Clinicoepidemiological Findings and Pathological Characteristics of Different Types of Cutaneous Warts

Cutaneous warts, which result from infections by human papillomaviruses, are a common skin disease worldwide. They are categorized as  common, plantar, plane, genital, filiform, periungual and mosaic warts. Genital warts represent the most common sexually transmitted infections; however,  no sufficient information are available in Iraqi Kurdistan region, concerning their frequency rates; therefore, this study aims to determine the epidemiological and clinical features of patients with warts in this region, with special emphasis on estimating the frequency rates of genital warts and on analyzing their histopathological characteristics. A specially designed questionnaire was designed to collect socio-demographic and clinical data, such as age, gender, occupation, education and residency, from 420 patients with wart, together with the type and anatomical location of the warts. In addition, histopathological examination was performed for 20 patients with genital warts. Out of the total number of wart patients involved in this study, 255 were males, and 165 were females. Common warts were the most common type (39.0%) followed by the plantar and genital warts (30.5% and 11.9% respectively). Students were the most common individuals affected by the warts (46.0%), followed by self-employed persons (29.5%). Among patients with genital warts, most of the wart lesions were seen in multiple locations around the genital organs, and the papular form was the most frequent type seen. Histopathological examination of the genital wart lesions showed papillomatosis, acanthosis, koilocytosis, dysplasia, parakeratosis, and one case of squamous cell carcinoma in situ

Dealing with the Outlier Problem in Multivariate Linear Regression Analysis Using the Hampel Filter

Outliers in multivariate linear regression models can significantly distort parameter estimates, leading to biased results and reduced predictive accuracy. These outliers may occur in the dependent variable or both independent and dependent variables, resulting in large residual values that compromise model reliability. Addressing outliers is essential for improving the accuracy and robustness of regression models.  In this study, proposes a Hampel filter-modified algorithm to dynamically detect and mitigate extreme values, enhancing parameter estimation and predictive performance. The algorithm optimizes window size and threshold parameters to minimize mean square errors, making it a robust approach for handling outliers in multivariate regression analysis. To assess its effectiveness, simulations and real datasets were analyzed using a MATLAB-based implementation. The algorithm was compared with the classical Hampel approach to evaluate improvements in outlier detection and suppression. The results indicate that the proposed method effectively identifies and removes extreme values, leading to improved parameter estimation accuracy, enhanced model stability, and greater predictive performance and the performance was analyzed using the Mean Squared Error (MSE). The adaptive nature of the filter minimizes the impact of outliers, ensuring a more reliable regression model. The Hampel filter-modified algorithm provides an effective and adaptive solution for handling outliers in multivariate regression models. By dynamically identifying and mitigating extreme values, it enhances model accuracy, strengthens predictive capabilities, and ensures greater resilience against data variability. This approach offers a valuable tool for researchers and practitioners working with outlier-prone datasets, significantly improving the reliability of multivariate regression analysis

AI-Based Load Balancing Using Decision Tree Regressor for Parallel Matrix Computation in Cloud Environments

Cloud computing is an evolving technology of current information systems that supports dynamic sharing and elastic provision of resources and services. With increasing demands for computational resources, efficient workload assignment has become an important challenge. Current load balancing methods based on traditional approaches fail to suit dynamic server performance and contribute to the inefficient utilization of available resources, latency, and delays. In response to this challenge, this paper suggests an AI-driven load balancer based on a decision tree regressor to dynamically control task allocation within a parallel cloud system. The system operates to handle computationally heavy tasks, i.e., matrix multiplication, across different servers based on real-time performance measures such as Central Processing Unit (CPU) usage, memory utilization, time of execution, and networking latency. Model training was done with historical data obtained from past executions, incorporated into the web server to facilitate adaptive decision-making. It was tested experimentally with different levels of server scalability as well as matrix complexity. It was contrasted with a static, manual load balancer. All critical performance measures were found to be significantly improved by the AI-based methodology, with the total execution time reduced from 7,060 milliseconds to 1,000 milliseconds; network latency was also reduced to 5.12 ms, down from 214 ms; and the method reduced the overall use of CPU by 33% and overall use of memory by more than 85%. These findings confirm that intelligent, data-driven load balancing offers superior scalability, responsiveness, and efficiency for cloud-based parallel processing systems

Formulation, Phytochemical Characterization, and Clinical Assessment of a Novel Natural Supplement Targeting Body Composition in Physically Active Individuals: A Randomized Clinical Trial

Nutritional supplementation plays a pivotal role in optimizing body composition, recovery, and performance in physically active individuals. This study aimed to evaluate the effects of an 8-week intervention with a novel natural supplement (NNS) on body composition participants. In a randomized, placebo-controlled trial, 55 participants (NNS: n = 28; placebo: n = 27) consumed either the NNS formulation comprising whey and pea protein, oats, flaxseed, spinach, beetroot, and chia or a placebo. Body composition (muscle mass, weight, BMI, fat %), oxygen saturation, and heart rate were measured at baseline and post-intervention. After 8 weeks, The NNS group showed a significant increase in muscle mass by 41.9%, rising from 12.96 kg to 18.41 kg (p = 0.000), while the placebo group only increased from 13.94 kg to 14.44 kg. Body weight in the NNS group decreased by 8.14 kg, from 76.54 kg to 68.40 kg (p < 0.001), whereas the placebo group gained 2.46 kg. BMI improved in the NNS group, dropping from 30.98 kg/m² to 25.7 kg/m² (p < 0.001), while remaining stable in the placebo group. Oxygen saturation increased from 95.85% to 98.62% (p = 0.001), and heart rate decreased from 76.00 bpm to 68.22 bpm (p = 0.004) in the NNS group. Fat percentage decreased from 30.63% to 27.11% (p = 0.0297). In conclusion, the novel natural multi-ingredient supplement significantly improved muscle mass, reduced body weight and BMI, and enhanced cardiopulmonary parameters, indicating its potential as a safe and effective nutritional strategy for improving body composition and performance in physically active individuals

Molecular Characterization of Biofilm-related Virulence and Resistance genes in Candida albicans Isolates from Women with Vulvovaginitis

One of the most prevalent reasons for gynecologic consultations is vulvovaginitis (VV), particularly vulvovaginal candidiasis (VVC). The etiology of VVC mostly associated with Candida albicans (C. albicans). The recurrence of VVs and the development of resistance to antimicrobials, along with efforts to find therapeutic alternatives are of paramount importance. Thus, this study aims to find the prevalence C. albicans virulence, resistance genes in addition to its susceptibility to antifungals. In this case control study, a total of 125 high vaginal cotton swabs attained in duplicate. from 100 wome clinically diagnosed with VVC and 25 controls (non-VVC). C. albicans was isolated with Hicrome differential agar and confirmed with species-specific primers using Polymerase chain reaction. Genes of the studied virulence determinants, Aglutinin-Like-Sequence (ALS1, ALS3), Hyphal Wall Protein1 (HWP1) as well as resistance determinants associated such as multidrug-resistance (MDR1) and Candida drug resistance (CDR1, CDR2) were also tested. The prevalence of Candida species were 70% and 32% in case and control groups, respectively. Further, the frequency of C. albicans were 88.57% (case group) and 100% (control group). The most common virulence gene was ALS3, present in 96.7% of case group and 87.5% of control group. Additionally, the results indicated that 98.39% of case group and 100% of control group exhibited MDR1 and CDR2 from confirmed isolates of C. albicans. Lastly, the result showed the highest antifungal resistance rates in case group were against voriconazole (70.97%) and fluconazole (40.32%), whereas in control the antifungal resistance was 75% for both voriconazole and fluconazole. In conclusion, high rate of virulence and resistance genes amongst women with VVC and therefore, the study suggests the importance of these genes to be targeted in new antifungal drugs

A Hybrid Approach to Cloud Data Security Using ChaCha20 and ECDH for Secure Encryption and Key Exchange

Cloud computing has transformed data storage and processing by offering on-demand resources and global accessibility. However, this convenience introduces significant security risks due to the reliance on third-party services, raising concerns about data confidentiality and integrity. This research proposes a hybrid encryption model that combines the high-speed ChaCha20 algorithm for data encryption with the Elliptic Curve Diffie-Hellman (ECDH) protocol for secure key exchange. The model ensures robust data protection in Cloud environments by generating a ChaCha20 key, encrypting it with ECDH, and securely storing encrypted key fragments in the cloud for later reassembly and decryption. This approach enhances security during data transmission and storage while mitigating the common vulnerabilities found in single-algorithm solutions. The study evaluates and compares the performance of ChaCha20 with ECDH against Rivest-Shamir-Adleman (RSA) with advanced encryption standard (AES) and Blowfish with Elliptic-Curve Cryptography (ECC). The results show that ChaCha20 with ECDH provides the fastest encryption time of 2ms and a key generation time of 15.8ms, with moderate memory usage. By contrast, RSA with AES is slower but offers consistent memory usage, while Blowfish with ECC balances speed and memory efficiency. The proposed hybrid model outperforms traditional encryption methods in both speed and security, making it suitable for modern cloud applications requiring scalability and high performance. Future research could focus on optimizing this model for resource-constrained environments, such as IoT and mobile

Evaluation of Serum Interleukin-33 and Tumor Necrosis Factor-α Levels in Patients with Psoriasis: Correlation with Disease Severity

Psoriasis is an immune-mediated dermatological disorder marked by accelerated skin cell proliferation, leading to thickened, rough, scaly lesions capable of causing itching, discomfort, and inflammation. This study investigates Interleukin-33 (IL-33) in psoriasis pathogenesis and evaluates its therapeutic potential. By understanding its mechanisms, the research aims to create effective treatment strategies for use in clinical practice, improving the well-being of individuals with the disease. Serum concentrations of Interleukin-33 and tumor necrosis factor-α (TNF-α) were assessed using the ELISA test in 44 subjects with psoriasis and 44 matched healthy controls. The severity of psoriasis was evaluated using Psoriasis Area and Severity Index (PASI scores), which enabled stratification into mild, moderate, and severe forms. Data were statistically analyzed to compare cytokine levels in patients and controls and to examine the relationship between cytokine concentrations and disease severity. Compared to their matched controls, psoriasis patients exhibited significantly increased median concentrations of Interleukin-33 [(268: 235–316) and tumor necrosis factor-α (294: 241–435). Also, the serum TNF-α levels exhibited a notable correlation with PASI scores (r= 0.389, p value= 0.009), while IL-33 levels did not exhibit a statistically significant association with PASI scores (r= 0.251, p value= 0.100). This study demonstrated a significant elevation in serum TNF-α and IL-33 concentrations in individuals with the disease, suggesting their involvement in disease pathogenesis. Moreover, TNF-α levels showed a proportional correlation with disease severity, as reflected by PASI scores, indicating its potential role as a biomarker for monitoring psoriasis progression. This positive association suggests a possible interplay in disease progression. 

Influence of Cabling on Photovoltaic System Performance: Wire Length, Diameter, and Material

Despite advancements in solar PV technology, significant challenges remain in the Global South, including financial, human resource, environmental, and technological constraints. System losses—caused by reflection, temperature effects, inverter inefficiency, cabling losses, shading, and degradation—are a major concern. This study examines how cabling parameters—wire length, diameter, and material—affect PV system performance and energy losses. Using a computational model, it evaluates a 3 kWp PV system in Durban, South Africa, analyzing efficiency, specific annual yield, and avoidable CO₂ emissions across various cabling configurations. The study’s key findings include: at a constant wire diameter of 4 mm, specific annual yield decreases as wire length increases, dropping from 977.36 kWh/kW at 5 m to 966.32 kWh/kW at 50 m, reflecting efficiency losses; at a constant wire length of 20 m, yield improves with increasing diameter, rising from 970.71 kWh/kWp at 2.5 mm to 977.81 kWh/kWp at 20 mm. Beyond 25 mm, yield gains diminish, stabilizing around 978.39 kWh/kW at 90 mm; at a fixed wire length of 20 m, avoided CO₂ emissions increase with wire diameter up to 25 mm, after which gains level off from 30 mm to 90 mm; at a constant diameter of 4 mm, avoided CO₂ emissions increase from 1,378 kg/year at a wire length of 5 m to 1,363 kg/year at 50 m. These findings highlight the importance of optimizing cabling parameters to minimize system losses and enhance the overall efficiency and sustainability of PV systems

Isolation and Characterization of Listeria monocytogenes in Selected Food Products

Listeria monocytogenes is a significant foodborne pathogen capable of causing severe illness with a high mortality rate, especially in vulnerable populations. Its ability to survive under adverse environmental conditions and contaminate a wide range of foods, including ready-to-eat products, makes it a major public health concern. In Sulaymaniyah and Halabja provinces, there is a lack of systematic data on the prevalence, virulence characteristics, and antimicrobial resistance of L. monocytogenes in locally consumed foods, which limits effective risk assessment and control strategies. This study aimed to determine the prevalence, virulence gene profiles, and antimicrobial resistance patterns of L. monocytogenes in selected dairy, vegetable, and meat products using cultural isolation, biochemical identification, and Polymers Chain Reaction based molecular confirmation. A total of 124 food samples were collected and tested, with molecular detection targeting prs, lmo1030, and 16S rRNA genes, and virulence profiling for hlyA, prfA, and inlA. Antimicrobial susceptibility was assessed against ten antibiotics using the disk diffusion method. Twelve samples (9.6%) were positive for L. monocytogenes, with the highest prevalence in traditional semi-hard cheese (40%), lettuce (25%), and celery (25%). The prfA and inlA genes were each detected in 41.6% of isolates, and hlyA in 33.3%. All isolates were resistant to ampicillin but remained susceptible to most other antibiotics. Thus, these findings provide essential baseline data that can guide targeted food safety interventions and strengthen public health protection measures in this region. Future studies should expand sampling to a wider range of food categories, include seasonal monitoring, and apply whole-genome sequencing to better understand the epidemiology and resistance mechanisms of L. monocytogenes.

Assessing the Urban Design Features of Historical Street

This study offers a comprehensive and critical evaluation of the urban design elements of Goran Street, a central and historically significant artery in Sulaimani, located in the Kurdistan Region. The research focuses on four primary areas: the street's physical characteristics, spatial organization, the utilization of open spaces, and its ability to meet user needs. Employing a mixed-methods approach—including field measurements, photographic documentation, on-site observations, and user questionnaires—the study reveals several critical deficiencies in urban design quality. Physically, Goran Street suffers from inconsistent building heights and architectural styles, leading to visual disunity. The chaotic use of building materials and color palettes further contributes to an aesthetically jarring environment. Pedestrian pathways are poorly maintained, with inadequate paving, signage, and street furniture, making the area less inviting for foot traffic. User-centered analysis reveals that Goran Street meets only 40.8% of the surveyed expectations related to accessibility, personal safety, and the availability of public services. Despite this, the frequency of open space usage remains high, with 75% of respondents indicating regular engagement, primarily due to the street's central location, though the quality of these open spaces scores a concerning 0%. Additionally, the lack of cultural infrastructure, such as galleries, museums, or performance spaces, limits the street’s potential to serve as a cultural and tourism hub. The study concludes by calling for strategic interventions to enhance Goran Street’s urban appeal. Recommendations include improving walkability, redesigning public spaces, integrating cultural identity, and enforcing planning regulations to foster a vibrant, inclusive, and historically enriched urban environment.