Camera Illuminate - Arab Photography Post Arab Revolutions

“Arab Photography” has often been associated with the oriental(ist) aesthetic, showcasing the Arab “subjects” to the West as ‘photographed’ rather than “photographer.” This focus has shifted ever since the Arab revolutions presented a stage for artists to express themselves through “revolutionary art.” Photography was a particularly interesting medium since it acts as a “double actant”; the photographer is a witness and, by documenting the unfolding events, he also becomes an agent of these events. In the post-revolutionary period, however, state censorship is evermore invasive, and photography was forced to take the role of a “civil” form of art in order to avoid censorship. This paper makes a case for photography as methodology and argues that it can inform us about the young Arab subjectivities in ways which other communication mediums cannot. Photography can be considered as a visual discourse on identity where the choice of photographic subject is telling of the ways in which Arab photographers deal with their social, political and physical environments. This medium lends itself to be a discursive practice due to the agency it allows as well as its inherent process of Othering. The agential aspect is done through a careful selection/filtering of experience and othering allows for an othering of the self which is fertile ground for self-criticism. La “fotografía árabe” se ha asociado a menudo con la estética oriental(ista) que muestra a Occidente los “sujetos” árabes como “fotografiados” en lugar de “fotógrafos”. Este enfoque ha cambiado desde que las revoluciones árabes han propiciado un escenario para que los artistas pudiesen expresarse a través de un “arte revolucionario”. La fotografía ha sido un medio particularmente interesante, ya que actúa como un “doble actor”: el fotógrafo como testigo, al documentar los eventos que se desarrollan, también se convierte en un agente de estos eventos. Sin embargo, en el período postrevolucionario, la censura estatal se vuelve cada vez más invasiva, y la fotografía se ve obligada a asumir el papel de una forma de arte “civil” para evitarla. Este artículo aboga por la fotografía como metodología y argumenta que ésta puede informarnos sobre las más jóvenes subjetividades árabes como otros medios de comunicación no pueden hacer. La fotografía puede considerarse un discurso visual sobre la identidad en el que la elección del sujeto fotográfico explica las formas en que los fotógrafos árabes tratan sus entornos sociales, políticos y físicos. Este medio se presta para ser una práctica discursiva debido a la agencia que permite, así como a su proceso inherente de “Othering.” El aspecto agencial se realiza a través de una selección / filtrado cuidadoso de la experiencia, y el otro permite un intercambio del yo que es un terreno fértil para la autocrítica. 

A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling

The following interdisciplinary article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration. In recent years, there has been extensive research on metaheuristics and DRL techniques but focused on simple scheduling environments. However, there are few approaches combining metaheuristics and DRL to generate schedules more reliably and efficiently. In this paper, we first formulate a DRC-FJSSP to map complex industry requirements beyond traditional job shop models. Then we propose a scheduling framework integrating a discrete event simulation (DES) for schedule evaluation, considering parallel computing and multicriteria optimization. Here, a memetic algorithm is enriched with DRL to improve sequencing and assignment decisions. Through numerical experiments with real-world production data, we confirm that the framework generates feasible schedules efficiently and reliably for a balanced optimization of makespan (MS) and total tardiness (TT). Utilizing DRL instead of random metaheuristic operations leads to better results in fewer algorithm iterations and outperforms traditional approaches in such complex environments.Comment: This article has been accepted by IEEE Access on June 30, 202

Incremental Learning Approach for Enhancing the Performance of Multi-Layer Perceptron for Determining the Stock Trend

This paper introduces a new technique for achieving minimum risk of predicting stock trend using multi-layer perceptron. The proposed technique presents the method of classification the stock trend .the paper show a comparison among multi-layer perceptron, gene learning theory. The achieved results show the superior performance of the multi-layer perceptron which is based on mathematical back ground

Factors Affecting Outcomes of COVID-19 Infection among Older Adults with Type 2 Diabetes: A Single Center, Cross-Sectional Study

Objective: COVID-19 infection and the factors affecting it are major concerns worldwide. This retrospective study aimed to investigate clinical, laboratory and radiological characteristics associated with disease severity and hospitalization among older adults with type 2 diabetes mellitus (T2D) with COVID-19. Materials and methods: A retrospective case series study was conducted to review the records of older adults with T2D infected with COVID-19. Sociodemographic, COVID-19-related data, laboratory tests at the time of COVID-19 diagnosis and CT findings were collected. Bivariate and multivariate regression analysis were done to determine the predictors of the studied outcome, either hospitalization or complete recovery. Results: A total of 343 patients’ records were reviewed, with a mean age of 73.6 ± 6.4 years. Most of patients had fever and cough at the time of diagnosis and ground glass opacities was found on CT in 62.1% of patients. Hospitalized patients had higher duration of diabetes, suffered more from dyspnea, body aches and chest pain, had higher HbA1c, CRP and ferritin and lower lymphocytes and hemoglobin. Fasting plasma glucose and HbA1c positively affected the duration from onset of symptoms till resolution, while hemoglobin level negatively affected it. Logistic regression analysis revealed that duration of diabetes, HbA1c, ferritin and dyspnea were significant predictors of hospitalization. Conclusions: Among older adults with T2D infected with COVID-19, poor glycemic control is associated with higher risk of hospitalization and longer duration till recovery of symptoms. Longer duration of diabetes, high serum ferritin and the presence of dyspnea are associated with higher risk for hospitalization among these patients

Optimized D-α-tocopherol polyethylene glycol succinate/phospholipid self-assembled mixed micelles: A promising lipid-based nanoplatform for augmenting the antifungal activity of fluconazole

Fluconazole (FLZ) is the most widely used antifungal agent for treating cutaneous candidiasis. Although oral FLZ has been proved to be effective, the incidence of side effects necessitates the development of an effective formulation that could surpass the pitfalls associated with systemic availability. Accordingly, this research aimed at developing a self-assembled mixed micelles topical delivery system to enhance the topical delivery of the drug. Self-assembled mixed micelles were developed using D-α-tocopheryl polyethylene glycol 1000 succinate and phospholipids and optimized using Box-Behnken design. The optimized formulation with minimized size was then tested in vivo for the antifungal activity against C. albicans in immunocompromised mice. Treatment with the optimized formulation led to decreased peripheral erythema as well as lesions due to fungal infection in comparison to raw FLZ loaded gel. Therefore, the developed formulation was found to be a promising vehicle for the treatment of cutaneous candidiasis

Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images

Acute lymphoblastic leukemia (ALL) poses a significant health challenge, particularly in pediatric cases, requiring precise and rapid diagnostic approaches. This comprehensive review explores the transformative capacity of deep learning (DL) in enhancing ALL diagnosis and classification, focusing on bone marrow image analysis. Examining ten studies conducted between 2013 and 2023 across various countries, including India, China, KSA, and Mexico, the synthesis underscores the adaptability and proficiency of DL methodologies in detecting leukemia. Innovative DL models, notably Convolutional Neural Networks (CNNs) with Cat-Boosting, XG-Boosting, and Transfer Learning techniques, demonstrate notable approaches. Some models achieve outstanding accuracy, with one CNN reaching 100% in cancer cell classification. The incorporation of novel algorithms like Cat-Swarm Optimization and specialized CNN architectures contributes to superior classification accuracy. Performance metrics highlight these achievements, with models consistently outperforming traditional diagnostic methods. For instance, a CNN with Cat-Boosting attains 100% accuracy, while others hover around 99%, showcasing DL models’ robustness in ALL diagnosis. Despite acknowledged challenges, such as the need for larger and more diverse datasets, these findings underscore DL’s transformative potential in reshaping leukemia diagnostics. The high numerical accuracies accentuate a promising trajectory toward more efficient and accurate ALL diagnosis in clinical settings, prompting ongoing research to address challenges and refine DL models for optimal clinical integration

Tilapia aquaculture systems in Egypt: Characteristics, sustainability outcomes and entry points for sustainable aquatic food systems

The future demand for fish and other aquatic foods requires the sustainable intensification of related production systems. However, policy and investment decisions for the sustainable intensification of aquaculture systems are usually hindered by the lack of benchmarking data about their actual sustainability performance, often resulting in poorly developed and implemented interventions that ignore potential sustainability trade-offs. This is a reality in many of the leading aquaculture producers in the developing world like Egypt. In this study we analyzed farm-level data from 402 aquaculture producers in the Kafr El Sheikh governorate in Egypt, to characterize and benchmark the performance of tilapia production systems against key sustainability outcomes. For the analysis we used a combination of statistical tools such as ordinary least square regressions, simultaneous quantile regressions and propensity score matching. We focussed on how the production characteristics and practices of different tilapia production systems intersect with economic, food security, and environmental outcomes that cover multiple dimensions of sustainability. We found that differences in these production characteristics and practices were significantly associated with the sustainability performance of tilapia production systems. In particular, our results show that yields in monocultural systems (10,460.5 ton/ha) were significantly higher than in polyculture systems (8404.7 ton/ha). Furthermore, despite the generally positive economic, food security, and environmental outcomes of several of the studied systems, some trade-offs emerge both between and within these sustainability dimensions

Benchmarking Tilapia Aquaculture Systems in Egypt: Synergies, Trade-offs, and Entry Points for Sustainable Development

The growing demand for aquatic foods requires the sustainable intensification of aquaculture systems. However, policy and investment decisions for sustainable intensification of aquaculture systems are often hindered by a lack of benchmarking data related to performance of such systems. Abstract accepted and presented at WAS2022 – published in the book of abstracts at pag 606

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London