The article aims to assess the relationship between school proximity and quality education. Apart from social-economic inequalities, students are also placed at disadvantaged positions due to regional disparity, locational disadvantage which hinders their equitable access to education. Keeping in view UNESCO's idea of sustainable and inclusive education this paper presents the case of Higher Secondary School, Malothi of District Doda in Jammu and Kashmir and aims to analyze how distance can influence the academic performance and educational sustainability. By analyzing the data of hundred students of the HSS, Malothi the paper brings out the specific challenges and barriers that students residing in remote areas face while attempting to participate in sustainable education practices. Some suggestions for policy changes or program adjustments based on findings have been provided towards the end of this paper so as to enhance sustainable education practices for students in Malothi regardless of their distance from the school.

The Semantic Gradient Decoupling methodology introduces a novel approach to enhancing the performance of large language models through the separation of semantic and syntactic gradients during backpropagation. This technique addresses prevalent issues such as context fragmentation and gradient instability, thereby improving contextual coherence and semantic precision across various linguistic tasks. The implementation involves specific architectural modifications to opensource models, facilitating the integration of this methodology without significant computational overhead. Empirical evaluations demonstrate notable improvements in metrics including perplexity, cosine similarity, lexical diversity, and named entity recognition accuracy, showing the efficacy of this approach. Comparative analyses reveal that Semantic Gradient Decoupling outperforms baseline methods, highlighting its potential to influence the development of more robust and contextually aware natural language processing systems. The study also discusses the limitations of the current implementation and suggests potential directions for future research to further refine and expand the applicability of this methodology. The findings presented herein contribute to the ongoing discourse on optimizing large language model architectures and offer insights into achieving a balance between computational efficiency and linguistic performance.

In this paper, we attempt to introduce the singularity theory into multiplicative Euclidean space and investigate the singularities of multiplicative spherical Darboux image, multiplicative rectifying Gaussian surface, and multiplicative rectifying developable surface. By constructing three families of multiplicative functions and applying unfolding theory in multiplicative Euclidean space, we establish the relationships between singularities of these objects and the multiplicative geometric invariants. In addition, we find that the multiplicative geometric invariants of multiplicative space curves are closely related to the order of contact with multiplicative helices. Finally, to enhance visual comprehension, an example is used to demonstrate our theoretical results.

Rising temperatures and shifting fire regimes in the western United States are pushing fires upslope into areas of deep winter snowpack, necessitating a new understanding of how hydrologic processes change following wildfire. We quantified differences in the timing and magnitude of quickflow responses to summer rainstorms between six catchments of varying levels of burn severity and seasonal snowpack cover for years 1-3 after the 2020 Cameron Peak fire. Our objectives were to (1) determine how burn severity and snow persistence influenced the magnitude, timing, and likelihood of a quickflow response, (2) quantify change in responses over time, and (3) identify the influencing factors for these responses. We identified maximum 60-min rainfall intensity (MI 60) thresholds yearly for each catchment by determining which MI 60 value best separated rainstorms that generated quickflow from those that did not. We used generalized linear models to determine which predictors were correlated to both the probability of a quickflow response and four quickflow response metrics: peak quickflow, total event quickflow, stage rise, and lag to peak time. We found that rainfall intensity thresholds were only good at predicting a quickflow response in the intermittent snow zone (ISZ), and that these were slightly higher than other reported post-fire thresholds for this region. Both threshold analysis and model results showed that a response was more likely in the persistent snow zone (PSZ) than in the ISZ, likely due to the higher soil moisture content in that area. The effect that burn severity and year post-fire had on the quickflow response was ambiguous, yet model results for stage rise indicate that widespread overland flow only occurred at the severely burned ISZ site. These results demonstrate that the streamflow responses to fire vary between snow zones, indicating a need to account for elevation and snow persistence in post-fire risk assessments.

Introduction Atrial tachycardias are wide spectrum of arrythmias with both focal and macro reentrant etiology. Among them, especially in patients with corrected congenital heart defects, complex arrythmias such as dual-loop MRAT may develop. In such cases successful ablation is always challenging. Methods and Results A 31 year old female with known genetic disorder, post ASD II and VSD corrective surgery was admitted to the cardiology ward due to persistent atrial flutter and a possible tachycardiomyopathy for arrhythmia substrate ablation. During the procedure, difficulties with RA catheterization were observed with IVC anomaly, draining into the RA through azygos vein and SVC. Activation and potential maps were obtained during the tachycardia, revealing a dual-loop reentry around the tricuspid annulus and the superior part of crista terminalis. RF applications were delivered in the expected reentry isthmus on crista terminalis and in the “cavo-tricuspid isthmus”, however a persistent CTI block was not achieved. CT was later performed, revealing multiple venous anomalies, confirming an interrupted IVC draining through the azygos and SVC into the RA with an atrial part of IVC developing only from hepatic veins and a coronary sinus draining into the left atrium. The patient spontaneously converted into sinus rhythm on the next day after the procedure and was referred for a redo procedure at a later term. Results These findings underscore that ablation of complex arrhythmias such as dual loop RA MRAT especially in patients with known genetic disorders and congenital heart is technically demanding and should be performed using advanced technology, under general anesthesia to enhance catheter stability, patient safety, and overall procedural success.

This work introduces a compact deep learning architecture for depth image reconstruction from time-resolved single-photon histograms. Unlike most deep learning approaches that mainly rely on 3D convolutions, our network is implemented purely with 1D convolutions without assistance from other sensors or pre-processing. Both synthetic and real datasets were employed to evaluate the accuracy of our model for challenging signal-to-background ratios (SBRs), ranging from 5 to 1. Conventional maximum likelihood (ML) and another photon-efficient optimization-based algorithm were adopted for performance comparisons. Results from synthetic data show that our model achieves lower mean absolute error (MAE). Additionally, results from real data indicate that our model exhibits better reconstruction for high-ambient effects and provides better spatial information. Unlike existing 3D deep learning models, we process pixelwise histograms continuously instead of splitting the point cloud and stitching them afterward, saving memory and computational resources, thereby laying a foundation for real-world embedded applications.

Despite the frequent failure of aortic valves and pediatric usage of pulmonary valves as a replacement, comparative studies on their full-field collagenous architecture and macroscale geometries are limited. We applied laser micrometry and quantitative polarized light imaging (QPLI), a novel technique for assessing collagen fiber organization, to porcine aortic (n = 8) and pulmonary (n = 8) valve leaflets to non-destructively compare thickness and anisotropy. We confirmed (1) light intensity and sample thickness are inversely related; and (2) aortic valve leaflets are thicker with decreased fiber organization when unloaded. To demonstrate the ability of QPLI to capture dynamic collagen fiber alignment, we imaged leaflets during equibiaxial loading. There was an increase in the aortic valve leaflet’s degree of alignment throughout loading, whereas the pulmonary valve leaflet exhibited relatively unchanged alignment. Understanding the full-field organization of a leaflet’s heterogeneous ECM and how it is altered by pathology can inform therapy development.

A conceptual and mechanistic approach for bridging the fields of macroecology and historical biogeography has been a long-term aim in evolutionary biology. Such a bridge could increase understanding on the processes governing the spatial and temporal generation of biodiversity distribution patterns. This aim has been approached by evolutionary biogeographic inferential statistical models, which incorporate the contribution of environmental factors as scaling parameters. Here, we describe a spatially-explicit, forward-time, numerical (“automat”) model. Our model sets a series of rules to govern speciation, extinction, and dispersal of lineages within an environmentally heterogeneous, two-dimensional landscape. Unlike some previous approaches, niche conservatism is assumed but the model allows environmental conditions to vary spatially and temporally, by simulating over time-series of palaeoclimate data. Speciation is governed by a global speciation rate, whereas the background extinction rate depends on abiotic (palaeoenvironmental conditions) and biotic (species density) factors, hence giving a local background extinction rate. Furthermore, we propose a mechanistic approach in which species are linked through evolutionary history event from a single evolutionary origin. We set different rules to generate the resulting phylogenies to test different factors (time, environment) that govern the inheritance of range distributions. Dispersal follows a Poisson kernel model, with higher probability of migration to contiguous areas and rarer long-distance movements to distant areas. We present ways in which temporal dispersal barriers could modify the resultant spatial patterns. Model parameterisation is based on comparison of simulated spatial patterns with empirical patterns with statistic dependent variables, such as the species’ landscape spatial distribution, species’ geographic range size and location, and the shape of resultant phylogenies. Finally, we propose that our model could be used to evaluate the role played by niche conservatism, ecological vicariance and climatic-driven extinction in the generation of disjunct continental patterns, such as the Rand Flora pattern.

This study aims to isolate clinical adenovirus among hospitalized children in Seremban, Malaysia. After that, determination of the serotype and genetic relatedness of isolated adenovirus through phylogenetic analysis with the published database of human adenovirus from GenBank will be carried out. 26 HAdV-positive retrospective respiratory specimens were collected from pediatric patients with respiratory distress at the Pediatric Department of Hospital Tuanku Jaafar in Seremban, Malaysia. HAdV was isolated in A549 cell culture, sequenced and analysed for HAdV typing. Based on hexon, penton, DNA polymerase, fiber, and virus-associated (VA) RNAII genes, there were 22/26 samples (84.62%) and 3/26 samples (11.54%) belonging to serotypes 7 and 3, respectively. Also, 1/26 sample (3.85%) was determined as HAdV subgroup D. In short, this study serves as a continuation to monitor the prevalence of HAdV serotypes in Malaysia and our result shows HAdV B as the most predominant circulating group of HAdV in Malaysia between 2013 – 2017.

The escalating sophistication of cyber threats calls for innovative detection mechanisms capable of identifying and mitigating ransomware attacks with high precision. Traditional signature-based methods often falter against novel and polymorphic ransomware strains, showing the need for adaptive and intelligent detection frameworks. This research introduces the Dynamic Anomaly-Differential Neural Tensor framework, a novel approach that leverages multi-dimensional tensor representations to model complex system behaviors. By integrating differential operations, the framework adeptly captures subtle deviations indicative of malicious activities. Comprehensive evaluations demonstrate the framework's robustness, achieving high accuracy, precision, recall, and F1-score metrics across diverse ransomware variants. Notably, the system maintains low false positive and false negative rates, enhancing its reliability in distinguishing between benign and malicious activities. The framework's efficient resource utilization and scalability affirm its practicality for deployment in various operational environments. These findings contribute to the advancement of cybersecurity measures, offering a sophisticated and effective solution for mitigating the pervasive threat of ransomware.

Introduction: Duane-radial ray syndrome (DRRS), also known as Okihiro syndrome, is a rare autosomal dominant disorder characterized by the association of Duane anomaly and radial ray malformations. Method：In this study, we report a de novo mutation of the SALL4 gene in a Chinese girl affected by DDRS, leading to an array

The kākāpō is a critically endangered flightless parrot which suffers from exudative cloacitis, a debilitating disease resulting in inflammation of the vent margin or cloaca. Despite this disease emerging over 20 years ago, the cause of exudative cloacitis remains elusive. We used total RNA sequencing and metatranscriptomic analysis to characterise the infectome of lesions and cloacal swabs from nine kākāpō affected with exudative cloacitis, and compared this to cloacal swabs from 45 non-diseased kākāpō. We identified three bacterial species – Streptococcus gallolyticus, Enterococcus faecalis and Escherichia coli – as significantly more abundant in diseased kākāpō compared to healthy individuals. The genetic diversity observed in both S. gallolyticus and E. faecalis among diseased kākāpō suggests that these bacteria originate from exogenous sources rather than from kākāpō-to-kākāpō transmission. The presence of extraintestinal pathogenic E. coli (ExPEC)-associated virulence factors in the diseased kākāpō population suggests that E. coli may play a critical role in disease progression by facilitating iron acquisition and causing DNA damage in host cells, possibly in association with E. faecalis. No avian viral, fungal nor other parasitic species were identified. These results, combined with the consistent presence of one E. coli gnd sequence type across multiple diseased birds, suggests that this species may be the primary cause of exudative cloacitis. These findings shed light on possible causative agents of exudative cloacitis, and offer insights into the interplay of microbial factors influencing the disease.

Background and objectives: This randomized controlled trial evaluated whether higher doses of oseltamivir would improve virological and clinical outcomes in critically ill influenza patients requiring invasive mechanical ventilation. Methods: Forty intubated adult patients with severe influenza A or B from four intensive care units in Hong Kong were enrolled and randomized to receive either a double dose (300 mg/day) or a triple dose (450 mg/day) of oseltamivir for ten days. Baseline data were collected, and outcomes were assessed daily using SOFA and Murray scores. Viral RNA was quantified from nasopharyngeal and tracheal aspirates. The primary outcome was the viral clearance rate after five days of treatment; secondary outcomes included 28-day and hospital mortality rates, changes in viral load, and serial SOFA and Murray scores. Results: Viral clearance rates after five days of treatment were low and similar between the double (15%) and triple-dose groups (10%). No significant differences were observed in 28-day mortality, hospital mortality, ICU or hospital length of stay, or duration of mechanical ventilation between the double and triple-dose groups. However, patients receiving triple doses exhibited a faster decline in influenza A viral load. Conclusions: Triple doses of oseltamivir did not significantly improve virological or clinical outcomes compared to double doses in severe influenza.

Quantum compilation requires solving the initial mapping problem, which is crucial for optimizing qubit assignment and minimizing fidelity in compiled quantum circuits. This study investigates the application of Deep Reinforcement Learning (DRL) to address initial mapping across various qubit topologies, while considering varying qubit connection fidelities. By leveraging policy gradient algorithms-Advantage Actor-Critic (A2C), Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO), and PPO with action masking-DRL agents effectively compute high-quality mappings for both small-and medium-scale quantum architectures, but their efficiency decreases as the system size grows, highlighting the need for further optimization strategies. Fine-tuning hyperparameters and incorporating action masking are shown to be essential for preventing illegal actions and enhancing mapping accuracy, particularly in larger systems. This research also aids the continued development of initial mapping techniques by introducing a comprehensive qubit connectivity database for systematic evaluation of DRL methods across diverse architectures.

Background: Humeral stress fractures in racehorses can progress to catastrophic fracture if unrecognized. Scintigraphy is the current gold standard diagnostic technique but is limited by accessibility and cost. Radiographs are inconclusive until sufficient bone modeling occurs to be visible. It was hypothesized that ultrasound could be used to visualize caudoproximal humeral stress fractures. Objectives: Examine horses with caudoproximal humeral stress fractures with ultrasound. Study Design: Clinical case series. Methods: Seven racehorses that had a clinical history consistent with the presence of a humeral stress fracture were examined using humeral ultrasound and radiography with or without scintigraphy from June 2013 through June 2021. Clinical and imaging findings are described. Results: Seven Thoroughbred racehorses aged 2-4 years had a history of acute onset of a severe lameness with 4 returning to training 3-12 months after layup for an unrelated reason. Nine of 10 humeral stress fractures (2 bilateral, 3 left, 2 right) were identified with ultrasound. Ultrasound abnormalities included a step defect (5 humeri, 5 horses), periosteal callus/roughening (7 humeri, 4 horses) and/or an abnormally convex contour of the caudal aspect of the humeral neck (6 humeri, 5 horses). Radiographs revealed periosteal (8 humeri, 6 horses) and/or endosteal (6 humeri, 4 horses) proliferation adjacent the caudoproximal aspect of the humeral cortex. Scintigraphy of 5 horses identified increased radiopharmaceutical uptake in the caudoproximal aspect of 7 humeri. Serial recheck radiography and ultrasound (5 horses) revealed bone remodeling. Horses were returned to intended use as racehorses (4) or riding horses (2) or were retired (1). Main Limitations: Small case series. Conclusions: Ultrasound is useful for caudoproximal humeral stress fracture detection and can be utilized to monitor healing.

This paper introduces a novel algorithm for secure, authenticated communication in IoT networks, leveraging blockchain’s robustness. Using Practical Byzantine Fault Tolerance (PBFT) as a consensus method for data authentication ensures data genuineness before integration into the network which enhances authenticity across nodes. Additionally, Elliptic Curve Cryptography (ECC) signatures add cryptographic protection to the data, safeguarding data from unauthorized tampering and spoofing. This study includes a mixed-method approach of both quantitative and qualitative analyses to assess the algorithm’s effectiveness, efficiency, and security. Surveys with stakeholders yield empirical data that highlight improvements in network integrity and security, demonstrating PBFT and ECC’s potential for addressing IoT security challenges. This research contributes to existing literature by presenting a scalable, secure framework for IoT communication, underscoring the importance of advanced cryptographic techniques in an evolving digital landscape.

Background and Purpose: Melanoma is an aggressive cancer, and current immunotherapies like mRNA vaccines target tumor antigens to activate immune responses. However, NK cell absence limits efficacy. Gardiquimod enhances NK cell activity, and inhibiting PERK disrupts the unfolded protein response (UPR), inducing tumor apoptosis. This study develops the GD-LPR mRNA vaccine to activate dendritic cells and NK cells via Gardiquimod, combined with a PERK inhibitor (GSK) to shift macrophages to the M1 phenotype, boosting immune responses and tumor cell death. Experimental Approach: The GD-LPR vaccine, using DOTMA, delivers the Gp-100 antigen, stimulating CTLs and enhancing immune responses. It activates dendritic cells and boosts NK cell activity with Gardiquimod. When combined with the PERK inhibitor GSK, promotes apoptosis, and shifts the tumor microenvironment toward M1 macrophage, especially for subcutaneous tumors and lung metastases. Key Results: The GD-LPR vaccine effectively delivered the Gp-100 antigen, activating dendritic cells and CTLs. Gardiquimod enhanced NK cell activity, aiding tumor clearance. The combination with GSK disrupted the UPR, induced apoptosis, and shifted the tumor microenvironment to favor M1 macrophages, enhancing immune responses. This strategy shows potential for treating melanoma, particularly subcutaneous tumors and lung metastases. Conclusion and Implications: The GD-LPR vaccine activates immune responses against melanoma by delivering Gp-100, stimulating dendritic cells, CTLs, and enhancing NK cell activity with Gardiquimod. Combined with GSK, it disrupts the UPR, promotes apoptosis, and shifts the tumor microenvironment to M1 macrophages. This combination offers a promising new approach for treating melanoma, with potential for enhancing immune-based therapies. Keywords: mRNA vaccine; gardiquimod; PERK inhibitor; tumor microenvironment; melanoma

Title: High-volume bladder tumor within an inguinal hernia: A case reportAuthors: Zihan Xue ab†, Liliang Lic†， Yunkai Qie ab†， Guodong Songd， Tianxiao Zhangab， Changli Wuab， Chong Shenab， Zesheng Anab， Rongjiang Liab， Hailong Hu ab *Affiliations: a Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China; b Tianjin Key Laboratory of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China; cDepartment of Urology, Daqing Oilfield General Hospital, Daqing, China; dDepartment of Gastroenterology, The Second Hospital of Tianjin Medical University, Tianjin, China;Contact information: †These authors contributed equally to this work. *Corresponding authors. Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China (H. Hu). Primary Phone: (+86)13662096232. E-mail addresses: huhailong@tmu.edu.cn (H. Hu)

Title Page:Title: Robotic-Assisted Pancreaticoduodenectomy: Rare Report of Middle Colic Artery Originating from the Gastroduodenal Artery.Authors: Yusuf Althawadi, MB, BCh 1, Adnan Alseidi2, MD, Mohamed A Adam2, M.D.1General Surgery, Hamad Medical Cooperation, Doha, Qatar.2 Hepatobiliary & Pancreas Surgery, Gastrointestinal, Surgical Oncology, University of California, San Francisco, San Francisco, USA.Emails:Dr. Yusuf Althawadi: yjo-1998@hotmail.comDr. Adnan Alseidi: adnan.alseidi@ucsf.eduDr. Mohamed A Adam: Mohamed.Adam@ucsf.edu

A document by Mahdiye Abiyarghamsari. Click on the document to view its contents.

Invasive species are non-native plants and animals that can significantly harm societal and natural values such as agriculture, social amenity, the environment and native ecosystems and species. Efficient preparation for their incursion requires understanding their potential impact, influenced by factors such as introduction pathways, host material availability, climate suitability, and the value of affected agriculture. A crucial factor that is specific to the incursion and therefore unpredictable beforehand is the size of the outbreak at the time of detection, which can curtail the range of management options: if the invading population is small then eradication may be affordable, whereas if it is large then eradication may be impossible. We propose a statistical model for this random variable to aid decision-support systems. We analyze the relationship between surveillance and organism detection using survival analysis, treating detection as analogous to a failure event. This approach links the distribution of infestation size at detection with the probability of detecting an incursion—specifically, the hazard function describing the instantaneous detection rate. Under this survival model, we connect the probability density function of infestation size at detection to the hazard function. Moreover, we introduce an approximation using the Weibull distribution to model the population size before pest detection. This approximation holds when dealing with a small fixed number of traps or a low probability of detection per trap. By assuming a relationship between the invasive population size and the time it remains undetected, we estimate the probability density function for the population’s duration of occupancy. We develop a computer program to perform the analysis, using the Mediterranean fruit fly as a case study to demonstrate its application. We believe that representing the invasive population size at detection provides valuable insights into control and eradication strategies, potentially applicable to broader invasive species management efforts.

Tools used 3. Languages  PHP  HTML  CSS  BOOTSTRAP  MYSQL 4. Program  Home.php  Connect.php 5.

A document by Kamal Acharya. Click on the document to view its contents.

The escalating threat of malicious encryption activities requires the development of advanced detection mechanisms capable of identifying and mitigating ransomware attacks with high precision. The Dynamic Entropic Signature Analysis (DESA) framework introduces a novel approach that leverages entropy variations to detect unauthorized encryption processes in real-time. By monitoring entropy fluctuations, DESA effectively distinguishes between benign and malicious activities, thereby enhancing the accuracy of ransomware detection systems. The framework's adaptive computational divergence metrics allow for dynamic threshold adjustments, accommodating the evolving nature of ransomware behaviors and reducing false positive rates. Comprehensive evaluations demonstrate DESA's superior performance compared to traditional detection methods, with notable improvements in detection accuracy, precision, and recall. Additionally, DESA's low resource utilization ensures minimal impact on system performance, facilitating seamless integration into existing cybersecurity infrastructures. The insights gained from entropy variation analyses provide a deeper understanding of ransomware behaviors, reinforcing the validity of entropy monitoring as a reliable indicator of malicious activities. Through these contributions, DESA offers a robust and efficient mechanism for enhancing cybersecurity measures against the evolving threat of ransomware.