Tunisia Private University

Despite the availability of various anti-vascular endothelial growth factor (anti-VEGF) therapies for diabetic macular edema (DME), high treatment burden remains a significant issue in many European countries, including Italy. This burden impacts healthcare costs, clinic capacities, and patient adherence. Aflibercept 8mg was developed to reduce treatment burden by requiring only three monthly injections before allowing extended dosing intervals up to 6months, without compromising efficacy and safety. This analysis aimed to evaluate the efficacy, safety, and treatment burden of aflibercept 8mg regimens compared to other anti-VEGF options (aflibercept 2mg, ranibizumab 0.5mg, faricimab 6mg, brolucizumab 6mg, and bevacizumab 1.25mg) in Italy. A systematic literature review identified randomised controlled trials of anti-VEGF therapies for DME with a 2-year follow-up. This informed a Bayesian network meta-analysis (NMA) assessing efficacy, safety, and treatment burden. A cost-minimisation analysis (CMA) was conducted from the perspective of the Italian National Health System over a 2-year horizon. The NMA, involving nine studies, showed no significant differences in best-corrected visual acuity, anatomical outcomes, and safety between aflibercept 8mg and the other anti-VEGF agents. Aflibercept 8mg required fewer injections than the comparators, with a mean of 9.3 injections over 2years for T&E (Q12) and 8.4 for T&E (Q16) regimens. Economically, aflibercept 8mg was the least costly licensed and reimbursed anti-VEGF in Italy. Aflibercept 8mg may be the most advantageous anti-VEGF option for reducing treatment burden in Italian patients with DME, benefiting healthcare providers, clinics, and the healthcare system. Aflibercept 8mg offers comparable efficacy and safety to other anti-VEGF therapies, with fewer injections and potentially lower overall costs.

Hydroclimate variability in northern Africa, driven by the interactions between the mid-latitude westerlies and the West African Monsoon (WAM) and by variations in their intensity, represents a key control on hominin dispersal. However, the response of these atmospheric circulation systems to changing boundary conditions and their influence on precipitation during past climate states remain poorly constrained, largely because most well-dated terrestrial hydroclimate records are limited to the Holocene interglacial period. Here, we present a 300,000-year hydroclimate record from northernmost Africa based on speleothem growth in Tunisia. This record captures alternating humid and arid phases across multiple glacial–interglacial cycles, as well as superimposed millennial-scale variability. Speleothem growth occurred predominantly during interglacials, indicating humid conditions, whereas growth hiatuses correspond to arid glacial periods. The cave record suggests that humid phases in northernmost Africa were primarily driven by a southward displacement and intensification of the mid-latitude westerlies and the Mediterranean storm track during interglacials. Comparison with Saharan palaeolake records further indicates that both enhanced winter westerlies and a strengthened summer WAM were required to generate widespread wet conditions across northern Africa during interglacial periods. These results highlight the combined influence of winter and summer moisture sources in shaping long-term hydroclimate variability and defining habitable corridors across northern Africa over the past 300,000 years.

Abstract Introduction Vestibular migraine is a major cause of recurrent vertigo, yet its mechanisms and diagnostic markers remain limited. Abnormal vestibular–cervical integration and convergence insufficiency, reflected by impaired near point convergence (NPC),suggest multisensory dysfunction. This study tested whether cervical proprioceptive perturbation provokes vertigo and nystagmus in vestibular migraine and evaluated NPC as a predictor of these responses. Methods Fifty-one vestibular migraine patients and 12 controls underwent interictal vestibular testing. Peripheral function was assessed with vHIT. Participants received randomized 100-Hz cervical (proprioceptive) and mastoid (vestibular) vibration without visual fixation, with eye movements recorded via video Frenzel goggles and NPC measured using standard methods. Analyses included McNemar’s, Wilcoxon signed-rank, Mann–Whitney U, correlations, and multivariable logistic regression. Results Neck vibration provoked vertigo in all vestibular migraine patients and none of the controls, producing nystagmus in 76.5%. Horizontal, ipsiversive nystagmus predominated, while less frequent vertical responses showed higher velocities. Mastoid vibration elicited no nystagmus. NPC was the only independent predictor of nystagmus and correlated with slow-phase velocity and bilateral responses. Age correlated with drift velocity, whereas vestibulo-ocular reflex gain showed no association. Discussion Neck vibration elicits vertigo and nystagmus in vestibular migraine, providing the first objective physiological marker. NPC predicts and correlates with nystagmus severity, highlighting its value as a surrogate of multisensory dysfunction. Together, these findings implicate abnormal cervical–vestibular integration and position NPC and neck-vibration testing as practical tools for diagnosis and phenotyping. Key points Vestibular migraine affects ∼3% of population yet remains highly controversial. Objective measures reveal reproducible vertigo and nystagmus in vestibular migraine. Impaired convergence strongly predicts vibration-induced nystagmus in VM patients. Findings support sensory mismatch model linking cervical proprioception to vertigo.

Abstract IFRS 8 “Operating Segments” and the UK Reports on Payments to Governments Regulations (UK RPGR 2014 No. 3209) both produce geographically disaggregated information, yet they embody different regulatory logics and serve distinct constituencies. IFRS 8’s management-oriented, business-model approach governs geographic segment reporting (GSR), while the UK RPGR mandates public Country-by-Country Reporting (CbCR) of extractive payments (EPD) to enhance accountability and governance in resource-rich contexts. This study investigates whether mandatory EPD adoption is associated with changes in GSR practices among UK-listed extractive multinationals. Using hand-collected data over 2010–2021, we document consistently high compliance with EPD requirements and substantively more detailed country-level information in EPD reports than in IFRS-based segment notes. Panel regressions indicate that mandatory EPD adoption is not systematically associated with changes in geographic segment aggregation or disclosure extent. The findings point to an institutional decoupling between public transparency regimes and international accounting standards. This has policy relevance for the International Accounting Standards Board, European regulators, and civil society advocates, underscoring limits of current segment reporting frameworks and the need for coordinated approaches to geographic transparency.

Purpose This paper aims to investigate how renewable energy index interacts and interrelates with developed and emerging stock markets and what the effect of uncertainties surrounding the pandemic and economic policy is on their connectedness from December 29, 2000, to March 23, 2022. Design/methodology/approach The Diebold and Yilmaz (2012, 2014) methodology is used to measure connectedness from three perspectives: total connectedness, total directional connectedness and pairwise directional connectedness. Furthermore, the authors examine the effects of economic, pandemic and financial uncertainty indices on this connectedness. Findings The authors find significant evidence that the volatility connectedness changes over time and reaches peak during global financial crisis and the onset of the COVID 19 crisis. In addition, the connectedness index has consistently reported that advanced equity markets are more connected with clean energy stocks than that in emerging economies. Moreover, the specific uncertainty indices all have a positive impact on the connectedness except the EPU effect in emerging countries. Originality/value This paper takes an initial attempt to provide a clear-cut investigation about the connectedness between renewable energy index and different stock markets with emphasis on the significant crisis periods. These insights can be used by investors in renewable energy to inform diversification benefits at different investment horizons and policymakers to create a stable and predictable renewable energy regulatory framework.

The study of dialects is not merely a scientific whim that we can be wary of or reject under the guise of intellectualism, but rather an urgent scientific and cognitive necessity in an era dominated by tendencies toward hegemony and influence in all its forms. This type of study can provide an objective and accurate sociocultural reading of society, as it focuses on the essence of local cultural identity and what that may represent in terms of self-esteem and pride on the one hand, and, on the other hand, the stigmatization and discrimination against others on that basis, despite the fact that the differentiation between dialects is not in line with the modern linguistic view that recognizes the dynamism and social nature of language. We highlighted this in the first part of the research, before moving on to present a historical overview of the phenomenon of linguistic stigmatization through the term “barbarism,” which is considered its oldest manifestation. We then highlighted how language and dialect can transform from stigmatizing conflict to distinctive glottophobia. In the field, we showed that dialect stigmatization in Tunisia is a manifestation of regionalism and its other, more dangerous and painful side, as stigmatization can turn into discriminatory practices that threaten social cohesion and unity.

A key component of retail marketing is the study of consumer behavior, which helps with decision-making about the layout of the product mix that is presented. In the past, retailers found it challenging to forecast customer behavior because the process was expensive and time-consuming due to the large amount of data that needed to be analyzed. However, access to consumer data has become less expensive and time-consuming due to the substantial developments in information and communications technology. Processing this data and turning it into marketing decisions that support customer requirements and aspirations has been made easier by artificial intelligence. Technologies such as machine learning, deep learning, robotics, facial recognition, and augmented reality have been used at retail points of sale by well-known brands like Adidas, Sephora, IKEA, and Amazon Go to understand and meet customer needs.

This research introduces a first-order Sliding Mode Control (SMC) approach applied to two-mass, variable-speed wind turbine systems, with particular attention to the regulation of the pitch angle β. Moreover, a Robust Integral of the Sign of the Error (RISE) control framework is employed to enhance the system’s robustness and ensure improved dynamic performance. The proposed approach leverages robust control techniques to ensure that power is maintained at a desired and stable level. Maintaining power stability is crucial in industrial and energy applications, as it guarantees operational stability, efficiency, and compliance with established standards. In electrical grids, delivering consistent power helps balance supply and demand, preventing disruptions such as frequency variations. Additionally, maintaining steady power minimizes mechanical and thermal stress on critical components, such as wind turbines and generators, thereby extending their lifespan and reducing wear caused by sudden fluctuations. The proposed SMC and RISE model leverages the robustness of control this ensures optimal performance even in uncertain conditions. Simulation results carried out in Matlab/Simulink confirm the model's effectiveness in achieving the desired dynamic performance objectives.

In the context of industrial applications, ensuring medium access control is a fundamental challenge. Industrial IoT devices are resource-constrained and must guarantee reliable communication while reducing energy consumption. The IEEE 802.15.4e standard proposed time-slotted channel hopping (TSCH) to meet the requirements of the industrial Internet of Things. TSCH relies on time synchronization and channel hopping to improve performance and reduce energy consumption. Despite these characteristics, configuring an efficient schedule under varying traffic conditions and interference scenarios remains a challenging problem. The exploitation of reinforcement learning (RL) techniques offers a promising approach to address this challenge. AI enables TSCH to dynamically adapt its scheduling based on real-time network conditions, making decisions that optimize key performance criteria such as energy efficiency, reliability, and latency. By learning from the environment, reinforcement learning can reconfigure schedules to mitigate interference scenarios and meet traffic demands. In this work, we compare various reinforcement learning (RL) algorithms in the context of the TSCH environment. In particular, we evaluate the deep Q-network (DQN), double deep Q-network (DDQN), and prioritized DQN (PER-DQN). We focus on the convergence speed of these algorithms and their capacity to adapt the schedule. Our results show that the PER-DQN algorithm improves the packet delivery ratio and achieves faster convergence compared to DQN and DDQN, demonstrating its effectiveness for dynamic TSCH scheduling in Industrial IoT environments. These quantifiable improvements highlight the potential of prioritized experience replay to enhance reliability and efficiency under varying network conditions.