Evaluating 3D local descriptors for future LIDAR missiles with automatic target recognition capabilities

Future light detection and ranging seeker missiles incorporating 3D automatic target recognition (ATR) capabilities can improve the missile’s effectiveness in complex battlefield environments. Considering the progress of local 3D descriptors in the computer vision domain, this paper evaluates a number of these on highly credible simulated air-to-ground missile engagement scenarios. The latter take into account numerous parameters that have not been investigated yet by the literature including variable missile – target range, 6-degrees-of-freedom missile motion and atmospheric disturbances. Additionally, the evaluation process utilizes our suggested 3D ATR architecture that compared to current pipelines involves more post-processing layers aiming at further enhancing 3D ATR performance. Our trials reveal that computer vision algorithms are appealing for missile-oriented 3D ATR

Automatic X-ray Image Segmentation and Clustering for Threat Detection

Firearms currently pose a known risk at the borders. The enormous number of X-ray images from parcels, luggage and freight coming into each country via rail, aviation and maritime presents a continual challenge to screening officers. To further improve UK capability and aid officers in their search for firearms we suggest an automated object segmentation and clustering architecture to focus officers’ attentions to high-risk threat objects. Our proposal utilizes dual-view single/ dual-energy 2D X-ray imagery and is a blend of radiology, image processing and computer vision concepts. It consists of a triple-layered processing scheme that supports segmenting the luggage contents based on the effective atomic number of each object, which is then followed by a dual-layered clustering procedure. The latter comprises of mild and a hard clustering phase. The former is based on a number of morphological operations obtained from the image-processing domain and aims at disjoining mild-connected objects and to filter noise. The hard clustering phase exploits local feature matching techniques obtained from the computer vision domain, aiming at sub-clustering the clusters obtained from the mild clustering stage. Evaluation on highly challenging single and dual-energy X-ray imagery reveals the architecture’s promising performance

AI-Driven Analysis of Diagnostic Profiles in COVID-19 Patients: Implications for Healthcare Interventions

The COVID-19 crisis has strained global healthcare systems, highlighting the significance of investigating comorbidities and secondary diagnoses in patients. Harnessing of data-driven insights, as facilitated by artificial intelligence (AI), has shown remarkable promise in enhancing the efficacy of healthcare strategies and ameliorating patient outcomes. PURPOSE: To identify diagnostic profiles in COVID-19 patients via AI-driven analysis, focusing on comorbidities and secondary diagnoses. METHODS: The analytical groundwork was established upon the scrutiny of 42,974 patients with PCR-confirmed COVID-19 diagnosis. Each record was characterized by 850 diagnostic indicators encompassing a spectrum of ailments, such as demyelinated diseases, seizure disorders, and various additional comorbidities. The predominant racial composition of the sample was White (n = 31,329, 73%). A majority of patients were of the female gender (n = 23,534, 55%). Data were collected using Electronic Medical Records through the Cerner system from 31 hospitals in a large health system. Finite mixture modeling, a form of model-based unsupervised machine learning, was employed to ascertain the presence of latent, distinguishable patterns among secondary diagnoses. Of the approximately 850 secondary diagnoses considered, 221 exhibited prevalence in over 50 patients. A sequence of mixture models was estimated, incrementally augmenting the number of latent profiles via maximum likelihood estimation with robust standard errors. Model solutions were subjected to rigorous evaluation, culminating in the selection of three diagnostic profiles predicated on statistical model-data fit, parsimony, and interpretability. RESULTS: The selected model revealed the presence of three distinct diagnostic profiles. These profiles were characterized by patients who: (1) exhibited a notably low likelihood of presenting with secondary diagnoses, (2) demonstrated heightened probabilities of manifesting commonly observed diagnoses within the United States, such as hypertension, hyperlipidemia, and a history of tobacco use, or (3) displayed elevated probabilities of harboring multiple comorbid diagnoses, spanning domains such as lung, heart, and kidney-related conditions. The initial profile encompassed 27,002 patients (63%), followed by the second profile comprising 11,419 patients (27%), and the third profile, accounting for 4,553 patients (11%). Patients were individually assigned probabilities denoting their affiliation with each profile, with respective average classification probabilities of .98, .89, and .94, signifying a high degree of classification confidence. CONCLUSION: Our findings demonstrate the potential application of AI in informing healthcare interventions, such as tailored treatment plans, early intervention, resource allocation, patient education, research and development, and healthcare policy

Temporal changes in the expression and distribution of adhesion molecules during liver development and regeneration

We have compared by immunocytochemistry and immunoblotting the expression and distribution of adhesion molecules participating in cell-matrix and cell-cell interactions during embryonic development and regeneration of rat liver. Fibronectin and the fibronectin receptor, integrin alpha 5 beta 1, were distributed pericellularly and expressed at a steady level during development from the 16th day of gestation and in neonate and adult liver. AGp110, a nonintegrin fibronectin receptor was first detected on the 17th day of gestation in a similar, nonpolarized distribution on parenchymal cell surfaces. At that stage of development haemopoiesis is at a peak in rat liver and fibronectin and receptors alpha 5 beta 1 and AGp110 were prominent on the surface of blood cell precursors. During the last 2 d of gestation (20th and 21st day) hepatocytes assembled around lumina. AGp110 was initially depolarized on the surface of these acinar cells but then confined to the lumen and to newly-formed bile canaliculi. At birth, a marked increase occurred in the canalicular expression of AGp110 and in the branching of the canalicular network. Simultaneously, there was enhanced expression of ZO-1, a protein component of tight junctions. On the second day postpartum, presence of AGp110 and of protein constituents of desmosomes and intermediate junctions, DGI and E-cadherin, respectively, was notably enhanced in cellular fractions insoluble in nonionic detergents, presumably signifying linkage of AGp110 with the cytoskeleton and assembly of desmosomal and intermediate junctions. During liver regeneration after partial hepatectomy, AGp110 remained confined to apical surfaces, indicating a preservation of basic polarity in parenchymal cells. A decrease in the extent and continuity of the canalicular network occurred in proliferating parenchyma, starting 24 h after resection in areas close to the terminal afferent blood supply of portal veins and spreading to the rest of the liver within the next 24 h. Distinct acinar structures, similar to the ones in prenatal liver, appeared at 72 h after hepatectomy. Restoration of the normal branching of the biliary tree commenced at 72 h. At 7 d postoperatively acinar formation declined and one-cell-thick hepatic plates, as in normal liver, were observed

Decisional tool for cost of goods analysis of bioartificial liver devices for routine clinical use

BACKGROUND AIMS: Bioartificial liver devices (BALs) are categorized as advanced therapy medicinal products (ATMPs) with the potential to provide temporary liver support for liver failure patients. However, to meet commercial demands, next-generation BAL manufacturing processes need to be designed that are scalable and financially feasible. The authors describe the development and application of a process economics decisional tool to determine the cost of goods (COG) of alternative BAL process flowsheets across a range of industrial scales. METHODS: The decisional tool comprised an information database linked to a process economics engine, with equipment sizing, resource consumption, capital investment and COG calculations for the whole bioprocess, from cell expansion and encapsulation to fluidized bed bioreactor (FBB) culture to cryopreservation and cryorecovery. Four different flowsheet configurations were evaluated across demands, with cell factories or microcarriers in suspension culture for the cell expansion step and single-use or stainless steel technology for the FBB culture step. RESULTS: The tool outputs demonstrated that the lowest COG was achieved with microcarriers and stainless steel technology independent of the annual demand (1500-30 000 BALs/year). The analysis identified the key cost drivers were parameters impacting the medium volume and cost. CONCLUSIONS: The tool outputs can be used to identify cost-effective and scalable bioprocesses early in the development process and minimize the risk of failing to meet commercial demands due to technology choices. The tool predictions serve as a useful benchmark for manufacturing ATMPs

Corporate Social Responsibility in a context of sustainable development

“The future we want”, the main document summarizing the action areas advocated by the Rio+20 conference (Rio de Janeiro, Brazil, June 20-22nd), advocates “green economy” as a main instrument to eradicate poverty, while maintaining the healthy functioning of the environment. “Green economy” is a reply to global capitalism and the excesses of its elite practitioners, as they became manifest during the recent economic crisis. A classical contribution of the private business sector to sustainable development is corporate social responsibility (CSR). The concept dovetails in the doctrine that a company is not only responsible for a positive economic performance, but also has to take care about the environmental, social and ethical aspects of its activities. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3164

A Cross-Disciplinary Outlook of Directions and Challenges in Industrial Electronics

[EN] How to build a sustainable society in view of industrial electronics has been discussed from energy, information and communication technologies, cyber-physical systems (CPSs), and other viewpoints. This paper presents a cross-disciplinary view that integrates the fields of human factors, professional education, electronic systems on chip, resilience and security for industrial applications, technology ethics and society, and standards. After explaining the efforts and challenges in these fields, this paper shows a methodology for cross-disciplinary technology that integrates the technical committees in Cluster 4, Industrial Electronics Society. A project, which was launched in March 2022, implements a 'Proof of Concept' trial of the methodology.The work of Jinhua Sh was supported by JSPS Grant-in-Aid for Scientific Research B under Grant 22H03998 (Japan).She, J.; Guzman-Miranda, H.; Huang, V.; Chen, AC.; Karnouskos, S.; Dunai, L.; Ma, C.... (2022). A Cross-Disciplinary Outlook of Directions and Challenges in Industrial Electronics. IEEE Journal of Emerging and Selected Topics in Industrial Electronics (Online). 3:375-391. https://doi.org/10.1109/OJIES.2022.3174218375391