Dry direct-seeded rice has higher energy and carbon production efficiencies without reducing economic benefits.

BACKGROUND: Water and energy shortages and food security challenges associated with global warming are essential issues affecting human survival and development. Rice is one of the world's top three staple food crops. In the present study, the energy footprint (EF), carbon footprint (CF), energy production efficiency (EPE), carbon production efficiency (CPE), and economic benefit (EB) of dry direct-seeded rice (DSR), wet direct-seeded rice (WSR), and transplanted rice (TPR), respectively, were integrated and assessed. RESULTS: The yield of WSR and TPR was 9.64% and 13.76% higher than that of DSR, respectively. However, DSR has less irrigation and less intensive field tillage; therefore, its energy input, and CF, were 37.47% and 44.29%, and 29.35% and 34.77%, lower than those of WSR and TPR, respectively; the EPE and CPE were obviously improved; and the EB was not lower than that of WSR and TPR. Current rice production relies heavily on non-renewable energy inputs such as nitrogen fertilizer, diesel, and machinery. Therefore, the key to improving EPE, CPE, and EB in rice production at the same time as reducing CF was to improve water use efficiency and decrease the intensity of paddy tillage. CONCLUSION: The DSR has higher social, economic, and ecological benefits and can be the preferred rice planting method for rice production areas. © 2023 Society of Chemical Industry.

Stretchable, Self-Healing, and Skin-Mounted Active Sensor for Multipoint Muscle Function Assessment.

Assessment of muscle function is an essential indicator for estimating elderly health, evaluating motor function, and instructing rehabilitation training, which also sets urgent requirements for mechanical sensors with superior quantification, accuracy, and reliability. To overcome the rigidity and vulnerability of traditional metallic electrodes, we synthesize an ionic hydrogel with large deformation tolerance and fast self-healing ability. And we propose a stretchable, self-healing, and skin-mounted (Triple S) active sensor (TSAS) based on the principles of electrostatic induction and electrostatic coupling. The skin modulus-matched TSAS provides outstanding sensing properties: maximum output voltage of 78.44 V, minimal detection limit of 0.2 mN, fast response time of 1.03 ms, high signal-to-noise ratio and excellent long-term service stability. In training of arm muscle, the functional signals of biceps and triceps brachii muscles as well as the joint dexterity of bending angle can be acquired simultaneously through TSAS. The signal can also be sent wirelessly to a terminal for analysis. With the characteristics of high sensitivity, reliability, convenience, and low-cost, TSAS shows its potential to be the next-generation procedure for real-time assessment of muscle function and rehabilitation training.

Interactions Between Biological Products and Product Packaging and Potential Approaches to Overcome Them.

Biological products such as protein-based biopharmaceuticals are playing an important role in the healthcare and pharmaceutical industry. The interaction between biological products and packaging materials has become the focus of many studies since it can reduce the effectiveness of biological products. These interactions are heavily influenced by the surface properties and physicochemical nature of the therapeutic agents and the packaging materials. Therefore, it is critical to understand the interactions between packaging materials and biological products in order to design biocompatible packaging materials and develop approaches to minimize adverse interactions. We describe the interactions that occur when using several common packaging materials, including glass and polymer. We discuss the interaction between these materials and biological products such as blood, blood derivatives, recombinant proteins, monoclonal antibodies, and gene therapeutics. We also summarize approaches for overcoming these interactions. Understanding the interactions between biological materials and packaging materials is critical for the development of novel packaging materials that improve the safety of pharmaceutical products.

Analytic derivation of pinhole collimation sensitivity for a general source model using spherical harmonics.

Pinhole collimators are widely used for single photon emission computed tomography (SPECT) imaging of small organs and animals. There has also been renewed interest in using pinhole arrays for clinical cardiac SPECT imaging to achieve high sensitivity and complete data sampling. Overall sensitivity of a pinhole array is critical in determining a system's performance. Conventionally, a point source model has been used to evaluate the sensitivity and optimize the system design. This model is simple but far from realistic. This work addresses the use of more realistic source models to assess the sensitivity performance of pinhole collimation. We have derived an analytical formula for pinhole collimation sensitivity with a general source distribution model using spherical harmonics. As special cases of this general model, we provided the pinhole sensitivity formulae for line, disk and sphere sources. These results show that the point source model is just the zeroth-order approximation of the other source models. The point source model overestimates or underestimates the sensitivity relative to the more realistic model. The sphere source model yields the same sensitivity as a point source located at the center of the sphere when attenuation is not taken into account. In the presence of attenuation, the average path length of emitted gamma rays is 3/4 of the radius of the sphere source. The calculated sensitivities based on these formulae show good agreement with separate Monte Carlo simulations in simple cases. The general and special sensitivity formulae derived here can be useful for the design and optimization of SPECT systems that utilize pinhole collimators.

Neighborhood effects on an individual's health using neighborhood measurements developed by factor analysis and cluster analysis.

This study suggests a multivariate-structural approach combining factor analysis and cluster analysis that could be used to examine neighborhood effects on an individual's health. Data were from the Taiwan Social Change Survey conducted in 1990, 1995, and 2000. In total, 5,784 women and men aged over 20 years living in 428 neighborhoods were interviewed. Participants' addresses were geocoded with census data for measuring neighborhood-level characteristics. The factor analysis was applied to identify neighborhood dimensions, which were used as entities in the cluster analysis to generate a neighborhood typology. The factor analysis generated three neighborhood dimensions: neighborhood education, age structure, and neighborhood family structure and employment. The cluster analysis generated six types of neighborhoods with combinations of the three neighborhood dimensions. Multilevel binomial regression models were used to assess the effects of neighborhoods on an individual's health. The results showed that the biggest health differences were between two neighborhood types: (1) the highest concentration of inhabitants younger than 15 years, a moderate education level, and a moderate level of single-parent families and (2) the highest educational level, a median level of single-parent families, and a median level of elderly concentrations. Individuals living in the first type had significantly higher chances of having functional limitations and poor self-rated health than the individuals in the second neighborhood type. Our study suggests that the multivariate-structural approach improves neighborhood measurements by addressing neighborhood diversity and examining how an individual's health varies in different neighborhood contexts.