In recent years, the rapid convergence of artificial intelligence (AI) and low-altitude flight technology has driven significant transformations across various industries. These advancements have showcased immense potential in areas such as logistics distribution, urban air mobility (UAM) and national defense. By adopting the AI technology, low-altitude flight technology can achieve high levels of automation and operate in coordinated swarms, thereby enhancing efficiency and precision. However, as these technologies become more pervasive, they also raise pressing ethical or moral concerns, particularly regarding privacy, public safety, as well as the risks of militarisation and weaponisation. These issues have sparked extensive debates. In summary, while the integration of AI and low-altitude flight presents revolutionary opportunities, it also introduces complex ethical challenges. This article will explore these opportunities and challenges in depth, focusing on areas such as privacy protection, public safety, military applications and legal regulation, and will propose strategies to ensure that technological advancements remain aligned with ethical or moral principles.

Tight focusing with very small f-numbers is necessary to achieve the highest at-focus irradiances. However, tight focusing imposes strong demands on precise target positioning in-focus to achieve the highest on-target irradiance. We describe several near-infrared, visible, ultraviolet and soft and hard X-ray diagnostics employed in a ∼1022 W/cm2 laser–plasma experiment. We used nearly 10 J total energy femtosecond laser pulses focused into an approximately 1.3-μm focal spot on 5–20 μm thick stainless-steel targets. We discuss the applicability of these diagnostics to determine the best in-focus target position with approximately 5 μm accuracy (i.e., around half of the short Rayleigh length) and show that several diagnostics (in particular, 3$\omega$ reflection and on-axis hard X-rays) can ensure this accuracy. We demonstrated target positioning within several micrometers from the focus, ensuring over 80% of the ideal peak laser intensity on-target. Our approach is relatively fast (it requires 10–20 laser shots) and does not rely on the coincidence of low-power and high-power focal planes.

Oxidation-reduction processes within natural systems greatly influence the properties of sediments, soils and clays. The objective of this experimental study was to gather new evidence for the effects of changes in redox conditions (reduction and reoxidation) on structural properties of ferruginous smectite and to understand better the mechanisms involved. The <2 µm fraction of a ferruginous smectite (sample SWa-1), which contains 17.3 wt.% of total structural Fe, was studied by infrared (IR) spectroscopy. The pure Na-saturated clay was reduced by Na dithionite for 10 to 240 min to obtain various Fe(II):(total Fe) ratios ranging from 0 to 1.0. Selected reduced samples were then reoxidized completely by bubbling O2 gas through the suspensions for up to 12 h. Infrared spectra of the initially unaltered, reduced and reduced-reoxidized samples were collected. Reduction generated changes in the three studied spectral regions (O-H stretching, M-O-H deformation, and Si-O stretching), indicating that major modifications occurred within the clay crystal beyond merely a change in Fe oxidation state. partial dehydroxylation and redistribution of Fe, and perhaps Al, cations occurred upon reduction of SWa-1, changing the structural properties of its tetrahedral and octahedral sheets. Water molecules, probably generated by dehydroxylation within the octahedral sheet upon reduction, were tightly bound to the clay surface and were possibly trapped within the clay structure. Except for dehydroxylation and the Fe oxidation state, all these modifications were largely irreversible. The tightly bound water was not completely removed upon reoxidation and the cationic rearrangements generated during reduction were not reversed: either they were preserved as in the reduced state or cations were redistributed into a different configuration from the unreduced clay.

Visitors to zoos are a source of potential stress to certain captive-housed animals. Much research has focused on Europe and America, whereas the effect of human audiences on the behaviour of captive animals in Chinese parks has so far not been investigated. Sika deer (Cervus nippon) housed in Zhu-Yu-Wan Park, Yangzhou City, Jiangsu Province, China, were studied to determine the effect of different visitor density levels on the animals’ activity. From June 21 to December 10, 2006, and again from February 21 to July 10, 2007, 21 subjects were observed for 10 h per week for a total of 44 weeks. Continuous focal animal sampling was used to quantify behaviours, and visitor density was recorded every minute. Friedman's tests were used to examine the effects of visitor density on the behaviour of sika deer. Results showed that high visitor density was significantly related to foraging, resting, watching and ‘non-visible’ behaviours. The findings demonstrate that high numbers of visitors have an effect on the welfare of sika deer.

Understanding cross-cultural differences in attitudes to animal welfare issues is important in maintaining good international relations, including economic and trade relations. This study aimed to investigate the attitudes of stakeholders towards improving the welfare of animals during slaughter and transport in four key SE and E Asian countries: China, Thailand, Vietnam and Malaysia. Logistic regression analysis of the associations between demographic factors and attitudes identified nationality as the most significant factor influencing attitude. Motivating factors for improving welfare were ranked according to their importance: religion, knowledge levels, monetary gain, availability of tools and resources, community issues, approval of supervisor and peers. Strong beliefs in the influence of animal welfare laws, the power of the workplace and the importance of personal knowledge were shared by all countries. In addition, religion and peer consideration were significantly associated with attitudes in Malaysia and Thailand, respectively. The findings of this research will assist in the development of international animal welfare initiatives.

Weapon target allocation (WTA) is an effective method to solve the battlefield fire optimisation problem, which plays an important role in intelligent automated decision-making. We researched the multitarget allocation problem to maximise the attack effectiveness when multiple interceptors cooperatively attack multiple ground targets. Firstly, an effective and reasonable fitness function is established, based on the situation between the interceptors and targets, by comprehensively considering the relative range, relative angle, speed, capture probability and radiation source matching performance and thoroughly evaluating them based on the advantage of the attack effectiveness. Secondly, the optimisation performance of the particle swarm optimisation (PSO) algorithm is adaptively improved. We propose an adaptive simulated annealing-particle swarm optimisation (SA-PSO) algorithm by introducing the simulated annealing algorithm into the adaptive PSO algorithm. The proposed algorithm can enhance the convergence speed and overcome the disadvantage of the PSO algorithm easily falling into a local extreme point. Finally, a simulation example is performed in a scenario where ten interceptors cooperate to attack eight ground targets; comparative experiments are conducted between the adaptive SA-PSO algorithm and PSO algorithm. The simulation results indicate that the proposed adaptive SA-PSO algorithm demonstrates great performance in convergence speed and global optimisation capabilities, and a maximised attack effectiveness can be guaranteed.

Let $\mathcal {O}(\pi )$ denote the number of odd parts in an integer partition $\pi$. In 2005, Stanley introduced a new statistic $\operatorname {srank}(\pi )=\mathcal {O}(\pi )-\mathcal {O}(\pi ')$, where $\pi '$ is the conjugate of $\pi$. Let $p(r,\,m;n)$ denote the number of partitions of $n$ with srank congruent to $r$ modulo $m$. Generating function identities, congruences and inequalities for $p(0,\,4;n)$ and $p(2,\,4;n)$ were then established by a number of mathematicians, including Stanley, Andrews, Swisher, Berkovich and Garvan. Motivated by these works, we deduce some generating functions and inequalities for $p(r,\,m;n)$ with $m=16$ and $24$. These results are refinements of some inequalities due to Swisher.

Quaternary selenide, Pb4In2.6Bi3.4Se13 (x = 2.4 member of the Pb4(InxBi6-xSe13 solid solution), was synthesized by a solid-state technique, and its structure was determined using powder X-ray diffraction (XRD). Pb4In2.6Bi3.4Se13 crystallizes in the orthorhombic space group Pbam (No. 55) with Z = 4. Lattice parameters and calculated density were determined to be a = 22.152(5) Å, b = 27.454(5) Å, and c = 4.1354(6) Å, V = 2515.0(11) Å3, and Dx = 7.490 g cm−3. The structure consists of Z-shaped ribbon units and corner-shared infinite one-dimensional [InSe4]∞ chains running parallel to the c-axis. The chains and ribbons are further connected by Pb atoms to form a three-dimensional network. Pb atoms are situated in the center of bicapped trigonal prisms. The compound exhibits a semiconductor feature. The Seebeck coefficient of Pb4In2.6Bi3.4Se13 was found to be −180 μV K−1 at 295 K and −380 μV K−1 at 600 K. Combining the values of Seebeck coefficient, electrical conductivity, and thermal conductivity yield a figure of merit, ZT, of about 0.175 at 700 K. The powder XRD pattern of Pb4In2.6Bi3.4Se13 was also determined.

Understanding core statistical properties and data features in mortality data are fundamental to the development of machine learning methods for demographic and actuarial applications of mortality projection. The study of statistical features in such data forms the basis for classification, regression and forecasting tasks. In particular, the understanding of key statistical structure in such data can aid in improving accuracy in undertaking mortality projection and forecasting when constructing life tables. The ability to accurately forecast mortality is a critical aspect for the study of demography, life insurance product design and pricing, pension planning and insurance-based decision risk management. Though many stylised facts of mortality data have been discussed in the literature, we provide evidence for a novel statistical feature that is pervasive in mortality data at a national level that is as yet unexplored. In this regard, we demonstrate in this work a strong evidence for the existence of long memory features in mortality data, and second that such long memory structures display multifractality as a statistical feature that can act as a discriminator of mortality dynamics by age, gender and country. To achieve this, we first outline the way in which we choose to represent the persistence of long memory from an estimator perspective. We make a natural link between a class of long memory features and an attribute of stochastic processes based on fractional Brownian motion. This allows us to use well established estimators for the Hurst exponent to then robustly and accurately study the long memory features of mortality data. We then introduce to mortality analysis the notion from data science known as multifractality. This allows us to study the long memory persistence features of mortality data on different timescales. We demonstrate its accuracy for sample sizes commensurate with national-level age term structure historical mortality records. A series of synthetic studies as well a comprehensive analysis of real mortality death count data are studied in order to demonstrate the pervasiveness of long memory structures in mortality data, both mono-fractal and multifractal functional features are verified to be present as stylised facts of national-level mortality data for most countries and most age groups by gender. We conclude by demonstrating how such features can be used in kernel clustering and mortality model forecasting to improve these actuarial applications.

The Dayao Paleolithic site, located in Inner Mongolia on the eastern margin of China's vast northwestern drylands, was a lithic quarry-workshop utilized by Pleistocene human migrants through the region. Determining the age of this activity has previously yielded controversial results. Our magnetostratigraphic and OSL dating results suggest the two artifact-bearing paleosols are correlated with MIS 5 and 7, respectively. Correlating paleoclimatic data with marine δ18O records leads us to conclude that two sandy gravel layers containing many artifacts in the lower part of the Dayao sequence were formed during MIS 9 and 11, if not earlier. Our results reveal that the earliest human occupation at the Dayao site occurred before ca. 400 ka during a relatively warm and moist interglacial period, similar to several subsequent occupations, documenting the earliest and northernmost archaeological assemblage yet reported in China's arid northwest. We conclude that the northward and southward displacements of the East Asian summer monsoon rain belt during past interglacial-glacial cycles were responsible for the discontinuous human occupation detected at the Dayao site. The penetration of this precipitation regime into dryland ecologies via the Huanghe (Yellow River) Valley effectively created a corridor for hominin migration into China's arid northwest.

Mortality projection and forecasting of life expectancy are two important aspects of the study of demography and life insurance modelling. We demonstrate in this work the existence of long memory in mortality data. Furthermore, models incorporating long memory structure provide a new approach to enhance mortality forecasts in terms of accuracy and reliability, which can improve the understanding of mortality. Novel mortality models are developed by extending the Lee–Carter (LC) model for death counts to incorporate a long memory time series structure. To link our extensions to existing actuarial work, we detail the relationship between the classical models of death counts developed under a Generalised Linear Model (GLM) formulation and the extensions we propose that are developed under an extension to the GLM framework known in time series literature as the Generalised Linear Autoregressive Moving Average (GLARMA) regression models. Bayesian inference is applied to estimate the model parameters. The Deviance Information Criterion (DIC) is evaluated to select between different LC model extensions of our proposed models in terms of both in-sample fits and out-of-sample forecasts performance. Furthermore, we compare our new models against existing models structures proposed in the literature when applied to the analysis of death count data sets from 16 countries divided according to genders and age groups. Estimates of mortality rates are applied to calculate life expectancies when constructing life tables. By comparing different life expectancy estimates, results show the LC model without the long memory component may provide underestimates of life expectancy, while the long memory model structure extensions reduce this effect. In summary, it is valuable to investigate how the long memory feature in mortality influences life expectancies in the construction of life tables.

In this paper, the generation of relativistic electron mirrors (REM) and the reflection of an ultra-short laser off the mirrors are discussed, applying two-dimension particle-in-cell simulations. REMs with ultra-high acceleration and expanding velocity can be produced from a solid nanofoil illuminated normally by an ultra-intense femtosecond laser pulse with a sharp rising edge. Chirped attosecond pulse can be produced through the reflection of a counter-propagating probe laser off the accelerating REM. In the electron moving frame, the plasma frequency of the REM keeps decreasing due to its rapid expansion. The laser frequency, on the contrary, keeps increasing due to the acceleration of REM and the relativistic Doppler shift from the lab frame to the electron moving frame. Within an ultra-short time interval, the two frequencies will be equal in the electron moving frame, which leads to the resonance between laser and REM. The reflected radiation near this interval and corresponding spectra will be amplified due to the resonance. Through adjusting the arriving time of the probe laser, a certain part of the reflected field could be selectively amplified or depressed, leading to the selective adjustment of the corresponding spectra.

Phytase has long been used to decrease the inorganic phosphorus (Pi) input in poultry diet. The current study was conducted to investigate the effects of Pi supplementation on laying performance, egg quality and phosphate–calcium metabolism in Hy-Line Brown laying hens fed phytase. Layers (n = 504, 29 weeks old) were randomly assigned to seven treatments with six replicates of 12 birds. The corn–soybean meal-based diet contained 0.12% non-phytate phosphorus (nPP), 3.8% calcium, 2415 IU/kg vitamin D3 and 2000 FTU/kg phytase. Inorganic phosphorus (in the form of mono-dicalcium phosphate) was added into the basal diet to construct seven experimental diets; the final dietary nPP levels were 0.12%, 0.17%, 0.22%, 0.27%, 0.32%, 0.37% and 0.42%. The feeding trial lasted 12 weeks (hens from 29 to 40 weeks of age). Laying performance (housed laying rate, egg weight, egg mass, daily feed intake and feed conversion ratio) was weekly calculated. Egg quality (egg shape index, shell strength, shell thickness, albumen height, yolk colour and Haugh units), serum parameters (calcium, phosphorus, parathyroid hormone, calcitonin and 1,25-dihydroxyvitamin D), tibia quality (breaking strength, and calcium, phosphorus and ash contents), intestinal gene expression (type IIb sodium-dependent phosphate cotransporter, NaPi-IIb) and phosphorus excretion were determined at the end of the trial. No differences were observed on laying performance, egg quality, serum parameters and tibia quality. Hens fed 0.17% nPP had increased (P < 0.01) duodenum NaPi-IIb expression compared to all other treatments. Phosphorus excretion linearly increased with an increase in dietary nPP (phosphorus excretion = 1.7916 × nPP + 0.2157; R2 = 0.9609, P = 0.001). In conclusion, corn–soybean meal-based diets containing 0.12% nPP, 3.8% calcium, 2415 IU/kg vitamin D3 and 2000 FTU/kg phytase would meet the requirements for egg production in Hy-Line Brown laying hens (29 to 40 weeks of age).

Three-dimensional (3D) tomographic imaging of the structural, chemical, and physical properties of a material provides key knowledge that links the structure of a material to both its processing and structure that is central to studies across a broad spectrum of materials. For many decades, tomography using x-rays or electrons has proven to be an essential 3D characterization tool. In recent years, advances in technology have significantly pushed the envelope of these techniques in many respects, enabling new imaging capabilities at the nanometer and atomic scale. This article highlights several such developments in nanoscale x-ray and electron tomography. The five articles that appear in this issue of MRS Bulletin discuss research frontiers that include multimodal x-ray tomography at the nanoscale, x-ray spectroscopic tomography, dark-field x-ray microscopy, electron nanotomography for functional nanomaterials, and atomistic imaging by electron tomography. These articles give a holistic view of the status of these techniques and promising future directions, as well highlighting their applications for scientific problems.

Feathers play a critical role in thermoregulation and directly influence poultry production. Poor feathering adversely affects living appearance and carcass quality, thus reducing profits. However, producers tend to ignore the importance of feather development and do not know the laws of feather growth and development. The objective of this study was to fit growth curves to describe the growth and development of feathers in yellow-feathered broilers during the embryonic and posthatching periods using different nonlinear functions (Gompertz, logistic and Bertalanffy). Feather mass and length were determined during the embryonic development and posthatching stages to identify which growth model most accurately described the feather growth pattern. The results showed that chick embryos began to grow feathers at approximately embryonic (E) day 10, and the feathers grew rapidly from E13 to E17. There was little change from E17 to the day of hatching (DOH). During the embryonic period, the Gompertz function (Y = 798.48e−203 431exp(−0.87t), Akaike’s information criterion (AIC) = −0.950 × 103, Bayesian information criterion (BIC) = −0.711 × 103 and mean square error (MSE) = 559.308) provided the best fit for the feather growth curve compared with the other two functions. After hatching, feather mass and length changed little from the DOH to day (D) 14, increased rapidly from D21 to D91 and then grew slowly after D91. The first stage of feather molting occurred from 2 to 3 weeks of age when the down feathers were mostly shed and replaced with juvenile feathers, and the second stage occurred at approximately 13 to 15 weeks of age. The three nonlinear functions could overall fit the feather growth curve well, but the Bertalanffy model (Y = 116.88 × (1−0.86e−0.02t)3, AIC = 1.065 × 105, BIC = 1.077 × 105 and MSE = 11.308) showed the highest degree of fit among the models. Therefore, the Gompertz model exhibited the best goodness of fit for the feather growth curve during the embryonic development, while the Bertalanffy model was the most suitable model due to its accurate ability to predict the growth and development of feathers during the growth period, which is an important commercial characteristic of yellow-feathered chickens.