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This study investigates the data-based consensus problem of discrete-time heterogeneous multi-agent systems (MASs) through the development of distributed protocols. A type-3 (T3) fuzzy logic system (FLS) is presented to effectively model the inherent nonlinearities present in MASs, allowing for more accurate representation of agent dynamics. Utilizing a Lyapunov stability approach alongside a linear matrix inequalities (LMIs) framework, the sufficient conditions are derived for designing the data-driven distributed control protocols. The results ensure that the consensus error dynamics are asymptotically stable, thereby guaranteeing the achievement of consensus among agents. Furthermore, the optimal linear quadratic regulator (LQR) problem is addressed within the context of heterogeneous MASs, providing an additional layer of control performance optimization. Also, the design procedure is analyzed under conditions of noisy data, which is critical for real-world applications where sensor inaccuracies are prevalent. To validate the theoretical findings, extensive simulations are conduced that showcase the effectiveness and robustness of the proposed methods. The implementation is demonstrated on a network comprising four multiple autonomous underwater vehicles (AUVs), hyper-chaotic systems, and various configurations of discrete-time MASs. The results highlight the advantages of suggested data-driven control approach, emphasizing its practicality and efficiency in achieving consensus in complex multi-agent environments. This work paves the way for future research in robust control strategies for heterogeneous physical and financial systems operating under uncertainty. For example, by the use of the suggested data-driven approach to achieve consensus among heterogeneous agents, the financial institutions can enhance their decision-making processes, and optimize performance under uncertainty.

Deep brain stimulation (DBS) is recognized as one of the prominent solutions to treat Parkinson’s disease. In the DBS, an accelerometer sensor is often adopted to measure the value of the tremor and then wirelessly transmit the measured signals to the programming device using a communication network. False data injection (FDI) attacks are among the most prevalent attacks in DBS, where the attacker injects false data into the communication infrastructure. In this paper, an event triggered adaptive defense mechanism is designed to mitigate the effect of FDI attacks in the DBS system. To the best of our knowledge, this is the first work focusing on the design of defense mechanisms against cybersecurity threats directed toward deep brain stimulation systems. To this end, a fractional order extended state observer (FOESO) is adopted to predict the tremor measurement signals of the accelerometer sensor. To achieve accurate prediction, Heuristic Adaptive Dynamic Programming (HADP) is utilized to adjust the coefficients embedded in the FOESO structure. Using a neural networks-based approach, the HADP agent regulates the parameters of the FOESO by maximizing a reward (reinforcement) signal according to the desired system requirements. An event-triggered mechanism is also utilized to mitigate manipulated signals measured by sensors and this strategy can address challenges such as bandwidth limitations and high network traffic. The comprehensive simulations of DBS under various levels of FDI threats reveal the feasibility of the suggested defense mechanism against cyber threats. Moreover, comparative dynamic responses and real-time examination of tremors demonstrate that by adopting the suggested adaptive defense, a superior performance than other state-of-the-art schemes can be achieved.

Abstract This paper presents RL4CEP, a reinforcement learning (RL) approach to dynamically update complex event processing (CEP) rules. RL4CEP uses Double Deep Q-Networks to update the threshold values used by CEP rules. It is implemented using Apache Flink as a CEP engine and Apache Kafka for message distribution. RL4CEP is a generic approach for scenarios in which CEP rules need to be updated dynamically. In this paper, we use RL4CEP in a financial trading use case. Our experimental results based on three financial trading rules and eight financial datasets demonstrate the merits of RL4CEP in improving the overall profit, when compared to baseline and state-of-the-art approaches, with a reasonable consumption of resources, i.e., RAM and CPU. Finally, our experiments indicate that RL4CEP is executed quite fast compared to traditional CEP engines processing static rules.

IntroductionWilliams-Beuren syndrome (WBS) is a rare genetic disorder characterized by congenital heart defects, dysmorphic features, intellectual delay, and a distinctive social behavioral profile. This highly recurrent and homogeneous phenotype has been curiously reported to be associated with autism spectrum disorders (ASD). Both genetic and environmental origins have been implicated. This study aimed to describe Tunisian patients associated with WBS and ASD and explore the underlying etiologies.MethodsThirty-one clinically suspected WBS were referred for genetic exploration. A comprehensive evaluation using karyotyping, fluorescence in situ hybridization (FISH), and array comparative genomic hybridization (array-CGH) was performed.ResultsAll patients were clinically diagnosed and confirmed to have WBS through karyotyping and FISH analysis. Notably, six patients with complex or atypical clinical presentations underwent array-CGH. Two of these patients presented with ASD. Array CGH showed microdeletions ranging from 1.4 to 1.7 Mb in the 7q11.2 region. Further analysis of the extended region deletion identified a gene closely located in the deleted region, the HIP1 gene, involved in the central nervous system trafficking protein.DiscussionThe recurrent deletion in WBS, as well as the mirror duplication, may contribute to ASD development in some cases, suggesting a potential involvement of the ASD genes pathway in this region. However, recessive genetic origins should also be considered, particularly in consanguineous families. Furthermore, our findings highlight the potential role of genetic factors and regulatory elements within the deleted region in modulating gene expression, notably the HIP1 gene. This underscores the implications of gene dosage and environmental factors in the broader WBS region, notably with language and social development.ConclusionThe presence of ASD in WBS patients emphasizes the need to investigate all WBS patients for autistic traits to establish a better genotype–phenotype correlation. We underline the utility of array-CGH as a valuable genetic diagnostic tool for characterizing WBS cases, and we shed light on the complex interplay of behavioral disorders in the 7q11.2 region rearrangements.

Abstract This study analyzes the effect of environmental, social, and governance (ESG) performance on banking stability in the digital era for a selected group of European banking institutions. Unlike many research, this paper incorporates the digital environment in which the European banking system operates into its investigation to capture the relationship between ESG scores, digitization, and bank stability from 2005 to 2022. This study uses a regime-switching model to investigate the non-linear hypothesis of the relationship, an aspect that has received insufficient attention in previous research. The empirical results confirm the non-linearity between ESG performance and banking stability in the digital era, identifying three ESG performance regimes. Moreover, higher ESG scores are associated with a lower risk of bank failure, aligning with stakeholder theory. Finally, European banks with weak ESG scores fail to avoid the fragility caused by investments in technological infrastructure. Overall, our results suggest that critics and proponents of banks’ commitment to social responsibility are, to some extent, right. Indeed, low or moderate ESG performance (Regimes 1 and 2) aligns with the classical perspective, while high ESG engagement (Regime 3) is consistent with the stakeholder view. Consequently, European banks need to adopt a digital strategy based on improving sustainability to leverage the positive impact of digitization and ESG performance on bank stability. On the policymakers’ side, they need to strengthen the legal infrastructure to support digitization and engagement in social responsibility activities.

Most multicystic dysplastic kidneys (MCDKs) are detected prenatally, yet there is no clear consensus on initial evaluation and follow-up. We report the case of a 4-year-old male diagnosed with right pyelocalyceal dilation in utero, confirmed postnatally as MCDK. Follow-up ultrasounds revealed rapid growth of three upper pole cysts, reaching 8 cm. A right upper pole nephrectomy was performed, and histopathology revealed a renal dermoid cyst within segmental multicystic renal dysplasia. This case highlights the critical role of postnatal imaging and follow-up in detecting atypical features, including rare entities like intrarenal teratomas, enabling timely surgical intervention and improved outcomes.

Abstract The primary objective of this manuscript is to investigate the existence and uniqueness of solutions for the Langevin ( k , φ ) $(\mathtt{k},\varphi )$ -Hilfer fractional differential equation of different orders with multipoint nonlocal fractional integral boundary conditions. We consider the generalized version of the Hilfer fractional diferential equation called as ( k , φ ) $(\mathtt{k},\varphi )$ -Hilfer fractional differential equation. We provide some significant outcomes about ( k , φ ) $(\mathtt{k},\varphi )$ -Hilfer fractional Langevin differential equation that requires deriving equivalent fractional integral equation to ( k , φ ) $(\mathtt{k},\varphi )$ -Hilfer Langevin fractional differential equation. The existence result is established using the Krasnoselskii’s fixed-point theorem, while the uniqueness is addressed with the help of Banach contraction principle. Additionally, we investigate the different forms of Ulam stability for the solution of the mentioned problem, under specific conditions. To validate our main outcomes, we present a detailed example at the end of the manuscript.

In an era where artificial intelligence (AI) is reshaping educational paradigms, this study explores AI-based chatbot adoption in higher education among students and educators. Employing a Structural Equation Modeling (SEM) approach, the research focuses on developing and validating a comprehensive model to understand the multifaceted factors impacting the acceptance and use of these chatbots. The methodology integrates an extensive literature review, construction of a theoretical model, administration of a detailed questionnaire to a representative sample from the higher education sector, coupled with advanced SEM techniques for data analysis and interpretation. The SEM analysis validates the model's robustness and highlights the relationships between several key factors affecting users' perspectives and chatbots adoption. Results reveal a predominantly positive perception towards AI-chatbots among both students and educators, underscoring the potential to substantially enrich their educational journey. However, it also uncovers critical concerns pertaining to trust, privacy, response bias, and information accuracy. Moreover, the study offers valuable insights into how moderators such as technological proficiency, user roles, and gender influence the adoption model relationships. This emphasizes the need for customizing AI-chatbots deployment to meet the diverse needs of users effectively. Contributing a robust framework for understanding users' perceptions towards AI-chatbots and their adoption patterns, this study offers actionable insights for educational leaders, policymakers, and technology developers. It also lays the groundwork for future research, including longitudinal studies to evaluate the long-term impact of these technologies, investigations into their effect on learning outcomes, and explorations of the ethical and privacy considerations involved.

Abstract Background The climatic changes crossing the world menace the green life through limitation of water availability. The goal of this study was to determine whether Moringa oleifera Lam. trees cultivated under Tunisian arid climate, retain their tolerance ability to tolerate accentuated environmental stress factors such as drought and salinity. For this reason, the seeds of M. oleifera tree planted in Bouhedma Park (Tunisian arid area), were collected, germinated, and grown in the research area at the National Institute of Research in Rural Engineering, Waters and Forests (INRGREF) of Tunis (Tunisia). The three years aged trees were exposed to four water-holding capacities (25, 50, 75, and 100%) for 60 days to realise this work. Results Growth change was traduced by the reduction of several biometric parameters and fluorescence (Fv/Fm) under severe water restriction (25 and 50%). Whereas roots presented miraculous development in length face to the decrease of water availability (25 and 50%) in their rhizospheres. The sensitivity to drought-induced membrane damage (Malondialdehyde (MDA) content) and reactive oxygen species (ROS) liberation (hydrogen peroxide (H2O2) content) was highly correlated with ROS antiradical scavenging (ferric reducing antioxidant power (FRAP) and (2, 2’-diphenyl-1-picrylhydrazyle (DPPH)), phenolic components and osmolytes accumulation. The drought stress tolerance of M. oleifera trees was associated with a dramatic stimulation of superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR), ascorbate peroxidase (APX), and glutathione peroxidase (GPX) activities. Conclusion Based on the several strategies adopted, integrated M. oleifera can grow under drought stress as accentuated adverse environmental condition imposed by climate change.

Toxoplasma gondii is one of the most common zoonotic parasites worldwide, with infections in humans as well as in all mammals, including dromedaries, that affect reproductive health, leading to malformations and abortions. In the current study, we estimated, for the first time in Tunisia, the seroprevalence of T. gondii antibodies and its associated risk factors in dromedaries (Camelus dromedarius) using the enzyme-linked immunosorbent assay (ELISA) technique. A total of 248 sera samples were collected from dromedaries living in Médenine and Tataouine governorates, South Tunisia. The overall seroprevalence of T. gondii was estimated to be 29.8 ± 2.9% (74/248) with a significantly higher seroprevalence rate in dromedaries from Médenine (52.5 ± 7.9%; 21/40) compared to Tataouine (25.5 ± 3%; 53/208); it was also higher in dromedaries kept in intensive farming systems (55 ± 11.1%; 11/20) compared to extensive systems (27.6 ± 3%; 63/228), in non-pregnant females (39 ± 4%; 57/146) compared to pregnant ones (16.7 ± 3.7%; 17/102), and in those in contact with cats (66.7 ± 8.6%; 20/30) compared to those with no contact with cats (24.8 ± 2.9%; 54/218). The present study should be followed by others to explore the role of dromedaries in the epidemiological cycle of T. gondii in Southern Tunisia.

This paper explores the mild solutions of partial impulsive fractional integro-differential systems of order 1<α<2 in a Banach space. We derive the solution of the system under the assumption that the homogeneous part of the system admits an α-resolvent operator. Krasnoselskii’s fixed point theorem is used for the existence of solution, while uniqueness is ensured using Banach’s fixed point theorem. The stability of the system is analyzed through the framework of Hyers–Ulam stability using Lipschitz conditions. Finally, examples are presented to illustrate the applicability of the theoretical results.

Adopting sustainable agricultural practices is considered as an effective strategy to mitigate climate change and to improve soil health and crop production.This study aims to assess the impact of tillage systems (no-tillage (NT) and conventional tillage (CT)), crop rotation (faba beans - oat and faba beans - durum wheat) and nitrogen fertilization rates (0, 20 and 40 kg N.ha ^−1 ) on soil properties and growth and yield of fenugreek. Soil samples were collected at two stages: at the 50% blossoming stage and after harvest and fenugreek quality and yield and its components were measured.The results showed that tillage systems and crop rotation treatments had great influence on soil properties. In general, the highest soil organic carbon (SOC) (13.4 and 14.2 g.kg ^−1 ), total nitrogen (1.0 and 1.1 g.kg ^−1 ), nitric nitrogen (75.73 and 62.42 mg.kg ^−1 ), ammonium-nitrogen (14.90 and 19.08 mg.kg ^−1 ). were recorded in no tillage practice with durum as a previous crop at 50% blossoming and harvest stage respectively. Exception for SOC, the highest nitrogen fertilization rate improved most of soil variables at both growth stages. Greater biomass and grain protein content were obtained under NT system and 40 kg N.ha ^−1 . Previous crops had no effect on grain protein content. Also, there was a significant synergy among soil fertility and fenugreek production. Conservation tillage, durum wheat previous crop and highest nitrogen fertilization rate gave the best aboveground biomass, yield and yield attributes performances. Overall, NT associated with durum wheat as a previous crop and nitrogen fertilization (40 kg N.ha ^−1 ) are the suitable combination to improve both soil quality, and fenugreek yield.

Extracting mathematical formulas from images of scientific documents and converting them into structured data for storage in a database is essential for their further use. However, recognizing and extracting math formulas automatically, rapidly, and effectively can be challenging. To handle this problem, we have proposed a system, with a deep learning architecture, that uses the formula combination features to train the YOLOv8 model. This system can detect and classify the formula inside and outside the text. Once extracted, we built a robust end-to-end math formula recognition system that automatically identifies and classifies math symbols, using the faster R-CNN object detection, then a Convolution Graphical Neural network (ConvGNN) to analyze the math formula layout, as the formula is better represented as a graph with complex relationships and object interdependency. ConvGNN can predict formula linkages without resorting to laborious feature engineering. Experimental results on the IBEM and CROHME 2019 datasets reveal that the proposed approach can accurately extract isolated formulas with mAP of 99.3\%, embedded formulas with mAP of 80.3%, detect symbols with mAP of 87.3%, and analyze formula layout with an accuracy of 92%. We also showed that our system is competitive with related work.

The vein of Galen aneurysmal malformation is a rare congenital malformation of the cerebral blood vessels. It is a result of the persistence of an embryonic vessel that drains multiple arteriovenous shunts. This malformation can cause a multitude of symptoms ranging from cardiac failure to headaches depending on the age of presentation. In the fetus, cardiac manifestations are rare and are linked to a very poor prognosis. That’s why prenatal diagnosis is crucial in early detection and management. We present a case of a vein of Galen aneurysmal malformation, diagnosed prenatally with ultrasonography. The newborn developed widely a high-output cardiac failure. Prenatal diagnosis facilitates the early detection of this malformation as well as predicting the prognosis.

Rehabilitation devices such as actuated exoskeletons can provide mobility assistance for patients suffering from paralysis or muscle weakness. In order to improve the well-being of patients, the control design of exoskeletons is of paramount importance and highest priority. In this paper, we present a sliding reinforcement learning (RL) method control for an upper-limb exoskeleton, enabling it to learn following a desired trajectory in the Cartesian space. The deep deterministic policy gradient (DDPG) using an actor-critic architecture is employed to continuously adjust the non-singular terminal sliding mode control (NSTSMC) control inputs, based on previous experiences. The designed actor network learns the policy and the critic evaluates the quality of the actions chosen by the actor. The robustness of the proposed approach is studied when the system is subjected to random disturbances. The simulation results demonstrate that the proposed approach based on the RL method effectively fulfills exoskeleton tracking tasks. Moreover, a comparative analysis with the standard NSTSMC, computed torque (CT), and RL-based CT shows the superiority of the proposed approach in terms of position tracking error. These findings are further confirmed by various performance evaluation metrics.

Osteomyelitis of the pubic symphysis is a rare infectious disease commonly caused by the opportunistic bacteria Staphylococcus aureus and Pseudomonas aeruginosa . The clinical context includes fever, and pubic, or abdominal pain, thus should prompt a biological, microbiological, and radiological assessment. We report the case of a 16-year-old patient who developed inflammatory pygalgia in a febrile context laboratory examination showed an elevation of C-reactive protein and sedimentation rate. Pelvic X-rays and magnetic resonance showed bilateral bone lysis and destruction of the edges of the pubic symphysis, in addition to a pubic pelvic collection. Bacteriological examination revealed by methicillin-sensible S. aureus . The patient was put on antibiotic therapy for 2 months with good clinical and radiological evolution without surgical debridement. In conclusion, osteomyelitis of pubic symphysis can be misdiagnosed because of its atypical manifestations. Recovery is often achieved if adequate treatment is started early.

Background and aim: As the presence of single nucleotide polymorphisms (SNPs) in the interleukin (IL)-10 gene continues to be a major challenge in the development of effective therapies for digestive cancers, this case-control study was conducted to assess the possible influence of genotype, haplotype and diplotype for two SNPs (−1082A/G (rs1800896) and −592A/C (rs1800872)) located in the promoter region of IL-10 gene on the incidence, severity and prognosis of colorectal cancer (CRC) in Tunisians. Methods: IL-10 gene SNPs were analyzed in 130 CRC cases and 165 healthy subjects (HS) using PCR-SSP. Results: For the IL-10 -1082A/G SNP, the comparison of genotype frequencies between cases and HS groups showed that the G allele significantly reduced CRC risk under the recessive model (GG vs. AA + AG: OR [95%CI] = 0.44 [0.21–0.93], p = 0.03). Conversely, a positive association was observed between the codominant model (AG vs. AA + GG) and high susceptibility (OR [95%CI] = 1.65 [1.02–2.63], p = 0.04). After stratification by disease site, the recessive model was also found to reduce susceptibility to colon cancer (OR [95%CI] = 0.18 [0.04–0.72], p = 0 0.01), while the homozygote model (AA vs. GG) was suggested as a risk factor (OR [95%CI] = 5.16 [1.31–23.26], p = 0.02). Furthermore, the codominant model (AG vs. AA + GG) doubled the risk of rectum cancer (OR [95%CI] = 1.98 [1.07–3.70], p = 0.03). For the IL-10 -592A/C SNP, the codominant model (AC vs. AA + CC) has a protective effect against the development of CRC (OR [95%CI] = 0.59 [0.36–0.94], p = 0.03). The IL-10 gene haplotype was not associated with CRC risk. A stratified analysis by disease site demonstrated that the presence of Hap3 (−1082G and −592C alleles) specifically reduced the risk of developing colon cancer (OR [95%CI] = 0.51 [0.32–0.80], p = 0.003). Moreover, homozygous Hap3/Hap3 diplotype significantly reduced susceptibility to CRC (OR [95%CI] = 0.35 [0.14–0.85], p = 0.02). Interestingly, this diplotype has not been identified in colon cancer patients. Kaplan-Meier analysis showed that the homozygous Hap2/Hap2 diplotype was significantly associated with decreased overall survival (Log-rank: p = 0.01). This association was also observed in the colon cancer subgroup (Log-rank: p = 0.001). Conclusion: Our findings provide preliminary indications that the −1082A/G and −592/AC SNPs within the IL-10 gene may exhibit significant associations with the pathogenesis and prognostic outcomes of CRC. However, further investigations are still warranted to validate and establish the veracity of our findings.

Bacterial exopolysaccharides (EPS) have emerged as one of the key players in the field of heavy metal-contaminated environmental bioremediation. This study aimed to characterize and evaluate the metal biosorption potential of EPS produced by a novel Psychrobacillus strain, NEAU-3TGS, isolated from an iron ore deposit at Tamra iron mine, northern Tunisia. Genomic and pan-genomic analysis of NEAU-3TGS bacterium with nine validated published Psychrobacillus species was also performed. The results showed that the NEAU-3TGS genome (4.48 Mb) had a mean GC content of 36%, 4,243 coding sequences and 14 RNA genes. Phylogenomic analysis and calculation of nucleotide identity (ANI) values (less than 95% for new species with all strains) confirmed that NEAU-3TGS represents a potential new species. Pangenomic analysis revealed that Psychrobacillus genomic diversity represents an “open” pangenome model with 33,091 homologous genes, including 65 core, 3,738 shell, and 29,288 cloud genes. Structural EPS characterization by attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy showed uronic acid and α-1,4-glycosidic bonds as dominant components of the EPS. X-ray diffraction (XRD) analysis revealed the presence of chitin, chitosan, and calcite CaCO3 and confirmed the amorphous nature of the EPS. Heavy metal bioabsorption assessment showed that iron and lead were more adsorbed than copper and cadmium. Notably, the optimum activity was observed at 37°C, pH=7 and after 3 h contact of EPS with each metal. Genomic insights on iron acquisition and metabolism in Psychrobacillus sp. NEAU-3TGS suggested that no genes involved in siderophore biosynthesis were found, and only the gene cluster FeuABCD and trilactone hydrolase genes involved in the uptake of siderophores, iron transporter and exporter are present. Molecular modelling and docking of FeuA (protein peptidoglycan siderophore-binding protein) and siderophores ferrienterobactine [Fe+3 (ENT)]-3 and ferribacillibactine [Fe+3 (BB)]-3 ligand revealed that [Fe+3 (ENT)]-3 binds to Phe122, Lys127, Ile100, Gln314, Arg215, Arg217, and Gln252. Almost the same for [Fe+3 (ENT)]-3 in addition to Cys222 and Tyr229, but not Ile100.To the best of our knowledge, this is the first report on the characterization of EPS and the adsorption of heavy metals by Psychrobacillus species. The heavy metal removal capabilities may be advantageous for using these organisms in metal remediation.

Abstract Background Abortion in ewes causes high economic losses and represents a threat for human health due to abortive zoonotic pathogens. Objective The present study aimed to assess the knowledge, attitudes and practices (KAP) among sheep owners in the northern Tunisia regarding ewes’ abortions. Methods Between February 2021 and May 2022, a structured questionnaire containing both close and open‐ended questions was applied to 120 sheep owners in northern Tunisia. The data collected were analysed by chi‐square test using Epi info 6 software. Results The majority (75%) of participants reported a history of abortion in their sheep flocks. Sheep owners thought that the most frequent cause of abortion was physical factors, such as trauma, climate and stress (60% ± 5.5%; 48/80), followed by toxicity (15% ± 4%; 12/80), metabolic and nutritional conditions (12.5% ± 3.7%; 10/80), vaccination (5% ± 2.4%; 4/80) and infectious causes (7.5% ± 2.9%; 6/80) (p

Abstract Dental fluorosis (DF) is a prevalent developmental defect of tooth enamel caused by exposure to excessive fluoride, with the severity dependent on various factors. This study aimed to investigate the association between DF and a specific genetic polymorphism (rs412777) in the COL1A2 gene among a Tunisian population. A case-control study was conducted from July to November 2022, involving a total of 95 participants including 51 cases and 44 controls. Dental examinations and genetic analysis were performed to assess the relationship between the COL1A2 gene polymorphism and DF. The results of allelic distribution revealed that A allele carriers were significantly protected against (DF) when compared to those with the C allele (C vs. A, p = 0.001; OR = 0.375 (0.207–0.672)). This suggests a strong correlation between the presence of the C allele and the risk of developing DF. Additionally, significant association between the CC genotype of rs412777 and an increased risk of DF was found under both codominant and dominant genetic models (P = 0.002 and P

Abstract The relationship between imported technology and employment is a controversial issue. This study aims to test the hypothesis that the relationship between imported technology and employment is non-linear and evolves with the level of technology imports. The study covers two groups of developed and developing countries over the period 2000–2019. The Panel Smooth Transition Regression (PSTR) model is used to estimate the technology import threshold and its impact on industrial employment. The study finds a positive relationship between imported technology and industrial employment for technology import rates above the 13.667% threshold, above which imported technology begins to improve industrial employment in developed countries. In contrast, for developing countries, the results showed a negative relationship between imported technology and industrial employment for technology import rates above the 3.44% threshold above which imported technology begins to reduce industrial employment. These results suggest that the threshold level of technology imports can be considered as an indicator for promoting innovation policies in both developed and developing countries to minimize the negative effect of process innovation resulting from imported technology.

Electrocardiogram (ECG) data collection during emergency situations is challenging, making ECG data generation an efficient solution for dealing with highly imbalanced ECG training datasets. In this paper, we propose a novel approach for ECG signal generation using Generative Adversarial Networks (GANs) and statistical ECG data modeling. Our approach leverages prior knowledge about ECG dynamics to synthesize realistic signals, addressing the complex dynamics of ECG signals. To validate our approach, we conducted experiments using ECG signals from the MIT-BIH arrhythmia database. Our results demonstrate that our approach, which models temporal and amplitude variations of ECG signals as 2-D shapes, generates more realistic signals compared to state-of-the-art GAN based generation baselines. Our proposed approach has significant implications for improving the quality of ECG training datasets, which can ultimately lead to better performance of ECG classification algorithms. This research contributes to the development of more efficient and accurate methods for ECG analysis, which can aid in the diagnosis and treatment of cardiac diseases.

This paper introduces the Web-Based Intelligent Packaging Evaluation (WIPE) platform, a novel approach to assess the performance of product and packaging systems within the e-commerce distribution sector. Unlike traditional methods that primarily rely on laboratory evaluations under controlled conditions, WIPE addresses the unique challenges posed by e-commerce distribution, such as increased handling points and unforeseen hazards that standard physical tests may not capture. Leveraging advanced machine learning algorithms and association rule mining, WIPE extracts insights about packaging defects directly from customer reviews on e-commerce platforms. Analyzing both images and text from these reviews establishes connections between frequently used words and the predicted damages, causes, and effects. This innovative approach was exemplified in two case studies involving laundry detergent liquid bottles and pods sold on the Amazon. The findings from these studies demonstrate WIPE’s capability to extract pertinent information from customer feedback and identify specific packaging defects and predict their potential causes. This integration of sentiment analysis and association rule mining into the packaging evaluation process marks a significant advancement in the field. The introduction of WIPE represents a transformative step in packaging evaluation, offering a more dynamic, real-world analysis that can significantly enhance product and packaging design, ultimately leading to improved customer satisfaction in the rapidly evolving e-commerce landscape.

This paper proposes a fault location and protection method for power distribution system with Distributed generation (DG) resources, considering fault impedance. A multi-layer perceptron (MLP) Artificial Neural Network (ANN)-based approach using the Levenberg-Marquardt algorithm to train the neural network for fault location and identification within distribution networks with DGs is developed. The proposed method emphasizes the incorporation of fault impedance in the analytic process. It also leverages the robust computational capabilities and inherent simplicity of ANNs to accurately detect the fault type and subsequently identify the fault location. Furthermore, it implements a zoning strategy, which partitions the distribution system into multiple independent sections to facilitate a more organized and effective fault management process. Simulation results and conducted analyses confirmed the effectiveness of the proposed approach in localizing faults within the network thereby minimizing downtime during faulty conditions and improving power system’s capabilities and reliability. Among the key advantages of the proposed fault detection approach are its ability to adeptly handle the complexities introduced by the wide integration of DGs in distribution networks.

Research into solar photovoltaic (PV) fed grid-tied multilevel inverters (MLIs) has increased in recent years due to their distinct features compared to two-level voltage source inverters (VSIs). However, maintaining the power quality of MLIs is a challenging problem that encompasses the reduction of total harmonic distortion (THD), dc-link voltage imbalance issues, power loss reduction and thermal stress mitigation; which are significantly affected by the applied pulse width modulation (PWM) technique. In order to tackle the above mentioned power quality problems, this work presents a modified hybrid PWM technique which combines a modified modulating signal with a modified carrier signal. The comparison between the proposed and the existing PWM techniques is performed under different conditions and using a wide range of modulation index variations. A grid-tied three-phase three-level T-type neutral point clamped (NPC) inverter is adopted for the power quality assessment. The proposed hybrid PWM offers 25.57% output line voltage THD and 11.8 V peak-to-peak dc-link capacitor voltage difference which are lower than existing PWM techniques. Additionally, by reducing the switching and conduction losses of the power switches, the proposed hybrid PWM technique offers lower thermal stress than existing PWM techniques. The comparative analysis between the existing and the proposed hybrid PWM technique is carried out using MATLAB/Simulink and PLECS simulation environments. The proposed hybrid PWM technique is further validated using a reduced scale prototype developed in our research laboratory.

Background: Congenital Chylothorax (CC) is a rare condition, which is defined as an accumulation of the chyle in the pleural cavity;moreover, it is associated with significant morbidities, including respiratory distress, malnutrition, immunodeficiency, and infections. Case Report: The diagnosis of chylothorax was made upon count cell analysis of the pleural fluid with ≥80% lymphocytesdetected before birth or within 28 days after birth. In this study, we presented five cases of CC infants. They were dischargedfrom our tertiary center at Hedi Chaker Hospital, Sfax, Tunisia, from January 2010 to December 2018. There were three malesand two females. Prenatal diagnosis was made in four cases. There were four full-term newborns and one near-term of 36weeks. Pleural effusion was on the right side in three cases, on the left side in one case, and bilateral in one case. Four casesrequired mechanical ventilation. Somatostatin was indicated in one case. The treatment was successful in four cases. One casepresented a dysmorphic syndrome and died of pneumothorax.Conclusion: The treatment of CC is based on conservative management. Somatostatin or its analog octreotide is considered anadjunctive treatment of CC. However, the refractory cases are treated with chemical pleurodesis or surgical treatment. Wepropose an algorithm for the treatment of CC.

Closed-loop deep brain stimulation (cl-DSB) is recognized as one of the most prominent schemes of neurostimulation therapy which aims to reduce the level of hand tremor in Parkinson’s disease (PD). Recently, modern neurostimulation has integrated wireless sensors and programming devices with communication infrastructures to monitor and regulate hand speed remotely. However, deploying wireless communication technologies introduces degradation procedures (i.e., latency, time delay, and so on) which adversely affect the neurostimulation treatment. Thus, in this paper, an adaptive fractional-order model predictive control (FOMPC) is designed to address the challenges of wireless neurostimulation in the cl-DSB system. For this purpose, a deep on-policy learning (DOL) algorithm is adopted to design parameters of the FOMPC in such a way that minimizes the fluctuations of hand speed and eliminates side effects of stimulation. Compared with the integer version of the model predictive controller, further flexibility is achieved by the non-integer orders. Deep neural networks (DNNs) of on-policy learning are trained to regulate tremor speed by dynamically adjusting the parameters of FOMPC in an episodic manner. The main novelty of this work is to consider the challenges of unreliable wireless communications, as it allows us to investigate the effects of latency and time delays in cl-DSB systems. This makes the suggested controller a prominent option for real-time applications where the presence of time delay can threaten the system’s stability. Extensive simulation scenarios under various levels of random communication delays are considered to assess the feasibility of suggested stimulators for wireless cl-DSB systems. Comparative analysis of cl-DBS’s responses reveals the superiority of the suggested FOMPC controller (designed by deep-on-policy learning) to regulate hand tremors over other state-of-the-art strategies.

Digital transformation of warehousing is revolutionizing operations by integrating advanced technologies. Automated Guided Vehicles (AGVs) optimize the movement of goods, while Internet of Things (IoT) enhances connectivity and data collection for improved decision-making. Robots automate tasks like picking and packing, increasing efficiency and reducing errors. Blockchain technology enhances transparency and traceability in the supply chain, building trust and reducing fraud. Digital twins create virtual replicas of warehouses for simulation and optimization, enabling proactive problem-solving and improved resource allocation. Such transformation addresses the increasing complexity and dynamic nature of warehousing in the modern marketplace. This systematic literature review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, investigates the role of optimization models in this digital shift from 2010 to 2023. The findings highlight the crucial enhancements and practical implications brought about by these technologies, showcasing their impact on streamlining operations. These improvements include advancements in path planning, task allocation, inventory management and storage assignment which significantly enhance operational effectiveness within warehousing. The results demonstrate a 25-30% increase in perishable food freshness, a 10% reduction in picking time, a 14.8% increase in space utilization, and a 9.95% decrease in total costs. Moreover, the findings call attention to the insufficient analysis on the economic implications of digital transformations in warehousing, notably in cost reductions and Return on Investment (ROI) evaluations which are crucial metrics for evaluating the financial success and wider benefits of digital transformation initiatives, such as customer satisfaction and revenue growth. The review also identifies critical research gaps, including the need to incorporate sustainability into optimization models, strategies for adapting to dynamic environments, the exploration of collaborative robots (cobots), and the optimization of reverse logistics and returns. This review provides a comprehensive foundation for future research and emphasizes the potential of digitalization to revolutionize warehousing practices through innovative optimization strategies and real-world applications.

Quadrotor unmanned aerial vehicles (QUAVs) are widely recognized for their versatility and advantages across diverse applications. However, their inherent instability and underactuated dynamics pose significant challenges, particularly under external disturbances and parametric model uncertainties. This paper presents an advanced observer-based control framework to address these challenges, introducing a high-gain disturbance observer (HGDO) integrated with a neural-network-based adaptive fractional sliding mode control (NN-AFSMC) scheme. The proposed HGDO-NN-AFSMC ensures robust position and attitude tracking by effectively compensating for external disturbances and model uncertainties. A direct control approach is employed, significantly reducing computational complexity by minimizing the need for frequent online parameter updates while maintaining high tracking precision and robustness. The stability of the control system is rigorously analyzed using Lyapunov theory, and comprehensive simulation studies validate the proposed scheme’s superior performance compared to other advanced control approaches, particularly in dynamic and uncertain operational environments. The proposed HGDO-NN-AFSMC achieves a position tracking error of less than 0.03 m and an attitude tracking error below 0.02 radians, even under external disturbances and parametric uncertainties of 20%. Compared to conventional robust feedback linearization (RFBL) and nonsingular fast terminal sliding mode control (NFTSMC), the proposed method improves position tracking accuracy by 25% and reduces settling time by approximately 18%.

Arsenic contamination of water endangers the health of millions of people worldwide, affecting certain countries and regions with especial severity. Interest in the use of Fe-based metal organic frameworks (MOFs) to remove inorganic arsenic species has increased due to their stability and adsorptive properties. In this study, the performance of a synthesized Nano-{Fe-BTC} MOF, containing iron oxide octahedral chains connected by trimesic acid linkers, in adsorbing As(III) and As(V) species was investigated and compared with commercial Basolite®F300 MOF. Despite their similarities in composition, they exhibit distinct structural characteristics in their porosity, pore size, and surface areas, which affected the adsorption processes. The kinetic data of the adsorption of As(III) and As(V) by both Fe-MOFs fitted the pseudo second-order model well, with the kinetic constant being higher for Basolite®F300 given its higher porosity. Intraparticle diffusion was, in both cases, the rate controlling step with the contribution of film diffusion in the adsorption processes, which achieved equilibrium after 1 h. The maximum adsorption capacity for As(V), 41.66 mg g−1, was obtained with Basolite®F300 at the 6.5–10 pH range, whereas Nano-{Fe-BTC} showed a different behaviour as maximum adsorption (14.99 mg g−1) was obtained at pH 2. However, both adsorbents exhibited the same performance for As(III) adsorption, which is not adsorbed at pH < 9. The Langmuir adsorption isotherm model fitted well for As(III) and As(V) adsorption by Nano-{Fe-BTC} and As(III) by Basolite®F300, whereas the Freundlich model fitted best for As(V) given its superior structural properties.

The primary objective of this study was to investigate the pulmonary damage resulting from isolated or combined exposure to inhaled toluene (300 ppm) and noise 85 dB (A), with a focus on evaluating the potential protective effects of Olea europaea L. leaf extract (OLE). Forty-eight male Wistar rats were divided into eight groups: control (C), OLE treatment (O), noise exposure (N), noise exposure with OLE treatment (N+OLE), toluene exposure (T), toluene exposure with OLE treatment (T + OLE), co-exposure to toluene and noise (NT), and co-exposure with OLE treatment (NT + OLE). OLE (40 mg/kg/day) was administered daily for six weeks via oral gavage. Exposure to toluene and noise resulted in significant disruption of the pulmonary tissue structure, accompanied by oxidative stress, as evidenced by increased lipid peroxidation, diminished catalase and superoxide dismutase activities, and elevated pro-inflammatory cytokines IL6, IL-β, and TNF-α. Notably, the administration of OLE effectively mitigated oxidative stress and inflammation and preserved pulmonary histology. In conclusion, exposure to toluene and its combination with noise significantly elevated oxidative stress, inflammatory responses, and histological disruptions in the lung tissue. In contrast, noise exposure alone is characterized by minimal effects, although it is still associated with an inflammatory response. Notably, Olea europaea L. leaf extract (OLE) exhibits a substantial protective role, effectively mitigating the adverse effects of combined exposure and highlighting its potential as a therapeutic agent for lung health.

The use of series shunt compensators along with wind energy in power systems has increased significantly. One of the main disadvantages of wind energy and series compensation is the phenomenon of sub-synchronous amplification. In this paper, a control method with fuzzy system based on wide area measurement systems (WAMS) and considering time delay in order to increase the damping of sub-synchronous oscillations in compensated networks with capacitive compensators and wind turbines. The DFIG type is provided in the GSC converter’s voltage control loop for the system’s stable operation. The simulation results using MATLAB/Simulink software on Modified IEEE First Bench Mark test networks show the effectiveness of the proposed method in damping sub-synchronous oscillations in power systems compensated with series capacitors.

In this work, we present two advanced models for identifying script writers, leveraging the power of deep learning. The proposed systems utilize the new vision Swin Transformer and ResNeSt-50. Swin Transformer is known for its robustness to variations and ability to model long-range dependencies, which helps capture context and make robust predictions. Through extensive training on large datasets of handwritten text samples, the Swin Transformer operates on sequences of image patches and learns to establish a robust representation of each writer’s unique style. On the other hand, ResNeSt-50 (Residual Neural Network with Squeeze-and-Excitation (SE) and Next Stage modules), with its multiple layers, helps in learning complex representations of a writer’s unique style and distinguishing between different writing styles with high precision. The SE module within ResNeSt helps the model focus on distinctive handwriting characteristics and reduce noise. The experimental results demonstrate exceptional performance, achieving an accuracy of 98.50% (at patch level) by the Swin Transformer on the CVL database, which consists of images with cursively handwritten German and English texts, and an accuracy of 96.61% (at page level) by ResNeSt-50 on the same database. This research advances writer identification by showcasing the effectiveness of the Swin Transformer and ResNeSt-50. The achieved accuracy underscores the potential of these models to process and understand complex handwriting effectively.

Background/objectives: Sewing machine operators (SMO) are the most likely workers to experience a high prevalence of musculoskeletal disorders in the textile, clothing, and footwear industries. We conducted a cross-sectional and exhaustive study among SMO working in the leather and footwear industry to describe the prevalence of multi-site musculoskeletal symptoms (MMS) and evaluate factors associated with their occurrence. Methods: Musculoskeletal symptoms declared by these operators were assessed through the modified Nordic questionnaire. The psychosocial work environment was assessed using the Karasek model. The variables associated with MMS were issued from binary logistic regression and decision tree using R software. Results: Of 145 operators, 65.5 % of men and 72.4 % of women had MMS. Based on binary logistic regression, a history of musculoskeletal disorders (MSDs) increased the risk of developing MMS by 8 folds. The binary decision tree identified five main nodes: history of MSDs, professional seniority, often finding the pace of work restrictive and male gender. Conclusion: Identifying homogeneous profiles of MMS's occurrence will help the implementation of an effective and targeted preventive strategy.

BackgroundIn Tunisia, the number of cardiac implantable electronic devices (CIEDs) is increasing, owing to the increase in patient life expectancy and expanding indications. Despite their life-saving potential and a significant reduction in population morbidity and mortality, their increased numbers have been associated with the development of multiple early and late complications related to vascular access, pockets, leads, or patient characteristics. ObjectiveThe study aims to identify the rate, type, and predictors of complications occurring within the first year after CIED implantation. It also aims to describe the demographic and epidemiological characteristics of a nationwide sample of patients with CIED in Tunisia. Additionally, the study will evaluate the extent to which Tunisian electrophysiologists follow international guidelines for cardiac pacing and sudden cardiac death prevention. MethodsThe Tunisian National Study of Cardiac Implantable Electronic Devices (NATURE-CIED) is a national, multicenter, prospectively monitored study that includes consecutive patients who underwent primary CIED implantation, generator replacement, and upgrade procedure. Patients were enrolled between January 18, 2021, and February 18, 2022, at all Tunisian public and private CIED implantation centers that agreed to participate in the study. All enrolled patients entered a 1-year follow-up period, with 4 consecutive visits at 1, 3, 6, and 12 months after CIED implantation. The collected data are recorded electronically on the clinical suite platform (DACIMA Clinical Suite). ResultsThe study started on January 18, 2021, and concluded on February 18, 2023. In total, 27 cardiologists actively participated in data collection. Over this period, 1500 patients were enrolled in the study consecutively. The mean age of the patients was 70.1 (SD 15.2) years, with a sex ratio of 1:15. Nine hundred (60%) patients were from the public sector, while 600 (40%) patients were from the private sector. A total of 1298 (86.3%) patients received a conventional pacemaker and 75 (5%) patients received a biventricular pacemaker (CRT-P). Implantable cardioverter defibrillators were implanted in 127 (8.5%) patients. Of these patients, 45 (3%) underwent CRT-D implantation. ConclusionsThis study will establish the most extensive contemporary longitudinal cohort of patients undergoing CIED implantation in Tunisia, presenting a significant opportunity for real-world clinical epidemiology. It will address a crucial gap in the management of patients during the perioperative phase and follow-up, enabling the identification of individuals at particularly high risk of complications for optimal care. Trial RegistrationClinicalTrials.gov NCT05361759; https://classic.clinicaltrials.gov/ct2/show/NCT05361759 International Registered Report Identifier (IRRID)RR1-10.2196/47525

Abstract Unlike for advanced liver fibrosis, the practical rules for the early non-invasive diagnosis of cirrhosis in NAFLD remain not well defined. Here, we report the derivation and validation of a stepwise diagnostic algorithm in 1568 patients with NAFLD and liver biopsy coming from four independent cohorts. The study algorithm, using first the elastography-based tests Agile3+ and Agile4 and then the specialized blood tests FibroMeterV3G and CirrhoMeterV3G, provides stratification in four groups, the last of which is enriched in cirrhosis (71% prevalence in the validation set). A risk prediction chart is also derived to allow estimation of the individual probability of cirrhosis. The predicted risk shows excellent calibration in the validation set, and mean difference with perfect prediction is only −2.9%. These tools improve the personalized non-invasive diagnosis of cirrhosis in NAFLD.

Abstract Diabetes and hypertension are a serious public health problem worldwide. In the last decades, prevalence of these two metabolic diseases has dramatically increased in the Middle East and North Africa region, especially in Tunisia. This study aimed to determine the prevalence of type 2 diabetes (T2D) and High Blood Pressure (HBP) in Zaghouan, a North-East region of Tunisia. To this end, an exploratory study with stratified random sampling of 420 participants has been carried out. Various data were collected. Blood samples and urine were drawn for biochemical assay. Then, all data were analyzed using the statistical R software. Results showed an alarming situation with an inter-regional difference in prevalence of obesity (50.0%, CI 95.0%), HBP (39.0%, CI 95.0%) and T2D (32.0%, CI 95.0%). This study allowed the discovery of 24, 17 and 2 new cases of T2D, HBP and T2D&HBP respectively. The association of some socio-economic factors and biochemical parameters with these chronic diseases has been highlighted. To conclude, the health situation in the governorate of Zaghouan requires urgent interventions to better manage the growing epidemic of non-communicable diseases (NCD) in the region. This study demonstrated the importance of engaging health policy makers in road mapping and implementing national NCD prevention programs.

Minimizing job waiting time for completing related operations is a critical objective in industries such as chemical and food production, where efficient planning and production scheduling are paramount. Addressing the complex nature of flow shop scheduling problems, which pose significant challenges in the manufacturing process due to the vast solution space, this research employs a novel multiobjective genetic algorithm called distance from ideal point in genetic algorithm (DIPGA) to identify Pareto-optimal solutions. The effectiveness of the proposed algorithm is benchmarked against other powerful methods, namely, NSGA, MOGA, NSGA-II, WBGA, PAES, GWO, PSO, and ACO, using analysis of variance (ANOVA). The results demonstrate that the new approach significantly improves decision-making by evaluating a broader range of solutions, offering faster convergence and higher efficiency for large-scale scheduling problems with numerous jobs. This innovative method provides a comprehensive listing of Pareto-optimal solutions for minimizing makespan and total waiting time, showcasing its superiority in addressing highly complex problems.

Background:: Examining functions and characteristics of DNA sequences is a highly challenging task. When it comes to the human genome, which is made up of exons and introns, this task is more challenging. Human exons and introns contain millions to billions of nucleotides, which contributes to the complexity observed in this sequences. Considering how complicated the subject of genomics is, it is obvious that using signal processing techniques and deep learning tools to build a strong predictive model can be very helpful for the development of the research of the human genome. Results:: After representing human exons and introns with color images using Frequency Chaos Game Representation, two pre-trained convolutional neural network models (Resnet-50 and GoogleNet) and a proposed CNN model having 13 hidden layers were used to classify our obtained images. We have reached a value of 92% for the accuracy rate for Resnet-50 model in about 7 h for the execution time, a value of 91.5% for the accuracy rate for the GoogleNet model in 2 h and a half for the execution time. For our proposed CNN model, we have reached 91.6% for the accuracy rate in 2 h and 37 min. Conclusions:: Our proposed CNN model is faster than the Resnet-50 model in terms of execution time. It was able to slightly exceed the GoogleNet model for the accuracy rate value.

This paper proposes a barrier function-based adaptive control strategy with a nonlinear sliding surface for a class of uncertain dynamic systems with input saturation. Its main objectives are to ensure robustness to parametric and non-parametric uncertainties whilst eliminating the reaching phase. A global nonlinear switching surface is defined and an adaptive barrier function sliding mode control approach is derived. The barrier strategy is adopted to ensure the convergence of the states to the desired values without overestimating the control gains. System stability is analyzed using the Lyapunov stability theory. The control parameters are obtained using the linear matrix inequality (LMI) approach. The control law is designed to remove the reaching phase. The effectiveness of the proposed approach was validated using a simulation study. The proposed control approach can easily be implemented to various nonlinear uncertain systems.

Abstract In this study, a novel method based on a robust adaptive fuzzy control approach is developed for nonlinear teleoperation systems. Its main objectives are to ensure system stability and properly mitigating parametric uncertainties stemming from external disturbances and un‐modelled dynamics. For the communication channel, instead of the direct transmission of environmental torque signals, the approximated environmental parameters by the fuzzy system are transmitted to the master side for the prediction of environmental torque, thus successfully avoiding the transmission of the power signals in the delayed communication channel and solving the passivity problem in the teleoperation system. Besides, a trajectory generator is employed in the master side, whereas a trajectory smoothing is provided in the slave side. Theoretically, it was proven that both position tracking and force feedback problems are attained. Using Lyapunov stability analysis, this work illustrates that the robust adaptive fuzzy controller based on the backstepping approach guarantees the system's asymptotic stability. Simulation results confirm the efficiency of the suggested control technique in achieving the stability and tracking objectives of the uncertain nonlinear teleoperation system.

INTRODUCTION: Disorders of sexual development (DSD) are a heterogeneous group of genital defects affecting chromosomal, gonadal and anatomical sex. 46,XY DSD is a subset of DSD which covers a wide range of phenotypes in which 46,XY gonadal dysgenesis (GD) is the most severe form. In this study, we report on the clinical and molecular cytogenetic findings of a study on a Tunisian girl with the syndromic form of 46,XY DSD. METHODS: This case was a phenotypic female patient having several congenital anomalies including growth retardation. Karyotype, fluorescence in situ hybridization and array Comparative Genome Hybridization (array CGH) were performed. RESULTS: The proband exhibited a de-novo 46,X,der(Y) karyotype. Array CGH revealed a pathogenic 27.5Mb gain of an Xp21.2 chromosome segment leading to Xp functional disomy. No deletion was observed in the Y-chromosome. The duplicated region encompassed the NR0B1 (DAX1) and MAGEB genes, located within the dosage sensitive sex (DSS) reversal locus, known as promote genes responsible for human sex reversal and testis repression. The extra-dosage and interactions of these genes with different specific genes could result in the impairment of the male sex pathway. Over-dosage of KAL1 and IL1RAPL1 genes fall within the somatic features observed in the patient. DISCUSSION AND CONCLUSION: To the best of our knowledge, we report on the fourth case of Xp21.2-pter duplication within Xp;Yp translocation associated with XY GD. Our findings suggest that when duplicated, the NR0B1 and MAGEB genes could be a major cause of XY GD. Therefore, we emphasize the usefulness of a combined cytogenetic approach in order to provide an accurate genetic diagnosis for those patients having syndromic XY DSD in a clinical setting.

الملخص: اهداف البحث: تعتبر الساركوما خلف الصفاق من اورام اللحمة المتوسطة النادرة. تهدف هذه الدراسة الى مناقشة مختلف الجوانب السريرية العلاجية، والإنذارية للساركوما خلف الصفاق التي تم علاجها في مؤسستنا. طرق البحث: تم اجراء دراسة مرجعية في قسم الأورام الطبية المستشفى الجامعي مستشفى جامعة الحبيب بورقيبة في صفاقس، تضمنت ۱٩ مريضا عولجوا من الساركوما خلف الصفاق بين عامي ۱٩٩٩ و٢٠۱٦ النتائج: كان متوسط العمر ٤٩ عاما (يتراوح بين ۱٨-٨۳). لوحظ ان غالبيتهم من الإناث (٦٨٫٤٪). كانت الأعراض الأكثر شيوالما البطن (٨٨٪). كان متوسط حجم الورم ۱٥ سم (٤-۳٠ سم). تم الاستئصال الكامل في خمس حالات فقط (٢٦٫۳٪). كانت الأنواع الفرعية النسيجية الاكثر شيوعا الساركوما الشحمية (٤٧٫٤٪) والساركوما العضلية (٢٦٫۳٪). كان لدى ثمانية من المرضى ورم عالي الدرجة (الدرجة ٢=٢ أو الدرجة ۳=٦). تم إعطاء العلاج الإشعاعي المساعد ٥ من المرضى (٢٦٪). تم علاج سبعة عشر مريضا بالعلاج الكيميائي، ستة منهم اخذوه في وضع مساعد، ثلاثة كعلاج جديد، وثمانية في المرحلة التلطيفية. لوحظ عودة المرض لدى ٥٨٪ من الحالات. في جميع المرضى، كانت النجاة بشكل عام ٨٩٫٥٪ في العام الأول و٤٠٫۳٪ بعد خمسة أعوام. كانت عوامل النذير التي أثرت على النجاة بشكل عام نوع الجنس، حدود الاستئصال، عودة المرض، والعلاج الإشعاعي. في التحليل متعدد المتغيرات، كان العلاج الإشعاعي ونوع النسيج الفرعي عوامل مستقلة تؤثر على النجاة بشكل عام والنجاة خاليا من المرض على التوالي الاستنتاجات: في هذه الدراسة، تبين أن علاج الساركوما خلف الصفاق يعتمد على الجراحة مع الاستئصال التام. العوامل الأخرى مثل العلاج الإشعاعي ومعاودة المرض لها تأثير على النجاة بشكل عام. لتسهيل الجراحة والحصول على حدود استئصال سالبة، يفضل العلاج الإشعاعي قبل الجراحة على مرضى معينين لديهم خطورة عالية للانتكاس. المزيد من المحاولات المستقبلية لها ما يبررها لاختيار العلاجات المثلى بأقل سمية وأفضل فعالية لتقليل عودة المرض، بشكل رئيسي موضعي Abstract: Objectives: Retroperitoneal sarcomas (RPSs) are rare mesenchymal tumors. The objective of this study was to discuss the different clinical, therapeutic and prognostic aspects of RPS in our institution. Methods: This was a retrospective study conducted at the Department of Medical Oncology in the Habib Bourguiba University Hospital in Sfax, including 19 patients who were treated for RPSs between 1999 and 2016. Results: The median age was 49 years (range: 18–83 years); 68.4% of the patients were female. The commonest symptom was abdominal pain (88%) and the median tumor size was 15 cm (range: 4–30 cm). Complete resection was achieved in only five cases (26.3%). The most common histological subtypes were liposarcoma (47.4%) and leiomyosarcoma (26.3%). Eight patients had a high-grade tumor (G2 = 2 or G3 = 6). Adjuvant radiotherapy was administered in 5 patients (26%). Seventeen patients were treated with chemotherapy; six received chemotherapy in an adjuvant treatment, three as a neoadjuvant treatment, and eight were treated during the palliative phase. Relapse was observed in 58% of cases. For all patients, the overall survival (OS) was 89.5% at 1 year and 40.3% at 5 years. Prognostic factors influencing OS were sex (p = 0.037), resection margins (p = 0.02), recurrence (p = 0.042), and radiotherapy (p = 0.023). In multivariate analysis, radiotherapy (p = 0.031) and histological subtype (p = 0.028) were independent factors influencing OS and disease-free survival (DFS) respectively. Conclusion: We showed that the treatment of RPSs relies on surgery with complete resection. Other factors, such as radiotherapy and the occurrence of relapse, also have an impact on OS. To facilitate surgery and to obtain negative resection margins, preoperative radiotherapy is indicated in selected patients with a high risk of relapse. Further prospective trials are warranted to select optimal therapies with less toxicity and better efficacy in reducing recurrences, mainly at a local level.

The public’s health depends on a reliable drug supply chain. Recently, the number of drug counterfeit has increased drastically, resulting in thousands of victims suffering from poisoning and/or treatment failures, which have resulted in new expectations on drug supply chain traceability. Indeed, the drug supply chain involves many parties having heterogeneous interests and are usually reluctant to share traceability data with each other. Furthermore, existing traceability and provenance systems for drug supply chains suffer from separated data storage, lack of information sharing transparency, and trust. Decentralized blockchain-based solutions are advocated to address these limitations by realizing decentralized trustless systems. In this work, we present a fully decentralized, blockchain-based drug traceability solution, worthy of seamless integrating IoT devices throughout the chain. It uses both smart contracts and decentralized off-chain storage to remove the need for middleman and to provide trusted, secure and immutable transaction history. Moreover, to preserve the traceability of drugs, it ensures and enforces data provenance and data integrity in the proposed IoT environment by using blockchain Non-Fungible Tokens (NFTs). We give a test and validation of the approach’s effectiveness in enhancing drug traceability supply chains, as well as an analysis of the approach’s costs and security.

Abstract Background Forty-six ,XY Differences/Disorders of Sex Development (DSD) are characterized by a broad phenotypic spectrum ranging from typical female to male with undervirilized external genitalia, or more rarely testicular regression with a typical male phenotype. Despite progress in the genetic diagnosis of DSD, most 46,XY DSD cases remain idiopathic. Methods To determine the genetic causes of 46,XY DSD, we studied 165 patients of Tunisian ancestry, who presented a wide range of DSD phenotypes. Karyotyping, candidate gene sequencing, and whole-exome sequencing (WES) were performed. Results Cytogenetic abnormalities, including a high frequency of sex chromosomal anomalies (85.4%), explained the phenotype in 30.9% (51/165) of the cohort. Sanger sequencing of candidate genes identified a novel pathogenic variant in the SRY gene in a patient with 46,XY gonadal dysgenesis. An exome screen of a sub-group of 44 patients with 46,XY DSD revealed pathogenic or likely pathogenic variants in 38.6% (17/44) of patients. Conclusion Rare or novel pathogenic variants were identified in the AR, SRD5A2, ZNRF3, SOX8, SOX9 and HHAT genes. Overall our data indicate a genetic diagnosis rate of 41.2% (68/165) in the group of 46,XY DSD.

Micro-Grid (MG), a paradigm shift in conventional distribution power systems, facilitates the integration of many Renewable Energy Resources (RERs), storage units, and loads. The key catalyst behind this emerging paradigm is the increased attention to environment-energy sustainability nexus. This novel concept exhibits various attractive features such as sustainability, reliability, resilience, improved power quality, energy security, and liberalization of electric service industries. However, the integration of RER units and load participation into the MG brings various challenges to the stability and operation of the system. These challenges arise as a consequence of the intermittent nature of RERs due to their stochastic behavior and increased level of non-linearity associated with smart load participation. Furthermore, in recent years, the development and deployment of RER in MG networks have demonstrated exponential growth. Therefore, in order to achieve a holistic analysis, a comprehensive review study about various aspects of MG should be investigated. In this regard, this rigorous survey paper presents the meticulous study of various aspects, historical evolution, and key enabling yet transdisciplinary technologies of MG, such as various components, generation resources, load classification, communication infrastructure, energy management, control and optimization, operational modes, and various frameworks, configurations, architectures, and topologies-including the emerging concept of Networked MG with flexible boundaries. This study also reviewed various storage and protection systems in MG, considering the attention to their contributions to the stability of the system. This review also underscores many key issues, challenges, and factors related to the sustainable development of the MG system. Lastly, an all-inclusive cross-sectoral analysis that includes cyber-physical systems, power quality, information and data management, conversion systems, synthetic inertia, and some governance issues has been provided, along with the future directions, progression, and latest development in the field of MG. This survey, therefore, greatly assists and enables researchers to study and analyze the development and prospect of MG technology conveniently.

Introduction: Monogenic diabetes (MD) accounts for 3%–6% of all cases of diabetes. This prevalence is underestimated due to its overlapping clinical features with type 1 and type 2 diabetes. Hence, genetic testing is the most appropriate tool for obtaining an accurate diagnosis. In Tunisia, few cohorts of MD have been investigated until now. The aim of this study is to search for pathogenic variants among 11 patients suspected of having MD in Tunisia using whole-exome sequencing (WES).Materials and methods: WES was performed in 11 diabetic patients recruited from a collaborating medical center. The pathogenicity of genetic variation was assessed using combined filtering and bioinformatics prediction tools. The online ORVAL tool was used to predict the likelihood of combinations of pathogenic variations. Then, Sanger sequencing was carried out to confirm likely pathogenic predicted variants among patients and to check for familial segregation. Finally, for some variants, we performed structural modeling to study their impact on protein function.Results: We identified novel variants related to MD in Tunisia. Pathogenic variants are located in several MODY and non-MODY genes. We highlighted the presence of syndromic forms of diabetes, including the Bardet–Biedl syndrome, Alström syndrome, and severe insulin resistance, as well as the presence of isolated diabetes with significantly reduced penetrance for Wolfram syndrome-related features. Idiopathic type 1 diabetes was also identified in one patient.Conclusion: In this study, we emphasized the importance of genetic screening for MD in patients with a familial history of diabetes, mainly among admixed and under-represented populations living in low- and middle-income countries. An accurate diagnosis with molecular investigation of MD may improve the therapeutic choice for better management of patients and their families. Additional research and rigorous investigations are required to better understand the physiopathological mechanisms of MD and implement efficient therapies that take into account genomic context and other related factors.

IntroductionType 2 diabetes (T2D) is a multifactorial disease involving genetic and environmental components. Several genome-wide association studies (GWAS) have been conducted to decipher potential genetic aberrations promoting the onset of this metabolic disorder. These GWAS have identified over 400 associated variants, mostly in the intronic or intergenic regions. Recently, a growing number of exome genotyping or exome sequencing experiments have identified coding variants associated with T2D. Such studies were mainly conducted in European populations, and the few candidate-gene replication studies in North African populations revealed inconsistent results. In the present study, we aimed to discover the coding genetic etiology of T2D in the Tunisian population.MethodsWe carried out a pilot Exome Wide Association Study (EWAS) on 50 Tunisian individuals. Single variant analysis was performed as implemented in PLINK on potentially deleterious coding variants. Subsequently, we applied gene-based and gene-set analyses using MAGMA software to identify genes and pathways associated with T2D. Potential signals were further replicated in an existing large in-silico dataset, involving up to 177116 European individuals.ResultsOur analysis revealed, for the first time, promising associations between T2D and variations in MYORG gene, implicated in the skeletal muscle fiber development. Gene-set analysis identified two candidate pathways having nominal associations with T2D in our study samples, namely the positive regulation of neuron apoptotic process and the regulation of mucus secretion. These two pathways are implicated in the neurogenerative alterations and in the inflammatory mechanisms of metabolic diseases. In addition, replication analysis revealed nominal associations of the regulation of beta-cell development and the regulation of peptidase activity pathways with T2D, both in the Tunisian subjects and in the European in-silico dataset.ConclusionsThe present study is the first EWAS to investigate the impact of single genetic variants and their aggregate effects on T2D risk in Africa. The promising disease markers, revealed by our pilot EWAS, will promote the understanding of the T2D pathophysiology in North Africa as well as the discovery of potential treatments.

Airway management in neonates is difficult because of the risk of rapid hypoxia. It presents a challenge even for an experienced anesthesiologist. Oral tumors in neonates can obstruct the airway or feeding problems in the newborn. Surgical excision is the treatment of choice but these tumors can seriously worsen the conditions of intubation. To surmount these difficulties, a particular multidisciplinary approach and special precautions are needed. We describe the airway management and precautions taken in the anesthesia for surgical removal of a case of large congenital palate teratoma associated with a wide cleft palate in a 25-day-old girl. Impossible intubation was predicted on magnetic resonance imaging. The difficult airway management cart as well as an otorhinolaryngologist skilled in performing emergency tracheostomies in neonates were available. The patient was intubated by conventional laryngoscopy under sevoflurane inhalation anesthesia. The tumor was successfully resected. This case poses a challenge for managing the airway because of the possibility of obstruction of the airway and the difficulty of the airway that radiological exams have allowed us. So, a multidisciplinary team effort is needed for successful neonatal airway management.