and no vacancies can be formed through transition metal oxidation, the material is electrochemically inert. Upon introduction of vacancies, the material can support high degrees of anion redox, even in the absence of transition metal oxidation. The model system offers fundamental insights to deepen our understanding of structure-property relationships that govern reversible anion redox in sulfides and demonstrates that cation vacancies are required for anion oxidation, in which persulfides are formed.

Soil salinization is among the most critical threats to agriculture and food security. Excess of salts adversely affects soil structure and fertility, plant growth, crop yield, and microorganisms. It is caused by natural processes, such as dry climates and low precipitations, high evaporation rate, poor waterlogging, and human factors, such as inappropriate irrigation practices, poor drainage systems, and excessive use of fertilizers. The growing extremization of climate with prolonged drought conditions is worsening the phenomenon. Nature-based solutions (NBS), combined with precision or conservation agriculture, represent a sustainable response, and offer benefits through revitalizing ecosystem services. This perspective explores NBS that can be adopted, along with their challenges and implementation limitations. We also argue that NBS could not be enough to combat hunger in the worlds most vulnerable regions and fully achieve the Sustainable Development Goal - Zero Hunger (SDG2). We therefore discuss their possible combination with salt-tolerant crops based on bioengineering.

Abstract: Addressing sustainable energy storage remains crucial for transitioning to renewable sources. While Li‐ion batteries have made significant contributions, enhancing their capacity through alternative materials remains a key challenge. Micro‐sized silicon is a promising anode material due to its tenfold higher theoretical capacity compared to conventional graphite. However, its substantial volumetric expansion during cycling impedes practical application due to mechanical failure and rapid capacity fading. A novel approach is proposed to mitigate this issue by incorporating trace amounts of aluminum into the micro‐sized silicon electrode using ball milling. Density functional theory (DFT) is employed to establish a theoretical framework elucidating how grain boundary sliding, a key mechanism involved in preventing mechanical failure is facilitated by the presence of trace aluminum at grain boundaries. This, in turn, reduces stress accumulation within the material, reducing the likelihood of failure. To validate the theoretical predictions, capacity retention experiments are conducted on undoped and Al‐doped micro‐sized silicon samples. The results demonstrate significantly reduced capacity fading in the doped sample, corroborating the theoretical framework and showcasing the potential of aluminum doping for improved Li‐ion battery performance.

Grain boundary (GB) segregation models are derived for multi-principal element alloys (MPEAs) and high-entropy alloys (HEAs). Differing from classical models where one component is taken as a solvent and others are considered solutes, these models are referenced to the bulk composition to enable improved treatments of MPEAs and HEAs with no principal components. An ideal solution model is first formulated and solved to obtain analytical expressions that predict GB segregation and GB energy in MPEAs and HEAs. A regular solution model is further derived. The GB composition calculated using the simple analytical expression derived in this study and data from the Materials Project agrees well with a prior atomistic simulation for NbMoTaW. The simplicity of the derived analytical expressions makes them useful for not only conveniently predicting GB segregation trends in HEAs but also analyzing nascent interfacial phenomena in compositionally complex GBs. As an application example, the models are used to further derive a set of equations to elucidate an emergent concept of high-entropy grain boundaries. [ABSTRACT FROM AUTHOR]