People can contract influenza, an infectious respiratory illness. Fever, sore throat, runny nose, cough, headache, aches in the muscles, and fatigue are among the symptoms. In severe cases, pneumonia frequently results in death. There are worldwide influenza pandemic outbreaks, despite the fact that these illnesses are marked by sporadic seasonal epidemics and irregular and unpredictable occurrences. a zoonotic viral strain. Severe influenza seasons are linked to the H3N2 subtype. Three of the previous five extremely severe influenza seasons were dominated by H3N2 viruses. influenza viruses are evolving so quickly, even with known techniques, The best ways to avoid and cure influenza is viewed as a tremendous task. Only thorough research on the currently dominating H3N2 influenza viruses will lead to improvements in vaccination efficacy and pandemic risk assessment. The different characteristics of the H3N2 viruses and their ability to cause seasonal outbreaks and pandemics are covered in great length in this article.

Reliability-Centered Maintenance (RCM) plays a crucial role in minimizing operational downtime and lifecycle costs in petrochemical industries. However, conventional RCM approaches often lack dynamic failure diagnosis and prioritization capabilities under uncertain operating conditions. This study proposes an enhanced framework integrating Failure Mode and Effects Analysis (FMEA) with data-driven linguistic rule extraction to improve maintenance decision-making for centrifugal pumps. The proposed methodology utilizes OREDA-based failure classification to identify critical failure modes and introduces a weighted severity–occurrence model to overcome limitations of traditional Risk Priority Number (RPN) ranking. The framework establishes relationships between failure causes and key operational parameters such as flow rate, discharge pressure, vibration, temperature, and efficiency using linguistic variables. A rule-based diagnostic system is developed to enable real-time fault identification and maintenance scheduling. The framework is validated through a case study of centrifugal pumps in a petrochemical aromatic plant. Results demonstrate improved fault detection accuracy, reduced maintenance time, and enhanced system reliability. The proposed approach provides a scalable and intelligent decision-support tool for predictive maintenance and industrial asset management.

Nickel sulphate (NiSO4) is a common industrial substance employed in various applications such as electroplating, battery production, and metal coating. However, its continuous occupational and environmental exposure has elicited toxicological issues. This review is a critical assessment of the histopathological, biochemical and molecular alterations of the liver in mice exposed to nickel sulphate, correlating the findings from both animal and mechanistic tests. Studies evidently show that NiSO4 leads to dose-related liver cell damage, necrosis, steatosis, inflammatory infiltration, and degeneration in animals, closely linked to the increase of serum transaminases and oxidative-stress biomarkers. From a mechanistic perspective, nickel exposure affects redox homeostasis, mitochondrial integrity, and lipid metabolism leading to ferroptosis, apoptosis, and endoplasmic reticulum stress signalling. Additionally, comparative data analysis between hepatic injuries induced by soluble and particulate nickel salts shows that the former is more fatal, highlighting the relevance of compound bioavailability and exposure pathway. The hepatoprotective effects of antioxidants and flavonoid supplements (e.g. selenium, silymamarin, hesperidin, etc.) against hepatic injury are seen in preclinical models. Cross-species researchers also show that there are conserved oxidative and inflammatory systems of damage, suggesting it may be applicable in human risk assessment. According to the review, early biomarkers, multi-omics, and mechanistically directed interventions are needed to enhance toxicological assessment. Overall, long-term exposure to nickel sulphate is a realistic risk for hepatic damage; therefore, increased occupational preventive and mechanistic research should be used to improve preventive and therapeutic strategies.

The ongoing use of chatbots in healthcare, education, customer service, and mental health has made more apparent the weaknesses of entirely task-focused conversational systems that are non-emotional. Emotion detection has become an essential process of improving human-computer interaction that allows the chatbots to detect the affective states of users and react in a more human-centric and situational behalf. This paper gives a synthesis of the research on emotion-aware chatbot systems and how emotion detection methods, data modalities, and architecture can be used to enhance the quality of interaction. Fifty chosen studies were systematically analyzed to study the trends of publications, prevalent emotion detecting techniques, effectiveness of modality, and system design method. The results show that there is an increasing concentration of quality research in traditional human-computer interaction and artificial intelligence outlets, and there is a growing global concern in the last few years. The use of text-based emotion detection is the most popular in that it is more scalable, whereas the speech, visual, and multimodal detection use more emotion expressiveness and resilience in real life. Multimodal architectures can capture more complex emotional cues better than other electric stimuli, but face difficulties in terms of complexity, privacy and evaluation of the system. The review also shows that most of the current chatbot frameworks are more focused on the technical measures of performance rather than long-term, human-focused evaluation outcomes. In general, the present study provides an insight into the achievements and limitations of the existing research on emotion-sensitive chatbots and emphasizes the necessity to create ethically oriented, culturally sensitive and systematically tested conversational agents in order to promote the development of emotionally intelligent human-computer interaction.

Smartphone applications have significant benefits in various specialties. The medical field is no exception to this trend, but more research is needed on the use of smartphone medical imaging apps among Saudi Arabian students. This study aimed to evaluate the awareness of mobile radiology applications (Apps), and the effectiveness of mobile applications. The findings of the study are important to enhance the integration of mobile technological resources in the teaching-learning process of radiology which is still an unexplored issue. A descriptive, cross-sectional survey was conducted during January and February 2025. four hundred ninety participants, including undergraduate medical imaging students from governmental and private institutions across Saudi Arabia, participated in the study. Most of the undergraduate students agreed (40.7%) and strongly agreed (28.6%) that the mobile medical imaging Apps were easy, quick, well organized, and gained the confidence of the users. 50.5% of the participants used mobile apps during their daily clinical practice, and 42.9% agreed that mobile apps improved their practical skills. 33% of the participants reported that mobile medical imaging Apps were more effective than logbooks or pocketbooks, while 40.7% showed supplement logbooks or pocketbooks were more effective. The study found that participants who installed medical imaging apps had better knowledge and skills, particularly in practical skills (p-value = .017), radiographic positioning (p-value = .030), x-ray collimation (p-value < .001), radiographic anatomy (p-value < .001), and evaluating x-ray image criteria (p-value < .001), compared to those who did not install the apps. The findings of this study indicate that mobile medical imaging apps can enhance the practical skills and knowledge of radiology students. The positive outcomes observed suggest that these applications should be integrated into radiology curricula to provide students with accessible, efficient learning tools. Future research should investigate the long-term effects of mobile apps on clinical practice, as well as explore how their integration can be optimized across different educational settings.

This paper will provide a Multimodal Critical Discourse Analysis (MCDA) of the ways in which both Iran and Israel used Visual and Linguistic Weaponization Strategies to engage each other throughout the Digital Conflict in 2025 and 2024. A Multimodal Critical Discourse Analysis will be conducted in order to understand how both sides of the conflict were able to utilize the combination of Visual Imagery, Textual Narratives, and Symbolic Representations to create Disinformation, Manipulate Public Perception, and to further Strategic Communication Objectives. Utilizing theoretical constructs from Critical Discourse Analysis (CDA), Multimodal Discourse Analysis, and Visual Rhetoric; this research will illustrate the various types of Visual and Linguistic manipulation that took place through Official State Media Channels, Social Media Platforms, and Strategic Communication Outlets. The results will indicate that both Iranian and Israeli Actors were able to successfully deploy Sophisticated Multimodal Disinformation Campaigns that included Mislabeled Images, Out-Of-Context Video Footage, Fabricated Claims, and Emotionally Charged Visual Rhetoric. These findings will contribute to the ever-growing body of literature on Information Warfare in the Digital Age and will also demonstrate the Urgent Need for Developing Enhanced Media Literacy Frameworks capable of Addressing the Complex Semiotic Landscape of Contemporary Conflict Zones.

Colorectal cancer (CRC) most often metastasizes to the liver, lung, lymph nodes, and peritoneum; involvement of the gallbladder or extrahepatic biliary region is distinctly uncommon and can create diagnostic and therapeutic uncertainty. We report a 67-year-old woman with a history of treated left breast cancer who presented in April 2025 with hematochezia and anemia. Colonoscopy demonstrated a large obstructing ascending colon mass; biopsy confirmed adenocarcinoma. Staging PET-CT (June 2025) showed an FDG-avid right colonic mass with FDG-avid peri colonic and porta hepatis/portacaval nodal disease and no definite visceral organ uptake. Serum carcinoembryonic antigen (CEA) was markedly elevated (2690). She underwent laparoscopic right hemicolectomy with partial omentectomy (July 2025). Histopathology revealed an 8.5-cm moderately-to-poorly differentiated adenocarcinoma invading the visceral peritoneum (pT4a) with extensive nodal involvement (12/13 nodes; pN2b) and omental metastasis, consistent with stage IV disease. Multidisciplinary tumor board recommended systemic therapy; however, the patient initially declined chemotherapy. By late December 2025, rising symptoms and imaging demonstrated progressive retroperitoneal/mesenteric nodal and peritoneal disease and a new lytic C7 lesion. Before planned palliative chemotherapy, she developed obstructive jaundice with right upper quadrant pain (January 2026). CT and MRCP showed progressive intra-and-extrahepatic biliary dilatation and new marked diffuse duodenal wall thickening with mass effect at the ampulla, causing secondary biliary and pancreatic duct obstruction. She was managed with percutaneous transhepatic biliary drainage and subsequent endoscopic duodenal stenting to re-establish enteral intake. This case highlights the need to consider metastatic CRC in atypical biliary/duodenal obstruction patterns, to distinguish secondary involvement from a new primary periampullary process, and to use multidisciplinary palliation to enable systemic therapy when appropriate.