Manufar wannan takarda ita ce nazartan yadda ‘yan siyasa suke amfani da abubuwa na ɓatanci ga abokan hamayyarsu na siyasa. Tunanin wannan takarda ya taso ne duba da yadda ‘yan siyasa suka mayar da wannan abu tamkar in babu shi to siyasar ma ba za ta yiwu ba. Wannan ya sa aka ga dacewar a shiga cikin ayyukan adabi na rubutaccen zube a gani shin ‘yarfe’ yana da asali ne tun kafuwar siyasar jam’iyyu ko kuwa bai daɗe da samuwa ba? An yi nazarin yarfe a cikin tarihin siyasa da aka kundace a cikin ayyukan rubutaccen zube. An ɗora binciken kan ra’in ‘Tarihanci’ inda ya yi jagorancin zaƙulo tarihin yarfe a harkokin siyasar ƙasar Hausa. Takardar ta gano cewa, yarfe abu ne da ya ginu a cikin siyasar jam’iyyu tun daga farkon lamari. Bugu da ƙari, har yanzu ana amfani da shi domin neman ƙarin mabiya da kuma shafa wa abokan hamayyar siyasa kashin kaji, wanda hakan kan taimaka ga samun nasara ko faɗuwa a zaɓe.

The act of forging seals and documents of state agencies and organizations for the purpose of fraudulent appropriation of property constitutes a serious form of crime, simultaneously infringing upon administrative management order and threatening the security of property relations. In Vietnam, this conduct is primarily governed by Article 341 of the Penal Code in conjunction with provisions on the offense of fraud. However, practical challenges remain in delineating the boundary between forgery and fraud, particularly when forged documents are employed as instruments of misappropriation. This article examines the Vietnamese criminal law framework and contrasts it with the experiences of selected jurisdictions such as the United States, China, and Singapore, thereby identifying both commonalities and differences in the constituent elements of offenses and applicable sanctions. On this basis, the study proposes orientations for improving criminal legislation and judicial practice in Vietnam.

Nanotechnology has become the revolutionary movement in terms of promoting the global agenda of sustainable development to make innovations on the frontiers of materials science, environmental engineering, biotechnology and renewable energy. The tunable physicochemical properties, the high surface reactivity, and multifunctionality of nanoparticles are central to the development of sustainable solutions to the complex problems of resource scarcity, energy requirement, environmental degradation and human health. This review is a comprehensive study of multidisciplinary uses of nanoparticles in ensuring sustainability in various fields such as clean energy production, pollution mitigation, precision agriculture, green manufacturing, and biomedical uses. It is devoted to the latest successes of environmentally friendly production routes, in particular, bioinspired and waste-based nanoparticles, in accordance with the principles of green chemistry and the idea of a circular economy. The review also addresses the role of the nanoparticle-enabled technologies towards the United Nations of the Sustainable Development Goals (SDGs) by increasing energy efficiency, environmental stability, and sustainable production. Using materials innovation and sustainability science, this paper provides valuable critical reflections on how nanotechnology can make the transition to a more fair, low-carbon, resource-saving future. The discussion has pointed out the necessity of having a cross-disciplinary approach and regulatory vision to ensure a safe and ethical use of nanomaterials in sustainable systems.

Inappropriate long-term use of Proton Pump Inhibitors (PPIs) is a public health challenge. It is uncertain if this "implementation gap" is driven by knowledge deficits or other behavioural barriers. This study aimed to quantify systematic de-prescribing protocol (SDP) use and identify barriers to PPI de-prescribing among Internal Medicine (IM) and Gastroenterology (GI) physicians. A cross-sectional survey was conducted among 310 physicians (IM, GI) from three Indian states. A validated questionnaire assessed knowledge, self-efficacy, barriers, and use of an SDP (the primary outcome). A multivariable logistic regression model, robust for clustering, identified predictors of SDP use. Physician knowledge was high (mean 3.7/4.0), but a "knowledge–implementation gap" was evident: 85.2% agreed de-prescribing was "crucial," yet only 28.4% used an SDP. Gastroenterologists (40.0%) were twice as likely as IM physicians (20.0%; p=0.0002) to use an SDP. In the adjusted model, GI specialty (aOR 2.59) and higher self-efficacy (aOR 1.82) were the strongest predictors. The highest-rated barriers were 'Patient resistance/anxiety' (78.0%) and 'Consultation time constraints' (69.2%). In this cohort, PPI over-prescription appears driven by implementation failure and clinical inertia, not knowledge deficits. This is associated with low self-efficacy and barriers like patient anxiety and time pressure. The specialty disparity suggests confidence, not just knowledge, is key. Interventions must pivot from education to systemic solutions targeting these behavioural barriers.

Artificial intelligence (AI) in crop science is redefining the agriculture issue by being accurate, scalable, and predictive. It is an overview of the recent developments in AI-based crop modeling in the context of its advancement, management, and sustainability. We criticize the application of machine learning (ML), deep learning (DL), reinforcement learning (RL) and computer vision to fields of high-throughput phenotyping, genomic prediction, yield forecasting and stress detection. Convolutional neural networks and vision transformers have assisted in new developments in image-based prediction of characteristics of UAVs, satellites, and ground sensors, and recurrent and graph neural networks to new developments in spatiotemporal modeling of crop-environment interactions. This is possible by combination of predictive modeling and crop simulation systems and enables dynamic decision support of the changing climatic conditions. Moreover, explainable AI (XAI) technique is also in progressive use to increase transparency of models and make them acceptable to breeders and farmers. However, there are still serious obstacles like the heterogeneity of the data, models transferability is not applicable across the regions, annotation bottlenecks, and the failure to incorporate the biological knowledge into the AI structures. The other fact, which we highlight, is the unavailability of AI to smallholder systems and the uniformity of standard and open-source datasets. Future directions It concentrates on the use of multi-omics, remote sensing, and on-farm data in individual AI systems, and physics-informed and hybrid modelling. Such integrative practices are necessary to make AI tools more powerful, decipherable and scalable. Ultimately, the strategic application of next generation AI models will be in sustainable increment, resultant reduction in environmental footprints, and crop production systems in a manner that will be resilient to the changing climatic conditions in order to feed the ever-growing world population which is increasing at an accelerated rate.

The transition from pediatric to adult care is a critical period for young people with type 1 diabetes. In Djibouti, the Young Diabetic Center has implemented a structured therapeutic education program to support this transition. A one-year prospective study of 100 patients aged 16 to 21 evaluated the impact of this process on glycemic control (HbA1c), autonomy, and satisfaction among young adults. The results show a modest but significant improvement in HbA1c after 12 months, despite the persistence of imbalance in nearly half of the participants. Female gender, low autonomy, and insufficient medical follow-up appear to be vulnerability factors. This experience highlights the importance of a coordinated, multidisciplinary, and patient-centered transition.

Inflammation plays a very important role in the development of non-communicable diseases in the general population, while it remains the leading cause of morbidity and mortality among HIV-positive patients treated with HAART. We aimed to investigate the association between IL-6 and hs-CRP with the components of metabolic syndrome among HIV-positive patients on HAART. We conducted a prospective study among HIV-positive HAART naïve patients. Eighty-six HIV-positive HAART naïve patients and eighty-six HIV-negative subjects to serve as controls at baseline were recruited. The baseline data was taken and recorded before commencing the patients on HAART. Then, the patients were placed on lamivudine, tenofovir and dolutegravir (DTG) HAART regiment and followed up for 24 months. Both serum IL-6 and hs-CRP are higher among HIV-positive patients than healthy control and among patients with and without MetS. Both age, gender, anthropometric and blood pressure FBC and LDL-c show an inverse correlation with the development of MetS at baseline, but not IL-6 and hs-CRP. However, 24 months post-HAART hs-CRP shows a significant correlation with MetS (P= 0.010) but IL-6 has a weak positive correlation with MetS (P= 0.055). In regression model, IL-6 has a strong negative correlation with the development of MetS (r –0.085 p= 0.042), while hs-CRP have a strong positive correlation with the development of MetS (r 0.117 p= 0.008). Interleukin-6 and hs-CRP are found to be highly elevated among patients with HIV than healthy controls, as well as in patients with MetS than those without MetS. Both IL-6 and hs-CRC are associated with the development of MetS among HIV patients on HAART than HAART naïve.

Artificial Intelligence (AI) is revolutionizing industrial process automation by introducing intelligent decision-making and adaptive control to traditionally deterministic systems. In the petrochemical and oil & gas industries where safety, efficiency, and reliability are paramount, AI technologies such as machine learning, deep learning, and digital twins enhance plant operations through predictive maintenance, process optimization, and asset integrity management. Despite challenges in certification, data quality, and cybersecurity, AI continues to evolve as an indispensable enabler of smart and self-optimizing industrial plants. This research examines the integration of AI within programmable logic controllers (PLCs), distributed control systems (DCS) and supervisory control and data acquisition (SCADA) frameworks, the improvements it brings in efficiency, energy management, and maintenance scheduling as well as examines the real-world implementations from major automation vendors such as Honeywell, Emerson, Yokogawa and Siemens.

Extraintestinal pathogenic E. coli (ExPEC) strains are associated with causing a wide range of infections such as urinary tract infections and bacteraemia. Over the past two decades, the levels of antimicrobial resistance of ExPEC strains have increasingly been reported worldwide, and this is attributed to the global emergence and dissemination of a single ExPEC lineage, known as E. coli ST131. This review explored the current knowledge of E. coli ST131 in Saudi Arabia, focusing on its antimicrobial resistance, virulence capacity and metabolic potential. Many local reports have shown that the antimicrobial resistance levels of E. coli ST131 were higher than non-ST131, particularly to front-line agents used for the empirical treatment of infections caused by ExPEC. Furthermore, E. coli ST131 strains have been associated with high virulence capacity, which could drive the current success of this clone locally. The metabolic activity of ST131 was also found to be slightly higher than non-ST131 strains that obtained from blood population. Taken together, the future studies should focus on elucidating the factors that drive the success of ST131.

Rotator cuff (RC) tears, both partial and full-thickness lesions, are a common source of pain and disability in the adult’s shoulder. Methods: In this study a retrospective cross-sectional study was conducted in 129 patients with shoulder pain, stiffness, restricted motion and trauma. Their age were between 20–65 years those attended into the clinical centers in Sudan (2021–2024). High-resolution ultrasonography (HR-US) was used in a uniform tendon-specific protocol in order to increase the diagnostic precision, reproducibility, then to study the Specificity and Sensitivity of HR-US clinical orientation on a partial and full- thickness tear on Rotator Cuff muscles. The examinations were performed with a 7.5-/12-MHzlinear-array transducer following ESSR guidelines. SPSS v23 was used for statistical analysis and level of significant at p < 0.05. Results: HR-US was over 90% sensitive and over 94% specific in identifying full-thickness tears and had excellent agreement with MRI. Supraspinatus and infraspinatus tendons had optimal diagnostic accuracy, with HR-US superior to MRI in the identification of some partial subscapularis tears. Standardized protocols improved inter-operator variability elimination and diagnostic consistency. Conclusion: HR-US is a highly precise, cost-effective, and interactive first-line imaging modality for evaluating rotator cuff disease. Its real-time imaging can provide functional information beyond MRI, leading to early and precise diagnosis. If performed under formalized protocol, HR-US reduces operator reliance and enables wide acceptance, particularly in resource-poor health care settings.

Driven by the dual forces of the digital economy and the rural revitalization strategy, China's agricultural live-stream e-commerce has become the core engine for reconfiguring the agricultural value chain. This article takes the tilapia industry in Maoming City, Guangdong Province as the research object. By analyzing its "live-streaming + industry" integration model, it reveals how the digitalized scenario reconstruction promotes the transformation of characteristic agricultural products from traditional sales to branding and globalization. The research finds that the Maoming tilapia industry achieved an average annual output value growth of 21% from 2020 to 2024 and an export volume exceeding 300 million US dollars in 2024 through three innovative strategies: "dual-scenario linked live-streaming", "full-chain data monitoring", and "geographical indication brand building". The research proposes a "technology-scenario-ecology" tripartite development framework, providing a Chinese solution for the digital transformation of the global tropical aquaculture industry.

The primary concern in healthcare services within hospitals is patient safety. Quality management and patient safety are not only related to medical factors; several non-medical factors also influence them. These factors are personal, organizational culture, motivation, and patient safety culture. There has been no research at Koja Regional Hospital to date regarding patient safety culture related to personal factors, organizational culture, and motivation, which are crucial for implementing a patient safety culture as a primary step in improving service quality. This study aimed to identify and explain the factors that influence patient safety culture, including personal factors, organizational culture, and motivation. It used a cross-sectional design. Primary data were collected through questionnaires with 116 respondents. This study involved a dependent variable, Patient Safety Culture, two independent variables, Personal Factors and Organizational Culture, and one mediating variable, Motivation. It was analyzed using path analysis via Structural Equation Modeling (SEM). The results showed that personal factors have a significant positive effect on patient safety culture, indicated by a path coefficient of 0.311, a T-statistic of 3.603 (p = 0.000). Correspondingly, organizational culture plays a positive and significant role, with a path coefficient of 0.396 and a T-statistic of 2.957 (p = 0.003). The influence of organizational culture on motivation shows a path coefficient of 0.778 and a T-statistic of 11.996 (p < 0.000). To improve patient safety culture, Koja Regional Hospital requires simultaneous, complementary, and continuous interventions. The success of a patient safety program depends not only on individual abilities but also on innovation within a supportive organizational culture, good motivation, and a work environment that fosters patient safety.

Charcoal stoves constitute an essential energy provision for millions residing in sub-Saharan Africa; however, conventional designs exhibit inefficiency and pose health risks, contributing to approximately 3.2 million premature fatalities each year due to household air pollution. This systematic review consolidates advancements in charcoal stove technology, with an emphasis on enhancing thermal efficiency, minimizing emissions, and ensuring user safety. Utilizing a methodologically rigorous approach, a total of 52 peer-reviewed studies (1994–2025) were meticulously examined from databases such as Scopus and ScienceDirect, employing standardized testing protocols (e.g., Water Boiling Test). The findings indicate that innovative designs, including rocket and gasifier stoves, attain thermal efficiencies ranging from 17% to 87%, in contrast to the 11% to 16% efficiencies observed in traditional models, alongside reductions in carbon monoxide emissions by as much as 75% and a decrease in fuel consumption by 70%. Nonetheless, performance outcomes exhibit variability in practical applications, influenced by user behavior and the durability of materials employed. The review emphasizes the imperative for validation through field-based studies and the development of economically accessible designs to promote widespread adoption. These technological innovations hold the potential to provide sustainable cooking solutions, thereby contributing to public health and the achievement of environmental objectives such as Sustainable Development Goal 7.

Background: Accurate segmentation and characterization of brain tumors from magnetic resonance imaging (MRI) are paramount for diagnosis, treatment planning, and monitoring. While deep learning models like U-Net have set a high standard for segmentation, they can fail to detect complex, multifocal disease and often lack the tools for in-depth clinical characterization beyond basic volume. Methods: A comprehensive, web-based platform built upon a hybrid Convolutional Neural Network (CNN) and Transformer architecture is presented. An end-to-end workflow is provided by the system from medical image upload (DICOM/NIfTI) to final analysis. It features a dedicated, interactive 3D reconstruction environment with real-time controls for mesh manipulation, lighting, and data export (STL for 3D printing, JSON for reports). All measurements are performed in native medical imaging units (millimeters) to ensure clinical accuracy. The platform also includes a detailed analysis tool for calculating a full suite of morphological and clinical metrics, including an estimated WHO grade. Results: Quantitatively, the Hybrid model achieved a mean Dice coefficient of 0.91 and a mean sensitivity of 0.94 across the test set, outperforming the U-Net (0.86 Dice, 0.88 sensitivity) and a traditional algorithm (0.72 Dice, 0.75 sensitivity). In a representative case of multifocal glioma, the hybrid model identified three distinct tumor foci with a total volume of 67,480 mm³, whereas the U-Net identified only a single mass of 15,140 mm³, representing a 4.4-fold increase in detected tumor burden. These results were visualized and explored in the platform's interactive 3D viewer, which provided real-time statistics and allowed for immediate export of the 3D model. Conclusion: Our work demonstrates a complete platform that not only leverages a state-of-the-art segmentation model but also provides the necessary tools for interactive visualization, analysis, and data dissemination. By seamlessly integrating a high-performance algorithm with a user-centric interface, the system serves as a powerful tool for medical education, clinical training, and reproducible research.

The development of multifunctional photocatalysts that efficiently operate under visible light remains a fundamental challenge for sustainable wastewater treatment and oxygen evolution. In this work, we report a novel reticular and high-entropy TiO₂-based nanotubular hybrid system integrated with Cu:ZnO nanoparticles and carbon quantum dots (C-dots), designed to achieve synergistic enhancement in charge dynamics and surface reactivity. The high-entropy configuration introduces lattice distortion and defect sites that extend the optical absorption edge and promote rapid charge separation, while the Cu:ZnO interface accelerates electron transport and facilitates multi-pathway redox reactions. Simultaneously, the C-dots serve as photonic antennas, enabling visible-light sensitization through π–π conjugation and energy up-conversion. Structural and optical analyses confirm the formation of a reticular nanotubular network providing hierarchical porosity and large interfacial area for catalytic interactions. Under simulated solar irradiation, the hybrid demonstrates remarkable photocatalytic efficiency, achieving over 95% degradation of organic contaminants and enhanced oxygen evolution activity compared to pristine TiO₂. The introduced design concept coupling reticular high-entropy stabilization with optoelectronic co-catalyst modulation presents a new paradigm for next-generation photocatalysts capable of simultaneous environmental remediation and clean energy generation.