Hanby Environmental (United States)

Introduction: Chronic urticarial (CU) is the condition characterized by the occurrence of itchy, erythematous and evanescent wheals for prolonged duration, usually more than 6 weeks. Its recurrence and flares have been linked to decreased quality of life and work productivity in vast majority of patients. Herein, we have attempted to delve into prevalence and association of this chronic yet intractable entity. This will aid in assessing concomitant factors associated with this chronic condition and facilitate holistic treatment for this chronic condition. Methods: A total of 125 patients with diagnosis of CU were included in this hospital-based observational study. Demographic details of the patients along with the associated factors were recorded in a pre-formed proforma and analyzed. Results: More than three fourth of our patients had Chronic Spontaneous Urticaria (CSU), and amongst them, generalized wheal was present in 92% of them. Angioedema was present in 40% the CU patients. An increased incidence of CU was seen in almost three fourth (72%) of our patients in age group of 16-45 years with predilection in females with a ratio of 1.7:1. Forty eight percent of our patients had severe itch and the mean duration of CU in our patient was 34.98 ± 5.26 months. Our study also showed an increased incidence of ASST positivity in 48% of CU subjects studied. Conclusions: This study re-iterates clinic-epidemiological and associated factors in chronic urticaria. Thus, relevant associated factors such as angioedema, severity of itch, thyroid dysfunction, helminthic co-infestation, exacerbating factors and atopiform predilection can be identified and managed timely to treat this recalcitrant and chronic entity which often takes a toll on patient’s quality of life and daily activities.

Cyber threats are evolving rapidly, making traditional malware detection approaches insufficient in handling sophisticated attacks such as zero-day exploits, polymorphic malware, and advanced persistent threats. As a solution to these problems, this study presents a novel hybrid AI-based data analysis framework for cyber threat intelligence and anomaly detection. We specifically proposed a model that combines autoencoders, random forests, and CNN-LSTM architectures to improve anomaly detection and classification. The dataset used for training and evaluation is the Microsoft Malware Dataset, a publicly available dataset. The AE-RF module learns the normal behavior of the system to detect abnormal behavior, and the CNN-LSTM model identifies spatial and temporal properties of malware for execution patterns. This article ensures that the dataset is well-preprocessed, including feature extraction, PCA, and SMOTE for class balancing to provide optimal model performance. The experimental results show that AE-RF has an area under curve (AUC) score of 0.91 and CNN-LSTM has 0.94, which demonstrates that unsupervised and supervised techniques together have high classification accuracy. This hybrid approach not only improves malware detection accuracy but also enhances cyber threat intelligence by providing interpretable insights into attack patterns. The study concludes that integrating multiple AI techniques can significantly improve cybersecurity defenses against evolving threats, paving the way for real-time, AI-powered malware detection solutions.

ABSTRACTMedium‐voltage back‐to‐back (MVB2B) converters can connect two distribution systems and quantifiably transfer power between them. This function can enable the MVB2B converter to exchange distributed energy resource (DER)‐generated power between two systems and bring significant value to enhancing distribution system DER adoption. Our previous work analysed and demonstrated the value MVB2B converter can bring to DER integration. As continuous work, this paper presents a methodology that helps address the MVB2B converter sizing and location selection problem in distribution systems with high DER penetrations. The proposed methodology aims to address three critical problems for MVB2B converter implementation in the real world: (1) which distribution systems are better to be connected, (2) what converter size is appropriate for connecting the distribution systems, and (3) where the optimal connection points are in the systems for connecting the MVB2B converter. The proposed methodology has been demonstrated by case studies that include various scenarios involving distribution systems with different dominated load types and high photovoltaic penetrations. The results demonstrate that selecting the optimal converter size based on net revenue and time of return considerations leads to a balance between maximizing energy savings and minimizing financial payback periods. Furthermore, feeder pair selection based on load profile standard deviation effectively identifies systems that derive the greatest value from MVB2B integration. Finally, an optimized connection point selection approach using a voltage load sensitivity matrix ensures minimal system impact while facilitating efficient power exchange. These findings provide practical insights for the real‐world deployment of MVB2B converters to enhance DER hosting capacity and improve grid resilience.

Background: Early childhood is a critical period for language acquisition, with parent-child interaction serving as a foundational determinant of linguistic development. While numerous studies have explored this relationship in high-income Western contexts, limited evidence exists from South Asian populations, where cultural, socioeconomic, and educational dynamics may influence outcomes. Objective: To examine the association between the quality of parent-child interaction and language development in children aged 3 to 6 years and assess differences across demographic subgroups. Methods: An analytical cross-sectional study was conducted among 63 children and their primary caregivers in Chakwal, Pakistan. Participants were selected through purposive sampling. Language development was assessed using the Parent Questionnaire for Early Language Development, while cognitive baseline was controlled using the Slosson Intelligence Test (SIT). Descriptive statistics, Pearson correlation, independent sample t-tests, and one-way ANOVA were performed using SPSS v20. Results: A statistically significant positive correlation was observed between parent-child interaction scores and language development (r = 0.33, p < 0.001). Rural families reported significantly higher interaction scores than urban families (p = 0.023), though no significant differences were observed in language outcomes across gender, parental education, or occupation. Conclusion: Higher-quality parent-child interactions are associated with improved language development in early childhood. Urban children with low interaction scores may be at greater risk for language delays, underscoring the need for targeted early interventions.

The National Helpline 333 is Bangladesh’s nationwide non‑emergency, citizen‑service contact center. As the platform’s scope grows from information and grievance services to proactive referrals and social protection, its human‑agent–only operating model faces constraints in scale, cost, and 24/7 availability. This paper examines whether and how Artificial Intelligence (AI) Voice Bots can augment 333. Using a qualitative, mixed‑sources approach (policy/operational documents, international case studies, and expert inputs), we (i) map opportunities across access, efficiency, inclusivity, and analytics; (ii) identify technical, ethical, and socio‑organizational risks; (iii) propose an architecture for Bangla/English voice automation integrated with CRM/KMS and human escalation; and (iv) outline policy and governance guardrails aligned with international AI principles. Findings suggest that a tiered ‘AI‑first, human‑assured’ model can reduce average handling time, increase first‑contact resolution, and extend after‑hours coverage—provided investments are made in local‑language ASR/NLU, quality knowledge bases, human‑in‑the‑loop review, and robust data governance (UNDP, 2023; OECD, 2019).

Machine learning methods have become instrumental in the prediction of heart diseases, offering a potent means of early detection and personalized risk assessment. By harnessing various algorithms and techniques, these models analyze extensive datasets encompassing patient demographics, medical histories, and clinical measurements. The interpretability of machine learning models remains a pertinent issue, as the complex algorithms often lack transparency, hindering clinicians’ understanding of the underlying decision-making process. Furthermore, model bias and generalization issues can arise, particularly when datasets are not representative of diverse populations or when algorithms are trained on data from specific healthcare settings. Addressing these challenges requires a concerted effort to improve data collection practices, enhance model interpretability, mitigate bias, and ensure equitable access to healthcare technologies. This paper proposed the Entropy Hidden Markov Model (EHMM) for heart disease prediction. The proposed EHMM model uses the estimation of entropy with consideration of local and global features for the estimation of modalities in the heart. With the estimated entropy Hidden Markov Model (HMM) is employed for disease detection and prevention. With the proposed EHMM the feature points are computed for the heart diseases. The experimental results demonstrated that the proposed EHMM model achieves a higher prediction rate of 0.98 with minimal loss than the conventional ML with the classifiers SVM and RF.

Scrub typhus is a mite-born acute febrile illness endemic to a part of the world known as the “tsutsugamushi triangle,” which also includes Nepal. It has a wide range of presentations with multiple organ involvement, including meningoencephalitis. We present a unique case of a 30-year-old lady with scrub typhus meningoencephalitis, which showed drastic improvement with doxycycline. This case highlights the importance of high suspicion in an endemic area with limited diagnostic facilities. With the early initiation of empirical therapy, fatal complications of scrub typhus infection such as meningoencephalitis can be prevented.