How to optimize global food production Keeping societies stable and managing Earth's resources sustainably depend on doing a good, steady job producing and distributing food. West et al. asked what combinations of crops and regions offer the best chance of progress. Their analysis focused on reducing greenhouse gas emissions, nutrient pollution, water use, and food waste. They identify regions that are likely to yield the best balance between applying fertilizer to increase crop yields versus the resulting environmental impact. Science, this issue p. 325 A limited set of interventions could disproportionately improve crop production and environmental sustainability. Achieving sustainable global food security is one of humanity’s contemporary challenges. Here we present an analysis identifying key “global leverage points” that offer the best opportunities to improve both global food security and environmental sustainability. We find that a relatively small set of places and actions could provide enough new calories to meet the basic needs for more than 3 billion people, address many environmental impacts with global consequences, and focus food waste reduction on the commodities with the greatest impact on food security. These leverage points in the global food system can help guide how nongovernmental organizations, foundations, governments, citizens’ groups, and businesses prioritize actions.
There is worldwide concern about the environmental costs of conventional intensification of agriculture. Growing evidence suggests that ecological intensification of mainstream farming can safeguard food production, with accompanying environmental benefits; however, the approach is rarely adopted by farmers. Our review of the evidence for replacing external inputs with ecosystem services shows that scientists tend to focus on processes (e.g., pollination) rather than outcomes (e.g., profits), and express benefits at spatio-temporal scales that are not always relevant to farmers. This results in mismatches in perceived benefits of ecological intensification between scientists and farmers, which hinders its uptake. We provide recommendations for overcoming these mismatches and highlight important additional factors driving uptake of nature-based management practices, such as social acceptability of farming.
Abstract Marine protected areas (MPAs) generate powerful interactions between social, economic and environmental interests, manifest at a circumscribed and often local scale. Consequently the designation and management of an individual MPA typically plays out in microcosm the general challenge of sustainable development in the marine environment. Some universally relevant questions relating to four commonly held defining attributes of MPAs are articulated. However, while many of the questions are universal, in practice the answers vary greatly. Consequently there are few MPAs which would not provide an informative case study elucidating the dynamics at the intersection between science, policy and management in the marine realm. The papers in this collection exemplify a range of key issues across this spectrum of disciplines. In practice most contentious issues relate to the balance within MPAs between environmental and socio-economic considerations, not least relating to fishing. In this respect greater attention in MPA management plans, to the economic benefits of MPAs for local communities is encouraged. However we also recognise that glib assertions that a secure sustainable balance between conservation and exploitation can be established in practice, typically with few resources in a largely unseen and often data-poor environment, may sometimes be politically expedient but scientifically questionable. Yet it is ultimately the work of all those involved directly with MPAs to collectively achieve the task of transforming the rhetoric of marine conservation policy into a successful reality on the ground and we commend the authors of this collection for their efforts to achieve that goal.
Audio DNNs have demonstrated impressive performance on various machine listening tasks; however, most of their representations are computationally costly and uninterpretable, leaving room for optimization. Here, we propose a novel approach centered on spectrotemporal modulation (STM) features, a signal processing method that mimics the neurophysiological representation in the human auditory cortex. The classification performance of our STM-based model, without any pretraining, is comparable to that of pretrained audio DNNs across diverse naturalistic speech, music, and environmental sounds, which are essential categories for both human cognition and machine perception. These results show that STM is an efficient and interpretable feature representation for audio classification, advancing the development of machine listening and unlocking exciting new possibilities for basic understanding of speech and auditory sciences, as well as developing audio BCI and cognitive computing.
This study explores the influence of environmental colors on human behavior, specifically focusing on aggressiveness and passiveness. Color is widely regarded as an influential environmental factor shaping human behavior, yet existing studies present conflicting evidence regarding its impact on aggressiveness and passiveness. This study employed Minecraft as a controlled digital platform to investigate whether exposure to different colors influences both the frequency and nature of participant interactions (aggressive versus non-aggressive), and whether prolonged exposure amplifies these effects. Anonymous online participants were exposed to various colors before interacting with non-player characters simulating human-like encounters. Three key outcomes were measured: (1) total interactions per color, (2) ratios of aggressive to non-aggressive interactions per color, and (3) the effect of varying exposure durations on aggressiveness. While no significant overall differences in interaction frequency were observed among the colors, post-hoc analyses revealed that Red and Black elicited significantly more interactions compared to Green. Additionally, Red, Yellow, and Black were associated with higher ratios of aggressive behavior relative to Green or White. Prolonged exposure to Red also appeared to intensify aggressive responses. These findings underscore the potential role of environmental color in shaping online social behaviors and highlight the importance of environmental settings in areas ranging from online communication platforms to digital marketing strategies.
Despite the growing emphasis on intelligent buildings as a cornerstone of sustainable urban development, significant energy inefficiencies persist due to suboptimal design, material choices, and user behavior. The applicability of integrated Building Information Modeling (BIM) and solarpowered environmental monitoring systems for energy optimization in low-carbon smart buildings remains underexplored. Can BIM-driven design improvements, combined with photovoltaic systems, achieve substantial energy savings while enabling self-powered environmental monitoring? This study conducts a case analysis on a retrofitted primary school building in Guangdong, China, utilizing BIM-based energy simulations, material optimization, and solar technology integration. The outcomes reveal that the proposed approach reduced annual energy consumption by 40.68%, with lighting energy use decreasing by 36.59%. A rooftop photovoltaic system demonstrated a payback period of 7.46 years while powering environmental sensors autonomously. Hardware system integrates sensors and an ARDUINO-based controller to detect environmental factors like rainfall, temperature, and air quality. It is powered by a 6W solar panel and a 2200 mAh/7.4 V lithium battery to ensure stable operation. This study underscores the potential of BIM and solar energy integration to transform traditional buildings into energy-efficient, self-sustaining smart structures. Further research can expand the scalability of these methods across diverse climates and building typologies.
We propose a novel approach for optimizing topological quantum devices: instead of merely isolating qubits from environmental noise, we engineer the environment to actively suppress decoherence. For a Majorana qubit in a topological superconducting wire, the exponentially small energy splitting $ε\sim e^{-L/ξ}$ provides protection against local perturbations but renders it highly susceptible to pure dephasing from low-frequency environmental noise. We show that coupling via a parity-conserving operator ($iγ_Lγ_R$) to a bosonic environment yields a dephasing rate $Γ_φ\propto S(ε)$, where $S(ε)$ is the environmental noise power at the qubit splitting frequency. In the experimentally relevant regime where $k_B T \gtrsim \hbarε$ (with $T \sim 10-100$ mK), the noise power scales as $S(ε) \propto ρ(ε) k_B T/\hbarε$, leading to a dephasing rate $Γ_φ\propto ρ(ε) T/ε$. This exposes a fundamental challenge: the dephasing rate diverges as $1/ε$ for a standard environment, e.g., a 1D system with linear dispersion where $ρ(ε)$ is constant. We overcome this by designing environments with a suppressed density of states following $ρ_{\text{engineered}}(ε) = ρ_{\text{free}}(ε) (ε/ω_c)^α$. This creates an ``inverse Purcell effect'' that yields a temperature-independent suppression factor $F_P = (ε/ω_c)^α$. For $α> 1$, the engineered dephasing rate decreases exponentially with wire length, $Γ_{φ,\text{engineered}} \propto e^{-(α-1)L/ξ}$, meaning longer wires provide better coherence protection. This provides a quantitative design principle where environmental engineering transforms detrimental noise into a tool for coherence stabilization, while respecting fermion parity superselection rules.
Rengasamy Anbazhakan, Xin-Meng Zhu, Neng-Qi Li
et al.
<i>Malania oleifera</i> Chun & S.K. Lee, an endemic monotypic species that belongs to the family Olacaceae, is under continuous pressure of decline owing to several ecological and physiological factors. The present study aimed to establish an efficient in vitro protocol for callus-mediated indirect somatic embryogenesis in <i>M. oleifera</i> by alleviating tissue browning. Internodes and leaves obtained from seedlings were used as explants. Antioxidant pre-treatment (ascorbic acid, AA) followed by different carbon sources (sucrose, maltose, glucose, and fructose) and plant growth regulators in various concentrations and combinations were employed in Woody Plant Medium (WPM) to alleviate explant browning and induce callus formation from the explants. AA pre-treatment and subsequent culture on maltose at a concentration of 116.8 mM were optimal for controlling phenolic exudation on >90% of both explants. The highest responses of 53.77% and 57.43% for embryogenic calli were induced from internode and leaf explants, respectively. The highest responses, 85.22% and 93.80%, were observed for somatic embryos that matured into the globular, heart-shaped and torpedo stages at different percentages on NAA 2.5 mg/L in combination with BA 1.0 mg/L for both explants. The matured somatic embryos were finally germinated at a maximum concentration of GA<sub>3</sub>, 2.0 mg/L. All plantlets were successfully hardened and acclimatized under culture room conditions and then transferred to the greenhouse. The current study suggests an efficient protocol for indirect somatic embryogenesis by alleviating phenolic exudation from the explants of <i>M. oleifera</i>. This first successful report of in vitro culture establishment in <i>M. oleifera</i> may offer an effective alternative measure to conserve this species and provide a system for analyzing bioactive chemicals and for use in the oil industry.
Sobhana Mummaneni, Venkata Chaitanya Satya Ramaraju Mudunuri, Sri Veerabhadra Vikas Bommaganti
et al.
A facial recognition system is a biometric security and surveillance system that can identify and monitor individuals in a crowded area. Manually monitoring a crowded environment is a difficult and error-prone task. Therefore, in such contexts, a model that automatically detects and recognises people's faces is needed to improve security. The automation of face recognition brings the benefit of a more efficient and accurate solution. This paper proposes an advanced model that has the ability to detect and recognise faces in dense crowds by using deep learning techniques. Where the input is live video, the process involves splitting the video into frames and each frame is fed into the model. The Multi-Task Cascaded Convolutional Neural Networks (MTCNN) algorithm is used for face detection. It accurately locates faces in frames and images and generates boundaries around the faces as output. The detected faces are then fed as input to a model, where they are compared with data from the database. If a face is recognised, the name of the recognised person is displayed in the boundary box of the frame, otherwise it is displayed that the person is unknown. FaceNet is used for face recognition tasks.