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Security in the Internet of Things (IoT)-consumer electronics is decisive in safeguarding connected devices from possible vulnerabilities and threats. Strong security measures are required to protect against unauthorized access, data breaches, and cyberattacks as these smart devices gather, transfer, and save sensitive information. Employing regular software updates, secure authentication protocols, and strong encryption are indispensable approaches to guarantee the integrity and privacy of user details. Drones offer users a bird's-eye view that can be started and implemented anywhere and anytime. But, the malicious use of drones has developed among criminals and cyber-criminals. The possibility and frequency of these attacks are maximum, and their effect is highly unsafe and devastating. Thus, the desire for protective, preventive counter-measures and detective are needed. Intrusion detection utilizing deep learning (DL) drones control advanced neural network (NN) structures to improve security surveillance in dynamic outdoor environments. Prepared with sophisticated sensors and onboard processing abilities, these drones autonomously examine aerial imagery to identify and classify possible risks like suspicious activities, unauthorized personnel, or vehicles. DL approaches allow drones to learn complex patterns and anomalies in real-time, enabling quick response and proactive security procedures. This study introduces an enhanced Mathematical Modeling-based Blockchain with Mountain Gazelle Optimization and Attention to Deep Learning for Cybersecurity (MGOADL-CS) technique in the drone's platform. The MGOADL-CS method aims to improve cybersecurity using BC technology in the drone's environment by detecting attacks using optimal DL models. In the initial stage, the MGOADL-CS technique uses a linear scaling normalization (LSN) approach to normalize the input data. The MGOADL-CS technique uses an improved tunicate swarm algorithm (ITSA) based feature selection approach for dimensionality reduction. Besides, the attention long short-term memory neural network (ALSTM-NN) model is employed to detect and classify cyberattacks. Finally, the MGO-based hyperparameter tuning process is performed to adjust the hyperparameter values of the ALSTM-NN model. To highlight the enhanced attack detection results of the MGOADL-CS technique, a detailed simulation set is accomplished under the NSL dataset. The performance validation of the MGOADL-CS method portrayed a superior accuracy value of 99.71 % over existing approaches.

Background: Prevalence of seropositivity following SARS-CoV-2 infection is vital in evaluating herd immunity. However, depending on illness severity, it remains unclear whether the breadth and magnitude of immune response to SARS-CoV-2 infection is for short or long term. Objective: To test the persistence of humoral antibody responses after SARS-CoV-2 exposure in patients with different illness severity and among volunteers who had been vaccinated. Methods: This study was conducted in two Saudi Arabian tertiary hospitals. Participants were categorized as critically ill COVID-19 patients, non-critically ill COVID-19 patients, or vaccinated volunteers. We collected demographic data, COVID-19 exposure history, symptoms, vaccination details, and serum samples to analyze antibody persistence. We evaluated SARS-CoV-2 antibody concentrations in COVID-19 patients with varying disease severity and age groups, as well as in BNT162b2-vaccinated individuals, focusing on IgG levels against the S.FL and S1 domains of the spike protein. Results: The study included 172 adults: 92 unvaccinated hospitalized COVID-19 patients and 80 vaccinated volunteers. All vaccinated subjects demonstrated seropositivity to the SARS-CoV-2 spike protein, with nearly 80% having a median antibody titer of 13,500 AU/mL. Notably, vaccinated subjects exhibited significantly higher IgG levels than naturally infected patients (P < 0.001), including higher S.FL and S1 titers, regardless of severity. Age, comorbidities, and previous infections influenced S-specific antibody levels. Among hospitalized patients, 58% required intensive care, with 28- and 90-day mortality rates of 23% and 43%, respectively. Conclusion: These findings shed light on the immune response dynamics following SARS-CoV-2 infection compared to vaccinated individuals, where the latter showed significantly higher level of antibodies response, providing crucial insights for evaluating short-term herd immunity and the effectiveness of natural infection-induced immunity.

هدف البحث الى إعداد منهج تعليمي باستعمال التعليم المصغر في تطوير مهاره الرمي بالمسدس من الوقوف والبروك لدى طلاب المرحلة الثالثة في كلية الشرطة ,اعتمد الباحثان المنهج التجريبي لملائمته طبيعة مشكلة البحث إذ إن المنهج التجريبي يعطي نتائج حقيقية ملموسة ومثل مجتمع البحث طلاب كلية الشرطة المرحة الثالثة للعام الدراسي (2023-2024) وكان عدد العينة (20) طالب للمجموعتين التجريبية والضابطة واستنتجنا ان التدريس المصغر له تأثير فعال في تطوير المهارات الرمي بالمسدس من الوقوف والروك قيد الدراسة للمجموعة التجريبية والمجموعة الضابطة باختلاف المستويات وعلية تم التوصية الحث على استعمال التدريس المصغر واعطاء مهارات التدريس لكونها تعمل على الاختصار بالوقت والجهد المبذول.

Microgrid protection and ground fault management are critical aspects of modern power distribution systems, especially with the increasing integration of Distributed Generators (DGs) such as renewable energy sources. Effective protection schemes are essential to ensure the reliability, safety, and resilience of microgrids under various fault conditions. This study addresses a new advancement in microgrid protection and ground fault management. Firstly, the research integrates zero sequence components into the time-inverse characteristics of phase Overcurrent Relays (OCR) and creates a dynamic scheme between two group settings for phase and ground faults. This enhancement improves ground fault detection and provides robust backup for ground OCR, thereby enhancing the overall reliability of microgrid protection schemes. Secondly, the study demonstrates the use of Configurable Function Blocks (CFCs) in digital relays to dynamically adjust relay settings based on zero sequence current detection. This functionality optimizes relay performance under varying fault conditions, addressing mis-coordination issues in low-value ground fault scenarios at traditional OCR scheme and improving fault detection and clearance times. The proposed strategy is extensively validated through Hardware-in-the-Loop (HIL) testing, ensuring its feasibility and effectiveness in real-world scenarios. HIL testing confirms the practical applicability and robustness of the proposed protection scheme, enhancing its reliability. Finally, the study provides a comprehensive framework for the implementation of the proposed protection strategy in real-case protective relays. It includes a detailed methodology and validation process, offering practical guidance for operators to implement and optimize microgrid protection systems.

Abstract Background/aim Conditioned media derived from Mesenchymal stem cells (MSC-CM) was suggested as a promising alternative cell-free regenerative therapy. It is hypothesized that the synergistic effect of MSC-CM with anticancer drugs may improve their antiproliferative and antimetastatic effects against cancer cells. Herein, the MSC-CM was impregnated with Wortmannin, a pan-PI3K/Akt/mTOR inhibitor, and their combined effect was investigated against breast cancer cells. Materials and methods To explore this, the cytotoxic, apoptotic, and autophagic potentials were assessed in luminal-A breast cancer cells (MCF-7). Results We found that incubation of MCF-7 to Wort-containing-CM induced apoptosis- and autophagy-mediated cell death, meanwhile prolonged exposure caused massive necrotic cell death. The involvement of MSC-CM effectively reduced Wortmannin IC50 observed in Wort-treated cells. Also, Wort-loaded-CM induced nuclear DNA fragmentation and reduced in vitro cell migration. These findings were associated with a Wort-dependent reduction in cell viability, the formation of the phosphorylated Akt and mTOR proteins, reduced the expression of mRNA, and downregulated the expression of the catalytic domain of phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K-Ca). Conclusion These findings revealed the promising antiproliferative and antimetastasis effects of combining pan-PI3K/Akt/mTOR inhibitors with MSC-derived-CM in breast cancer via the downregulation of PI3K/AKT/mTOR signaling pathways. Further studies are required to validate this chem-regenerative strategy in cancer treatment.

Abstract To address the challenges of global warming and the greenhouse effect, extensive research has been dedicated to microgrids (MGs) powered by renewable energy sources (RESs). This paper presents an innovative control mechanism, the synergetic simplified super-twisting algorithm (SSSTA), designed specifically for a DC-MG incorporating a battery energy storage system (BESS), a solar photovoltaic (PV) unit, and DC loads. The PV system connects to a shared DC bus via a unidirectional DC–DC boost converter, optimized for maximum power point tracking from the PV generator. At the same time, the BESS is linked using a bidirectional DC–DC buck-boost converter to the same bus, aimed at maintaining supply–demand balance within the DC-MG through charging and discharging. The SSSTA is designed to regulate each power control unit in the MG. It ensures the desired voltage level at the common DC bus while tailoring energy allocation to meet load requirements. The study shows that SSSTA improves the performance and stability of DC-MG systems incorporating solar PV and batteries. By implementing SSSTA, the stability of the MG system is sustained even under varying load conditions, thereby minimizing the impact of disturbances such as fluctuations in load demand and solar irradiation. As a result, implementing this control strategy enhances the reliability and efficiency of MGs integrated with RESs, promoting broader adoption. Furthermore, to offer a clearer understanding of the proposed control approach, the results of the proportional-integral control are also presented. Simulation experiments in MATLAB confirm the effectiveness of the designed control mechanism.

Abstract Background The management of severe SARS-CoV-2 pneumonia, alongside logistical constraints, evolved between the first and subsequent COVID-19 waves. This study aimed to compare the prevalence of early bacterial pulmonary co-infections and the incidence of ventilator-associated lower respiratory tract infections (VA-LRTI) across the first and second waves of the pandemic, and to characterize their microbiology. Methods Latter part of a multicenter retrospective European cohort analysis conducted in 35 ICUs. Adult patients admitted for SARS-CoV-2 pneumonia and requiring invasive mechanical ventilation ≥ 48 h were consecutively included from both waves (February-May 2020 for period 1, October 2020-April 2021 for period 2). Co-infections were defined by bacterial isolation in respiratory secretions or blood cultures, or a positive pneumococcal urinary antigen test, within 48 h after intubation. VA-LRTI, including ventilator-associated tracheobronchitis (VAT) and ventilator-associated pneumonia (VAP), were diagnosed using clinical, radiological and quantitative microbiological criteria. The 28-day cumulative incidence of first VA-LRTI episodes was estimated using the Kalbfleisch and Prentice method, with co-infection prevalence and VA-LRTI incidence compared using multivariable logistic regression and Fine-and-Gray models, respectively. Results The study included 1,154 patients (558 in period 1 and 596 in period 2). Co-infection prevalence significantly rose from 9.7% in period 1 to 14.9% in period 2 (adjusted odds ratio (95% confidence interval) 1.52 (1.04–2.22), p = 0.03). Gram-positive cocci dropped from 59 to 48% of co-infections between periods 1 and 2. The overall incidence of VA-LRTI was similar across periods (50.4% and 53.9%, adjusted sub distribution hazard ratio (sHR) 1.14 (0.96–1.35), p = 0.11), with a significant increase in VAP incidence in period 2 (36% to 44.8%, adjusted sHR 1.37 (1.12–1.66), p = 0.001), predominantly occurring within the initial 14 days after intubation, and a concurrent significant decrease in VAT incidence (14.3% to 9.1%, adjusted sHR 0.61 (0.42–0.88), p = 0.007). Gram-negative bacilli, led by Pseudomonas aeruginosa, Enterobacter spp., and Klebsiella spp., were responsible for 89% and 84% of VA-LRTI in periods 1 and 2, respectively. Conclusions Between the first and second COVID-19 waves, the prevalence of early bacterial pulmonary co-infections significantly increased among intubated patients. Although the overall incidence of VA-LRTI remained stable, there was a significant shift from VAT to VAP episodes.

Parliaments in political systems are not limited to the function of legislation but also perform a range of roles that reflect their influence and effectiveness in relation to other authorities and independent bodies. In addition to lawmaking, parliaments exercise various competencies that shape their interaction with the executive branch. The Iraqi Parliament, under Article 61 of the 2005 Constitution of the republic of Iraq, holds several significant powers, including the issuance of parliamentary decisions affecting the executive authority. Among these powers is the election of the President of the Republic by a two-thirds majority of the House of Representatives and the ability to hold the president accountable for high treason, perjury of the constitutional oath, or violations of the constitution. In such cases, the parliament can issue a decision to remove the president from office by an absolute majority vote.

Background: Although an integral component of cardiac valve operations, manual knot tying has been linked with increased operative times and greater costs. The introduction of the Cor-Knot device (LSI Solutions) has eliminated hand-tied knots through an automatic titanium fastener system. However, adverse outcomes related to this device remain unknown. We thus used a nationally representative cohort to characterize adverse events of the Cor-Knot. Methods: All adverse events for the Cor-Knot from 2015-2023 were tabulated from the Manufacturer and User Facility Device Experience database. Reports were screened to assess incident type and complication. Device and patient complications were categorized and reported as proportions to further ascertain factors contributing to the development of adverse incidents. Results: Of an estimated 74 adverse events, the number of reported occurrences increased over the study period from 1 in 2015 to 13 in 2023. The greatest proportion of adverse events involved the Cor-Knot Mini (41.9%) or the Cor-Knot (37.4%), with malfunction representing the most frequent device incident (63.5%). Problems related to device usage (22.8%) or misfire (22.8%) constituted the most frequent complications after Cor-Knot usage. The most frequent complications included valve insufficiency (10.8%), presence of a foreign body (8.1%), or hemorrhage (2.7%). Conclusions: Of all reported adverse events, malfunction was most likely to occur due to misfire or device usage issues. Patient complications comprised valve insufficiency, foreign body presence, or hemorrhage. As adoption and utilization of the Cor-Knot increases, future work is necessary to ensure adequate device training and minimize the incidence of adverse events.

The complexity of hardware realization for the Bell function forces most researchers to replace it with trapezoidal, negatively affecting accuracy. The implementation complexity of the Bell function on FPGA comes from bundling the division operator by FPGA. This requires large hardware resources due to the limited number of logic elements in FPGA devices, such as Lock-Up Tables (LUTs), slices, and DSP48 units, specifically when utilizing the Spartan-3A DSP sc3sd3400a Xilinx FPGA kit. This work investigates hardware resource minimization of Bell Membership Functions (MFs) based on FPGA implementation. A comparative analysis of seven proposed designs with a predesigned system is studied, focusing on resource utilization and performance evaluation. Approach 1 uses the highest number of DSP48 blocks (10). Conversely, Approach 7 achieves the highest area minimization, using the least number of slices (79) and LUTs (144) with zero DSP48 blocks with a reduction rate equal to (97.7%) for slices and (96.7%) LUTs regarding previous work. The absence of multiplication blocks for implementing Approach 7 comes from modifying the precomputation method using half memory size. Performance evaluation indicates varying error rates of 1.06%, 1.14%, and 1.16% for Approaches 1, 2, and 3, respectively. Approaches 6 and 7 exhibit the lowest error rates (0.17%), suggesting superior accuracy, including the previous work. To sum up, the modification in Approach 7 is a very useful method that reduces the hardware platform area and enhances the performance. Thus, this approach is adopted as the best method for designing the Bell function by FPGA.

Abstract The Oleaceae family, encompassing key genera such as Fraxinus (ash trees), Olea (olives), Jasminum (jasmine), Syringa (lilac), and Forsythia, plays a crucial ecological and economic role. Despite their importance, the evolutionary dynamics and immune system adaptations of their NLR (Nucleotide binding leucine-rich repeats) gene family remain largely unexplored. This study employs high-throughput comparative genomics to investigate NLR gene evolution across the Oleaceae family. The genus Fraxinus is widely distributed across both the New and Old Worlds, with 23 distinct species analyzed in this study. Our results reveal a predominant strategy of gene conservation in the evolution of the NLR gene family across these species. Geographical adaptation has played a significant role, particularly in Old World ash tree species, which exhibit dynamic patterns of gene expansion and contraction within the last 50 million years. Notably, genes acquired from an ancient whole genome duplication event (~ 35 Mya) have been retained across Fraxinus lineages. In contrast, the genus Olea (olives) has undergone extensive gene expansion driven by recent duplications and significant birth of novel NLR gene families. These differences in NLR gene evolution likely enhance Olea's ability to recognize diverse pathogens through recent expansions, while Fraxinus maintains specialized immune responses through conserved genes, with potential trade-offs in pathogen adaptation and energy efficiency. In terms of NLR distribution, all species of the Oleaceae family show an enhanced pseudogenization of TIR-NLRs and expansion in CCG10-NLR. However, the comparative RNA-seq expression analysis in olive suggests that partial NLR genes, despite their incomplete structure, have significant expression and may play important roles in plant immune responses. This study provides a comprehensive analysis of NLR gene evolution within the Oleaceae family, offering insights into the adaptive mechanisms of immune response evolution across diverse genera.

Abstract This paper investigates the effects of die design geometrical parameters on self-piercing riveting (SPR) operations and the joint quality of 5052 aluminum alloy. Piped-die types with various designed diameters and depths were prepared. Subsequently, a 2D axisymmetric simulation model was developed to analyze the riveting efficiency and forming mechanism of SPR joints. The forming quality of specimens was studied by evaluating the cross-sections of joints obtained from the 2D simulation model and experiments. The mechanical properties of the specimens were analyzed by comparing the maximum shear load and energy absorption. The results indicate that a larger die diameter results in better rivet flaring, forming quality and mechanical properties of the specimens. Additionally, the forming quality improves with an increase in die depth, while the mechanical properties initially decrease and then increase. A smaller die depth benefits rivet flaring but affects the rivet penetration process. The maximum interlock value of 0.28 mm was recorded for the specimen obtained using a die with a depth of 1.25 mm; however, the forming quality deteriorated due to a large head height of 0.37 mm.

Abstract Background Postoperative recurrent or permanent recurrent laryngeal nerve injury is one of the most serious complications in the field of thyroid surgery, in either benign or malignant thyroid disease, significantly affecting patients’ quality of life. The importance of recurrent laryngeal nerve identification intraoperatively reduces the risk of injury. We report herein a young patient who underwent nerve monitoring in total thyroidectomy that led to recurrent laryngeal nerve injury. Case presentation We report a 30-year-old Arab male patient who presented to our clinic with a longstanding thyroid swelling, which was reported as papillary thyroid carcinoma, and was scheduled for total thyroidectomy and lymph node dissection. Nerve monitoring was used to identify the recurrent laryngeal nerve, leading to recurrent laryngeal nerve nerve fatigability/paresis that was seen during the postoperative course. Conclusion Visual identification of the recurrent laryngeal nerve is the gold standard for nerve protection. We recommend the use of nerve monitoring as an adjunct in challenging cases but not in routine settings, as it does not decrease the incidence of injuries compared with visualization alone in our experience.

Abstract Background Turkey arthritis reovirus (TARV) causes arthritic lameness in market-age turkeys. Since 2011, highly pathogenic TARV strains have caused significant economic losses in the turkey industry due to increased culling, reduced market weights, and decreased carcass quality, necessitating more effective control measures. Autogenous vaccine prevention strategies have been inefficacious partly due to a limited understanding of age-related susceptibility of turkeys to TARV. This study investigated age-related host and gut microbiota responses to TARV infection in commercial turkeys derived from vaccinated breeder hens. Poults with known maternally derived antibody titers were orally challenged with TARV O’Neil strain at 1-, 3-, and 7- weeks of age (WOA) and monitored for cloacal virus shedding, gastrocnemius tendon viral tropism, tendon inflammation, weight gain, and changes in gut microbiota. Results A transient TARV-induced weight gain suppression was evident in poults infected at 1- and 3- WOA during the first 3 weeks post-infection. Age-dependent variations in cloacal viral shedding, virus isolation from tendons, and tendon inflammation severity were also observed. There was significant dissimilarity in ileal and cecal bacterial communities between mock and infected groups, but the effect of age of infection was unclear. Conclusions Age dependent host response was observed to TARV based on cloacal virus shedding, weight gain suppression and viral tendon tropism. Our study also indicates that maternally derived antibodies appeared insufficient to prevent virus translocation to the tendons and subsequent pathological changes. This study lays the groundwork for future investigations of better vaccines/vaccination strategies and alternative preventive measures. Importance Turkey arthritis reovirus (TARV) causes lameness due to arthritis and tenosynovitis, commonly in market-age turkeys, resulting in significant economic losses. As a control strategy, the turkey industry used autogenous vaccines, prepared from field TARV isolates in breeder hens, to protect the poults in the early stage of life through maternally derived antibodies (MDAs). This study establishes the level of protection provided by MDAs in young poults with age-based responses to TARV O’Neil reovirus strain. Additionally, this study reveals the dynamics of gut dysbiosis in infected poults at different timepoints, paving the way to ground-breaking investigations into gut microbiome modulation interventions that could potentially improve vaccine efficacy and reduce virus transmission and disease severity.

Background: Staphylococcus is a Gram-positive (GM+) facultative anaerobic, non-motile coccus. It's frequently results in a variety of illnesses in human. Staphylococcus sp. considered the most common organisms that can infect the thyroid gland. Methods: Genomic DNA was extracted from 20 thyroiditis patients' blood samples and analyzed for 16S rRNA using PCR, employing the Universal gene. Gene amplification was performed in a thermocycler under specific cycling conditions, including initial denaturation, annealing, extension, and final extension. For the purpose of partially DNA sequencing for the 16S rRNA gene, six PCR results were selected in order to investigate any possible association between the results and the global records in GenBank. The data was analyzed using Statistical Package for Social Sciences (SPSS) version 27, using chi-square test and basic ratios, with P-values between 0.05 and 0.01 indicating statistical significance. Results: Findings showed that 6/20 (30%) were Staphylococcus sp. isolates. The 16S rRNA PCR product was registered in GenBank in accordance with the official accession numbers of (PP396084, PP396083, PP396085, PP396086, PP396082 and PP396093). The local Staphylococcus sp. isolates have distinct molecular links with those of similar strains worldwide according to the phylogenetic tree generated by the MEGA-10 program. Conclusions: Thyroiditis continues to be one the most threating health problems around the world. Thyroiditis patients are more susceptible to GM+ microorganisms. Staphylococcus sp. is an important opportunistic pathogen and it's associated with substantial morbidity rate.

Abstract Current prognostic scores in multiple myeloma (MM) currently rely on disease burden and a limited set of genomic alterations. Some studies have suggested gene expression panels may predict clinical outcomes, but none are presently utilized in clinical practice. The tyrosine kinase WEE1 is a critical cell cycle regulator during the S-phase and G2M checkpoint. Abnormal WEE1 expression has been implicated in multiple cancers including breast, ovarian, and gastric cancers, but its prognostic signal in MM has not been thoroughly reported. We, therefore, analyzed the MMRF CoMMpass dataset (N = 659) and identified a high-risk group (top tertile) and a low-risk group (bottom tertile) based on WEE1 expression sorted in descending order. PFS was significantly different (p

Abstract Lattice structures (LS) have been utilized in various fields, from engineering to biomedical sciences. In the lattice structures, the triply periodic minimal surface (TPMS) LS attains benefits in terms of higher productivity and less material usage, a step towards greener 3D printing. However, no automated system exists that can effectively recommend LS parameters to reduce material waste, which is often neglected in traditional methods. To overcome these challenges, this study presents a machine learning (ML) and Deep Learning (DL) based framework recommending TPMS LS according to specific requirements. Initially, a dataset of 144 samples was created using the Material Extrusion (ME) technique. The four TPMS LS were chosen (Split-P, Gyroid, Diamond, and Schwarz) and manufactured with Polylactic acid (PLA). This dataset was used to train both ML and DL algorithms. ML algorithms included Bayesian regression (BR), K-nearest neighbors (KNN), Random Forest (RF), Decision Tree (DT), and DL algorithm convolutional neural network (CNN). These models were used to predict the key parameters of TPMS LS, including wall thickness, unit cell type, loading direction, and unit cell size. Extensive testing was performed to evaluate the performance of the algorithms, employing R-squared values and root mean square error (RMSE). The results showed that the machine learning models, specifically the RF and DT algorithms, performed the best, achieving R-squared scores of 0.993 and 1.0 and RMSE scores of 0.1180 and 0.0795, respectively. The deep learning model, CNN, achieved an RMSE value of 0.46 and an R-squared score of 97%. This study not only contributes to a better understanding of automated TPMS lattice structures but also has significant implications for sustainable design and innovation, particularly in enhancing efficient and environmentally friendly 3D printing technologies.

Abstract Background Psychological stress is linked to elevated salivary cortisol levels, potentially worsening periodontitis by exacerbating inflammation. This study examines the impact of stress on cortisol levels in periodontitis patients, aiming to explore salivary cortisol as a biomarker for disease severity and its role in improving management strategies. Methods This case-control study, conducted at Dow University of Health Sciences in Karachi from January to December 2022, involved 120 dental clinic patients aged 30–60. Participants were selected based on the presence (cases) or absence (controls) of periodontitis, excluding those with systemic diseases, taking hydrocortisone, immunosuppressives, antidepressants, systemic antibiotics, having oral ulcers and pregnancy or lactation. Periodontal parameters, stress levels (using the Perceived Stress Scale), and morning salivary cortisol (measured via ELISA) were assessed. Statistical analyses were performed using SPSS version 23, including descriptive statistics, independent t-tests, ANOVA, and odds ratio calculations with a 95% confidence interval at 0.05. Results The study cohort comprised 120 subjects, equally divided between periodontitis patients and healthy controls. Predominantly male (63%) with a mean age of 41.75 years, participants were subjected to comprehensive evaluations. Oral health indices (Gingival index, Plaque index, Clinical attachment loss, Periodontal probing depth, Tooth mobility) and stress markers (Perceived Stress Score, Salivary cortisol levels [SCLs]) were significantly higher in cases compared to controls (p

Abstract Chaos theory, with its unique blend of randomness and ergodicity, has become a powerful tool for enhancing metaheuristic algorithms. In recent years, there has been a growing number of chaos-enhanced metaheuristic algorithms (CMAs), accompanied by a notable scarcity of studies that analyze and organize this field. To respond to this challenge, this paper comprehensively analyzes recent advances in CMAs from 2013 to 2024, proposing a novel classification scheme that systematically organizes prevalent and practical approaches for integrating chaos theory into metaheuristic algorithms based on their strategic roles. In addition, a list of 27 standard chaotic maps is explored, and a summary of the application domains where CMAs have demonstrably improved performance is provided. To experimentally demonstrate the capability of chaos theory to enhance metaheuristic algorithms that face common issues such as susceptibility to local optima, non-smooth transitions between global and local search phases, and decreased diversity, we developed a chaotic variant of the recently proposed RIME optimizer, which also encounters these challenges to some extent. We tested C-RIME on the CEC2022 benchmark suite, rigorously analyzing numerical results using statistical metrics. Non-parametric statistical tests, including the Friedman and Wilcoxon signed-rank tests, were also used to validate the findings. The results demonstrated promising performance, with 14 out of 21 chaotic variants outperforming the non-chaotic variant, whereas the piecewise map-based variant achieved the best results. In addition, C-RIME outperformed ten state-of-the-art metaheuristic algorithms regarding solution quality and convergence speed.

Abstract Background Nodular fasciitis (NF), also known as pseudosarcoma fasciitis, is defined as a benign, self-limiting, vascular, or fibrovascular proliferation. It is commonly found on the trunk and upper extremities. NF is rarely found in the orbit and periorbital regions, with only a few case studies reported since it was initially described by Font et al. in 1966. NF usually presents as a rapidly growing solitary tumor and is often mistaken for a sarcoma due to its high cellularity and increased mitotic activity. Case presentation We present a case of NF in a 41-year-old Saudi man with a 1-year history of a gradually enlarging left medial subbrow mass in the left eye. The mass was not associated with pain, visual disturbances, double vision, or a history of trauma. An external ophthalmic examination revealed a firm, oval mass over the nasal aspect of the superior orbital rim of the left eye. The mass was non-tender, measured 1 × 2 cm in diameter, and fixed to deep tissues. Computed tomography (CT) with contrast revealed a well-demarcated left para-orbital soft tissue round lesion with peripheral enhancement occupying the nasal aspect of the superior orbital rim above the inner canthus. Debulking and incisional biopsies were performed, and the histopathological findings were consistent with those of NF. Conclusions NF is a rare benign tumor that infrequently presents in the periorbital region. It lacks distinctive features for a definitive diagnosis and should be suspected when imaging results are inconclusive. Management typically involves complete local excision or debulking.

Abstract Pasteurella multocida is incriminated in fowl cholera in poultry and snuffles in rabbits, causing significant economic losses in the poultry industry worldwide. This study investigated the prevalence, kmt1 gene sequencing, antibiogram, pathogenicity, and PCR detection of the virulence and resistance genes of P. multocida recovered from chickens, ducks, and rabbits. Approximately 150 samples were collected from diseased chickens, ducks, and rabbits (50 for each) from commercial farms in Ismailia government, Egypt. The collected samples were bacteriologically examined, and the recovered isolates were subsequently subjected to kmt1 gene sequencing, antibiogram, pathogenicity test, and PCR-based detection of virulence and antibiotic resistance genes. The overall prevalence of P. multocida was 20% (30/150). The highest prevalence was recorded in rabbits (24%; 12/50), followed by ducks (20%; 10/50) and chickens (16%; 8/50). The lung was the most predominant infected organ. Moreover, the kmt1 phylogenetic analysis emphasized that the tested P. multocida strains presented remarkable genetic identity with other P. multocida strains from the USA, Denmark, China, India, and Iran. Chloramphenicol and ciprofloxacin exhibited promising results in treating P. multocida. Moreover, the prevalence of oma87, ptfA, luxS, tadG, and nanB virulence genes was 100%, 86.7%, 76.6%, 73.3%, and 56.7% respectively. Herein, 13.3% of the tested strains were multidrug-resistant (MDR) to 5 antibiotic classes and carried bla ROB−1 and tetH genes. Besides, 10% of the tested P. multocida strains were extensively drug resistant (XDR) to 7 antibiotic classes and harbored the bla ROB−1, tetH, and ermX genes. Moreover, 10% of the recovered P. multocida strains were MDR to 5 antibiotic classes and had tetH and ermX genes. Pathogenicity test revealed that the mortality rate was 20% at 1 day post-infection, reaching its highest point (100%) at 2 days post-infection. Briefly, this study highlights the occurrence of MDR and XDR P. multocida in domestic birds and rabbits, revealing a public health threat. The oma87, ptfA, luxS, and tadG virulence genes and the bla ROB−1, tetH, and ermX antibiotic resistance genes are usually associated with emerging MDR P. multocida strains.

Abstract Accurate time series forecasts are crucial for various applications, such as traffic management, electricity consumption, and healthcare. However, limitations in models and data quality can significantly impact forecasts’ accuracy. One common issue with data quality is the absence of data points, referred to as missing data values. It is often caused by sensor malfunctions, equipment failures, or human errors. This paper proposes Hinge-FM2I, a novel method for handling missing data values in univariate time series data. Hinge-FM2I builds upon the strengths of the Forecasting Method by Image Inpainting (FM2I). FM2I has proven effective, but selecting the most accurate forecasts remains a challenge. To overcome this issue, we proposed a selection algorithm. Inspired by door hinges, Hinge-FM2I drops a data point either before or after the gap (left/right-hinge), then uses FM2I for imputation. In fact, it selects the imputed gap based on the lowest error of the dropped data point. Hinge-FM2I was evaluated on a comprehensive sample composed of 1356 time series. These latter are extracted from the M3 competition benchmark dataset, with missing value rates ranging from 3.57 to 28.57%. Experimental results demonstrate that Hinge-FM2I significantly outperforms established methods such as linear/spline interpolation, K-Nearest Neighbors, and ARIMA. Notably, Hinge-FM2I achieves an average Symmetric Mean Absolute Percentage Error score of 5.6% for small gaps and up to 10% for larger ones. These findings highlight the effectiveness of Hinge-FM2I as a promising new method for addressing missing values in univariate time series data.

Abstract Flood is among the most disastrous natural disasters since they are responsible for massive damage to infrastructure, severe fatalities and injuries, innumerable economic losses, and social disruptions worldwide. These damages caused by floods have been worsening in recent years worldwide because of environmental degradation, climatic change, and high-speed urbanization. A rising precipitation rate increases the chances of floods in flood-vulnerable areas. A flash flood is a rapid flooding of geomorphic low-lying regions caused by remarkably high rainfall in a short duration. On September 23rd, 2023 a flooding event in the Nagpur, Maharashtra, it is directly impact on the human death and economic loss entire city. In the present study, the change in the dynamics of Nagpur city was analysed by employing remote sensing and GIS techniques to assess the change in the land use and land cover patterns. Landsat imagery of year 2000, 2010, 2020, and 2023 was used for land use and land cover classification. This analysis reveals that there is an increase in built-up area from 72.85 sq. km in year 2000 to 185.4 sq. km in year 2023. The built up land is increased this changes where directly affects the infiltration rate of rainwater into the soil. The total area covered by water bodies is reduced to 2.29 sq. km in 2023 which were 12.2 sq. km in year 2000. It is indicates the encroachment of built-up land on the water bodies. On the day of flash flood occurrence, it was observed that Nagpur city received 145 mm rainfall which is highest in the month of September, 2023. The Shannon entropy model was used to estimate the population dynamics and growth patterns of Nagpur city. Higher entropy values were obtained during the analysis which indicates the rapid transformation of city in all directions. Population dynamics of Nagpur city also indicate the inflation in population from 4,067,637 in 2000 to 4,653,570 in 2010. The SAR water index was calculated using Google Earth Engine to detect the water surges in residential areas during the flood. Precautionary measures should be taken by governing authorities to avoid such disasters. Proper city planning and improvements in drainage systems are recommended within the city. It is needed for an hour to develop a river monitoring system and early warning system, as well as preventive measures that should be implemented, like the construction of retaining walls to control the flood water.

Abstract Background Students have long been admitted into medical colleges using standardized tests/interviews. However, limited evidence exists on their association with academic achievement during medical education. Moreover, the relationship between its individual components and subsequent academic achievement remains unexplored. This study aims to determine the association between medical students’ demographics and admission scores with their academic performance during medical college. Methods This cross-sectional study was conducted in 2023 at one of the oldest private medical colleges in a South Asian low- and middle-income country, where data for medical students graduating with a Bachelor of Medicine, Bachelor of Surgery (MBBS) degree in 2018–2022 were retrieved electronically. Through an extreme groups approach (EGA) sampling, factors leading to students falling within the top 20% scorers in preclinical, clinical, procedural, and non-procedural clerkships were identified. Binary logistic regression models assessed the association between demographics and admission scores with their academic performance in medical college. Results From data of 418 students, EGA sampling included a total of 168 and 169 students for comparison between the top and bottom 20% scorers in preclinical and clinical rotations, respectively. Female sex (adjusted OR:4.10, 95% CI:1.94,8.65) and higher biology, physics, and mathematical reasoning scores on the university’s admission test independently predicted preclinical academic achievement. Female sex and higher mathematical reasoning scores significantly predicted academic achievement in clinical, procedural, and non-procedural clerkships. Higher biology scores also independently predicted achievement in non-procedural clerkships (adjusted OR:1.03, CI:1.01,1.06). Prior schooling from the British education system was significantly associated with higher mean percentage for admission scores and clinical clerkships for applicants compared to the local education system. Conclusion Higher scores on medical college admission tests can predict medical students’ academic achievement during undergraduate studies. Understanding the specific test components associated with students’ success can refine the selection process, ultimately fostering efficient healthcare professionals.

This study explores the integration of open innovation in education with a focus on technology and cultural aspects, aiming to foster improvements in educational management and policy within Oman learning environments. The research investigates the relationships between technological literacy (TL), cultural compatibility (CC), human face-to-face communication methods (HFtFCM), and student motivation and engagement (SMaE). A survey was conducted among 1,436 Oman students, and the data were analyzed using partial least squares and structural equation modeling to assess the moderating effects of TL and CC on these relationships. Results indicate that incorporating TL and CC as moderators significantly strengthens the mediation framework, enhancing the connection between HFtFCM and student perception of communication effectiveness (SPoCE). Specifically, the p-value decreased from 0.11 to 0.005, highlighting increased statistical significance, and the path from HFtFCM to SPoCE to SMaE improved from -0.051 to 0.071, demonstrating a stronger mediation effect. Conversely, the indirect effect from Technology-Based Communication Methods (TBCM) to SPoCE to SMaE decreased from 0.047 to 0.008. Additionally, notable paths such as TL → SPoCE and CC → SPoCE emerged, illustrating the enhanced explanatory power of these moderators. Conclusion: These findings underscore the potential of TL and CC to elevate student engagement and communication effectiveness, offering valuable insights for educational policy development and leadership programs. Doi: 10.28991/ESJ-2025-09-01-025 Full Text: PDF

Abstract One crucial factor that aids the evaluation of oil and gas well productivity is the flowing bottom-hole pressure (FBHP). However, accurately determining the FBHP has been challenging for the oil and gas industry. Traditional methods, such as using downhole gauges or relying on empirical and mechanistic models, have limitations, prompting the exploration of alternative approaches such as machine learning (ML). However, most ML models operate as black box models, lacking transparency and interpretability. In this study, the multivariate adaptive regression splines algorithm was used to develop a FBHP estimation model. The model includes eight input variables and was built using 1001 data points from literature. The results show that the model achieved a coefficient of correlation, root mean square error and average absolute percentage error values of 0.94, 130 and 4.2% respectively. Compared to existing models, the developed model exhibited improved predictive accuracy. Sensitivity analysis indicates that water flow rate and depth had the largest effect on FBHP estimation, each contributing 17.5%, while oil API gravity had the least effect with a contribution of 3.5%. This study showcases a novel model that is explicitly presented, interpretable and physically validated, making the model suitable for integration into software applications. These attributes are lacking in many existing FBHP estimation models. By utilizing this model, the costs associated with downhole gauges can be saved, and real-time estimations can be obtained in the field. The model would be useful to oil industry players producing from unconventional wells where downhole gauges are rarely installed.

Abstract Document source identification in printer forensics focuses on determining the source printer of a document by analyzing characteristics such as printer model, serial number, defects, or unique artifacts. This is crucial in forensic investigations involving counterfeit documents or anonymous threats. However, identifying consistent patterns across different printers remains challenging, especially when perpetrators attempt to obscure these artifacts. Machine learning models in this field must identify discriminative features that differentiate printers while minimizing noise. In particular, choosing an appropriate distance metric for K-Nearest Neighbors (KNN) classifiers is critical and requires experimentation. This study proposes a quantum-inspired approach to improve KNN’s performance in printer source identification. By exploring alternative number of neighbors (K), quantum-inspired computing can optimize feature space calculations, even in noisy conditions. This allows the system to iteratively refine and select the optimal K value based on classification performance, ensuring that the best K is identified for the specific dataset and task. The system utilizes the Grey Level Co-occurrence Matrix (GLCM) for feature extraction, which is robust to changes in rotation and scale. Experimental results demonstrate that the Quantum-inspired KNN (QKNN) classifier outperforms classical KNN, achieving higher accuracy in identifying subtle printing artifacts, even under variable conditions.

Abstract This study investigated the potential of indigenous entomopathogenic bacterial (EPB) strains from Egypt to control the two most prevalent locust species, Schistocerca gregaria (Forsskål) (Orthoptera: Acrididae), and Locusta migratoria migratorioides (Reiche & Fairmaire) (Orthoptera: Acrididae). To assess the efficacy of the EPB strains, experiments were conducted in the laboratory, semi field, and field. The results showed that Xenorhabdus nematophila (Thomas et Poinar) BA2 (Enterobacterales: Morganellaceae) and Photorhabdus luminescens (Thomas et Poinar) EGAP3 (Enterobacterales: Morganellaceae) were the most effective strains against S. gregaria and L. migratoria migratorioides in laboratory settings. Under semi-field conditions, X. nematophila BA2 recorded nymphal mortality rates of 89.31% and 85.00% against the 2nd and 5th nymph instars of S. gregaria, respectively, and P. luminescens EGAP3 showed nymphal mortality rates of 88.00% and 80.00% against the 2nd and 5th nymph instars of S. gregaria, respectively. In field trials, X. nematophila BA2 exhibited the highest nymphal mortality rate of 88.70% at 7 days after spraying. Overall, the findings of this study suggest that X. nematophila BA2 and P. luminescens EGAP3 are promising candidates for environment-friendly, safe locust pest management. Further research is needed to explore and develop these bacteria for commercial use in agriculture.

Abstract Recently, chaotic maps (CMs) have been employed in many optimization algorithms as a motivator to find a better solution to non-convex engineering problems since they can avoid local optima and find the near-optimal solution rapidly. In this article, a metaheuristic, physics-based algorithm called chaotic transient search optimization (CTSO) algorithm is developed to solve 23 benchmark functions, including uni- and multi-modal optimization functions. Nine CMs integrated into the TSO to improve its search capabilities by applying various scenarios for improving the TSO random numbers. Further, the proposed CTSO was compared with the original TSO using the Wilcoxon p-value test, non-parametric sign test, t-test, convergence curves, and elapsed time. Furthermore, the proposed CTSO algorithm has been employed for solving real-life engineering design problems, including coil spring, welded beam, and pressure vessel design, where CTSO performed better than some recent optimization algorithms in finding the best design.

Abstract Hybrid energy systems (HESs) are integrated systems that have successfully addressed the problems of meeting the increasing demand for electrical power. Like all known power systems, the energy and stream quality are among the most important issues in addition to the durability of the HES. In this study, the battery-powered HES is presented, where this designed system consists of a wind system and a photovoltaic (PV) system. The strategy of maximum power point (MPP) tracking (MPPT) based on the adaptive neuro-fuzzy inference system (ANFIS) method is used to command the PV system and the wind system, and the MPPT based on the neural method is used. These proposed strategies do not need the mathematical model of the studied system and augment the robustness and stability, where the system performance is great. Also, the fractional-order proportional-integral regulator and the integral sliding mode control approach are combined to control the battery-based storage system, and the particle swarm optimization approach was used to estimate the gain values of the resulting controller. The HES was realized using MATLAB, where the competence is tested under different work scenarios. The results showed excellent efficacy of the designed control and were compared with conventional control. The simulation results showed that using the neural MPPT strategy in the case of the wind speed being 12 m/s, the values of rise time, response time, MPP, and steady-state error (SSE) are improved by rates estimated at 99.32%, 60%, 1.5%, and 60%, respectively compared to the perturbations and observations-based MPPT approach. Compared to the traditional strategy, the ANFIS-MPPT strategy improves the values of MPP, response time, SSE, and rise time in the case of irradiation, which takes the value of 1000 W/m2, by percentages estimated at 18%, 60%, 94.70%, and 69.23%, respectively. Also, the PSO-FOPI-ISMC strategy improves the harmonic distortion of the current value in the second test by 55.20% and 72.90% for mode 1 and mode 2, respectively, compared to the traditional approach. These results make the designed approach of great importance in the future in other industrial applications.

Abstract Genetic testing is important in the diagnosis of genetic disorders. Genetic counseling integrates the interpretation of the results of genetic testing to reach informed decisions concerning genetic disorders. Palestine has an increased incidence of genetic disorders primarily due to the continued practice of consanguineous marriage. Nevertheless, limited research has been conducted to explore public awareness regarding genetic testing and genetic counseling. The current study aimed to assess the public knowledge, attitudes, and practices of Palestinians toward genetic testing and genetic counseling. A cross-sectional study was performed using an online questionnaire that gathered information from Palestinians whose ages were 18 years or older between April and July 2024. The questionnaire gathered demographic information about the participants and assessed their genetic test usage patterns and their knowledge, practices, and attitudes toward genetic testing and counseling. A total of 1056 participants (408 males and 648 females) completed the questionnaire. The mean age of participants was 31.18 years. Sixty-seven point 6% of the participants reported their knowledge about the term genetic testing; however, only 35.5% of them knew the term genetic counseling. Knowledge of genetic testing was significantly associated with younger ages, higher levels of education, and higher income (p

Abstract Background The compound eyes of insects exhibit extensive variation in ommatidia number and size, which affects how they see and underlies adaptations in their vision to different environments and lifestyles. However, very little is known about the genetic and developmental bases of differences in eye size. We previously showed that the larger eyes of Drosophila mauritiana compared to D. simulans are generally caused by differences in ommatidia size rather than number. Furthermore, we identified an X-linked chromosomal region in D. mauritiana that results in larger eyes when introgressed into D. simulans. Results Here, we used a combination of fine-scale mapping and gene expression analysis to further investigate positional candidate genes on the X chromosome. We found earlier expression of orthodenticle (otd) during ommatidial maturation in D. mauritiana than in D. simulans, and we show that this gene is required for the correct organisation and size of ommatidia in D. melanogaster. We discovered that the activity of an otd eye enhancer is consistent with the difference in the expression of this gene between species, with the D. mauritiana enhancer sequence driving earlier expression than that of D. simulans. When otd expression is driven prematurely during D. melanogaster eye development, the ommatidia grow larger, supporting a possible role for the timing of otd expression in regulating ommatidial size. We also identified potential direct targets of Otd that are differentially expressed between D. mauritiana and D. simulans during ommatidial maturation. Conclusions Taken together, our results suggest that differential timing of otd expression may contribute to natural variation in ommatidia size between D. mauritiana and D. simulans, which provides new insights into the mechanisms underlying the regulation and evolution of compound eye size in insects.

The cold rolling effect on the microstructure and mechanical properties of recycled AA6111 alloy with Zr or/and Ni were investigated. All alloys were produced by the stir-casting method. homogenization heat treatment (500 ºC for 6 hours) was performed on AA6111 alloy only. The alloys were solutionized (at 540 ºC for 30 minute) and artificially aged (at 180 ºC for 10 hours). The optical micrograph, scanning-electron microscope (SEM) and X-ray Diffraction (XRD) studies for all samples. The microstructure analysis displayed different fine intermetallic phases distributed in large amounts in alloys containing Zr or/and nickel compared to AA6111 alloy. The combined alloying elements' addition effect with the cold rolling effect and artificial aging led to great improvements in the mechanical properties, where the hardness and tensile properties of all alloys increased. The AA6111+0.41wt%Ni alloy showed the highest hardness value, as it increased to 161 HV after artificial aging, while the AA6111+0.54wt%Zr alloy showed the highest YS and UTS, which increased to 252 MPa and 342 MPa, respectively.

Abstract: In newly diagnosed multiple myeloma (NDMM), measurable residual disease (MRD) status is prognostically important, but its role in treatment decisions remains unclear. In a phase 2 trial, we assessed daratumumab, carfilzomib, lenalidomide, and dexamethasone (Dara-KRd) induction followed by a next-generation sequencing–based MRD-adapted strategy. The primary outcome was complete response (CR) and stringent CR (≥CR) after induction. Flow cytometry was used to profile T cells. Among 39 patients, 21 (54%) achieved ≥CR after induction (P = .375), with MRD-negative rates of 59% (10−5) and 41% (10−6). Patients who were MRD-negative (n = 24, group A) received lenalidomide maintenance, showing sustained MRD negativity in 14 of 18 (77.8%) for ≥12 cycles. MRD-positive transplant-eligible patients (n = 8, group B) underwent autologous stem cell transplantation, with 62.5% converting to MRD-negative at 10−5 (37.5% at 10−6) posttransplant. MRD-positive, transplant-ineligible patients (n = 4, group C) received KRd consolidation. Best MRD-negative rates improved to 77% (10−5) and 72% (10−6). No new safety concerns were identified for Dara-KRd. With a median follow-up of 30.1 months, 3, 2, and 1 patient(s) in groups A, B, and C, respectively, have progressed or died. We observed that Dara-KRd strongly activated memory T cells, which was associated with an MRD-negative state post induction. Although the primary outcome was not met, Dara-KRd induction in NDMM achieved high ≥CR and MRD-negative rates without new safety concerns. The post induction MRD-adapted strategy deepened responses in MRD-positive patients and maintained durable MRD control in MRD-negative patients. This trial was registered at www.clinicaltrials.gov as #NCT04113018.

Distraction can cause delayed decision-making and slower awareness, posing significant risks in driving. For reliable driving systems, continuous monitoring of driver behavior is essential to mitigate the impact of distractions. Current strategies for distraction detection widely rely on machine learning models, but the non-linear relationships among various data modalities complicate the identification of optimal combinations. To address these challenges, we propose a novel model, Self-DSNet, for efficient driving distraction detection. The proposed Self-DSNet model utilizes Self-Organizing Neural Network (Self-ONN) layers to enhance complex pattern learning within the data. We employed a publicly available multimodal dataset encompassing three data categories: physiological, vehicle dynamics, and vision-based data. Our approach aims to identify the normal state and three types of distractions: cognitive, emotional, and sensorimotor. The model was evaluated using both single-modality and combined-modality data, focusing on binary classification to distinguish between distracted and non-distracted driving states. The Self-DSNet model demonstrated an impressive accuracy of 94.23% when using vision-based data alone. Incorporating additional physiological data, such as heart rate and breathing rate, alongside vehicle dynamics data, such as steering behavior, further enhanced the model’s performance. The combined data approach achieved a 95.13% accuracy in detecting driving distractions. Specifically, the binary classification yielded a 96.58% accuracy with vision-based data, which increased to 97.31% when steering, breathing rate, and heart rate data were included. Our approach significantly outperformed state-of-the-art methods in terms of classification accuracy. The proposed Self-DSNet model offers a robust solution for driving distraction detection by effectively leveraging multimodal data and enhancing complex pattern recognition through the Self-ONN layers. The model’s high accuracy rates underscore its potential for improving driving safety by providing reliable and continuous monitoring of driver behavior. Future research may focus on real-time implementation and the integration of additional data sources to further refine and validate the model’s effectiveness in diverse driving scenarios.

Among the renewable energy sources, photovoltaic (PV) solar power represents one of the most potential. The use of grid-integrated solar power is much more popular than off-grid systems. The total system cost and weight can be reduced by eliminating transformers for grid-tied applications. However, a leakage current with a high peak value flows across the system in the absence of galvanic isolation between an ac grid and PV. In addition, reactive power injection must be ensured in future grid-tied systems. In this paper, the authors have proposed a new hybrid topology using both decoupling and mid-point clamping techniques to reduce the root mean square (RMS) and peak value of leakage current. In addition, the proposed topology can provide both active and reactive power to the grid. The topology is simulated in the Matlab/Simulink environment for both the open loop and closed loop conditions and then validated through experiments. In the open loop conditions, leakage current issue has been addressed. In the closed loop conditions, active power (P) and reactive power (Q) are injected into the grid using model predictive control (MPC). Finally, a comparative analysis with other similar topologies has been performed.

Acute Lymphoblastic Leukemia (ALL), a cancer affecting the blood and bone marrow, requires precise classification for accurate diagnosis, personalized treatment plans, and improved predictive assessments to enhance patient survival and quality of life. This study presents LEU3, a novel classification model designed to improve the accuracy of leukemia detection from peripheral blood smear (PBS) images. LEU3 leverages an attention-based convolutional neural network (CNN) architecture, incorporating pooling layers, a global average pooling layer, and dense layers with dropout for regularization. The model is trained with an Adam optimizer comprising with four classes: Benign, early malignant pre-B, malignant pre-B, and malignant pro-B. Data augmentation techniques were employed to increase training set diversity. Additionally, Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are used to enhance interpretability and transparency in the model’s decision-making process. LEU3 achieved a test accuracy of 99% and a validation accuracy of 99% on 484 PBS images, demonstrating a 3% improvement over the baseline model. These results underline the potential of LEU3 in supporting medical professionals by reducing diagnostic workload and improving the accuracy of leukemia classification.

Superquadratic function is a generalization of convex functions. Results based on superquadratic functions are more refined than the results obtained using the notion of convexity. This work aims to provide a new class of superquadratic functions called superquadratic fuzzy-interval-valued function (superquadrtic $ F_{I.V.F} $) and demonstrate its properties using fuzzy order relations. In the space of fuzzy intervals, this relation is also termed as the Kulisch-Miranker order relation defined on such a space level-wise. By leveraging the definition and features of superquadrtic $ F_{I.V.F} $, we come up with improved integral inequalities such as Hermite-Hadamard (H.H) and Jensen type for superquadrtic $ F_{I.V.F} $. Furthermore, we offer fractional representation of inequalities of H.H's types for superquadrtic $ F_{I.V.F} $ with respect to fuzzy interval Riemann-Liouville fractional integral operators. These findings are further validated through specific numerical examples and graphical illustrations, which demonstrate the practical relevance and applicability of the results. We have no doubt that these results will open new avenues for researchers to further explore the notion of superuadraticity.

Crime analysis using data mining techniques can uncover valuable information to assist law enforcement investigations. This study analyses a crime data set from the Kingdom of Bahrain to demonstrate an integrated framework using exploratory analysis and machine learning for predictive modelling. The study data set, comprising 720 cases, was first explored to reveal patterns, including prevalent theft and assault crimes concentrated in four areas. Data sensitivity measures were implemented by anonymising specific geographic areas, represented by characters A to T. Supervised learning algorithms such as Additive Regression, Random Forest, Naïve Bayes, and Decision Tree are then applied to predict unknown offender characteristics such as age, sex, and occupation. The algorithms achieve strong predictive performance. Additive regression achieved a high correlation coefficient of 0.889 for the prediction of the age of the offender, and Random Forest achieved 83% precision in predicting sex through 10-fold cross-validation. However, challenges arise with unbalanced nationality data. A survey of 22 police officers was conducted, discussing the study results and the feasibility of the predictive modelling approach to enhance investigative efficiency. The responses received were mostly positive, reflecting a strong endorsement of the potential of the proposed method to enhance law enforcement practices. This research highlights a promising methodology that combines exploratory and machine learning techniques to extract actionable insights from crime data. With appropriate caution, it can improve investigative efficiency by making data-driven predictions of the attributes of the offender. Further work should expand data sets and explore advanced algorithms while addressing ethical concerns regarding predictive modelling in criminal justice.

Purpose – This research aims to investigate the negative aspects of digital transformation (DT) impacting various segments of society in the Emirate of Abu Dhabi, one of the seven Emirates comprising the United Arab Emirates. Specifically, it focuses on perceptions of participants to well-being: social connections and activities, physical health, mental health, the impact on the younger generation, and security/cybercrime. Design/methodology/approach – Using data from the 3rd Abu Dhabi Quality of Life Survey, which is an annual large-scale survey, this study employs analysis of variance and regression analysis to explore the associations between the perceived negative impact of DT and various well-being indicators. Findings – The study reveals that the perceived negative impact of digital transformation on the younger generation is of the highest concern, followed by concerns about mental health and social relationships. Results also show significant variations among different demographic categories. Among the perceived negative impacts, mental health, social trust, and happiness emerge as the most affected well-being indicators. Originality/value – The originality and value of this study lie in its large social survey sample of over 80,000 participants. The findings suggest that the negative effects of DT are not uniformly experienced across different segments of the population. The study highlights the need for further research on the adverse effects of DT and recommends that policymakers develop targeted strategies to address the specific needs of different community segments, particularly vulnerable groups. Additionally, it emphasizes the importance of adaptive policies for younger age groups to prepare them for a digital future.

Abstract In wind energy generation systems, ensuring high energy quality is critical but is often compromised due to the limited performance and durability of conventional regulators. To address this, this work presents a novel controller for managing the machine-side inverter of a single-rotor large wind turbine system using an induction machine-type generator. The proposed controller is designed using proportional, integral, and derivative error-based mechanisms, which fundamentally differ from traditional proportional-integral (PI) regulators. Key features of the proposed regulator include its simplicity, cost-effectiveness, ease of implementation, reduced number of gains, and rapid dynamic response. This regulator enhances the direct power control (DPC) approach, as it integrates two tailored controllers alongside a pulse width modulation strategy to manage the machine inverter. The DPC strategy incorporating the proposed controller was implemented and tested using MATLAB, with various simulations to evaluate its performance and effectiveness. The proposed regulator demonstrated a significant improvement over the PI regulator, with reductions in active power ripples of 69%, 61.70%, and 59.14% across different tests. Additionally, the steady-state error of reactive power was reduced by 54.84%, 85.23%, and 62.68%, and the total harmonic distortion of current decreased by 48.12%, 50.55%, and 56.05%. These results underscore the high efficiency, robustness, and effectiveness of the proposed controller in improving system performance compared to conventional PI regulators. The controller’s outstanding performance makes it a promising solution for broader industrial applications.

Abstract Previous research indicates that Transforming growth factor beta-3 (TGFβ3) expression levels correlate with breast cancer metastasis, and elevated TGFβ3 levels have been linked with poor overall survival in breast cancer patients. The study used computational methods to examine curcumin’s effects on TGFβ3, a chemical with antiviral and anticancer characteristics. The curcumin has low Molecular Weight 368.130 (MW) and follows Lipinski Rule, Pfizer Rule, GSK Rule, Golden Triangle, BMS Rule, zero PAINS alert and Acute Toxicity Rule with zero alert. Any drug-like contender must follow these qualities. Through molecular docking analyses, curcumin displayed favourable binding affinities at the TGFβ3 binding pocket, forming key interactions such as hydrogen bonds with residues like ASP323, ARG325, VAL333, HIS334, PRO336, LYS337, GLY393, and ARG394. 500 ns molecular dynamic simulations examined docking interactions. Molecular dynamics (MD) simulations trajectories analysis, by calculating lower structural deviation, minimal residual fluctuations, structural compactness assessment by calculating radius of gyration, surface area calculation which interact with solvent, role of hydrogen bonding, and secondary structural analyses. Furthermore, principal component, Gibbs free energy landscape and MMPBSA analysis, signifying system stability. These data suggest curcumin may inhibit TGFβ3, providing a framework for developing new compounds targeting this protein.

Abstract No consensus exists on the optimal therapy for resectable gastric cancer (GC) and gastroesophageal junction (GEJ) tumors, including the effectiveness of chemoradiotherapy versus perioperative chemotherapy (PC). Our study aimed to compare overall survival (OS) outcomes associated with the recommended treatment modalities for GC and GEJ tumors and evaluate treatment trends from 2010 to 2020. A national registry cohort identified patients with ≥ cT2 nonmetastatic GC and GEJ cancer. Treatment modalities were classified as neoadjuvant chemotherapy (NC), neoadjuvant chemoradiotherapy (NCR), PC, adjuvant chemotherapy (AC), and adjuvant chemoradiation (ACR). Kaplan-Meier curve and multivariable Cox regression models evaluated factors associated with OS. A cohort of 7665 patients were included. Patients who received PC had the highest OS (median 86.80 months, 95% CI 73.40-NE), while chemoradiotherapy in the neoadjuvant and adjuvant settings had worse OS than PC and NC (NCR median 47.15 months, 95% CI 44.58–52.27, and ACR median 52.67 months, 95%CI 42.78–63.93). The Cox proportional hazards model showed that NCR and NC had worse survival than PC (HR 1.74, 95% CI 1.50–2.02, p

Abstract Background Malignant mesotheliomas are aggressive forms of tumors arising from mesothelial cells. The most common type is malignant pleural mesothelioma, which progresses rapidly and leads to pleural effusion. It typically affects older men and is strongly associated with asbestos exposure. However, a few studies have reported cases of malignant pleural mesothelioma resulting from non-asbestos factors, including radiotherapy for breast cancer, viruses, chronic inflammation, and BRCA1-associated protein-1-associated genetic mutations. Breast cancer is the most common sporadic cancer among women, and a small percentage of cases are related to genetic factors, such as BRCA1/2 and BRCA1-associated protein-1 mutations. While breast cancer can be linked with other primary malignancies through germline mutations, the synchronous occurrence of breast cancer with pleural mesothelioma is extremely rare. Case presentation We present the case of a 40-year-old Pashtun woman diagnosed with primary breast cancer. She underwent surgery followed by chemotherapy (paclitaxel). During chemotherapy, she developed right-sided chest pain and dyspnea. A computed tomography scan revealed pleural thickening, and a pleural biopsy confirmed the diagnosis of malignant pleural mesothelioma, with positive results for the diagnostic markers WT1 and D240. Conclusion This case represents a rare occurrence of synchronous breast cancer and pleural mesothelioma in a 40-year-old female, and is the first case reported in Khyber Pakhtunkhwa, Pakistan. These findings demonstrate the importance of comprehensive diagnostic testing and the potential role of genetic mutations in concurrent cancers. The challenge of simultaneously treating these cancers highlights the need for further research and the importance of multidisciplinary approaches.

Abstract Background Hydroxyapatite (HAP) resembles the components of biological hard tissue. Recent research has been interested in the biomedical application of HAP nanoparticles (HAP-NPs) in cancer treatment, HAP-NPs have high cytotoxic activity against cancerous cells, in addition, they are nontoxic to healthy normal cells, biocompatible, biodegradable, and have a high absorption rate within the tissue. Therefore, this study evaluated HAP-NPs' antitumoral activity in Ehrlich solid carcinoma (ESC)-bearing mice, in addition, we examined the anticancer efficacy of combined treatment of a common chemotherapeutic drug such as Cisplatin (CDDP) and HAP-NPs in ESC-bearing mice. Methods Forty female mice were inoculated with 200 µl of diluted ascites fluid containing approximately two million viable cancer cells in the mice's left thigh, after 14 days of inoculation, the mice were distributed into four groups: 10 mice in each. ESC group was administrated distilled water, the HAP-NPs group was treated orally with 100 mg/kg of hydroxyapatite nanoparticles, the CDDP group was administrated intraperitoneally with 5 mg/kg of Cisplatin, the HAP-NPs + CDDP group was treated with both doses of hydroxyapatite and cisplatin, the animal treatment was conducted for 20 days. Antitumor activity was assessed for two durations after 10 and 20 days. DNA damage assessment was performed using comet assay in ESC, in addition, we measured the expression of the following genes (P53, Bcl2, and Bax,) using quantitative real-time PCR, and the apoptotic-related proteins (P53 and Ki-67) using immunohistochemical analysis. A histopathological examination of ESC was performed. Results The obtained data illustrated a promising anticancer activity of HAP-NPs, and the combined treatment of HAP-NPs and CDDP illustrated a higher anticancer efficacy. HAP-NPs, CDDP, and HAP-NPs + CDDP resulted in significant (P

Introduction Bell palsy (BP) is an acquired, idiopathic facial palsy linked to lower motor neuron malfunction of the seventh cranial nerve. Several studies have identified BP as one of the many neuropathies that coronavirus disease 2019 (COVID-19) patients have developed, while other studies disagree.

This study introduces an innovative method for assessing ECG interpretation abilities in medical professionals via eye-tracking data. We examine eye movement patterns from five separate groups of cardiology practitioners utilizing a combination of neuromorphic computing models, including Spiking Neural Networks (SNN), Spiking Convolutional Neural Networks (SCNN), Recurrent Spiking Neural Networks (RSNN), and Spiking Convolutional Long Short-Term Memory (SCLSTM). Utilizing eye movement data, we analyze the skill levels of practitioners in diverse medical positions, including consultants, nurses, and technicians, during ECG evaluations. Our proposed work combines spiking neuron activations with convolutional and recurrent architectures to analyze spatial and temporal gaze patterns that reflect clinical expertise. The suggested SNN, SCNN, RSNN, and SCLSTM models attained accuracies of 84.35%, 93.04%, 94.68%, 99.76% respectively, exceeding standard machine learning approaches in both precision and recall for identifying expertise levels based on visual attention patterns. This paradigm has the potential to construct skill evaluation tools in medical education, specifically for ECG interpretation training, thereby addressing prevalent difficulties related to inconsistent ECG diagnosis methods.

Machine Learning (ML) has been exploited across diverse fields with significant success. However, the deployment of ML models on resource-constrained devices, such as edge devices, has remained challenging due to the limited computing resources. Moreover, training such models using private data is prone to serious privacy risks resulting from inadvertent disclosure of sensitive information. Split Learning (SL) has emerged as a promising technique to mitigate these risks through partitioning neural networks into the client and the server subnets. One should note that although only the extracted features are transmitted to the server, sensitive information can still be unwittingly revealed. Existing approaches addressing this privacy concern in SL struggle to maintain a balance of privacy and utility. This research introduces a novel privacy-preserving split learning approach that integrates: 1) Adversarial learning and 2) Network channel pruning. Specifically, adversarial learning aims to minimize the risk of sensitive data leakage while maximizing the performance of the target prediction task. Furthermore, the channel pruning performed jointly with the adversarial training allows the model to dynamically adjust and reactivate the pruned channels. The association of these two techniques makes the intermediate representations (features) exchanged between the client and the server models less informative and more robust against data reconstruction attacks. Accordingly, the proposed approach enhances data privacy without ceding the model’s performance in achieving the intended utility task. The contributions of this research were validated and assessed using benchmark datasets. The experiments demonstrated the superior defense ability, against data reconstruction attacks, of the proposed approach in comparison with relevant state-of-the-art approaches. In particular, the SSIM between the original data and the data reconstructed by the attacker, achieved by our approach, decreased significantly by 57%. In summary, the obtained quantitative and qualitative results proved the efficiency of the proposed approach in balancing privacy and utility for typical split learning frameworks.

Abstract Background C-reactive protein (CRP) is one of the most commonly monitored inflammatory markers in patients with COVID-19 to gain insight into the inflammation level in the body and to adopt effective disease management and therapeutic strategies. COVID-19 is now less prevalent, and the study of CRP as a biomarker of inflammation still needs deeper understanding, particularly in understanding its role among patients with comorbidities, which are known to influence inflammatory responses and increase the risk of severe outcomes during acute and chronic infectious diseases. The objective of this study was to evaluate the association of major comorbidities such as ischemic heart diseases, diabetes, chronic kidney disease, hypertension, and lung infections e.g. tuberculosis with serum CRP levels in hospitalized COVID-19 patients. Methods This study involves a retrospective observational framework to monitor CRP levels among hospitalized COVID-19 patients after getting ethical approval and patient consent. The information on underlying health conditions or comorbidities and age was collected from the patient data files. The requirement of ventilation, ICU admission, mortality & survival, and CRP levels were monitored based on their daily updates in the data file. Furthermore, the association of CRP levels was evaluated with disease severity and mortality. Results In this study 618 out of 750 hospitalized COVID-19 patients, of which 62.6% were male and 37.4% were female, the levels of serum CRP were significantly influenced by age and comorbidities. No case of hospitalization was observed in children (≤ 14 years) during the study period, while 38.3% of patients belonged to the old age group (≥ 65 years). Comorbidity status varied, with 36.1% of patients without having any comorbidities, 27.8% with one, 23.6% with two, and 12.5% with three or more comorbidities. Descriptive statistics revealed that the CRP levels in the study population averaged 88.92 mg/L (SD = 63.95), ranging from