Variety Of Features Research Articles

Genetic Characterization of Plasmodium falciparum Histidine-Rich Protein 2 Deletions and Their Impact on Malaria Interventions in Odisha, India.

Diagnostic escape via Plasmodium falciparum (P. falciparum) histidine-rich protein 2 (pfhrp2) gene deletions is a major potential hurdle for global malaria elimination efforts. We investigated the prevalence of pfhrp2 gene deletions in 15 malaria-endemic villages in the state of Odisha, India, and modeled their impact on an ongoing in-country malaria intervention program. We found that 61.6% of subpatent P. falciparum infections (i.e., rapid diagnostic test [RDT]-negative and positive by polymerase chain reaction [PCR]) had pfhrp2 gene deletions, which were predominantly located in the exon 2 region (96.2%) and largely identified in samples from febrile individuals (82.6%). DNA sequencing and protein diversity features were characterized in a subset of samples from individuals with subpatent infections carrying intact pfhrp2 exon 2 loci. Our analyses revealed novel amino acid repeat motifs (231-293 amino acids), and these variant repeat sequences differed from those of RDT+/PCR+ samples. We also evaluated the state-sponsored mass screening and treatment intervention in the context of pfhrp2 gene deletions. We found that mass screening and treatment conducted alongside additional interventions (e.g., long-lasting insecticidal net distribution, indoor residual spraying) reduced the relative risk of infection for both P. falciparum parasites harboring a pfhrp2 deletion (adjusted relative risk ratio [aRRR] = 0.3; 95% CI = 0.1-1.0) and P. falciparum parasites with intact pfhrp2 genes (aRRR = 0.4; 95% CI = 0.2-1.1) when compared with the use of mass screening and treatment by RDT alone. Combined, our findings highlight the need for alternative diagnostic targets and tools as India moves toward its goal of malaria elimination by 2030.

Does Character–Audience Matching Increase the Persuasive Impact of Anti-Sugar-Sweetened-Beverage (SSB) Consumption Messages?

ABSTRACT It is widely believed that persuasion is enhanced by including characters in a message whose features correspond with those of the audience. Matching on gender, age, and ethnicity may be especially appealing because these features are immediately perceivable. We examined the persuasive impact of matching on these variables using a large sample of real-world messages (k = 91) drawn from a single topic/context (messages that advocate for reduced consumption of sugar-sweetened beverages). Contrary to prediction, the data (N = 692) showed no significant main effects. Gender-matching, however, was moderated by prior SSB consumption such that it produced higher levels of perceived message effectiveness among heavier drinkers. Neither age- nor ethnicity-matching yielded a similar interaction. A composite similarity (gender + age + ethnicity) effect was observed though it was likely the product of gender-matching alone. The results suggested that the matching principle is intuitively attractive, but empirically suspect. There may be principled reasons to include demographically diverse characters in health campaigns, but anti-SSB message designers should keep their expectations low regarding advantages from demographic matching.

Analysis of Tidal Cycle Wave Breaking Distribution Characteristics on a Low-Tide Terrace Beach Using Video Imagery Segmentation

Wave breaking is a fundamental process in ocean energy dissipation and plays a crucial role in the exchange between ocean and nearshore sediments. Foam, the primary visible feature of wave breaking areas, serves as a direct indicator of wave breaking processes. Monitoring the distribution of foam via remote sensing can reveal the spatiotemporal patterns of nearshore wave breaking. Existing studies on wave breaking processes primarily focus on individual wave events or short timescales, limiting their effectiveness for nearshore regions where hydrodynamic processes are often represented at tidal cycles. In this study, video imagery from a typical low-tide terrace (LTT) beach was segmented into four categories, including the wave breaking foam, using the DeepLabv3+ architecture, a convolutional neural networks (CNNs)-based model suitable for semantic segmentation in complex visual scenes. After training and testing on a manually labelled dataset, which was divided into training, validation, and testing sets based on different time periods, the overall classification accuracy of the model was 96.4%, with an accuracy of 96.2% for detecting wave breaking foam. Subsequently, a heatmap of the wave breaking foam distribution over a tidal cycle on the LTT beach was generated. During the tidal cycle, the foam distribution density exhibited both alongshore variability, and a pronounced bimodal structure in the cross-shore direction. Analysis of morphodynamical data collected in the field indicated that the bimodal structure is primarily driven by tidal variations. The wave breaking process is a key factor in shaping the profile morphology of LTT beaches. High-frequency video monitoring further showed the wave breaking patterns vary significantly with tidal levels, leading to diverse geomorphological features at various cross-shore locations.

PDSS1 mutations-associated steroid-resistant nephrotic syndrome: case report and review of literature.

PDSS1 mutations hamper Coenzyme Q10 biosynthesis and cause a rare multisystem mitochondrial disease characterized by diverse clinical features and limited treatment options. To date, renal involvement has been reported in only one patient. We report a new female patient with compound heterozygous PDSS1 mutations and the clinical outcome following a trial of Coenzyme Q10 therapy. Our patient presented with developmental delay and regression at age three, which progressed to steroid-resistant nephrotic syndrome at age six, leading to stage 5 chronic kidney disease. Whole exome sequencing identified two pathogenic variants in the PDSS1 gene. High doses of Coenzyme Q10 therapy had no effect at this advanced stage of disease. Coenzyme Q10 treatment did not appear to improve the clinical outcome in this patient. Further data is needed to better understand the phenotypic spectrum of PDSS1-associated disruption, and the potential benefit of early Coenzyme Q10 therapy.

Unveiling Key Correlation Studies in Local Germplasm of Green Gram [Vigna radiata (l.) Wilczek

The present investigation entitled “Unveiling key correlation studies in local germplasm of green gram [Vigna radiata (L.) Wilczek]” was conducted at Agricultural Botany section farm, College of Agriculture, Dhule during kharif-2022. The experimental material included 42 genotypes of mungbean which were evaluated in a Randomized Block Design (RBD) with two replications, representing different eco-geographic regions and the source of material was Assistant Pulse Breeder, Oilseed Research Station, Nimkhedi, Jalgoan and local collection from Dhagdaon, Akkalkua, Nandurbar of Maharashtra state. Genotypes were evaluated for twelve diverse characters and correlation coefficient studies were conducted. The weight of 100 seeds (0.5054), the number of seeds per pod (0.4168), the thickness of the pods (0.339), the number of clusters per plant (0.2602), the number of pods per plant (0.2232), and the seed output per plant were the most significant and favourable relationships. It has a negative and non-significant correlation with the number of pods per cluster (-0.0534), but a positive correlation with plant height (0.1748), pod length (0.1737), protein content (0.159), 50 per cent flowering (0.0282) and days to maturity (0.0071). Based on the results, the highest yield of mungbean may be obtained from genotypes with higher protein content, early flowering and ripening, maximum plant height, long and thick pod size, more seeds per pod, and superior seed weight.

Personalized College English Learning Experience Assisted by Artificial Intelligence: An Algorithm-Driven Adaptive Learning Approach

Personalized College English Learning offers customized education based on individual necessities and aims. Students enhance their linguistic ability efficiently over engaging sessions, specific curricula, and professional teachers. This study aims to progress a smart Artificial Intelligence (AI)-based adaptive learning method for enhancing personalized college English learning experience. Our proposed model provides an intelligent detecting device-enabled setting for learning English over big data analysis and machine learning based on a DM approach. Intelligent sensing strategies detention significant statistics from college English students and big data analysis examines the data to produce beneficial insights that increase real-time personalizing of English learning experiences. We suggest an innovative Starling Murmuration fine-tuned Dynamic Weighted Random Forest (SM-DWRF) system for classifying the data. Our model influences perceptions from collective behavior in starling flocks to dynamically alter feature weights in DWRF arrangement. It integrates fine-tuning mechanisms to adaptively weight features, developing classification accurateness and strength in adapted college English learning settings. We implemented our suggested model in Python software. In the assessment stage, we accurately measure the effectiveness of our proposed SM-DWRF model in identifying diverse features of English learning across various parameters. Our experimental findings incontestably showcase the greater performance of our model associated with conventional approaches in classifying content from multimodal statistics. Significantly, we perceive prominent enhancements in reliability and robustness, when acclimating to dynamic learning settings.

Adult Intussusception in Jordan: Demographics, Clinical Features, and Outcomes from a Tertiary Hospital.

BACKGROUND Adult intussusception (AI) is a rare condition with diverse clinical presentations and management challenges. Despite its rarity, understanding its epidemiology, clinical features, and predictive factors distinguishing benign and malignant lead points is crucial for effective management. This study aimed to assess the demographic and clinical characteristics of patients with pathological AI and examine factors associated with malignant lead points. MATERIAL AND METHODS Medical records of patients aged >18 years with diagnosis of AI between January 1, 2014, and January 1, 2024 were retrospectively analyzed. Patients were classified based on location and etiology of intussusception. Predictive factors for malignant lead points were assessed, including age, sex, presenting symptoms, location, and size of intussusception. Computed tomography (CT) scan images were reviewed for diagnosis confirmation. Transient small bowel intussusceptions and intussusceptions related to feeding tubes were excluded. RESULTS Twenty-six cases of pathological AI were identified over 10 years, with a male predominance (69.2%) and a mean age of 53.3 years. Abdominal pain was the most common presenting symptom (65.4%), with bowel obstruction diagnosed in 23.1% of cases. CT scans were the primary diagnostic modality (92.3%). Colocolic intussusceptions were most prevalent (53.8%), and surgical management was common (69.2%). Histopathological examination revealed benign lead points in the majority (57.7%) of cases, with lipomas and polyps being the most common. Bleeding per rectum was significantly associated with malignant lead points (P=0.011). CONCLUSIONS AI presents with diverse clinical features. It predominantly affects the colon. Bleeding per rectum indicates a higher likelihood of malignant lead points. A multidisciplinary approach is essential for optimal case-based management.

Biotin-Thiamine-Responsive Basal Ganglia Disease Case report

Introduction Biotin-thiamine responsive basal ganglia disease (BTBGD) is a rare autosomal recessive neurometabolic disorder characterized by diverse and variable phenotypic features, which can make diagnosis challenging. However, prompt treatment with thiamine and biotin can effectively manage the condition. Diagnosis relies on the identification of biallelic pathogenic variants in the SLC19A3 gene. This case report describes two novel variants of uncertain significance in the SLC19A3 gene, which may be correlated with the phenotypic manifestations of BTBGD. Case presentation Our case is a 7-month-old female infant presented with a 3-week history of irritability, altered behavior, and refusal of newly introduced solid foods. Symptoms started with an upper respiratory tract infection, followed by lethargy, floppiness, and abnormal movements. The patient was admitted to the pediatric ward with a broad differential diagnosis. Extensive laboratory evaluations revealed lactic acidosis. MRI brain showed symmetric restricted diffusion affecting the bilateral basal ganglia, thalami, and cortical regions. Whole genome sequencing identified biallelic variants of the SLC19A3: a c.1364T>G p.Met455Arg missense variant in the maternal allele and a 2.3 kb deletion of intron 3 of the paternal allele. Both variants were identified as variants of uncertain significance. However, given the clinical picture, MRI brain findings, resolution of symptoms with empiric biotin and thiamine supplementation, and biallelic SLC19A3 variants of unknown significance, the patient most likely suffers from BTBGD. Patient continues to show sustained developmental progress on biotin and thiamine supplementation. Conclusion This case highlights the fact that genetic testing remains a vital but improvable tool for the diagnosis of BTBGD. As of yet, genetic testing and diagnosis of BTBGD continues to be limited by the knowledge of which SLC19A3 variants are established to be pathogenic variants. Thus, further research is required to study other SCL19A3 variants of unknown significance to further improve genetic testing and diagnosis of BTBGD in the future.

Puzzle sine cosine optimization-based secure communication and brain tumor classification in IoT‐healthcare system

A brain tumor (BT) refers to an irregular accumulation of cells within the brain that proliferates uncontrollably, resulting in the formation of a mass. The accurate classification and early detection are important for effective treatment. In previous researches, the BT exhibited diverse features in terms of size, shape, and location. Moreover, the images used for segmentation, which suffered from image noise, low contrast, and shifting intensities within tissues. These issues are overcome by developing an effective method in this paper named Puzzle Sine Cosine Optimization enabled Deep Kronecker Network (PSCO-DKN) for classifying BT in the Internet of Things (IoT) healthcare system. Firstly, an IoT network is simulated, where the IoT device is used to capture the patient’s Magnetic Resonance Imaging (MRI) images. Further, the images are routed to the Base Station (BS) by employing PSCO. The routing is accomplished by contemplating several fitness parameters including delay, energy, and distance. At the BS, the process for BT classification is implemented as follows. Initially, the pre-processing is done by utilizing the median filter. Afterwards, the segmentation process is done by applying Spatial Attention U-Net (SA-Unet). After that, Statistical features and Shape Local Binary Texture (SLBT) are extracted. At last, BT classification is performed by utilizing the DKN, which is structurally optimized by using PSCO developed by the hybridization of Puzzle Optimization Algorithm (POA) and Sine Cosine Algorithm (SCA). Finally, PSCO-DKN attained superior outcomes of True Negative Rate (TNR) at 90.9 %, True Positive Rate (TPR) at 92.6 %, and accuracy at 87.7 %.

Chinese expert consensus on the diagnosis and treatment of adult T-cell leukemia/lymphoma (2024)

Adult T-cell leukemia/lymphoma (ATL), a rare and unique proliferative tumor of T lymphocytes, is associated with human T-cell leukemia virus type 1 (HTLV-1) infection. Due to its diverse clinical features, multidrug resistance of malignant cells, frequent opportunistic infection, and poor prognosis, ATL remains a clinical challenge. A unified understanding of ATL is currently lacking, and its diagnosis and treatment are not standardized in China due to the small number of cases. Therefore, to strengthen our understanding of ATL, hematology experts have formulated the "Chinese expert consensus on the diagnosis and treatment of adult T-cell leukemia/lymphoma (2024)" based on the latest research progress at home and abroad. This consensus proposes the latest diagnostic and subtype criteria for ATL in China. Furthermore, it briefly describes the infection situation of HTLV-1 in coastal areas of China, the epidemic trends in China, and the latest evidence and recommendations regarding the pathogenesis, clinical manifestations, and treatment strategies, with the aim to standardize the clinical diagnosis and treatment of ATL and ultimately improve the overall survival of patients with ATL.

Left Ventricular False Tendons: A Morphological Study by Echocardiography.

This is a prospective study aimed to investigate the morphology of left ventricular false tendons (LVFTs) using echocardiography and explore its associations with age, sex, body mass index (BMI), congenital heart structural abnormalities, and premature ventricular contractions (PVCs). We analyzed data from 889 individuals who underwent consecutive echocardiograms at our ultrasound department between December 2023 and February 2024. Routine echocardiograms were performed to detect congenital structural abnormalities, with a focus on identifying LVFT. We examined the prevalence, number, and distribution of LVFTs, as well as their correlation with age, sex, BMI, and congenital heart structural abnormalities. LVFTs were detected in 460 of 889 cases (51.74%), totaling 672 LVFTs. LVFT prevalence significantly differed not only between sexes but also between ages. LVFT prevalence was higher in individuals with lower BMI. There was no significant difference in congenital heart structural abnormalities between the groups, but the composition of distinct types of structural abnormalities differed between the groups. The incidence of PVCs in the LVFT-positive group was significantly higher than in the LVFT-negative group. The prevalence of LVFTs is notably higher in males than females and tends to decrease with advancing age and increasing BMI. LVFTs display diverse morphological features and may interact synergistically with certain congenital heart structural abnormalities. LVFTs can easily lead to PVCs in healthy people, but the risk of leading to malignant PVCs does not seem to be high. Correctly recognizing the morphological characteristics of LVFTs helps to distinguish similar ultrasonic images of different diseases, thus avoiding missed diagnoses and misdiagnoses in ultrasound work and clinical practice.

Metabolic and physiological functions of Patatin-like phospholipase-A in plants.

Patatin-like phospholipase-A (pPLA) is a class of lipid acyl hydrolase enzymes found in both the animal and plant kingdoms. Plant pPLAs are related to the potato tuber storage protein patatin in solanaceous plants. Despite extensive investigation of pPLA functions in the animal system, the mechanistic functional details and regulatory roles of pPLA are poorly understood in plants. In recent years, research pertaining to pPLAs has gain some momentum as some of the key members of pPLA family have been characterized functionally. These findings have provided key insights into the structural features, biochemical activities, and functional roles of plant pPLAs. In this review, we are presenting a holistic overview of pPLAs in plants and providing the latest updates on pPLA research. We have highlighted the genomic diversity and structural features of pPLAs in plants. Importantly, we have discussed the role of pPLAs in lipid metabolism, including sphingolipid metabolism, lignin and cellulose accumulation, lipid breakdown and seed oil content enhancement. Moreover, regulatory roles of pPLAs in physiological processes, such as plant stress response, plant-pathogen interactions and plant development have been discussed. This information will be critical in the biotechnological programs for crop improvement.

Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations

Predictive modeling potentially increases the reproducibility and generalizability of neuroimaging brain-phenotype associations. Yet, the evaluation of a model in another dataset is underutilized. Among studies that undertake external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies (i.e., dataset shifts). Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized developmental samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. Through advanced methodological approaches, we demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features. Results indicate the potential of functional connectome-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of brain-phenotype associations in real-world scenarios and clinical settings.

Basaloid Squamous Cell Carcinoma of the Oral Cavity: A Report of Two Cases with Review of Literature

Basaloid Squamous Cell Carcinoma (BSCC) is a rare, aggressive variant of squamous cell carcinoma. It exhibits diverse histological features. The present case reports provide valuable insights by focusing on BSCCs of the tongue and gingiva, including a detailed review of 19 cases in the literature specific to gingival involvement, which is a less common location for BSCC. Case 1 involves a 76- year-old female with a history of BSCC who presented with a painful 4x4 cm recurrent lesion on her upper lip. Case 2 is a 62-yearold male smoker with a history of thyroid carcinoma who exhibited a rapidly growing, painful 2x2 cm lobulated mass on his tongue. In both cases, a thorough clinical evaluation and incisional biopsy were performed to establish a diagnosis. Histopathological examination confirmed BSCC and both lesions were surgically excised with wide margins, taking their age into consideration. Both patients have since remained recurrence-free under regular follow-up, emphasising the efficacy of timely intervention. The present study findings suggest that while radiation exposure may contribute to the development of BSCC of the tongue, it is not the only causative factor. Other factors, such as smoking or genetic predispositions, may also play a role. The aggressive nature of BSCC, particularly in the gingiva, highlights the need for a comprehensive diagnostic approach, including biopsy and histopathological evaluation. Furthermore, the present cases align with evidence suggesting that combining surgery with postoperative chemotherapy or radiation therapy may not significantly improve survival outcomes. Long-term follow-up remains important to monitor for any potential complications.

A Compact X and Ku Band Quad Port MIMO Antenna with Path Loss aModel for Urban Macro and Micro Environments

The prospective of multiple-input multiple-output (MIMO) technology to increase network reliability and enhanced data throughput has made it essential in modern wireless communication systems. In this study, a new X and Ku band MIMO antenna with improved isolation is designed and analyzed using a shared radiator. To improve system performance, the MIMO antenna system was built to provide greater isolation between antenna elements by inserting inverted H-shape parasitic components within the radiators, resulting in an out-of-phase current of a comparable magnitude to the coupling current. The antenna achieves an isolation level greater than 15 dB across the operational frequency of 8.5-18.5 GHz with overall dimensions of 25 × 22 × 0.5 mm3 on RT duroid 5880 substrate. Furthermore, the developed MIMO antenna is investigated for radiation characteristics, with a maximum gain of 5.8 dB and acceptable diversity features. A path loss study for the designed antenna is performed using the ABG and CI models under the urban macro and micro model scenarios. The proposed shared radiator quad port MIMO antenna is optimized to realize compactness, wider impedance bandwidth, and inter-port isolation. The antenna design outcomes from simulation and experimental data make it a good contender for use in contemporary communication systems.

Enhanced detection of tomato leaf diseases using ensemble deep learning: INCVX-NET model

<p align="justify"><span lang="EN-US">Automated leaf disease detection quickly identifies early symptoms, and saves time on large farms. Traditional methods like visual inspection and laboratory detection are prevalent despite being labor-intensive, time-consuming, and susceptible to human error. Recently, deep learning (DL) has emerged as a promising alternative for crop disease recognition. However, these models usually demand extensive training data and face problems in generalization due to the diverse features among different crop diseases. This complexity makes it difficult to achieve optimal recognition performance across all scenarios. To solve this issue, a novel ensemble approach INCVX-Net is proposed to integrate the three DL models, ‘Inception, visual geometry group (VGG)-16, and Xception’ using weighted averaging ensemble for tomato crop leaf disease detection. This approach utilizes the strengths of three DL models to recognize a wide range of disease patterns and captures even slight changes in leaf characteristics. INCVX-Net achieves an impressive 99.5% accuracy in disease detection, outperforming base models such as InceptionV2 (93.4%), VGG-16 Net (92.7%), and Xception (95.2%). This significant leap in accuracy demonstrates the growing power of ensemble DL models in disease detection compared to standalone DL models. The research paves the groundwork for future advancements in disease detection, enhancing precision agriculture through ensemble models.</span></p>

International PCOS guideline clinical research priorities roadmap: a co-designed approach aligned with end-user priorities in a neglected women’s health condition

Polycystic ovary syndrome (PCOS) is a common endocrinopathy with significant reproductive, metabolic, and psychological complications. Consensus on PCOS clinical research priorities across end-users is fundamental and necessitates a robust co-development of a clinical research roadmap to guide international research efforts. A multistage process included: i) international surveys of women and healthcare providers to identify research priorities and unmet needs; ii) interrogation of systematic reviews conducted for the International PCOS Guideline to identify research gaps; iii) International PCOS Guideline Network consensus generated clinical research roadmap; and iv) international peer review for external validation. A codesigned survey engaging 1278 women with PCOS and 1474 healthcare providers found general concordance on research priorities. International PCOS Guideline development processes identified gaps in the literature and coproduced over 150 research priorities throughout the women's life course, affirmed in international peer review. Key themes included: 1) Optimizing PCOS diagnosis; understanding natural history across diverse populations and life stages; detecting and preventing complications, and integrating and interrogating large data assets; 2) developing evidence-based resources, exploring optimal modes for information provision and models of care; 3) exploring effective lifestyle and weight management strategies; minimising weight stigma; 4) exploring intervention effects (including treatment efficacy, safety, cost-effectiveness, and long-term follow-up) on diverse features of PCOS across subgroups; and 5) optimising preconception care and fertility treatments in PCOS. This rigorously coproduced International PCOS Guideline clinical research roadmap addresses stakeholder priorities to guide future clinical research in this common yet neglected condition. The roadmap complements the established PCOS Core Outcome Set to enhance research quality, and tackles evidence-practice gaps to improve health outcomes for women with PCOS throughout their life course. The survey, International PCOS Guideline Network and 2018 and 2023 International PCOS Guidelines were funded by the Australian National Health and Medical Research Council (NHMRC) Centres of Research excellence in PCOS (APP1078444) and in Women's Health in Reproductive life (APP1171592). Guideline partners, American Society for Reproductive Medicine (ASRM), Endocrine Society, European Society of Human Reproduction and Embryology (ESHRE), and European Society of Endocrinology (ESE), provided additional funding and assisted in guideline development. HT and AM are NHMRC Research Fellows. LM was funded by a Heart Foundation Future Leader and Veski Fellowship and CTT by the NHMRC Centres of Research excellence in Women's Health in Reproductive life. All disclosures of interest were declared before commencing GDG involvement and updated before all major milestones and are available alongside the PCOS Guideline (https://www.monash.edu/__data/assets/pdf_file/0009/3371292/Register-of-disclosures-of-interest.pdf).

Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis

Accurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices. Doi: 10.28991/ESJ-2024-08-06-01 Full Text: PDF

Pneumonia detection on x-ray image using improved depthwise separable convolutional neural networks

A single neural network model cannot capture intricate and diverse features due to its ability to learn only a finite set of patterns from the data. Additionally, training and utilising a single model can be computationally demanding. Experts propose incorporating multiple neural network models to address these constraints to extract complementary attributes. Previous research has highlighted challenges network models face, including difficulties in effectively capturing highly detailed spatial features, redundancy in network structure parameters, and restricted generalisation capabilities. This study introduces an innovative neural network architecture that combines the Xception module with the inverse residue structure to tackle these issues. Considering this, the paper presents a model for detecting pneumonia in X-ray images employing an improved depthwise separable convolutional network. This network architecture integrates the inverse residual structure from the MobileNetV2 model, using the rectified linear unit (ReLU) non-linear activation function throughout the entire network. The experimental results show an impressive recognition rate with a test accuracy of 97.24% on the chest x-ray dataset. This method can extract more profound and abstract image features while mitigating overfitting issues and enhancing the network's generalisation capacity.

10-camphor sulfonic acid: A simple and efficient organocatalyst to access anti-SARS-COV-2 Benzoxanthene derivatives

10-Camphor sulfonic acid (10-CSA) has gained popularity as an organocatalyst due to its broad range of solubility and user-friendliness. Affordable multicomponent reactions (MCRs) for the preparation of benzoxanthenes (4a-4 h) (5a-5i) are presented in this work. Extensive investigations and records have been conducted on the diverse biological features exhibited by xanthenes and benzoxanthenones, such as their antiviral, antibacterial, and anti-inflammatory capabilities.Using β-naphthol, dimedone, and aldehydes, we demonstrate a cost-effective and environmentally friendly catalytic method. Under ideal circumstances, the 10-CSA catalyzes one-pot reaction, procuring impressive amounts of benzoanthenes (85–95 %). All the synthesized compounds were characterized by 1H NMR and 13C NMR. A wide variety of suitable chemicals, simple work-up procedures, and solvent-free synthesis outperforms numerous existing methods for procuring biologically relevant benzoxanthene derivatives are some of the interesting features of this organocatalyzed bronsted acid process. Therefore this synthesis is industrially inevitable. Furthermore, computational studies such as molecular docking and ADMET data analysis were performed on a number of the synthesized benzoxanthene molecules. This has led to the identification of the most potent synthetic against the SARS-CoV-2 spike protein. Additionally, to mimic how medicinal compounds interact to target proteins, computational docking and dynamics techniques were used. These studies showed that, in terms of binding affinity and other crucial traits, 4a, 4b, and 5a are potential possibilities. Overall, the current study should be of great help in the development of benzoxanthene analogs which can be potential drugs for treatment of COVID-19.