The workplace is a key environmental determinant of health and well-being. Food choices can be influenced by several workplace-related factors including, but not limited to, working hours, the workplace food environment, job roles and workplace culture. Therefore, the workplace is increasingly viewed as an important place for public health nutrition interventions. However, research in this area is fragmented and heterogeneous due to the wide range of workplace settings and occupational groups. This editorial summarises the research presented at The Nutrition Society Workplace Diet and Health Special Interest Group Satellite Symposium in July 2024 as part of the inaugural Nutrition Society Congress.

Acid activation of clay minerals is one of the most effective methods for production of materials with increased acidity and porosity. In this review, the benefits of infrared (IR) spectroscopy in studies of acid-treated clay minerals are demonstrated. Protons penetrating into the clay mineral layers evoke structural modifications that can be followed readily by changes in the characteristic absorption bands attributed to the vibrations of the OH and Si–O groups. In the first part of the review the effect of the clay mineral type, composition, layer charge, non-swelling layers, and organo-modification on the dissolution rate is reported. The identification of the acid sites via pyridine adsorption also included. The purpose was to gather the IR results published in previous studies in a single summary paper. In the second part select recent studies reporting the utilization of IR spectroscopy for the characterization of acid-activated clay minerals, mainly applied as catalysts or adsorbents, are discussed. IR spectroscopy as a simple and non-destructive technique deserves attention also today.

Machine learning is increasingly being utilised across various domains of nutrition research due to its ability to analyse complex data, especially as large datasets become more readily available. However, at times, this enthusiasm has led to the adoption of machine learning techniques prior to a proper understanding of how they should be applied, leading to non-robust study designs and results of questionable validity. To ensure that research standards do not suffer, key machine learning concepts must be understood by the research community. The aim of this review is to facilitate a better understanding of machine learning in research by outlining good practices and common pitfalls in each of the steps in the machine learning process. Key themes include the importance of generating high-quality data, employing robust validation techniques, quantifying the stability of results, accurately interpreting machine learning outputs, adequately describing methodologies, and ensuring transparency when reporting findings. Achieving this aim will facilitate the implementation of robust machine learning methodologies, which will reduce false findings and make research more reliable, as well as enable researchers to critically evaluate and better interpret the findings of others using machine learning in their work.

We present a versatile method to generate asymmetric profiles and use it to create Gaussian-like, Cauchy-like, and Pseudo-Voigt-like profiles in terms of elementary functions. Furthermore, this method guarantees that the position and magnitude of the global maximum are independent of the asymmetry parameter, which substantially facilitates the convergence of an optimizer when fitting the peaks to real data. This investigation shows that the method developed here exhibits favorable practical properties and is particularly well suited for various applications where asymmetric peak profiles are observed. For example, in X-ray diffraction (XRD) measurements, the use of asymmetric profiles is essential for obtaining accurate outcomes. This is because diffractometers can introduce asymmetry into the diffraction peaks due to factors such as axial divergence in the beam path. By taking this asymmetry into account during the modeling process, the resulting data obtained can be corrected for instrumental effects. The results of the study show that the evaluation of XRD using nearly defect-free LaB6 allows a precise characterization of the peak broadening caused by the diffractometer itself. Additional size-strain effects of ZnO are determined by considering the asymmetric peak profile of the diffractometer.

Determining the chemical composition of sub-micrometer rock-forming minerals is still a challenging task. The electron probe micro-analyzer (EPMA) is considered the most accurate analytical way to obtain chemical data on amorphous and crystalline materials. However, performing EPMA analyses on sub-micrometer-sized grains is uncertain not recommended as the risk of obtaining analyses contaminated from the surrounding phases. A transmission electron microscope (TEM) equipped with an energy dispersive X-ray spectrometer (EDS) provides a greater spatial resolution, making it possible to obtain trustworthy chemical information on sub-micrometer-sized material. In this work, we present a fast and cheap data-reduction protocol for TEM-EDS chemical analysis, where k-factors derived experimentally for each element of interest and absorption correction are implemented. The results are compared with those determined using standardless and non-corrected TEM-EDS protocols. The k-factor for oxygen plays a fundamental role and its value should be calculated from compounds similar to the phase of interest. For absorption correction, the contribution of hydrogen during structural formula recalculation is taken into account, like a lower net valence of oxygen. The robustness of this protocol was tested by performing TEM-EDS analyses on white mica grains from metapelites, belonging to the Internal Ligurian Units exposed in the Northern Apennines, the chemical composition of which is well constrained. Such a protocol has proven to provide high-quality results from both statistical and crystallo-chemical perspectives. Remarkably, the tested data-reduction protocol for TEM-EDS analysis provided chemical compositions consistent with the EPMA results previously obtained from the same samples.

In this paper I look at the philosophical work of Sophie Germain, a woman mathematician and philosopher in nineteenth-century France. Although forgotten after her death, Germain’s contribution to mathematical sciences has been revisited and reappraised in recent years, but with very few notable exceptions, her philosophical work is still in the margins. In addressing this gap in the literature, I revisit Germain’s contribution to the history of ideas, particularly focusing on her contribution to process epistemologies. I argue that Germain was a truly transdisciplinary thinker avant la lettre and that her philosophical work should be mapped in the wider field of process philosophies. In doing so I make connections between Sophie Germain and Alfred Whitehead’s philosophy of the organism, particularly focusing on their take on feelings, prehensions, happy ideas, and events.

Although childhood maltreatment is associated with externalizing symptoms, not all individuals with these experiences develop externalizing behaviors and some exhibit positive adjustment. To address this multifinality, we used latent growth curve modeling to identify trajectories of (a) externalizing symptoms and (b) subjective wellbeing from late adolescence through young adulthood, determine whether types of childhood maltreatment and domains of executive functioning (EF) are associated with initial levels and growth (slopes) of externalizing symptoms or subjective wellbeing, and investigate whether EF moderates these relations. Participants were youth recruited at ages 10–12 (N = 775; 69% male, 31% female; 76% White, 21% Black/African American, 3% multiracial). We examined EF at ages 10–12, childhood maltreatment reported retrospectively at age 25, and externalizing symptoms and subjective wellbeing at multiple points between ages 16 and 28. Experience of childhood maltreatment and certain EF domains were associated with externalizing symptoms and subjective wellbeing at age 16. EF domains were associated with rate of change in externalizing problems, though not in expected directions. EF variables moderated the relation between maltreatment and initial levels of both outcomes and change in externalizing symptoms. Findings have implications for intervention efforts to mitigate externalizing problems and bolster positive adjustment.

Aiming at the problem of fast and consensus obstacle avoidance of multiple unmanned aerial systems in undirected network, a multi-quadrotor unmanned aerial vehicles UAVs (QUAVs) finite-time consensus obstacle avoidance algorithm is proposed. In this paper, multi-QUAVs establish communication through the leader-following method, and the formation is led by the leader to fly to the target position automatically and avoid obstacles autonomously through the improved artificial potential field method. The finite-time consensus protocol controls multi-QUAVs to form a desired formation quickly, considering the existence of communication and input delay, and rigorously proves the convergence of the proposed protocol. A trajectory segmentation strategy is added to the improved artificial potential field method to reduce trajectory loss and improve the task execution efficiency. The simulation results show that multi-QUAVs can be assembled to form the desired formation quickly, and the QUAV formation can avoid obstacles and maintain the formation unchanged while avoiding obstacles.

In response to its severe environmental problems, China's government is pursuing a national goal to “build an ecological civilization.” One approach used to theorize about China's environmental governance is environmental authoritarianism (EA). Drawing on work in political steering theory and the governmentality tradition, this paper addresses the “soft” side of EA by analysing the eco-civilization discourse on food and eating in policy documents and consumer guidebooks. It argues that China's EA works not only through coercion but also through citizen responsibilization. The emerging discourse of eco-civilization outlines a cultural nationalist programme focused on virtue and vice, in which consumer behaviour is morally charged. Consumers are expected to cultivate themselves into models of ecological morality to fulfil their civic duty and support the state's goal of building an ecological civilization.

This paper presents Hybrid Modified A* (HMA*) algorithm which is used to control an omnidirectional mecanum wheel automated guided vehicle (AGV). HMA* employs Modified A* and PSO to determine the best AGV path. The HMA* overcomes the A* technique’s drawbacks, including a large number of nodes, imprecise trajectories, long calculation times, and expensive path initialization. Repetitive point removal refines Modified A*’s path to locate more important nodes. Real-time hardware control experiments and extensive simulations using Matlab software prove the HMA* technique works well. To evaluate the practicability and efficiency of HMA* in route planning and control for AGVs, various algorithms are introduced like A*, Probabilistic Roadmap (PRM), Rapidly-exploring Random Tree (RRT), and bidirectional RRT (Bi-RRT). Simulations and real-time testing show that HMA* path planning algorithm reduces AGV running time and path length compared to the other algorithms. The HMA* algorithm shows promising results, providing an enhancement and outperforming A*, PRM, RRT, and Bi-RRT in the average length of the path by 12.08%, 10.26%, 7.82%, and 4.69%, and in average motion time by 21.88%, 14.84%, 12.62%, and 8.23%, respectively. With an average deviation of 4.34% in path length and 3% in motion time between simulation and experiments, HMA* closely approximates real-world conditions. Thus, the proposed HMA* algorithm is ideal for omnidirectional mecanum wheel AGV’s static as well as dynamic movements, making it a reliable and efficient alternative for sophisticated AGV control systems.