The study of correlation between forward head posture and neck pain in Iranian office workers

Objectives: Factors such as prolonged sitting at work or improper posture of head during work may have a great role in neck pain occurrence among office employees, particularly among those who work with computers. Although some studies claim a significant difference in head posture between patients and pain-free participants, in literature the forward head posture (FHP) has not always been associated with neck pain. Since head, cervical and thoracic postures and their relation with neck pain has not been studied in Iranian office employees, the purpose of this study was to investigate the relationship between some work-related and individual factors, such as poor posture, with neck pain in the office employees. Material and Methods: It was a cross-sectional correlation study carried out to explore the relationship between neck pain and sagittal postures of cervical and thoracic spine among office employees in forward looking position and also in a working position. Forty-six subjects without neck pain and 55 with neck pain were examined using a photographic method. Thoracic and cervical postures were measured using the high thoracic (HT) and craniovertebral (CV) angles, respectively. Results: High thoracic and CV angles were positively correlated with the presence of neck pain only in working position (p 0.05). Conclusions: Our findings have revealed that office employees had a defective posture while working and that the improper posture was more severe in the office employees who suffered from the neck pain

Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital and physical world. Many human activities now not only operate in geographical (physical) space but also in cyberspace. Such changes have triggered a paradigm shift in geographic information science (GIScience), as cyberspace brings new perspectives for the roles played by spatial and temporal dimensions, e.g., the dilemma of placelessness and possible timelessness. As a discipline at the brink of even bigger changes made possible by machine learning and artificial intelligence, this paper highlights the challenges and opportunities associated with geographical space in relation to cyberspace, with a particular focus on data analytics and visualization, including extended AI capabilities and virtual reality representations. Consequently, we encourage the creation of synergies between the processing and analysis of geographical and cyber data to improve sustainability and solve complex problems with geospatial applications and other digital advancements in urban and environmental sciences

Parallels between the future for MedTech and Agri-Tech, perspectives drawing on the British experience

In this chapter we explore the future for innovation in two related, but distinct, sectors. We consider the linkages between medical technology(MedTech) and agricultural technology (Agri-Tech) innovation in the UK. We ask and discuss questions: Who are the key actors in the innovation systems of Medtech and Agri-Tech in the UK? What are the core technologies driving the current waves of innovation in these two sectors? Can one industry learn from the other? Where is the scope for cooperation and synergies? We notice that both sectors are technologically linked through foundational technologies underpinning the majority of the observed innovation e.g. big data, AI, IoT and robotics. The outputs of these technologies rely crucially on digital data for insight and decision support. However, Agri-Tech benefits from less complex stakeholder issues regarding data security and privacy. Both sectors are important to the UK going forwards, and both will be exposed to Brexit and the consequences of the COVID pandemic. Our discussion on the future of innovation should be of particular interest to start-up leaders, entrepreneurs, investors, managers and policy-makers in MedTech, Agri-Tech and cognate sectors

Performance Review of Meta LLaMa 3.1 in Thoracic Imaging and Diagnostics

ABSTRACT Background The integration of artificial intelligence (AI) in radiology has opened new possibilities for diagnostic accuracy, with large language models (LLMs) showing potential for supporting clinical decision‐making. While proprietary models like ChatGPT have gained attention, open‐source alternatives such as Meta LLaMa 3.1 remain underexplored. This study aims to evaluate the diagnostic accuracy of LLaMa 3.1 in thoracic imaging and to discuss broader implications of open‐source versus proprietary AI models in healthcare. Methods Meta LLaMa 3.1 (8B parameter version) was tested on 126 multiple‐choice thoracic imaging questions selected from Thoracic Imaging: A Core Review by Hobbs et al. These questions required no image interpretation. The model’s answers were validated by two board‐certified diagnostic radiologists. Accuracy was assessed overall and across subgroups, including intensive care, pathology, and anatomy. Additionally, a narrative review introduces three widely used AI platforms in thoracic imaging: DeepLesion, ChexNet, and 3D Slicer. Results LLaMa 3.1 achieved an overall accuracy of 61.1%. It performed well in intensive care (90.0%) and terms and signs (83.3%) but showed variability across subgroups, with lower accuracy in normal anatomy and basic imaging (40.0%). Subgroup analysis revealed strengths in infectious pneumonia and pleural disease, but notable weaknesses in lung cancer and vascular pathology. Conclusion LLaMa 3.1 demonstrates promise as an open‐source NLP tool in thoracic diagnostics, though its performance variability highlights the need for refinement and domain‐specific training. Open‐source models offer transparency and accessibility, while proprietary models deliver consistency. Both hold value, depending on clinical context and resource availability

APP-CEP: Adaptive Pattern-level Privacy Protection in Complex Event Processing Systems

Although privacy-preserving mechanisms endeavor to safeguard sensitive information at the attribute level, detected event patterns can still disclose privacy-sensitive knowledge in distributed complex event processing systems (DCEP). Events might not be inherently sensitive, but their aggregation into a pattern could still breach privacy. In this paper, we study in the context of APP-CEP the problem of integrating pattern-level privacy in event-based systems by selective assignment of obfuscation techniques to conceal private information. Compared to state-of-the-art techniques, we seek to enforce privacy independent of the actual events in streams. To support this, we acquire queries and privacy requirements using CEP-like patterns. The protection of privacy is accomplished through generating pattern dependency graphs, leading to dynamically appointing those techniques that have no consequences on detecting other sensitive patterns, as well as non-sensitive patterns required to prov ide acceptable Quality of Service. Besides, we model the knowledge that might be possessed by potential adversaries to violate privacy and its impacts on the obfuscation procedure. We assessed the performance of APP-CEP in a real-world scenario involving an online retailer’s transactions. Our evaluation results demonstrate that APP-CEP successfully provides a privacy-utility trade-off. Modeling the background knowledge also effectively prevents adversaries from realizing the modifications in the input streams

Quantitation of supercoiled DNA content during plasmid processing a fluorescence-based method

A method for quantifying the proportion of supercoiled circular (SC) forms in DNA solutions is described. The method (SCFluo) takes advantage of the reversible denaturation property of SC forms and the high specificity of the PicoGreen fluorochrome for double-stranded (ds)DNA. Fluorescence values of forms capable of reversible denaturation after a 5 min heating, 2 min cooling step are normalised to fluorescence values of total dsDNA present in the preparation. For samples with a SC content >20–30%, good regression fits were obtained when values derived from densitometric scanning of an agarose gel and those derived from the SCFluo method were compared. The method represents an attractive alternative to currently established methods because it is simple, rapid and quantitative. During large-scale processing and long-term storage, enzymatic, chemical and shear degradation may substantially decrease the SC content of plasmid DNA preparations. Regulations for pharmaceutical grade products for use in gene therapy and DNA vaccination may require >90% of the plasmid to be in the SC form. In the present study the SC content of 6.9, 13 and 20 kb plasmid preparations that had been subjected to chemical and shear degradation was successfully quantified using the new method