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IntroductionEffective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes. MethodsTo tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference (SAHI), along with applying a detection model. HTL pioneers a multi-step knowledge transfer paradigm, harnessing intermediary in-domain datasets to facilitate model adaptation. Moreover, slicing-aided hyper inference subdivides images into overlapping patches, conducting independent object detection on each patch before merging outcomes for precise, comprehensive results. ResultsThe outcomes underscore the substantial improvement achievable in detection results by integrating a diverse and expansive in-domain dataset within the HTL method, complemented by the utilization of SAHI. DiscussionWe also present a hardware and software infrastructure for deploying such models for real-life applications. Our results can assist researchers and practitioners looking for solutions for insect-pest detection and quantification on yellow sticky traps. 

It is widely accepted that the interaction of swift heavy ions with many complex oxides is predominantly governed by the electronic energy loss that gives rise to nanoscale amorphous ion tracks along the penetration direction. 

To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in information and communication technology, precision agriculture and data analytics, are creating a perfect opportunity for the creation of smart connected farms (SCFs) and networked farmers. A network and coordinated farmer network provides unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. The aim of this article is to provide a comprehensive overview of the state of the art in SCF including the advances in engineering, computer sciences, data sciences, social sciences and economics including data privacy, sharing and technology adoption. More specifically, we provide a comprehensive review of key components of SCFs and crucial elements necessary for its success. It includes, high-speed connections, sensors for data collection, and edge, fog and cloud computing along with innovative wireless technologies to enable cyber agricultural system. We also cover the topic of adoption of these technologies that involves important considerations around data analysis, privacy, and the sharing of data on platforms. From a social science and economics perspective, we examine the net-benefits and potential barriers to data-sharing within agricultural communities, and the behavioral factors influencing the adoption of SCF technologies. The focus of this review is to cover the state-of-the-art in smart connected farms with sufficient technological infrastructure; however, the information included herein can be utilized in geographies and farming systems that are witnessing digital technologies and want to develop SCF. Overall, taking a holistic view that spans technical, social and economic dimensions is key to understanding the impacts and future trajectory of Smart and Connected Farms. 

In this study, we report the length dependence of thermal conductivity ( k ) of zinc blende-structured Zinc Selenide (ZnSe) and Zinc Telluride (ZnTe) for length scales between 10 nm and 10 μm using first-principles computations, based on density-functional theory. The k value of ZnSe is computed to decrease significantly from 22.9 W m −1 K −1 to 1.8 W m −1 K −1 as the length scale is diminished from 10 μm to 10 nm. The k value of ZnTe is also observed to decrease from 12.6 W m −1 K −1 to 1.2 W m −1 K −1 for the same decrease in length. We also measured the k of bulk ZnSe and ZnTe using the Frequency Domain Thermoreflectance (FDTR) technique and observed a good agreement between the FDTR measurements and first principles calculations for bulk ZnSe and ZnTe. Understanding the thermal conductivity reduction at the nanometer length scale provides an avenue to incorporate nanostructured ZnSe and ZnTe for thermoelectric applications. 

Abstract The multi‐principal element alloy nanoparticles (MPEA NPs), a new class of nanomaterials, present a highly rewarding opportunity to explore new or vastly different functional properties than the traditional mono/bi/multimetallic nanostructures due to their unique characteristics of atomic‐level homogeneous mixing of constituent elements in the nanoconfinements. Here, the successful creation of NiCoCr nanoparticles, a well‐known MPEA system is reported, using ultrafast nanosecond laser‐induced dewetting of alloy thin films. Nanoparticle formation occurs by spontaneously breaking the energetically unstable thin films in a melt state under laser‐induced hydrodynamic instability and subsequently accumulating in a droplet shape via surface energy minimization. While NiCoCr alloy shows a stark contrast in physical properties compared to individual metallic constituents, i.e., Ni, Co, and Cr, yet the transient nature of the laser‐driven process facilitates a homogeneous distribution of the constituents (Ni, Co, and Cr) in the nanoparticles. Using high‐resolution chemical analysis and scanning nanodiffraction, the environmental stability and grain arrangement in the nanoparticles are further investigated. Thermal transport simulations reveal that the ultrashort (≈100 ns) melt‐state lifetime of NiCoCr during the dewetting event helps retain the constituent elements in a single‐phase solid solution with homogenous distribution and opens the pathway to create the unique MPEA nanoparticles with laser‐induced dewetting process. 

Silica nanosprings (NS) were coated with gallium nitride (GaN) by high-temperature atomic layer deposition. The deposition temperature was 800 °C using trimethylgallium (TMG) as the Ga source and ammonia (NH3) as the reactive nitrogen source. The growth of GaN on silica nanosprings was compared with deposition of GaN thin films to elucidate the growth properties. The effects of buffer layers of aluminum nitride (AlN) and aluminum oxide (Al2O3) on the stoichiometry, chemical bonding, and morphology of GaN thin films were determined with X-ray photoelectron spectroscopy (XPS), high-resolution x-ray diffraction (HRXRD), and atomic force microscopy (AFM). Scanning and transmission electron microscopy of coated silica nanosprings were compared with corresponding data for the GaN thin films. As grown, GaN on NS is conformal and amorphous. Upon introducing buffer layers of Al2O3 or AlN or combinations thereof, GaN is nanocrystalline with an average crystallite size of 11.5 ± 0.5 nm. The electrical properties of the GaN coated NS depends on whether or not a buffer layer is present and the choice of the buffer layer. In addition, the IV curves of GaN coated NS and the thin films (TF) with corresponding buffer layers, or lack thereof, show similar characteristic features, which supports the conclusion that atomic layer deposition (ALD) of GaN thin films with and without buffer layers translates to 1D nanostructures.