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Considering the role of the energy grid mix on indirect water use in dairy barns
No full textWater use is an important environmental concern for the dairy sector. There are two kinds of water use in the dairy sector, direct and indirect. Electricity generation (e.g., cooling water, evaporation, etc.) is an indirect use of water and a significant contributor to the overall water budget depending on how electricity is generated. In Canada, the dairy industry is distributed across 10 provinces each with a wide range of electricity generation sources in their grid mix, making it an interesting case study. For a dairy farm that uses 1021 kWh cow-1 y-1 (9.4 – 10.6 kWh per 100 kg milk, depending on the province), the average water use related to generating electricity was estimated to be 3.48 L kg-1 milk (range: 1.40 – 5.77 L kg-1, depending on the electricity grid). Energy conservation technologies could reduce electricity use by as much as 30 % and thus reduce water use by 1.04 L kg-1 milk on average (range: 0.42 – 1.73 L kg-1). Installing an on-farm solar array (0.40 kWp cow-1; i.e. one 400-watt solar panel per cow) could lower grid-electricity-related water use by 35 – 51 % (or by 0.57 – 2.71 L kg-1). Solar array sized with the capacity to reach net-zero electricity is feasible and can eliminate grid-electricity-related water use. This study highlights that dairy farms can achieve substantial water savings by strategically using electricity conservation and renewables, with the magnitude depending on the electricity grid mix, a relationship that has yet to be analyzed in current literature
Mechanistic insight into cooperative catalysis with pentanuclear nickel clusters: catalytic alkene dimerization and silyl-silylene and silylyne clusters.
No full textThis work gives structural insights into potential intermediates relevant to the mechanism of stereoselective catalysis by pentanuclear Ni complexes, such as the previously reported stereoselective dimerization of norbornene to the C2 symmetric (Z) anti-(bis-2,2'-norbornylidene) by [(iPr3P)Ni]5H6 (1). Attempts to generate a polymer by reaction of norbornadiene with catalytic 1 instead selectivity gave the exo-trans-exo 2+2 cyclodimerized product, 1,4,4a,4b,5,8,8a,8b-octahydro-1,4:5,8-dimethanobiphenylene. The reaction of cyclopentene with catalytic [(iPr3P)Ni]5H6 (1) gave a mixture of 1,1'-bi(cyclopentylidene) and 1-cyclopentylcyclopentene as organic products. Catalysis terminated when 1 was fully converted to the twisted trapezoidal pentanuclear cluster (iPr3P)4Ni5(C10H13)H5 (2). Complex 2 reacted with H2 to give back 1. Attempts to functionalize the organic fragment in 2 with diphenylsilane instead gave (iPr3P)4Ni5(SiPh2)(SiPh2H)H5 (3), which retains the twisted trapezoidal geometry. Complex 3 was also prepared by direct reaction of Ph2SiH2 with 1. Triethylsilane reacts with 1 to give a new distorted pentagonal cluster [(iPr3P)Ni]5(μ5-SiEt)H7 (4) from Si‒H and multiple Si‒C bond cleavages. The new structures demonstrate the remarkable flexibility in the geometry of the persistent Ni5 core and provide insight into the structures of intermediates in the reactions with [(iPr3P)Ni]5H6
AI‐Enhanced PV Power Forecasting Using Cloud Thickness and Motion in Kayseri, Türkiye
No full textThe incorporation of renewable energy in photovoltaic (PV) systems has made significant progress. The inherent intermittency nature of PV generation, nevertheless poses an obstacle to accurate energy forecasting. Historical PV production plus meteorological data such as temperature, humidity, and atmospheric pressure are largely utilized in present methods of forecasting. However, cloud thickness and dynamics‐integrated system, has not been investigated and tested in real‐world examples yet. This research seeks to fill this gap in research through the development of a new AI‐based PV forecasting model that incorporates cloud thickness, cloud motion, and solar position into the forecasting model. Cloud properties and their impact on solar radiation are computed through a deep learning‐based panel‐shadowing model. For cloud movement forecasting, a gated recurrent unit (GRU) is used, while multiple convolutional neural networks (CNNs) are used for estimating cloud thickness. These outcomes are then integrated with measurements from environmental sensors to improve the accuracy of the predictions. The system was implemented and tested at Abdullah Gül University and exhibited a remarkable improvement in forecasting accuracy compared to current models. The results prove that cloud motion and thickness improve the accuracy of PV predictions, which is important for energy market stability and power grid operations.Qatar National Librar
Improved order selection method for hidden Markov models: A case study with movement data
No full textHidden Markov models (HMMs) are a versatile statistical framework commonly used in ecology to characterize behavioural patterns from animal movement data. In HMMs, the observed data depend on a finite number of underlying hidden states, generally interpreted as the animal's unobserved behaviour. The number of states is a crucial hyperparameter, controlling the trade-off between the ecological interpretability of behaviours (fewer states) and the goodness of fit of the model (more states). Selecting the number of states, commonly referred to as order selection, is notoriously challenging. Common model selection metrics, such as Akaike information criterion (AIC) and Bayesian information criterion (BIC), often perform poorly in determining the number of states, particularly when models are misspecified. Building on existing methods for HMMs and mixture models, we propose a double penalised maximum likelihood estimate (DPMLE) for the simultaneous estimation of the number of states and parameters of non-stationary HMMs. The DPMLE differs from traditional information criteria by using two penalty functions on the stationary probabilities and state-dependent parameters. For non-stationary HMMs, forward and backward probabilities are used to approximate stationary probabilities. Using a simulation study that includes scenarios with additional complexity in the data, we compare the performance of our method with that of AIC and BIC. We also illustrate how the DPMLE differs from AIC and BIC using narwhal (Monodon monoceros) movement data. The proposed method outperformed AIC and BIC in identifying the correct number of states under model misspecification. Furthermore, its capacity to handle non-stationary dynamics allowed for more realistic modelling of complex movement data, offering deeper insights into narwhal behaviour. Our method is a powerful tool for order selection in non-stationary HMMs, with potential applications extending beyond the field of ecology
Excitation Signal Design for Fast Electrochemical Impedance Spectroscopy in Battery Testing
No full textElectrochemical impedance spectroscopy (EIS) is a widely used technique for analyzing battery dynamics over a broad frequency spectrum. Conventional state-of-the-art EIS methods involve applying a sequence of sinusoidal excitation signals, ranging from very low to very high frequencies, to capture the impedance response of the battery. However, this process is time-consuming, often requiring several hours to complete. Alternatively, approaches using pulse-based excitation have shown promise in reducing test time but often suffer from challenges in handling measurement noise and poor frequency resolution, especially at low frequencies. This work presents an improved rectangular pulse-based impedance characterization technique that enhances low-frequency resolution, increases robustness to noise, and reduces experimental time. This is accomplished through the following three key contributions of this paper: First, it establishes statistical noise properties in the Fourier-transformed signals, enabling effective noise reduction through averaging. Second, it proposes a log-frequency clustering approach to average impedance data, enhancing the accuracy of the impedance spectrum. Third, it presents a systematic pulse design method using the knowledge of the approximate time constants of the system to select the sampling interval, pulse width, and rest duration for reduced test time, improved low-frequency resolution, and enhanced signal-to-noise ratio (SNR). Together, the proposed approach enables faster and more accurate impedance characterization. Simulation analysis and experimental results confirm that the proposed approach enhances spectral resolution at low frequencies, mitigates the impact of noise at high frequencies, and significantly improves the reliability of impedance estimates at a faster measurement time frame.Natural Sciences and Engineering Research Council of Canada (Award: RGPIN-2024-04557
Shallow seamounts are “oases” and activity hubs for pelagic predators in a large-scale marine reserve
No full textSeamounts have been likened to “oases” of life in the comparative deserts of the open ocean, often harbouring high densities of threatened and exploited pelagic top predators. However, few such aggregations have been studied in any detail and the mechanisms that sustain them are poorly understood. Here, we present the findings of an integrated study of 3 previously unexplored seamounts in the tropical Atlantic, which aimed to investigate their significance as predator “hotspots” and inform their inclusion in one of world’s largest marine reserves. Baited underwater video and visual census transects revealed enhanced diversity and biomass of pelagic top predators, including elevated abundances of 7 species of sharks, predatory fish, and seabirds, within 5 km of 2 shallow seamounts (<100 m), but not a third deeper seamount (260 m). Hydroacoustic biomass of low- and mid-trophic level “prey” was also significantly elevated within 2.5 km of shallow seamounts. However, we found no evidence of enhanced primary productivity over any feature, suggesting high faunal biomass is sustained by exogenous energy inputs. Relative biomass enrichment also increased with trophic level, ranging from a 2-fold increase for zooplankton to a 41-fold increase for sharks. Tracking of the dominant predator species revealed that individual sharks (Galapagos, silky) and tuna (yellowfin, bigeye) often resided around seamounts for months to years, with evidence of connectivity between features, and (in the case of sharks) were spatially aggregated in localised hotspots that coincided with areas of high mid-trophic biomass. However, tuna and silky sharks also appeared to use seamounts as “hubs” in more extensive pelagic foraging ranges, which may help explain disproportionately high predator density. Our results reinforce the conservation significance of shallow seamounts for many marine top predators and offer fundamental insights into their functional roles as both prey “oases” and activity hubs for these species.European Commission (Award: BEST Initiative (no. 1599))Darwin Initiative (Award: DPLUS063)National Geographic Society (Award: Pristine Seas Expedition)Conflict, Stability and Security Fun
The Lance: School Year 1990-1991
No full textSchool Year 1990-1991 Vol. 63: no. 1 (1990: Aug. 29) 16p.Vol. 63: no. 2 (1990: Sept. 5) 20p.Vol. 63: no. 3 (1990: Sept. 12) 16p.Vol. 63: no. 4 (1990: Sept. 19) 16p.Vol. 63: no. 5 (1990: Sept. 26) 20p.Vol. 63: no. 6 (1990: Oct. 3) 20p.Vol. 63: no. 7 (1990: Oct. 10) 24p.Vol. 63: no.8 (1990: Oct. 17) 20p.Vol. 63: no.9 (1990: Oct. 24) 20p.Vol. 63: no. 10 (1990: Oct. 31) 20p.Vol. 63: no. 11 (1990: Nov. 7) 16p.Vol. 63: no. 12 (1990: Nov. 14) 20p.Vol. 63: no. 13 (1990: Nov. 21) 20p.Vol. 63: no. 14 (1990: Nov. 28) 24p.Vol. 63: no. 15 (1991: Jan. 9) 16p.Vol. 63: no. 16 (1991: Jan. 16) 16p.Vol. 63: no. 17 (1991: Jan. 23) 16p.Vol. 63: no. 18 (1991: Jan. 30) 16p.Vol. 63: no. 19 (1991: Feb. 6) 20p.Vol. 63: no. 20 (1991: Feb. 13) 16p.Vol. 63: no. 21 (1991: Feb. 20) 16p.Vol. 63: no. 22 (1991: Mar. 6) 16p.Vol. 63: no. 23 (1991: Mar. 13) 20p. Missing pages 15 and 16Vol. 63: no. 24 (1991: Mar. 20) 16p.Vol. 63: no. 25 (1991: Mar. 27) 16p.Vol. 63: no. 26 (1991: Apr. 3) 16p.Vol. 63: no. 27 (1991: Apr. 10) 20p
Hierarchical Multi-Scale Patch Attention and Global Feature-Adaptive Fusion for Robust Occluded Face Recognition
No full textOccluded face recognition remains a challenging problem in biometric identification, where real-world obstructions such as masks, sunglasses, scarves, and hands obscure key facial features. To address this, we introduce a dual-branch architecture that combines a Local Multi-Patch Attention Module (LMPAM) for extracting localized features with a Global Self-Attention Channel Module (GSACM) to enhance overall feature representation. The local branch utilizes Multi-Scale Patch Attention to adaptively emphasize visible facial regions, ensuring robust feature learning from unoccluded areas. Meanwhile, the global branch employs Self-Attention with Channel Recalibration to enhance discriminative features, capturing long-range dependencies while suppressing occlusion-induced noise. The two branches are integrated using Dynamic Weighted Local-Global Fusion (DW-LG), allowing the model to balance local and global information effectively. Unlike predefined occlusion-aware methods, our approach generalizes across occlusions of varying types, regions, and sizes and demonstrates robustness on multiple datasets with changes in illumination, pose, and facial expression—without requiring explicit localization. Extensive evaluations on CASIA-WebFace, LFW, and AR datasets demonstrate the effectiveness of our approach, achieving higher recognition performance under severe occlusion conditions
