Innovative food supply chain through spatial computing technologies: A review.

The global food supply chain faces significant challenges related to inefficiencies, quality variability, and traceability issues, all of which contribute to food waste and consumer distrust. Spatial computing (SC) technologies, including augmented reality (AR), virtual reality (VR), and digital twins, offer promising solutions by enhancing precision agriculture, logistics, manufacturing, and retail operations. This review explores SC's potential across the entire food supply continuum, emphasizing improvements in resource management, supply chain transparency, and consumer engagement.

Novel Approach for Biosensor-Based Imaging of Cysteine Levels in Ischemeic Heart Disease: Insights from Preclinical Models and Human Samples.

Cardiovascular diseases (CVDs) pose a serious threat to human health, with atherosclerosis being a leading cause of heart disease and stroke. Elevated cysteine (Cys) levels have been closely linked to an increased risk of cardiovascular diseases, underscoring its significance in cardiovascular health. However, current detection methods for cysteine in serum and atherosclerotic plaques present challenges in sensitivity, specificity, dynamic monitoring, and invasiveness.

Integrating Vision-Language Models for Accelerated High-Throughput Nutrition Screening.

Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision-language models (VLMs) with chemical analysis techniques. A cutting-edge VLM is unveiled, utilizing the expansive UMDFood-90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro-AUCROC of 0.

Flash heating process for efficient meat preservation.

Maintaining food safety and quality is critical for public health and food security. Conventional food preservation methods, such as pasteurization and dehydration, often change the overall organoleptic quality of the food products. Herein, we demonstrate a method that affects only a thin surface layer of the food, using beef as a model.

Highly defective copper-based metal-organic frameworks for the efficient adsorption and detection of organophosphorus pesticides: An experimental and computational investigation.

Organophosphorus pesticide (OP) residues pose a serious threat to human health, motivating the search for novel adsorbents and detection methods. Herein, defective copper-based metal organic frameworks (Cu-MOFs) were synthesized by the reaction of Cu ions and 1,3,5-benzenetricarboxylate linkers in the presence of acetic acid. As the amount of acetic acid increased, the crystallization kinetics and morphology of the Cu-MOFs changed, leading to mesoporous Cu-MOFs with many large surface pores (defects).

A Scheduling Method of Using Multiple SAR Satellites to Observe a Large Area.

This paper presents a scheduling problem of using multiple synthetic aperture radar (SAR) satellites to observe a large irregular area (SMA). SMA is usually considered as a kind of nonlinear combinatorial optimized problem and its solution space strongly coupled with geometry grows exponentially with the increasing magnitude of SMA. It is assumed that each solution of SMA yields a profit associated with the acquired portion of the target area, and the objective of this paper is to find the optimal solution yielding the maximal profit.

A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization.

For positioning tasks of mobile robots in indoor environments, the emerging positioning technique based on visual inertial odometry (VIO) is heavily influenced by light and suffers from cumulative errors, which cannot meet the requirements of long-term navigation and positioning. In contrast, positioning techniques that rely on indoor signal sources such as 5G and geomagnetism can provide drift-free global positioning results, but their overall positioning accuracy is low. In order to obtain higher precision and more reliable positioning, this paper proposes a fused 5G/geomagnetism/VIO indoor localization method.

Development of Stable Pickering Emulsions with TEMPO-Oxidized Chitin Nanocrystals for Encapsulation of Quercetin.

Pickering emulsions stabilized by TEMPO-oxidized chitin nanocrystals (T-ChNCs) were developed for quercetin delivery. T-ChNCs were synthesized by TEMPO oxidation chitin and systematically characterized in terms of their physicochemical properties. T-ChNCs were rod-like with a length of 279.

Neural network in food analytics.

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.

Mapping a highly conserved linear neutralizing epitope at the N-terminus of the gD glycoprotein of bovine herpesvirus type I using a monoclonal antibody.

Bovine herpesvirus type 1 (BoHV-1), a member of the Alphaherpesvirinae subfamily, causes significant economic losses to the cattle industry worldwide. Envelope glycoprotein D (gD) of BoHV-1 plays an essential role in the viral entry into permissive cells and possibly cooperates with other envelope glycoproteins. The herpesvirus gD induces a protective immune response against diseases in cattle or animal models.

Determination of 20 perfluoroalkyl substances in greenhouse vegetables with a modified one-step pretreatment approach coupled with ultra performance liquid chromatography tandem mass spectrometry(UPLC-MS-MS).

A new rapid modified one-step QuEChERS (quick, easy, cheap, effective, rugged, and safe) approach was developed for the simultaneous determination of 20 perfluoroalkyl substances (PFASs) from samples of cucumber, lettuce, eggplant, tomato, and leek. Several parameters were optimized. Similar pretreatment approaches were employed for comparison; the results verified the satisfactory performance of this new method.